Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Yousefi, Samuel
Doctor of Philosophy - PhD
University of British Columbia
The increasing complexity of global supply chains and the limitations of traditional supply chain management systems have highlighted the need for enhanced transparency, sustainability, and resilience. Blockchain technology, with its decentralized and immutable ledger system, has emerged as a promising solution to these issues, offering potential improvements in data integrity and traceability. However, the practical adoption of blockchain is hindered by significant barriers, including high implementation costs, technical complexities, and managerial conservatism. Previous studies do not effectively bridge the gap between theoretical insights and practical applications in this area. This gap emphasizes the need for advanced, context-specific tools to comprehensively assess the benefits and challenges of integrating blockchain into supply chains. This research aims to develop a feasibility study to examine the potential of blockchain for improving sustainable supply chain management. Therefore, advanced decision support frameworks are proposed to systematically model and address the decision-making problems inherently linked with the adoption of blockchain technology. These frameworks are designed by incorporating organizational theories, systems modeling, robust decision-making, and mathematical programming. The research objectives are fourfold: (i) to develop a robust system analysis-based framework to explore and prioritize key enablers of blockchain adoption; (ii) to introduce a risk management-enabled decision approach to mitigate potential barriers to blockchain adoption; (iii) to propose decision support framework based on the technology-organization-environment theory to select the best-fitting blockchain platform; and (iv) to develop an optimization model that incorporates blockchain compatibility in strategic sourcing and supply chain coordination under competitiveness. The importance of the developed frameworks lies in their ability to handle both the strategic and operational complexities of blockchain adoption under uncertainty. This, in turn, facilitates the successful integration of blockchain to improve sustainable supply chain performance and coordination. This study also helps organizations navigate the complexities of global supply chains while achieving their sustainability and resilience goals. By leveraging these advanced decision support frameworks, organizations learn how to optimize their operations through blockchain characteristics and transform traditional and linear supply chains into more dynamic and integrated networks.
DECISION SUPPORT FRAMEWORKS FOR UNLOCKING THE POTENTIAL OF BLOCKCHAIN IN SUSTAINABLE SUPPLY CHAIN MANAGEMENT by Samuel Yousefi B.Sc., Urmia University of Technology, 2012 M.Sc., Urmia University of Technology, 2014 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) November 2024 © Samuel Yousefi, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the dissertation entitled: Decision Support Frameworks for Unlocking the Potential of Blockchain in Sustainable Supply Chain Management submitted by Samuel Yousefi in partial fulfillment of the requirements for the degree of Doctor of Philosophy Examining Committee: Dr. Babak Mohamadpour Tosarkani, School of Engineering Supervisor Dr. Kasun Hewage, School of Engineering Supervisory Committee Member Dr. Ahmad Al-Dabbagh, School of Engineering Supervisory Committee Member Dr. Mohammad Hossein Zarifi, School of Engineering University Examiner Dr. Guoqing Zhang, Faculty of Engineering, University of Windsor External Examiner iii Abstract The increasing complexity of global supply chains and the limitations of traditional supply chain management systems have highlighted the need for enhanced transparency, sustainability, and resilience. Blockchain technology, with its decentralized and immutable ledger system, has emerged as a promising solution to these issues, offering potential improvements in data integrity and traceability. However, the practical adoption of blockchain is hindered by significant barriers, including high implementation costs, technical complexities, and managerial conservatism. Previous studies do not effectively bridge the gap between theoretical insights and practical applications in this area. This gap emphasizes the need for advanced, context-specific tools to comprehensively assess the benefits and challenges of integrating blockchain into supply chains. This research aims to develop a feasibility study to examine the potential of blockchain for improving sustainable supply chain management. Therefore, advanced decision support frameworks are proposed to systematically model and address the decision-making problems inherently linked with the adoption of blockchain technology. These frameworks are designed by incorporating organizational theories, systems modeling, robust decision-making, and mathematical programming. The research objectives are fourfold: (i) to develop a robust system analysis-based framework to explore and prioritize key enablers of blockchain adoption; (ii) to introduce a risk management-enabled decision approach to mitigate potential barriers to blockchain adoption; (iii) to propose decision support framework based on the technology-organization-environment theory to select the best-fitting blockchain platform; and (iv) to develop an optimization model that incorporates blockchain compatibility in strategic sourcing and supply chain coordination under competitiveness. The importance of the developed frameworks lies in their ability to handle both the strategic and operational complexities of blockchain adoption under iv uncertainty. This, in turn, facilitates the successful integration of blockchain to improve sustainable supply chain performance and coordination. This study also helps organizations navigate the complexities of global supply chains while achieving their sustainability and resilience goals. By leveraging these advanced decision support frameworks, organizations learn how to optimize their operations through blockchain characteristics and transform traditional and linear supply chains into more dynamic and integrated networks. v Lay Summary As supply chain networks become increasingly complex and global, traditional management methods are proving inadequate. To address this issue, digital tools such as blockchain technology are being examined as solutions to enhance supply chain operations. This research examines how blockchain technology could streamline processes, improve performance, and increase resilience within supply chain networks. This investigation divides the problem of blockchain adoption into several sub-problems, including the analysis of enablers, barriers, platform selection, and incorporation of blockchain. This study first highlights the main benefits (e.g., increased transparency) and challenges (e.g., technical complexity) associated with blockchain adoption in sustainable supply chains. Then, the decision support frameworks are developed to offer practical insights to help organizations modernize their supply chains by incorporating the best-fitting blockchain platform effectively. This can result in more efficient operations and support sustainability efforts, such as reducing environmental impact and encouraging ethical sourcing practices. vi Preface This dissertation is conducted by Samuel Yousefi under the supervision of Dr. Babak Mohamadpour Tosarkani at the Industrial & Systems Engineering (ISE) Research Group at The University of British Columbia. The original research carried out in this dissertation has led to multiple journal publications and conference presentations. No generative AI was used for any aspects of this work. The details of all publications extracted from this dissertation and the contributions of the author are listed as follows: Journal Publications A version of Chapter 2 has been submitted to the Technological Forecasting and Social Change. In this article, I was responsible for problem definition, methodology development, software, analysis of the results, and writing the manuscript: â¢ Yousefi, S., & Tosarkani, B. M. (2024). Unlocking Blockchain Potential for Supply Chain Transformation: A Robust System Analysis for Enhanced Strategic Performance. Technological Forecasting and Social Change. (Under Review) A version of Chapter 3 has been published in the Engineering Applications of Artificial Intelligence under a CC BY-NC 4.0 license. In this article, I was responsible for problem definition, methodology development, software, analysis of the results, and writing the manuscript: â¢ Yousefi, S., & Tosarkani, B. M. (2024). Enhancing Sustainable Supply Chain Readiness to Adopt Blockchain: A Decision Support Approach for Barriers Analysis. Engineering Applications of Artificial Intelligence, 133, 108151. https://doi.org/10.1016/j.engappai.2024.108151 vii A version of Chapter 4 has been published in the Computers & Industrial Engineering under a CC BY-NC 4.0 license. In this article, I was responsible for problem definition, methodology development, software, analysis of the results, and writing the manuscript: â¢ Yousefi, S., & Tosarkani, B. M. (2024). A decision support framework for best-fitting blockchain platform selection in sustainable supply chains under uncertainty. Computers & Industrial Engineering, 197, 110577. https://doi.org/10.1016/j.cie.2024.110577 A version of Chapter 5 has been submitted to the Transportation Research Part E: Logistics and Transportation Review. In the following article, I was responsible for problem definition, methodology development, software, investigation, analysis of the results, and writing the manuscript. Mr. Khodoomi, a member of the ISE research group, helped with the methodology and software. â¢ Yousefi, S., Khodoomi, M. R., & Tosarkani, B. M. (2024). Data-Driven Robust Strategic Sourcing Considering Supply-Side Competition: Insights into Blockchain Adoption for Coordination, Transportation Research Part E: Logistics and Transportation Review. (Under Review) Conference Presentations I defined the problems, developed the conceptual and analytical frameworks, implemented the frameworks, analyzed the results, and presented the following works: â¢ Yousefi, S., & Tosarkani, B. M. (2024). Blockchain Platform Selection for Sustainable Supply Chains: Integrating a Causal-Based Model with TOE Theoretical Framework. Supply Chain Sustainability Session, IISE Annual Conference & Expo 2024, Montréal, Canada. viii â¢ Yousefi, S., & Tosarkani, B. M. (2023). Blockchain in Supply Chain Management: A Decision Support System for Platform Selection. Information Systems Division, ASAC 2023 Conference, Toronto, Canada. (Best Student Paper Award) â¢ Yousefi, S., & Tosarkani, B. M. (2023). A Risk Assessment Approach for Blockchain Technology Adoption in Sustainable Supply Chains. Sustainable Supply Chains Session, 2023 CORS-Optimization Days conference, Montreal, Canada. (Sustainability Travel Award) â¢ Yousefi, S., & Tosarkani, B. M. (2022). Blockchain Technology Adoption for Sustainable Supply Chain Performance Improvement. Sustainable Operations Track, 2022 CORS/INFORMS International Conference, Vancouver, Canada. ix Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ...................................................................................................................................v Preface............................................................................................................................................ vi Table of Contents ........................................................................................................................... ix List of Tables ............................................................................................................................... xiv List of Figures .............................................................................................................................. xvi List of Abbreviations ................................................................................................................... xix Acknowledgements ...................................................................................................................... xxi Dedication ................................................................................................................................... xxii Chapter 1: Introduction ....................................................................................................................1 1.1 Background ..................................................................................................................... 1 1.1.1 Supply Chain Digitalization ........................................................................................ 1 1.1.2 Blockchain Technology .............................................................................................. 2 1.2 Motivation and Research Gaps ....................................................................................... 4 1.3 Objectives ....................................................................................................................... 6 1.4 Thesis Outline ............................................................................................................... 11 Chapter 2: Unlocking Blockchain Potential for Supply Chain Transformation: A Robust System Analysis for Enhanced Strategic Performance ..............................................................................12 2.1 Introduction ................................................................................................................... 12 2.2 Literature Review.......................................................................................................... 16 2.2.1 BT Adoption Enablers Analysis ............................................................................... 17 x 2.2.2 Research Gaps ........................................................................................................... 19 2.3 Preliminaries ................................................................................................................. 22 2.3.1 FCM Technique ........................................................................................................ 22 2.3.2 DEA-CCR and Robust DEA Models ........................................................................ 25 2.4 Proposed RSAF ............................................................................................................. 27 2.5 Case Description and Results ........................................................................................ 35 2.5.1 Implementation of the Proposed Framework ............................................................ 39 2.5.1.1 Developing the Causal-Based Model of Enablers ............................................ 39 2.5.1.2 Assessing the Impact of Enablers on SCP Metrics ........................................... 44 2.5.1.3 Prioritizing the Factors Enabling BT Adoption ................................................ 46 2.5.2 Comparative Analysis ............................................................................................... 49 2.6 Theoretical, Managerial, and Practical Implications .................................................... 52 2.7 Conclusions ................................................................................................................... 57 Chapter 3: Enhancing Sustainable Supply Chain Readiness to Adopt Blockchain: A Decision Support Approach for Barriers Analysis........................................................................................59 3.1 Introduction ................................................................................................................... 59 3.2 Literature Review.......................................................................................................... 64 3.2.1 BT Adoption Barriers Analysis ................................................................................ 64 3.2.2 Research Gaps ........................................................................................................... 71 3.3 Preliminaries ................................................................................................................. 72 3.3.1 Z-number Theory ...................................................................................................... 73 3.3.2 FCM Model ............................................................................................................... 76 3.4 Proposed Approach ....................................................................................................... 80 xi 3.5 Analysis of the Results.................................................................................................. 86 3.5.1 Case Description ....................................................................................................... 86 3.5.2 Implementation of the Proposed Approach .............................................................. 88 3.5.3 Comparative Analysis ............................................................................................... 93 3.6 Managerial and Practical Implications.......................................................................... 95 3.7 Conclusions ................................................................................................................. 100 Chapter 4: A Decision Support Framework for Best-Fitting Blockchain Platform Selection in Sustainable Supply Chains under Uncertainty .............................................................................102 4.1 Introduction ................................................................................................................. 102 4.2 Literature Review........................................................................................................ 106 4.2.1 BT Adoption in SCs ................................................................................................ 106 4.2.2 BT Platform Evaluation and Selection ................................................................... 107 4.2.3 Research Gaps ......................................................................................................... 109 4.3 Preliminaries ............................................................................................................... 113 4.3.1 Z-number Theory .................................................................................................... 113 4.3.2 Fuzzy Inference System .......................................................................................... 114 4.3.3 FCM Modeling........................................................................................................ 116 4.3.3.1 FCM Construction .......................................................................................... 116 4.3.3.2 FCM Learning ................................................................................................. 117 4.3.4 Fuzzy CoCoSo Method ........................................................................................... 119 4.4 Proposed Decision Support Framework ..................................................................... 122 4.5 Case Description and Results ...................................................................................... 126 4.5.1 Comparing BT Platforms and Forming Fuzzy Decision Matrix ............................ 129 xii 4.5.2 Determining the Weights of Evaluation Criteria .................................................... 132 4.5.3 Prioritizing BT Platforms ........................................................................................ 136 4.6 Comparative Analysis ................................................................................................. 137 4.7 Discussion of Implications .......................................................................................... 140 4.7.1 Theoretical Implications ......................................................................................... 140 4.7.2 Practical Implications.............................................................................................. 141 4.7.3 Managerial Implications ......................................................................................... 143 4.8 Conclusions ................................................................................................................. 146 Chapter 5: Data-Driven Robust Strategic Sourcing Considering Supply-Side Competition: Insights into Blockchain Adoption for Coordination ................................................................................147 5.1 Introduction ................................................................................................................. 147 5.2 Literature Review........................................................................................................ 151 5.2.1 BT for SC Coordination .......................................................................................... 151 5.2.2 Research Gaps ......................................................................................................... 154 5.3 Problem Statement ...................................................................................................... 157 5.4 Proposed Model .......................................................................................................... 159 5.4.1 Non-Competitive MOP Model ............................................................................... 159 5.4.2 Competitive MOP Model ........................................................................................ 167 5.4.3 Augmented ε-constraint Method ............................................................................. 170 5.4.4 DDRMOP Model .................................................................................................... 171 5.4.4.1 Data-driven Uncertainty Set ........................................................................... 171 5.4.4.2 Robust Counterpart Formulation .................................................................... 173 5.5 Results and Discussion ............................................................................................... 177 xiii 5.5.1 Case Descriptions.................................................................................................... 177 5.5.2 Analysis of the Results............................................................................................ 181 5.5.2.1 Implementing the MOP Model Under Supply-Side Competition .................. 181 5.5.2.2 Implementing the Proposed DDRMOP Model ............................................... 184 5.5.2.3 Strategic Sourcing Outcomes Under Uncertainty ........................................... 186 5.6 Sensitivity Analysis .................................................................................................... 190 5.7 Discussions of Implications ........................................................................................ 195 5.7.1 Theoretical Implications ......................................................................................... 195 5.7.2 Practical Implications.............................................................................................. 196 5.7.3 Managerial Implications ......................................................................................... 200 5.8 Conclusions ................................................................................................................. 202 Chapter 6: Conclusion..................................................................................................................204 6.1 Summary of Main Findings ........................................................................................ 204 6.2 Research Contributions ............................................................................................... 206 6.3 Future Work Recommendations ................................................................................. 208 Bibliography ................................................................................................................................211 Appendices ...................................................................................................................................244 Appendix A ............................................................................................................................. 244 xiv List of Tables Table 2.1 A summary of features of the current research compared to the previous studies in the field of enablers analysis ................................................................................................................20 Table 2.2 The identified factors enabling BT adoption in sustainable SCs ...................................37 Table 2.3 The obtained scores for CSFs enabling BT adoption in sustainable SCs ......................48 Table 2.4 The obtained ranking from BWM-robust DEA compared to conventional models ......50 Table 2.5 The impact of each metric on the strategic SCP improvement ......................................53 Table 3.1 A comparison between the specifications of the current research and other studies .....69 Table 3.2 The linguistic terms for specifying the value of the uncertainty component .................82 Table 3.3 The linguistic terms for specifying the value of the reliability component ...................82 Table 3.4 The defined rules for transforming Z-numbers into triangular fuzzy numbers .............83 Table 3.5 The identified barriers to BT adoption in SCs considering the sustainability concept ..87 Table 3.6 Calculating the fuzzy weight of the direct CR between B5 and B2 ..............................89 Table 3.7 The mean of fuzzy values assigned to RFs associated with BT adoption barriers ........91 Table 3.8 The obtained priority scores for BT adoption barriers using Z-FCM and different versions of the FMEA technique ...................................................................................................92 Table 3.9 Investigating the role of the treatment level factor on BT adoption barriers analysis ...97 Table 4.1 A conceptual comparison between the features of the present research and others ....111 Table 4.2 The identified evaluation criteria for selecting a BT platform ....................................129 Table 4.3 The calculated weights of evaluation criteria using the ZIS-based automatic FCM modeling ......................................................................................................................................136 Table 4.4 The obtained scores and ranks for candidate BT platforms .........................................137 xv Table 4.5 The ranks assigned to BT platforms considering the TOE-related contexts ...............142 Table 5.1 A comparative overview of the features of the current research and other studies .....155 Table 5.2 The nomenclature used in formulating the studied problem .......................................160 Table 5.3 The values of purchasing, transportation, holding, partnership and coordinating information systems .....................................................................................................................178 Table 5.4 The capacity of suppliers and the estimated demand by the buyer .............................179 Table 5.5 BT implementation progress and the Oracle validation process-related parameters ...179 Table 5.6 The inputs and outputs of the supplier evaluation process ..........................................180 Table 5.7 The performance of objective functions considering supply-side competition ...........182 Table 5.8 Exploring the strategic role of the efficient suppliers in optimizing the SC network .198 Table A1. The linguistic variables allocated to the importance of CSFs in manufacturing firms 244 Table A2. Z-number-based linguistic variables for RFs of each potential barrier ......................245 Table A3. Converting the Z-number-based values of RFs to fuzzy numbers for potential barriers 246 Table A4. The linguistic variables and their corresponding membership functions (Onari et al., 2021) ............................................................................................................................................247 Table A5. Transformation rules for converting Z-numbers to triangular fuzzy numbers ...........247 Table A6. The Z-values of evaluation criteria .............................................................................248 Table A7. The constructed rules for designing the ZIS system ...................................................249 Table A8. The aggregated triangular fuzzy values of evaluation criteria using the ZIS system .250 xvi List of Figures Figure 1.1 Leveraging BT to overcome major challenges of traditional SCs .................................3 Figure 1.2 Organization of the dissertation....................................................................................10 Figure 2.1 Explanatory model of BT adoption for enhancing strategic SCP ................................13 Figure 2.2 The structured framework of the FCM modeling technique ........................................23 Figure 2.3 The implementation phases of the developed RSAF ...................................................29 Figure 2.4 The potential inner CRs among factors enabling BT adoption considering BSC perspectives ....................................................................................................................................40 Figure 2.5 The potential outer CRs among factors enabling BT adoption ....................................43 Figure 2.6 The impact analysis of enablers on strategic metrics: a) SCP1, b) SCP2, c) SCP3, and d) SCP4 ..........................................................................................................................................45 Figure 2.7 Comparing the scores obtained from BWM-robust DEA with conventional models ..51 Figure 2.8 Sensitivity analysis of the obtained rankings within different scenarios .....................54 Figure 2.9 Comparing the role of CSFs in designing different SC strategies................................56 Figure 3.1 A holistic framework for leveraging risk management to analyze BT adoption barriers 62 Figure 3.2 A BT-based sustainable SC network ............................................................................65 Figure 3.3 A simple Z-number X= (A, R) ......................................................................................74 Figure 3.4 A sample of an FCM model considering the recurrent CRs and loops ........................77 Figure 3.5 The framework of the decision support approach for analyzing BT adoption barriers in sustainable SCs ..............................................................................................................................81 Figure 3.6 The Z-FCM model of barriers to BT adoption in sustainable SCs...............................90 Figure 3.7 The impact of considering uncertainty, reliability, and CRs on the prioritization .......94 xvii Figure 3.8 A structured framework for handling BT adoption barriers in sustainable SCs ..........96 Figure 3.9 Comparing the rankings obtained from the conventional FMEA (SOD) technique with a) FMEA (SODT) b) F-FMEA (SODT) c) Z-FMEA (SODT) d) Z-FCM (SODT) ............................................97 Figure 4.1 The supportive BT-based solutions for multistakeholder LSCM ...............................103 Figure 4.2 The framework of the feasibility study of BT adoption .............................................107 Figure 4.3 The implementation phases of the proposed DSF for solving the BT platform selection problem ........................................................................................................................................123 Figure 4.4 The implementation phases of the ZIS system ...........................................................124 Figure 4.5 The BT-based architecture for textile and apparel SCs ..............................................128 Figure 4.6 The designed ZIS system to map out given inputs to an output .................................131 Figure 4.7 The potential inner CRs between evaluation criteria considering TOE contexts .......133 Figure 4.8 The FCM model of evaluation criteria for BT platform selection .............................134 Figure 4.9 The solutions separability of the DSF compared to conventional MOORA and CoCoSo methods ........................................................................................................................................139 Figure 4.10 The solutions separability of the DSF compared to the extended versions of the CoCoSo method ...........................................................................................................................139 Figure 4.11 The impact of evaluation criteria on the BT platform selection process: a) technological factors, b) organizational factors, and c) environmental factors ...........................143 Figure 4.12 The impact of increasing the weight of significant evaluation criteria on BT platform selection .......................................................................................................................................145 Figure 5.1 The proposed methodology to address the strategic sourcing and SC coordination ..158 Figure 5.2 The uncertainty data along with the data-driven uncertainty set U (PCA-Robust KDE) .....175 xviii Figure 5.3 Heat map of the solution profile for DDRMOP under different values of α and Ω: a) non-competitive Z1; b) competitive Z1; c) non-competitive Z2; d) competitive Z2 ......................185 Figure 5.4 A comparative analysis of primary objective functions under demand uncertainty ..187 Figure 5.5 The list of selected suppliers for minimizing SC coordination costs in a) non-competitive environment and b) competitive environment .........................................................188 Figure 5.6 Z1 optimization-based order allocation in a) non-competitive environment and b) competitive environment .............................................................................................................188 Figure 5.7 The list of selected suppliers for minimizing defective and delivery delay rates in a) non-competitive environment and b) competitive environment ..................................................189 Figure 5.8 Z2 optimization-based order allocation in a) non-competitive environment and b) competitive environment .............................................................................................................190 Figure 5.9 Pareto front of Z1 and Z2 in non-competitive and competitive environments ............191 Figure 5.10 The impact of changing the value of Nmax on sourcing decisions made by a) Z1, b) Z2, and c) Z3 .......................................................................................................................................192 Figure 5.11 The order quantity determined by Z1 and Z3 through changing the resilience threshold 194 Figure 5.12 Investigating changes in sourcing decisions made by the buyer in a non-competitive environment .................................................................................................................................199 Figure 5.13 Investigating changes in sourcing decisions made by the buyer in a competitive environment .................................................................................................................................200 Figure 5.14 The impact of ending partnerships on buyerâs shortage level in a non-competitive environment .................................................................................................................................201 Figure 5.15 The impact of ending partnerships on buyerâs shortage level in a competitive environment .................................................................................................................................202 xix List of Abbreviations BT Blockchain Technology BSC Balanced Scorecard BWM Best-Worst Method CR Causal Relationship CoCoSo Combined Compromise Solution CSF Critical Success Factor DDRMOP Data-Driven Robust Multi-Objective Programming DDRO Data-Driven Robust Optimization DE Differential Evolution DEA Data Envelopment Analysis DEMATEL Decision-Making Trial and Evaluation Laboratory DM Decision-Maker DMU Decision-Making Unit DSF Decision Support Framework FCM Fuzzy Cognitive Map FMEA Failure Mode and Effect Analysis ISM Interpretive Structural Modeling KDE Kernel Density Estimation LSCM Logistics and Supply Chain Management MCDM Multi-Criteria Decision-Making MILP Mixed-Integer Linear Programming xx MOORA Multi-Objective Optimization on the basis of Ratio Analysis MOP Multi-Objective Programming NHL Nonlinear Hebbian Learning PCA Principal Component Analysis RF Risk Factor RSAF Robust System Analysis-based Framework SC Supply Chain SCM Supply Chain Management SCP Supply Chain Performance TOE Technology-Organization-Environment TOPSIS Technique for Order of Preference by Similarity to Ideal Solution Z-FCM Z-number-based Fuzzy Cognitive Map ZIS Z-number-based Inference System xxi Acknowledgements I would like to express my heartfelt gratitude to my supervisor, Dr. Babak Mohamadpour Tosarkani, for his invaluable guidance and mentorship throughout my research journey. Beyond imparting essential research skills, Dr. Tosarkani has consistently encouraged me to approach challenges with perseverance and creativity, fostering a resilient and innovative mindset. His mentorship has not only shaped the researcher I am today but has also prepared me to navigate future academic and professional endeavors with confidence. I am deeply grateful for his unwavering support and belief in my potential, which has been pivotal in my academic growth. My deepest appreciation goes to my committee members, Dr. Hewage and Dr. Al-Dabbagh. Your insightful questions and innovative perspectives have consistently opened up new pathways for exploration to enrich my work significantly. I would also like to extend my gratitude to Dr. Mohammad Hossein Zarifi, my University Examiner, and Dr. Guoqing Zhang, my External Examiner, for their thorough evaluation of my work and their valuable feedback. I am deeply thankful to my colleagues in the ISE research group, who made this journey both enjoyable and memorable. I also extend my gratitude to my off-campus friends, who encouraged me throughout my Ph.D. I thank my lovely mother and brother, whose unwavering support and encouragement have been a cornerstone of my educational endeavors. Their belief in my abilities has been a constant source of motivation, enabling me to strive toward my goals with determination. Lastly, I would like to thank my partner, Mahsa, for continuously inspiring me to pursue what is meaningful in life. Her understanding and encouragement have been a beacon of light during the most challenging times of my doctoral studies. Her support has not only enriched my personal growth but has also reminded me of the importance of perseverance in academic and personal pursuits. xxii Dedication This dissertation is dedicated to all who have profoundly shaped my journey, both personally and professionally, standing as pillars of support and inspiration throughout my academic pursuit.1 Chapter 1: Introduction 1.1 Background 1.1.1 Supply Chain Digitalization A Supply Chain (SC) is a network of entities working together through a sequence of processes to deliver a product or service to consumers (Wu et al., 2024). Globalization has significantly reshaped the structure of SC networks and made them more complex and interconnected than ever before. As SC complexity increases, the need for enhanced transparency, responsiveness, and efficiency in managing stakeholdersâ relationships becomes crucial (Kouhizadeh et al., 2021; Tiwari et al., 2023). This complexity requires robust management systems capable of handling the diverse dimensions of modern SCs. Traditional Supply Chain Management (SCM) systems, which often rely on manual processes and isolated information sources, are inadequate for addressing these challenges. This necessitates innovative SCM strategies that leverage digitalization to improve information flow throughout SCs. Digitalization, which involves the integration of digital technologies into SCM systems, offers the opportunity to transform manual processes into data-driven, adaptable, and interconnected systems (Chang et al., 2019; Görçün et al., 2023). This transformation enables managers and practitioners to efficiently implement the necessary changes across the SC network to enhance operational performance and increase SC resilience (Wamba et al., 2020). Furthermore, the digitalization of SCs in recent years has supported organizations to be more innovative than ever before in managing stakeholdersâ relationships and fulfilling demand (Tiwari et al., 2023). 2 1.1.2 Blockchain Technology Todayâs organizations are increasingly focusing on implementing digital technologies to unlock new opportunities and revolutionize their businesses. In the meantime, several emerging technologies have been innovated to expedite the digitalization of SCs (Agrawal and Narain, 2023). Blockchain Technology (BT) stands out among these emerging technologies due to its transformative potential for SCs and reshaping the relationships between entities (Queiroz and Wamba, 2019). Blockchain is a decentralized, immutable ledger that ensures secure and transparent transactions across the network and supports the real-time exchange of information (Orji et al., 2020; Han and Fang, 2024). BT comprises a sequence of cryptographically linked blocks to ensure the authenticity of product provenance and secures data transmission and storage (Cao et al., 2022; Tsai, 2023). Blockchain was first introduced by Nakamoto (2008) for financial applications (i.e., cryptocurrency Bitcoin). Following rapid technological advancements, BT has significantly impacted numerous industries and major global companies (e.g., Microsoft and Walmart), compelling them to rethink their strategies and capabilities (Chapkanovska, 2023; Ying et al., 2018). In 2021, spending on BT solutions reached $6.6 billion, with projections indicating this amount will increase to $19 billion by 2024 (Chapkanovska, 2023). As depicted in Fig. 1.1, the BT offers various solutions to address the shortcomings of traditional SCs and transform them into more integrated networks (CRI, 2018). 3 Figure 1.1 Leveraging BT to overcome major challenges of traditional SCs The immutability and encryption of BT directly improve the transparency and traceability within sustainable SCs and logistics services (van Hoek, 2020). Additionally, smart contracts, a key feature of BT, enable automated responses to predefined business conditions to maintain continuous operations during disruptions and enhance SC resilience (IBM., 2022). These contracts also facilitate responsible sourcing, reduce carbon emissions, and ensure regulatory compliance, thereby aligning SCM with broader sustainability goals (Saberi et al., 2019; Alvarenga et al., 2019). By transforming information flows and financial processes, BT eliminates the need for third-party intermediaries to further streamline SCM operations (Chang et al., 2019). Consequently, BT significantly improves dispute management, provides end-to-end visibility, and manages various SC functions such as sourcing and logistics (IBM, 2022). This impact is evident in the BT market for SCM, which was valued at $375.6 million in 2021 and is projected to grow at a compound annual growth rate of 69.5% through 2030 (QMI, 2021). 4 1.2 Motivation and Research Gaps Despite the potential of BT for resolving SCM challenges, understanding its effective integration into SC networks remains crucial (Ferdous et al., 2024). Studying the BT adoption process can also uncover the practical outcomes (e.g., performance improvement) and barriers organizations may face (e.g., implementation costs and technological compatibility) (Kouhizadeh et al., 2021; Yadav et al., 2023; Surucu-Balci et al., 2024). Previous studies have examined each category of these factors separately to help Decision-Makers (DMs) explore the potential of BT for SCM. However, a thorough examination of the adoption process can provide insights into how BT can be adapted to different industries and operational contexts effectively (Zafar et al., 2021). Therefore, DMs must conduct a comprehensive feasibility assessment to evaluate the potential drivers and barriers to BT adoption when developing their information technology strategies. For instance, while BT can bridge flexibility and integrity through improved information sharing, high implementation costs and technical complexities pose significant barriers. A feasibility assessment involves a detailed evaluation of the factors associated with BT adoption to help organizations make informed strategic decisions about its incorporation into their SCs. In this regard, DMs are required to address various problems associated with BT adoption, such as enablers analysis, barriers analysis, and platform evaluation and selection. In the enablers analysis problem, DMs should explore the characteristics that will be added to their SC network (e.g., environmental sustainability and social responsibility) through supporting features of BT. On the other hand, there are several managerial and organizational barriers to BT adoption. Thus, the potential challenges for BT adoption and their negative impacts are identified and analyzed in the barriers analysis problem. DMs are also required to investigate the trade-off between enablers and barriers (e.g., performance efficiency and implementation costs) to having a successful BT 5 integration into their SCs. This trade-off can be considered in the BT platform evaluation and selection procedure. After analyzing the potential enablers and barriers and choosing the best-fitting platform, DMs need to investigate the impact of BT implementation on SC functions. This highlights the practical outcomes of using BT for strategically designing SC networks that prioritize sustainability and resilience. BT adoption in SCs is still in its infancy, making it essential to address the uncertainties involved (Queiroz et al., 2020; Lim et al., 2021). The complexities associated with BT integration, such as interoperability issues, regulatory compliance, and technological scalability, require careful and simultaneous consideration (Zhang and Song, 2022; Erol et al., 2023; Xu et al., 2023). Effectively managing these uncertainties and complexities ensures a smooth and successful integration process with the aim of maximizing BTâs potential benefits for SCM. Besides, BT adoption is a decision-making challenge compounded by managerial conservatism (Vafadarnikjoo et al., 2023; Surucu-Balci et al., 2024). Therefore, there is a crucial need for developing efficient and robust Decision Support Frameworks (DSFs) to address the BT adoption-related problems under uncertainty. These frameworks must address multiple decision dimensions to offer clear insights into the feasibility and impact of BT implementation in order to enable DMs to make well-informed choices. Such frameworks can support the alignment of BT adoption with strategic goals to transform traditional and linear SCs into more dynamic and integrated networks. 