Management, Faculty of (Okanagan)
DSpace
UBCO
Henderson, John David
Doctor of Philosophy - PhD
University of British Columbia
Given the emergence of new automated text-based measurement and machine learning analytical techniques such as Natural Language Processing (NLP) and Topic Modelling, which serve as research analytical methodologies, there is growing interest in their use and application to business scenarios as well as theory development. Issues arise, however, as existing theory may not be properly reflected in such novel methods, such as computer-assisted text analysis (CATA), natural language processing, and topic modeling. This research joins one such conceptual debate regarding entrepreneurial orientation (EO), a psychometric evaluation of a firm’s behavioral nature related to risk-taking, innovativeness, proactiveness, autonomy, and competitive aggressiveness. It has been recently argued that entrepreneurial words, which form the text-based EO measurement, alone are not necessarily aligned with EO concepts as a behavioral construct (Covin & Wales, 2018). This research proposes a novel extension to EO as a messaging construct called entrepreneurial posture (EP) within an Entrepreneurial Posture in Communications (EPIC) framework, where the moderating influence between EO and Firm Performance (FP) is conceptualized as candor, the quality of being honest and straightforward in attitude and speech. Methodological segmentation by natural language processing (NLP) and machine learning topic modelling techniques is also explored in this research. Topic model segmentation identified communities with differing EO to FP profiles, which supports the theoretical view that firm communities differ in candor profiles. Results from this research demonstrates unique EP expression in firms’ communications with respect to numerous accounting and market measures and across time. The results also identified communities with differing EO to FP conceptual relationships, where specific candor differences were suggested. Conclusions drawn suggest that the combined assessment of EP and candor, using automated text-based analysis exclusively, provides predictive insights in keeping with strategic posture. Methodologically, the novel application of NLP and machine learning topic modelling presented in this research improved the proportion of explain variance in the EO to Firm Performance model from 0.04 to 0.21, an affect size substantially higher than typically presented in EO literature.
i Entrepreneurial Posture in Communications Entrepreneurial Orientation (EO) Analysis in Financial Communications with Computer-Assisted Text Analysis Combined with EO Custom Dictionaries, Natural Language Processing, and Machine Learning By John David Henderson MBA, Sauder School of Business, The University of British Columbia, 2003 Master of Science, The University of Waterloo, 1995 Bachelor of Economics (Honours), The University of Waterloo, 1995 Bachelor of Science (Honours), Western University, 1991 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY In The College of Graduate Studies (Interdisciplinary Studies) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2021 © John David Henderson, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Entrepreneurial Posture in Communications: Entrepreneurial Orientation (EO) Analysis in Financial Communications with Computer-Assisted Text Analysis Combined with EO Custom Dictionaries, Natural Language Processing, and Machine Learning submitted by John David Henderson in partial fulfillment of the requirements for the degree of Doctor of Philosophy Dr. Barb Marcolin Supervisor Dr. Arjun Bhardwaj Supervisory Committee Member Dr. Brian O’Connor Supervisory Committee Member Dr. Apurva Narayan University Examiner Dr. Thomas Stafford External Examiner iii ABSTRACT Given the emergence of new automated text-based measurement and machine learning analytical techniques such as Natural Language Processing (NLP) and Topic Modelling, which serve as research analytical methodologies, there is growing interest in their use and application to business scenarios as well as theory development. Issues arise, however, as existing theory may not be properly reflected in such novel methods, such as computer-assisted text analysis (CATA), natural language processing, and topic modeling. This research joins one such conceptual debate regarding entrepreneurial orientation (EO), a psychometric evaluation of a firm’s behavioral nature related to risk-taking, innovativeness, proactiveness, autonomy, and competitive aggressiveness. It has been recently argued that entrepreneurial words, which form the text-based EO measurement, alone are not necessarily aligned with EO concepts as a behavioral construct (Covin & Wales, 2018). This research proposes a novel extension to EO as a messaging construct called entrepreneurial posture (EP) within an Entrepreneurial Posture in Communications (EPIC) framework, where the moderating influence between EO and Firm Performance (FP) is conceptualized as candor, the quality of being honest and straightforward in attitude and speech. Methodological segmentation by natural language processing (NLP) and machine learning topic modelling techniques is also explored in this research. Topic model segmentation identified communities with differing EO to FP profiles, which supports the theoretical view that firm communities differ in candor profiles. Results from this research demonstrates unique EP expression in firms’ communications with respect to numerous accounting and market measures and across time. The results also identified communities with differing EO to FP conceptual relationships, where specific candor differences were suggested. Conclusions drawn suggest that the combined assessment of EP and candor, using automated text-based analysis exclusively, provides predictive insights in keeping with strategic posture. Methodologically, the novel application of NLP and machine learning topic modelling presented in this research improved the proportion of explain variance in the EO to Firm Performance model from 0.04 to 0.21, an affect size substantially higher than typically presented in EO literature. iv LAY SUMMARY This research explores the assessment of whether strategic posture insights can be derived from financial communications. Background literature and research suggests that organizations with the behavioral qualities of risk-taking, innovativeness, and proactiveness, as well as autonomy and competitive aggressiveness, as defined by the Entrepreneurial Orientation (EO) Theory, possess a competitive advantage and gain superior firm performance over competitive rivals. The amount of EO, whether high or low, is considered predictive of future firm performance and is called strategic posture (SP). SP is, therefore, insightful for investors, managers, and entrepreneurs. Possessing EO is different to simply communicating EO, which is viewed as entrepreneurial posture (EP) and may or may not clearly reflect real intentions to act entrepreneurially. The association between EO and EP is conceptualized in this research as moderated by candor and defined by honest and straightforward attitude and speech. v PREFACE This dissertation is an original, unpublished, independent work by the author, John D. Henderson. vi TABLE OF CONTENTS ABSTRACT ................................................................................................................................................................. iii LAY SUMMARY ........................................................................................................................................................ iv PREFACE ..................................................................................................................................................................... v TABLE OF CONTENTS ............................................................................................................................................ vi LIST OF TABLES ...................................................................................................................................................... ix LIST OF FIGURES ..................................................................................................................................................... x LISTS OF SYMBOLS, ABBREVIATIONS OR OTHER ...................................................................................... xi GLOSSARY ................................................................................................................................................................ xii ACKNOWLEDGMENTS ........................................................................................................................................ xiii DEDICATION ........................................................................................................................................................... xiv 1. INTRODUCTION .............................................................................................................................................. 1 2. EO LITERATURE REVIEW ........................................................................................................................... 3 2.1. Entrepreneurial Orientation (EO) .............................................................................................................. 3 2.2. Firm Performance (FP) .............................................................................................................................. 5 2.3. EO to FP Relationship (EO - FP) ............................................................................................................... 6 2.4. EO - FP Moderators ................................................................................................................................... 7 2.5. Entrepreneurial Posture ............................................................................................................................. 7 3. Entrepreneurial Orientation as a Messaging Construct ................................................................................. 8 3.1. Candor in Financial Communications ........................................................................................................ 8 3.2. Strategic Posture in the EPIC Framework ................................................................................................. 9 4. RESEARCH QUESTIONS .............................................................................................................................. 11 5. RESEARCH METHODS ................................................................................................................................. 14 5.1. DATA Sources & Time Frame .................................................................................................................. 14 5.2. Computer-Aided Text Analysis (CATA) .................................................................................................... 16 5.3. EO Dictionaries ........................................................................................................................................ 16 5.3.1. Autonomy ............................................................................................................................................. 17 vii 5.3.2. Competitive Aggressiveness ................................................................................................................ 17 5.3.3. Innovativeness ...................................................................................................................................... 