6 1.3 Objectives The primary goal of this study is to explore the role of BT in improving SCM systems, identify the requirements needed to facilitate a smoother transition and understand the lessons learned from early adopters. By integrating these insights, the study seeks to maximize the benefits of BT for improved SC functions and sustainability. To achieve this overall goal, the current study develops DSFs to address the problems associated with the BT adoption. These frameworks integrate systems modeling, robust decision-making, and mathematical programming to assist DMs in investigating various decision dimensions. This study also adopts organizational theories to support the proposed DSFs in designing more efficient adoption strategies that align with broader organizational goals and sustainability objectives. These tools enable organizations to navigate the intricacies of BT implementation and optimize their SCM systems. As shown in Fig. 1.2, each chapter contributes to the overall goal mentioned above through the following more specific objectives: Objective 1: Explore the inherent features of BT adoption and investigate their role in improving Supply Chain Performance (SCP) and responsiveness to sustainability-related issues. In Chapter 2, the analysis of BT adoption enablers is conducted to identify the most effective features of this technology in enhancing SCP. While BT offers innovative SCP improvement solutions for various types of SC networks, theoretical and empirical analyses are needed to understand BT adoption enablers. Therefore, a Robust System Analysis-based Framework (RSAF) is developed to reveal the features and outcomes of BT adoption for sustainable SC networks. The RSAF uses critical success factor theory to identify BT adoption enablers impacting SCP for effective integration and implementation. Meanwhile, the impact of other BT features (e.g., smart 7 contracts) on improving environmental sustainability and social responsibility is analyzed. The balanced scorecard is also employed to translate the organizationâs strategy into performance metrics (e.g., cost-effectiveness) to help managers monitor performance. In the following, the systems theory-enabled Fuzzy Cognitive Map (FCM) is developed to model the potential Causal Relationships (CRs) among enablers and metrics, addressing both organizational and SC levels. Once the causal-based model is created, scenario-making and FCM learning algorithms quantify the impact of enablers on metrics. The robust Data Envelopment Analysis (DEA) model is then embedded in the RSAF to prioritize enablers, considering managerial conservatism and different possible scenarios. The RSAF not only offers better discriminatory power but also greater flexibility in adjusting adoption strategies to enhance SCP. Objective 2: Identify the barriers that affect an organizationâs readiness to adopt BT in sustainable SCs and leverage risk management to mitigate their deterrent effects. In Chapter 3, an analysis of the barriers to BT adoption is conducted to pinpoint the most critical challenges to implementing this technology, taking into account three dimensions of sustainability. To put it precisely, while BT presents numerous benefits for improving SCM, its implementation is closely tied to various challenges and uncertainties. Therefore, a decision support approach is proposed by integrating Z-number theory with FCM to analyze barriers in an uncertain environment. The Z-FCM approach helps DMs examine interactions among economic, social, and environmental barriers while handling numerous variables and complex CRs. The Z-number theory combines fuzzy set and probability theories to enhance information reliability and allow DMs to incorporate it in setting Risk Factor (RF) values and modeling CRs. Given that treatment level can directly affect an organizationâs readiness, this factor is considered in addition to 8 conventional RFs for analyzing BT adoption barriers. In the following, a hybrid FCM learning algorithm estimates the criticality of each barrier, which is derived from the specified RF values and potential CRs. Incorporating the multiple RFs and reliability concept in barrier analysis yields more accurate and reliable inputs for the mitigation process compared to other traditional techniques. Objective 3: Investigate the trade-off between the expected enablers and the existing barriers in the form of BT platform evaluation to determine the most feasible platform for SCM. In Chapter 4, the evaluation and selection of BT platforms are discussed from different perspectives. Each BT platform comes with its own features and adoption requirements, so organizations need to choose the platform that best meets their unique needs. Therefore, a Technology-Organization-Environment (TOE)-enabled DSF is developed to investigate conflicting criteria (i.e., advantages and challenges) in the BT platform selection process for decision-making under uncertainty. The TOE helps examine decision criteria for BT platform selection across technological, organizational, and environmental contexts. Then, an advanced fuzzy inference system in the Z-number environment is proposed to aggregate linguistic descriptions for a more reliable BT platform evaluation process. Furthermore, an automatic FCM is enhanced by the Z-number-based inference system to build a causal model of evaluation criteria in an uncertain environment. This model feeds the scenario-making technique and the hybrid FCM learning algorithm to determine the effectiveness of each criterion while minimizing biased judgments. This, in turn, leads to the determination of the weights of evaluation criteria. To incorporate the effectiveness of criteria, a fuzzy combined compromise solution is embedded in the DSF to prioritize candidate BT platforms under uncertain weights. The DSF outputs identify 9 key criteria within TOE contexts and offer more stable, reliable, and distinguishable solutions for the proposed problem compared to the traditional approaches. Objective 4: Investigate the benefits of integrating BT compatibility and sustainability-related criteria into strategic supplier evaluation for enhancing SC coordination. In Chapter 5, the focus is on a buyer-driven strategic sourcing problem that aims to help select efficient suppliers who align with the buyerâs goals regarding information sharing and sustainability practices. While the adoption of BT offers significant benefits, challenges related to complexity and the need for broad stakeholder engagement can limit its effectiveness in improving information coordination. Consequently, buyers should evaluate BT compatibility criteria when forming strategic partnerships to fully capitalize on the advantages of integration. Chapter 5 introduces a Multi-Objective Programming (MOP) model integrated with DEA to tackle buyer-driven strategic sourcing and enhance SC resilience. This model leverages BT adoption to improve information coordination between buyers and suppliers, who should be evaluated based on their BT-friendliness and sustainability score. The developed model chooses suppliers to reduce SC coordination costs, minimize delivery delays and defective rates, and maximize sourcing efficiency. Further refinement occurs by analyzing how competition among suppliers influences the evaluation of those suppliers and the development of resilient sourcing strategies. Given the dynamic interactions among supply chain entities, it is essential to account for demand uncertainty in order to make effective decisions. To handle the complexities of multidimensional demand uncertainty, the Data-Driven Robust Optimization (DDRO) approach is utilized. This method creates uncertainty sets that capture correlations and distributional asymmetries to result in a more accurate and less conservative representation of the situation. The developed data-driven robust 10 multi-objective programming model improves the robustness and applicability of the SC coordination model, providing an effective solution for managing demand uncertainties. Chapter 2: Unlocking Blockchain Potential for Supply Chain Transformation: A Robust System Analysis for Enhanced Strategic PerformanceChapter 1: Background, Motivation, Objectives, and Thesis OutlineChapter 6: Summary of Key Findings and Potential Directions for Future StudiesChapter 3: Enhancing Sustainable Supply Chain Readiness to Adopt Blockchain: A Decision Support Approach for Barriers AnalysisChapter 4: A Decision Support Framework for Best-Fitting Blockchain Platform Selection in Sustainable Supply Chains under UncertaintyChapter 5: Data-Driven Robust Strategic Sourcing Considering Supply-Side Competition: Insights into Blockchain Adoption for CoordinationFeasibility Study of Blockchain AdoptionBlockchain Adoption Enablers Analysis Blockchain Adoption Barriers Analysis Blockchain Platform Evaluation and SelectionBlockchain Incorporation for Improved Functions Figure 1.2 Organization of the dissertation 11 1.4 Thesis Outline This dissertation comprises six chapters, with Chapters 2 through 5 forming the core of the research. Chapter 2 addresses the importance of analyzing enablers of BT adoption in SC networks and reviews recent analytical approaches and theoretical frameworks. It contributes to the literature by integrating a strategic perspective into BT adoption practices to improve SCP and introducing an RSAF to provide SC managers with a comprehensive view of BT enablers. Chapter 3 examines the deterrent impact of organizational, system-related, and external barriers on sustainable SC readiness for BT adoption. It proposes a DSF to analyze BT adoption barriers considering information uncertainty and reliability and offers a holistic framework for leveraging risk management to address these barriers. Chapter 4 emphasizes the importance of selecting feasible BT platforms for transforming SCM systems. It reviews designed conceptual and analytical frameworks for BT platform evaluation and introduces a three-phase DSF for BT platform selection in sustainable SCs, considering technological, organizational, and environmental contexts. Chapter 5 highlights the practical outcomes of BT adoption and reviews recent studies on improved SC coordination. It develops an MOP model to explore BT compatibility in improving strategic partnerships within a competitive environment and employs a DDRO approach to address demand uncertainty in a bi-level SC network. The chapter compares the findings of the developed models with those that do not consider supply-side competition. Finally, Chapter 6 summarizes the key findings and contributions and suggests potential directions for future studies. 12 Chapter 2: Unlocking Blockchain Potential for Supply Chain Transformation: A Robust System Analysis for Enhanced Strategic Performance 2.1 Introduction The growing importance of social-environmental issues has increased the pressure on conventional SC entities to cooperate in sustainability (Dehshiri and Amiri, 2024; Zkik et al., 2023). As Supply Chains (SCs) can be complex and geographically spread, organizations may lose visibility over their network topology (Khan et al., 2022a). Meanwhile, creating a responsive and sustainable SC requires adaptability to change and readiness to recover from unforeseen disruptions (Pandey et al., 2024). Therefore, organizations strive to incorporate real-time and transparent information sharing into their traditional Supply Chain Management (SCM) to reinforce SC capabilities (Yousefi and Tosarkani, 2024a). Recent technological advancements are transforming SCM systems, leading to the exploration of new operations strategies (Duan et al., 2024). One of the most promising technologies that has emerged is Blockchain Technology (BT) (Guo et al., 2024). Although BT was initially developed for handling cryptocurrencies, this technology is being utilized in SCM, where the estimated value of BT in the SC market is expected to range from US$ 1.4 billion to 4.21 billion by 2028 (Medina et al., 2024). The decentralization and smart contracts created by BT eliminate intermediaries and make SCs automated and sustainable, enhancing productivity and adaptability to market dynamics (Saberi et al., 2019; Zhang et al., 2022; Duan et al., 2024). BT adoption is a complex process because it involves upstream and downstream entities and depends on various drivers and enablers (e.g., transparency and traceability) that affect SC capabilities and consequently Supply Chain Performance (SCP) (Wamba et al., 2020; Dutta et al., 13 2020; Surucu-Balci et al., 2024). As shown in Fig. 2.1, BT adoption without a comprehensive investigation and analysis of its characteristics may lead to suboptimal outcomes for SCM. The managers are required to consider how fast-growing technology adoptions and their associated characteristics could result in improving strategic SCP (Akhtar, 2018; Adaryani et al., 2024). Previous studies have focused on exploring and ranking the BT adoption enablers in different SCs (Gupta et al., 2021; Bai et al., 2022; Yontar, 2023; Shardeo et al., 2023). Although enablers influencing adoption strategies can be inherently interrelated, the potential Causal Relationships (CRs) have been considered in limited studies using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) techniques (Hong and Hales, 2021; Agi and Jha, 2022; Samad et al., 2023). Furthermore, as the BT adoption in SCs is still infancy, the uncertainty in dealing with this problem should be considered (Zhu et al., 2022; Yousefi and Tosarkani, 2022a). Nevertheless, previous studies have not focused considerably on applying uncertainty in the analysis or prioritization process (Yadav and Singh, 2020; Kayikci et al., 2022; Sahebi et al., 2022). Moreover, there is a lack of strategic perspective to analyze the enablers and how they can affect SCP. Most previous studies have identified the most significant BT adoption enablers without considering their impact on SCP through the mediators. Decentralization Blockchain Technology CharacteristicsSmart ContractSecurity Transparency Traceability Supply Chain CapabilitiesCollaboration and Relationship ManagementStrategic Sourcing and ProcurementInventory ManagementProduction and Operations ManagementCost-effectivenessImproved Quality Lead Time MinimizationIncreased Customer SatisfactionStrategic Supply Chain Performance......Blockchain-based Supply Chain Figure 2.1 Explanatory model of BT adoption for enhancing strategic SCP 14 Failure to understand the potential of BT adoption in various dimensions of strategic SCP (e.g., cost efficiency and quality) will lead to unsuccessful adoption (Yousefi and Tosarkani, 2022a). Although BT proposes innovative solutions to improve SCP (Sharma et al., 2023), a theoretical reconsideration and empirical examination are needed to analyze the enablers of BT adoption to provide valuable insights for SC managers. However, there is still a limited understanding of the relationship between organizational theories and BT-related phenomena (Xu et al., 2022). Thus, Decision-Makers (DMs) need to integrate such theories and systems modeling to explore the impact of BT on improving strategic SCP. This can persuade managers to incorporate BT into their SCs. The current study endeavors to address four main questions: i) How do we explore enablers of BT adoption in SCs considering a strategic perspective? ii) How do enablers contribute to establishing a BT-based SC network? iii) How do these enablers impact strategic SCP through mediators? iv) How can we address the inherent uncertainty associated with the BT adoption problem? This study aims to propose a Robust System Analysis-based Framework (RSAF) to uncover the outcomes of BT adoption for sustainable SC networks. The RSAF is supported by the Critical Success Factor (CSF) theory to recognize the potential BT adoption enablers affecting SCP. This theory enables managers to effectively integrate and implement cutting-edge technologies into their organizations by identifying key elements (Govindan, 2024). Then, the Balanced Scorecard (BSC) is used to translate the organizationâs strategy into several performance metrics, helping managers monitor the organizationâs performance (Rezaee et al., 2021; Kaplan and Norton, 1996). Accordingly, the potential enablers are diversified into several categories to guide DMs to explore the role of enablers in enhancing business functions and strategic management of SCP metrics. As cost-effectiveness, improved quality, lead time minimization, and increased customer satisfaction 15 affect the SCP (Pattanayak et al., 2024), this study considers these four metrics to evaluate the impact of each enabler (i.e., BT characteristic) on the strategic SCP. In the following, the systems theory-enabled Fuzzy Cognitive Map (FCM) is developed to model the potential CRs among enablers and metrics. When modeling CRs, managers can encompass both the organizational and SC levels through the systems theory (Hong and Hales, 2021). After developing the causal-based model of enablers and SCP metrics, a scenario-making technique and an FCM learning algorithm are adopted to quantify the impact of enablers on the metrics. Then, the BT adoption enablers analysis problem is formulated in an uncertain environment to calculate the efficiency score of each enabler for improving SCP. Therefore, the robust Data Envelopment Analysis (DEA) model, integrated with the Best-Worst Method (BWM), is embedded in the proposed RSAF. Robust decision-making performs well across a range of possible scenarios (Moazzeni et al., 2023), helping DMs prioritize the enablers considering managerial conservatism. In addition to offering higher discrimination, the RSAF allows greater flexibility power in adjusting adoption strategies as needed to improve SCP. The rest of this chapter is structured as follows: Section 2.2 conducts a comprehensive literature review on the analysis of enablers of BT adoption in SC networks to identify the research gaps. The FCM, DEA-CCR (the first letter of the Charnes, Cooper and Rhodes), and robust DEA models are introduced in Section 2.3. In the following, a holistic framework is introduced in Section 2.4 to deal with the BT adoption enablers analysis problem. Then, the outcomes of implementing the proposed RSAF are discussed in Section 2.5. In this section, a comparative analysis is also provided to illustrate the effectiveness of the RSAF over conventional approaches. Section 2.6 discusses the theoretical, practical, and managerial implications. Section 2.7 concludes with final remarks and outlines the potential for future research. 16 2.2 Literature Review The adoption of BT is still relatively new and unexplored in the SCM field (Alazab et al., 2021). Hence, it is crucial to conduct a comprehensive and systematic investigation to recognize the key advantages and opportunities that BT can bring to SCM across different industries (Duan et al., 2024). In recent years, some researchers have explored the perceived attributes and enablers of BT adoption for SCM (Manzoor et al., 2022; Han and Fang, 2024; Duan et al., 2024). The BT characteristics and CSFs have also been examined in various SCs, such as sustainable SC (Ferdous et al., 2024), food SC (Li et al., 2023; Vu et al., 2023; Akram et al., 2024), agri-food SC (Akella et al., 2023), maritime SC (Surucu-Balci et al., 2024) and logistics SC (Karakas et al., 2021). To successfully adopt BT in SCs, it is also essential to investigate the importance of the identified enablers throughout the adoption process. Some researchers have explored the significance of these enablers, which has become one of the fastest-growing research streams in the discipline. Hong and Hales (2021) also utilized the DEMATEL technique supported by the systems of systems theory to assess the performance of BT in the SC network considering environmental, economic, customer, and information domains. This helps practitioners to find the CRs among performance measures and realize the BT attributes for SCM. Agi and Jha (2022) proposed a comprehensive framework based on the diffusion of innovation theory, a business technology adoption model, and the DEMATEL technique to identify the enablers and evaluate their impact on BT adoption. The results emphasized that the advantage of the BT and the external pressure are considered as the most significant groups of enablers affecting BT adoption in SCs. Another application of diffusion of innovation theory was shown by Nath et al. (2022) to recognize the enablers of BT adoption in the apparel SC. They developed a Technology-Organization-Environment (TOE)-diffusion of innovation framework 17 integrated with structural equation modeling and showed that trust, information sharing and collaborative culture are the most effective factors for BT adoption. 2.2.1 BT Adoption Enablers Analysis To investigate the hierarchical CRs among CSFs, Elhidaoui et al. (2022) employed the ISM approach to address the BT adoption for green SCM. The findings revealed that recording and trading-related CSFs may affect BT adoption. Kuei and Chen (2023) used the ISM-MICMAC approach, along with the theory of dynamic SC capabilities, to explore the hierarchical CRs between BT adoption enablers for SCM. This study implied that risk management facilitation can be considered as the most significant enabler. Samad et al. (2023) employed the ISM-DEMATEL approach to identify and investigate the hierarchical and network-based CRs among enablers of BT adoption in logistics SCs. The outputs of this study showed that traceability and real-time connectivity are the most influencing enabler and resulting enabler, respectively. Yontar (2023) introduced the analytic network process-multiattribute ideal-real comparative analysis approach to analyze CSFs considering the degree of influence. This study implied that increased food security and product lifecycle tracking are significant enablers in adopting BT in agri-food SCs. Shoaib et al. (2020) applied the analytic hierarchy process method to recognize and rank the effective factors in the incorporation of BT into sustainable SC networks. The findings emphasized that traceability and trackability are the main success factors of a BT-based sustainable SC. Similarly, Gökalp et al. (2022) utilized the analytic hierarchy process-supported TOE theory to explore and rank the BT adoption determinants in SCM systems. The results showed that environment-related enablers (e.g., inter-organizational trust) are more important than organization-related or technology-related ones. To address the shortcomings of a traditional 18 weighting Multi-Criteria Decision-Making (MCDM) method, Gupta et al. (2021) proposed the additive value function- BWM to evaluate the digitization enablers affecting the SCP. The results of their study implied that tracking and localization of products is one of the most important digitization enablers helping organizations improve their SCP. In the following, Bai et al. (2022) introduced a TOE framework along with using the BWM method to identify and evaluate the importance of enablers of BT implementation in agricultural SCs. The outputs of their study illustrated that smart contracts, security, and tracking capability are the most significant factors influencing the adoption process. Considering uncertainty in making decisions is crucial to accurately assess potential benefits and challenges for effective BT adoption. To deal with the inherent uncertainty, Sahebi et al. (2022) employed a fuzzy ISM-DEMATEL approach to highlight the benefits of BT adoption in renewable energy SC networks and recognize the CRs between the identified advantages. They demonstrated that the secure database, immutability, and decentralized database are the prominent enablers. Kayikci et al. (2022) proposed the integrated FCM and fuzzy BWM to investigate CSFs for BT integration in circular SCs considering their network-based CRs. The findings illustrated that network collaboration is the most important enabler. Shardeo et al. (2023) also showed the application of the fuzzy BWM method to explore and rank CSFs of BT adoption in the agri-food SC. The obtained ranking implied that quality control, trust, provenance tracking, and traceability are the major CSFs. Yousefi and Tosarkani (2024b) developed an analytical approach using the FCM and Combined Compromise Solution (CoCoSo) techniques to investigate the effect of BT adoption enablers on the improvement of sustainable SCP. The outcomes of their study reflected that smart contracts, traceability, and transparency added by BT to the SC network affect the SCP significantly. 19 2.2.2 Research Gaps According to Table 2.1, many studies have focused on identifying the BT adoption enablers through surveys and systematic literature reviews. To extend the scope of the enablers analysis problem, some researchers have identified the main enablers by employing expert-dependent weighting techniques. Nevertheless, they have not investigated the inherent CRs between the enablers. In the following, different approaches have been introduced based on the ISM and DEMATEL to consider the hierarchical CRs and model network-based CRs, respectively. Despite the importance of considering the information uncertainty, previous studies have rarely considered this concept in the analysis. Although some of these studies have adopted organizational theories to support the proposed approaches, they have not analyzed the impact of enablers on sustainable SC capabilities or performance. To do this, it is required to define several mediators or metrics associated with the SCP. Moreover, since BT adoption is a long-term investment, the strategic perspective should be involved in the impact analysis of BT adoption. Given the importance of BT adoption enablers analysis problem in making adoption decisions, the current research proposes the CSF-BSC theoretical framework to explore the potential BT adoption enablers. Then, the systems theory-enabled FCM model is proposed to model the potential CRs among BT adoption enablers and SCP metrics. This is followed by the implementation of the hybrid learning algorithm to measure the effect of each enabler on the SCP metrics. This algorithm helps organizations investigate the interactions between enablers and mediators to observe performance improvements with a dynamic perspective. In addition to employing a learning algorithm to provide more reliable results, this research introduces the most effective enablers for enhancing strategic SCP in an uncertain environment through a robust DEA model. This model also empowers DMs to deal with potential managerial conservatism during BT adoption in SC networks.20 Table 2.1 A summary of features of the current research compared to the previous studies in the field of enablers analysis Author(s) SC Type Enablers Analysis Scope Problem Background Methodological Framework Identification Structural/ Hierarchical CRs Modeling Network-Based CRs Impact Analysis Prioritization Theory-Driven Strategic Perspective Socio-Environmental Enablers SCP Concept SCP Metrics (including Customer Satisfaction) Managerial Conservatism Methodology Uncertainty Robustness Reliable Outputs High Discriminatory Power Yadav and Singh (2020) Sustainable SC â â â PCA-Fuzzy DEMATEL â Shoaib et al. (2020) Sustainable SC â â â Analytic Hierarchy Process Kamble et al. (2020) Agriculture SC â â â ISM-DEMATEL Gupta et al. (2021) SCM â â â Additive value function-BWM Mishra and Maheshw (2021) Food SC â Conceptual Framework Karakas et al. (2021) Logistics SC â Narrative Network Analysis Hong and Hales (2021) SCM â â â Systems of systems â â DEMATEL Nath et al. (2022) Apparel SC â â Diffusion of innovation-TOE Structural Equation Modeling Gökalp et al. (2022) SCM â â TOE Analytic Hierarchy Process Elhidaoui et al. (2022) Green SCM â â â ISM-MICMAC Bai et al. (2022) Sustainable SC â â TOE â BWM Khan et al. (2022b) General SC â â TOPSIS Kayikci et al. (2022) Circular SC â â â CSF â FCM-Fuzzy BWM â Sahebi et al. (2022) Renewable energy SC â â â Fuzzy ISM-DEMATEL â Agi and Jha (2022) General SC â â â Diffusion of innovation DEMATEL Manzoor et al. (2022) SCM â Systematic Literature Review Yousefi and Tosarkani (2022a) Mineral SC â â â â Network Theory â â Fuzzy Inference System-FCM-Fuzzy DEA â â â Li et al. (2023) Food SC â Systematic Literature Review Shardeo et al. (2023) Agri-food SC â â Fuzzy BWM â Akella et al. (2023) Agri-food SC â Systematic Literature Review 21 Continued to Table 2.2 Author(s) SC Type Enablers Analysis Scope Problem Background Methodological Framework Identification Structural/ Hierarchical CRs Modeling Network-Based CRs Impact Analysis Prioritization Theory-Driven Strategic Perspective Socio-Environmental Enablers SCP Concept SCP Metrics (including Customer Satisfaction) Managerial Conservatism Methodology Uncertainty Robustness Reliable Outputs High Discriminatory Power Vu et al. (2023) Food SC â â Conceptual Framework Kuei and Chen (2023) SCM â â Dynamic SC capabilities â ISM-MICMAC Mohammed et al. (2023a) Food SC â Systematic Literature Review Samad et al. (2023) Logistics SC â â â ISM-DEMATEL Yontar (2023) Agri-food SC â â â â Analytic Network Process- Multiattribute Ideal-Real Comparative Analysis Zkik et al. (2023) Agriculture SC â â â â Pythagorean Fuzzy Cumulative Prospect Theory-VIKOR â Han and Fang (2024) SCM â Thematic analysis Ferdous et al. (2024) Sustainable SC â â Conceptual Framework Pattanayak et al. (2024) SCM â Relational View â â Thematic analysis Duan et al. (2024) SCM â Technology Acceptance Model-Diffusion of Innovation â Conceptual Framework Surucu-Balci et al. (2024) Maritime SC â â â Systematic Literature Review Akram et al. (2024) Food SC â Interpretive Research Strategy Yousefi and Tosarkani (2024b) Sustainable SC â â â â â â FCM-CoCoSo â The current study Sustainable SC â â â â CSF-Systems Theory â â â â â BSC-FCM-BWM-Robust DEA â â â â 22 2.3 Preliminaries 2.3.1 FCM Technique FCM is a system analysis model that utilizes fuzzy logic and artificial intelligence to tackle novel decision-making issues (Bakhtavar et al., 2021). This technique is also able to model complex systems having various variables and CRs (e.g., direct, diverse, recurrent, and loops) compared to conventional methods, such as MCDM and structural equation modeling (Yousefi and Tosarkani, 2024a). Another main advantage of FCM modeling is its ease of use in modeling and knowledge display, along with handling incomplete or even conflicting data, enabling DMs to compare theoretical models more quickly with reality and improve them (Amirkhani et al., 2020). The FCM techniqueâs benefits in fuzzy reasoning, flexibility, adaptability, and interdisciplinary applications have led to its wide usage across various domains (Bakhtavar et al., 2021). As shown in Fig. 2.2, a causal-based model developed by FCM comprises nodes and edges representing the systemâs concepts (Ci) and causal links or CRs among them (Wij, i,j=1,â¦,n). The information of a system (e.g., variables, attributes, characteristics, states, and qualities) can be denoted by the concept nodes, which are classified into conventional concepts and target concepts. In the FCM model, a weight (Wij) is assigned to each CR between two concepts (Ci and Cj), which could be negative, positive, or neutral (i.e., absence of a meaningful relation). These three states reflect the types of CRs among the identified concepts and their causal degrees. The FCM modeling process can be implemented in four main steps: Step 1: Identifying FCMâs concepts: In this step, potential concepts describing the problem under study are explored through literature surveys or expert knowledge. DMs can consider a few objectives or measures as target concepts to evaluate the status of the system when a learning algorithm is running. 23 W12Step 1: Identifying FCM conceptsStep 2: Developing the causal-based modelExploring the potential attributes of the systemDefining the target conceptsExploring the potential CRs among FCM conceptsDetermining the matrix of causality weightsStep 3: Scenario-making ProcessDefining different scenariosInitializing the values of the conventional conceptsStep 4: Learning processChoosing an FCM learning algorithm Implementing the learning algorithm to train the modelC4 C5Target(C6)C1C3C2Recurrent relationshipsLoopW13W24W56W45W54 Figure 2.2 The structured framework of the FCM modeling technique Step 2: Exploring the potential CRs and determining the matrix of causality weights: In this step, the CRs among the concepts, identified in the first step, are explored and weighted through one of the FCM construction methods (i.e., semi-automatic and automatic). The matrix of causality weights is specified based on the expertsâ opinions when we adopt the semi-automatic approach. In this case, the expert(s) specify the weight of all potential CRs using values in the range of [-1,1] or [0,1] (Bakhtavar et al., 2021). On the other hand, in the automatic construction approach, DMs can determine the weight of the potential CRs based on the historical data or data provided in the 24 literature (Schneider et al., 1998). Eqs. (2.1) and (2.2) are employed to respectively calculate the strength of direct and inverse CRs between two specific concepts. dr =|x1(vr)-x2(vr)| (2.1) dr =|x1(vr)-(1-x2(vr))| (2.2) Where x1(vr) and x2(vr) show the membership degrees of r in the vectors associated with C1 and C2. As dr reflects the distance between the two r elements, the mean distance between them (AD) is calculated through AD=â |dr|mr=1m, where m denotes the number of elements. Lastly, S=1-AD is utilized to obtain the similarity between two specific vectors (S), indicating the weight of the potential CR. Step 3: Defining scenarios for analyzing the causal-based model: In this step, several scenarios are defined based on the problem under study to evaluate the status of the developed causal-based model. In each scenario, the initial value (e.g., value of 1) for each conventional concept is assumed to enable monitoring of the changes in the value of target concepts. Generally, the defined scenarios are considered as inputs of the learning algorithms implemented in the last step. The scenario-making process can also be employed to assess the performance of FCM models through target concepts under complex scenarios (Onari et al., 2021). Step 4: Implementing each scenario through the learning algorithm: In this step, an FCM learning algorithm (e.g., Hebbian-based and population-based algorithms) is selected and implemented for defined scenarios (Bakhtavar et al., 2021). The main inputs of the learning algorithm are the initial matrix of weights of the potential CRs and the values of concepts. The FCM learning algorithm assists DMs in determining the final (updated) values of target concepts in each scenario and reduces the dependency of these values on biased judgments (Yousefi and Tosarkani, 2024a). Once 25 a learning algorithm is performed, Eq. (2.3) is applied to interchange values of the identified concepts until the causal-based model attains stability (Papageorgiou et al., 2006). In Eq. (2.3), Ai(k) and Aj(k) indicate the values of Ci and Cj in iterations k, respectively. Ai(k+1) shows the value of Cj in iteration k and f(x) represents the transformation function (e.g., sigmoid function). This function assists in training the causal-based model by adjusting the values resulting from the multiplication of two matrices to fall within the specified ranges for the conceptsâ values. Ai(k+1)=f (Ai(k)+ â Aj(k).njâ i,j=1Wji) (2.3) 2.3.2 DEA-CCR and Robust DEA Models The nonparametric method of DEA was first introduced by Charnes et al. (1978) to measure the relative efficiency scores of several Decision-Making Units (DMUs) with multiple homogenous inputs and outputs. The DEA model measures the relative efficiency of DMUs as a ratio of a weighted sum of their outputs to a weighted sum of their inputs. More precisely, DEA uses linear programming to construct an efficiency frontier from the most efficient DMUs to allow the assessment of other units concerning this frontier and thereby identify both efficient and inefficient DMUs (Omrani et al., 2021). The first model of DEA, namely CCR, was introduced as Model (2.4): 11 11. :0 ,10 ,m so i ioi mm s mi ij i iji m imi ioiiMax w ys tw y w x jw xw iï±+= ++= + ===â ï£ ï¢=ï³ ï¢ï¥ï¥ ï¥ï¥ (2.4) 26 Where m inputs (1 ,...,j mjx x ) are used to produce s outputs ( ( 1) ( ),...,m j m s jy y+ + ) to measure the relative efficiency of DMU under consideration ( oï± ). In this model, j shows the number of existing DMUs ( 1,...,j n= ) and iw represents the weight of ith criterion ( 1,...,i m s= + ). In various real-world problems, accessing accurate values of inputs and outputs for DEA applications is often nearly impossible. Conventional DEA models do not consider data uncertainty, so even a small change in the data could significantly impact the results and rankings (Yousefi et al., 2018). A perturbation in the data of Model (2.4) may violate1 10m s mi ij i iji m iw y w x+= + =â ï£ï¥ ï¥ , leading to the obtained efficiency scores greater than one and, consequently, unreliable ranking. To measure the efficiency scores of DMUs under uncertainty, Sadjadi and Omrani (2008) first developed a robust DEA model, which was later extended by Omrani (2013). This model can manage uncertain or unknown probability distributions to find a robust solution, ensuring it remains close to optimal within conventional DEA constraints. They utilized the linear robust optimization model, proposed by Bertsimas and Sim (2004), to apply uncertainty in the coefficients of the equality constraints (e.g., 11mi ioiw x==ï¥ ) and maintain the linear structure to develop a robust DEA model as follows: 27 1 11 11. :0 , ,1, 1,...,, 1,...,ojm s mi io o i io o o ioi m i o Jm s mi ij j i ij j j iji m i j Jm siij ij ij ij ij ij ij ij ii i iMax w y w x p qS tw y w x p q i jwp q e x z j i mp q e y z j i m m sz w z iï±ï±+= + = ï+= + = ï+=â â ï ââ + ï + ï£ ï¢=+ ï³ ï¢ ï¬ =+ ï³ ï¢ ï¬ = + +â ï£ ï£ ï¬ ï¢ ï¥ ï¥ ï¥ï¥ ï¥ ï¥ï¥, , 0 , ,ij ij iw ip q z i j ï³ ï¥ ï¬ ï¢ ï³ ï¢ (2.5) Where m and s respectively show the number of inputs and outputs, which are considered as criteria. In Model (2.5), pi and qij are the dual variables, and jï± indicates the efficiency score of jth DMU, obtained from Model (2.4). Besides, ije is the perturbation value of ith criterion in jth constraint. oJ and jJ respectively denote the set of coefficients in the objective function and constraint jth that are exposed to uncertainty.jï regulates the DEA modelâs robustness against the conservatism level of the solutions. When 0jï = in Model (2.5), there is no protection against any uncertainty. While jï can be considered as the number of criteria to have full protection (Omrani et al., 2021). Compared to Model (2.4), 11mi ioiw x==ï¥ is replaced by 11m siiw+==ï¥ in Model (2.5). 2.4 Proposed RSAF This research develops a Decision Support Framework (DSF) to enable DMs to explore the role of BT in improving strategic SCP. The developed RSAF integrates two organizational theories, the systems modeling perspective, and a mathematical programming-based technique. As depicted in Fig. 2.3, this framework can be summarized in five main phases as follows: 28 Phase I: Exploring and categorizing the CSFs within BSC perspectives First, a theoretical background is used to describe the BT adoption enablers analysis problem with the lens of CSF after conducting the literature review on factors that enable the successful implementation of BT in SCs. CSF theory is significant for analyzing complex systems and provides a better understanding of the variables that affect successful technological integration and overall organizational performance (Hastig and Sodhi, 2020; Govindan, 2024). In this regard, the current research identifies and considers the potential BT adoption enablers, which generate positive outcomes and improve the performance of sustainable SCs. More precisely, these enablers relate to the key characteristics of BT (e.g., decentralization and immutability) and support organizations to achieve their strategic objectives. In the following, the identified CSFs enabling the BT adoption are categorized considering BSC perspectives (i.e., financial, customer, internal business process, and organizational capacity) to realize their role in the strategic management of SCP metrics. These metrics can be used to assess and optimize the performance of various SCs. Thus, BSC bridges the gap between strategy formulation and its adoption to assist managers in monitoring strategic performance improvement (Rezaee et al., 2021). Phase II: Developing a causal-based model of the CSFs and SCP metrics In this phase, the system theory is integrated with the systems modeling to evaluate the impact of enablers in each BSC perspective on the specified mediators or SCP metrics (e.g., cost-effectiveness, improved quality, and lead time minimization). The potential CRs among BT adoption enablers and metrics associated with strategic SCP are modeled using the systems theory-enabled FCM model. In this model, the nodes represent the concepts (i.e., enablers and metrics), and edges indicate CRs among concepts. After exploring the potential CRs, the initial weights of these relations are specified through the automatic construction approach. To do this, the fuzzy 29 version of Eqs. (2.1) and (2.2) are employed to calculate the strength of CRs ( jd ). Then, the weights of CRs are determined through 1( , , )1ml m mj j jjd d dSm== âï¥. Accordingly, a causal-based model of enablers and SCP metrics is developed to help DMs evaluate the direct and indirect influence of enablers on strategic SCP. Figure 2.3 The implementation phases of the developed RSAF 30 Phase III: Quantifying the potential impact of the CSFs on the SCP metrics In this phase, the impact of each BT characteristic on improving SCP is quantified through the scenario-making and learning processes. To do this, a unique scenario is considered for each enabler by assigning a value of 1 to its associated concept (i.e., activated node) and a value of zero to other existing concepts (i.e., deactivated nodes in the initial stage). Then, a hybrid FCM algorithm is run to analyze the interactions among all enablers. The hybrid learning algorithm used in this study includes two main stages: The Nonlinear Hebbian Learning (NHL) algorithm and the Differential Evolution (DE) learning algorithm. The NHL-DE algorithm incorporates the characteristics of both population-based and Hebbian-based algorithms to generate solutions with meaningful separability. Unlike the NHL algorithm, the hybrid algorithm makes concepts more distinguishable and the decision-making procedure more reliable (Onari et al., 2021). As shown in Algorithm 2.1, the first stage aims to evaluate the influence of the enabler under study on target concepts (e.g., SCP metrics). More precisely, in the implementation stage of the NHL, the FCM conceptsâ values and the non-zero weights of CRs are updated in various iterations using the provided equations until reaching the termination condition. γ and η donate learning rates, while sgn(wji) shows the type of CRs within the FCM model. In the second stage, the DE learning algorithm is utilized to update the values generated in the previous stage more accurately while keeping the identified CRs during the learning step. After initializing the population, this stage is repeated for all input concept states considering a weight constraint, ensuring values fall between 0 and 1 and enabling qualitative comparisons in the scenario-making phase. When the termination condition of the second stage is met, the updated values of the concepts associated with the SCP metrics are obtained. These values represent the impact of the enabler under consideration on the 31 different dimensions of the strategic SCP. After investigating each enabler in a unique scenario, the influence of all enablers on SCP metrics is obtained. Algorithm 2.1 The proposed learning algorithm for impact evaluation of enabler Inputs: The initial matrix of weights of potential CRs and the concept state matrix in each scenario Repeat the first and second stages for each scenario until all enablers are studied First stage: NHL algorithm 1.1 Repeat steps 1.2 to 1.4 for each iteration k until the termination condition is met: 1.2 Exchange values of concepts associated with enablers to calculate Ai(k+1) using Eq. (2.3). 1.3 Update the weights of CRs using ( )( )( ) ( ) ( ) ( ) ( ) ( ) ( )1 1 1 1 1 1. k k k k k k kji ji i j ji ji iw w A A sgn w w Aï§ ï¨â â â â â â= + â 1.4 Investigate the termination function 1.5 Return the updated weights to the second stage Second stage: DE algorithm 2.1 Initialize the population in the neighborhood of the updated weights and within weight constraints 2.2 Repeat for each input concept state (k): 2.3 Repeat steps 2.4 to 2.7 for i=1 to the number of populations: 2.4 MUTATION (Wi(k)) â Mutant_Vector 2.5 CROSSOVER (Mutant_Vector) â Trial_Vector 2.6 If F(Trial_Vector) â¤ F(Wi(k)): Accept Trial_Vector for the next generation 2.7 Continue to reach a slight difference between values of target concepts in two consecutive iterations Outputs: The final concept state matrix for each scenario Phase IV: Formulating the BT adoption enablers analysis problem under uncertainty In this phase, enablers serve as DMUs, while SCP metrics are used as the decision criteria. The obtained values for each enabler from the learning process in the previous phase are the values of four decision criteria. Since the uncertainty is inherently interconnected with these values, a fixed-input robust DEA model is employed in the last phase to deal with managerial conservatism. This model helps DMs to consider a range of possible scenarios and calculates an aggregated score for each potential enabler of BT adoption in sustainable SCs. Furthermore, since decision criteria (inputs and outputs) have relative contributions to overall performance measurement, ignoring the issue may lead to unreliable results (Güner, 2018). Although DMUs freely choose the input-output weights to achieve the efficient frontier in conventional DEA models, some DMs seek more 32 reliable weights in the evaluation process. More precisely, the high flexibility in weight selection by DMUs may cause the reduced discriminatory power of conventional DEA models (Omrani et al., 2021). Therefore, incorporating strategic thinking into the evaluation procedure helps DMs make more reliable adoption decisions. Model (2.6) shows a robust DEA integrated with the BWM, in which value judgments are applied in estimating the relative contributions of criteria (Omrani et al., 2021): 11 11 112. :0 ,1, 1,...,, 1,...,ojm s mi io o i io o o ioi m i o Jm s mi ij j i ij j j iji m i j Jm siij ij ij ij ij ij ij ij ii i iMax f w y w x p qS tMw y w x p q jwp q e x z j i mp q e y z j i m m sfzaxw zï±ï¸ï±+= + = ï+= + = ï+== â â ï ââ + ï + ï£ ï¢=+ ï³ ï¢ ï¬ =+ ï³ ï¢ ï¬ = + +â ï£ ï£ ï¬ = âï¥ ï¥ ï¥ï¥ ï¥ ï¥ï¥, , 0 , ,iB Bij ij iii iW Wiw iiip q z i jw a ww a wï¸ï¸ ï¢ ï³ ï¥ ï¬ ï¢ ï¬ ï¢ ï¬â ï¢ ï£ï¢ï£âï³ (2.6) In Model (2.6), 1f shows the robust DEAâs objective function and 2f represents the BWMâs objective function. The second objective aims to obtain the optimal weights of SCP metrics while minimizing the absolute maximum difference of BiBiawwâ and iWiWawwâ or maximizing -ï¸ . Here, Bia indicates the preference of the best criterion over criterion i, while iWa denotes the preference of criterion i over the worst criterion. Considering 11m siiw+==ï¥ and non-negativity constraints, B Bi iw a w ï¸â ï£ and i iW Ww a w ï¸â ï£ , specific for the linear BWM model (Rezaei, 2016), are 33 added to Model (2.6). Compared to Model (2.5), the bi-objective model includes these new constraints, which can be converted to four linear constraints: B Bi iw a w ï¸â ï£ , Bi i Ba w w ï¸â ï£ , i iW Ww a w ï¸â ï£ , and iW W ia w w ï¸â ï£ . In Model (2.6), 11m siiw+==ï¥ is considered as a constraint for both DEA and BWM models, while pairwise comparisons are performed for outputs ( 1,...,i m m s= + + ) of the fixed-input robust DEA. Phase V: Identifying the most effective BT adoption enablers on strategic SCP improvement In the last phase, the BWM-robust DEA model is solved to calculate the scores of all enablers. The potential conflict between the two distinct objective functions in Model (2.6) may result in deviations from the optimal solution for each function. Therefore, this bi-objective programming model should be transformed into a single-objective model. To do this, the min-max technique is employed to incorporate strategic thinking or BWMâs weights into the robust DEA model. This technique seeks to minimize the maximum deviations of the two functions from their ideal values (Omrani et al., 2021). These optimal values are calculated by solving each objective function separately while considering all the constraints of the problem. Accordingly, ( )* *1 21 1,om s mi io o i io o o ioi m i o JMinMax f w y w x p q fï± ï¸+= + = ïï¬ ï¼ï© ï¹ï¦ ï¶ï¯ ï¯ï© ï¹â â â ï â â âïª ïºï§ ï·ï­ ï½ï« ï»ï§ ï·ïª ïºï¨ ï¸ï¯ ï¯ï« ï»ï® ï¾ï¥ ï¥ ï¥ is designed to achieve an optimal solution for the non-convex multi-objective optimization, with *1f and *2f denoting the ideal values of the first and second functions, respectively. Considering ï¡ as the maximum deviations of the first and second objective functions (i.e., robust DEA and BWM) from *1f and *2f , the above-mentioned function is transformed into linear programming: 34 ( )*11 1*21 11. :,0 ,1, 1,...,, 1,...,ojm s mi io o i io o o ioi m i o Jm s mi ij j i ij j j iji m i j Jm siij ij ij ij ij ij ij ij iiMinS tf w y w x p q jfw y w x p q jwp q e x z j i mp q e y z j i m m szï¡ï± ï¡ï¸ ï¡ï±+= + = ï+= + = ï+=ï¦ ï¶â â â ï â ï£ ï¢ï§ ï·ï§ ï·ï¨ ï¸â â ï£â + ï + ï£ ï¢=+ ï³ ï¢ ï¬ =+ ï³ ï¢ ï¬ = + +âï¥ ï¥ ï¥ï¥ ï¥ ï¥ï¥, , 0 , ,B Bi iBi i Bi iW WiWjW ii iij i iw z iw iiiiip q z i jw a wa w ww a wa w wï¸ï¸ï¸ï¸ï£ ï£ ï¬ ï¢ ï³ ï¥ ï¬ ï¢ ï¬ ï¢ ï¬ ï¢ ï¬ â ï£â ï£â ï£ ï¢ ï¬ â ï³ï£ ï¢ ï¢ (2.7) After obtaining the optimal weights of SCP metrics ( *iw ), Eq. (2.8) is employed to calculate the score of each enabler ( *jï± ). Subsequently, the potential enablers are ranked such that the enabler with the highest score occupies the first position. This ranking assists DMs in identifying the main enablers for improving the strategic SCP and consequently adopting BT successfully. ** 1*1m si iji mj mi ijiw yWeighted OutputsWeighted Inputsw xï±+= +== =ï¥ï¥ï¥ ï¥ (2.8) 35 2.5 Case Description and Results The developed RSAF is applied to tackle the BT adoption enablers analysis problem and observe the performance improvement within sustainable SCs. Since a BT platform may have various characteristics (e.g., consensus mechanism, authorization and authentication, and contracts), it can add extensive capabilities to sustainable SC networks (Farshidi et al., 2020; Yousefi and Tosarkani, 2024b). For example, smart contracts integrated with BT adoption affect the sustainability of SC networks through the mutually agreed terms and conditions on socio-environmental issues. BT adoption in the manufacturing sector is pivotal for improving SC transparency and traceability, thereby promoting sustainable practices (e.g., ethical sourcing and waste reduction). By leveraging BT for sustainable SCM, manufacturers can drive efficiency while mitigating environmental impact and meeting regulatory standards. Once a transaction is performed in the BT-based sustainable SCs, a block containing data, a hash block, and a previous hash block, are added to the distributed leader through the predefined frameworks. As each block includes the hash of the preceding block and the blocks are linked together through cryptography, it helps stakeholders detect unauthorized changes to block information (Agi and Jha, 2022; Bamakan et al., 2020). In addition to the immutability of data, smart contracts help stakeholders build encrypted and real-time data exchange within sustainable SCs to support the continuous tracking of goods and services. To investigate the role of BT in improving manufacturing SCP, this study first explores the BT features (e.g., traceability) enabling the successful implementation of BT in sustainable SCs through the CSF theory. A comprehensive list of BT enablers affecting the adoption of BT in sustainable SCs is identified through a literature review and provided in Table 2.2. In addition to describing each BT enabler, this table categorizes these enablers into four BSC perspectives. 36 According to the descriptions of enablers and definition of BSC perspectives, five BT enablers (i.e., synchronized and verified transactions automation, improved inventory control, reduced administrative cost, trust-worthy system, and environmental sustainability) directly affecting the financial performance of SC are assigned to the financial perspective. As shown in Table 2.2, the other five BT enablers (i.e., customer centricity, anonymity and privacy, social responsibility, streamlined communication, and security) with a significant impact on enhancing customer satisfaction are allocated to the customer perspective. The internal business process perspective includes seven BT enablers (i.e., traceability, integrity, distributed and shared database, smart contract, auditability, flexibility, and decentralization) focusing on improving operational performance to meet SC goals. Lastly, six BT enablers influencing the SC competitiveness (i.e., transparency, provenance tracking, interoperability and collaboration, improved regulation and policy compliance, immutability and encryption, adaptability and scalability) are categorized into the organizational capacity perspective. Considering BT adoption in sustainable SCM, the significance of each identified factor enabling BT adoption in five manufacturing firms (MF) is derived from Zhang et al. (2022). More precisely, three scenarios are designed for each manufacturing firm, resulting in 15 different states. As depicted in Table A1 provided in Appendix A, this study adopts 10 linguistic variables, including Absolutely High (AH), Very Very High (VVH), Very High (VH), High (H), Moderate High (MH), Moderate (M), Moderate Low (ML), low (L), very low (VL), Absolutely Low (AL), None (N), to describe the importance of each enabler in adopting BT in an uncertain environment. 