17 5.3.4. Proactiveness ........................................................................................................................................ 17 5.3.5. Risk-taking ........................................................................................................................................... 17 5.3.6. Inductively Derived Words .................................................................................................................. 18 5.4. EO-CATA Measurement ........................................................................................................................... 18 5.5. EO-FP Relationship .................................................................................................................................. 18 5.6. Machine Learning Topic Modeling ........................................................................................................... 19 6. RESULTS .......................................................................................................................................................... 20 6.1. Assessment of EO Drawn from Letters to Shareholders ........................................................................... 20 6.2. EO Dimensionality .................................................................................................................................... 20 6.3. Comparing EO Expression Across Exchanges ......................................................................................... 21 6.4. EO - Firm Performance Relationship Modeling ....................................................................................... 23 6.4.1. EO - FP (Tobin’s q) .............................................................................................................................. 23 6.4.2. EO - FP (% Tobin’s q Change) ............................................................................................................ 24 6.4.3. EO - FP (Revenues) .............................................................................................................................. 25 6.4.4. EO - FP (% Revenue Change) .............................................................................................................. 26 6.4.5. EO - FP (Profit) .................................................................................................................................... 27 6.4.6. EO - FP (% Profit Change) .................................................................................................................. 28 6.4.7. EO - FP (R&D Expense) ...................................................................................................................... 29 6.4.8. EO - FP (% R&D Expense) .................................................................................................................. 30 6.4.9. EO - FP (Cash Flow from Financings) ................................................................................................. 31 6.4.10. EO - FP (% Cash Flow from Financings Change) .......................................................................... 32 6.4.11. EO - FP (Cash Flow from Operations) ............................................................................................ 33 6.4.12. EO - FP (% Cash Flow from Operations) ....................................................................................... 34 6.5. Summary for Part I ............................................................................................................................... 35 6.6. Machine Learning Topic Modeling ........................................................................................................... 35 6.7. EO Subdimensions Expressed in Each Topic Model Cluster ................................................................... 38 6.8. EO-FP (Tobin’s q t+1 and % Tobin’s q t+1) in Each Topic Model ........................................................ 39 viii 6.9. Clear Candor vs Non-Clear Candor Firm Communities .......................................................................... 42 7. DISCUSSION .................................................................................................................................................... 45 7.1. Does EO Measurement Using CATA Measure EO or EP? ...................................................................... 45 7.2. Candor ...................................................................................................................................................... 47 7.3. Entrepreneurial Orientation ..................................................................................................................... 48 7.4. Firm Performance ..................................................................................................................................... 48 7.5. Entrepreneurial Posture (EP) ................................................................................................................... 49 7.6. Entrepreneurial Posture as Predictor ....................................................................................................... 50 7.7. Implications to Investing ........................................................................................................................... 51 7.8. Automatic Computational Analysis in EO Research ................................................................................ 52 7.9. Limitations & Suggestions for Further Research ..................................................................................... 54 8. CONCLUSIONS ............................................................................................................................................... 54 9. BIBLIOGRAPHY ............................................................................................................................................. 56 ix LIST OF TABLES Table 1. One-sided t Test (mean >0) for EO Expression .................................................................................... 20 Table 2. t Test Comparisons of EO Expression Between the Russel 2000 and the TSX .................................... 22 Table 3. t Test Comparing Difference Between R2K and TSX in 2012 ............................................................. 22 Table 4. Regression Analysis for Tobin’s q ........................................................................................................ 24 Table 5. Regression Analysis for % Tobin’s q .................................................................................................... 25 Table 6. Regression Analysis for Revenues ........................................................................................................ 26 Table 7. Regression Analysis for Revenue Change ............................................................................................ 27 Table 8. Regression Analysis for Profits ............................................................................................................. 28 Table 9. Regression Analysis for % Profit Change ............................................................................................. 29 Table 10. Regression Analysis for R&D Expenditures ....................................................................................... 30 Table 11. Regression Analysis for % Change in R&D Expenditures ................................................................. 31 Table 12. Regression Analysis for Cash Flow from Financings ......................................................................... 32 Table 13. Regression Analysis for % Change in Cash Flow from Financings ................................................... 33 Table 14. Regression Analysis for Cash Flow from Operations ......................................................................... 34 Table 15. Regression Analysis for % Change in Cash Flow from Operations ................................................... 34 Table 16. Top 10 Words in Each Topic Model Grouping ................................................................................... 38 Table 17. % Sector Representation in Each Topic Model Grouping .................................................................. 38 Table 18. EO Subdimension Expression in Each Topic Model .......................................................................... 39 Table 19. Regression Analyses for EO Measures and Tobin’s Q ....................................................................... 40 Table 20. Regression Analyses EO Measures to % Tobin Q Change ................................................................. 41 Table 21. EO to % Tobin’s q +1 Year ................................................................................................................. 45 x LIST OF FIGURES Figure 1. EO Theory current literature .................................................................................................................. 5 Figure 2. Candor and Entrepreneurial Orientation as a Messaging Construct (Entrepreneurial Posture) ............ 8 Figure 3. Entrepreneurial Posture In Communications (EPIC) Conceptual Framework .................................... 10 Figure 4. Entrepreneurial Posture in Communications (EPIC) Analytical Model ............................................. 13 Figure 5. Bivariant Correlations Amongst EO Subdimensions .......................................................................... 21 Figure 6: Company Size (Number of Employees) for TSX and Russel Listed Firms ........................................ 22 Figure 7. Perplexity of LTS Corpus .................................................................................................................... 36 Figure 8. Calculating Number of Topics to Minimize Perplexity and Maximize Cohesiveness ........................ 37 Figure 9. Actual vs Modeled % Change in Tobin’s q t+1 .................................................................................. 41 Figure 10. Modeled R-Square Values for Each Topic Cluster ........................................................................... 42 Figure 11. Model Predicted vs Actual % Change Tobin’s q t+1 ........................................................................ 44 Figure 12. Modeled R Square Values ................................................................................................................. 44 Figure 13. Pie chart for LDA5 Firms and their Respective Exchange ............................................................... 45 Figure 14: Emergent Entrepreneurial Orientation / Entrepreneurial Posture Model .......................................... 50 xi LISTS OF SYMBOLS, ABBREVIATIONS OR OTHER CATA Computer-Assisted Text Analysis EM Entrepreneurial Messaging EO Entrepreneurial Orientation EP Entrepreneurial Posture EPIC Entrepreneurial Posture in Communications FP Firm Performance GO Growth-oriented LDA Latent Dirichlet Allocation LTS Letters to Shareholders SBO / BO Small Business Oriented / Business Oriented SP Strategic Posture xii GLOSSARY Candor Candor is defined as the degree to which a company’s rhetoric expresses transparent information about its entrepreneurial orientation. Entrepreneurial Posture (EP) Firms, through financial communications, may signal a particular entrepreneurial orientation (EO), but are not necessarily nor obligated to behave as they espouse. In such cases, the EO measurement may not be viewed as equivalent to EO as a behavioral construct, as conceptualized in EO Theory; rather, EO drawn from rhetoric is viewed as entrepreneurial posture (EP). Entrepreneurial Posture in Communications (EPIC) Framework Entrepreneurial Posture (EP) is a messaging construct