37 Table 2.2 The identified factors enabling BT adoption in sustainable SCs Perspective Enabler Description References Financial Synchronized and Verified Transactions Automation (CSF1) BT streamlines invoicing and verifiability of transactions by eliminating costly intermediaries and automates transactions (e.g., payment and inventory replenishment) through smart contracts to minimize verification time and overhead costs. Li et al. (2023); Akram et al. (2024); Ferdous et al. (2024); Samad et al. (2023) Improved Inventory Control (CSF2) BT facilitates the management of inventory by recording and distributing a copy of the purchase order information across the network and reducing forecast errors. Yadav and Singh (2020); Zhang et al. (2022); Ferdous et al. (2024) Reduced Administrative Cost (CSF3) BT reduces paperless transactions and provides better management and administration through the shared database to reduce transaction costs (e.g., contract establishment, monitoring, and resolving disputes). Surucu-Balci et al. (2024); Sahebi et al. (2022); Yadav and Singh (2020); Samad et al. (2023) Trust-Worthy System (CSF4) BT validates business transactions through its immutable and decentralized database to build up trust among network members and ensure their adherence to the reliability of data and quality requirements. Akram et al. (2024); Manzoor et al. (2022); Pattanayak et al. (2024) Environmental Sustainability (CSF5) BT ensures the permanence of eco-friendly practices through smart contracts and enables tracking goods to minimize environmental costs (e.g., greenhouse gas emissions and waste) and foster accountability. Surucu-Balci et al. (2024); Asadi et al. (2023) Meier et al. (2023) Customer Customer Centricity (CSF6) BT makes communications between SC entities and customers more streamlined, transparent, and automated to enhance the trust level and provide customers with knowledge of product origins. Yadav and Singh (2020); Zhang et al. (2022); Asadi et al. (2023) Anonymity and Privacy (CSF7) BT organizes cryptographic private keys and restricts access rights to guarantee the confidentiality and anonymity of data shared by different entities within the network. Han and Fang (2024); Duan et al. (2024); Sahebi et al. (2022); Yousefi and Tosarkani (2024b) Social Responsibility (CSF8) BT sets up efficient revenue-sharing mechanisms and ethical and quality-oriented frameworks via smart contracts to promote awareness of human rights and responsible sourcing. Yadav and Singh (2020); Zhang et al. (2022); Ferdous et al. (2024) Streamlined Communication (CSF9) BT provides end-to-end visibility and authorizes information sharing through a distributed ledger to create strategic relationships and remove intermediaries (i.e., the need for verification by external agencies) to reduce lead times. Surucu-Balci et al. (2024); Kuei and Chen (2023); Pattanayak et al. (2024) Security (CSF10) BT offers secure transactions through the cryptographic hashing functions and tamper-proof infrastructure and keeps a record of every activity by decentralization of information to create trust among participants. Surucu-Balci et al. (2024); Li et al. (2023); Duan et al. (2024); Asadi et al. (2023) 38 Continued to Table 2.2 Perspective Enabler Description References Internal Business Process Traceability (CSF11) BT enables tracing the flows of products and funds and determining their origins through the provenance of data and time stamps to provide real-time traceability within SC networks. Li et al. (2023); Han and Fang (2024); Biswas et al. (2023); Kuei and Chen (2023) Integrity (CSF12) BT creates an integrated SC network from suppliers to customers by integrating barcodes, digital tags, and sensors to manage and track the physical flow of products. Yadav and Singh (2020); Zhang et al. (2022); Shahaab et al. (2023) Distributed and Shared Database (CSF13) BT stores transactions and data across multiple locations to prevent a single point of failure and shares them through distributed authorization to foster a collaborative ecosystem and provide seamless information flow. Surucu-Balci et al. (2024); Han and Fang (2024); Sahebi et al. (2022); Tokkozhina et al. (2023) Smart Contract (CSF14) BT enables the creation of self-executing protocols and automated electronic contracts, all based on mutually agreed terms and conditions, thereby fostering document exchanges and seamless transactions among SC entities. Han and Fang (2024); Duan et al. (2024); Manzoor et al. (2022); Oudani et al. (2023) Auditability (CSF15) BT ensures precise, visible, and traceable data across the SC network to preserve information integrity and auditability, affecting the flow of high-quality production of goods and services. Sahebi et al. (2022); Akella et al. (2023); Ferdous et al. (2024) Flexibility (CSF16) BT transforms existing traditional structures through adaptable consensus, customizable smart contracts, and improved interoperability to enhance SC flexibility and enable agile responses to dynamic market changes. Ferdous et al. (2024); Han and Fang (2024); Yousefi and Tosarkani (2024b) Decentralization (CSF17) BT distributes data across multiple nodes rather than a single centralized server to foster a decentralized database architecture and establish trustful relations within SC networks. Duan et al. (2024); Sahebi et al. (2022); Yadav and Singh (2020) Organizational Capacity Transparency (CSF18) BT provides identical network copies across nodes and records any modifications through a consensus mechanism to make transactions transparent and visible to SC entities and extends the accountability of the decisions. Li et al. (2023); Han and Fang (2024); Duan et al. (2024) Provenance Tracking (CSF19) BT utilizes a transparent and immutable ledger to facilitate the identification of potential issues and verify the origin of a product or the authenticity of transaction data in the SC network. Sahebi et al. (2022); Surucu-Balci et al. (2024); Manzoor et al. (2022); Pattanayak et al. (2024) Interoperability and Collaboration (CSF20) BT enhances interoperability and simplifies the SC ecosystem to support collaborative workflows among multiple entities, sharing resources, and coordination across the network through decentralization and shared databases. Akella et al. (2023); Ferdous et al. (2024); Shahaab et al. (2023); Erol et al. (2022) Improved Regulation and Policy Compliance (CSF21) BT utilizes transparent information sharing and smart contracts to improve compliance with government policies, certifications, and reporting while incentivizing sustainable practices and adhering to circular economy regulations in SCs. Yadav and Singh (2020); Zhang et al. (2022); Singh and Sharma (2023) Immutability and Encryption (CSF22) BT securely stores transaction data across several nodes and creates an immutable audit trail through timestamp and permission control limitations to minimize risks of tampering and forgery. Han and Fang (2024); Duan et al. (2024); Sahebi et al. (2022); Manzoor et al. (2022) Adaptability and Scalability (CSF23) BT offers an extensive and inclusive database accessible through various channels without compromising data consistency, enhancing the scalability of SC operations in response to changes in business needs and market conditions. Yadav and Singh (2020); Zhang et al. (2022); Iranmanesh et al. (2023) 39 2.5.1 Implementation of the Proposed Framework 2.5.1.1 Developing the Causal-Based Model of Enablers After exploring the significance of factors enabling BT adoption in manufacturing firms, the potential CRs among BT adoption enablers are explored and modeled. To do this, first, the existence of CRs between factors is recognized through the literature review (Pundir et al., 2020; Yadav et al., 2023; Kuei and Chen, 2023; Samad et al., 2023; Yousefi and Tosarkani, 2024b; Pattanayak et al., 2024) considering the nature of enablers and metrics. Then, the fuzzy values associated with linguistic variables mentioned in Table A1 are used to determine the initial weight of potential CRs under uncertainty. Figure 2.4 shows the causal-based model of CSFs within each BSC perspective. Each part of this figure illustrates the inner CRs among CSFs by the solid lines and potential CRs between CSFs and strategic SCP metrics by the dotted lines. SCP metrics, as the target nodes, are added to the initial causal-based model of CSFs to enable evaluation of the system status throughout the learning process. SCP1 through SCP4, respectively, show cost-effectiveness, improved quality, lead time minimization, and increased customer satisfaction. SCP1 evaluates how efficiently resources are being utilized within the network and how process improvement reduces total SC cost. SCP2 measures quality in the SC may include defect rates, customer complaints, product returns, and adherence to quality standards or specifications. SCP3 refers to minimizing lead time through order processing time and overall order fulfillment cycle time. The increased customer satisfaction is indicated by SCP4 and is affected by on-time delivery performance, perfect order rate, and customer retention rates. By focusing on these metrics, the current study aims to make appropriate BT adoption decisions to drive continuous improvement within the sustainable SC network. 40 CSF1(0.533, 0.733, 0.853)CSF2CSF3CSF4CSF5Financial Perspective-Related CSFsStrategic Supply Chain Performance MetricsSCP1SCP2SCP3SCP4(0.533, 0.733, 0.907) CSF6CSF7CSF8CSF9CSF10SCP2SCP3SCP4(0.640, 0.840, 0.960)(0.613, 0.813, 0.920)(0.613, 0.813, 0.920)Customer Perspective-Related CSFsStrategic Supply Chain Performance Metrics (a) (b) CSF11CSF12CSF15CSF16CSF17 SCP2SCP3(0.633, 0.833, 0.913)(0.633, 0.833, 0.900)CSF13 (0.593, 0.793, 0.913)CSF14(0.573, 0.773, 0.907) (0.647, 0.847, 0.913)(0.620, 0.820, 0.927)(0.600, 0.800, 0.920)(0.627, 0.827, 0.933)(0.567, 0.767, 0.860)Internal Business Process Perspective-Related CSFsStrategic Supply Chain Performance Metrics CSF19CSF23CSF20CSF22SCP1SCP2SCP4CSF18(0.647, 0.847, 0.927)CSF21Organizational Capacity Perspective-Related CSFsStrategic Supply Chain Performance Metrics(0.593, 0.793, 0.913)(0.640, 0.840, 0.933)(0.560, 0.760, 0.907) (c) (d) Figure 2.4 The potential inner CRs among factors enabling BT adoption considering BSC perspectives 41 As shown in Fig.2.4a, some factors (i.e., CSF1) may affect others (CSF2 and CSF3) and strategic SCP metrics simultaneously. It implies that CSF1 (i.e., synchronized and verified transactions automation) influences SCP1 directly and indirectly through other CSF2 and CSF3. Considering the financial perspective, BT simplifies invoicing by cutting out costly intermediaries and automating transactions to minimize verification time and, consequently, reduce overhead and SC costs (Ferdous et al., 2024). Furthermore, since CSF1 affects CSF2 (i.e., improved inventory control) and CSF3 (i.e., reduced administrative cost) directly, both factors improve the SC cost-effectiveness by reducing inventory costs and expenses associated with disputes, compliance, or other risks (Pattanayak et al., 2024). In comparison with the frequently used techniques in the literature (e.g., analytic network process), the developed RSAF is able to consider the recurrent CRs within complex systems. As illustrated in Fig. 2.4b, the recurrent CRs between CSF7 (i.e., anonymity and privacy) and CSF10 (i.e., security) are considered within the causal-based model developed for the customer perspective. These two factors intensify each other to ensure the confidentiality and anonymity of data and enhance trust in a multi-stakeholder environment (Yousefi and Tosarkani, 2024b). While there are no potential inner CRs among these two factors and other ones, CSF7 and CSF10 can impact BT adoption decisions through the potential outer CRs. The outer CRs refer to relations among two factors from different BSC perspectives and provide DMs with a holistic view of the BT enablers analysis problem. Figure 2.4c highlights the importance of intermediate factors within the internal business process perspective. Take CSF15 as an example, CSF12, CSF13, and CSF14 directly influence auditability, guaranteeing the precise data, visible ecosystem, and high-quality production of products and services (i.e. SCP2) (Pattanayak et al., 2024; Yousefi and Tosarkani, 2024b). As shown in this figure, CSF15 highlights the role of integrity, distributed and shared database, and 42 the smart contract in improving the strategic SCP. Figure 2.4d illustrates that CSF19 (i.e., provenance tracking) and CSF23 (i.e., adaptability and scalability) are intensified by enhancing interoperability and collaboration (i.e., CSF20) within the SC network. Consequently, CSF19 and CSF23 enable tracking back to identify the origin and practices of the products and make conventional SC entities more agile in fulfilling customer requirements (Pattanayak et al., 2024). As illustrated in Fig. 2.4d, the organizational capacity-related CSFs donât affect SCP3, while this metric is impacted by other factors explored from financial, customer, and internal business perspectives. Focusing on the internal business perspective, it can be seen that CSF14 (i.e., smart contract) and CSF16 (i.e., flexibility) have a considerable impact on minimizing lead time through fostering seamless transactions and document exchanges among SC entities and transforming existing traditional structure (Han and Fang, 2024; Ferdous et al., 2024). Other inner CRs among CSFs can be investigated by focusing on the nature of enablers and metrics. In addition to the inner CRs, there are potential outer CRs among CSFs explored considering various perspectives. These relations enable DMs to analyze the enabler adoption analysis problem more comprehensively. Figure 2.5 shows a causal-based model of enablers from four BSC perspectives simultaneously. CSF9 (i.e., streamlined communication) has three input edges from two internal business process perspective-related CSFs (i.e., CSF13 and CSF17) and CSF18, an organizational capacity perspective-related factor. In addition to the direct impact of CSF9 on minimizing lead time by enabling strategic partnerships and removing intermediaries (see Fig. 2.4b), this enabler can affect other metrics through the output edges to CSF1, CSF11, CSF12, CSF18, and CSF20. As depicted in Fig. 2.5, CSF11 (i.e., traceability) is also improved by three outer and two inner CRs and, consequently, affects four external CSFs. Overall, the proposed model shows how the identified enablers or taking advantage of BT characteristics can result in improved strategic SCP.43 Organizational Capacity Perspective-Related CSFsFinancial Perspective-Related CSFsInternal Business Process Perspective-Related CSFsCustomer Perspective-Related CSFsCSF1CSF2CSF3CSF4CSF5CSF6CSF7CSF8CSF9CSF10CSF11CSF12CSF16CSF17CSF13CSF14CSF19CSF23CSF20CSF22CSF18(0.693, 0.893, 0.933)(0.567, 0.767, 0.900)(0.620, 0.820, 0.927)(0.640, 0.840, 0.933)(0.560, 0.760, 0.907)(0.687, 0.887, 0.953)(0.533, 0.733, 0.867)(0.567, 0.767, 0.873)(0.613, 0.813, 0.933)(0.580, 0.780, 0.927)(0.607, 0.807, 0.900)(0.613, 0.813, 0.920)(0.607, 0.807, 0.940)(0.600, 0.800, 0.933)(0.667, 0.867, 0.920)(0.587, 0.787, 0.893)(0.600, 0.800, 0.893)(0.593, 0.793, 0.940)(0.647, 0.847, 0.940)(0.600, 0.800, 0.920)(0.580, 0.780, 0.913)(0.533, 0.733, 0.880)(0.560, 0.760, 0.893)(0.627, 0.827, 0.907)(0.647, 0.847, 0.913)(0.607, 0.807, 0.900)(0.633, 0.833, 0.940)CSF21(0.627, 0.827, 0.933)CSF15(0.613, 0.813, 0.933)(0.560, 0.760, 0.907)(0.573, 0.773, 0.920)(0.620, 0.820, 0.927)(0.653, 0.853, 0.933)(0.587, 0.787, 0.920)(0.640, 0.840, 0.933)(0.673, 0.873, 0.940)(0.667, 0.867, 0.960)(0.607, 0.807, 0.900)(0.573, 0.773, 0.920)(0.600, 0.800, 0.933)(0.640, 0.840, 0.933)(0.547, 0.747, 0.880)(0.607, 0.807, 0.940)(0.613, 0.813, 0.920)(0.553, 0.753, 0.887)(0.580, 0.780, 0.913)(0.587, 0.787, 0.933)(0.613, 0.813, 0.933)(0.600, 0.800, 0.920)(0.567, 0.767, 0.873) Figure 2.5 The potential outer CRs among factors enabling BT adoption 44 2.5.1.2 Assessing the Impact of Enablers on SCP Metrics After developing a causal-based model, the scenario-making technique is employed to quantify the potential impact of the CSFs on the SCP metrics. In each scenario, the node associated with the CSF under study is activated, and then its impact on other nodes (including SCP metrics) is investigated. More precisely, the activation of a CSF affects other existing nodes through the potential CRs until reaching a stable state. This learning process can be performed using an FCM learning algorithm. The present study adopts the hybrid NHL-DE algorithm to analyze the interactions among all factors and, consequently, evaluates the influence of the enabler under study on SCP metrics. As shown in Algorithm 2.1, the conceptsâ values are exchanged and the non-zero weights of CRs in various iterations are updated until reaching the termination condition. In the following, the algorithm updates the generated values more accurately to improve the separability of solutions. Lastly, the obtained values for concepts associated with SCP metrics show the impact of the enabler under consideration on the different dimensions of the strategic SCP. Figure 2.6 illustrates the impact of all potential enablers on four SCP metrics separately. As shown in Fig. 2.6a, CSF5, CSF2, and CSF20 are the most effective enablers on cost-effectiveness within sustainable SC networks. These enablers aim to highlight the role of BT in minimizing costs related to greenhouse gas emissions and waste, inventory control, and the lack of coordination. As depicted in the developed causal-based model, these CSFs are intensified by other BT features, such as smart contracts, decentralization, and shared databases. Considering the metric of improved quality, CSF15, CSF11, and CSF19 have a significant impact on the adherence to quality standards or specifications. If these enablers are considered in a conventional SC network through the BT adoption, they enable preserving information integrity and auditability, providing real-time and continuous traceability, and facilitating the identification of potential 45 issues. CSF1, CSF16, and CSF9 are the main enablers helping todayâs SC networks minimize lead time through order processing time and overall order fulfillment cycle time. BT streamlines invoicing, removes intermediaries, and revolutionizes existing traditional structures to reduce lead times and enable agile responses to dynamic market changes. Customer satisfaction can be increased significantly through several BT adoption enablers, such as CSF5, CSF8, and CSF6. More precisely, BT and its smart contract can foster accountability regarding eco-friendly practices, enhance awareness of human rights and ethical sourcing, and improve the trust level within a sustainable SC network. (a) (b) (c) (d) Figure 2.6 The impact analysis of enablers on strategic metrics: a) SCP1, b) SCP2, c) SCP3, and d) SCP4 46 2.5.1.3 Prioritizing the Factors Enabling BT Adoption After investigating the impact of BT adoption enablers on improving each metric, we aim to measure the overall impact of enablers on the strategic SCP. First, the BT enablers analysis problem is formulated in an uncertain environment through the fixed input BWM-robust DEA (see Model 2.7). In this model, 23 enablers and four SCP metrics are considered as the DMUs and outputs (i.e., decision criteria), respectively. The values of these decision criteria for each DMU have been obtained from the learning process in the previous phase (see Table 2.3). In addition to incorporating strategic thinking into the evaluation procedure, this model can consider a range of possible scenarios for managerial conservatism in calculating the aggregated scores for potential enablers. More precisely, the pairwise comparisons among these decision criteria are performed using the BWM to help DMs apply their preferences in the robust DEA-based evaluation procedure. In this case, DMs first choose the best and worst criteria, and then create vectors of Bia (i.e., the preference of the best criterion over criterion i) and iWa (i.e., the preference of criterion i over the worst criterion). For such companions, values of 1 to 9 can be used, where 1 shows two criteria are equally important and 9 represents the first criterion is absolutely important compared to another one. In this section, it is supposed that DMs donât have any preferences, indicating all SCP metrics are equally important. On the other hand, the robust DEA can adjust the modelâs robustness against the conservatism level of the solution through the parameter of jï . To find the value of this parameter, it is assumed that the probability of constraint violations is 5%, meaning constraints can be violated with a maximum probability of 95%. As jï can be considered as the sum of the number of inputs and outputs to have full protection, we can calculate the value of jï through 0.95=1 â eâð¤ð22Ã|ð|. As there 47 is one input with a fixed value and four outputs, |i| is equal to 5 and, consequently, the value of jï is approximately 5.47. To address the inherent uncertainty of the BT adoption enablers analysis problem, 10% perturbation ( ije ) is considered for all criteria. After implementing Model (2.7) using GAMS software, the weights of decision criteria are specified considering the DEAâs constraints and DMâs preferences. Then, Eq. (2.8) is adopted to estimate the scores of the identified enablers of BT adoption in sustainable SCs. As shown in Table 2.3, CSF14 (i.e., smart contract) with a score of 0.8182 is the most significant enabler affecting manufacturing SCP and, consequently, the adoption of BT in SC networks. While CSF14 is not the most effective enabler on each metric, this enabler has the highest overall impact on SCP improvement because of its direct impact on other CSFs. Smart contract improves SCP indirectly through other mediators, including CSF1, CSF5, CSF8, CSF15, CSF16, and CSF21. In other words, BT can apply mutually agreed terms and conditions through self-executing protocols and automated electronic contracts to automate verification of transactions, adherence to environmental sustainability and social responsibility, and enable agile responses to dynamic market changes. CSF11 (i.e., traceability) is ranked second with a score of 0.8135. This enabler is intensified by several factors (e.g., distributed and shared database, transparency, and immutability) and then affects SCP directly and indirectly. More precisely, improving traceability after adopting BT can facilitate inventory management, eco-friendly practices and responsible sourcing, and the identification of potential issues or verification of the origin within sustainable SCs. This significant impact is transferred to the SCP metrics indirectly through CSF2, CSF5, CSF8, and CSF19, while CSF11 also minimizes lead time directly (see Fig. 2.4c). As the internal business process perspective includes several highest influencing CSFs (i.e., 48 CSF14 and CSF11), it is implied that BT adoption can significantly enhance the efficiency of the existing systems. Based on Table 2.3, CSF9 (i.e., streamlined communication) and CSF19 (i.e., provenance tracking) are the other two main enablers of BT adoption with scores of 0.8069 and 0.7990, respectively. These enablers have one of the greatest impacts on reducing the lead time and response time within the SC network. This results in CSF9 and CSF19 being ranked third and fourth, respectively, from the perspectives of customer and organizational capacity. Focusing on the financial perspective, CSF5 (i.e., environmental sustainability) is an important factor within the studied context, despite being ranked eighth overall. This can be attributed to the low impact of this enabler on SCP2 and SCP3. Table 2.3 The obtained scores for CSFs enabling BT adoption in sustainable SCs Perspective Enabler SCP1 SCP2 SCP3 SCP4 BWM-Robust DEA Score Priority Financial CSF1 0.6971 0.4210 0.7856 0.5422 0.7443 15 CSF2 0.7408 0.4346 0.6998 0.5570 0.7380 17 CSF3 0.7206 0.4125 0.6095 0.5092 0.6863 22 CSF4 0.5562 0.7122 0.5713 0.6092 0.7344 18 CSF5 0.7460 0.5619 0.5289 0.7563 0.7839 8 Customer CSF6 0.6648 0.6579 0.5614 0.7076 0.7775 10 CSF7 0.6768 0.4616 0.5326 0.6493 0.6990 21 CSF8 0.5857 0.6627 0.5032 0.7130 0.7465 14 CSF9 0.5978 0.6775 0.7609 0.6601 0.8069 3 CSF10 0.6043 0.5732 0.5043 0.6073 0.6855 23 Internal Business Process CSF11 0.5677 0.7620 0.6796 0.7092 0.8135 2 CSF12 0.7253 0.5485 0.6513 0.7060 0.7899 5 CSF13 0.6660 0.5948 0.7165 0.5536 0.7589 13 CSF14 0.6914 0.6606 0.7218 0.6632 0.8182 1 CSF15 0.6856 0.7767 0.4962 0.6568 0.7877 7 CSF16 0.6865 0.5379 0.7805 0.5854 0.7789 9 CSF17 0.5841 0.6840 0.7324 0.5274 0.7610 12 Organizational Capacity CSF18 0.6214 0.7156 0.5689 0.6790 0.7746 11 CSF19 0.6524 0.7450 0.5606 0.7053 0.7990 4 CSF20 0.7373 0.5535 0.7111 0.6281 0.7886 6 CSF21 0.6152 0.6642 0.4706 0.6113 0.7089 19 CSF22 0.7039 0.6138 0.4975 0.5029 0.7054 20 CSF23 0.6108 0.4623 0.6861 0.6891 0.7402 16 49 2.5.2 Comparative Analysis Compared to the previously developed approaches (e.g., MCDM techniques), the proposed RSAF is able to deal with several aspects of the BT adoption enablers analysis problem. In addition to modeling the potential CRs among enablers, the RSAF considers the uncertainty and managerial conservatism inherently interrelated with the BT adoption problem. Furthermore, the embedded CSF-BSC theoretical framework enables involving the strategic perspective in the impact analysis of BT adoption on SCP. As the separability of solutions can help DMs make more reliable BT adoption decisions, discriminatory power is considered as the indicator for comparison in this section. In Phase III of the developed RSAF, the separability of solutions (i.e., the obtained values for SCP metrics) is improved through the hybrid FCM algorithm compared to the NHL. Then, the BWM-robust DEA keeps the separability of the ranks assigned to enablers high to improve the quality of rankings, reduce ambiguity, and facilitate precise trade-off analysis. Figure 2.7 shows a comparison of the scores obtained from the implementation of the BWM-robust DEA, embedded in the proposed RSAF, and other conventional models (i.e., DEA-CCR and robust DEA). As shown in Table 2.4, the DEA-CCR model assigns thirteen different ranks to 23 CSFs, showing the low separability of ranking. This results from the calculated equal score for CSF1, CSF2, CSF5, CSF9, CSF11, CSF12, CSF14, CSF15, CSF16, CSF19, and CSF20. Other 12 CSFs are placed in the second to the thirteenth ranks. In the following, 10% perturbation is considered for all criteria to deal with the inherent uncertainty of the studied problem. After implementing the robust DEA (see Model 2.5), the new scores are calculated for all CSFs. Figure 2.7 illustrates that the mean of scores obtained through the robust DEA is 0.7942, while the mean of scores calculated by the DEA-CCR is 0.9740. This decline demonstrates that the robust DEA model maintains constraints despite rising uncertainty levels to find optimal solutions. Notably, 50 increasing the value of jï leads to a worsening of the objective function and, consequently, a decrease in the score values. Focusing on the ranking provided by the robust DEA, this model assigns fourteen different ranks to 23 DMUs. The first rank is assigned to CSF1, CSF2, CSF5, CSF9, CSF11, CSF12, CSF14, CSF15, CSF16, and CSF20, while other 13 CSFs are placed in the second to the fourteenth ranks. As depicted in Table 2.4, as uncertainty is considered the same for all criteria, rankings obtained from the DEA-CCR and robust DEA models remained almost unchanged. The main difference between these two rankings is assigning the second priority to CSF19 by the robust DEA, showing a slight improvement in the separability of the ranking. This enabler, like the other 10 CSFs, has the first priority based on the DEA-CCR model. Table 2.4 The obtained ranking from BWM-robust DEA compared to conventional models Enablers DEA-CCR Model Robust DEA Model BWM-Robust DEA Model Score Priority Score Priority Score Priority CSF1 1.0000 1 0.8182 1 0.7443 15 CSF2 1.0000 1 0.8182 1 0.7380 17 CSF3 0.9698 6 0.7876 8 0.6863 22 CSF4 0.9283 10 0.7530 11 0.7344 18 CSF5 1.0000 1 0.8182 1 0.7839 8 CSF6 0.9787 4 0.7984 5 0.7775 10 CSF7 0.9103 11 0.7367 12 0.6990 21 CSF8 0.9780 5 0.7981 6 0.7465 14 CSF9 1.0000 1 0.8182 1 0.8069 3 CSF10 0.8631 13 0.6937 14 0.6855 23 CSF11 1.0000 1 0.8182 1 0.8135 2 CSF12 1.0000 1 0.8182 1 0.7899 5 CSF13 0.9651 9 0.7864 10 0.7589 13 CSF14 1.0000 1 0.8182 1 0.8182 1 CSF15 1.0000 1 0.8182 1 0.7877 7 CSF16 1.0000 1 0.8182 1 0.7789 9 CSF17 0.9848 3 0.8044 4 0.7610 12 CSF18 0.9658 8 0.7867 9 0.7746 11 CSF19 1.0000 1 0.8147 2 0.7990 4 CSF20 1.0000 1 0.8182 1 0.7886 6 CSF21 0.8982 12 0.7256 13 0.7089 19 CSF22 0.9695 7 0.7905 7 0.7054 20 CSF23 0.9903 2 0.8094 3 0.7402 16 51 Figure 2.7 Comparing the scores obtained from BWM-robust DEA with conventional models The low separability of ranking provided by the DEA-CCR and robust DEA models is attributed to the high flexibility in choosing weights by CSFs. To deal with this issue, the BWM-robust DEA model is able to estimate the weights of decision criteria considering the DEAâs constraints and DMâs preferences (i.e., strategic thinking). Considering the relative contributions of decision criteria in the evaluation process affects the separability of the rankings significantly. The results imply that the BWM-robust DEA model has greater discriminatory power compared to other approaches. As shown in Table 2.4, the BWM-robust DEA provides the highest possible separability in the ranking of CSFs. This, in turn, enhances the reliability of adoption decisions, making them less susceptible to data errors or minor changes, and supports complex decision scenarios. Compared to the robust DEA, the BWM-robust DEA model distinguishes all CSFs with a score of 1. According to the obtained ranking, CSF14, CSF11, and CSF9 are ranked first to third priorities, respectively, while these enablers have the same priority in other provided rankings. On the other hand, CSF1 and CSF2 are among the enablers with a score of 1, whereas BWM-robust DEA assigns the fifteenth and seventeenth ranks to these two enablers considering the relative 52 contributions of decision criteria. As depicted in Fig. 2.7, the mean of scores calculated by the BWM-robust DEA (i.e., 0.7577) is less than the other two models. Accordingly, the BWM-robust DEA outperforms these two DEA models in terms of discriminatory power and consequently results in a more reliable ranking. 2.6 Theoretical, Managerial, and Practical Implications The current study has contributed to the literature by integrating the strategic perspective into BT adoption practices for improving SCP. In addition to bridging the gap between theory and practice, the developed RSAF has sought to enable SC managers to get a more holistic view of the BT adoption enablers analysis problem. Previous studies have primarily analyzed enablers from an individual perspective, while BT adoption involves different aspects. Accordingly, the proposed RSAF has been supported by a CSF-BSC theoretical framework to describe the studied problem. The CSF theory provides managers with a better understanding of the key elements enabling successful technological integration in SCs and overall organizational performance. The BSC framework seeks to explore the CSFs within the financial, customer, internal business process, and organizational capacity perspectives. Compared to the literature, the BSC integrated with the CSF theory empowers DMs to investigate the strategic role of CSFs in improving SCP and existing business functions. The current research enables SC managers to monitor the impact of BT adoption on the strategic SCP through the potential CRs among CSFs and metrics. Focusing on strategic SCP helps managers effectively bridge the gap between organizational performance and business transformation. In this study, the potential CRs have been modeled using the systems theory-enabled FCM approach to observe performance improvements with a dynamic perspective. This study has also adopted a mathematical programming-based approach to address the inherent 53 uncertainty related to the BT adoption problem while considering the managerâs strategic thinking. This, in turn, allows greater flexibility in responding to new information about BT adoption enablers to help DMs adjust their information technology strategies as needed to improve SCP. By considering the robustness of various scenarios, DMs can also identify strategies that are less vulnerable to the high managerial conservatism level. Thus, the findings will help policymakers in designing effective strategies for faster implementation of BT in sustainable SCs. Table 2.5 shows the role of each CSF in improving SCP while managers have different preferences. Table 2.5 The impact of each metric on the strategic SCP improvement Enablers SCP1-Focused Scenario SCP2-Focused Scenario SCP3-Focused Scenario SCP4-Focused Scenario Score Priority Score Priority Score Priority Score Priority CSF1 0.7657 8 0.5583 22 0.8088 3 0.6500 20 CSF2 0.7966 4 0.5648 21 0.7444 9 0.6584 19 CSF3 0.7640 9 0.5300 23 0.6624 15 0.6058 23 CSF4 0.6613 23 0.7530 5 0.6550 16 0.6961 14 CSF5 0.8156 1 0.6641 14 0.6446 17 0.8182 1 CSF6 0.7545 11 0.7282 9 0.6643 14 0.7817 5 CSF7 0.7367 14 0.5664 20 0.6154 19 0.7116 13 CSF8 0.6850 21 0.7198 10 0.6131 20 0.7739 6 CSF9 0.7178 16 0.7526 6 0.8141 2 0.7588 9 CSF10 0.6789 22 0.6373 18 0.5914 22 0.6771 17 CSF11 0.6993 18 0.8150 2 0.7591 8 0.7962 2 CSF12 0.8035 3 0.6591 16 0.7301 11 0.7845 4 CSF13 0.7502 13 0.6784 12 0.7657 7 0.6652 18 CSF14 0.7889 5 0.7448 7 0.7895 4 0.7638 7 CSF15 0.7736 6 0.8165 1 0.6258 18 0.7472 11 CSF16 0.7717 7 0.6478 17 0.8182 1 0.6945 15 CSF17 0.6926 20 0.7417 8 0.7776 5 0.6480 21 CSF18 0.7223 15 0.7685 4 0.6683 13 0.7602 8 CSF19 0.7531 12 0.7977 3 0.6721 12 0.7875 3 CSF20 0.8126 2 0.6621 15 0.7718 6 0.7283 12 CSF21 0.6944 19 0.7100 11 0.5777 23 0.6876 16 CSF22 0.7584 10 0.6713 13 0.5941 21 0.6090 22 CSF23 0.7018 17 0.5842 19 0.7363 10 0.7557 10 In each scenario, it is assumed that one metric is the most important one compared to other ones. Figure 2.8 illustrates the changes in the rankings within each scenario compared to the original 54 scenario. As the first SCP metric is considered as the main criterion in the SCP1-focused scenario, Bia and iWa are (1,9,9,9) and (9,1,1,1), respectively. This shows a focus on cost-effectiveness, while the other three metrics have equal importance. If a manager prefers reducing total SC cost instead of optimizing all metrics, they should take advantage of BT in enhancing environmental sustainability, interoperability and collaboration, and integrity. As shown in Table 2.5, CSF5, CSF20, and CSF12, with scores of 0.8156, 0.8126, and 0.8035, have a significant impact on SCP. While these three CSFs are placed in the eighth, sixth, and fifth ranks, respectively, if the manager seeks to optimize all metrics equally (see Fig. 2.8a). (a) (b) (c) (d) Figure 2.8 Sensitivity analysis of the obtained rankings within different scenarios Considering the SCP2-focused scenario, it can be seen that CSF15 and CSF11, with scores of 0.8165 and 0.8150, are among the most influential enablers. In other words, BT supports 55 auditability and traceability to enhance SCP significantly if the manager focuses on improving quality. Whereas, Fig. 2.8b illustrates that CSF15 is not among the five most important enablers in the original scenario. Based on Table 2.5, flexibility (CSF16), streamlined communication (CSF9), and synchronized and verified transactions automation (CSF1) are the most effective enablers for minimizing lead time and improving SCP in the SCP3-focused scenario. In the SCP4-focused scenario, it is assumed that SCP4 is the main criterion, where Bia and iWa are equal to (9,9,9,1) and (1,1,1,9), respectively. In this case, the manager seeks to adopt the BT to increase customer satisfaction through different enablers, such as CSF5, CSF11, and CSF19. Compared to the original scenario shown in Fig. 2.8d, increasing the importance of SCP4 has led to changing the priority of CSF5 from 8 to 1. The study would not only empower managers to understand the different aspects of the BT adoption problem but also help practitioners explore the role of CSFs in achieving various goals. As the trade-off between SCP metrics affects these goals, practitioners can investigate the impact of BT on different types of SC strategies by changing the importance of the metrics. Figure 2.9 illustrates the roles of BT and its associated CSFs in implementing various SC strategies. As shown in this figure, the change in the importance of metrics results in different rankings of CFSs. This implies that managers and practitioners can choose a specific BT platform to adopt in their organizations considering their unique needs. If an organization seeks to adopt the BT for having an efficient SC, cost-effectiveness should be considered as the main metric. In this type of SC, lead time minimization is the second important metric, while SCP3 and SCP4 have the lowest importance. Therefore, it is assumed that Bia and iWa are equal to (1,5,3,5) and (5,1,3,1), respectively. As depicted in Fig. 2.9, BT can assist practitioners in minimizing costs and optimizing resource utilization within an efficient SC by facilitating collaborative workflows 56 among entities, resource sharing, and network-wide coordination (i.e., CSF20). This results from adding important BT characteristics, including decentralization, shared databases, and self-executing protocols, to the conventional SC networks. Figure 2.9 Comparing the role of CSFs in designing different SC strategies If an organization aims to ensure SC continuity during disruptions (e.g., natural disasters and geopolitical events), some BT characteristics will help managers create a resilient SC. Accordingly, maintaining service levels and customer satisfaction during disruptions is considered as the most important metric, while cost-effectiveness is the least important one. In this case, it is supposed that Bia = (8,3,3,1) and iWa = (1,6,6,8). Findings imply that improving streamlined communication (CSF9) and strategic partnerships within SC networks through BT adoption enables quick response to unexpected disruptions. Enhancing traceability (CSF11) and creating smart contracts (CSF14) also ensure real-time monitoring and automated processes, allowing for efficient problem resolution and maintaining resilience during unforeseen events. Considering the lead time minimization as the main metric can lead to an agile SC network. In this type of SC, it 57 is assumed that Bia and iWa are (8,4,1,6) and (1,5,8,3), respectively. As shown in Fig. 2.9, BT increases flexibility (CSF16) by transforming existing traditional structures through customizable smart contracts and adaptable consensus. Therefore, a BT-based SC network will be able to rapidly adjust itself to changes in demand, supply disruptions, and market conditions. To conclude, the proposed RSAF can be adopted by various organizations to uncover the outcomes of the incorporation of BT into their SCs. 2.7 Conclusions Recent technological advancements have increasingly affected SCM systems and operational strategies. In the meantime, BT inherently offers an immutable, transparent, and decentralized database to build trustful relationships and enhance the sustainability of SC networks. However, BT adoption is complex due to its intricate challenges involving managerial conservatism, technical integration, and alignment with strategic goals. Accordingly, there is a need to systematically investigate performance improvement while implementing BT adoption in SC networks. Exploring enablers of BT adoption in SC networks from a strategic perspective is crucial for leveraging its potential in enhancing SCP. Therefore, this study has incorporated organizational theories, systems modeling, and robust decision-making to develop a holistic DSF for evaluating the impact of BT characteristics. This can assist organizations in adding innovative BT features to their conventional SC networks. The current research first has theoretically described the BT adoption enablers problem through CSF theory and BSC to feed the proposed RSAF. Then, the role of each BT enabler in improving SCP has been explored and quantified by the developed RSAF. This framework has enabled DMs to model the potential interactions among enablers and SCP metrics. Considering the inherent uncertainty and managerial conservatism, the proposed 58 RSAF has also identified the most effective enablers in dealing with different SC strategies. The findings imply that smart contracts, traceability, streamlined communication, provenance tracking, and integrity act as the most important enablers in the association between BT adoption and strategic SCP. The results also show that the weights of SCP metrics can lead to the change of impact of each CSF on designing SC strategies. As BT adoption can influence the stakeholdersâ partnerships, future researchers are suggested to investigate how BT characteristics and SCP improvement rebuild stakeholder relations within SC networks. Each BT platform has its own unique features to help organizations meet their specific needs. Thus, future researchers can develop a theoretical-based DSF to deal with the BT platform selection problem, enabling managers to consider various perspectives to design adoption strategies. 59 Chapter 3: Enhancing Sustainable Supply Chain Readiness to Adopt Blockchain: A Decision Support Approach for Barriers Analysis 3.1 Introduction Due to recent technological advancements, sustainable Supply Chain (SC) operations are transforming from manual operations into information technology-based procedures (Chang et al., 2019). The asymmetric information and issues related to product authentication in the traditional SC networks and logistics systems affect business transactions and consequently increase operation costs (Xu and He, 2022). The inadequate real-time information handling also disrupts the SCs and poses a longstanding adverse impact on their performance (Mishra et al., 2023). Many organizations try to adopt emerging technologies like Blockchain Technology (BT) because of their inherent features (e.g., decentralization, traceability, and transparency) to optimize their existing operations (Yousefi and Tosarkani, 2024b). Due to such potential features and applications of BT, forecasts suggest that investments in BT practices will continue to grow and reach up to US$ 19 billion by 2024 (Taylor, 2022). BT solutions have been also introduced for Supply Chain Management (SCM) to enable managers to gain end-consumersâ confidence and enhance operational efficiency (Modak et al., 2024). Focusing on the SC industry, the BT solutions for this industry are expected to be worth between US$ 1.4 billion and 4.21 billion by 2028 (Medina et al., 2024). Furthermore, SC sustainability has recently been revolutionized by the emergence of technologies like BT (Dehshiri and Amiri, 2024; Zhang et al., 2024). Since the adoption of BT provides continuous tracking of products and real-time information sharing, it makes SCs more responsible for following the standards and improves sustainability functions through the mediation of SC integration (Ferdous et al., 2024; Surucu-Balci et al., 2024). 60 In BT-based sustainable SCs, entities generate and update data based on a distributed consensus algorithm to increase the authenticity of productsâ provenance information and guarantee data transmission and storage (Cao et al., 2022; Niranjanamurthy et al., 2019). Based on its structure, BT is a sequence of blocks linked using cryptography (Asadi et al., 2023). In each block, the hash block, the previous hash block, and a list of transactions are accumulated to provide a secure record of historical transactions (Bonab et al., 2023; Siddiqui and Haroon, 2023). The immutability and encryption of BT improve the transparency and traceability within SC networks and affect trust relationships (Menon and Jain, 2024; Chen and Su, 2022). Smart contracts, as one of the main advantages of BT adoption, also help managers and practitioners formulate sustainable SC entitiesâ rights and obligations by reducing third-party costs (Helo and Hao, 2019). Furthermore, such contracts enable organizations to consider the pre-determined conditions to address socio-environmental concerns, such as irresponsible sourcing and unsustainable practices and environmental costs due to increasing carbon emissions and non-compliance with regulations (Yousefi and Tosarkani, 2024b; Dehshiri and Amiri, 2024). Although BT has numerous advantages, its adoption in SCM remains challenging; a variety of antecedent factors and barriers affect an organizationâs readiness to adopt BT (Bai and Sarkis, 2022; Medina et al., 2024). These factors include a series of organizational, system-related, and external barriers (Saberi et al., 2019). Analyzing such barriers and identifying the most significant ones in the path of BT adoption can help managers and practitioners handle the innovation resistance to implementing this technology (Friedman and Ormiston, 2022). This also enhances the readiness of sustainable SCs to take advantage of BT to improve their performance (Yousefi and Tosarkani, 2024b). In this regard, some researchers have put their efforts into addressing the barriers analysis problem by developing theoretical and applying decision-making approaches to 61 get a successful BT implementation in SCs (Khan et al., 2023a; Karuppiah et al., 2023; Singh et al., 2023a; Almutairi et al., 2023; Surucu-Balci et al., 2024). Due to the increasing importance of the socio-environmental impacts of initiatives and projects in various domains (Selicati and Cardinale, 2023), Decision-Makers (DMs) are required to focus on sustainability dimensions in the BT adoption process. Considering the sustainability context, few studies have focused on analyzing BT adoption barriers and risks in sustainable SCs (Zhang and Song, 2022). The previous studies mainly adopt qualitative approaches or employ Multi-Criteria Decision-Making (MCDM) techniques to address the BT barriers and risk analysis problem (Vafadarnikjoo et al., 2023; Khan et al., 2023b; Singh et al., 2023b). Most studies have not considered inherent uncertainty, vagueness, and Causal Relationships (CRs) in barriers modeling and analysis simultaneously. Since uncertainty is the main feature of the risk management process and barriers are inherently interrelated, taking into account the uncertainty and potential CRs between barriers and their corresponding Risk Factors (RFs) can affect the reliability of the prioritization process. Considering these important features, this study aims to address four main questions: i) What is the impact of simultaneous consideration of uncertainty and reliability concepts on BT adoption barriers modeling and analysis? ii) How can the potential CRs between the BT adoption barriers change the priorities for implementing the mitigation measures? iii) What is the impact of multiple RFs corresponding to barriers on the prioritization of potential barriers? iv) What are the critical sustainability-related barriers affecting the organizationâs readiness to adopt BT into sustainable SCs? 62 Figure 3.1 A holistic framework for leveraging risk management to analyze BT adoption barriers To address the research questions, we seek to leverage risk management to provide a framework, including identifying, assessing, modeling, prioritizing, and mitigating barriers to BT adoption in sustainable SCs (see Fig. 3.1). This, in turn, enables DMs to reduce the deterrent effects of such barriers and consequently improves the readiness of sustainable SCs to adopt BT to address their specific needs. Accordingly, we develop a decision support approach incorporating the Z-number theory into the Fuzzy Cognitive Map (FCM) to model and analyze the identified barriers simultaneously in an uncertain environment. The proposed Z-number-based Fuzzy Cognitive Map (Z-FCM) approach also helps DMs to explore the interactions between the identified economic, social, and environmental barriers. FCM empowers DMs to consider the unlimited and different variables and model complex systems having loops or recurrent CRs compared to MCDM techniques and structural equation modeling (Rezaee and Yousefi, 2018). Furthermore, the Z-number theory integrates the fuzzy set theory for imprecise information and the probability theory for uncertain information to facilitate applying the reliability of information (AydoÄan et al., 2020). Therefore, the Z-number theory embedded in the Z-FCM approach enables DMs to consider the reliability concept in specifying the values of RFs and modeling CRs under uncertainty. Since the capability of treatment is one of the significant challenges in the risk management process (Zhang and Song, 2022), we assume the impact of Treatment level (T) for modeling and analyzing 63 potential barriers to BT adoption in addition to conventional RFs used in the Failure Mode and Effect Analysis (FMEA) technique (i.e., Severity (S), Occurrence probability (O), Detection probability (D)). The treatment level describes the degree of reduction, aversion, and transferability of each potential BT adoption barrier in sustainable SCs when managers and practitioners face (Zhang and Song, 2022). Therefore, considering the T factor in the barriers modeling and analysis can provide more accurate and reliable inputs for the mitigation process. After determining the values of RFs and exploring potential CRs between barriers to create a causal-based model, a hybrid FCM learning algorithm is employed to determine the criticality degree of each barrier. This criticality degree results from aggregating the values of RFs into a score with respect to the potential CRs. This learning algorithm can also reduce the outputsâ dependency on biased judgments to help DMs make more reliable decisions (Onari et al., 2021). Compared to the techniques used in the literature (i.e., FMEA and MCDM techniques), the proposed approach takes advantage of the Z-number theory and FCM learning algorithm to improve the reliability of the final ranking of barriers in an uncertain environment. The rest of this study is organized as follows: Section 3.2 reviews the studies in the field of BT adoption barriers analysis in various SCs to demonstrate the research gaps. The Z-number theory and FCM technique are introduced in Section 3.3. Then, a decision support approach is proposed in Section 3.4 to address the BT adoption barriers and risk analysis problem. Section 3.5 provides and discusses the outputs of the proposed approach for analyzing the barriers to BT adoption in sustainable SCs. The managerial and practical implications of the current research are discussed in Section 3.6. The conclusions and future research avenues are provided in Section 3.7. 64 3.2 Literature Review 3.2.1 BT Adoption Barriers Analysis BT has experienced significant application and growth in managing sustainable SC networks (Zhu et al., 2022). The adoption of BT can help managers and practitioners avoid imposing extra costs on the network in addition to improving response time and increasing the capability of real-time tracking of products in sustainable SCs (Yousefi and Tosarkani, 2024b). BT adoption also facilitates the exchange of information between BT-based sustainable SC network entities (i.e., actors, certifiers, registrars, and standard organizations) and consequently improves transparency within the network. To do this, smart contracts are defined to consider the agreed terms of stakeholders to approve transactions and document exchanges (Scott, 2017). In a BT-based sustainable SC shown in Fig. 3.2, adopted from Yousefi and Tosarkani (2022a), registrars provide a unique identity to conventional network actors (e.g., raw material suppliers, manufacturers, distributors, end-users, and recycling centers) to continue their activities. Furthermore, the certifiers are the representatives of government agencies to certify the existing actorsâ participation in the sustainable SC network. Besides, standard organizations direct sustainable SC operations by determining procedures and protocols based on government policy and operations nature. 