where EP reflects the organizational EO as moderated by a facet conceptualized as Candor. The concept of EP and Candor contributes two new strategic postures to EO research: Promoter, a firm who expresses EP higher than EO, and Stealth, a firm who expresses EP lower than EO. Strategic Posture In a unilateral three-dimensional model of EO, firms expressing a high measure of risk-taking, innovativeness, and proactiveness are said to have a growth-oriented (GO) strategic posture. Firms with low measures are said to have a business oriented (BO) strategic posture. In a five-dimensional multilateral model, strategic posture cannot be interpreted in absolute terms. Instead, it is interpreted as the unique EO construct (topology of EO subdimensions) amongst a family of EO constructs that align to a particular firm performance metric, as indicated by the strength of the entrepreneurial orientation to firm performance relationship (EO-FP). xiii ACKNOWLEDGMENTS I wish to express sincere gratitude to my committee members for all of their guidance and support, and special thanks to Dr. Barb Marcolin, my advisor, for her persistence, patience, and encouragement during the entire Ph.D. program. I would also like to thank Dr. Bill Wales for his time in discussing some of the conceptual facets proposed in this dissertation such as my introduction of Candor to the EO domain as well as my novel application of natural language processing (NLP) and machine learning topic modelling. xiv DEDICATION This dissertation is dedicated to my wife (Leigh) and children (Tyler and Faith) for their loving support during my time in the Ph.D. program. 1 1. INTRODUCTION Communications play an important and critical role in today’s financial environment. Financial communications, in one form or another, serve to inform internal and external stakeholders, such as senior management, business partners, employees, customers, competitors, regulatory agencies, and investors. For public companies in particular, investors, management, media, and security regulators are constantly scrutinizing financial communications; hence, the fundamental business, ethical, and legal importance to its accurate and honest representation. Though financial communication is generally valued for its accurate depiction of nomothetic information, such as historical performance results, there is mounting evidence that predictive ideographic insights, information about the sender, can be extracted when organizational behavioral theories are combined with emerging text-based measurement tools. Using automated text analysis directed at financial communications, this research seeks predictive firm performance insights in keeping with Entrepreneurial Orientation. Entrepreneurial Orientation (EO) is represented by firm-level behavioral characteristics: proactiveness, innovativeness, risk-taking, autonomy, and competitive aggressiveness (Covin & Lumpkin, 2011). EO is conceptualized to reflect systems and decisional processes that collectively guide and direct specific firm behaviours. Firms possessing EO are theorized to possess better access to premium market segments and charge higher prices because they monitor market changes, move faster, and respond more quickly than their competitors, thereby capturing emerging opportunities. EO is thereby viewed as a strategic resource and causal determinant of firm performance (FP) (Rauch, Wiklund, Lumpkin, & Frese, 2009). The most popular measurement tool of EO, developed by Miller (Miller, 1983) and Covin and Slevin (Covin & Slevin, 1988), is a survey-based, self-perceptive scale. Although the operationalisation and measurement of the EO construct is equivalent across many empirical studies, various conceptualisations of what this self-perceptive EO construct really measures have been proposed: entrepreneurial behavior (Moruku, 2013), strategic posture (Madison, Runyan, & Swinney, 2014), or entrepreneurial posture (Covin & Slevin, 1988). The Miller/Covin & Slevin scale is generally considered relevant only if it reflects the actions undertaken by the firm (Wiklund, 1998), thereby measuring entrepreneurial behavior. Firms with more EO should behave more entrepreneurially than firms with less EO. As a behavioral construct, EO is considered predictive of firm performance. As a predictor, EO is said to represent a strategic posture to the sustainment of firm’s future viability (Gürbüz & Aykol, 2009). EO literature distinguishes two strategic posture orientations: Firms possessing high EO, cited as Growth-oriented (Ferreira, Azevedo, & Ortiz, 2011), or Opportunistic (Lessner & Knapp, 1974), which predicts high future FP. Firms 2 possessing low EO are deemed Small Business Way-of-Thinking (Wiklund, 1998), or simply Business Oriented or Craftsman (Lessner & Knapp, 1974), which both predict low future FP. Entrepreneurial posture, in contrast to strategic posture, is fundamentally a firm’s “representation” of entrepreneurial orientation, whether currently existing or is simply desired (Anderson, Kreiser, Kuratko, Hornsby, & Eshima, 2015). EO Theory places no particular obligation nor commitment that a firm’s entrepreneurial posture must reflect its strategic posture, as Covin and Wales (2018) state “through discourse, firms may signal a particular posture, but not necessarily and strictly behave as they espouse.” Signaling is an essential mechanism resulting from information asymmetry existing between two parties, the sender and receiver (Colombo & Grilli, 2010; Gangloff, Connelly, & Shook, 2016; Gimmon & Levie, 2010; Levie & Gimmon, 2008). Signalling theory has been extensively reviewed in management literature, (Connelly, Certo, Ireland, & Reutzel, 2010). EO as a behavior signal has been recently explored within the context of seeking venture capital investment such as crowdfunding (Calic & Shevchenko, 2020). It is important to distinguish, however, that EO itself is conceptualized as a behavioral construct whereas the emergent view of EP, though related to EO, is fundamentally entrepreneurial orientation intentions (signalling) drawn from entrepreneurial rhetoric. Again, as to Covin and Wales (2018) assessment, EO is not necessarily equivalent to EP. Increased research interest appears to be following the theme presented in this dissertation with regards to exploring the nuances of EO rhetoric as a signal of entrepreneurial posture, (Schüler, Wales, & Baum, 2021). The distinction between the nature of EO versus EP has emerged with the recent introduction of a novel, non-survey-based approach to EO measurement derived from the application of custom EO dictionaries and computer-assisted text analysis. Combining custom dictionaries with automated text-based measurement tools, such as computer-assisted text analysis (CATA) holds exciting promise for the future of business applications and business theory development. Though the CATA technique has been in use for some time, CATA, in combination with custom EO dictionaries (EO-CATA), was pioneered by Short (2010). As with the introduction of any novel application, concerns arise over its misuse and misinterpretation of theoretical constructs. Fittingly, there appears to be a lively debate ongoing in the EO literature as to whether this technique measures EO, as the behavioral construct (McKenny, Aguinis, Short, & Anglin, 2016; McKenny, Short, & Payne, 2013; Short, Broberg, Cogliser, & Brigham, 2010), or the EP construct (Covin & Wales, 2018). Exploring the EO literature, there is very little research on the associations between EP and EO. Consequently, there is little research documenting the nature of the EP-FP relationship. Addressing this gap in the EO literature, this research explores the attributes of EO versus EP. This research reconceptualizes the EO 3 construct as a messaging construct, mirroring the conceptualization of EP, within entrepreneurial posturing in a communications (EPIC) framework. Since there is little constraint on EP to perfectly reflect EO, EP expressed in financial messaging is theorized in this research to be either equal to, higher than or lower than EO. Through the lens of the proposed EPIC framework, the difference between entrepreneurial posture (EP) and EO is conceptualized as candor. Candor means purity and openness, and stems from the Latin word candere which means to shine. In modern language, candor is defined as the “quality of being honest and straightforward in attitude and speech” (Rittenhouse, 2013). EO literature does not have the equivalent concept of candor; however, candor has been studied extensively in management communications related research (Avolio, 2016; Bennis, Goleman, & O'Toole, 2008; Minter, 2012; O’Toole & Bennis, 2009; Rittenhouse, 2013). Candor is introduced here to EO literature as the degree to which a company exhibits pure information about its entrepreneurial orientation. The EPIC framework conceptualizes firms possessing different states of candor. Firms possessing clear candor (C = 0) act on entrepreneurial posturing proportionally to their entrepreneurial orientation. Firms with negative candor (C = -1) are those whose entrepreneurial posturing statements are greater than subsequent entrepreneurial behaviors, EP>EO. Such firms are deemed to be Promoter Oriented. In contrast, firms who seek to underrepresent entrepreneurial intentions and thereby appear to overdeliver on entrepreneurial behavior, where EP<EO, are conceptualized to possess positive candor (C = +1). Positive candor firms under promise and overdeliver which are referred to in the EPIC framework as Stealth Oriented. Through the EPIC framework, this research suggests that the measurement of EP along with an assessment of candor may provide predictive insights in keeping with EO strategic posture. This research therefore seeks insights based on EP as well as candor derived exclusively from text-based analysis incorporating computer assisted text analysis with custom dictionaries, natural language processing and topic modeling. Text analysis measurements for this research are drawn from Letters to Shareholder contained in Annual Reports of 525 publicly listed companies listed on the Toronto Stock Exchange (TSX) and the Russell 2000 (R2K). 2. EO LITERATURE REVIEW 2.1. Entrepreneurial Orientation (EO) One of the most prominent theories to emerge from the field of Strategic Management and Entrepreneurship has been the conceptualization and operationalization of a firm-level entrepreneurship framework known as 4 Entrepreneurial Orientation (EO) Theory. EO Theory draws from the strategic management perspective that a competitive advantage stems from key strategic competencies, vis à vis the Resource Based View of Firms (Penrose, 1995). Extending the strategic foundations laid by Henry Mintzberg (Mintzberg, 1973; Mintzberg, Raisinghani, & Théorêt, 1976), Danny Miller (1983) was the first to conceive the entrepreneurial firm construct when he shifted the focus of entrepreneurship from the individual to that of the firm. Miller noted that “firms that engage in product market innovation, undertake somewhat risky ventures, and are usually first to come up with ‘proactive’ innovations, ‘beating competitors to the punch,’ can be described as being entrepreneurially oriented”. According to Miller (1983), the entrepreneurial label is “most defensible as a descriptor of established firms when those organizations are simultaneously risk-taking, innovative, and proactive with respect to their overall business operations, product offerings and technologies, and interactions with competitors, respectively.” EO, as an attribute of organizations, is, therefore, a behavioral construct defined by firm actions (Covin & Wales, 2018). The three subdimensional conceptualization of EO