65 Figure 3.2 A BT-based sustainable SC network The incorporation of BT into varied SC networks is challenging because organizations may face potential barriers and managerial conservatism. Therefore, identifying and analyzing these barriers is critical for minimizing their deterrent effects on the BT adoption process. To analyze challenges associated with BT adoption in agricultural SCs, Yadav et al. (2020) developed an integrated approach using Interpretive Structural Modeling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques. The findings of this study implied that lack of trust among stakeholders and regulatory uncertainty are root barriers to BT adoption. In another application of the ISM to model hierarchical CRs, Mathivathanan et al. (2021) employed the total ISM-MICMAC approach to investigate the relationships between BT adoption barriers in business SCs. This research showed that the lack of familiarity with BT is one of the most significant barriers to the process of BT adoption. In the following, Kouhizadeh et al. (2021) analyzed barriers to BT 66 adoption in sustainable SCs by applying the DEMATEL technique advanced by the Technology-Organization-Environment (TOE) framework. Their findings revealed that SC and technological barriers are the key barriers. Manzoor et al. (2024) also applied the DEMATEL method to identify the most significant enablers and barriers for BT-enabled resilient SCs. The results showed that the lack of acceptance and standardization, along with complexity, are the critical barriers affecting BT adoption for enhancing the resilience of SCs. In another application of the DEMATEL technique, Kaur et al. (2022) employed the analytic hierarchy process-DEMATEL to identify and prioritize the BT adoption challenges in SC finance practices considering CRs to show that technological challenges are the most important barriers. Yadav and Kumar (2023) also explored the barriers to BT adoption in vaccine SCs using the TOE-fuzzy DEMATEL approach to show that lack of technical expertise and the requirement for large-scale infrastructure are significant barriers. In addition to using the DEMATEL technique to recognize and investigate the main barriers to BT adoption in SCs by Khan et al. (2023b), its fuzzy version was employed by Vern et al. (2023) to model such barriers in agri-food SCs (e.g., high investment cost, unfamiliarity with BT technology, and lack of regulations). In the following, Kumar Singh et al. (2024) developed the grey DEMATEL approach to analyze CRs between the barriers on the way of the integration of radio frequency identification and BT adoption in developing public distribution systems. The results implied that the lack of familiarity and unclear return on investment and benefits have a high deterrent impact on BT adoption. Kumar et al. (2024) utilized the rough DEMATEL method to analyze the barriers associated with Industry 4.0 technologies adoption in sustainable food SCs. This study illustrated that technological immaturity, high investment, and lack of awareness and customer acceptance affect the adoption process significantly. 67 Focusing on the integration of MCDM techniques in an uncertain environment, Zhang and Song (2022) developed the Best-Worst Method (BWM)- Combined Compromise Solution (CoCoSo) approach using the rough set theory. They used the approach to evaluate the sustainability RFs of applying BT in sustainable SCs (e.g., increased training costs and repeated audits and certifications). In another study, Han and Rani (2022) developed an integrated approach based on the CoCoSo and CRITIC methods in the Pythagorean fuzzy environment to analyze the BT implementation barriers in manufacturing SCs. To investigate the BT adoption barriers in manufacturing SCs, Vafadarnikjoo et al. (2023) also applied the neutrosophic analytic hierarchy process to indicate that transaction-level uncertainty is one of the most critical barriers. Khan et al. (2023a) employed the BWM method to analyze the barriers associated with BT implementation in food SCs and recognized that technological and organizational barriers are critical BT adoption barriers. In the following, the fuzzy Delphi technique and the integrated approach based on the grey DEMATEL-WASPAS approach were introduced by Karuppiah et al. (2023) to analyze barriers to BT adoption in manufacturing SCs. This research revealed that the lack of knowledge about BT and new organizational policies is among the main challenges. Almutairi et al. (2023) proposed a hybrid gray MCDM approach to evaluate and rank the barriers to BT adoption in renewable energy SCs. This study indicated that the high investment cost is the critical barrier in the path of adoption of BT in energy SCs. Considering the uncertainty in analyzing barriers, Kumar and Barua (2023) applied a hesitant fuzzy set theory to address the BT adoption in petroleum SCs and recognized that lack of general standards, trust among partners, and understanding are significant barriers. Additionally, Singh et al. (2023b) employed the Pythagorean fuzzy analytic hierarchy process method to analyze potential barriers to BT adoption in construction SCs and revealed that the transparency range is 68 the most important factor. Govindan et al. (2023) investigated barriers to adopting BT platforms in healthcare SCs from a BSC perspective and prioritized using a structural approach. The results implied that financial and security issues, lack of knowledge, and regulatory uncertainty as the main barriers. Focusing on the BT adoption problem, Ferdous et al. (2024) provided a comprehensive overview of the enablers of BT contributing to sustainability in SCs and its associated potential barriers. Recently, Surucu-Balci et al. (2024) conducted a systematic literature review to explore dynamic capabilities, enablers, and barriers of BT adoption in maritime SCs and discussed the application of this technology in various SC-related business functions. Table 3.1 represents the characteristics of the present study compared to previous ones. This table compares various features, including considering the sustainability concept in identifying barriers, considering CRs, uncertainty, and reliability concept in analyzing the potential barriers. This table also illustrates the dependency of the outputs of the used approach in the literature on expertsâ feedback. Besides, multiple RFs refer to those who have employed sub-factors to analyze the identified barriers to BT adoption.69 Table 3.1 A comparison between the specifications of the current research and other studies Authors Application field Methodology Barriers Modeling Risk Treatment Sustainability-related barriers Multiple decision criteria/RFs CRs Uncertainty Reliability Low outputsâ dependency Ãztürk and YildizbaÅi (2020) General SC Analytic hierarchy process-TOPSIS â Yadav et al. (2020) Agricultural SC ISM-DEMATEL â Network Orji et al. (2020) Freight logistics Analytic network process Network Patil et al. (2021) Humanitarian SC BWM â Kouhizadeh et al. (2021) Sustainable SC TOE-DEMATEL â Network Mathivathanan et al. (2021) Business SC ISM-MICMAC Hierarchical Balci and Surucu-Balci (2021) Maritime SC ISM-MICMAC Hierarchical KaraÅan et al. (2021) General SC DEMATEL-Cognitive map â Network â â â Zhang and Song (2022) Sustainable SC BWM-CoCoSo â â â â Mangla et al. (2022) Tea SC Analytic hierarchy process â â Han and Rani (2022) Manufacturing SC CoCoSo-CRITIC â â Govindan et al. (2023) Healthcare SC BSC-Structural approach Hierarchical Vafadarnikjoo et al. (2023) Manufacturing SC Analytic hierarchy process â Khan et al. (2023a) Food SC BWM Khan et al. (2023b) General SC DEMATEL â Network Karuppiah et al. (2023) Manufacturing SC DEMATEL-WASPAS â Network â 70 Continued to Table 3.1 Authors Application field Methodology Barriers Modeling Risk Treatment Sustainability-related barriers Multiple decision criteria/RFs CRs Uncertainty Reliability Low outputsâ dependency Singh et al. (2023a) Construction SC Pythagorean fuzzy DEMATEL â Network â Singh et al. (2023b) Construction SC Pythagorean fuzzy analytic hierarchy process â â â Almutairi et al. (2023) Energy SC SWARA-EDAS â â Vern et al. (2023) Agri-food SC Fuzzy-DEMATEL â Network â Yadav and Kumar (2023) Vaccine SC TOE-Fuzzy DEMATEL â â Network â Kumar and Barua (2023) Petroleum SC Hesitant fuzzy-based system â Perumal et al. (2023) General SC ISM-MICMAC Hierarchical Ferdous et al. (2024) Sustainable SC Conceptual framework Surucu-Balci et al. (2024) Maritime SC Conceptual framework Kumar Singh et al. (2024) Distribution system Grey DEMATEL â Network â Akram et al. (2024) Food SC Conceptual framework â Manzoor et al. (2024) General SC DEMATEL â Network Dhingra et al. (2024) Healthcare SC BWM-ISM- MICMAC Hierarchical Kumar et al. (2024) Food SC Rough DEMATEL â Network â The current study Sustainable SC Z-FCM â â â â Network â â â 71 3.2.2 Research Gaps According to the summary of the previous studies presented in Table 3.1, it can be seen that most of them have focused on using weighting and outranking MCDM techniques (e.g., analytic hierarchy process, analytic network process, and DEMATEL) to analyze and prioritize the BT adoption barriers in various SCs. To implement the weighting MCDM techniques, DMs are required to form the comparison matrix, which may be challenging and time-consuming when there are numerous evaluation criteria. Some MCDM outranking techniques (e.g., Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)), also suffer from information distortion and ignorance of the target-based criteria because of having one normalization technique (Lai and Liao, 2021). Based on the literature, the ISM technique has been used to model CRs among barriers (Mathivathanan et al., 2021; Balci and Surucu-Balci, 2021; Perumal et al., 2023), but this technique can consider the hierarchical CRs not recurrent ones (i.e., network-based CRs) between factors and subfactors. The DEMATEL technique has been used frequently to model network-based CRs among barriers (Yadav et al., 2020; Khan et al., 2023b; Karuppiah et al., 2023). However, in most cases, this technique should be jointly implemented with other methods (e.g., ISM) to consider the importance of evaluation criteria and model CRs between them simultaneously in the prioritization process. Additionally, the outputs of this technique are highly dependent on the expertâs opinions (Yousefi and Tosarkani, 2022a). These shortcomings can reduce the reliability and separability of the outputs of the prioritization process (Onari et al., 2021). Considering the sustainability concept, the current study applies the weights of multiple RFs in the barriers analysis and models the existing CRs between barriers through the Z-FCM approach. This approach aggregates the impacts of multiple RFs, including the used factors in the 72 FMEA technique (S, O, and D) and T, and other barriers on the studied barrier into a score (i.e., criticality degree) through CRs. The developed Z-FCM approach and its hybrid learning algorithm simultaneously consider the reliability and uncertainty concepts in the impact assessment and prioritization stages and reduce the dependency of the findings on direct judgments, respectively. The embedded Z-number theory helps DMs deal with vague input in the face of a new decision-making problem, like BT adoption barriers and risk analysis, to make the rationale of decisions during the mitigation process. Furthermore, considering the risk treatment capability in the barriers analysis stage can ensure assigning more accurate priorities to the potential barriers to reduce the difficulty of mitigating them. Compared to the conventional FMEA and MCDM techniques, the proposed Z-FCM approach provides a reliable and separable barrier prioritization to help managers take effective and sustainable mitigation measures to control the deterrent effect of barriers with a high criticality degree. 3.3 Preliminaries This research proposes a decision support approach for analyzing the BT adoption barriers in sustainable SCs and modeling their CRs in an uncertain environment. Before providing the framework of this decision support approach, taking advantage of the Z-number theory and FCM technique, these methods are introduced in detail in this section. 73 3.3.1 Z-number Theory Due to the rapid advancement of information technology, traditional decision-making approaches face significant challenges when dealing with complex and uncertain scenarios. To address the ambiguous borders between various alternatives or imprecise data in such scenarios, fuzzy sets play an important role in the decision-making process (Khan and Wang, 2023). The fuzzy set theory introduced by Zadeh (1965) aims to manage imprecise and uncertain data by assigning membership grades, usually between 0 and 1. To handle the inherent uncertainty involved in many decision-making problems, alternative frameworks, such as interval-valued fuzzy sets, fuzzy soft sets, and hesitant fuzzy sets, have been also widely applied in recent years (Xiao, 2022). On the other hand, some researchers have focused on developing appropriate mathematical models to integrate DMsâ preferences into a collective preference for processing information during the decision-making process (Jana and Pal, 2023). Although the reliability of information may directly affect the accuracy of decision-making (Zadeh, 2011), most of the uncertain sets, especially classical fuzzy sets, assume that the decision information is completely reliable (Shen and Wang, 2018). To consider the reliability of information and the confidence of the evaluations in the decision-making process, Zadeh (2011) introduced the Z-number theory. Z-numbers add a reliability level to fuzzy linguistic values, enabling DMs to describe human knowledge, handle greater uncertainty, and produce more decisive results in an uncertain environment (Aboutorab et al., 2018; Dhalmahapatra et al., 2022). If X= (AÌ, RÌ) is considered as a Z-number, A indicates a fuzzy set (i.e., constraint), and R represents the reliability (i.e., certainty degree) of A. The certainty degree can be expressed as a fuzzy set or probability density function (Z= {x | x ϵ A with a certainty degree equal to R}). Thus, we have a probability constraint: R(X)=X is A, where A describes the probability distribution X (see Eq. (3.1)). 74 R(X): X is A â Poss (X=u) =μA(u) (3.1) Where u indicates the general value of X and μA is the membership function of A. μA can be also described as a constraint that is related to R(X). In this case, μA(u) is the degree that u satisfies the constraint mentioned in Eq. (3.2). R(X): X is p â Prob (uâ¤Xâ¤u+du) = p(u)du (3.2) Where p represents the probability density function of X (Zadeh, 2011). If it is supposed that AÌ=(la,ma,ua) and RÌ=(lr,mr,ur) are triangular fuzzy numbers and their membership functions are denoted by Eq. (3.3). Figure 3.3 shows a Z-number, in which the left is part of constraint (uncertainty), and the right is part of reliability. (3.3) ð?Ì?(ð¥) ={ 0 ð¥ â (ââ, ðð)ð¥ â ðððð â ððð¥ â [ðð, ðð]ð¢ð â ð¥ð¢ð âððð¥ â [ðð, ð¢ð]0 ð¥ â (ð¢ð, â)âââââââââââââââââââð?Ì?(ð¥) ={ 0 ð¥ â (ââ, ðð)ð¥ â ðððð â ððð¥ â [ðð , ðð]ð¢ð â ð¥ð¢ð âððð¥ â [ðð , ð¢ð]0 ð¥ â (ð¢ð , â) Figure 3.3 A simple Z-number X= (AÌ, RÌ) If the DM assigns the linguistics variables âMediumâ (M) and âHighâ (H) to the uncertainty (A) and reliability (R) concepts of a Z-number, respectively, we can assume that Z= (M, H) = 75 [(0.5,0.6,0.7), (0.5,0.7,0.9)]. Notably, the membership functions associated with each linguistic variable (e.g., M and H) will be defined in Section 3.4. The declaration of problems described based on the Z-number theory is comparatively easy while solving such problems can be difficult because of complex computation (Zadeh, 2011). Accordingly, DMs can dispose of Z-numbers straightforwardly with the existing operations or transform them into crisp numbers or other forms of fuzzy numbers to solve these problems (Onari et al., 2021). In this regard, Kang et al. (2012) transformed Z-numbers into triangular fuzzy numbers to use arithmetic operations defined in the fuzzy environment. To do this, transformation rules should be introduced based on the integration of membership functions used for determining values of uncertainty and reliability components. If we consider AÌ={(x, Î¼AÌ(x)) | x â [0,1]} and RÌ={(x, Î¼RÌ(x)) | x â [0,1]} are triangular fuzzy number membership functions, Eq. (3.4) can be employed to convert the value of reliability component into a crisp number (Kang et al., 2012). ( )( )RRx x dxx dxï­ï¡ï­=ï²ï² (3.4) Where Î¼RÌ(x), Î¼AÌÎ±(x), and α represent the membership degree of x â X on RÌ, the membership degree of x â X on AÌÎ±, and the reliability weight, respectively. As Z= (M, H) = [(0.5,0.6,0.7), (0.5,0.7,0.9)], α is computed equal to 0.7 based on Eq. (3.4). In the following, the reliability weight should be added to the first component (i.e., ZÌÎ±= (0.5, 0.6, 0.7 ; 0 .7)). The weighted Z-number can be shown in Eq. (3.5) ï ïï» ï½( , ) | ( ) ( ), 0,1AA AZ x x x xï¡ ï¡ï¡ ï­ ï­ ï¡ï­= = ï (3.5) Now, the assumed Z-number can be transformed into a triangular fuzzy number using Eq. (3.5): ZÌ'= (â0.7Ã0.5,â0.7Ã0.6,â0.7Ã0.7) = (0.4184, 0.5020, 0.5857). Likewise, other transformation 76 rules can be obtained to enable DMs to employ arithmetic operations. If AÌ=(la,ma,ua) and BÌ=(lb,mb,ub) are triangular fuzzy numbers and it is assumed that λ>0 (i.e., constant number greater than zero), the common arithmetic operations are provided as Eqs. (3.6) to (3.10). Besides, if AÌ=(la,ma,ua) be a triangular fuzzy number, Eq. (3.11) can be used to convert this number to crisp one. (3.6) ( , , )a b a b a bA B l l m m u uï = + + + (3.7) ( , , )a b a b a bA B l l m m u uï = (3.8) A ( , , )a b a b a bB l u m m u l= â â â (3.9) A ( / , / , / )a b a b a bB l u m m u l= (3.10) ( , , ) ( , , )a a a a a aA l m u l m uï¬ ï¬ ï¬ ï¬ ï¬= = (3.11) R(AÌ)=la+4ma+ua6 3.3.2 FCM Model FCM is introduced as a system analysis model inheriting the advantages of artificial intelligence and fuzzy logic to address complex decision-making problems (Bakhtavar et al., 2021). Compared to conventional techniques (e.g., MCDM), FCMs help DMs model the potential CRs between different variables when they face novel and complex situations (Rezaee and Yousefi, 2018). An FCM model consists of nodes and arcs indicating the concepts of the system (Ci) and CRs between concepts (Wij, i,j=1,â¦,n). These concepts are considered as representative of variables, attributes, characteristics, and states of the system and can be divided into two groups, including conventional and target concepts (Bakhtavar et al., 2021). Generally, the target concepts are defined by DMs to assess the system status or monitor the behavior of conventional concepts. Figure 3.4 illustrates a 77 sample of an FCM model in which nodes (Ci, i=1,â¦,6) show the concepts, and arcs (Wij, i,j=1,â¦,6) represent the CRs between concepts. C2C1C4C5LoopW13C6 (Traget)C3LoopW36W41W56W25 Figure 3.4 A sample of an FCM model considering the recurrent CRs and loops The sign and value assigned to each arc (Wij) represent the type of CR between Ci and Cj and its degree of causality. Therefore, Wij>0, Wij<0, and Wij=0 illustrate a direct CR (i.e., positive weight), an inverse CR (i.e., negative weight), and the absence of a meaningful CR between Ci and Cj concepts, respectively. The implementation of the FCM technique can be summarized in four steps. In the first step, FCM concepts are identified using the literature surveys. In the second step, one of the semi-automatic and automatic FCM construction approaches can be used to specify the weights of CRs between the concepts identified in the previous step. After recognizing the type of CRs between the concepts (i.e., direct, inverse, and none) due to their descriptions, in the semi-automatic FCM construction approach, the expert(s) are asked to directly determine the weight of extracted CRs in the range of [0,1] or [-1,1] based on their previous experience (Bakhtavar et al., 2021). While DMs calculate the weight of the CRs based on the relations introduced by Schneider et al. (1998) in the case of using the automatic FCM 78 approach. Eq. (3.12) and Eq. (3.13) are employed for starting the calculation of the strength of direct and inverse CR between two concepts (e.g., concepts 1 and 2), respectively. dr =|x1(vr) - x2(vr)| (3.12) dr =|x1(vr) - (1-x2(vr))| (3.13) Where x1(vr) and x2(vr) represent the membership degrees of r in the vectors belonging to the first and second concepts. dr shows the distance between the two r elements of these vectors. Then, the mean distance between these two vectors (AD) is calculated using Eq. (3.14). AD=â |dr|mr=1m (3.14) Where m shows the number of vector elements. The weight of the CR between two concepts or the similarity between the two vectors (S) is obtained through S=1-AD. In the third step, after constructing the initial FCM model, multiple scenarios should be defined to analyze the current and future system states. To define the scenarios, the initial values are allocated to the concepts (e.g., the value of 1) to activate them. Notably, the performance of an FCM model can also be investigated using the scenario-making process because this represents the ability of the model in describing the behavior of a system through target concepts (Onari et al., 2021). After determining the weights of CRs and defining the scenarios by assigning the initial values to concepts in the previous steps, DMs should implement a learning algorithm to describe the behavior of the studied system. These learning algorithms also enhance the reliability of the decision-making framework and accuracy of weight estimation by reducing the direct dependency of the final outputs on expertsâ judgments when we face new issues (Yousefi and Tosarkani, 2022b). 79 In the last step, an FCM learning algorithm is run for each scenario based on the determined matrix of initial weights of CRs to calculate the final values of target concept(s). More precisely, this algorithm enables DMs to calculate the values of other concepts related to one concept having a determined value in the scenario. To do this, Eq. (3.15) is used to exchange values of concepts until this model reaches a termination condition (e.g., a stable state, in which there is a slight difference between Ai(k+1) and Ai(k)) (Papageorgiou et al., 2006). Ai(k+1)=f (Ai(k)+ â Aj(k).njâ i , j=1Wji) (3.15) Where ( )kiA and ( 1)kiA+ show the values of concept Ci in iterations k and k+1, respectively. ( )kjA represent the value of concept Cj in iteration k and f(x) indicates the transformation function, respectively. The transformation functions (e.g., the sigmoid function) train the FCM model and return the values resulting from multiplying two matrices to the defined ranges for the value of concepts (i.e., assigning a significance level to each output). Eq. (3.15) is embedded in learning algorithms to train the developed FCM model. Accordingly, an appropriate FCM learning algorithm is selected among three main categories, including Hebbian-based, population-based, and hybrid learning algorithms, based on their characteristics and their compatibility with the studied system (Bakhtavar et al., 2021). An appropriate FCM learning algorithm makes concepts distinguishable (i.e., providing valid solutions with acceptable separability) and improves the reliability of the decision support approach (Onari et al., 2021). 80 3.4 Proposed Approach The proposed decision support approach for analyzing the potential barriers to BT adoption in sustainable SCs is described in this section. This approach is implemented in five main phases, including identifying the barriers considering the sustainability concept, determining the values of RFs, modeling CRs between the identified barriers, calculating the criticality degree in an uncertain environment to prioritize barriers, and categorizing barriers for tracking and mitigation. The implementation phases of the developed decision support approach are presented in Fig. 3.5. In Phase I, the potential barriers affecting the successful BT adoption in SCs are recognized through a literature survey and then finalized considering sustainability dimensions. Therefore, these barriers are explored in the sustainability context and categorized into economic, social, and environmental barriers. In Phase II, the values of RFs (i.e., S, O, D, and T) for all the identified BT adoption barriers can be determined through the literature review or by a cross-functional team. Considering the T factor along with conventional RFs in the barriers modeling and analysis enables DMs to focus on impact assessment and mitigation stages simultaneously, in addition to providing more reliable and accurate prioritization. If a cross-functional team is used, each member can employ the Z-number-based linguistic terms to specify the values of RFs for all potential barriers and the certainty degree of these values. Tables 3.2 and 3.3 represent the defined linguistic variables and their corresponding membership functions associated with uncertainty and reliability concepts (Onari et al., 2021; Yousefi and Tosarkani, 2022b). 81 Phase I: IdentificationPhase II: Assessment Defining the linguistic terms for expressing the uncertainty Defining the linguistic terms for expressing the reliabilityConverting Z-numbers to triangular fuzzy numbersCalculating the weights of CRs using the automatic construction approachPhase IV: PrioritizationConsidering the sustainability dimensionsConducting literature surveysPhase III: ModelingDefining the transformation rulesIdentifying the type of potential CRs between sustainability-related barriersDefining the scenarios based on the values of RFsImplementing the hybrid learning algorithmChoosing the FCM learning algorithmIdentifying potential barriers to BT adoption in sustainable SCsDetermining the values of traditional RFs (SOD) in Z-environment Specifying the level of risk treatment (T) in the Z-environmentDeveloping the Z-FCM model of BT adoption barriers in sustainable SCsPrioritizing the sustainability-related barriers to BT adoption Phase V: MitigationIdentifying barriers with high criticality level (Intolerable and Major barriers)Implementing tracking stage to re-assess the tolerable barriersProposing and implementing mitigation measures Identifying watch-listed barriers (Tolerable barriers)Calculating the concept values (criticality degree) for the all barriers Figure 3.5 The framework of the decision support approach for analyzing BT adoption barriers in sustainable SCs 82 Table 3.2 The linguistic terms for specifying the value of the uncertainty component Linguistic term Membership function Linguistic term Membership function Very High (VH) (0.9, 1.0, 1.0) Medium Low (ML) (0.4, 0.5, 0.6) Almost Very High (AVH) (0.8, 0.9, 1.0) Low (L) (0.3, 0.4, 0.5) High (H) (0.7, 0.8, 0.9) Slight (S) (0.2, 0.3, 0.4) Medium High (MH) (0.6, 0.7, 0.8) Very Slight (VS) (0.1, 0.2, 0.3) Medium (M) (0.5, 0.6, 0.7) None (N) (0.1, 0.1, 0.2) Table 3.3 The linguistic terms for specifying the value of the reliability component Linguistic term Membership function Very High (VH) (0.7, 1.0, 1.0) High (H) (0.5, 0.7, 0.9) Medium (M) (0.3, 0.5, 0.7) Low (L) (0.1, 0.3, 0.5) Very Low (VL) (0, 0, 0.3) In Phase III, potential CRs between the identified BT adoption barriers should be modeled to monitor the impact of each barrier on other ones. To do this, the developed FCM approach in the Z-number environment is employed to model the existing CRs between the barriers while determining their weights. After identifying the type of potential CRs based on the definitions of the potential barriers, the fuzzy weights of the direct and inverse relationships are calculated using Eqs. (3.16) to (3.18), respectively. These equations are the extended version of Eqs. (3.12) to (3.14) in the fuzzy environment to calculate the fuzzy weight of the CR between two barriers according to its type (direct or inverse). drÌ=|x1(vrÌ)-x2(vrÌ)|=|x1(vr l ,vrm,vru)-x2(vrl ,vrm,vru)| (3.16) drÌ=|x1(vrÌ)-(1-x2(vrÌ))| =|x1(vrl ,vrm,vru)-(1-x2(vrl ,vrm,vru))| (3.17) S Ì=1-ADÌ = 1-â |(dr l, dr m, dr u)|mr=1m (3.18) Where ð?Ì? indicates the fuzzy distance between the two fuzzy vectors x1(vrÌ)=x1(vrl ,vrm,vru) and x2(vrÌ)=x2(vr l ,vrm,vru). Each of these vectors includes the fuzzy values of RFs associated with a 83 specific barrier. Before using Eqs. (3.16) and (3.17), the specified Z-number-based linguistic variables for RFs corresponding to each barrier are converted to triangular fuzzy number-based ones using the transformation rules defined in Table 3.4. These transformation rules are obtained using Eqs. (3.4) and (3.5) by integrating the membership functions used for determining values of uncertainty and reliability components (see Tables 3.2 and 3.3). The outputs of this phase provide the matrix of fuzzy weights of the CRs. Based on this matrix, an initial causal-based model of BT adoption barriers can be developed. Table 3.4 The defined rules for transforming Z-numbers into triangular fuzzy numbers Z-linguistic variable Transformation rule Z-linguistic variable Transformation rule (VH, VH) (0.8563, 0.9514, 0.9514) (ML, VH) (0.3806, 0.4757, 0.5708) (VH, H) (0.7530, 0.8367, 0.8367) (ML, H) (0.3347, 0.4184, 0.5020) (VH, M) (0.6364, 0.7071, 0.7071) (ML, M) (0.2828, 0.3536, 0.4243) (VH, L) (0.4929, 0.5477, 0.5477) (ML, L) (0.2191, 0.2739, 0.3286) (VH, VL) (0.2835, 0.3150, 0.3150) (ML, VL) (0.1260, 0.1575, 0.1890) (AVH, VH) (0.7611, 0.8563, 0.9514) (L, VH) (0.2854, 0.3806, 0.4757) (AVH, H) (0.6694, 0.7530, 0.8367) (L, H) (0.2510, 0.3347, 0.4184) (AVH, M) (0.5657, 0.6364, 0.7071) (L, M) (0.2121, 0.2828, 0.3536) (AVH, L) (0.4382, 0.4929, 0.5477) (L, L) (0.1643, 0.2191, 0.2739) (AVH, VL) (0.2520, 0.2835, 0.3150) (L, VL) (0.0945, 0.1260, 0.1575) (H, VH) (0.6660, 0.7611, 0.8563) (S, VH) (0.1903, 0.1260, 0.3806) (H, H) (0.5857, 0.6694, 0.7530) (S, H) (0.1673, 0.2510, 0.3347) (H, M) (0.4950, 0.5657, 0.6364) (S, M) (0.1414, 0.2121, 0.2828) (H, L) (0.3834, 0.4382, 0.4929) (S, L) (0.1095, 0.1643, 0.2191) (H, VL) (0.2205, 0.2520, 0.2835) (S, VL) (0.0630, 0.0945, 0.1260) (MH, VH) (0.5708, 0.6660, 0.7611) (VS, VH) (0.0951, 0.1903, 0.2854) (MH, H) (0.5020, 0.5857, 0.6694) (VS, H) (0.0837, 0.1673, 0.2510) (MH, M) (0.4243, 0.4950, 0.5657) (VS, M) (0.0707, 0.1414, 0.2121) (MH, L) (0.3286, 0.3834, 0.4382) (VS, L) (0.0548, 0.1095, 0.1643) (MH, VL) (0.1890, 0.2205, 0.2520) (VS, VL) (0.0315, 0.0630, 0.0945) (M, VH) (0.4757, 0.5708, 0.6660) (N, VH) (0.0951, 0.0951, 0.1903) (M, H) (0.4184, 0.5020, 0.5857) (N, H) (0.0837, 0.0837, 0.1673) (M, M) (0.3536, 0.4243, 0.4950) (N, M) (0.0707, 0.0707, 0.1414) (M, L) (0.2739, 0.3286, 0.3834) (N, L) (0.0548, 0.0548, 0.1095) (M, VL) (0.1575, 0.1890, 0.2205) (N, VL) (0.0315, 0.0315, 0.0630) 84 In Phase IV, the fuzzy criticality degree of each barrier is calculated based on the fuzzy weights of potential CRs and the mean of fuzzy values assigned to RFs. As stated, the fuzzy values of RFs are obtained by converting Z-numbers to triangular fuzzy numbers using transformation rules. To calculate the criticality degree of barriers, the scenario-making approach and a hybrid FCM algorithm are employed in this phase. In this study, a scenario represents the values of each RF for all potential barriers. For example, if we have three barriers (B1, B2, and B3) and four RFs (S, O, D, and T) associated with these barriers, the scenario should be defined as follows [B1, SB1, OB1, DB1, TB1, B2, SB2, OB2, DB2, TB2, B3, SB3, OB3, DB3, TB3]. In this scenario, we seek to calculate the concept values associated with B1, B2, and B3 based on the mean of fuzzy values assigned to each RF associated with these barriers. To do this, a learning algorithm is employed to train the initial causal-based model to determine the final value for each barrier (i.e., criticality degree). This research uses the hybrid Nonlinear Hebbian Learning-Differential Evolution (NHL-DE) algorithm that incorporates the characteristics of two learning algorithms (i.e., Hebbian-based and population-based algorithms). This hybrid learning algorithm provides outputs having high separability compared to the NHL taking advantage of the DE algorithm (Onari et al., 2021). This algorithm aims to update non-zero weights in various iterations and keeps the potential CRs during the learning process. The NHL part of this hybrid learning algorithm updates the values of concepts and the weights of CRs based on Eqs. (3.15) and (3.19), respectively, until reaching the termination condition. ( )( )( ) ( ) ( ) ( ) ( ) ( ) ( )1 1 1 1 1 1. k k k k k k kji ji i j ji ji iw w A A sgn w w Aï§ ï¨â â â â â â= + â (3.19) Where γ and η illustrate the two learning rates, and sgn(wji) indicates the sign of weights or type of CRs (direct or inverse) in the initial causal-based model. Based on Algorithm 3.1, the DE part of the hybrid algorithm updates the input concept values obtained from the previous step more 85 accurately after initializing its population. In this stage, the weight constraint (i.e., the values must be fallen between 0 and 1) is used to analyze CRs between the concepts and make qualitative comparisons (Yousefi and Tosarkani, 2024b). Now, defined scenarios are implemented using the hybrid learning algorithm based on the matrix of the fuzzy weights of CRs between barriers. More precisely, by implementing this learning algorithm for the triple bounds of the matrix of weights (l, m, u), the final values of all concepts associated with barriers are obtained in the fuzzy form. These fuzzy values are considered as criticality degrees to prioritize the identified BT adoption barriers in sustainable SCs. In Phase V, all barriers are classified into four categories to introduce the critical barriers and watch-listed or tolerable barriers. To deal with the critical barriers, DMs should define and implement mitigation measures by highlighting the inherent features of BT. DMs also should track and re-assess tolerable barriers during the adoption process to prevent them from being critical barriers. Algorithm 3.1 The hybrid NHL-DE learning algorithm First stage: NHL algorithm Inputs: The initial weighting matrix and the concept state matrix 1.1 Repeat steps 1.2 to 1.4 for each iteration k until the termination condition is met: 1.2 Calculate Ai(k+1) through Ai(k+1)=f (Ai(k)+â Aj(k).njâ i , j=1Wji) 1.3 Update the weights using ( )( )( ) ( ) ( ) ( ) ( ) ( ) ( )1 1 1 1 1 1. k k k k k k kji ji i j ji ji iw w A A sgn w w Aï§ ï¨â â â â â â= + â 1.4 Investigate the termination function 1.5 Return the final weights to the second stage Second stage: DE algorithm 2.1 Initialize the population in the neighborhood of the final weights and within weight constraints 2.2 Repeat for each input concept state (k): 2.3 Repeat steps 2.4 to 2.7 for i=1 to the number of populations: 2.4 MUTATION (Wi(k)) â Mutant_Vector 2.5 CROSSOVER (Mutant_Vector) â Trial_Vector 2.6 If F(Trial_Vector) â¤ F(Wi(k)): Accept Trial_Vector for the next generation 2.7 Continue to reach the termination condition Outputs: The final concept state matrix 86 3.5 Analysis of the Results 3.5.1 Case Description To demonstrate its applicability, the proposed decision support approach is implemented to analyze the barriers to BT adoption in a sustainable SC network. The main objective of this SC is to adopt a BT platform (e.g., Ethereum and Hyperledger) to support the real-time and continuous tracking of products and data, build trustful relations between SCs entities (e.g., raw material suppliers, end-users, and recycling centers), and address socio-environmental concerns. However, there are some potential barriers affecting successful BT adoption in the SC network. Therefore, based on the first phase of the proposed approach, the potential barriers are explored through the literature review. The initial list of the potential barriers to BT adoption in sustainable SCs is adopted from Zhang and Song (2022). Then, this list can be modified after reviewing more recent studies and considering the problem under study (i.e., barriers modeling and analysis). The final list of potential barriers, along with their descriptions, is provided in Table 3.5. 87 Table 3.5 The identified barriers to BT adoption in SCs considering the sustainability concept Symbol Barrier Brief explanation (Sustainability Dimension) Sources B1 Adoption costs This results from technological complexities and the required infrastructures (e.g., initial development costs), the lack of familiarity with BT (e.g., increased training costs), and sustainability costs (Economic) Vern et al. (2023); Kumar and Barua (2023); Surucu-Balci et al. (2024) B2 Extra audits This refers to recertification and extra audits to address operation complexity and lack of trust in sustainable SC networks to guarantee real-world compliance (Economic) Cole et al. (2019); Zhang and Song (2022) B3 Opportunity loss This refers to the value of a lost potential benefit resulting from the long pay-back period of investment in the BT adoption process (Economic) Ferdous et al. (2024); Kumar Singh et al. (2024) B4 Operation complexity This describes the resistance to adoption resulting from the complexity associated with BT development, maintenance, and operation (Social) Patil et al. (2021); Zhang and Song (2022); B5 Lack of trust This refers to the lack of trust in BT within the SC network resulting from regulatory uncertainty and entitiesâ unfamiliarity with its inherent features (Social) Kumar and Barua (2023); Xu et al. (2023); Manzoor et al. (2024) B6 Regulatory uncertainty This arises from the shortage of pre-determined frameworks and standards and existing uncertainty for regulating sustainable SC entitiesâ behavior (Social) Zhang and Song (2022); Xu et al. (2023) B7 Lack of standardization This represents the lack of predefined protocols and standards to adopt BT in sustainable SC networks successfully (Social) Kumar and Barua (2023); Ferdous et al. (2024) B8 Energy consumption This involves increasing electricity consumption to perform innumerable meaningless calculations throughout the network (Environmental) Zhang and Song (2022); Yadav and Kumar (2023) 88 3.5.2 Implementation of the Proposed Approach After identifying the potential barriers in the first phase of the proposed approach, the values of RFs (i.e., S, O, D, and T) are specified for all barriers. The degrees of uncertainty and reliability for each RF are approximated from the existing literature considering the description of each barrier. Accordingly, the linguistic descriptions of uncertainty and reliability are specified in five scenarios (Sc1, Sc2, Sc3, Sc4, and Sc5), which can be considered as the supposed cross-functional team members, using the Z-number-based linguistic variables (see Table A2 in Appendix A). In the following, these linguistic variables are transformed into triangular fuzzy number-based terms based on the rules defined in Table 3.4. This helps DMs apply Eqs. (3.16) and (3.17) to model the potential CRs between barriers. The converted values of these linguistic variables are presented in Table A3 in Appendix A. The automatic FCM construction approach is then used to determine the weight of all meaningful CRs to create the initial causal-based model of adoption barriers. The fuzzy weight of each direct CR is determined using Eq. (3.18), where drÌ is calculated based on Eq. (3.16), and m is equal to 20. For example, the calculation process for determining the fuzzy weight of the direct CR between B5 (i.e., lack of trust) and B2 (i.e., extra audits) is presented in Table 3.6. Since increasing trust risk resulting from regulatory uncertainty and unfamiliarity with BT directly affects the increasing audits to address the lack of trust in sustainable SC networks, there is a direct CR between B5 and B2 with the fuzzy weight of (0.5722, 0.7285, 0.8642). Now, after identifying potential barriers (i.e., concepts) and specifying the fuzzy weights of all CRs (i.e., arcs), the causal-based model of BT adoption barriers can be developed as Fig. 3.6. In this figure, FCM concepts assigned to economic, social, and environmental barriers are shown by orange, blue, and green borders, respectively. 89 Table 3.6 Calculating the fuzzy weight of the direct CR between B5 and B2 B5 B2 ð ?Ì? Sc1 S (0.2121, 0.2828, 0.3536) (0.5857, 0.6694, 0.7530) (0.2321, 0.3866, 0.5409) O (0.2828, 0.3536, 0.4243) (0.5020, 0.5857, 0.6694) (0.0777, 0.2321, 0.3866) D (0.0837, 0.1673, 0.2510) (0.2510, 0.3347, 0.4184) (0.0000, 0.1674, 0.3347) T (0.1673, 0.2510, 0.3347) (0.5020, 0.5857, 0.6694) (0.1673, 0.3347, 0.5021) Sc2 S (0.1643, 0.2191, 0.2739) (0.6660, 0.7611, 0.8563) (0.3921, 0.5420, 0.6920) O (0.4184, 0.5020, 0.5857) (0.4243, 0.4950, 0.5657) (0.0070, 0.0070, 0.1614) D (0.4184, 0.5020, 0.5857) (0.2854, 0.3806, 0.4757) (0.0573, 0.1214, 0.3003) T (0.2510, 0.3347, 0.4184) (0.4184, 0.5020, 0.5857) (0.0000, 0.1673, 0.3347) Sc3 S (0.2510, 0.3347, 0.4184) (0.5857, 0.6694, 0.7530) (0.1673, 0.3347, 0.5020) O (0.2191, 0.2739, 0.3286) (0.4243, 0.4950, 0.5657) (0.0957, 0.2211, 0.3466) D (0.0837, 0.1673, 0.2510) (0.2854, 0.3806, 0.4757) (0.0344, 0.2133, 0.3920) T (0.1414, 0.2121, 0.2828) (0.5020, 0.5857, 0.6694) (0.2192, 0.3736, 0.5280) Sc4 S (0.2121, 0.2828, 0.3536) (0.7611, 0.8563, 0.9514) (0.4075, 0.5735, 0.7393) O (0.2510, 0.3347, 0.4184) (0.4950, 0.5657, 0.6364) (0.0766, 0.2310, 0.3854) D (0.6694, 0.7530, 0.8367) (0.4184, 0.5020, 0.5857) (0.0837, 0.2510, 0.4183) T (0.2828, 0.3536, 0.4243) (0.5857, 0.6694, 0.7530) (0.1614, 0.3158, 0.4702) Sc5 S (0.2828, 0.3536, 0.4243) (0.4950, 0.5657, 0.6364) (0.0707, 0.2121, 0.3536) O (0.1643, 0.2191, 0.2739) (0.4243, 0.4950, 0.5657) (0.1504, 0.2759, 0.4014) D (0.2121, 0.2828, 0.3536) (0.6694, 0.7530, 0.8367) (0.3158, 0.4702, 0.6246) T (0.3536, 0.4243, 0.4950) (0.3536, 0.4243, 0.4950) (0.0000, 0.0000, 0.1414) ð¨?Ì? (0.1358, 0.2715, 0.4278) ?Ì? (0.5722, 0.7285, 0.8642) 90 (0.5436,0.6970,0.8071)Adoption costsExtra auditsOpportunity lossOperation complexityLack of trustRegulation uncertaintyLack of standardizationEnergy consumption(0.6367,0.7920,0.9516)(0.5995,0.7484,0.8517)(0.7348,0.8719,0.9311)S_B1O_B1 D_B1T_B1 S_B3O_B3 D_B3T_B3S_B6O_B6D_B6T_B6S_B8O_B8D_B8T_B8T_B5D_B5O_B5S_B5 T_B4D_B4O_B4S_B4 Figure 3.6 The Z-FCM model of barriers to BT adoption in sustainable SCs In the fourth phase, the fuzzy criticality degree of each BT adoption barrier is calculated based on the fuzzy weights of exiting CRs, shown in Fig. 3.6, and the mean of fuzzy values of RFs, shown in Table 3.7. To do this, the scenario-making approach and the hybrid learning algorithm are employed to calculate the fuzzy criticality degree of all BT adoption barriers. In this study, a scenario is defined for each bound of the matrix of fuzzy weights of the CRs. Each of these scenarios represents the values of RFs for all identified barriers. More precisely, based on the determined values for RFs related to eight barriers, the concept values associated with B1 to B8 are calculated using the NHL-DE algorithm. This hybrid algorithm updates the values of concepts of these barriers and calculates the new weights of CRs between them until reaching a slight 91 difference between outputs of the learning procedure in two consecutive iterations. This algorithm is implemented to obtain the final values of the concept associated with the identified barriers in the fuzzy form according to the matrix of weights and the mean of values assigned to RFs. The calculated fuzzy criticality degrees for the BT adoption barriers in sustainable SCs are provided in Table 3.8. To prioritize these barriers, Eq. (3.11) is also applied to calculate the crisp values in Table 3.8. Table 3.7 The mean of fuzzy values assigned to RFs associated with BT adoption barriers S O D T B1 (0.6694, 0.7530, 0.8200) (0.3347, 0.4183, 0.5020) (0.2579, 0.3462, 0.4344) (0.5492, 0.6352, 0.7212) B2 (0.6187, 0.7044, 0.7900) (0.4540, 0.5273, 0.6006) (0.3819, 0.4702, 0.5584) (0.4723, 0.5534, 0.6345) B3 (0.0862, 0.1474, 0.2085) (0.3705, 0.4490, 0.5275) (0.2264, 0.2982, 0.3700) (0.1737, 0.2522, 0.3307) B4 (0.1203, 0.1715, 0.2227) (0.1962, 0.2637, 0.3312) (0.1723, 0.2456, 0.3189) (0.1512, 0.2181, 0.2850) B5 (0.2245, 0.2946, 0.3648) (0.2671, 0.3367, 0.4062) (0.2935, 0.3745, 0.4556) (0.2392, 0.3151, 0.3910) B6 (0.2123, 0.2824, 0.3526) (0.2957, 0.3579, 0.4202) (0.1361, 0.2041, 0.2722) (0.3471, 0.4279, 0.5087) B7 (0.1602, 0.2088, 0.2575) (0.3550, 0.4225, 0.4900) (0.3486, 0.4245, 0.5004) (0.3096, 0.3849, 0.4602) B8 (0.5259, 0.6093, 0.6785) (0.3170, 0.4004, 0.4838) (0.1648, 0.2458, 0.3269) (0.4580, 0.5463, 0.6346) 92 Table 3.8 The obtained priority scores for BT adoption barriers using Z-FCM and different versions of the FMEA technique Barrier FMEA (SOD) F-FMEA (SOD) Z-FMEA (SOD) Z-FCM (SODT and CRs) Score Priority F-score Crisp Value Priority Z-score Crisp Value Priority Improved Z-score Crisp Value Priority B1 96 3 (0.3174, 0.2952, 0.3018) 0.3000 3 (0.1917, 0.1490, 0.1895) 0.1628 2 (0.9847, 0.9895, 0.9920) 0.9891 1 B2 168 1 (0.3125, 0.2930, 0.3497) 0.3057 2 (0.2315, 0.2120, 0.2421) 0.2203 1 (0.9738, 0.9843, 0.9891) 0.9833 2 B3 15 8 (0.0271, 0.0748, 0.1008) 0.0712 8 (0.0059, 0.0133, 0.0276) 0.0144 8 (0.8742, 0.8781, 0.8814) 0.8780 8 B4 18 7 (0.0838, 0.1435, 0.1747) 0.1388 6 (0.0089, 0.0189, 0.0235) 0.0180 7 (0.9359, 0.9370, 0.9394) 0.9372 7 B5 48 5 (0.0832, 0.1016, 0.1316) 0.1036 7 (0.0290, 0.0482, 0.0433) 0.0442 4 (0.9620, 0.9662, 0.9714) 0.9664 5 B6 30 6 (0.0887, 0.1571, 0.1598) 0.1461 5 (0.0133, 0.0172, 0.0268) 0.0182 6 (0.9646, 0.9677, 0.9702) 0.9676 3 B7 100 2 (0.1426, 0.2210, 0.2400) 0.2111 4 (0.0145, 0.0246, 0.0310) 0.0240 5 (0.9359, 0.9374, 0.9401) 0.9376 6 B8 48 4 (0.3494, 0.3969, 0.4474) 0.3974 1 (0.1432, 0.1228, 0.1879) 0.1370 3 (0.9634, 0.9668, 0.9716) 0.9670 4 According to Table 3.8, B1 (i.e., adoption costs) is considered as the main critical barrier in the BT adoption process according to the score obtained from the implementation of the Z-FCM model. In fact, this barrier is one of the most significant barriers that SC managers face because of the increased costs resulting from technological complexities, the lack of familiarity with BT, and sustainability-related issues. Similarly, B2 (i.e., extra audits), with a score of 0.9833, is placed in the second rank. This economic barrier indicates the potential recertification and extra audits to address operation complexity and lack of trust (two social barriers) in the BT-based sustainable SC networks. In the following, the third and fourth priorities are assigned to B6 (i.e., regulatory uncertainty) and B8 (i.e., energy consumption), with scores of 0.9676 and 0.9670. Due to the existing shortage of pre-determined protocols and standards for BT adoption in sustainable SCs, B6 (a social barrier) prevents many organizations from adopting this technology in their networks. Furthermore, the proposed approach introduces energy consumption (an environmental barrier) as one of the most critical barriers in the path of BT adoption. Despite the effective role of smart contracts in considering socio-environmental issues for managing sustainable SCs, increasing 93 electricity consumption to perform innumerable meaningless calculations throughout the network plays the main barrier to BT adoption. 3.5.3 Comparative Analysis In this section, the obtained barrier ranking using the Z-FCM score is compared with three versions of the FMEA, including the conventional FMEA, Fuzzy FMEA (F-FMEA), and Z-number-based FMEA (Z-FMEA) (see Table 3.8). Based on the conventional FMEA and Z-FMEA, B2 is placed in the first rank, while B8 is considered the most critical barrier based on the F-FMEA. In fact, the patterns of ranks assigned to BT adoption barriers are changed after adding uncertainty, reliability, and CRs between barriers. However, all of these techniques illustrate that B3 (i.e., opportunity loss) is an acceptable barrier with the minimum criticality degree compared to other barriers. Thus, the advantages of BT adoption can persuade managers to accept this barrier without requiring any specific mitigation measure. Figure 3.7 focuses on investigating the changing ranks of the identified barriers after adding uncertainty, reliability, and potential CRs between barriers step-by-step. In the following, to have a comparative analysis between FMEA, F-FMEA, Z-FMEA, and Z-FCM model, we investigate the priorities assigned to B1, B2, B6, and B8 (i.e., the main barriers identified using the Z-FCM model). Since the lack or incompleteness of information affects the assessment process and barriers are inherently interrelated, the obtained barrier prioritization resulting from the conventional FMEA technique can be far from reality. Based on Fig. 3.7, B1 is still the third most critical barrier after adding the uncertainty in the barrier prioritization using the F-FMEA, while the rank assigned to B2 has been changed from 1st to 2nd. After considering the uncertainty, B8 is placed in the first rank according to the F-FMEA, whereas this barrier was ranked in the fourth 94 priority according to the conventional FMEA. The Z-FMEA takes into account the reliability concept in addition to the uncertainty in barrier prioritization. Thus, the Z-FMEA technique introduces B2 and B1 as the more critical barriers compared to the F-FMEA by assigning the first and second priorities to these barriers. Figure 3.7 The impact of considering uncertainty, reliability, and CRs on the prioritization Figure 3.7 also illustrates that considering potential CRs between barriers along with reliability and uncertainty in the barriers analysis stage affects the final ranking provided by the conventional FMEA technique significantly. Accordingly, B1 and B6 are placed in the first and third ranks based on the Z-FCM model, while these barriers have the third and sixth priorities according to the conventional method. As stated, the Z-FCM calculates criticality degrees by considering uncertainty, reliability, CRs between barriers, and the multiple RFs simultaneously. Thus, this approach can provide a more reliable prioritization compared to the different versions of the FMEA 95 technique (i.e., conventional FMEA, F-FMEA, and Z-FMEA). Due to its structure and capabilities, the Z-FCM can also provide a barrier ranking with more separability in dealing with a complex problem having numerous barriers and RFs. 3.6 Managerial and Practical Implications The advancement of information technologies (e.g., BT) and their potential features have affected SC networks in recent years. The literature highlights the benefits of BT for improving SC performance, but BT is an emerging technology supplanting existing structures within SC networks. Despite increasing attention to BTâs inherent features, its adoption process has remained ambiguous for many managers, causing various barriers to adopting this technology. The adoption of BT in sustainable SCs, in practice, is still infancy. Therefore, there is an inherent uncertainty when managers deal with the BT adoption process in their organizations. In addition to considering the uncertainty, this research has aimed to leverage risk management to develop a decision support approach for analyzing potential barriers and consequently addressing managerial conservatism. Compared to the previously developed methodologies, this approach helps DMs consider the indirect impact of each barrier on the BT adoption process through the potential CRs in a highly uncertain environment. More precisely, the proposed decision support approach applies the reliability of information and the confidence of the evaluations in the barriers modeling and analysis to produce more decisive results. Focusing on the theoretical background, the current research delivers more holistic risk management of the BT adoption barriers by considering the uncertainty and reliability concepts, CRs between sustainability-related barriers, and multiple RFs simultaneously. Furthermore, the proposed framework enables SC managers to identify, analyze, and plan to mitigate the BT adoption barriers simultaneously. As shown in Fig. 3.8, after 96 identifying the potential barriers to BT adoption, the Z-FCM model helps managers focus on barriers analysis, including impact assessment (Phase II), modeling (Phase III), and prioritization (Phase IV), to recognize critical BT adoption barriers. In the following, the outputs of the Z-FCM approach are considered as the main input for Phase V to enable DMs to propose more effective mitigation measures to improve sustainable SCâs readiness to adopt BT. 1. Identification2. Impact Assessment5. Mitigation 4. PrioritizationTrackingIdentifying potential BT adoption barriersDetermining the values of traditional risk factors and treatment levelProposing and implementing mitigation measures Identifying barriers with high criticality level (Intolerable and major category)Introducing the watch-listed barriers (Tolerable category)Re-assessing the watch-listed barriers3. ModelingModeling the potential CRs between BT adoption barriers Figure 3.8 A structured framework for handling BT adoption barriers in sustainable SCs In addition to the conventional RFs, this study also considers the T factor, as the representative of the mitigation stage, in the barriers modeling and analysis. This, in turn, helps DMs achieve more accurate and reliable inputs for the mitigation process. To explore the role of the T factor in barriers analysis, we implement the conventional FMEA, F-FMEA, Z-FMEA, and the proposed Z-FCM model by considering S, O, D, and T factors. Then, the outputs of these approaches (see Table 3.9) are compared with the conventional FMEA without considering the T factor. The priorities assigned to BT adoption barriers based on the above-mentioned approaches compared to the conventional FMEA (SOD) are shown in Fig. 3.9. 