represents the “strategic posture” of the firm emphasizing risk-taking, innovativeness, and proactiveness (Covin & Slevin, 1989; Miller, 1983). EO, within this scope, is a strategic posture whose conceptual domain consists of firm-level preferences, beliefs, and behaviors expressed by top-level managers (Covin, Green, & Slevin, 2006). The strategic posture of a firm thereby defines an inextricable link between EO, as a behavior, and firm performance (FP) represented in the EO-FP relationship. The EO strategic posture has been further refined into two forms: firms with high EO considered to be Growth-oriented, whereas firms with low EO are deemed Business Oriented (Madison et al., 2014). Lumpkin and Dess (G. Tom Lumpkin & Dess, 1996) extended the Covin and Slevin EO definition by including competitive aggressiveness and autonomy, as illustrated in Figure 1. They defined EO as “the processes, practices, and decision-making activities that lead to new-entry,” thereby identifying a five-dimensional EO defined by the propensity to act autonomously, a willingness to innovate and take risks, and a tendency to be aggressive toward competitors and proactiveness relative to marketplace opportunities (Lumpkin & Dess, 1996 p. 136). The five subdimensions in the Lumpkin and Dess measurement scale are thought to be related but independent, where each dimension may have a distinct relationship (multidimensional) with firm performance (Covin & Lumpkin, 2011). Lumpkin and Dess’ (1996) introduction of EO as a multidimensional construct makes the original conceptualization of strategic posture less intuitive, where the growth-oriented posture, for instance, would be reflected in an optimal configuration or topology of EO subdimensions set to achieve high FP. 5 FIGURE 1. EO THEORY CURRENT LITERATURE This figure depicts the 5-factor multidimensional construct of entrepreneurial orientation (EO) and its moderators to firm performance (FP) construct. 2.2. Firm Performance (FP) The firm performance (FP) construct, in EO Theory, is conceptualized as a general indicator of overall firm performance outcome. To operationalize the overall firm performance construct, EO researchers have used non-financial and financial measures. Non-financial are subjective performance measures, commonly adopted when historical data is not available, for example, in the case of small firms or new ventures (Dess & Robinson, 1984). Even though subjective performance constructs and measures may be prone to various reporting biases, empirically they exhibit relatively high correlations with objective constructs and measures (Dess & Robinson, 1984). No single dominant FP construct or indicator has emerged that captures the entire essence of a firm’s performance as conceptualized in EO Theory (Rosenbusch et al., 2011). As a result, there are various conceptualizations of the firm performance (FP) constructs defined using various measurements, (Brush & Vanderwerf, 1992; Hult et al., 2008; Murphy et al., 1996). Financial performance measures are typically derived using either accounting metrics or market metric. Examples of accounting measures used to define the FP construct include return on assets (ROA), return on sales (ROS), and return on equity (ROE), profit, firm growth in terms of number of employees (Wu et al., 2008) and revenue growth (Lumpkin et al., 2006). Examples of market measures (mostly directed at publicly listed companies on stock market exchanges) include values such as Tobin’s q (Keil et al., 2017) and market return (Combs et al., 2005; Hoskisson et al., 1999; Hult et al., 2008). Meta-analyses of the relationship between different indicators of performance indicate the most common FP constructs and measures are accounting measures profitability and growth, and market measure in Tobin’s q (Combs et al., 2005). 6 The lack of explicit and detailed depictions of the FP construct and measurements is a noted concern within the EO research. As EO Theory proports a moderately strong connection between EO and FP, the rationale for the interchangeable use of accounting measures and market measures to define the FP construct appears overlooked in EO literature (Rosenbusch, Rauch, & Bausch, 2013). EO is typically conceptualized as a behavioral construct based on the entrepreneurial behavior of the firm. Firm behaviors are governed by firm’s management, and the outcome of those behaviors typically captured in accounting measures. Yet, firm performance is often defined using market measures such as Tobin’s q. Market measures are the behaviors of investors. If there is an implicit assumption that market measures mirror accounting measures, this is not reflected in empirical research, which indicates a very low correlation between accounting measures and market measures, approximately 10% (Gentry & Wei, 2010). 2.3. EO to FP Relationship (EO - FP) Of particular interest to strategic entrepreneurship scholars is the relationship between EO and firm performance (Stam & Elfring, 2008). EO researchers have extensively studied the relationship between various EO constructs and resulting manifestations of firm performance (Hughes & Morgan, 2007). Since the emergence of the EO concept, it has generally been acknowledged that there exists a positive relationship between EO and FP (Miller, 1983, 2011). Meta-analysis studies have found a relatively consistent EO-FP relationship result to be significant (Rauch et al., 2009), although seemingly weak (Cohen, 1988), with Rosenbusch (2011) reporting R = .26 and Rauch (2009) reporting R = .24, which amounts to an overall average effect size for the EO-FP relationship of r^2 = .05, (Rauch et al., 2009; Rosenbusch, Rauch, & Bausch, 2011). One of the main tenets of EO Theory is that higher EO leads to increased performance, as depicted in the growth-oriented strategic posture. In the multidimensional view, the proportional