97 Table 3.9 Investigating the role of the treatment level factor on BT adoption barriers analysis Barrier FMEA (SODT) F-FMEA (SODT) Z-FMEA (SODT) Z-FCM (SODT) Score Priority F-score Crisp Value Priority Z-score Crisp Value Priority Improved Z-score Crisp Value Priority B1 576 2 (0.2476, 0.2184, 0.2136) 0.2225 2 (0.0999, 0.0717, 0.1066) 0.0822 2 (0.9847, 0.9895, 0.9920) 0.9891 1 B2 1008 1 (0.2075, 0.1945, 0.2168) 0.2004 3 (0.1222, 0.1089, 0.1432) 0.1168 1 (0.9738, 0.9843, 0.9891) 0.9833 2 B3 30 8 (0.0127, 0.0428, 0.0476) 0.0385 8 (0.0013, 0.0035, 0.0063) 0.0036 8 (0.8742, 0.8781, 0.8814) 0.8780 8 B4 36 7 (0.0329, 0.0637, 0.0964) 0.0640 6 (0.0024, 0.0051, 0.0056) 0.0047 7 (0.9359, 0.9370, 0.9394) 0.9372 7 B5 144 5 (0.0336, 0.0455, 0.0742) 0.0483 7 (0.0110, 0.0203, 0.0192) 0.0186 4 (0.9620, 0.9662, 0.9714) 0.9664 5 B6 120 6 (0.0394, 0.0999, 0.1176) 0.0928 5 (0.0030, 0.0049, 0.0077) 0.0050 6 (0.9646, 0.9677, 0.9702) 0.9676 3 B7 400 3 (0.0673, 0.1317, 0.1651) 0.1265 4 (0.0039, 0.0073, 0.0093) 0.0071 5 (0.9359, 0.9374, 0.9401) 0.9376 6 B8 240 4 (0.2823, 0.3572, 0.3830) 0.3490 1 (0.0774, 0.0693, 0.1001) 0.0758 3 (0.9634, 0.9668, 0.9716) 0.9670 4 (a) (b) (c) (d) Figure 3.9 Comparing the rankings obtained from the conventional FMEA (SOD) technique with a) FMEA (SODT) b) F-FMEA (SODT) c) Z-FMEA (SODT) d) Z-FCM (SODT) 98 Based on the comparison between the rankings obtained from the FMEA (SOD) and FMEA (SODT) techniques (see Fig. 3.9a), adding the T factor makes some changes in the list of critical barriers. More precisely, the priority assigned to B1 is changed from 3rd to 2nd, and consequently, B7 is placed in the third rank. Figure 3.9b shows the impact of adding the uncertainty concept and T factor on the prioritization process. The consideration of these two features causes making significant changes in the priorities of the identified barriers compared to the FMEA (SOD) technique. According to the ranking resulting from the F-FMEA (SODT), B8 has the first priority, whereas the fourth rank has been assigned to this barrier using the conventional technique. Besides, the rank of B2 falls from 1st to 3rd based on the ranking obtained from the F-FMEA (SODT) technique. By considering the reliability in addition to uncertainty and the T factor in barriers modeling and analysis, it can be seen that B2 becomes the most critical barrier (see Fig. 3.9c). B1 is still the second most important barrier based on the score obtained from the Z-FMEA (SODT) technique. Lastly, CRs between barriers are considered, in addition to other previously mentioned features, in Fig. 3.9d. This figure illustrates that B1 is placed in the first rank, while this barrier has the second priority based on the FMEA (SODT), F-FMEA (SODT), and Z-FMEA (SODT) techniques, and the third priority using the conventional FMEA (SOD). Taking B7 as an example, the rank of this barrier falls in a stepwise manner from 2 (assigned by the FMEA (SOD)) to 6 (assigned by the Z-FCM (SODT)) after adding each feature. To conclude, adding an extra RF in addition to the conventional ones in an environment can affect the final ranking significantly. According to the obtained criticality degrees for potential barriers, they can be categorized into four groups (i.e., the intolerable, major, tolerable, and minor barriers) in the fifth phase. Then, the deterrent effects of these barriers should be mitigated by removing or controlling their causes, including autonomous factors and root barriers. Based on the obtained ranking using the proposed 99 approach, DMs can propose more effective mitigation measures or select a sustainable strategy (i.e., aversion, reduction, and transfer) to control the deterrent effects of significant barriers. More precisely, DMs can make an informed decision on how they should treat barriers with various criticality levels and define mitigation measures for each barrier according to its criticality degree and the existing resource limitations. Since B1 and B2 have a very high deterrent effect on the adoption process in sustainable SCs compared to other ones, these barriers are considered as intolerable barriers. Therefore, DMs focus on proposing mitigation measures to reduce the deterrent effects of such barriers without paying attention to resource limitations. While the criticality degree of B6, B8, and B5 (i.e., major barriers) should be reduced to a reasonable level by paying attention to the availability of resources. Generally, DMs and practitioners first should identify the inherent features of BT to have a successful implementation of BT in sustainable SCs. Then, they can highlight the impact of such enablers and correspond them to the critical barriers (i.e., intolerable and major barriers) to propose effective mitigation measures. For example, DMs can restrict the role of the B1 (i.e., adoption costs) in increasing managerial resistance by highlighting the impact of its inherent features on reducing the costs of BT-based sustainable SC networks. In fact, BT enhances environmental sustainability by providing continuous tracking of data and goods to reduce costs resulting from non-compliance with environmental standards and the agreed regulations (Yousefi and Tosarkani, 2022a). BT also provides smart contract-driven trade transactions to enable SC managers and practitioners to eliminate third-party intermediaries and reduce overhead and transaction costs in sustainable SCs (Yadav and Singh, 2020). After developing and implementing the mitigation measures, the DMs will be able to evaluate the effectiveness of each measure by reassessing the potential barriers. In addition to introducing critical barriers for the mitigation phase, a list of 100 barriers having a medium criticality degree (i.e., tolerable or watch-listed barriers: B7 and B4) can be provided to implement a tracking stage to monitor them. 3.7 Conclusions Due to digital transformation and its impact on todayâs businesses, managers and practitioners try to adopt BT to optimize sustainable SC operations by taking advantage of this technology (e.g., smart contracts, traceability, and transparency). Since BT is known as a radical innovation, there are some barriers on the path of BT adoption in sustainable SCs that increase the resistance to implementing this technology successfully. This study has proposed a decision support approach to support a holistic management framework for analyzing barriers to BT adoption in sustainable SCs. This approach has integrated the FCM and Z-number theory to develop the Z-FCM to model the CRs between potential barriers considering the context of sustainability. Considering the uncertainty and reliability concepts, CRs between barriers, and multiple RFs simultaneously in barrier modeling and analysis, the proposed approach helps DMs calculate the reliable criticality degrees for potential barriers. This, in turn, enables SC managers to identify critical barriers and then propose more effective mitigation measures to control their deterrent effects during the adoption process. In comparison with various versions of FMEA as a popular technique for risk analysis, it is shown that the proposed approach empowers DMs to obtain unbiased ranking by taking advantage of the hybrid FCM learning algorithm. The findings of this research have implied that the adoption costs and extra audits as economic barriers are the most significant barriers affecting BT adoption in sustainable SCs. Additionally, this study has shown that regulatory uncertainty and lack of trust (social barrier) and energy consumption (environmental barrier) have a high deterrent effect on BT adoption. 101 This study has focused on providing a list of critical barriers to enable managers to develop effective mitigation strategies. After implementing such mitigation measures, the DMs should evaluate the effectiveness of each measure during the BT adoption process. Future researchers can evaluate the effectiveness of each measure by reassessing the potential barriers using a Bayesian belief network model. The BT implementation may include different stages, such as creating a proof of concept and choosing the BT platform and consensus protocol. Considering this multi-stage perspective, future researchers are suggested to analyze the effects of potential barriers associated with each step of BT implementation on the barriers of the next steps and, consequently, resistance to BT adoption using the multi-stage version of the proposed model. Some barriers, like development costs, can be considered as time-related barriers according to the advancement of BT platforms. Therefore, exploring a comprehensive list of barriers, including time-related barriers, and then analyzing their deterrent impacts to get an operational competitive advantage in the market can be considered in future research. 102 Chapter 4: A Decision Support Framework for Best-Fitting Blockchain Platform Selection in Sustainable Supply Chains under Uncertainty 4.1 Introduction The increasing significance of socio-environmental issues and the need for cooperation in sustainability have heightened the pressure on conventional Supply Chain (SCs) to leverage a real-time information-sharing mechanism (Yousefi and Tosarkani, 2024b). Accordingly, traditional Logistics and Supply Chain Management (LSCM) systems are being replaced by information-based alternatives to enable the monitoring of assets across SC networks and addressing a wide range of societal and environmental challenges (Chang et al., 2019). Some organizations seek to integrate Blockchain Technology (BT) with sustainable SCs because of its inherent capabilities to support network coordination (Zhu et al., 2024). BT is a distributed database technology that has the potential to revolutionize organizationsâ shape and size (Risso et al., 2023). It also supports sustainability certifications, information exchange, real-time traceability, and engagement with external stakeholders (Saberi et al., 2019; Kamble et al., 2023). BT and its features (e.g., decentralization and smart contracts) improve SC integration through the implementation of integrated changes in managing the existing operations, contributing towards improving agility (Kamble et al., 2023; Oliveira-Dias et al., 2022). Such BT practices have exerted influence on more than 80% of the worldâs top 100 public companies, compelling them to rethink their strategies (Ying et al., 2018; Chapkanovska, 2023). Figure 4.1 represents the BT-based innovative solutions for managing processes and enhancing end-to-end visibility throughout the SC network (IBM, 2022). 103 Sourcing Procurement Manufacturing Logistics Distributionâ¢ Responsible Sourcing â¢ Supplier Information Managementâ¢ Dispute Managementâ¢ Contingent Labor Management â¢ Contract Managementâ¢ Responsible Sourcing â¢ Enterprise Asset Managementâ¢ Parts Provenance and Parts Maintenanceâ¢ Dispute Managementâ¢ End-to-End Visibilityâ¢ Delivery Transaction Intelligenceâ¢ Dispute Management Figure 4.1 The supportive BT-based solutions for multistakeholder LSCM Despite the potential of BT for addressing LSCM-related issues, there is still a need to learn about how this innovative technology can be effectively integrated into SCs (Ferdous et al., 2024). Every BT platform has its own set of characteristics to address organizationsâ unique needs (Farshidi et al., 2021). Accordingly, the variety of features makes it challenging to choose a feasible and effective BT platform for a business application (Zafar et al., 2021). As there are some adoption requirements for each platform, Decision-Makers (DMs) need to make a trade-off between conflicting criteria when selecting the appropriate platform. However, previous studies have not thoroughly focused on the theoretical background of the problem and frequently explored the characteristics of BT platforms from an individual perspective (Lin, 2023). Besides, most studies on evaluating and selecting BT platforms have treated evaluation criteria as independent (Lai et al., 2022; Görçün et al., 2023; Erol et al., 2023). However, there are often interdependencies among these criteria and considering potential Causal Relationships (CRs) enhances the accuracy of the criteria weights, thereby influencing the ranking process (Lai and Liao, 2021; Tavana et al., 2023). While traditional Multi-Criteria Decision-Making (MCDM) methods have typically been used to assess the importance of evaluation criteria, they can be time-consuming when dealing with a large set of criteria (Lai and Liao, 2021). As BT adoption in SCs is still in its early stages, inherent uncertainty remains a significant challenge (Yousefi and Tosarkani, 2022a; Bonab et al., 2023). 104 Nevertheless, the uncertainty and reliability of information have not been applied in the weighting and ranking processes simultaneously. Therefore, this study seeks to support sustainable SCs to benefit from BT practices by addressing four main questions: i) Can evaluation criteria be theoretically explored and categorized into multiple contexts? ii) How can we determine the importance of evaluation criteria using causal-based modeling? iii) How can we apply the importance of each factor to find the best-fitting BT platform? iv) What is the impact of simultaneous consideration of uncertainty and reliability on the BT platform selection process? The main objective of this study is to develop a Decision Support Framework (DSF) supported by the Technology-Organization-Environment (TOE) theory to choose the best-fitting platforms under uncertainty. We first leverage TOE to explore the evaluation criteria affecting the BT platform selection within technological, organizational, and environmental contexts. This provides a comprehensive evaluation considering the outer and inner dynamics of organizations. Then, DSF is implemented in three main phases to address the shortcomings of the previously developed approaches. The Z-number theory can be employed to tackle the challenges arising from uncertainty and partially reliable information (Aboutorab et al., 2018). Accordingly, an advanced version of the fuzzy inference system in the Z-number environment is introduced in the first phase to aggregate the linguistic descriptions associated with the BT platform evaluation process. In the second phase, an automatic Fuzzy Cognitive Map (FCM) advanced by a Z-number-based Inference System (ZIS) is proposed to create a causal-based model of evaluation criteria. Then, a hybrid FCM learning algorithm, along with a scenario-making technique, is utilized to determine the weight of each criterion. Such learning algorithms reduce direct dependency on biased judgments, supporting DMs to make more reliable decisions in the presence of limited data (Onari 105 et al., 2021). In the third phase, the embedded fuzzy Combined Compromise Solution (CoCoSo) in the proposed DSF is applied to prioritize candidate BT under uncertain weights. The outputs of the developed DSF reveal the main evaluation criteria within the TOE contexts and differentiate BT platforms to select the best-fitting ones for adoption in sustainable SCs. Covering shortcomings of the previous studies, the DSF also outperforms MCDM approaches, such as Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) and the extended versions of the CoCoSo, in dealing with the BT platform selection problem. The remainder of this study is organized as follows: Section 4.2 reviews the latest studies in the field of BT adoption in SCs and BT platform selection and identifies the research gaps. The Z-number theory, fuzzy inference system, FCM, and fuzzy CoCoSo methods are introduced in Section 4.3. Then, a DSF is developed in Section 4.4 to address the BT platform evaluation and the selection problem. Section 4.5 discusses the outputs of implementing the proposed framework for a case study. Section 4.6 provides a comparison between the developed DSF and traditional approaches. The theoretical, practical, and managerial implications of this study are discussed in Section 4.7. Finally, the concluding remarks and the scope for future research are presented in Section 4.8. 106 4.2 Literature Review 4.2.1 BT Adoption in SCs BT has experienced significant application and growth in multistakeholder LSCM (Zhu et al., 2022; Collier and Sarkis, 2021). Incorporating BT into varied SCs is different and challenging because each industry seeks to adopt this technology to address its unique problems. Thus, a feasibility study can be done in different industries to adopt a suitable BT platform (Erol et al., 2020). For conducting a feasibility assessment for BT applications in an organization, the potential enablers (i.e., characteristics) and barriers (i.e., adoption requirements) should be analyzed (Yadav and Kumar, 2023; Yousefi and Tosarkani, 2024a). Some researchers have focused on investigating the role of intrinsic characteristics of BT, such as traceability, immutability, and security, that increase the technology adoption rate in SC networks (Saurabh and Dey, 2021; Bai et al., 2022; Li et al., 2023; Han and Fang, 2024). On the other hand, some studies have analyzed barriers to BT adoption in various SCs to help DMs learn more about the potential challenges, such as high implementation costs, regulatory uncertainty, and lack of scalability (Kouhizadeh et al., 2021; Mathivathanan et al., 2021; Yadav et al., 2023; Surucu-Balci et al., 2024). Such analyses enable DMs to make informed decisions when developing business information technology strategies and selecting a BT platform. According to Fig. 4.2, the impact of expected enablers (e.g., consensus support and security) should be highlighted to address the existing managerial barriers (e.g., implementation costs and interoperability). Such enablers and barriers can affect the BT platform selection significantly. To make a trade-off between these factors, the enablers and barriers can be related to the evaluation criteria for choosing the appropriate BT platform. 107 Identifying Blockchain Adoption EnablersIdentifying Potential Adoption BarriersExploring the Deterrent Impact of BarriersExploring the Facilitative Impact of Enablers Enablers AnalysisBarriers AnalysisExpected Blockchain Characteristics Potential Blockchain RequirementsBlockchain Platform Evaluation and SelectionBlockchain CharacteristicsBlockchain Adoption Requirements Figure 4.2 The framework of the feasibility study of BT adoption 4.2.2 BT Platform Evaluation and Selection Nowadays, more organizations are trying to adopt BT to respond to their specific needs and enable themselves to improve their performance in SCs (Ãolak et al., 2020). Since organizations face several platforms with various supporting features, the BT platform evaluation and selection process has become more significant today (Lai and Liao, 2021). Hence, there is a need for decision tools to estimate which BT platform is appropriate (Liu et al., 2020). Based on the type of access to data, BT platforms are defined as public and private platforms. In a public BT, any participant can record and track the transactions (i.e., information and financial flows) and interact with other users because ledgers are publicly available. On the opposite side, in a private BT, data access is restricted to the private group of authorized participants because ledgers are shared among the permissioned users (Yousefi and Tosarkani, 2022a). Every platform has also its characteristics, such as consensus mechanism, authorization and authentication, contracts, and innovation-related characteristics (e.g., Internet of Things and artificial intelligence) (Farshidi et al., 2021). 108 Some studies have focused on dealing with the BT platform selection in recent years. Yang et al. (2021) developed a decision model using the fuzzy analytic hierarchy process and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods to identify the suitable BT platform for a knowledge-based conversation system. Zafar et al. (2021) proposed an MCDM approach using the combination of entropy and CRITIC methods to rank public BT platforms considering popularity, sustainability, and profitability. The findings implied that Bitcoin is placed in the first rank in the popularity category, while it is ranked in the thirteenth in the sustainability category. Nanayakkara et al. (2021) introduced a four-stage methodology (i.e., identification, selection, evaluation, and validation) for the selection of a best-fitting BT to develop an enterprise system. They employed a simple multi-attribute rating technique to select a best-fitting platform (i.e., Hyperledger Fabric). Similarly, Büyüközkan and Tüfekçi (2021) combined multiple preference formats with the VIKOR method to evaluate alternatives to show that Hyperledger Fabric is the appropriate enterprise platform. Filatovas et al. (2022) proposed an MCDM-based framework using the weighted sum model, TOPSIS, and VIKOR methods to choose the most useful consensus protocols. The findings showed that EOSIO is the most suitable consensus protocol for an application for bike renting, considering technological requirements. Considering the uncertainty, Lai et al. (2022) developed an advanced fuzzy CoCoSo method based on the hesitant Fermatean fuzzy sets to select a BT platform to enhance the social donation system. They considered cost, ease of use, level of support, and performance as the criteria to rank existing platforms. Görçün et al. (2023) developed a hybrid MCDM framework using the Delphi, full consistency method, and multiattribute ideal-real comparative analysis techniques in the Fermatean fuzzy environment to identify the most feasible BT platform. Considering multiple criteria (e.g., productivity and reliability), they reported that Microsoft and IBM are the most 109 feasible options for the logistics industry. Tavana et al. (2023) employed the interval-weighted influence non-linear gauge system-VIKOR approach to calculate the weights of four main evaluation criteria (e.g., popularity and support) and assess the suitability of the BT-Internet of Things platforms in SCs. Erol et al. (2023) integrated the extent of analysis-based rough analytic hierarchy process-compromise programming to choose a BT platform considering the requirements of a healthcare organization. Based on the multiple evaluation criteria (e.g., security, scalability, and throughput), they showed that Ethereum, Cardano, and the entrepreneurial operating system are the most appropriate platforms. 4.2.3 Research Gaps Based on Table 4.1, most of the studies have primarily focused on using conceptual frameworks (Dabbagh et al., 2020; Saleh et al., 2021; Connors and Sarkar, 2023) and MCDM methods (Zafar et al., 2021; Lai et al., 2022; Tavana et al., 2023) to address the BT platform evaluation and selection problem. In the meantime, some of these MCDM methods are time-consuming to implement, particularly when dealing with a large number of evaluation criteria, and suffering from information distortion and ignorance of the target-based criteria (Yousefi and Tosarkani, 2022a; Lai and Liao, 2021). Furthermore, most of the existing studies in the field of BT platform evaluation and selection have assumed that the evaluation criteria are independent (Görçün et al., 2023; Bonab et al., 2023), while there are often CRs between criteria. Considering CRs can improve the accuracy of the obtained weights for such criteria and, consequently, affect the ranking of BT platforms (Lai and Liao, 2021; Tavana et al., 2023). This study considers CRs to calculate the weights of evaluation criteria through the FCM construction approach advanced by the ZIS system. 110 Although BT adoption is contingent on technological, organizational, and environmental contexts (Lin, 2023), most studies have examined the characteristics of platforms from an individual perspective. Furthermore, concepts associated with BT adoption are often subjective (Ganguly, 2024). Hence, we first employ the TOE theory to explain innovation adoption behavior to explore the BT platform evaluation criteria. Additionally, the developed DSF considers the uncertainty and reliability simultaneously in the weighting process through the Z-number theory. The proposed framework reduces the dependency on biased judgments using the hybrid FCM learning algorithm (Bakhtavar et al., 2021). This capability allows DMs to effectively handle vague input, characterized by a higher level of uncertainty compared to classical fuzzy sets (Zadeh, 2011), and enhance the reliability of the weighting and ranking processes. Furthermore, the CoCoSo methodology, embedded in the proposed DSF, delivers a reliable ranking with high separability compared to the utility value-based MCDM methods (e.g., TOPSIS and VIKOR) (Valipour et al., 2022; Lai et al., 2022). 111 Table 4.1 A conceptual comparison between the features of the present research and other studies Author(s) Application field Research domain Approaches Theory Low dependency on biased inputs Methodology Weighting process Ranking Process Platform evaluation Platform selection Considering uncertainty Considering reliability Considering CRs Considering uncertainty Considering reliability Weights of criteria Liu et al. (2020) Numerical example â Intuitionistic fuzzy Best-Worst Method (BWM)-Entropy-TOPSIS â â â Monrat et al. (2020) Private blockchains â Conceptual framework Bamakan et al. (2020) Consensus algorithms â Conceptual framework â Farshidi et al. (2020) Common BT platforms â MoSCoW prioritization â Dabbagh et al. (2020) Private blockchains â Conceptual framework Yang et al. (2021) Conversation systems â Fuzzy analytic hierarchy process-TOPSIS â â Saleh et al. (2021) Common BT platforms â Conceptual framework Farshidi et al. (2021) Model-driven development platforms â MoSCoW prioritization â Zafar et al. (2021) Public blockchains â Entropy-CRITIC â â â Nanayakkara et al. (2021) Common BT platforms â Simple multi-attribute rating technique â Büyüközkan and Tüfekçi (2021) Numerical example â Multiple format preferences-VIKOR â â â Lai and Liao (2021) Numerical example â Linguistic D-CRITIC- DNMA â â â â Zhou and Chen (2022) Major banks/financial institutions â Pythagorean fuzzy analytic hierarchy process-TODIM â â â 112 Continued to Table 4.1 Author(s) Application field Research domain Approaches Theory Low dependency on biased inputs Methodology Weighting process Ranking Process Platform evaluation Platform selection Considering uncertainty Considering reliability Considering CRs Considering uncertainty Considering reliability Weights of criteria Filatovas et al. (2022) Consensus algorithms â Weighted sum model-TOPSIS-VIKOR â Lai et al. (2022) Numerical example â Hesitant Fermatean fuzzy CoCoSo â â â Görçün et al. (2023) Common BT platforms â Fermatean fuzzy -Delphi-Full consistency method-multiattribute ideal-real comparative analysis â â â Tavana et al. (2023) BT-Internet of Things platforms â Interval weighted influence non-linear gauge system-VIKOR â â â â Connors and Sarkar (2023) Common BT platforms â Conceptual framework Erol et al. (2023) Numerical example â Rough analytic hierarchy process-Compromise programming â â â Kubler et al. (2023) Distributed ledger technologies â Analytic hierarchy process-TOPSIS â Bonab et al. (2023) Common BT platforms â Spherical fuzzy BWM-MARCOS â â â Krishankumar et al. (2024) BT service providers â CRITIC-CRADIS-Copeland algorithm â â â â The current study Private blockchains â â ZIS-FCM-Fuzzy CoCoSo TOE â â â â â â â 113 4.3 Preliminaries 4.3.1 Z-number Theory Fuzzy sets have been widely applied to handle the inherent uncertainty of MCDM problems in recent years. In this regard, fuzzy set theory, introduced by Zadeh (1965), has been extended to alternative frameworks (e.g., intuitionistic fuzzy sets and hesitant fuzzy sets). However, the reliability of information can directly affect the accuracy of decision-making and formulation of algorithms (Zadeh, 2011). Most of the uncertain sets assume that the information required for decision-making is completely reliable. Classical fuzzy sets are not able to consider the uncertainty in the information generation process and the reliability of decision information (Shen and Wang, 2018). Thus, Zadeh (2011) introduced the Z-number theory to consider reliability in addition to the concept of uncertainty with the aim of computing non-reliable numbers. This theory ensures the confidence of the evaluations in the decision-making process to make the results more decisive in an uncertain environment (Abbasi Kamardi et al., 2022). If it is supposed that AÌ=(la,ma,ua) and RÌ=(lr,mr,ur) are triangular fuzzy numbers, X= (AÌ, RÌ) can be a Z-number, where A represents a fuzzy set and R indicates the degree of certainty or reliability of A. Accordingly, the Z-number theory-based linguistics variables can be used by DMs to describe the values of criteria for several alternatives. Take the Z= (M, H) = [(0.25, 0.50, 0.75), (0.50, 0.70, 0.90)] as an example, the DM allocates the values of âMediumâ (M) and âHighâ (H) to the first component (uncertainty) and the second component (reliability). Based on the study of Kang et al. (2012), we can define transformation rules to convert Z-numbers to triangular fuzzy numbers with the aim of using arithmetic operations in the fuzzy environment. Eqs. (4.1) and (4.2) can be employed to convert the reliability into a crisp number, if AÌ={(x, Î¼AÌ(x))| xâ[0,1]} and RÌ={(x, Î¼RÌ(x))| xâ[0,1]} are triangular fuzzy functions. 114 ( )( )RRx x dxx dxï­ï¡ï­=ï²ï² (4.1) ï ïï» ï½( , ) | ( ) ( ), 0,1AA AZ x x x xï¡ ï¡ï¡ ï­ ï­ ï¡ï­= = ï (4.2) In the above equations, Î¼RÌ(x), Î¼AÌÎ±(x), and α represent the membership degree of x ϵ X on ?Ì?, the membership degree of x ϵ X on AÌÎ±, and the reliability weight, respectively. If Z=(M, H)= [(0.25, 0.50, 0.75),(0.50, 0.70, 0.90)], the value of α is computed equal to 0.7 based on Eq. (4.1). In the following, the value of α is applied in Eq. (4.2) which means ZÌÎ±= (0.25, 0.50, 0.75 ; 0 .7). Accordingly, the above-mentioned Z-number is converted into a triangular fuzzy number: ZÌ'= (0.25Ãâ0.7,0.50Ãâ0.7,0.75Ãâ0.7) = (0.2092, 0.4183, 0.6275). Other transformation rules can be obtained to enable employing the arithmetic operations provided for triangular fuzzy numbers. 4.3.2 Fuzzy Inference System Fuzzy logic can be a flexible and useful way to deal with vagueness and uncertainty and transform linguistic descriptions into mathematical expressions. In fuzzy logic, a crisp input converted to fuzzy input based on the membership function can be processed through an inference engine. This engine is embedded in the fuzzy inference system and consists of several rules. The fuzzy output can be reverted to the crisp output using defuzzification after making inferences about the fuzzy input (Rezaee et al., 2020). One of the most popular fuzzy inference systems was proposed by Mamdani (1974) to aggregate expertsâ points of view. Executive steps for implementing the fuzzy inference system can be provided as follows: 115 i) Defining the linguistic variables and their corresponding membership functions to declare the importance of the inputs, ii) Defining the rules, including antecedents (if) and consequences (then), to design the inference engine to map out the given inputs to inference output, iii) Fuzzifying non-fuzzy inputs using the defined membership functions, iv) Considering implication operator (AND) for presenting propositions and evaluating rules, v) Combining results using the aggregation operator (OR), and vi) Converting fuzzy outputs to crisp values using defuzzification methods. Different defuzzification methods can be used to propose an approximation for inference output (Rezaee et al., 2020). The Largest of Maximum (LOM), Middle of Maximum (MOM), and Smallest of Maximum (SOM) are three popular defuzzification methods. These methods help DMs compute the upper, middle, and lower bounds of a fuzzy output. SOM, MOM, and LOM are determined using Eqs. (4.3) to (4.5). In Eqs. (4.3) and (4.4), supu and infu represent lower and upper bounds, respectively. Also, cocequeï­ illustrates the membership function of the fuzzy set u and U represents the range of output values. SOM=supu{uâU: μcnceqe(u)=maxu{μcnceqe(u)}} (4.3) LOM=infu{uâU: μcnceqe(u)=maxu{μcnceqe(u)}} (4.4) MOM=SOM+LOM2 (4.5) 116 4.3.3 FCM Modeling 4.3.3.1 FCM Construction FCM is considered as a system analysis model inheriting the features of artificial intelligence and fuzzy logic to address complex decision-making issues (Bakhtavar et al., 2021). FCMs enable DMs to model complex systems having loop/recurrent CRs and consider the unlimited and various variables and CRs between them in comparison with structural equation modeling and MCDM methods, respectively (Papageorgiou et al., 2004; Yousefi and Tosarkani, 2022a). FCM learning algorithms also reduce the final outputsâ dependency on biased judgments when we face novel problems to increase the reliability of the decision-making and the accuracy of weight estimation (Yousefi and Tosarkani, 2024a). An FCM model consists of nodes and arcs that respectively represent the concepts of the system (Ci) and CRs among them (wij, i,j=1,â¦,n). These concepts are considered as the representative of attributes of the system under study and can be categorized into two groups based on their nature, including conventional concepts and target concepts (Bakhtavar et al., 2021). The sign and value of each arc (wij) illustrate the type of CR between Ci and Cj and its causality degree, respectively. Hence, wij>0, wij<0, and wij=0 show a direct CR, an inverse CR, and the absence of a meaningful CR between concepts i and j, respectively. The semi-automatic and automatic FCM construction approaches are used to calculate the weights of potential CRs among the concepts identified based on the literature. After exploring CRs between concepts and their type, in the semi-automatic FCM construction approach, the expert(s) are asked to directly specify the weight of each CR using values in the range of [0,1] or [-1,1] based on their background in the field of the study (Bakhtavar et al., 2021). In the automatic FCM approach, DMs prefer to use historical data or data provided in the literature to determine the weight of the extracted CRs based on the relations provided by Schneider et al. (1998). Eq. 117 (4.6) and Eq. (4.7) are used for starting the calculation of the strength of direct and inverse CR between two concepts (e.g., concepts 1 and 2), respectively. In these equations, x1(vr) and x2(vr) show the membership degrees of r in the vectors belonging to concepts 1 and 2. dr illustrates the distance between the two r elements of these vectors. The mean distance between these two vectors (AD) is calculated using Eq. (4.8), where m denotes the number of vector elements. Finally, the similarity between the two vectors (S) or the weight of their CR is obtained through S=1-AD. dr=|x1(vr)-x2(vr)| (4.6) dr=|x1(vr)-(1-x2(vr))| (4.7) AD=â |dr| mr=1m (4.8) 4.3.3.2 FCM Learning After constructing an FCM model, DMs can define scenarios by assigning specific values (e.g., value of 1) to concepts in the matrix of initial values to analyze the system states. The scenario-making process can be also used to evaluate the performance of FCM models because these models should describe the systemâs behavior through target concepts under complex scenarios (Onari et al., 2021). More precisely, an FCM learning algorithm is performed for each scenario considering the matrix of initial weights of CRs to compute the values of target concepts. To calculate the values of concepts related to one concept having a specified value, Eq. (4.9) is used to exchange the conceptsâ values until the model reaches a stable state (Papageorgiou et al., 2006). Ai(k+1)=f (Ai(k)+ â Aj(k).njâ i,j=1Wji) (4.9) Where Ai(k)and Ai(k+1) indicate the value of Ci in iterations k and k+1, respectively. ( )kjA and f(x) shows the value of Cj in iteration k and the transformation function, respectively. Eq. (4.9) is 118 embedded in the learning algorithm to train the created FCM model. This study uses a hybrid FCM learning algorithm to incorporate the characteristics of the population-based and Hebbian-based learning algorithms. This hybrid learning algorithm helps DMs find valid solutions with acceptable separability, improving the reliability of the decision-making procedure (Onari et al., 2021). The hybrid learning algorithm employed in this research includes two main stages: Nonlinear Hebbian Learning (NHL) and Differential Evolution (DE) processes. As indicated in Algorithm 1, the first stage aims to evaluate the influence of the concept under study on other concepts. In the implementation of the NHL algorithm, the FCM conceptsâ values and the non-zero weights of CRs are updated in various iterations through Eqs. (4.9) and (4.10), respectively, until reaching the termination condition. ( )( )( ) ( ) ( ) ( ) ( ) ( ) ( )1 1 1 1 1 1. k k k k k k kji ji i j ji ji iw w A A sgn w w Aï§ ï¨â â â â â â= + â (4.10) Where γ and η indicate the learning rates to optimize the obtained solutions and sgn(wji) represents the sign of weights (direct or inverse CRs). In Eq. (4.9), the sigmoid function (11+exp(-x)) is used as a transformation function to return the values generated from multiplying two matrices to the specified ranges for the conceptsâ values. This function is differentiable and helps DMs estimate probabilities within the interval from 0 to 1. In the last step of Algorithm 4.1, the obtained final weights are returned to the next stage of the learning process. Algorithm 4.1 The NHL implementation stage of the hybrid FCM learning algorithm Inputs: The initial weighting matrix and the concept state matrix Step 1 Repeat steps 2 to 4 for each iteration k until the termination condition is met: Step 2 Calculate the value of the concepts in iteration k+1 (Ai(k+1)) through Eq. (4.9) Step 3 Update the weights (wji(k)) through Eq. (4.10) Step 4 Investigate the termination function Step 5 Return the final weights to the next learning stage Outputs: The final weighting matrix 119 As shown in Algorithm 4.2, the second stage seeks to utilize the DE algorithm to update the values generated in the previous stage more accurately while keeping the potential CRs during the learning process. After initializing the population, this stage, including mutation, crossover, and selection process, is repeated for each input concept state considering a weight constraint. This constraint is employed to ensure values fall between 0 and 1, enabling qualitative comparisons in the scenario-making phase (Yousefi and Tosarkani, 2022a). When the termination condition of the second stage (i.e., a slight difference between outputs obtained from two consecutive iterations) is met, the final concept state matrix is obtained. This matrix includes the trained values for FCMâs concepts considering the interactions among the concepts in a causal-based model. Algorithm 4.2 The DE learning stage of the hybrid FCM learning algorithm Inputs: The final weighting matrix and the updated concept state matrix Step 1 Initialize the population in the neighborhood of the final weights and within weight constraints Step 2 Repeat for each input concept state (k): Step 3 Repeat steps 4 to 7 for i=1 to the number of populations: Step 4 MUTATION (Wi(k)) â Mutant_Vector Step 5 CROSSOVER (Mutant_Vector) â Trial_Vector Step 6 If F(Trial_Vector) â¤ F(Wi(k)): Accept Trial_Vector for the next generation Step 7 Continue to reach the termination condition Outputs: The final concept state matrix 4.3.4 Fuzzy CoCoSo Method The CoCoSo method was proposed to use three aggregator strategies to prioritize alternatives based on several decision-making criteria (Yazdani et al., 2019). The CoCoSo method produces more robust results in comparison with other MCDM methods (e.g., TOPSIS and VIKOR). The CoCoSo method, like other MCDM methods, has been extended in uncertain environments. Considering the framework of the fuzzy CoCoSo method, if dij=(dij l, dij m, dij u) represents the fuzzy 120 value of the criterion jth (j=1,2,â¦,n) for the option or alternative ith (j=1,2,â¦,m), the fuzzy decision matrix can be organized as follows: D= [(d 11l , d11 m, d11 u )â®(dm1 l, dm1 m, dm1 u )(d12 l, d12 m, d12 u )â®(dm2 l, dm2 m, d m2u )â¦â¦â¦(d1n l, d1n m, d1n u )â®(dmn l, dmn m, d mnu )] (4.11) Where the parameters l, m and u indicate the lower, middle, and upper bound values in a triangular fuzzy environment, respectively. After forming the fuzzy decision matrix, Eq. (4.12) (compromise normalization equations) is used for the normalization of benefit and cost criteria (Ecer and Pamucar, 2020). This step aims to create a fuzzy normalized decision matrix (see Eq. (4.13)). In this matrix, elements ξij= (ξ ijl , ξij m, ξij u) indicate the normalized fuzzy value. ξij= (ξij l ,ξij m,ξij u)={ ξ ijl =dij lmaxi(dij u) ; ξij m=dij mmaxi(dij u) ; ξij u=dij umaxi(dij u) if j â Benefit criteriaξ ijl =mini(dij l)dij u ; ξ ijm=mini(dij l)dij m ; ξij u=mini(dij l)dij l if j â Cost criteria} (4.12) ð = [(ξ 11l , ξ11 m, ξ11 u )â®(Î¾m1 l, ξm1 m, ξm1 u )(ξ12 l , ξ12 m, ξ12 u )â®(Î¾m2 l, ξm2 m, ξ m2u )â¦â¦â¦(Î¾1n l, ξ1n m, ξ1n u )â®(Î¾mn l , ξmn m , ξ mnu )] (4.13) To form the weighted normalized decision matrix, the weight of each decision criterion jw is determined by a weighting method (e.g., MCDM weighting methods) or directly by DMs. Next, the fuzzy exponential weight of comparability sequences Pi= (Pi l, Pi m, Pi u) and the fuzzy weighted comparability sequences Si= (Si l, Si m, Si u) are calculated for each alternative using Eqs. (4.14) and (4.15). The aggregated simple additive weighting and exponentially weighted product methods are employed to define Eqs. (4.14) and (4.15) (Valipour et al., 2022). Pi=â (ξij)wjnj=1 , âi (4.14) 121 Si=â (wj ξij)nj=1 , âi (4.15) In the following, three aggregator strategies kia = (kia l, kiam , kiau), kib = (kib l, kib m, kibu), and kic = (kicl , kic m, kicu) are calculated the relative weight of alternatives using Eqs. (4.16) to (4.18) (Yazdani et al., 2019). Notably, λ mentioned in Eq. (4.18) is usually considered equal to 0.5 (Valipour et al., 2022). In the last step, the final fuzzy score Ki = (Ki l, Ki m, Ki u) for each alternative can be specified based on Eq. (4.19). To prioritize the alternatives, this fuzzy parameter (ki) is converted to a crisp parameter using the graded mean integration representation or R(Ki) (see Eq. (4.20)). The output of this stage implies that the option with the maximum score R(Ki) is considered as the first rank. kia=Pi+Siâ (Pi+Si)mi=1 , âi (4.16) kib=Si mini Si+Pimin iPi , âi (4.17) kic=λ(Si)+(1-λ)(Pi)λ maxi Si+(1-λ) max iPi , 0â¤Î»â¤1 , âi (4.18) Ki = (kia.kib.kic)13 + 13(kia+kib+kic) , âi (4.19) R(Ki) =Kil+4Kim+Kiu6 , âi (4.20) 122 4.4 Proposed Decision Support Framework In this section, we provide a detailed description of the proposed DSF for evaluating BT platforms and selecting the appropriate one for multistakeholder LSCM. This framework is implemented in three main phases: comparing BT platforms, determining the weights of evaluation criteria, and prioritizing BT platforms. The implementation phases of the developed DSF are presented in Fig. 4.3. As shown in this figure, the evaluation criteria are explored within the TOE framework before the implementation of the first phase. This framework, unlike other behavior models, explicates behavioral intentions and innovation adoption at a firm level to reflect the impacts of multiple aspects on adoption decisions (Tornatzky et al., 1990). Accordingly, the identified criteria can be categorized into three groups (i.e., technological, organizational, and environmental factors). The technology-related factors consider the characteristics and availability of BT platforms. The organization-related criteria focus on the importance of organizational resources and capabilities that facilitate the adoption of each BT platform. The environment-related criteria include characteristics beyond the firmâs control (e.g., market conditions) that may affect decisions regarding the BT platform selection. Phase 1: Evaluating the candidate BT platforms in the Z-environment: In this phase, after identifying the evaluation criteria through literature review and the TOE framework, the values of each criterion for BT platforms are determined. The identified evaluation criteria for selecting an appropriate platform are closely related to the characteristics and adoption requirements of BT. In this phase, Z-number theory-based linguistics variables are employed to specify the values of criteria for all BT platforms and the certainty degree of these values. As decision matrices should be aggregated, we incorporate the Z-number theory into the fuzzy inference system to develop the ZIS system. The inputs of this system can be the initial values of 123 criteria provided in the multiple states, approximated from the literature or expressed by potential experts. The output of the ZIS system is the aggregated value considering the inputs. Identifying the evaluation criteriaLiterature ReviewForming decision matrices in the Z-number environmentEvaluating the Blockchain PlatformsDetermining the values of each criterion for each alternativeAggregating multiple scenarios using the ZIS systemExploring the alternatives (i.e., Blockchain platforms)Forming the decision matrix in the fuzzy environmentDetermining the Weights of Evaluation CriteriaExploring the potential CRs between criteria according to their descriptionsModeling the CRs through the ZIS-based automatic FCMDefining a scenario for each evaluation criterionImplementing the hybrid learning algorithmDetermining the importance of each criterion (i.e. weight)Forming the fuzzy weighted normalized decision matrixPrioritizing Blockchain PlatformsCalculating the aggregator strategies for each platformCalculating the final score for each platformRanking the blockchain platformsTechnological innovation decision-makingEnvironment OrganizationTechnologyConsidering the TOE theoryCreating the fuzzy normalized decision matrixZ-number theory-based linguistics variablesConsidering the studied criterion as the target concept Figure 4.3 The implementation phases of the proposed DSF for solving the BT platform selection problem As shown in Fig. 4.4, the ZIS system operates in four phases: initialization, fuzzification, inference, and finalization. In the initialization phase, Z-number theory-based linguistics variables and their corresponding membership functions related to uncertainty and reliability concepts are defined in Table A4 in Appendix A. Afterward, in the fuzzification phase, these linguistic variables are converted to triangular fuzzy-based representations using the transformation rules (see Table 124 A5 in Appendix A). In the meantime, an inference system is designed to map the provided inputs to outputs. In the inference phase, fuzzy propositions are generated through the implication operator, and the results are aggregated based on the aggregation operator. In the last phase of the ZIS system, SOM, MOM, and LOM defuzzification methods are employed to achieve the best result in the fuzzy environment. The above-mentioned methods determine the lower, middle, and upper bounds of the output to use in the automatic FCM construction approach and the fuzzy CoCoSo method. Consequently, the output of the first phase of the proposed DSF is a decision matrix in the fuzzy environment to use as the input for the subsequent phases. Initialization Phase (Knowledge base)Defining Z-number-based linguistic variables to define the importance of the inputsFuzzification PhaseInference PhaseConverting Z-number-based linguistic variables to triangular fuzzy numbers-based onesDefining linguistic variables associated with reliability Defining linguistic variables associated with uncertainty Constructing corresponding membership functions for each linguistic variable Constructing corresponding membership functions for triangular fuzzy linguistic variablesDesigning a rule basePresenting fuzzy propositions using the implication operator (AND)Combining the results using the aggregation operator (OR)Using SOM, MOM, and LOM defuzzification methodsObtaining fuzzy outputsFinalization Phase Figure 4.4 The implementation phases of the ZIS system Phase 2: Determining the weights of evaluation criteria through the ZIS-enabled FCM: In the second phase of the developed DSF, we apply an automatic construction approach to develop an FCM model, where evaluation criteria serve as concepts. In the automatic FCM approach, we can use the data provided in the literature or historical data to calculate the weights of CRs. More precisely, the extended version of Eqs. (4.6) and (4.7) in the fuzzy environment are used to 125 calculate the fuzzy weight of CR between two evaluation criteria (SÌ=1-ADÌ=â |drÌ|mr=1m ) based on its type (direct or inverse) and the obtained fuzzy decision matrix. In these new equations, drÌ is calculated based on the fuzzy vectors x1(vrÌ)=x1(vrl ,vrm,vru) and x2(vrÌ)=x2(vrl ,vrm,vru). Once the type of potential CRs between the concepts due to their descriptions are recognized and their weights are calculated, an FCM model is developed. In the following, we employ the scenario-making technique to determine the weight of each evaluation criterion. Each scenario activates the concept associated with the specific criterion by assigning a value of 1, while other concepts receive a value of 0 as their initial state. Then, each scenario is implemented using the hybrid learning algorithm based on the matrix of CR weights. By implementing this algorithm for triple bounds of the weight matrix, the updated value of the studied concept is obtained in the fuzzy form. After obtaining the updated values for all concepts, we can estimate the normalized weight of each evaluation criterion. Phase 3: Prioritizing BT platforms to find the best-fitting option: In the third phase of the proposed DSF framework in Fig. 4.3, the weighted normalized decision matrix is created based on Eq. (4.12). Then, the fuzzy weighted decision matrix can be formed by applying the obtained weights of evaluation criteria from the previous phase to the weighted normalized decision matrix. To do this, Eqs. (4.14) and (4.15) are used to create the fuzzy weighted decision matrix. In this matrix, the identified criteria and the BT platforms are assumed as the decision criteria and alternatives, respectively. Following the methodology of the fuzzy CoCoSo method, this matrix is the main input to calculate the aggregator strategies for each alternative (i.e., BT platform in this study). Therefore, Eqs. (4.16) to (4.18) are used to calculate three aggregator strategies (i.e., kia, kib, and kic), while Eqs. (4.19) and (4.20) are employed to obtain the final score 126 for all candidate BT platforms. According to the obtained final scores, the BT platforms are prioritized to select the most suitable one for adoption in sustainable SCs. 4.5 Case Description and Results To demonstrate the frameworkâs applicability, the proposed DSF is implemented to address a BT platform selection problem to manage sustainable SCs. The adoption of a BT platform in such networks supports real-time and continuous product tracking, leading to enhancing socio-environmental responsibility and building trustful relations between different stakeholders. Every BT platform has various characteristics such as consensus mechanism (e.g., proof of work and proof of authority), layers (i.e., protocol and network), authorization and authentication (i.e., private or public and permissioned or permissionless), contracts (e.g., smart contracts and virtual machine), and programming language support (e.g., Solidity and Python) (Farshidi et al., 2021). BT platforms enhance the integrity and sustainability of SC networks through smart contracts. Such contracts can be designed to account for pre-defined expected and unexpected conditions, enabling SCs to address socio-environmental concerns (Helo and Hao, 2019; Yousefi and Tosarkani, 2024b). Smart contracts support responsible sourcing, mitigate unsustainable practices, and ensure compliance with agreed regulations (Tian and Sarkis, 2024; Yousefi and Tosarkani, 2022a). Among various networks, the textile and apparel SC stands out due to its labor-intensive manufacturing, relatively high environmental impact (i.e., resource consumption, energy usage, water consumption and waste generation), and shorter product life cycles (Cai and Choi, 2020; Abbate et al., 2023). This is because the textile and apparel industry is continually introducing new products, considering the marketâs trends (Keshavarz-Ghorbani and Pasandideh, 2023). Since there are seven private platforms with the capability of LSCM (e.g., Hyperledger and R3 Corda) (Farshidi et al., 2020), the DMs should select the best-fitting platform that aligns with their 127 requirements. In conjunction with the industry characteristics, Fig. 4.5 provides an overview of a BT-based architecture, involving various stakeholders (e.g., yarn suppliers, fabric and clothing manufacturers, and consumers). In this architecture, new data or transactions can be added to the BT after meeting the predefined frameworks and standards through a smart contract. Approval is given based on the consensus algorithm. Then, this new data and transactions are stored in immutable blocks, accessible only to authorized users. BT is a sequence of blocks linked through cryptography, with each block containing a list of transactions to maintain a secure historical record. Each block contains data, a hash block, and a previous hash block, in which the hash determines the unique identity of each block (Yousefi and Tosarkani, 2024b). As each block includes the hash of the previous block and the blocks are linked together, any changes made in the information of a block cause changes in its hash number (Agi and Jha, 2022). As a result, this helps network stakeholders detect unauthorized changes to block information, implying the block becomes invalid for subsequent blocks (Bamakan et al., 2020). The smart contract, embedded in this architecture, also enables stakeholders to build encrypted and real-time data exchange in the system to support the continuous tracking of products. 128 Figure 4.5 The BT-based architecture for textile and apparel SCs To select the best-fitting BT platform from the candidate ones for managing sustainable SCs, a list of evaluation criteria is identified through a literature review focusing on BT adoption. As shown in Table 4.2, the identified evaluation criteria are categorized through the TOE framework into three groups. The five technology-related criteria (i.e., scalability, reliability, security, portability, and consensus support) refer to the technical characteristics of BT platforms. Organizational context includes three criteria (i.e., performance efficiency, interoperability, and cost) related to linking structures and organizational resources and capabilities. The environment-related criteria include âexperience and imageâ and âmaintainability and operabilityâ which are beyond the firmâs control (e.g., market) that may affect the BT platform selection. 129 Table 4.2 The identified evaluation criteria for selecting a BT platform Context Symbol Criteria Description Sources Technology C1 Scalability This criterion involves restrictions on the number of nodes, throughput capacity, and network latency to process transactions, record data, and reach consensus. Lai and Liao (2021); Ar et al. (2021); Erol et al. (2023) C2 Reliability This criterion consists of the maturity, availability, fault tolerance, and recoverability of the BT platform. Farshidi et al. (2020); Lai and Liao (2021); Görçün et al. (2023) C3 Security This criterion indicates the ability of the BT platform to guarantee the confidentiality, integrity, and authenticity of all data and information. Ar et al. (2021); Filatovas et al. (2022); Erol et al. (2023) C4 Portability This criterion refers to the replaceability and adaptability of the operating system, database and data structure (i.e., moving data and executable software). Lai and Liao (2021) C5 Consensus Support This criterion shows the ability of BT to support pluggable consensus services and includes several mechanisms (e.g., multi-signature of participants). Farshidi et al. (2020); Büyüközkan and Tüfekçi (2021); Filatovas et al. (2022) Organization C6 Performance Efficiency This criterion refers to throughput, visibility, network delay, block confirmation, and data access in the organization. Ar et al. (2021); Lai and Liao (2021); Tavana et al. (2023) C7 Interoperability This criterion focuses on interactivity with existing software and hardware systems and easily communicating within the organization. Ar et al. (2021); Büyüközkan and Tüfekçi (2021); Erol et al. (2023) C8 Implementation Costs This criterion includes consultation, infrastructure, development, and operation and maintenance costs. Büyüközkan and Tüfekçi (2021); Gopalakrishnan et al. (2021); Lai and Liao (2021) Environment C9 Experience and Image This criterion involves satisfactory working experience, perception of customers, and reputation of the BT platform in the market. Büyüközkan and Tüfekçi (2021); Erol et al. (2023) C10 Maintainability and Operability This criterion refers to supporting infrastructures of the BT platform in the market, modularity, reusability, and modifiability. Büyüközkan and Tüfekçi (2021); Lai and Liao (2021) 4.5.1 Comparing BT Platforms and Forming Fuzzy Decision Matrix In the first phase of the proposed framework, the values of each of the identified criteria for existing BT platforms should be determined in the Z-number environment. Seven private BT platforms with the capability of LSCM, including BP1: R3 Corda, BP2: Quorum, BP3: Hyperledger, BP4: BigChainDB, BP5: Chain, BP6: Symbiont, and BP7: Stratis (Azure BaaS), are considered as the candidate platforms (Farshidi et al., 2021). The degrees of uncertainty and reliability for each evaluation criterion are driven from the existing literature considering the features of the candidate 130 BT platform. To demonstrate the applicability of the proposed DSF, the degrees of uncertainty and reliability are specified in three scenarios (Sc1, Sc2, and Sc3), which can be replaced by the linguistic descriptions of three potential experts (see Table A6 in Appendix A). The decision matrices created in Sc1, Sc2, and Sc3 are aggregated using the ZIS. The output of the aggregation process within the ZIS system is the aggregated value considering the three inputs. In Table A6, âVLâ and âVHâ respectively indicate the lowest and highest value for each criterion when the evaluation process of each candidate BT platform proceeds. For example, if the value of (H, VH) is considered for the first criterion, it means that high scalability is expected for the R3 Corda platform with very high confidence (i.e., reliability). After determining the values of criteria in the Z-number environment (i.e., the initialization phase), these values are aggregated using the ZIS system. Based on the methodology of the ZIS system (see Fig. 4.4), the Z-number theory-based linguistics variables associated with uncertainty and reliability concepts are converted to triangular fuzzy-based variables using the transformation rules (see Table A5). Afterward, an independent multi-inputs-single-output ZIS system is designed for each evaluation criterion per BT platform. Each ZIS is designed based on the predefined rules to map out given inputs to an output. Thus, this study constructs 70 ZISs having a similar structure (see Fig. 4.6) to aggregate the values of all evaluation criteria for BT platforms. The inputs of each designed ZIS are the values defined in Sc1, Sc2, and Sc3 (three inputs), and the output is the aggregated value. Figure 4.6 consists of three inputs with the same membership functions, and we can use the five linguistic variables (see Table A4) to specify the value of each evaluation criterion (consisting of uncertainty and reliability concepts) per alternative. Besides, we use nine linguistic variables to represent the output of each designed fuzzy inference system (i.e. the aggregated value) based on a combination of these triple inputs. When the different values are aggregated, the 131 range of 0 and 1 are covered by the following membership functions: Very Low (VL), Low (L), Nearly Low (NL), Low Medium (LM), Medium (M), High Medium (HM), Nearly High (NH), High (H), and Very High (VH). 0 0.250 0.500 0.750 10111Decision Criteria Values (Mamdani)0.5VL L M H VHVH0 0.125 0.250 0.375 0.500 0.625 0.750 0.875 101Aggregated ValueVL NL M NH VH0 0.250 0.500 0.750010.5VL L M H0 0.250 0.500 0.750010.5VL L M H VHL LM HM HConverted ValuesSc1 (Z-Numbers)Uncertainty ReliabilityUsing Transformation RulesUsing Transformation RulesUsing Transformation RulesSc2 (Z-Numbers)Uncertainty ReliabilitySc3 (Z-Numbers)Uncertainty Reliability Figure 4.6 The designed ZIS system to map out given inputs to an output A fuzzy inference engine is constructed and embedded in each ZIS to find the best output considering the various combinations of inputs. In this engine, fuzzy propositions are generated through the implication operator, and the results are aggregated based on the aggregation operator. The 125 rules are defined in Table A7 to construct this inference engine. We set âmaxâ, âminâ, and âminâ operators for the aggregation function, implication function, and AND method, respectively, in the determination phase of the ZIS parameters. We also employ SOM, MOM, and LOM defuzzification methods to achieve a better output of ZISs in the final phase. Accordingly, each ZIS can produce an aggregated fuzzy value for each evaluation criterion for each BT platform. In other words, this study employs the ZIS, instead of traditional approaches (e.g., definitive average), to aggregate inputs to form the fuzzy decision matrix. The matrix provided in Table A8 in Appendix A is the main input of the ZIS-based automatic FCM modeling and the fuzzy CoCoSo 132 method to calculate the weights of criteria and rank BT platforms, respectively (i.e., the second and third phases of the proposed DSF). 4.5.2 Determining the Weights of Evaluation Criteria As the importance of evaluation criteria from technological, organizational, and environmental contexts is different, there is a need to determine their weights before evaluating the candidate platforms. These criteria may affect the influence of others and consequently affect the final adoption decision. Accordingly, the second phase of the developed DSF seeks to determine the weights of criteria considering the potential CRs between them, in addition to uncertainty and reliability concepts. Therefore, the ZIS-based automatic FCM is used to model the CRs between 10 evaluation criteria. To do this, we consider these criteria as the FCM concepts and then determine the strength of potential CRs considering the descriptions of the criteria. These CRs include inner and outer relationships among technology-, organization-, and environment-related criteria. Figure 4.7 shows the CRs between concepts within each category (i.e., inner CRs), while Fig. 4.8 considers the outer CRs between concepts from different categories. As shown in Fig. 4.7, there are two inner CRs between five technology-related criteria and an inner CR between C7 and C8 as the organization-related criteria. As this study incorporates three groups of criteria, all meaningful outer CRs are also explored and added to the FCM model (see Fig. 4.8). This figure illustrates a causal-based model of BT platform evaluation criteria, which are categorized into three groups. Generally, if one concept affects another one, the CRs between them can be direct or inverse. As shown in Fig. 4.8, since the scalability (C1) of a platform improves (i.e., enhancing the throughput capacity and reducing the network latency to process transactions), the implementation costs (C8) of that platform increase because of requiring further infrastructures 133 (i.e., the existence of the direct CR). The fuzzy version of Eqs. (4.6) and (4.8) are applied to estimate the weight of direct CRs between two criteria (e.g., C1 and C8). The calculated weight of the exiting direct CR between C1 and C8 will be (0.6857, 0.7679, 0.8500). Reliability(C2)Scalability(C1)Portability(C4)Consensus support (C5)Security(C3)(0.4700, 0.5536, 0.6371)(0.7614, 0.8393, 0.8957)Technological ContextInteroperability(C7)Performance efficiency (C6)Implementation costs (C8)(-0.7986, -0.7143, -0.6300)Maintainability and operability (C10)Experience and image (C9)Organizational ContextEnvironmental Context Figure 4.7 The potential inner CRs between evaluation criteria considering TOE contexts On the other hand, if increasing the value of one concept reduces the value of another concept, the existing CRs will be inverse. As interoperability (C7) of a platform increases, it means that its interactivity with existing software and hardware systems is high. In this case, the implementation costs (C8) decrease because the selected platform can be adopted without considering the specific change in the existing infrastructures, requiring a high investment. The extended version of Eqs. (4.7) and (4.8) in the fuzzy environment are used to determine the strength of inverse CRs between the two criteria (e.g., C7 and C8). The weight of the inverse CR between C7 and C8 will be (-0.7986, -0.7143, -0.6300). The inputs of Eqs. (4.6) and (4.7) are extracted from the obtained fuzzy decision matrix in the first phase. Two concepts sometimes affect each other at the same time. The proposed causal-based modeling also handles such situations to provide more reliable weights. Take the CRs between reliability (C2) and maintainability and operability (C10) as an example, 134 the recurrent CRs between these two concepts appear. If the reliability of a platform, referring to the availability and recoverability, increases, that platform will be operated properly. On the other hand, if the maintainability and operability, referring to supporting infrastructures of the BT platform in the market and reusability increase, that platform will be more reliable. Reliability(C2)Interoperability(C7)Performance efficiency (C6)Scalability(C1)Portability(C4)Maintainability and operability (C10)Consensus support (C5)Experience and image (C9)Security(C3)(0.7329, 0.8036, 0.8614)Implementation costs (C8)(0.7786, 0.8750, 0.9371)(0.4700, 0.5536, 0.6371)(0.6671, 0.7500, 0.8171)(0.7614, 0.8393, 0.8957)(0.7957, 0.8750, 0.9429)(0.6800, 0.7679, 0.8286)(0.7329, 0.8214, 0.8800)(0.6671, 0.7500, 0.8171)(0.6857, 0.7679, 0.8500)(0.8014, 0.8929, 0.9571)(-0.7986, -0.7143, -0.6300)(0.7200, 0.8214, 0.8757)(0.6629, 0.7500, 0.8200)(0.7786, 0.8750, 0.9371)(0.7571, 0.8571, 0.9400)Technology-related criterionOrganization-related criterionEnvironment-related criterion Figure 4.8 The FCM model of evaluation criteria for BT platform selection Now, the scenario-making technique and hybrid NHL-DE learning algorithm are utilized to calculate the weight of each evaluation criterion. To do this, we define a scenario for each criterion, in which the criterion under study is activated by allocating the value of 1 to its concept and assigning the value 0 to other concepts associated with the other nine criteria. As this study considers 10 main criteria to evaluate platforms, 10 scenarios are designed. Take C1 as an example, the value of its associated concept should be equal to 1, while other concepts (C2 to C10) receive 135 the value of 0 in the initial concept state matrix. Afterward, each scenario is implemented using the hybrid learning algorithm based on the matrix of weights of the potential CRs to support the exchange of conceptsâ values. This helps DMs find the updated concept state matrix and, consequently, the weights of evaluation criteria. By implementing this algorithm for triple bounds of the matrix of weights the updated value of the studied concept is obtained in the fuzzy environment. This algorithm has been implemented in MATLAB on a 64-bit system (RAM: 16.0 GB, CPU: 11th Gen Intel® Core â¢ i5-1135G7, 2.4 GHz). Given the moderate complexity of the developed causal-based model for the studied problem, the algorithm efficiently completes each run in approximately 10 seconds per scenario. However, the runtime of the hybrid algorithm can vary based on several factors, including the number of FCM concepts, the number of CRs, and the number of iterations required to achieve convergence. This adaptability ensures that the algorithm remains efficient across a range of model complexities. After obtaining the updated values for all evaluation criteria, the normalized weight of each criterion is determined, as shown in Table 4.3. The performance efficiency (C6) and implementation cost (C8) with weights of (0.1268, 0.1315, 0.1347) and (0.1070, 0.1118, 0.1196) are the most significant criteria for evaluating BT platforms from the organizational perspective. Besides, maintainability and operability (C10) in the environmental context and scalability (C1) in the technological context are other important evaluation criteria. 136 Table 4.3 The calculated weights of evaluation criteria using the ZIS-based automatic FCM modeling Context Criterion Concept Value Weight Technology C1 (0.5419, 0.5490, 0.5566) (0.0963, 0.0997, 0.1029) C2 (0.5399, 0.5449, 0.5497) (0.0960, 0.0990, 0.1016) C3 (0.4855, 0.4923, 0.5086) (0.0863, 0.0894, 0.0940) C4 (0.4764, 0.4838, 0.4991) (0.0847, 0.0879, 0.0923) C5 (0.5102, 0.5175, 0.5247) (0.0907, 0.0940, 0.0970) Organization C6 (0.7134, 0.7241, 0.7288) (0.1268, 0.1315, 0.1347) C7 (0.4911, 0.5058, 0.5194) (0.0873, 0.0919, 0.0960) C8 (0.602, 0.6156, 0.6468) (0.1070, 0.1118, 0.1196) Environment C9 (0.4968, 0.5079, 0.5134) (0.0883, 0.0923, 0.0949) C10 (0.5524, 0.5642, 0.5781) (0.0982, 0.1025, 0.1069) 4.5.3 Prioritizing BT Platforms The third phase of the proposed DSF aims to prioritize the candidate BT platforms to find the best-fitting one considering multiple adoption requirements. To do this, the outputs of the first and second phases are incorporated to construct the fuzzy weighted decision matrix. This matrix is formed by applying the obtained weights of criteria in the second phase in the fuzzy normalized decision matrix (i.e., the normalized version of the output of the first phase). Accordingly, the fuzzy exponential weight of comparability sequences and the fuzzy weighted comparability sequences are computed for each BT platform using Eqs. (4.14) and (4.15). Then, the aggregator strategies (i.e., kia, kib, and kic) are calculated using Eqs. (4.16) to (4.18). The calculated values of aggregator strategies for each platform are presented in Table 4.4. Now, Eqs. (4.19) and (4.20) can be applied to obtain the final score (Ki) for each platform based on the values of kia, kib, and kic. As shown in Table 4.4, BP3: Hyperledger, with a score of 2.2175, is ranked as the first priority. Therefore, this BT platform can be suitable to develop a BT-based architecture to manage sustainable SCs. While Hyperledger imposes a high implementation cost to the organization, this platform has the highest value for three important benefit evaluation criteria (i.e., performance 137 efficiency, maintainability and operability, and scalability) compared to other candidate BT platforms (see Table A8). Besides, BP1: R3 Corda has the second rank among the candidate platforms with a score of 2.1198. More precisely, BP1 has the highest values of performance efficiency (like BP3), reliability, and interoperability. These advantages make BP1 appropriate to adopt in an organization to better manage sustainable SC activities. BP2: Quorum is the third priority to create a private BT-based architecture in the organization. BP2 is a private platform and a soft fork from Ethereum. This private platform shows acceptable levels of security, portability, and performance efficiency. Table 4.4 The obtained scores and ranks for candidate BT platforms Platforms kia kib kic Ki R(Ki) Rank BP1 (0.1450, 0.1491, 0.1537) (2.3303, 2.9292, 3.6814) (0.9619, 0.9866, 1.0093) (1.8332, 2.1102, 2.4445) 2.1198 2 BP2 (0.1420, 0.1466, 0.1528) (2.1408, 2.7220, 3.5262) (0.9425, 0.9702, 1.0032) (1.7344, 2.0085, 2.3753) 2.0239 3 BP3 (0.1471, 0.1511, 0.1561) (2.4756, 3.1311, 3.9558) (0.9761, 1.0000, 1.0245) (1.9080, 2.2066, 2.5705) 2.2175 1 BP4 (0.1340, 0.1389, 0.1438) (1.7668, 2.1947, 2.7904) (0.8893, 0.9195, 0.9442) (1.5250, 1.7389, 2.0164) 1.7495 5 BP5 (0.1296, 0.1360, 0.1423) (1.5696, 2.0000, 2.6863) (0.8602, 0.9001, 0.9342) (1.4125, 1.6377, 1.9637) 1.6545 7 BP6 (0.1339, 0.1393, 0.1432) (1.8107, 2.2588, 2.8314) (0.8885, 0.9218, 0.9403) (1.5438, 1.7685, 2.0301) 1.7747 4 BP7 (0.1340, 0.1389, 0.1436) (1.7466, 2.1663, 2.7225) (0.8893, 0.9195, 0.9427) (1.5160, 1.7266, 1.9865) 1.7348 6 4.6 Comparative Analysis In this section, a conceptual comparison between the proposed DSF and other existing methods is made to highlight the features of this framework. The comparison is conducted according to three factors: (i) stability, (ii) separability, and (iii) reliability. Compared to other conventional MCDM methods (e.g., TOPSIS and VIKOR), the CoCoSo method provides stable solutions, not susceptible to changes in the distribution of evaluation criteria weights or the number of alternatives (Yazdani et al., 2019; Lai et al., 2022). Accordingly, the CoCoSo methodology is selected to embed in the proposed DSF to help DMs achieve solutions with higher stability. The outputs of the DSF can be compared with conventional CoCoSo and its extended versions in 138 uncertain environments. First, output validation is performed to confirm the priorities assigned to the BT platforms. The implementation of the mentioned three methods shows that BP3, BP1, and BP2 are the most suitable BT platforms for adoption. Since the Spearmanâs rank-order correlation between the DSF and conventional CoCoSo, fuzzy CoCoSo (FCoCoSo), and Z-number-based CoCoSo (Z-CoCoSo) methods is close to 1 (Ïâ0.89), the direction of association between the priorities allocated by these approaches is high. In addition to providing stable solutions, the DSF improves the outputsâ separability of the above-mentioned methods in the face of complex decision-making problems having high numbers of criteria and alternatives. The considerable separability of the solutions for a decision-making problem empowers DMs to have clear managerial insights to make more reliable decisions (Szwed, 2021). The separability of solutions from the implementation of the DSF is compared to the CoCoSo and MOORA. Since the MOORA method has excellent stability to consider tradeoffs between several conflicting criteria in comparison with its contemporaries (e.g., TOPSIS and VIKOR) (Chakraborty et al., 2023), it is chosen for a comparison of the separability of solutions. Figure 4.9 shows the separability of the DSF and conventional MOORA and CoCoSo methods. Overall, these conventional methods spread solutions in shorter intervals compared to the DSF. Thus, the accumulation of the generated solutions is near the regression line. In this case, when we increase the complexity level of the decision-making problem by adding more alternatives, there will be several options having similar or very close scores. Therefore, the lack of sufficient separability may eclipse the true rank of the alternatives and prevent DMs from reliably making decisions (Yousefi and Tosarkani, 2022a). Figure 4.10 illustrates the separability of the DSF in comparison with the FCoCoSo and Z-CoCoSo. According to Figs. 4.9 and 4.10, the solutions generated by the conventional CoCoSo, FCoCoSo, and Z-CoCoSo methods are in the range of [1.6652, 1.9400], 139 [1.7701, 2.1274], and [1.7612, 2.2200], respectively. While the proposed DSF generates solutions in the range of [1.6545, 2.2175], which is greater than the intervals generated by MOORA, CoCoSo, and its extended versions. In fact, the DSF employs a hybrid learning algorithm to improve the separability of solutions and consequently the reliability of final decisions. The proposed DSF also adds the reliability concept to the decision-making process to handle higher levels of uncertainty and provide more reliable and unbiased solutions compared to the FCoCoSo and CoCoSo methods. Figure 4.9 The solutions separability of the DSF compared to conventional MOORA and CoCoSo methods Figure 4.10 The solutions separability of the DSF compared to the extended versions of the CoCoSo method 140 4.7 Discussion of Implications 4.7.1 Theoretical Implications This study has aimed to empower sustainable SCs to harness the benefits of BT practices to improve their integrity and responsiveness. To achieve successful innovation adoption in sustainable SCs, DMs are required to evaluate the feasibility of BT adoption. The proposed DSF in this study has sought to bridge the gap between theory and practice, enabling DMs to conduct a more holistic feasibility assessment before implementing BT. The feasibility study can be implemented in three main stages, including enablers analysis, barriers analysis, and platform evaluation and selection. In the first stage, we can explore the characteristics that BT brings to an organization through its supportive features. For example, BT enhances integrity by focusing on information sharing and communication among stakeholders in the sustainable SC network. Despite its status as a cutting-edge technology, the adoption of BT faces several managerial and organizational barriers. The potential challenges for BT adoption and their negative impacts should be identified and analyzed in the second stage of the feasibility study. Lastly, DMs are required to investigate the trade-off between the impact of expected enablers (e.g., consensus support and security) and the existing managerial barriers (e.g., implementation costs and interoperability) to have a successful BT adoption. Therefore, the proposed DSF has considered several evaluation criteria, derived from enablers and barriers analyses, to enable managers to make informed BT platform selection decisions in the last stage. Previous studies have primarily analyzed BT platforms from an individual perspective, while BT adoption involves different aspects. To better understand the BT platform selection problem, there is a need to describe the adoption decisions made by an organization within a theoretical framework. Exploring various aspects of the BT platform problem allows DMs to identify the most 141 feasible platform for application in sustainable SC networks. This highlights the potential supporting features of the best-fitting BT platform compared to others, addressing the cost-related criteria that may deter DMs from adopting innovation. This study has employed the TOE framework along with the advanced FCM modeling to derive several implications for DMs to redirect intra- and inter-organizational strategies to promote BT adoption in multistakeholder LSCM. In this regard, the inputs of the BT platform selection process have drawn from the TOE framework to cover intra- and inter-organizational and technological aspects. Compared to the literature, the studied BT platform evaluation criteria encompass technological, organizational, and environmental factors, empowering DMs to select the suitable BT platform based on various requirements. 4.7.2 Practical Implications Since this study has considered BT platform evaluation criteria from various contexts, DMs can consider the characteristics and availability of BT platforms, the importance of organizational resources and capabilities, and characteristics beyond the organizationâs control simultaneously. This, in turn, helps DMs develop efficient information technology strategies to outline which BT platform should be adopted to meet business goals. Table 4.5 shows the ranks assigned to the candidate BT platforms considering technology-, organization-, and environment-related criteria individually. This table illustrates that each group of factors has a different impact on managersâ decisions. Besides, Fig. 4.11 shows the changes in original ranks obtained from the implementation of the TOE-based DSF when DMs only focus on a specific context. 142 Table 4.5 The ranks assigned to BT platforms considering the TOE-related contexts Platforms Technological Context Organizational Context Environmental Context TOE-Based DSF Score Rank Score Rank Score Rank Score Rank BP1 2.2342 2 2.2677 2 2.5408 2 2.1198 2 BP2 2.0250 3 2.4639 1 2.0191 3 2.0239 3 BP3 2.4057 1 2.1194 4 3.1335 1 2.2175 1 BP4 1.5463 7 2.1659 3 1.7982 5 1.7495 5 BP5 1.6083 6 1.7813 5 1.3346 7 1.6545 7 BP6 2.0008 4 1.4664 7 1.5728 6 1.7747 4 BP7 1.7509 5 1.6801 6 1.8027 4 1.7348 6 As shown in Fig. 4.11, BP3 attains the first rank when DMs involve technological and environmental contexts in the BT platform selection process. However, BP3 drops to the fourth rank when the focus shifts solely to organization-related evaluation criteria. Based on Table 4.3, the organizational context comprises two effective criteria, including performance efficiency and implementation costs. Since BP2 has higher performance efficiency and lower implementation costs than BP3, this platform emerges as the preferred choice for adoption. Whereas the TOE-based DSF ranks BP2 as the third option when all factors are considered simultaneously. BP4 has the third rank when focusing on organizational context, while this platform has the lowest score based on the technological context. In this case, if DMs seek to conduct a more holistic evaluation of candidate platforms, they should consider all three aspects indicated in Table 4.5. Therefore, BP4 is assigned the fifth rank as an aggregated priority based on the three aspects of TOE. 143 (a) (b) (c) Figure 4.11 The impact of evaluation criteria on the BT platform selection process: a) technological factors, b) organizational factors, and c) environmental factors 4.7.3 Managerial Implications This research has developed a holistic framework for directing discussion on the feasibility of BT adoption in sustainable SC networks. The research has incorporated the potential enablers (i.e., characteristics) and barriers (i.e., adoption requirements) associated with BT adoption to help SC managers choose a feasible platform. Accordingly, the proposed DSF enables managers to make informed decisions when addressing the BT platform selection problem by considering several evaluation criteria, derived from enablers and barriers analyses (e.g., consensus support and implementation costs). Since there are often correlations between criteria, the impact of each criterion depends on its nature and potential CRs with other existing criteria. Each evaluation criterion holds varying levels of importance, affecting the final ranking of candidate BT platforms. For example, if managers prioritize affordability in platform selection, they might opt for a less costly option, potentially sacrificing high performance. As shown in Fig. 4.8, the identified criteria can play the role of both cause factors and effect factors because of the existence of recurrent CRs or loops. According to the developed causal-based model, performance efficiency (C6) and implementation cost (C8) can be considered as the effect factors because they have input arcs linked with other factors. In this case, the impact of C6 144 on the BT platform ranking is intensified by three factors from the technological context (i.e., C1, C2, and C5). C8 is also directly affected by three factors from the technological and environmental contexts (i.e., C1, C5, and C10). However, since there is an inverse CR between C7 and C8, the impact of C8 is decreased by increasing the value of C7. Thus, the overall impact of C8 is less than C6, placing C8 in the second rank. This shows that the weight of each criterion should be determined considering the potential CRs among criteria to achieve a more reliable ranking. As there are three output arcs and one input arc associated with C1, this factor can be considered as the cause factor, increasing the impact of C6 and C8. Based on the literature, performance efficiency and implementation cost are the main factors affecting the BT platform selection (Dabbagh et al., 2020; Lai et al., 2022; Erol et al., 2023). While ignoring the potential CRs between the two above-mentioned factors (i.e., C6 and C8) and C1 could lead to a misrepresentation of their impact. In the following, the impact of increasing the weight of significant evaluation criteria, including C1, C6, C8, and C10, is investigated. Then, four sets of weights (i.e., SW1, SW2, SW3, and SW4) have been defined in addition to the original set (i.e. SW0 based on the weights presented in Table 4.3). We consider the original weight and weights of 0.25, 0.50, 0.75, and 1 for each under-study criterion in SW1 to SW4. The obtained ranking scores for all BT platforms according to these sets are shown in Fig. 4.12. This figure illustrates the changes in the priorities of BT platforms while increasing the weight of each criterion. 145 (a) (b) (c) (d) Figure 4.12 The impact of increasing the weight of significant evaluation criteria on BT platform selection As shown in Fig. 4.12a, the increasing importance of scalability only reduces the ranking scores assigned to BP4 and BP6. While Fig. 4.12b shows that significant growth in the weight of performance efficiency increases the ranking scores of BP1 and BP2, making them the most suitable platforms like BP3. Besides, the proposed DSF also provides a stable and distinguishable ranking for DMs until considering the weight of 1 for C6. According to Fig. 4.12c, implementation costs affect the ranking scores of all BT platforms considerably. In this case, the obtained score for BP5 increases from 1.6545 to 3.9632, whereas the score calculated for BP3 decreases from 2.2175 to 1.6420. This, in turn, makes BP5 the most appropriate platform and BP3 the inappropriate platform in terms of implementation costs. Figure 4.12d shows that highlighting the importance of maintainability and operability has an insignificant impact on the ranking scores of BP1 and BP5, while other platforms have been affected by this change. 146 4.8 Conclusions Todayâs organizations are actively pursuing the adoption of emerging technologies to establish effective support mechanisms for managing complex SC networks. Despite the benefits of BT, its platforms come with their unique features and adoption requirements. Therefore, DMs are required to find a viable platform for SCs to respond to their specific needs. This study has proposed a DSF to consider various technological, organizational, and environmental aspects in selecting the best-fitting BT platform. We have incorporated the TOE theoretical framework, ZIS-based FCM approach, and fuzzy CoCoSo method to develop a more holistic framework rarely found in existing literature. Compared to the traditional approaches (e.g., MOORA), the FCM modeling supported by the developed ZIS system considers both uncertainty and reliability simultaneously in modeling the CRs between evaluation criteria and determining the weight of each criterion. The proposed DSF is also able to offer a more stable, reliable, and distinguishable ranking of BT platforms. The outputs of this study have revealed that performance efficiency, implementation costs, maintainability and operability are the most influential factors in selecting the best-fitting BT platform. Additionally, Hyperledger and R3 Corda are suggested to be adopted in sustainable SCs based on the identified evaluation criteria. As numerous factors and managerial conservatism can affect BT platform selection decisions, future researchers need to explore a broader range of evaluation criteria from various perspectives to formulate more effective technology adoption strategies. This study identifies implementation costs as a crucial criterion in selecting BT platforms, while it encompasses several components, such as infrastructure, development, and operation costs. Thus, future researchers are suggested to develop optimization models to analyze the SC costs under various BT platform scenarios. 147 Chapter 5: Data-Driven Robust Strategic Sourcing Considering Supply-Side Competition: Insights into Blockchain Adoption for Coordination 5.1 Introduction Todayâs global Supply Chain (SCs) are vulnerable to various disruptions, including logistical challenges and geopolitical tensions. For instance, the Suez Canal blockage created significant delays in global shipping, while the Russia-Ukraine war and the Gaza-Israel crisis have led to supply constraints and increased uncertainty. These events often arise due to factors beyond an organizationâs control, which can temporarily hinder sourcing and impact SC stability (Jain et al., 2022). However, effective Supply Chain Management (SCM) practices can mitigate the negative impacts of such disruptions (Frederico et al., 2021). Effective SCM starts with robust supplier management; any delays or issues from suppliers can significantly disrupt the entire SC network (Mandal, 2021). Therefore, strategic sourcing and partnership can develop a readiness mechanism for enhancing SC resilience in the face of disruptions (Jain et al., 2022). Strategic sourcing enables organizations to select reliable suppliers that ensure operational continuity (Wowak et al., 2024). This, in turn, helps better positioning to adapt to SC interruptions and maintain product flow and customer satisfaction. By strategically sourcing, companies can optimize their SCs and enhance their competitive edge, especially in uncertain environments (e.g., pandemics) (Frederico, 2023). Organizations often prioritize traditional criteria (i.e., price, quality, and delivery time) when making sourcing decisions, and they frequently overlook socio-environmental and operational factors (Mubarik et al., 2021; Ghosh et al., 2022). Strategic sourcing involves evaluating suppliers not only on price but also on their capability to meet the buyerâs requirements (e.g., operational and technological) 148 consistently and sustainably-related standards. The selection of efficient suppliers is pivotal in forming robust partnerships, which affects improved SC coordination (Wu et al., 2023). In the meantime, transparency and information decentralization are some of the main factors affecting the alignment of buyerâs strategies with suppliersâ operations during disruptions (Wissuwa et al., 2022; Vaezi et al., 2024). By leveraging advanced information-sharing mechanisms, organizations can achieve greater synchronization and responsiveness to ensure that the entire SC operates smoothly and efficiently. Innovative technologies like Blockchain Technology (BT) are increasingly being integrated into business environments to enhance SC capabilities and resilience (Dubey et al., 2020; Dubey et al., 2024). BT and its associated characteristics (e.g., immutability and smart contracts) can provide verifiable information about supplier performance, quality, and compliance with sustainability criteria (Dutta et al., 2020). Moreover, BT facilitates real-time information sharing and traceability of goods, which is essential for effective partnerships within the SC network (De Prajapati et al., 2022; Ghomi-Avili et al., 2023). Despite the benefits of BT adoption, complexity challenges and the need for widespread stakeholder engagement can hinder its effectiveness for enhanced information coordination (Yousefi and Tosarkani, 2024a). Therefore, buyers should consider BT compatibility criteria when establishing partnerships to maximize the benefits of integration. Although previous research has investigated the economic and environmental performance of suppliers, only a few studies have examined BT compatibility in the evaluation process (Vaezi et al., 2024). Transparency and information decentralization facilitated by BT could serve as a source of competitive advantage for SC partners, especially in the face of potential disruptions (Ferdous et al., 2024). Other market structures, such as supply-side competition and demand uncertainty, must be taken into account when the buyer deals with sustainable and strategic sourcing (Yousefi 149 et al., 2021; Yang et al., 2023). More precisely, a significant challenge in developing resilient sourcing strategies arises from the dynamic interactions among SC entities (Echefaj et al., 2024). Accurately capturing demand uncertainty is also crucial for effective decision-making, particularly in consumer electronics and automotive industries, where demand variability dimensions play a key role. Motivated by the above observations, the current research seeks to address the following research questions: i) What benefits arise from integrating BT compatibility and sustainability into supplier evaluation? ii) How does BT enhance and coordinate information systems within SCs? iii) How can supply-side competition affect strategic sourcing in SC networks? iv) How can advanced modeling techniques improve the robustness of decision-making under uncertainty? This study aims to focus on buyer-driven strategic sourcing to enable the selection of efficient suppliers who align with the buyerâs objectives on information sharing and sustainability practices. To achieve this, we first propose a Multi-Objective Programming (MOP) model integrated with Data Envelopment Analysis (DEA) to simultaneously evaluate the BT compatibility and sustainability of suppliers and allocate orders. This model evaluates and selects the required suppliers to ensure minimized SC coordination costs, minimized defective and delivery delay rates, and maximized sourcing efficiency. The developed model is then advanced through the Nash game theory to investigate the impact of competitiveness on choosing efficient suppliers for strategic partnerships. By exploring the role of BT adoption, the model seeks to improve information coordination between buyers and suppliers (BT-friendly or BT-disabled). In this context, Oracle validation is incorporated to help buyers connect external information systems to 150 their BT platform, ensuring reliable and accurate information sharing within the bi-level SC network. In the following, the proposed MOP model is transformed into a single objective programming model using the augmented ε-constraint to analyze the impact of each objective function in strategic sourcing and SC optimization. In the face of market uncertainties, we also suggest a Data-Driven Robust Optimization (DDRO) approach that utilizes the Principal Component Analysis (PCA) and robust Kernel Density Estimation (KDE) to create uncertainty sets. Conventional uncertainty sets, like box or ellipsoidal sets, typically rely on worst-case assumptions that can result in overly conservative and inefficient solutions (Ning and You, 2019). In contrast, DDRO improves decision-making by dynamically adapting to actual data and accounting for correlations and distributional asymmetries in demand uncertainties, thereby offering a less conservative representation of uncertainty (Ghiasvand et al., 2024). The developed Data-Driven Robust Multi-Objective Programming (DDRMOP) model significantly advances the robustness and applicability of SC coordination models to provide a sourcing strategy for managing demand uncertainties effectively. This study assists managers in recognizing BT as a crucial driver of SC capabilities, particularly for organizations operating in environments characterized by high market uncertainties and intense competition. The remainder of this study is structured as follows: Section 5.2 examines recent literature on BT adoption for SCM and highlights the existing gaps. The studied SC coordination problem is described in Section 5.3. Then, two MOP models are developed in Section 5.4 to address the supplier evaluation and order allocation in non-competitive and competitive environments. Then, the DDRMOP model is proposed to deal with the multidimensional demand uncertainties. Section 5.5 discusses the outputs of implementing the proposed models for a case study. Section 5.6 provides a sensitivity analysis of the parameters affecting sourcing strategies. Section 5.7 discusses 151 the theoretical, practical, and managerial implications of the research, while Section 5.8 concludes with final remarks and outlines opportunities for future research. 5.2 Literature Review 5.2.1 BT for SC Coordination As traditional SC systems may lack the flexibility and transparency required to address customersâ requirements, they incur substantial administration and error-handling costs (Dutta et al., 2020). Due to the growing applications of BT in different industries, this emerging technology has shown a significant potential to revolutionize every stage of the SC network, from procuring raw materials to distributing products to customers (Babich and Hilary, 2020; Xu and Choi, 2021; Choi and Siqin, 2022; Tiwari et al., 2023). BT and its associated characteristics (e.g., decentralization and shared database) effectively help managers improve inter-chain management and inter-chain sustainability (Dutta et al., 2020; Lim et al., 2021; Yousefi and Tosarkani, 2022). More specifically, the real-time information sharing among SC entities, along with the transparency and traceability of product movements, impacts network coordination and partnerships. This demonstrates the need for designing an integrated network that incorporates various procurement strategies and sustainability-related issues through smart contracts (Pournader et al., 2020). The sourcing process is also critical for building a more resilient SC because it affects the upstream flow and consequently influences downstream processes (Frederico et al., 2021; Frederico, 2023). Thus, incorporating strategic sourcing into SC coordination can enhance operational efficiency and maintain a competitive edge in the market. In this case, BT allows for tracking suppliersâ historical sustainability performance, facilitating supplier development, and eliminating intermediaries (Dutta et al., 2020). BT-based coordination among buyers and vendors benefits organizations in selecting efficient suppliers and reducing adverse socio-environmental impacts. 152 While BT offers significant benefits for SCM, its effective adoption by SC entities is essential. Accordingly, most of the previous studies have focused on the feasibility study of BT adoption in different SC networks. These studies have investigated BT adoption from three main perspectives, including BT adoption enablers analysis (Surucu-Balci et al., 2024), BT adoption barriers analysis (Khan et al., 2023b), and BT platform selection (Erol et al., 2023). Emphasizing vendor management, the compatibility of BT can be considered as a key criterion for selecting the most suitable suppliers to enhance SC coordination. In this regard, Liang et al. (2024) developed a two-stage approach using robustness PROMETHEE and the internal interactive index to evaluate supplier performance to enhance SC resilience with BT-related assessment criteria. Alora and Gupta (2024) proposed a Delphi-Best-Worst Method (BWM)-VIKOR approach to address the supplier selection problem considering the BT adoption capability criterion. As BT capabilities affect various functions, some researchers have also studied the impact of this technology on integrating SCM. Lotfi et al. (2022) investigated the enhancement of agility in a two-echelon healthcare SC through the integration of BT. They developed a Mixed-Integer Linear Programming (MILP) model, demonstrating that the cost function of the primary problem without the implementation of BT is higher than that of the same problem with BT. Rahmanzadeh et al. (2023a) conceptualized open SCM, leveraging integrated physical and conceptual resources to enhance SC efficiency and flexibility. They developed a fuzzy tactical planning model for a three-echelon clothing SC, demonstrating that product design costs form only a small fraction of the total SC cost. Rahmanzadeh et al. (2023b) examined the impact of open innovation on the core activities of a three-echelon home appliance SC by proposing a robust fuzzy-stochastic optimization model. Their findings demonstrated the modelâs effectiveness in aiding managerial decisions during the mid-term planning process. Modares et al. (2023) proposed a comprehensive 153 approach combining the Bayesian BWM, fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and MOP to analyze a green two-echelon electronic SC under a vendor-managed inventory policy. This study integrated the weights derived from the Multi-Criteria Decision-Making (MCDM) approach into the proposed model. Goli (2023) developed a mathematical model to design a BT-enabled closed-loop SC and emphasized the importance of robust product portfolio design. The results demonstrated that a product portfolio designed using this robust model significantly enhances the SCâs financial performance, particularly in terms of changes in equity. The information gap between stakeholders leads to sub-optimal business performance and distrust, which can be mitigated through smart contract implementation, thereby enhancing trust and overall business performance (Dutta et al., 2020). De Carvalho et al. (2022) developed a mathematical programming model to optimize BT adoption alongside the design of a three-echelon floral SC. The findings indicated that strategically implementing BT at specific points in the SC network yields significant cost savings compared to a full-scale deployment across the entire SC. In another study, Prajapati et al. (2022) introduced a mixed-integer nonlinear programming model for a virtual closed-loop SC, incorporating BT, the Internet of Things, and sustainability principles. Their findings provided valuable insights for businesses and government officials to develop policies that enhance environmental sustainability and benefit end-users. Ghomi-Avili et al. (2023) developed an MOP model to design a BT-based system ensuring SC transparency and authenticity. The optimization process utilizes data from a distributed ledger, followed by a fuzzy satisfying approach to identify optimal solutions within the Pareto frontier for the glass industry. Recently, Vaezi et al. (2024) proposed a two-phase framework for integrating BT into sustainable supplier selection and order allocation. The framework combines the BWM 154 and MULTIMOORA for supplier evaluation and develops a bi-objective MILP model that incorporates BT-related costs and constraints to enhance procurement decisions. 5.2.2 Research Gaps While significant advancements have been made in exploring BT within SCM, the previous studies have not thoroughly focused on integrating sustainability and BT compatibility criteria into SC coordination and strategic sourcing problems. As shown in Table 5.1, existing studies do not address the complexities of SC networks that arise from intricate interactions and competitive dynamics. Accordingly, the current research first develops an MOP model integrated with the DEA to evaluate the efficiency of suppliers in terms of BT compatibility and sustainability for strategic sourcing and address the order allocation problem simultaneously. The application of the proposed model is also extended to a competitive environment. Compared to the previous studies, this research incorporates sourcing efficiency and supply-side competition to provide a holistic view of SC coordination. This, in turn, affects the alignment of information systems within the SC network considering BT implementation levels. This also enables the development of strategic practices that leverage BT, ultimately achieving a competitive advantage in an uncertain environment. Furthermore, there is still a lack of comprehensive studies addressing multidimensional uncertainty during dynamic competitive interactions. Thus, this study proposes the DDRMOP model to provide Decision-Makers (DMs) with actionable insights to enhance coordination and strategic alignment under uncertainty. Taking advantage of PCA and robust KDE methods, the developed model helps DMs dynamically investigate complex and multidimensional demand uncertainty. The outcomes can lead to better cost minimization and improved supplier evaluation and selection to ensure a resilient and efficient SC network. 155 Table 5.1 A comparative overview of the features of the current research and other studies Author(s) Problem Industry SC type Sustainability Supplier evaluation conditions Methodology Modeling characteristics Solution space Robustness Supplier evaluation Allocation Information coordination BT adoption BT compatibility Efficiency Competitiveness Strategic partnership Theoretical background Mathematical approach Integrated methods Single objective Bi-objective Multi-objective Single-product Multi-product Single-period Multi-period Deterministic Uncertain Robust approach Type of uncertain set Yousefi et al. (2021) â â Numerical example Two-echelon â â MINLP DEA â â â â Foroozesh et al. (2022) â Food Four-echelon â MILP â â â â â Lotfi et al. (2022) â â Healthcare Two-echelon â MILP EVaR â â â â â De Carvalho et al. (2022) â â â Floral Three-echelon MIQP â â â â Prajapati et al. (2022) â â â E-commerce Closed-loop â MINLP â â â â Tirkolaee et al. (2023) â â Vaccine Three-echelon â MIP BWM-T2NN-WASPAS â â â â â Saputro et al. (2023) â â Numerical example Two-echelon â FAHP-TOPSIS â â â â Rahmanzadeh et al. (2023a) â â Clothing Three-echelon MINLP â â â â Ghomi-Avili et al. (2023) â â â Glass Closed-loop â MILP â â â â Mohammed et al. (2023) â Food Three-echelon â MILP AHP-OCRA â â â â Rahmanzadeh et al. (2023b) â â Home appliance Three-echelon MINLP â â â â â Oudani (2023) â Logistics Transportation â MILP MARCOS-VIKOR â â â â Goli (2023) â â Finance Closed-loop MILP â â â â â 156 Continued to Table 5.1 Author(s) Problem Industry SC type Sustainability Supplier evaluation conditions Methodology Modeling characteristics Solution space Robustness Supplier evaluation Allocation Information coordination BT adoption BT compatibility Efficiency Competitiveness Strategic partnership Theoretical background Mathematical approach Integrated methods Single objective Bi-objective Multi-objective Single-product Multi-product Single-period Multi-period Deterministic Uncertain Robust approach Type of uncertain set Modares et al. (2023) â â Electronic Two-echelon â MINLP Bayesian BWM-Fuzzy TOPSIS â â â Abdolazimi et al. (2023) â Blood Four-echelon MILP TOPSIS â â â â â Vaezi et al. (2024) â â â â Numerical example Two-echelon â â MILP BWM-MULTIMOORA â â â â Liang et al. (2024) â Pork â RPROMETHEE Lotfi et al. (2024) â â Clothing Three-echelon â MILP Minimax +CVaR â â â â â Polyhedral Saha et al. (2024) â Healthcare â Probabilistic hesitant fuzzy MARCOS Alora and Gupta (2024) â Finance â TCE BWM-VIKOR Abdolazimi et al. (2024) â Construction Two-echelon â MILP BWM â â â â â Saghafinia et al. (2024) â Electronic â â LP Fuzzy DEA â â â â Tavana et al. (2024) â Lithium-ion battery Circular â MILP â â â â The current study â â â â Consumer electronics Two-echelon â â â â â DCV MINLP Nash game theory-based DEA â â â â â PCA-Robust KDE LP: Linear Programming; MIP: Mixed-Integer Programming; MINLP: Mixed-Integer Nonlinear Programming; MIQP: Mixed-Integer Quadratic Programming; TCE: Transaction Cost Theory; DCV: Dynamic Capability View157 5.3 Problem Statement This study addresses the coordination problem within a bi-level SC network consisting of a single buyer and multiple suppliers (vendors) under uncertainty. In this network, the buyer seeks to choose a set of efficient suppliers to form strategic partnerships and supply the demand for required products over the planning horizon. As shown in Fig. 5.1, the suppliers are analyzed to ensure minimized SC coordination costs, minimized defective and delivery delay rates, and maximized sourcing efficiency. Furthermore, the transactions and the buyer-supplier partnership are supported by the adoption of BT and its inherent features (e.g., security and decentralization) within the network. In this regard, there is a need to improve the interoperability between buyerâs information systems and external systems adopted by BT-friendly suppliers and non-validated suppliers. Oracle validation can play a main role in connecting the buyerâs platform and systems adopted by non-validated suppliers for sharing accurate data in a secure environment. This is followed by information coordination between buyer and candidate suppliers to improve the resiliency of the SC network in the face of potential disruptions. Accordingly, the buyer must cover the expense of Oracle validation and determine how much they want to attune their functions with BT. Besides addressing BT-related aspects, the buyer must also consider sustainability factors in the supplier selection process, ensuring the environmental and social responsibility of SCs. Figure 5.1 depicts the step-by-step procedure for assessing and choosing suppliers, along with the allocation of orders. In the following, an MOP model is proposed to incorporate BT compatibility and Oracle validation into supplier selection and SC coordination. Then, this model is advanced through the Nash game theory to explore the impact of supply-side competition on strategic sourcing in the presence of BT. After introducing the augmented ε-constraint method to transform the MOP model into single objective programming, the DDRO is adopted to manage demand uncertainties. 158 Determining the supplier evaluation criteriaBlockchain CompatibilitySustainabilityTraditional Criteria Incorporating the efficiency scores of candidate suppliers through the DEA modelDeveloping an MOP model for optimizing a bi-level SC networkMinimizing supply chain coordination costMinimizing delivery delay and defective ratesMaximizing the sourcing efficiency Partnership costPurchasing costHolding cost Shortage cost Transportation cost Validation cost Incorporating dynamics of supply-side competition through the Nash game theoryTransforming the MOP model to a single objective programming model using the augmented ε-constraint method Advancing the proposed model using the DDRO approach Integrating the principal component analysis and robust kernel density estimationBlockchain-friendly supplierBlockchain-friendly supplierBlockchain-disabled supplier Oracle ValidationBlockchain-disabled supplier Blockchain-friendly supplierRead operationRead operationWrite operationWrite operationBuyer-Driven Strategic Sourcing Choosing efficient suppliers for strategic partnerships and allocating orders Demand uncertainty analysisNew data or transaction Adding new block Figure 5.1 The proposed methodology to address the strategic sourcing and SC coordination 159 5.4 Proposed Model 5.4.1 Non-Competitive MOP Model To deal with the strategic sourcing and order allocation problems simultaneously, this study proposes an MOP model. The proposed model addresses coordination between a single BT-enabled buyer and multiple suppliers to select the most efficient vendors and determine the optimal order quantity in different periods. The three objective functions are minimizing the SC cost, reducing delivery delays and defect rates for various products, and maximizing the score of selected suppliers for reliable partnerships. To consider the BT features in the SC coordination, a threshold suggested by the buyer for attuning suppliers with its BT platform and the degree of the BT adoption advancements for suppliers are considered. The attunement level describes the organizationâs maturity and readiness to adopt and integrate BT (Vaezi et al., 2024). In this case, the interoperability and Oracle validation-related costs are incurred by the buyer. To conduct the evaluation process, the DEA is incorporated with the buyer-vendor coordination model to compare the candidate suppliers in terms of some qualitative factors. Therefore, the evaluation of suppliers is implemented based on their BT compatibility and sustainability-related criteria. The DEA model is able to specify the importance of all evaluation criteria inherently, while the outranking MCDM techniques should be integrated with the weighting MCDM methods. The outcomes of the proposed model can reveal the impact of BT features and sustainability-based efficiency scores on the buyerâs strategic sourcing decisions. The indices, parameters, and decision variables employed for developing the model are defined in Table 5.2. This model is proposed considering the following assumptions: - The demand is known over the planning horizon in the deterministic model. - The model is developed considering multiple periods. 160 - Multiple products can be ordered in periods. - The buyer seeks to build strategic partnerships with multiple suppliers. - BT adoption helps avoid trust issues and maximizes the ability to share information. - No suppliers have been already selected. - The capacity of suppliers and storage space of buyers is limited. - The shortage is allowed over the planning horizon. - The defective and delivery delay rates are constant over the planning horizon. Table 5.2 The nomenclature used in formulating the studied problem Indices i Set of inputs for supplier evaluation (i = 1, 2, â¦, m) j Set of outputs for supplier evaluation (j = 1, 2, â¦, s) k Set of candidate suppliers (k = 1, 2, â¦, n) p Set of products (p = 1, 2, â¦, l) t Set of time periods (t = 1, 2, â¦, r) Parameters ð¹ð¶ðð¡ Partnership cost between buyer and kth supplier in time period t ðð¶ððð¡ Unit price offered by kth supplier for product p in period t ðð¶ððð¡ Transportation cost for kth supplier to deliver product p during period t ð»ð¶ðð¡ Holding cost for product p during period t ðð¶ð Shortage cost for product p ðð¶ð Validation cost for the Oracle of kth supplier ððð The need for Oracle validation (0 or 1) ð¶ð¶ðð¡ Cost of coordinating information systems of the buyer and kth supplier during period t ð¼ðð Defective rate of product p supplied by kth supplier ð½ðð Delivery delay rate of product p shipped by kth supplier ðððð¡ Capacity of kth supplier for product p in period t ð·ðð¡ Demand of product p in period t 161 Continued to Table 5.2 Parameters ðð Required space for storage of product p ðð Resilience threshold for product p ðððð¥ Maximum storage space of buyer ðððð¥ Maximum number of required suppliers ð¸ Minimum sourcing efficiency score required for selecting a supplier ð¾ð Oracle validation of kth supplier ððð¡ BT implementation score for kth supplier in period t ð´ð¡ Minimum BT attunement level required in period t ð¼ðð The ith inputâs value of supplier k ððð The jth outputâs value of supplier k ð A large number ϵ A very small number Decision variables ðððð¡ Ordered quantity of product p from kth supplier in period t ð ððð¡ Shortage level of product p during period t ðððð¡ Inventory level of product p during period t ð¦ððð¡ A binary variable; ð¦ððð¡ = 1 if supplier k for supplying product p in period t is selected; otherwise ð¦ððð¡ = 0 ðð Inefficiency value of kth supplier for product p ð¥ðð¡ A binary variable; ð¥ðð¡ = 1 if supplier k is selected in period t; otherwise ð¥ðð¡ = 0 ð¦ð A binary variable; ð¦ð = 1 if supplier k is considered for cooperation; otherwise ð¦ð = 0 ð£ðð The importance (weight) of ith input of supplier k ð¢ðð The importance (weight) of jth output of supplier k The proposed model includes three objective functions. The first objective function (Z1) minimizes the SC coordination cost considering the buyerâs viewpoint. As shown in Eq. (5.1), the first part of the objective function is equal to the sum of partnership, purchasing, and transportation costs. The second part comprises holding and shortage costs for different products over the planning horizon. The final part aims to minimize the costs incurred by the buyer when improving SC 162 coordination when there is a lack of BT adoption. More precisely, BT-related costs comprise the costs of Oracle validation and information sharing between the buyer and the chosen suppliers. These costs vary depending on each supplierâs readiness to integrate with BT-enabled information systems. The second objective function (Z2) minimizes the defective rate and delivery delay rate of products purchased from selected suppliers to enhance the SC responsiveness (see Eq. (5.2)). The main quantitative economic criteria which are associated with the type of products over the planning horizon (i.e., price, quality, and lead time) are applied in Z1 and Z2. As presented in Eq. (5.3), the third objective function Z3 Non-competitive measures the sourcing efficiency in terms of BT compatibility (e.g., transparency and information decentralization) and sustainability. In other words, the third objective function seeks to guarantee the feasibility of BT adoption for improving SC sustainability. Considering these aspects, Z3Non-competitive aims to form a partnership with the required number of sustainable and BT-friendly suppliers that maximize overall sourcing efficiency. ððð ð1 =âââð¹ð¶ðð¡ ð¦ððð¡ + ðð¶ððð¡ ðððð¡ + ðð¶ððð¡ ðððð¡ðð¡=1ðð=1ðð=1+ââð»ð¶ðð¡ðððð¡ + ðð¶ðð ððð¡ðð¡=1ðð=1+ââðð¶ðððð + ð¶ð¶ðð¡ð¥ðð¡ðð¡=1ðð=1 (5.1) ððð ð2 =âââ(ð¼ðð + ð½ðð)ðððð¡ðð¡=1ðð=1ðð=1 (5.2) ððð¥ ð3Nonâcompetitive=â(1 â ðð )ð¦ððð=1 (5.3) 163 Equations (5.4) to (5.26) are the constraints of the proposed model. Equation (5.4) guarantees that the quantity of products purchased from the selected supplier in different periods must not exceed its capacity. On the other hand, Eq. (5.5) ensures that if a supplier is selected by the buyer, it is required to place an order to initiate a collaboration between them. Equation (5.6) controls the total number of suppliers that could be selected. This helps the buyer to manage its partnerships. Equation (5.6) also assures that the objective functions can be optimized with fewer suppliers when possible. However, if the equation uses an equal sign, the buyerâs policy requires order allocation to the specific number of suppliers which may lead to the selection of extra suppliers with a minimal order (see Eq. (5.5)) and potentially increasing costs due to higher purchase expenses. ðððð¡ â¤ ðððð¡ð¦ððð¡ âð, ð, ð¡ (5.4) ðððð¡ â¥ Ïµð¦ððð¡ âð, ð, ð¡ (5.5) âð¦ððð=1â¤ ðððð¥ (5.6) The inventory balance constraint is shown in Eq. (5.7). This constraint states that the inventory level of product p in period t depends on the inventory level of product p in period t-1 and other three factors in period t, including the shortage level, demand, and sum of received orders. Equation (5.8) limits the shortage to a specific proportion of the demand, where ðð acts as a cap on how much of the demand can remain unfulfilled. This constraint ensures that the SC resilience is maintained by preventing excessive shortages. Equation (5.9) indicates that the space occupied by the inventory of products transferred from period t-1 to the current period and newly received orders in period t must not exceed the available buyerâs warehouse storage space. Equation (5.10) specifies the minimum level of BT attunement required by the buyer, which is correlated with the 164 amount bought from each supplier. This constraint also involves the Oracle validation and the score of effective BT implementation by suppliers (Vaezi et al., 2024). Rigorous Oracle validation is crucial for maintaining the integrity and security of smart contracts on BT platforms, as it ensures that off-chain data remains immutable and verifiable. This validation imposes costs on the buyer and influences information coordination with suppliers, impacting purchasing decisions and the degree to which processes are attuned to BT integration (Vaezi et al., 2024). In Eq. (5.10), Oracle validation ranges from 0 to 1, where ð¾ð=0 indicates a complete rejection of the supplierâs Oracle. In this case, the validation process imposes a cost to the buyer. On the other hand, ð¾ð=1 signifies a full validation of the supplierâs Oracle and attributes to the suppliers who do not require validation. ðððð¡ â ð ððð¡ = ððð,ð¡â1 â ð·ðð¡ +âðððð¡ðð=1 âð, ð¡ (5.7) ð ððð¡ â¤ ððð·ðð¡ âð, ð¡ (5.8) ââðð(ðððð¡ + ððð,ð¡â1)ðð=1ðð=1â¤ ðððð¥ âð¡ (5.9) ââð¾ðððð¡ðððð¡ð·ðð¡ðð=1ðð=1â¥ ð´ð¡ âð¡ (5.10) Equations (5.11) to (5.16) are associated with the DEA integrated with the proposed model. Equations (5.11) and (5.12) guarantee that the suppliers with high-efficiency scores, obtained from The weighted sum of the outputs The weighted sum of the inputs , are suggested for cooperation. Equation (5.13) ensures that the weighted sum of the outputs for each supplier is equal to or less than the weighted sum of its corresponding inputs. Additionally, Eqs. (5.14) and (5.15) demonstrate that the non-negative weights can be assigned to inputs and outputs. Equation (5.16) also indicates that the weighted outputs do not exceed a value of 1. 165 âð£ððð¼ðððð=1= ð¦ð âð (5.11) âð¢ðððððð ð=1+ ðð = ð¦ð âð (5.12) âð¢ðððð§ðð ð=1ââð£ððð¼ð§ððð=1â¤ 0 âð, ð§ (ð â ð§) (5.13) ð£ðð â¥ Ïµð¦ð âð, ð (5.14) ð¢ðð â¥ Ïµð¦ð âð, ð (5.15) ð¢ððððð â¤ ð¦ð âð, ð (5.16) The integrated DEA model seeks to introduce the most efficient suppliers through the binary variable of ð¦ð. To incorporate the outcomes of the DEA into the coordination model, Eqs. (5.17) to (5.20) are added to the set of constraints. Eqs. (5.17) and (5.18) formulate two if-then conditions in the linear form: If ð¦ð = 1 then â ð¥ðð¡ ðð¡=1 â¥ 1 and if ð¦ð = 0 then â ð¥ðð¡ ðð¡=1 = 0. Equations (5.19) and (5.20) also enable formulating these two if-then conditions: If ð¥ðð¡ = 1 then â ð¦ððð¡ â¥ 1ðð=1 and If ð¥ðð¡ = 0 then â ð¦ððð¡ = 0ðð=1 . These constraints help DMs screen the initial list of candidate suppliers and introduce more BT-compatible and sustainable suppliers to the buyer. âð¥ðð¡ðð¡=1â¥ ð¦ð âð (5.17) âð¥ðð¡ðð¡=1â¤ ðð¦ð âð (5.18) 166 âð¦ððð¡ðð=1â¥ ð¥ðð¡ âð, ð¡ (5.19) âð¦ððð¡ðð=1â¤ ðð¥ðð¡ âð, ð¡ (5.20) As Z3non-competitive is a nonlinear function, it should be linearized before converting the MOP model to a single objective function (see Lemma 1). Accordingly, Eqs. (5.21) to (5.23) are added to the set of constraints. Equations (5.24) to (5.26) define the types of decision variables used in the proposed model. Lemma 1. The objective function Z3non-competitive, where ðð is a continuous variable and ð¦ð is a binary variable, can be linearized. Proof: The linearize the objective function â (1 â ðð )ð¦ððð=1 , an auxiliary variable ð§ð defined as ð§ð = (1 â ðð )ð¦ð. Then, ð§ð â¤ ðð¦ð, ð§ð â¤ 1 â ðð , ð§ð â¥ (1 â ðð ) â ð(1 â ð¦ð), and ð§ð â¥ 0 are added to the set of constraints to ensure that ð§ð correctly models the product under all possible values of ðð and ð¦ð. Thus, the original objective function can be rewritten as â ð§ððð=1 . â ð§ð â¤ ðð¦ð âð (5.21) ð§ð â¤ 1 â ðð âð (5.22) ð§ð â¥ (1 â ðð ) âð(1 â ð¦ð) âð (5.23) ðððð¡, ðððð¡, ð ððð¡ â â¤â¥0 âð, ð, ð¡ (5.24) ðð, ð§ð, ð£ðð , ð¢ðð â¥ 0 âð, ð, ð (5.25) ð¦ððð¡, ð¥ðð¡, ð¦ð â {0,1} âð, ð, ð¡ (5.26) 167 5.4.2 Competitive MOP Model In a competitive environment, suppliers need to adjust their information management strategies, which affects the buyerâs sourcing and partnership decisions. Understanding this landscape enables diversification of the list of suppliers for the buyer to mitigate supply disruptions and quality issues. Competition fosters innovation and improvement among suppliers, which ultimately benefits the buyer. Thus, incorporating the competitive environment can lead to more informed, strategic, and resilient procurement decisions (Yousefi et al., 2021). Focusing on the competitiveness among suppliers, the third objective can be extended using the Nash game theory (Z3Competitive) ensuring the selected suppliers meet the minimum sourcing efficiency score required from the buyerâs viewpoint. According to Nashâs (1950) approach, the bargaining solution is an agreement that maximizes the product of individualsâ utilities (see Eq. (5.27)). This leads to the Nash Equilibrium solution when individualsâ utilities (ð¢ð ) are attainable using the strategy set represented by S. This function can be optimized when the utilities are equal to or greater than the minimum required utility (ðð ). By integrating the DEA part of the proposed model and Nash game theory in Eq. (5.28), Z3Competitive seeks to maximize the product of suppliersâ sourcing efficiency scores (1 â ðð ). In a competitive environment, it is assumed that the buyer establishes a minimum sourcing efficiency standard (E) for forming a strategic partnership with the suppliers. Each supplier aims to meet the minimum efficiency requirement to secure a partnership with the buyer and sell their products. From the buyerâs perspective, this creates competition among suppliers, motivating them to meet the buyerâs criteria for BT compatibility and sustainability within the SC network. Notably, as Z3Competitive is nonlinear, we transform this objective function into a linear form, as shown in Lemma 2. 168 ððð¥?â? âð,?â? â¥?â? =â(ð¢ð ðð=1â ðð ) (5.27) ððð¥ ð3Competitive=â((1 â ðð )ðð=1â ð¸) (5.28) In addition to the constraints of the initial model (i.e., Eqs. (5.4) to (5.20)) along with non-negativity constraints, Eq. (5.29) is added to enable the selection of suppliers who meet the minimum required efficiency score for collaboration with the buyer. This constraint emphasizes that suppliers must enhance their performance to increase their market share. 1 â ðð â¥ ð¸ âð (5.29) Lemma 2. The nonlinear objective function ð3Competitive= â ((1 â ðð )ðð=1 â ð¸) can be approximated and transformed into a linear form using McCormick linearization (Castro, 2015), considering 1 â ðð â¥ ð¸ and ðð â¥ 0. Proof: If a new variable ð§ð = (1 â ðð ) â ð¸ is defined, the objective function becomes ð3Competitive= â ð§ð ðð=1 . This product can be linearized using McCormick linearization. Let ð¢ð be an auxiliary variable such that ð¢0 = ð§0 = (1 â ð0 ) â ð¸. For each subsequent term ð, the product can be iteratively defined using McCormickâs linear relaxation, where ð¢ð â¥ ð¢ðâ1 + ð§ð â1, ð¢ð â¤ ð¢ðâ1, and ð¢ð â¤ ð§ð. Thus, these constraints ensure that ð¢ð approximates â ð§ð ðð=1 . â To analyze the feasible Nash equilibrium solution of the proposed model, Lemmas 3 to 5 are provided to validate equilibrium points. These lemmas ensure the continuity of the objective function and confirm the compactness and convexity of the feasible region. 169 Lemma 3. The objective function Z3Competitive is continuous with respect to the decision variables. Proof: The objective function ð3Competitive= â ((1 â ðð )ðð=1 â ð¸) is continuous because each term (1 â ðð ) â ð¸ is a linear transformation of ðð , which is a continuous function. Subtracting ð¸ from 1 â ðð does not affect continuity, as linear transformations preserve continuity. Additionally, the product of continuous functions remains continuous. Therefore, since ð3ðððððð¡ðð¡ðð£ð is a product of continuous terms, it is continuous. Furthermore, the defined constraints are linear and ensure that the decision variables remain within a continuous range. Thus, the objective function is continuous. â Lemma 4. The feasible region is compact because it is both closed and bounded. Proof: The constraints define a closed set since they are linear constraints. Additionally, these constraints ensure that the solution space is bounded by restricting the variables to finite ranges. As the feasible region is both closed and bounded, therefore it is compact. â Lemma 5. The feasible region is convex because it is defined by convex constraints. Proof: The constraints of the proposed model are linear and convex. Consequently, the feasible region, defined as the intersection of these convex constraints, remains convex. Thus, any linear combination of feasible solutions will also fall within the convex region. â 170 5.4.3 Augmented ε-constraint Method As each of the developed models includes three objective functions (Z1, Z2, and Z3Non-competitive / Z3Competitive) to incorporate a variety of considerations into the decision-making process, it is required to find Pareto-optimal solutions. These solutions represent the best trade-offs between conflicting objectives. In such cases, the DMs are seeking the most preferred solution rather than the optimal one (Mavrotas, 2009; Seif et al., 2024). Accordingly, this study employs the augmented ε-constraint method to consider the role of each function in strategic sourcing and SC optimization. This method improves upon the traditional version of ε-constraint by incorporating an additional penalty term in the objective function to handle constraint violations more effectively. This penalty term ensures that solutions violating the ε-constraints are penalized, leading to a more efficient search for Pareto-optimal solutions. This method transforms an MOP problem into a series of single-objective problems by keeping one objective function while converting the remaining objectives into constraints with specified bounds. This, in turn, enables DMs to explore various scenarios, where the primary objective function is changed. In the following, the formulation of this method for the generalized version of the MOP problem (see Eq. (5.30)) is explained. ððð¥ (ð1(ð¥), ð2(ð¥),â¦ , ðð(ð¥)) (5.30) ð . ð¡. ðð(ð¥) â¤ 0 âð In the augmented ε-constraint method, the model is enhanced by incorporating slack variables and secondary objective functions as shown in Model (5.31) (Mavrotas, 2009). 171 ððð¥ (ð1(ð¥) + ððð (ð 2/ð2 +â¯+ ð ð/ðð)) (5.31) ð . ð¡. ð2(ð¥) â ð 2 = ð2 â® ðð(ð¥) â ð ð = ðð ðð(ð¥) â¤ 0 âð ð ð â¥ 0 âð Where ð ð is the slack variable for ðð(ð¥), ðð is the range of ðð(ð¥) from the payoff table, and ð is a small positive number to prioritize the primary objective. 5.4.4 DDRMOP Model In this section, a DDRMOP model is proposed by integrating robust optimization techniques with data-driven uncertainty quantification methods to enhance decision-making under uncertainty. By using data-driven approaches, the uncertainty sets are constructed. The following subsections describe the methodologies employed in constructing data-driven uncertainty sets using PCA and robust KDE, as well as the formulation of the robust counterpart. 5.4.4.1 Data-driven Uncertainty Set The construction of a data-driven uncertainty set through PCA and robust KDE involves several steps aimed at capturing the underlying uncertainties in a systematic and robust manner (Ning and You, 2018). Initially, the matrix of uncertainty data ð³ = [ð¢(1), â¦ , ð¢(ð)]Tâ âðÃð is considered. PCA is applied to this matrix to identify the principal components that represent the directions of maximum variance in the data, effectively converting the original correlated uncertain parameters into independent ones. The data matrix ð³ is first scaled to zero-mean, resulting in ð³0 = ð³ â eð0T, 172 where e is a ones vector and ð0 is the vector of the mean. The covariance matrix ð® =1ðâ1ð³0Tð³0 is then computed. Eigenvalue decomposition is performed on the covariance matrix to obtain eigenvalues ðð and eigenvectors ðð. Through eigenvalue decomposition, ð® = ð«Îð«T, where ð« =[ð 1, â¦ , ð ð] â âðÃð is the principal components matrix consisting of all the ð eigenvectors (which correspond to the principal components) and Î = diag(ð1, â¦ , ðð) is a diagonal matrix containing the eigenvalues. Without affecting generality, it is assumed ð1 â¥ ð2 â¥ â¯ â¥ ðð. Once the principal components are obtained, the uncertainty data is projected onto these components to form the matrix ð¯ð = [ðð(1), â¦ , ðð(ð)]T where ð¡ð(ð)= ð ðT(ð¢(ð) â ð0). The robust KDE method is then used to approximate the probability density function of each principal component to provide a robust representation of the distribution of latent uncertainties. The estimated probability density function ðð ð¾ð·ð¸(ð) (ðð) =1ðâ ð¾â(ðð, ðð(ð))ðð=1 and its cumulative density function ?Ì?ð ð¾ð·ð¸(ð)(ðð) are calculated. One of the common kernel functions is Gaussian kernel that is ð¾â(ðð, ðð(ð)) =1â2ðâ2exp (ââððâðð(ð)â22â2). Noteworthy, the bandwidth is a critical parameter that controls the smoothness of the estimated density. Silverman (2018)âs rule of thumb is commonly used to estimate the bandwidth for KDE that is â = (4ð+2)1(ð+4)ðâ1(ð+4)ð. In this method, ð is the dimension of data, ð is the number of data, and ð is the standard deviation of the data. The robust KDE addresses the high sensitivity of the quadratic loss function to outliers by minimizing a robust loss function ð. Since minðð½(ð®) = â ð(âðº(ðð(ð)) â ð®â)ðð=1 does not have a closed-form solution, it is solved using the kernelized iteratively reweighted least squares algorithm. The algorithm iteratively adjusts weights to robustly estimate the density function. 173 After estimating the density function, the cumulative density function ?Ì?ð ð¾ð·ð¸(ð)(ðð) of the uncertainty vector ðð is calculated. The corresponding quantile function is ð¹ð ð¾ð·ð¸(ð)â1(ð¼) = min {ðð ââ|?Ì?ð ð¾ð·ð¸(ð)(ðð) â¥ ð¼}. In this function, ð¼ is the confidence level for each scenario. This function is used to establish the confidence interval for ðð at a specified ð¼. By incorporating the distributional information and correlations obtained from the uncertainty data, the data-driven uncertainty set is constructed as shown in Eq. (5.32): ððð¶ð´+ð ð¾ð·ð¸ = {ð¢|ð¢ = ð0 + ðð, ð = ðð ð¾ð·ð¸ â ðâ + ðð ð¾ð·ð¸â ð+,0 â¤ ðâ, ð+ â¤ e,ðâ + ð+ â¤ e, eT(ðâ + ð+) â¤ ðº} (5.32) Where ðð ð¾ð·ð¸ = [?Ì?ð ð¾ð·ð¸(1)â1(ð¼),â¦ , ?Ì?ð ð¾ð·ð¸(ð)â1(ð¼)]Tand ðð ð¾ð·ð¸= [?Ì?ð ð¾ð·ð¸(1)â1(1 â ð¼),â¦ , ?Ì?ð ð¾ð·ð¸(ð)â1(1 â ð¼)]T. The parameters ðâ and ð+ are the backward and forward deviation vectors, respectively, and ðº is the uncertainty budget. This set captures the correlations and distributional characteristics of the data, providing a robust representation of uncertainty for the MOP model. 5.4.4.2 Robust Counterpart Formulation Identifying the worst-case scenario within the uncertainty set is essential to determine the counterpart of uncertain demand. In the presented model, constraints (5.7), (5.8), and (5.10) contain this uncertain demand. It might seem that the maximum values from historical data could be used to specify the maximum demand value. However, this approach may result in an overly conservative solution because the maximum demand values in the historical data might be outliers or occur in very low-density regions. Therefore, it is important to construct an uncertainty set that reflects the desired degree of conservatism, avoids excessive caution, and excludes outlier data. Once the uncertainty set is constructed, the data within it can be analyzed to determine the 174 maximum value for each dimension. Thus, if there is a sufficient amount of historical data for the specific form of uncertain demand, developing a robust counterpart model may not be necessary to find the counterpart values of demands (Ghiasvand et al., 2024). The data-driven uncertainty set for demands, referred to as ð, is presented in Eq. (5.33) (Ning and You, 2018). ð represents a consolidated version of the uncertainty sets derived from PCA combined with robust KDE. ð = {ð·ð |ð·ð = ðð + â ðð ð(ððððâ + ðððð+), âð ð ððâ + ðð+ â¤ 1, 0 â¤ ððâ, ðð+ â¤ 1, âð â (ððâ + ðð+)ð â¤ Ω } (5.33) Where ðð is the mean value of ð·ð , and ðð ð is the sth element of the rth principal component, as well as ðð and ðð respectively are the lower and upper bounds of uncertain demand. Robust counterparts are constructed for constraints involving ð·ðð¡ to ensure feasibility under all possible realizations within the defined uncertainty set. Figure 5.2 shows that ð¼ and ðº significantly influence the size and inclusiveness of the uncertainty sets. As ð¼ increases, the uncertainty sets expand, capturing a broader range of data points. Similarly, increasing ðº allows for more considerable deviations within the uncertainty sets, further increasing the feasible region. These changes suggest that higher ð¼ and ðº values lead to more inclusive and less conservative uncertainty sets. Accordingly, a robust optimization model based on larger ð¼ and ðº will be less conservative and potentially enable more flexible and cost-effective decisions while accommodating a wider range of scenarios. Conversely, lower values of ð¼ and ðº result in smaller, more conservative uncertainty sets, leading to a more cautious optimization approach that prioritizes feasibility under stricter uncertainty conditions. 175 Figure 5.2 The uncertainty data along with the data-driven uncertainty set U (PCA-Robust KDE) Considering uncertainty sets and applying the augmented ε-constraint method, the data-driven strategic sourcing and order allocation model is formulated. For instance, as presented in Model (5.34), Z1 is considered as the primary objective, while Z2 and Z3Competitive are in the constraints. Given the uncertain demand ð·ðð¡, which belongs to the uncertainty set ð (Eq. (5.33)) that is added to Model (5.34). Notably, this model can also be applied to other two possible situations, where Z2 or Z3Competitive is a primary function. Furthermore, this approach can be implemented in a non-competitive environment where the set of constraints includes Eqs. (5.4) to (5.26) and two secondary objective functions. 176 Minðððð¡,ð ððð¡,ðððð¡,ð¦ððð¡,ðð,ð¢ð,ð§ð,ð¥ðð¡,ð¦ð,ð£ðð,ð¢ððMaxð·ðð¡âðâââð¹ð¶ðð¡ ð¦ððð¡ +ðð¶ððð¡ ðððð¡ + ðð¶ððð¡ ðððð¡ðð¡=1ðð=1ðð=1+ââð»ð¶ðð¡ðððð¡ + ðð¶ðð ððð¡ðð¡=1ðð=1+ââðð¶ðððð + ð¶ð¶ðð¡ð¥ðð¡ðð¡=1ðð=1â ððð (ð 2/ð2 + ð 3/ð3) (5.34) ð . ð¡: âââ(ð¼ðð + ð½ðð)ðððð¡ðð¡=1ðð=1ðð=1+ ð 2 = ð2 âð§ð ðð=1â ð 3 = ð3 ðððð¡ â¤ ðððð¡ð¦ððð¡ âð, ð, ð¡ ðððð¡ â¥ Ïµð¦ððð¡ âð, ð, ð¡ â ð¦ððð=1â¤ ðððð¥ ðððð¡ â ð ððð¡ = ððð,ð¡â1 âð·ðð¡ +â ðððð¡ðð=1 âð, ð¡ ð ððð¡ â¤ ððð·ðð¡ âð, ð¡ ââ ðð(ðððð¡ + ððð,ð¡â1)ðð=1ðð=1â¤ ðððð¥ âð¡ ââ ð¾ðððð¡ðððð¡ð·ðð¡ðð=1ðð=1â¥ ð´ð¡ âð¡ âð£ððð¼ðððð=1= ð¦ð âð âð¢ðððððð ð=1+ ðð = ð¦ð âð âð¢ðððð§ðð ð=1ââð£ððð¼ð§ððð=1â¤ 0 âð, ð§ (ð â ð§) ð£ðð â¥ Ïµð¦ð âð, ð ð¢ðð â¥ Ïµð¦ð âð, ð ð¢ððððð â¤ ð¦ð âð, ð âð¥ðð¡ðð¡=1â¥ ð¦ð âð âð¥ðð¡ðð¡=1â¤ ðð¦ð âð 177 5.5 Results and Discussion 5.5.1 Case Descriptions In the fast-moving consumer electronics industry, a retailer coordinates its operations with multiple suppliers to manage the SC for high-demand products like televisions, tablets, and laptops. In this industry, where technology evolves quickly and consumer preferences shift rapidly, the management of demand uncertainty is also crucial. Accordingly, in this study, it is assumed that a retailer evaluates 10 candidate suppliers based on their ability to deliver on time, meet quality standards, share real-time information, and adhere to sustainability practices. The main aim of the retailer is to use market data for three specific products to predict demand and then establish partnerships with BT-compatible and sustainable suppliers to keep coordination costs low. Adopting BT ensures secure and decentralized data exchange, which mitigates the risk of errors. Oracle validation supports this by connecting disparate systems and allows seamless integration â ð¦ððð¡ðð=1â¥ ð¥ðð¡ âð, ð¡ â ð¦ððð¡ðð=1â¤ ðð¥ðð¡ âð, ð¡ 1 â ðð â¥ ð¸ âð ð§ð = (1 â ðð ) â ð¸ âð ð¢ð â¥ ð¢ðâ1 + ð§ð â 1 âð ð¢ð â¤ ð¢ðâ1 âð ð¢ð â¤ ð§ð âð ð = {ð·ð |ð·ð = ðð + â ðð ð(ððððâ + ðððð+), âð ð ððâ + ðð+ â¤ 1, 0 â¤ ððâ, ðð+ â¤ 1, âðâ (ððâ + ðð+)ð â¤ ðº} ðððð¡, ðððð¡, ð ððð¡ â â¤â¥0 âð, ð, ð¡ ðð, ð¢ð, ð§ð, ð£ðð, ð¢ðð â¥ 0 âð, ð, ð ð¦ððð¡, ð¥ðð¡, ð¦ð â {0,1} âð, ð, ð¡ 178 and accurate data sharing across the SC network. By building strong and transparent relationships with suppliers and leveraging advanced technologies like BT, the retailer ensures a resilient SC capable of adapting to market fluctuations and delivering consistent value to consumers. A summary of the data adopted from Vaezi et al. (2024) for applying the proposed deterministic model is provided in Tables 5.3 and 5.4. Table 5.3 includes various types of costs, and Table 5.4 indicates the values of demand and capacity over the planning horizon. Furthermore, it is supposed that the defective rate of Products 1 to 3 (P1 to P3), supplied by the candidate suppliers, are [0.05, 0.10], [0.05, 0.11], and [0.05, 0.10], respectively. Besides, the delivery delay rates of P1 to P3 are between 0.05 and 0.10. Table 5.5 shows the BT implementation progress for candidate suppliers in different periods, along with the values of parameters related to the Oracle validation process performed by the buyer. The buyer specifies a threshold for aligning suppliers with its BT platform, ranging from 0 to 4, alongside the suppliersâ progress in BT implementation, which spans from 0% to 100% (Vaezi et al., 2024). Based on the literature, the shortage costs for P1 to P3 are equal to 3560, 2700, and 3130, respectively, and the space required to store these products are 1.5 m2, 0.25 m2, and 0.5 m2. The maximum storage space of the buyer and the resilience shortage threshold for products are 650 m2 and 45%, respectively. Table 5.3 The values of purchasing, transportation, holding, partnership and coordinating information systems Parameters Period 1 Period 2 Period 3 Period 4 Min Max Min Max Min Max Min Max ðð¶ððð¡ P1 1050 1400 1090 1400 1120 1450 1160 1450 P2 390 500 390 500 395 590 415 590 P3 750 950 775 950 795 1020 820 1020 ðð¶ððð¡ P1 120 355 120 375 130 350 130 400 P2 7 25 7 26 9 24 9 28 P3 64 190 64 201 70 187 70 212 ð»ð¶ðð¡ P1 600 600 700 700 790 790 820 820 P2 19 19 22 22 28 28 33 33 P3 310 310 361 361 409 409 427 427 ð¹ð¶ðð¡ 920 1300 925 1250 1000 1320 1060 1400 ð¶ð¶ðð¡ 225 270 205 250 210 280 185 250 179 Table 5.4 The capacity of suppliers and the estimated demand by the buyer Parameters Period 1 Period 2 Period 3 Period 4 Min Max Min Max Min Max Min Max ðððð¡ P1 30 90 30 100 30 100 30 90 P2 140 370 160 350 180 330 190 330 P3 95 220 115 210 120 195 125 200 ð·ðð¡ P1 140 140 130 130 100 100 80 80 P2 800 800 900 900 600 600 1000 1000 P3 470 470 515 515 350 350 540 540 Table 5.5 BT implementation progress and the Oracle validation process-related parameters Period 1 Period 2 Period 3 Period 4 ð¶ð½ð ð¸ð ð½ðªð Supplier 1 3 (75%) 3 (75%) 3 (75%) 4 (100%) 0 1 0 Supplier 2 4 (100%) 4 (100%) 4 (100%) 4 (100%) 0 1 0 Supplier 3 2 (50%) 3 (75%) 3 (75%) 3 (75%) 0 1 0 Supplier 4 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 0.3 200 Supplier 5 0 (0%) 0 (0%) 1 (25%) 1 (25%) 1 0.65 130 Supplier 6 4 (100%) 4 (100%) 4 (100%) 4 (100%) 0 1 0 Supplier 7 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 0.3 200 Supplier 8 2 (50%) 2 (50%) 3 (75%) 4 (100%) 0 1 0 Supplier 9 3 (75%) 3 (75%) 4 (100%) 4 (100%) 0 1 0 Supplier 10 0 (0%) 0 (0%) 1 (25%) 2 (50%) 1 0.70 115 ð¨ð 2 2 3 3 Focusing on the supplier evaluation procedure, the values of the chosen input and four outputs are shown in Table 5.6. These factors affect BT compatibility and sustainability. Transparency (O1) and information decentralization (O2) are criteria associated with BT compatibility, and environmental responsibility (O3) and ethical trade practices (O4) are two sustainability-related 180 criteria. O1 refers to the openness in sharing information about processes and practices to ensure clarity in communication and foster trust between buyers and suppliers (Thakur et al., 2021; Yousefi and Tosarkani, 2022). O2 ensures the authenticity and validity of the supply information and enhances accountability and collaboration (Cui et al., 2020; Ling et al., 2024). O3 implies a track record of adherence to environmental regulations, including proactive measures to reduce pollution, responsible waste management, and compliance with emission standards (Hailiang et al., 2023; Vaezi et al., 2024). O4 indicates the suppliersâ commitment to fair trade practices to ensure equitable treatment and fair wages for workers (Hailiang et al., 2023). The integration capacity can be considered in the evaluation process using the DEA model, referring to the compatibility of the supplierâs systems and processes with BT (Alora and Gupta, 2024). In this regard, integration incapacity (I1) is considered as the input of this model and its lower value is preferred because the buyer seeks to form a strategic partnership with suppliers with a high BT compatibility level. This partnership can result in leveraging the BT characteristics for enhanced environmental sustainability and social responsibility. In this case, it is also assumed that the buyer prefers to choose a maximum of four suppliers with at least a score of 0.50 for strategic partnerships. Table 5.6 The inputs and outputs of the supplier evaluation process Input Outputs I1 O1 O2 O3 O4 Supplier 1 0.40 0.80 0.78 0.49 0.55 Supplier 2 0.25 0.95 0.98 0.53 0.55 Supplier 3 0.50 0.65 0.61 0.48 0.58 Supplier 4 1.00 0.45 0.42 0.45 0.48 Supplier 5 0.95 0.55 0.53 0.38 0.40 Supplier 6 0.25 0.95 0.93 0.65 0.65 Supplier 7 1.00 0.45 0.40 0.30 0.48 Supplier 8 0.50 0.75 0.69 0.48 0.50 Supplier 9 0.35 0.85 0.81 0.63 0.53 Supplier 10 0.90 0.55 0.58 0.45 0.40 181 5.5.2 Analysis of the Results 5.5.2.1 Implementing the MOP Model Under Supply-Side Competition In this section, the findings of the non-competitive and competitive MOP models are presented. This section aims to compare these two models to reveal the impact of supply-side competition on strategic sourcing and order allocation in the presence of BT adoption. Strategic sourcing involves choosing suppliers based on factors beyond cost, including their ability to meet criteria such as BT compatibility and sustainability. Focusing on individual optimization, the values for Z1, Z2, and Z3 in a non-competitive environment are 4,080,960, 333.02, and 3.22, respectively. However, when accounting for potential supply-side competition, these values change to 6,320,298, 394.73, and 2.69. This shift highlights the significant impact of competition on coordination costs, delivery delays, defective rates, and overall sourcing efficiency. Table 5.7 indicates the performance of the defined three objective functions in non-competitive and competitive environments when the primary objective is changed. When the primary objective is to minimize SC coordination costs (Z1), competition leads to a substantial increase of 45.21% in these costs and a slight increase of 4.47% in defective and delivery delay rates (Z2). This suggests that while sourcing efficiency (Z3) improves by 27.49% under competitive conditions, the trade-off is a notable rise in the overall cost of coordinating the SC network. By establishing long-term relationships and collaborative agreements with key suppliers, the buyer may be able to streamline coordination processes to offset the increased costs. 182 Table 5.7 The performance of objective functions considering supply-side competition Primary Secondary (Constraints) Non-competitive MOP Competitive MOP ðð ðð ðð ðð ðð ðð ð1 ð2, ð3 4,352,669 670.00 2.11 6,320,547 699.98 2.69 ð2 ð1, ð3 6,999,830 482.90 2.00 6,999,614 604.60 2.69 ð3 ð1, ð2 5,044,756 669.92 3.22 6,999,886 656.63 2.69 Considering Z2 as the primary objective function, the introduction of supply-side competition has a mixed impact on SC optimization. The most significant change is observed in Z2 itself, which unexpectedly increased by 25.20%. This indicates a potential challenge in maintaining timely and defect-free deliveries under competitive pressure. On the positive side, the sourcing efficiency improves substantially by 34.50%. This implies that competition drives suppliers to coordinate themselves with the buyerâs requirements, even though it may come at the cost of increased delays. Partnering with suppliers known for their performance in meeting delivery schedules and quality standards can help address the challenges posed by competition to ensure a better alignment with delivery and quality objectives. When the primary objective is maximizing Z3, supply-side competition generally leads to continued partnership with suppliers with high BT compatibility and terminating the agreement with other suppliers. In this case, the buyer must make different purchasing decisions, such as increasing order quantities from the selected BT-friendly and sustainable suppliers. To accommodate this shift, the value of Z3 is reduced by 16.46% due to the removal of a BT-disabled supplier. This also leads to a 38.76% increase in Z1, driven by higher purchasing, transportation, and shortage costs. Furthermore, the impact on Z2 is negligible, with a minimal decrease of 1.98% observed, indicating that competition does not significantly affect the delivery time and quality of products in this scenario. In this scenario, forming partnerships with suppliers that offer 183 competitive advantages in efficiency while maintaining manageable coordination costs can be beneficial. Comparing the impact of competition on different primary objectives, the most significant increase in Z1 is observed when it is the primary focus. This indicates that the SC cost structure is most sensitive to competitive pressures when minimizing coordination costs is the goal. Sourcing efficiency consistently improves in the first two scenarios, with the largest gain of 34.50% observed when Z2 is the primary objective. This improvement will be evident in the third scenario (where Z3 is the primary objective) when the list of candidate suppliers includes more than three efficient suppliers satisfying the minimum required sourcing efficiency score. As this depends on E and ðððð¥, a sensitivity analysis of these parameters will be provided. This implies that regardless of the main focus, supply-side competition tends to drive better supplier performance in terms of sustainability and BT compatibility, albeit with varying degrees of impact on other objectives. Considering the last two scenarios (Z2 and Z3) in a competitive environment, the supply-side competition has led to a higher value of Z2 when Z3 is a primary objective function. While maintaining cost and the list of selected suppliers (Suppliers 2, 6, and 9) in the third scenario, the proposed model seeks to recommend partnerships exclusively with more efficient suppliers. As a result, the buyer places larger orders with Suppliers 2 and 6 and reduces the amount of purchased products from Supplier 9 to improve the overall sourcing efficiency. This could compel the buyer to rely more on BT-friendly and sustainable suppliers, even if they exhibit slightly higher defective rates and delivery delays. 184 5.5.2.2 Implementing the Proposed DDRMOP Model Taking uncertainty into account is essential in robust optimization, as it helps develop models that can handle real-world variations and unexpected disruptions. Balancing conservativeness ensures that solutions are not excessively cautious, which allows for practical and cost-effective decisions without compromising resilience. This, in turn, supports more effective sourcing strategies to ensure that buyer-supplier relationships remain resilient under varying conditions. In the following, the values of objective functions are determined using the DDRMOP model under different values of ð¼ and ðº. The parameter ð¼ defines the size of the interval for each latent uncertainty, corresponding to a confidence level of (1-2ð¼). Meanwhile, ðº referred to as the uncertainty budget, indicates the maximum number of latent uncertainties permitted to deviate from their mean values (Ning and You, 2019). Figure 5.3 presents a heat map illustrating the variation in the values of Z1 and Z2, which are the most sensitive objective functions to demand fluctuations, as ð¼ and ðº change. As shown in Fig. 5.3, careful tuning of ð¼ and ðº is crucial for optimizing Z1 and Z2 as the primary objectives. Increasing the value of parameter ð¼ leads to reduced coordination costs for a specified uncertainty budget ðº. This makes the solution more profitable because a higher ð¼ is associated with a smaller confidence interval. Conversely, increasing the uncertainty budget leads to higher costs as it enlarges the data-driven uncertainty set. Additionally, the impact of ð¼ on coordination costs is more pronounced than that of ðº in both competitive and non-competitive environments, particularly when the uncertainty budget is three or more. Considering Fig. 5.3, increasing the value of parameter ð¼ leads to a lower value of Z2 for a given uncertainty budget ðº, whereas expanding the uncertainty budget results in higher defective rates and delivery delays. Moreover, the influence of ð¼ on the value of Z2 is more significant than that of ðº in both 185 competitive and non-competitive environments, especially when the uncertainty budget is two or more. Notably, when a focus is on Z3, the non-competitive and competitive models are more robust to changes in ð¼ and ðº. This implies that Z3 can help DMs maintain effective buyer-supplier partnerships and performance levels even in the face of uncertainties. (a) (b) (c) (d) Figure 5.3 Heat map of the solution profile for DDRMOP under different values of α and Ω: a) non-competitive Z1; b) competitive Z1; c) non-competitive Z2; d) competitive Z2 186 5.5.2.3 Strategic Sourcing Outcomes Under Uncertainty In this section, a comparative analysis of three objective functions under two distinct scenarios (i.e., non-competitive and competitive environments) is provided. The results are derived from a DDRMOP model with ð¼=0.03 and ðº=2. These parameter values reflect the level of conservativeness of the developed model in dealing with the worst-case scenarios and providing robust solutions under uncertainty. Figure 5.4 reveals how the introduction of supply-side competition impacts the objective functions under uncertainty. Focusing on Z1 as a primary objective function, there is a notable increase from 6,235,830 in the non-competitive scenario to 9,537,772 when competition is introduced. This implies that competition leads to an increase in coordination costs, possibly due to greater complexity and the need for more advanced management of relations between the buyer and two distinct types of suppliers (BT-disabled and BT-friendly). The value of Z2, as a primary objective function, rises from 511.45 in the non-competitive environment to 625.83 under supply-side competition. This indicates that while competition may improve certain aspects of supplier performance, it also introduces challenges that could lead to slightly higher defect rates and delays. This could be attributed to the pressure on suppliers to perform under competitive conditions, which might strain their capabilities. Compared to the deterministic status, the value of Z3 increases from 2.16 in the non-competitive scenario to 2.69 in the competition. This improvement reflects the competitive environmentâs tendency to favor more technologically advanced and efficient suppliers under demand uncertainty. The results show that competition can lead to improved sourcing efficiency but also raises SC costs and operational challenges, highlighting its dual-edged nature under uncertainty. 187 Figure 5.4 A comparative analysis of primary objective functions under demand uncertainty In the following, we investigate the sourcing-related decisions when DMs prioritize Z1 and Z2. In this analysis, the impact of supply-side competition is applied by considering Z3 as a secondary objective function. When minimizing coordination costs (Z1) is the primary objective, the model selects three BT-friendly suppliers and one BT-disabled supplier concerning a restriction on the number of suppliers (N=4). As shown in Fig. 5.5a, Supplier 1 is chosen due to its affordable purchasing cost. Although Supplier 5 also offers low costs, it is not selected due to its lower capacity and higher coordination and Oracle validation costs. Supplier 6, despite having the highest product prices, is chosen for its BT-friendliness and superior performance in terms of sustainability and information coordination. This supplier, along with Suppliers 2 and 7, is selected to ensure that the buyer remains within the resilience shortage threshold, particularly for the second product (see Fig. 5.6a). In response to supply-side competition, the buyer opts to maintain partnerships with Suppliers 2 and 6 and allocates a larger portion of orders to them (see Fig. 5.6b). As illustrated in Fig. 5.5b, the buyer replaces Suppliers 1 and 7 with Supplier 9 to enhance resilience against future disruptions through collaboration with BT-friendly suppliers. Notably, the BT 188 implementation progress for suppliers 1 and 7 in the third period is 0 and 75%, while supplier 9 shows 100% progress. Based on Table 5.6, Supplier 9 also outperforms Supplier 1 in terms of sustainability-related criteria, transparency, and information decentralization, and consequently, obtains a higher efficacy score. This implies the critical role of information coordination in strategic partnership decisions. (a) (b) Figure 5.5 The list of selected suppliers for minimizing SC coordination costs in a) non-competitive environment and b) competitive environment (a) (b) Figure 5.6 Z1 optimization-based order allocation in a) non-competitive environment and b) competitive environment 189 As shown in Fig. 5.7a, when the buyerâs primary objective is to minimize defective and delay rates (Z2), the model strategically selects four BT-friendly suppliers, excluding BT-disabled suppliers from partnerships. In the non-competitive environment, Suppliers 6 and 9, identified as the most efficient, are chosen to supply all products, while Suppliers 1 and 3 are selected for specific products during the planning horizon (see Fig. 5.8a). This decision is driven by the strategic sourcing approach that prioritizes the first supplierâs ability to deliver high-quality P2 and the third supplierâs capability to ensure timely delivery of high-quality P1 and P3. In a competitive environment, the buyer must establish partnerships with suppliers who not only meet the minimum efficiency requirements but also excel in BT compatibility and sustainability. The need for strategic alignment leads to the inclusion of Supplier 2, while Suppliers 1 and 3 are removed from the partnership list (see Fig. 5.7b). In this situation, as depicted in Fig. 5.8b, there is a greater emphasis on obtaining products from Suppliers 6 and 9. This reflects the strategic priority given to these two suppliers, compared to the previous focus on minimizing coordination costs. (a) (b) Figure 5.7 The list of selected suppliers for minimizing defective and delivery delay rates in a) non-competitive environment and b) competitive environment 190 (a) (b) Figure 5.8 Z2 optimization-based order allocation in a) non-competitive environment and b) competitive environment 5.6 Sensitivity Analysis In this section, the Pareto front and sensitivity analysis of the key parameters are provided. Figure 5.9 illustrates the trade-offs between SC coordination cost (Z1) and the defective and delivery delay rates (Z2) under different confidence levels. The results demonstrate that as the confidence level decreases from 0.05 to 0.01, the proposed model becomes more conservative and results in higher values of Z2 for a given Z1. This implies that minimizing the overall defective and delay rates requires accepting higher coordination costs, particularly when DMs are more risk-averse. Consequently, higher confidence levels (α=0.05) allow the model to achieve lower defective and delay rates, albeit with increased risk. The selection of the optimal trade-off on the Pareto front depends on the DMsâ risk tolerance. If minimizing costs is the priority, solutions closer to the Pareto front with α=0.05 might be preferred despite potentially higher defective and delay rates. Conversely, for those who prioritize reducing defective and delay rates, a more conservative approach with α=0.01 may be more suitable, although at a higher overall SC coordination cost. The varying positions of the Pareto fronts reflect the inherent trade-offs between the objective functions Z1 and Z2 and provide valuable insights for strategic decision-making in SCM. 191 (a) (b) Figure 5.9 Pareto front of Z1 and Z2 in a) non-competitive environment b) competitive environment When comparing the Pareto fronts in non-competitive and competitive environments, it is evident that the competitive model provides a more stable relationship between Z1 and Z2 across various confidence levels. This stability is attributed to the competitive environment, which is structured using the Nash game theory-based Z3. As shown in Fig. 5.9, the shifts in the Pareto fronts are less pronounced which indicates that the competitive environment may encourage more balanced trade-offs between cost and quality, even as risk preferences change. On the other hand, the non-competitive model demonstrates greater sensitivity to risk aversion, with more significant trade-offs necessary to achieve similar levels of cost and quality. This indicates that in a non-competitive environment, DMs may need to exercise greater caution when adjusting risk preferences, as the impact on SC performance could be more substantial. Overall, these analyses offer valuable insights into how competitive dynamics and risk preferences shape the balance between cost efficiency and SC responsiveness in different market contexts. In the following, the impact of key parameters derived from buyerâs expectations on sourcing decisions is investigated. One such parameter is the maximum number of required suppliers needed to manage potential relationships within the SC network. In a non-competitive environment, increasing ðððð¥ influences the number of suppliers selected when focusing on Z1 as 192 the primary objective function. As ðððð¥ increases, the model initially selects more suppliers to meet potential market demand, with a notable shift occurring when ðððð¥ reaches 8 (see Fig. 5.10a). Beyond this point, specifically at ðððð¥ values of 9 and 10, the model optimizes coordination costs by limiting the number of suppliers to eight. When Z2 is the primary objective function, Fig. 5.10b shows that the trend of increasing selected suppliers stops at ðððð¥=6. In this scenario, the model selects six suppliers to achieve the lowest defective and delay rates. As ðððð¥ exceeds 6, the model does not increase the number of chosen suppliers further. When DMs focus on Z3 in Fig. 5.10c, increasing ðððð¥ directly affects the number of selected suppliers. As ðððð¥ rises, the model may choose some suppliers with lower efficiency scores and impact reliable partnerships. Accordingly, DMs should consider supply-side competition and establish an efficiency score threshold to ensure optimal sourcing decisions. Figure 5.10 The impact of changing the value of Nmax on sourcing decisions made by a) Z1, b) Z2, and c) Z3 193 The purpose of considering supply-side competition is to enable the buyer to select more efficient suppliers and consequently foster strategic partnerships. This consideration significantly influences the number of suppliers chosen. As illustrated in Fig. 5.10, when the value of ðððð¥ increases, the objective functions of the proposed model in a competitive environment tend to select a limited number of suppliers who are more BT-friendly and sustainable. As the minimum required sourcing efficiency score (E) increases from 0.2 to 0.5 and beyond, the outcomes of the three objective functions begin to converge, ultimately selecting the same set of suppliers (i.e. Suppliers 2, 6, and 9). To deal with worst-case scenarios, the buyer must procure the necessary products from these suppliers. Additionally, Suppliers 2, 6, and 9 should work to align more closely with the buyerâs expectations to boost SC resilience. Figure 5.11 illustrates the impact of varying the resilience threshold on the ordering procedure for different products when Z1 and Z3 are the primary objective functions. In this analysis, it is assumed that E=0.2 and ðððð¥=10. As depicted in Fig. 5.11, when the resilience threshold increases over the planning horizon, the cost-efficient approach (Z1) responds by increasing the order quantity for Product 1 while maintaining the order quantity for Product 2 at a constant level until the threshold reaches 0.7. This strategy helps control the shortage and enhances SC resilience. In this worst-case scenario, where both suppliersâ capacity and buyer storage are constrained, Z1 prioritizes fulfilling the demand for Product 3 entirely and then gradually reduces orders for this product to free up space for other products. This reduction continues until the resilience threshold line is met. 194 (a) (b) (c) Figure 5.11 The order quantity determined by Z1 and Z3 through changing the resilience threshold When Z3 is the primary objective function and Z1 is the secondary objective, the proposed model prioritizes meeting the demand for Product 1 over Product 2 until the resilience threshold reaches 0.6. During this phase, the order quantities for Products 2 and 3 remain constant, despite changes in the resilience threshold, as the buyer works with a combination of inefficient and efficient BT-friendly suppliers (Suppliers 3, 6, 8, and 9) in a competitive environment. After the threshold of 0.6, the model begins increasing the order quantities for Products 2 and 3 by engaging Supplier 2, along with Supplier 6, who has the highest BT compatibility and sustainability scores. Simultaneously, the sourcing efficiency function directs the buyer to source Product 1 exclusively from Suppliers 3, 6, and 9. This strategy helps the system build inventory levels for Products 2 and 3 while maintaining acceptable overall sourcing efficiency. Once the resilience threshold is met, 195 the model shifts to a strategy that ensures a consistent supply of a specific percentage of the demand for all products in the worst-case scenario to facilitate quick recovery. These outcomes imply that as the resilience threshold increases, the model prefers partnerships with efficient BT-friendly suppliers who enhance the buyerâs ability to manage potential disruptions through improved information coordination. 5.7 Discussions of Implications 5.7.1 Theoretical Implications This study offers novel insights into the capabilities of BT-enabled SCs and enhances our understanding of strategic sourcing in dynamic, competitive environments. While traditional capabilities typically focus on daily operational tasks, dynamic capabilities enable fundamental changes in products, processes, or market approaches (Winter, 2003; Dubey et al., 2023). Theoretically, this research adopts a DCV to better define the problem and analyze evolving market conditions. Applying the DCV lens as an intra-organizational theory refines the existing middle-range theory on using BT adoption to transform SCM systems (Meier et al., 2023). Recognizing the need to improve SC dynamic capabilities in response to disruptions, this study explores the role of BT-friendly suppliers, examining how dynamic digital capabilities influence strategic sourcing. By understanding the connection between BT adaptability and resilience, organizations can adopt an effective sourcing approach and strengthen their long-term partnerships in a competitive environment. The integration of BT can significantly boost SC dynamic capabilities by improving transparency and information decentralization (Li et al., 2022; Yousefi and Tosarkani, 2022a). This research highlights strategic sourcing considerations between BT-friendly and BT-disabled 196 suppliers that advance the application of DCV. BT-friendly suppliers, with their advanced digital capabilities, are better equipped to handle crises and manage resources effectively through improved information coordination with buyers. They offer superior efficiency, faster responses to disruptions, and more transparent processes. Although procurement costs for these suppliers may be higher, the reduction in coordination costs and enhanced data accuracy provide substantial benefits. In contrast, BT-disabled suppliers may struggle with adaptability and incur higher coordination costs. This differentiation underscores the importance of evaluating suppliers based on their technological compatibility and their role in enhancing dynamic capabilities within SCs. 5.7.2 Practical Implications This study offers valuable insights for SC practitioners aiming to maintain reliable partnerships in the face of market uncertainties. By integrating a Nash game theory-enabled DEA model with a DDRMOP approach, the research provides a comprehensive framework for evaluating sourcing effectiveness. This model helps DMs identify suppliers who align with technological and sustainability standards. Setting minimum thresholds for supplier compatibility can facilitate the establishment of long-term partnerships with high-performance suppliers, ensuring better alignment with the buyerâs strategic goals. Additionally, the incorporation of BT into the evaluation process can significantly streamline information coordination between buyers and suppliers. Although the initial costs of digitalization may be significant, the long-term advantages include lower information coordination expenses and improved network efficiency, which contribute to a more resilient SC that can effectively adapt to disruptions (e.g., global pandemics). 197 Moreover, the advanced DDRMOP model equips DMs with tools to effectively manage varying levels of market uncertainty. By considering risk preferences, DMs can customize their sourcing strategies to achieve a competitive edge while navigating complex and uncertain market conditions. The ability of the developed model to balance multiple objectives (e.g., minimizing coordination costs and improving delivery performance) under different scenarios ensures that sourcing decisions are well-informed. The enhanced collaboration fostered by this approach not only improves real-time information sharing but also strengthens the overall agility of the SC network. As a result, organizations can better respond to fluctuating market demands and maintain robust SC operations despite external challenges. Table 5.8 represents the impact of changing the optimal sourcing decisions by ending partnerships with the most efficient suppliers on SC coordination costs and the shortage level. Table 5.8 highlights the role of each BT-enabled supplier in both non-competitive and competitive environments by comparing the impact of removing them on SC coordination costs and shortage levels. In the non-competitive setting, Supplier 2 plays a significant role, as ending the partnership increases the value of Z1 by 4.33% and shortage levels by 4.59%. Meanwhile, removing Supplier 6 has a smaller impact on coordination costs but a more significant effect on shortages, leading to an increase of 12.54%. This indicates the importance of a partnership with Supplier 6 in maintaining supply levels rather than coordination costs. Overall, in a non-competitive environment, the elimination of these suppliers causes some disruption, especially in terms of supply shortages, but the coordination cost of the SC remains relatively manageable. 198 Table 5.8 Exploring the strategic role of the efficient suppliers in optimizing the SC network Condition Scenario Description Change in SC Coordination Costs Change in Shortage Level Non-Competitive Scenario 1 Ending partnership with Supplier 2 4.33% â² 4.59% â² Scenario 2 Ending partnership with Supplier 6 0.70% â² 12.54% â² Competitive Scenario 1 Ending partnership with Supplier 2 10.04% â² 37.15% â² Scenario 2 Ending partnership with Supplier 6 2.34% â² 15.49% â² Scenario 3 Ending partnership with Supplier 9 1.07% â² 8.06% â² Figure 5.12 depicts procurement decisions across various periods, comparing the base case with two alternative scenarios that involve removing key suppliers in a non-competitive environment. In the base case, Suppliers 2 and 6 play crucial roles in mitigating shortages for Product 2 across all periods. However, in Scenario 1 (i.e., removal of Supplier 2), the buyer compensates by significantly increasing orders from Supplier 6, while also forming a partnership with Supplier 9, particularly in the first and second periods, to offset the impact of Supplier 2âs absence. In Scenario 2, when Supplier 6 is removed, the buyer maintains steady procurement from Supplier 2 but shifts additional reliance to less efficient suppliers, such as Supplier 8, to bridge the gap left by Supplier 6. This increased dependency on lower-performing suppliers results in a more pronounced imbalance in procurement, which reflects the heightened shortage levels observed in Scenario 2. These adjustments underline the essential role of both Suppliers 2 and 6 in ensuring SC resilience and highlight the challenges in maintaining efficiency when forced to rely on less optimal suppliers. 199 Figure 5.12 Investigating changes in sourcing decisions made by the buyer in a non-competitive environment As shown in Table 5.8, cost and shortage disruptions are significantly more severe in the competitive environment. Terminating the partnership with Supplier 2 prompts the model to suggest forming a new partnership with Supplier 3 for supplying Products 1 through 3 (see Fig. 5.13). However, due to the higher costs associated with purchasing and integrating information systems for Supplier 3, the value of Z1 increases by 10.04%. Similarly, the removal of Supplier 6 results in a noticeable increase in SC coordination costs (i.e., 2.34%). In this scenario, Supplier 6 is replaced by Supplier 3, and the remaining suppliers attempt to meet the buyerâs demand. As a result, the increase in Z1, relative to the base case scenario, is less pronounced than in Scenario 1. Supplier 9, which is evaluated in the competitive environment, generates smaller increases in costs compared to other selected suppliers. This comparison underscores the strategic importance of 200 suppliers in a competitive setting, particularly for critical partners like Supplier 2, whose removal causes substantial disruptions in SC coordination. Figure 5.13 Investigating changes in sourcing decisions made by the buyer in a competitive environment 5.7.3 Managerial Implications This study emphasizes the importance of strategically selecting and maintaining BT-compatible suppliers to strengthen SC resilience. Effective information coordination enhances resilience by enabling real-time visibility, improving decision-making during disruptions, and accelerating recovery through better communication and collaboration across all stakeholders. The proposed framework aids managers in assessing supplier performance based on BT compatibility and sustainability and enables them to understand how these factors contribute to organizational resilience. The model incorporates a resilience threshold to help managers manage shortage levels within the system. An increase in product shortages reveals vulnerabilities in the SC and 201 consequently prompts managers to adopt adaptive strategies that mitigate disruptions and maintain supply continuity. Figures 5.14 and 5.15 demonstrate the impact of various scenarios on product shortages over time to highlight the crucial role of strategic sourcing in enhancing SC resilience. For instance, Fig. 5.14 shows that ending the partnership with Supplier 2 (Scenario 1) results in the highest shortages for Product 2, due to its higher estimated demand. In a competitive environment, the model prioritizes replacing an efficient supplier with one that implements BT to avoid higher costs related to validation and coordination, aligning with long-term strategic goals. If BT-compatible suppliers are unavailable, the model is forced to select suppliers without BT, which increases information coordination costs for the buyer compared to BT-enabled suppliers. To minimize these additional costs, the model reduces order quantities, but this reduction also compromises system resilience. As shown in Fig. 5.15, replacing efficient Supplier 2 with relatively inefficient Supplier 3 leads to a 37.15% increase in shortage levels, mainly for Products 2 and 3, as Supplier 3 has lower capacity across all time periods. Scenario 2, which removes Supplier 6, results in a 15.49% increase in shortages, while Scenario 3 shows more moderate impacts. These fluctuations in shortages expose potential SC vulnerabilities and reinforce the need for careful selection and maintenance partnerships with BT-compatible suppliers to mitigate risks. Figure 5.14 The impact of ending partnerships on buyerâs shortage level in a non-competitive environment 202 Figure 5.15 The impact of ending partnerships on buyerâs shortage level in a competitive environment 5.8 Conclusions As global SCs become more complex, DMs must address market dynamics and uncertainties for strategic sourcing. This study introduces a robust framework for enhancing strategic sourcing within SC networks to emphasize the critical role of information coordination. We have developed an MOP model combined with DEA to highlight how BT compatibility and sustainability can be systematically considered in supplier evaluation and order allocation. The study has further explored the dynamics of supply-side competition and the impact of complexities of demand uncertainty on strategic sourcing decisions through the Nash game theory-enabled DEA model and DDRO approach. The findings have revealed that supply-side competition plays a significant role in influencing SC coordination costs, defective and delivery delay rates, and overall sourcing efficiency. Suppliers that demonstrate compatibility with BT and acceptable performance in sustainability metrics are preferred, particularly when the buyer aims to minimize defects and delays or maximize sourcing efficiency. Therefore, supply-side competition can foster ongoing collaboration with BT-friendly suppliers to streamline SC communications and positively impact partnerships. This helps the buyer enhance information coordination within the network to navigate uncertain market conditions properly. The proposed MOP model also equips DMs with 203 a more comprehensive framework to manage sourcing strategies effectively in order to improve SC capabilities and resilience. As this study has focused on a bi-level SC network, future research could extend this model to offer deeper insights into how BT affects end-to-end information flow. In this regard, the proposed model can be expanded to a multi-echelon SC network under conditions of uncertainty. Furthermore, exploring the integration of BT with the Internet of Things could improve real-time tracking and visibility across SCs. Therefore, future researchers could develop a comprehensive framework to examine this integration to improve SC coordination and sustainability. To enhance decision-making and increase the flexibility of solutions, reinforcement learning can be incorporated into the proposed model to continuously update uncertainty sets using real-time data. 204 Chapter 6: Conclusion This dissertation has aimed to address the limitations of existing theoretical frameworks and decision-making approaches concerning BT adoption in SC networks. To this end, a series of uncertainty-aware DSFs have been developed, incorporating organizational theories, systems modeling, robust decision-making, and mathematical programming. The applicability of these frameworks has also been examined in relation to four specific problems associated with BT adoption. This chapter summarizes the main findings and contributions of the research presented in the preceding chapters. Additionally, it discusses potential future research directions derived from these findings. 6.1 Summary of Main Findings Chapter 2 presented a framework for evaluating the impact of BT characteristics on SCP improvements. The proposed RSAF addresses inherent uncertainties and managerial conservatism by identifying key enablers for various SC strategies. Findings implied that improved inventory control and environmental sustainability are crucial for enhancing cost-effectiveness in sustainable SCs. Additionally, auditability and traceability significantly impacted adherence to quality standards, while synchronized transactions and streamlined communication reduced lead times and order fulfillment cycle times. Overall, key BT adoption enablers included smart contracts, traceability, streamlined communication, provenance tracking, and integrity. These enablers are necessary for aligning BT adoption with the improved strategic SCP. The RSAF not only helps managers understand adoption complexities through the potential CRs but also guides practitioners in exploring CSFs for achieving diverse SC goals. Unlike previously developed approaches, the 205 RSAF improves ranking quality, reduces ambiguity, facilitates trade-off analysis, and supports effective strategy design for faster BT implementation. Chapter 3 proposed a DSF to support a holistic management framework to analyze barriers to BT adoption within sustainability contexts. The implementation of this DSF revealed that adoption cost, driven by technological complexities, unfamiliarity with BT, and sustainability challenges, is a significant barrier for SC managers. Additionally, the framework identified energy consumption as a critical environmental barrier, as increasing electricity usage for numerous calculations in the network hampers BT adoption. The approach incorporates uncertainty and reliability concepts, as well as the interplay between sustainability-related barriers and multiple RFs, to offer a more holistic risk management strategy compared to the different versions of the FMEA technique. Applying a hybrid FCM learning algorithm improves the reliability of barrier prioritization and allows DMs to define more effective mitigation measures to control their effects. Chapter 4 developed a TOE-enabled DSF for selecting the feasible BT platform by evaluating technological, organizational, and environmental factors. The findings emphasized performance efficiency and implementation cost as key criteria from an organizational perspective. Maintainability and operability in the environmental context, as well as scalability in the technological context, were also identified as crucial. Based on the obtained ranking, Hyperledger emerged as the top platform for developing a BT-based architecture for managing sustainable SCs, despite its high implementation cost. Hyperledger excels in performance efficiency, maintainability, operability, and scalability compared to other platforms. R3 Corda ranked second and was noted for its high-performance efficiency, reliability, and interoperability. The FCM modeling, enhanced by the ZIS system, simultaneously addresses both uncertainty and reliability in evaluating the CRs between criteria and determining the weights of each criterion. The proposed 206 DSF has shown its ability to provide a more stable, reliable, and distinguishable ranking of BT platforms compared to conventional MCDM approaches. Chapter 5 proposed an MOP model, integrated with data DEA and Nash game theory, to address the strategic sourcing within the bi-level SC networks. Considering the supply-side competition, the results indicated that BT-friendly suppliers are preferred for their better alignment with sustainability and information coordination goals. The findings also demonstrated that while supply-side competition can increase coordination costs and introduce operational complexities, it ultimately enhances overall sourcing efficiency. The outcomes of the advanced DDRMOP model showed its effectiveness in managing demand uncertainties while providing a more precise and less conservative approach. The results emphasized the value of BT and sustainability for creating strategic partnerships between the buyer and the selected suppliers to make SC more resilient in the face of potential disruptions. 6.2 Research Contributions The study has bridged several research gaps in the existing literature and made significant contributions in the following areas: (i) Proposing a comprehensive framework for studying the feasibility of BT adoption in sustainable SC networks and breaking down the adoption procedure into specific problems. (ii) Incorporating organizational theories (i.e., CSF and TOE) to DSFs to integrate strategic and operational perspectives to address resistance to BT adoption and choose the best-fitting platform. 207 (iii) Developing the systems theory-enabled FCM models in uncertain environments to analyze the network-based CRs among factors affecting the BT adoption problems (e.g., enablers and barriers). (iv) Demonstrating the applicability of the hybrid learning algorithms along with scenario-making to train the causal models developed for BT adoption problems considering various objectives. (v) Incorporating robust optimization and strategic thinking into the developed DSFs to consider managerial conservatism in designing BT adoption strategies. (vi) Exploring the direct and indirect impact of BT characteristics in improving SCP and SC coordination under information uncertainty. (vii) Leveraging risk management to analyze the deterrent effect of potential barriers on the readiness of SCs to adopt BT in sustainable SCs. (viii) Simultaneously considering risk treatment capability, uncertainty, and reliability in barrier analysis to provide a more accurate ranking for mitigating high-criticality barriers. (ix) Introducing the ZIS system to specify weights of potential CRs among evaluation criteria to enable modeling the BT platform selection as a decision-making problem. (x) Investigating the impact of BT compatibility and competitiveness on enhanced strategic partnerships within a SC network. (xi) Incorporating sourcing efficiency into SC coordination through a DEA integrated with the MOP model in a competitive environment. (xii) Advancing an MOP model using a DDRO approach to effectively address market demand uncertainty and improve SC resilience. 208 By tackling these challenges and introducing novel methodologies, this dissertation has explored the applicability of DSFs for decision-making under uncertainty, specifically in the context of BT adoption. It has also demonstrated how BT can be leveraged to improve SCM systems and highlights its benefits for an efficient sustainable SC network. 6.3 Future Work Recommendations The potential directions for future research on BT adoption in SC networks can be categorized into two main groups: (i) feasibility studies on BT adoption to address managerial conservatism and (ii) the incorporation of BT for improved SC functions. In the following, the research directions for the first group, focusing on addressing three main BT adoption problems, are discussed. In the BT adoption enablers analysis (Chapter 2), it is assumed that various BT characteristics (e.g., decentralization, distributed and shared databases, and smart contracts) can be integrated into traditional SCM systems to create more efficient and responsive SC networks. As these characteristics affect different dimensions of SCM, it is suggested that future researchers investigate how BT influences partnerships and helps rebuild more trustful stakeholder relations within SC networks. Given the increasing importance of security and privacy, investigating the implications of BT on data governance and compliance within SCM systems is essential. Specifically, future research should first focus on proposing practices for integrating BT in SC ecosystems to secure data and streamline compliance processes. Then, an institutional theory-enabled analytical approach can be developed to evaluate the effectiveness of the proposed solutions and learn how external pressures from regulatory bodies and standards shape the adoption of the BT procedure. 209 In the BT adoption barriers analysis (Chapter 3), the aim is to identify the most challenging barriers preventing a successful BT incorporation into SC networks. To mitigate the deterrent effects of such barriers, it is needed to develop mitigation strategies. Due to the limited resources, managers should implement more efficient mitigation measures to improve the readiness of SCs. Therefore, future researchers are recommended to evaluate the impact of each measure during the BT adoption process by reassessing the potential barriers using a Bayesian belief network model. Certain barriers, such as development costs, can be categorized as time-related challenges linked to the progression of BT platforms. Thus, a comprehensive list of barriers, including time-related challenges, should be identified and their potential to hinder progress examined. This analysis will provide insights into how addressing these barriers can offer operational competitive advantages in the market. On the other hand, BT implementation typically involves several stages, including developing a proof of concept, selecting the BT platform, and choosing the consensus protocol. From this multi-stage perspective, future research should examine how challenges encountered at each stage of BT implementation can influence subsequent stages and contribute to overall resistance to BT adoption. Using an advanced multi-stage FCM in an uncertain environment will help in analyzing these interdependencies and barriers more comprehensively. In the BT platform selection (Chapter 4), it is essential to establish well-defined evaluation criteria to effectively compare candidate platforms in order to choose a platform that aligns closely with the SC network. This process ensures that the chosen platform is not only compatible well with the existing SC structure but also fulfills a critical requirement. To effectively evaluate BT platforms, future researchers are suggested to consider a wider range of criteria and perspectives, reflecting the diverse influences and managerial conservatism that can impact selection decisions. As implementation costs involve multiple components (e.g., infrastructure and development), 210 future studies should focus on developing optimization models that analyze SC costs comprehensively under various BT platform scenarios. By exploring cost components and their impact on SC optimization, DMs gain a deeper understanding of the cost implications and provide a more detailed perspective on the feasibility of different BT platforms. After discussing the research avenues for the feasibility study of BT adoption, further directions on the adoption of BT for improving SC coordination (Chapter 5) are provided. The role of BT in SC network design can be further developed by focusing on three key areas. 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The linguistic variables allocated to the importance of CSFs in manufacturing firms Symbol MF1 MF2 MF3 MF4 MF5 S1 S2 S3 S1 S2 S3 S1 S2 S3 S1 S2 S3 S1 S2 S3 CSF1 M H L MH L ML VVH MH ML MH L H H MH L CSF2 H VH H H H VH ML ML H M VH MH VH M VH CSF3 M VL M L MH H H L L VL M ML M VL MH CSF4 ML ML L MH ML L MH VH M H M MH MH MH M CSF5 H VH VH M VVH VH ML M MH M MH ML H L VL CSF6 VH MH M M VL ML VH H M H M MH H MH ML CSF7 ML VH H L MH H M MH ML MH ML MH VH M VL CSF8 VH H L M L M MH H L M VL ML ML L MH CSF9 H L ML H VH ML VL L L VH MH ML VH M VL CSF10 MH L ML VH H MH VH ML L H MH VH VH M VL CSF11 ML M H MH VVH H ML MH M MH MH ML L VL M CSF12 MH M H M M MH ML VL M VL M ML H L VL CSF13 M MH H ML H H MH ML M ML M VH VVH H VH CSF14 ML MH L L ML L ML MH M MH M ML H VL ML CSF15 M M H M MH MH ML VH MH VH MH M VVH H ML CSF16 VH VH ML ML L M M L VL VL L H H L MH CSF17 H ML VL VH MH VL M VH VL H ML VH H L VL CSF18 ML ML M L L ML MH ML M ML M MH H M VL CSF19 MH VH H MH VH H M VL ML VL ML ML VVH H ML CSF20 MH L M MH ML VL M L VL M VL MH M H L CSF21 M ML VL VH MH L MH L VL MH M L MH M VL CSF22 H M L VH MH VL H MH M H VH M M VL H CSF23 MH L VL MH ML L H M M MH M H VH VH H AH= (0.9,1,1); VVH= (0.8,0.9,1); VH= (0.7,0.8,0.9); H= (0.6,0.7,0.8); MH= (0.5,0.6,0.7); M= (0.4,0.5,0.6); ML= (0.3,0.4,0.5); L= (0.2,0.3,0.4); VL= (0.1,0.2,0.3); AL= (0,0.1,0.2); N= (0,0,0.1) 245 Table A2. Z-number-based linguistic variables for RFs of each potential barrier Symbol Sc1 Sc2 Sc3 Sc4 Sc5 S O D T S O D T S O D T S O D T S O D T B1 AVH, H M, H S, VH MH, VH AVH, H L, H L, H MH, H AVH, H M, H S, H MH, H VH, H S, H VS, VH AVH, H H, H M, H H, H MH, H B2 H, H MH, H L, H MH, H H, VH MH, M L, VH M, H H, H MH, M L, VH MH, H AVH, VH H, M M, H H, H H, M MH, M AVH, H M, M B3 VS, L ML, H L, M L, H VS, L MH, H L, H L, M VS, M ML, M L, VH L, H S, M S, H VS, L VS, H S, L AVH, M MH, L VS, M B4 VS, L S, M VS, H S, M L, VL S, M L, M S, L VS, H S, M VS, M S, H M, L ML, M MH, M ML, M L, VL M, L VS, M VS, L B5 L, M ML, M VS, H S, H L, L M, H M, H L, H L, H ML, L VS, H S, M L, M L, H AVH, H ML, M ML, M L, L L, M M, M B6 L, M H, VL S, M MH, H S, M H, L S, H M, M L, M H, M S, M MH, H S, H S, H S, VL VS, VH MH, L L, M S, H ML, M B7 L, L MH, M MH, H M, M S, L M, M MH, M M, L L, M MH, M MH, M M, H M, VL M, M L, H L, H M, VL ML, L S, M L, H B8 M, H L, H VS, M M, VH MH, VH S, H L, H S, H M, H L, M VS, H M, VH H, H L, VH VS, H MH, H VH, M AVH, H ML, H AVH, H 246 Table A3. Converting the Z-number-based values of RFs to fuzzy numbers for potential barriers Symbol Sc1 Sc2 Sc3 Sc4 Sc5 S O D T S O D T S O D T S O D T S O D T B1 (0.6694, 0.7530, 0.8367) (0.4184, 0.5020, 0.5857) (0.1903, 0.1260, 0.3806) (0.5708, 0.6660, 0.7611) (0.6694, 0.7530, 0.8367) (0.2510, 0.3347, 0.4184) (0.2510, 0.3347, 0.4184) (0.5020, 0.5857, 0.6694) (0.6694, 0.7530, 0.8367) (0.4184, 0.5020, 0.5857) (0.1673, 0.2510, 0.3347) (0.5020, 0.5857, 0.6694) (0.7530, 0.8367, 0.8367) (0.1673, 0.2510, 0.3347) (0.0951, 0.1903, 0.2854) (0.6694, 0.7530, 0.8367) (0.5857, 0.6694, 0.7530) (0.4184, 0.5020, 0.5857) (0.5857, 0.6694, 0.7530) (0.5020, 0.5857, 0.6694) B2 (0.5857, 0.6694, 0.7530) (0.5020, 0.5857, 0.6694) (0.2510, 0.3347, 0.4184) (0.5020, 0.5857, 0.6694) (0.6660, 0.7611, 0.8563) (0.4243, 0.4950, 0.5657) (0.2854, 0.3806, 0.4757) (0.4184, 0.5020, 0.5857) (0.5857, 0.6694, 0.7530) (0.4243, 0.4950, 0.5657) (0.2854, 0.3806, 0.4757) (0.5020, 0.5857, 0.6694) (0.7611, 0.8563, 0.9514) (0.4950, 0.5657, 0.6364) (0.4184, 0.5020, 0.5857) (0.5857, 0.6694, 0.7530) (0.4950, 0.5657, 0.6364) (0.4243, 0.4950, 0.5657) (0.6694, 0.7530, 0.8367) (0.3536, 0.4243, 0.4950) B3 (0.0548, 0.1095, 0.1643) (0.3347, 0.4184, 0.5020) (0.2121, 0.2828, 0.3536) (0.2510, 0.3347, 0.4184) (0.0548, 0.1095, 0.1643) (0.5020, 0.5857, 0.6694) (0.2510, 0.3347, 0.4184) (0.2121, 0.2828, 0.3536) (0.0707, 0.1414, 0.2121) (0.2828, 0.3536, 0.4243) (0.2854, 0.3806, 0.4757) (0.2510, 0.3347, 0.4184) (0.1414, 0.2121, 0.2828) (0.1673, 0.2510, 0.3347) (0.0548, 0.1095, 0.1643) (0.0837, 0.1673, 0.2510) (0.1095, 0.1643, 0.2191) (0.5657, 0.6364, 0.7071) (0.3286, 0.3834, 0.4382) (0.0707, 0.1414, 0.2121) B4 (0.0548, 0.1095, 0.1643) (0.1414, 0.2121, 0.2828) (0.0837, 0.1673, 0.2510) (0.1414, 0.2121, 0.2828) (0.0945, 0.1260, 0.1575) (0.1414, 0.2121, 0.2828) (0.2121, 0.2828, 0.3536) (0.1095, 0.1643, 0.2191) (0.0837, 0.1673, 0.2510) (0.1414, 0.2121, 0.2828) (0.0707, 0.1414, 0.2121) (0.1673, 0.2510, 0.3347) (0.2739, 0.3286, 0.3834) (0.2828, 0.3536, 0.4243) (0.4243, 0.4950, 0.5657) (0.2828, 0.3536, 0.4243) (0.0945, 0.1260, 0.1575) (0.2739, 0.3286, 0.3834) (0.0707, 0.1414, 0.2121) (0.0548, 0.1095, 0.1643) B5 (0.2121, 0.2828, 0.3536) (0.2828, 0.3536, 0.4243) (0.0837, 0.1673, 0.2510) (0.1673, 0.2510, 0.3347) (0.1643, 0.2191, 0.2739) (0.4184, 0.5020, 0.5857) (0.4184, 0.5020, 0.5857) (0.2510, 0.3347, 0.4184) (0.2510, 0.3347, 0.4184) (0.2191, 0.2739, 0.3286) (0.0837, 0.1673, 0.2510) (0.1414, 0.2121, 0.2828) (0.2121, 0.2828, 0.3536) (0.2510, 0.3347, 0.4184) (0.6694, 0.7530, 0.8367) (0.2828, 0.3536, 0.4243) (0.2828, 0.3536, 0.4243) (0.1643, 0.2191, 0.2739) (0.2121, 0.2828, 0.3536) (0.3536, 0.4243, 0.4950) B6 (0.2121, 0.2828, 0.3536) (0.2205, 0.2520, 0.2835) (0.1414, 0.2121, 0.2828) (0.5020, 0.5857, 0.6694) (0.1414, 0.2121, 0.2828) (0.3834, 0.4382, 0.4929) (0.1673, 0.2510, 0.3347) (0.3536, 0.4243, 0.4950) (0.2121, 0.2828, 0.3536) (0.4950, 0.5657, 0.6364) (0.1414, 0.2121, 0.2828) (0.5020, 0.5857, 0.6694) (0.1673, 0.2510, 0.3347) (0.1673, 0.2510, 0.3347) (0.0630, 0.0945, 0.1260) (0.0951, 0.1903, 0.2854) (0.3286, 0.3834, 0.4382) (0.2121, 0.2828, 0.3536) (0.1673, 0.2510, 0.3347) (0.2828, 0.3536, 0.4243) B7 (0.1643, 0.2191, 0.2739) (0.4243, 0.4950, 0.5657) (0.5020, 0.5857, 0.6694) (0.3536, 0.4243, 0.4950) (0.1095, 0.1643, 0.2191) (0.3536, 0.4243, 0.4950) (0.4243, 0.4950, 0.5657) (0.2739, 0.3286, 0.3834) (0.2121, 0.2828, 0.3536) (0.4243, 0.4950, 0.5657) (0.4243, 0.4950, 0.5657) (0.4184, 0.5020, 0.5857) (0.1575, 0.1890, 0.2205) (0.3536, 0.4243, 0.4950) (0.2510, 0.3347, 0.4184) (0.2510, 0.3347, 0.4184) (0.1575, 0.1890, 0.2205) (0.2191, 0.2739, 0.3286) (0.1414, 0.2121, 0.2828) (0.2510, 0.3347, 0.4184) B8 (0.4184, 0.5020, 0.5857) (0.2510, 0.3347, 0.4184) (0.0707, 0.1414, 0.2121) (0.4757, 0.5708, 0.6660) (0.5708, 0.6660, 0.7611) (0.1673, 0.2510, 0.3347) (0.2510, 0.3347, 0.4184) (0.1673, 0.2510, 0.3347) (0.4184, 0.5020, 0.5857) (0.2121, 0.2828, 0.3536) (0.0837, 0.1673, 0.2510) (0.4757, 0.5708, 0.6660) (0.5857, 0.6694, 0.7530) (0.2854, 0.3806, 0.4757) (0.0837, 0.1673, 0.2510) (0.5020, 0.5857, 0.6694) (0.6364, 0.7071, 0.7071) (0.6694, 0.7530, 0.8367) (0.3347, 0.4184, 0.5020) (0.6694, 0.7530, 0.8367) 247 Table A4. The linguistic variables and their corresponding membership functions (Onari et al., 2021) Table A5. Transformation rules for converting Z-numbers to triangular fuzzy numbers Uncertainty VL L M H VH Reliability VL (0.0000,0.0000,0.0789) (0.0000,0.0789,0.1577) (0.0789,0.1577,0.2366) (0.1577,0.2366, 0.3154) (0.2366,0.3154,0.3154) L (0.0000,0.0000,0.1369) (0.0000,0.1369,0.2739) (0.1369,0.2739,0.4108) (0.2739,0.4108,0.5477) (0.4108,0.5477,0.5477) M (0.0000,0.0000,0.1768) (0.0000,0.1768,0.3536) (0.1768,0.3536,0.5303) (0.3536,0.5303,0.7071) (0.5303,0.7071,0.7071) H (0.0000,0.0000,0.2092) (0.0000,0.2092,0.4183) (0.2092,0.4183,0.6275) (0.4183,0.6275,0.8367) (0.6275,0.8367,0.8367) VH (0.0000,0.0000,0.2379) (0.0000,0.2379,0.4758) (0.2379,0.4758,0.7137) (0.4758,0.7137,0.9516) (0.7137,0.9516,0.9516) Linguistic variable Very Low (VL) Low (L) Medium (M) High (H) Very High (VH) Uncertainty (0.00, 0.00, 0.25) (0.00, 0.25, 0.50) (0.25, 0.50, 0.75) (0.50, 0.75, 1.00) (0.75, 1.00, 1.00) Reliability (0.00, 0.00, 0.30) (0.10, 0.30, 0.50) (0.30, 0.50, 0.70) (0.50, 0.70, 0.90) (0.70, 1.00, 1.00) 248 Table A6. The Z-values of evaluation criteria Scenarios Platforms C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 Sc1 BP1 (H, VH) (H, H) (VH, VH) (L, VH) (VH, H) (VH, M) (H, VH) (H, M) (H, H) (M, M) BP2 (H, H) (H, M) (VH, VH) (H, H) (M, M) (VH, M) (M, H) (L, M) (M, M) (H, H) BP3 (H, VH) (H, VH) (VH, H) (H, VH) (H, H) (H, VH) (H, M) (VH, H) (H, VH) (H, H) BP4 (L, H) (H, VH) (H, VH) (L, H) (L, M) (M, M) (H, M) (L, M) (L, H) (H, M) BP5 (L, M) (M, VH) (VH, M) (L, M) (M, M) (L, H) (M, VH) (L, M) (L, M) (M, L) BP6 (L, H) (H, H) (VH, VH) (M, VH) (L, H) (L, H) (M, H) (M, H) (L, H) (H, M) BP7 (H, M) (H, M) (VH, H) (M, H) (L, H) (L, H) (H, M) (M, M) (M, M) (H, M) Sc2 BP1 (VH, H) (H, VH) (VH, H) (M, H) (VH, M) (H, H) (VH, H) (H, H) (VH, M) (M, H) BP2 (H, VH) (M, H) (H, VH) (H, VH) (M, M) (VH, H) (H, H) (M, L) (M, M) (H, H) BP3 (VH, H) (H, VH) (VH, VH) (VH, H) (H, H) (VH, H) (VH, H) (H, VH) (VH, H) (H, VH) BP4 (M, VH) (M, VH) (H, H) (M, L) (L, H) (M, H) (H, VH) (M, L) (L, H) (M, H) BP5 (M, H) (M, VH) (H, H) (L, H) (M, M) (L, H) (M, H) (L, L) (M, H) (M, M) BP6 (M, VH) (H, H) (H, VH) (H, H) (M, VH) (L, M) (H, H) (H, M) (M, M) (H, H) BP7 (H, M) (H, H) (H, VH) (L, VH) (L, VH) (L, H) (H, M) (M, M) (M, L) (H, M) Sc3 BP1 (H, VH) (VH, H) (VH, H) (M, H) (VH, H) (H, H) (H, VH) (H, M) (H, H) (VH, M) BP2 (H, M) (H, M) (VH, H) (H, H) (M, H) (H, VH) (M, H) (L, M) (M, H) (M, H) BP3 (VH, VH) (H, VH) (VH, H) (H, H) (H, VH) (H, H) (H, H) (H, H) (VH, VH) (H, H) BP4 (L, H) (M, VH) (H, VH) (M, M) (L, H) (M, H) (H, VH) (L, M) (H, M) (H, L) BP5 (M, M) (H, H) (VH, M) (L, H) (M, H) (M, M) (M, H) (L, M) (M, M) (M, H) BP6 (L, H) (H, H) (H, VH) (H, H) (M, H) (L, H) (H, H) (M, H) (L, H) (H, L) BP7 (M, H) (H, M) (VH, H) (M, H) (M, M) (M, H) (H, M) (M, H) (M, H) (H, H) 249 Table A7. The constructed rules for designing the ZIS system Sc1 Sc2 Sc3 VL L M H VH VL VL VL VL VL L NL L VL VL L NL LM M VL L NL LM M H L NL LM M M VH NL LM M M HM L VL VL VL L NL LM L VL L NL LM M M L NL LM M M H NL LM M M HM VH LM M M HM NH M VW VL L NL LM M W L NL LM M M M NL LM M M HM H LM M M HM NH VH M M HM NH H H VL L NL LM M M L NL LM M M HM M LM M M HM NH H M M HM NH H VH M HM NH H VH VH VL NL LM M M HM L LM M M HM NH M M M HM NH H H M HM NH H VH VH HM NH H VH VH 250 Table A8. The aggregated triangular fuzzy values of evaluation criteria using the ZIS system Platform C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 BP1 (0.730, 0.750, 0.770) (0.690, 0.750, 0.810) (0.840, 0.875, 0.910) (0.340, 0.375, 0.410) (0.720, 0.750, 0.780) (0.690, 0.750, 0.810) (0.730, 0.750, 0.770) (0.440, 0.500, 0.560) (0.690, 0.750, 0.810) (0.450, 0.500, 0.550) BP2 (0.570, 0.625, 0.680) (0.460, 0.500, 0.540) (0.840, 0.875, 0.910) (0.690, 0.750, 0.810) (0.200, 0.250, 0.300) (0.720, 0.750, 0.780) (0.440, 0.500, 0.560) (0.090, 0.125, 0.160) (0.200, 0.250, 0.300) (0.570, 0.625, 0.680) BP3 (0.840, 0.875, 0.910) (0.740, 0.750, 0.760) (0.840, 0.875, 0.910) (0.690, 0.750, 0.810) (0.690, 0.750, 0.810) (0.690, 0.750, 0.810) (0.570, 0.625, 0.680) (0.690, 0.750, 0.810) (0.840, 0.875, 0.910) (0.690, 0.750, 0.810) BP4 (0.230, 0.250, 0.270) (0.490, 0.500, 0.510) (0.690, 0.750, 0.810) (0.080, 0.125, 0.170) (0.090, 0.125, 0.160) (0.330, 0.375, 0.42) (0.610, 0.625, 0.640) (0.090, 0.125, 0.160) (0.230, 0.250, 0.270) (0.460, 0.500, 0.540) BP5 (0.200, 0.250, 0.300) (0.440, 0.500, 0.560) (0.690, 0.750, 0.810) (0.090, 0.125, 0.160) (0.200, 0.250, 0.300) (0.080, 0.125, 0.170) (0.460, 0.500, 0.540) (0.070, 0.125, 0.180) (0.200, 0.250, 0.300) (0.200, 0.250, 0.300) BP6 (0.230, 0.250, 0.270) (0.690, 0.750, 0.810) (0.840, 0.875, 0.910) (0.570, 0.625, 0.680) (0.340, 0.375, 0.410) (0.090, 0.125, 0.160) (0.570, 0.625, 0.680) (0.460, 0.500, 0.540) (0.080, 0.125, 0.170) (0.440, 0.500, 0.560) BP7 (0.460, 0.500, 0.540) (0.440, 0.500, 0.560) (0.730, 0.750, 0.770) (0.340, 0.375, 0.410) (0.080, 0.125, 0.170) (0.210, 0.250, 0.290) (0.490, 0.500, 0.510) (0.200, 0.250, 0.300) (0.200, 0.250, 0.300) (0.440, 0.500, 0.560)
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Tosarkani, Babak Mohamadpour
Decision support frameworks for unlocking the potential of blockchain in sustainable supply chain management
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