expression of EO subdimensions is viewed as an inextricably entwined topology to a specific FP. In this view, given a strong EO-FP relationship, a specific FP measurement is thereby associated to a particular topology of EO subdimensions. EO literature suggests the relationship between EO and performance may be more complex than just a straightforward positive EO-FP relationship (Rosenbusch et al., 2011). Some researchers have suggested a curvilinear EO-FP relationship (Tang, Tang, Marino, Zhang, & Li, 2008). Curvilinear EO-FP would imply that the positive impact of EO levels off or even becomes negative beyond a certain threshold. A curvilinear EO-FP relationship, however, is considered inconclusive as other studies that reported specific tests for the curvilinear relationship did not find a significant impact (Anderson et al., 2015). These differences suggest that there may be moderators that cap the positive impact of EO at a certain level, or that certain populations of companies do not usually reach the level of EO, at which point the concepts become less effective. 7 2.4. EO - FP Moderators Considerable empirical evidence supports this conceptualization of EO and the positive EO-FP relationship. Yet, researchers have also presented studies with conflicting results, leading EO researchers to believe the relationship between EO and performance is more complex than originally presumed (Covin & Lumpkin, 2011; G. T. Lumpkin, Brigham, & Moss, 2010; G. Tom Lumpkin & Dess, 1996). Thus, leading EO researchers should explore more variables, in addition to EO, which could influence firm performance in more complex ways (Anderson et al., 2015). Variations in the EO-FP relationship attributed to moderators influencing the relationship between EO and FP is well noted in EO literature (G. Thomas Lumpkin & Dess, 2001; G. T. Lumpkin, Wales, & Ensley, 2006; Zahra & Garvis, 2000). A context-dependency of the link of EO and its constituent dimensions with firm performance has been established from the earliest work on EO (Covin & Slevin, 1991; G. Tom Lumpkin & Dess, 1996; Miller, 1983). Based on meta-analysis studies, the most common EO moderators investigated include company size and industry classification. Related findings suggest that EO influences company performance more for micro-businesses than for small businesses (Gupta & Wales, 2017). As for industry effects, EO was found to be more closely associated with high-tech industries rather than non-high-tech industries (Lomberg, Urbig, Stöckmann, Marino, & Dickson, 2017). Many more potential moderators are being investigated, such as country effects (Kreiser, Marino, & Weaver, 2002), dynamics of the environment (van Doom, Heyden, & Volberda, 2017), munificence (Iyer & Doucette, 2003), complexity (García-Villaverde, Ruiz-Ortega, & Canales, 2013), and other organizational factors (Al-Swidi & Mahmood, 2011). 2.5. Entrepreneurial Posture Entrepreneurial posture (EP) is defined in EO Literature as “fundamentally similar to EO in that the same component dimensions are assessed” (Covin & Wales, 2018). Unlike EO, which is a behavioral construct, EP does not require entrepreneurial behaviors to exist, and is viewed primarily as a measurement of verbiage. In contrast to EO, firms may express an entrepreneurial posture (EP) but may not act entrepreneurially based on it such that, “through discourse, firms may signal a particular posture, but not necessarily and strictly behave as they espouse,” (Covin & Wales, 2018). Though EO literature has defined EP and acknowledges that EP is itself a form of posture, there has been little to no exploration of EP with respect to strategic posture. EO Theory is, therefore, lacking the means to explain the relatedness of EP to EO with respect to strategic posture. 8 3. Entrepreneurial Orientation as a Messaging Construct 3.1. Candor in Financial Communications Delving into management-related disciplines, a close approximate in conceptual nature relating EP relative to EO is the concept of candor (Avolio, 2016; Bennis et al., 2008; O’Toole & Bennis, 2009). History is filled with examples of firms varying in open, honest, and transparent financial communications (Langevoort, 2002, 2004). In management studies, the common applications of candor have been leadership (Bennis et al., 2008), senior management (Rittenhouse, 2015), organizational culture (O’Toole & Bennis, 2009), and investment performance (Rittenhouse, 2013). Essentially, candor is about people wanting to do business with people they trust. People trust those who are honest. In business, one gauges honesty when, for example, one’s words and one’s behaviors are in sync. Candor takes this notion one step further, where words and behaviors are a pure reflection of who you are, either as an individual or an organization. Candor is, therefore, considered an excellent characterization of EO relative to EP. Candor, as conceptualized in the Entrepreneurial Posture in Communications (EPIC) model in Figure 2, is a contingency variable reflecting the degree of clear information regarding EO and strategic posture as drawn from entrepreneurial rhetoric found in its financial communications. Candor is conceptualized in the EPIC Model such that if entrepreneurial posturing perfectly reflects an organization’s EO (i.e., possesses EO equivalent to its entrepreneurial posture [EP], then candor is deemed clear [C = 0]). If EP does not reflect EO, meaning that one does not possess EO similar to one’s entrepreneurial posture, this represents the absence of candor and is depicted in negative form (C = -1) or positive form (C = +1). FIGURE 2. CANDOR AND ENTREPRENEURIAL ORIENTATION AS A MESSAGING CONSTRUCT (ENTREPRENEURIAL POSTURE) 9 The idea that firms share similar propensities for candor, which, in turn, would be reflected in their financial messaging is well-established in financial research (Krueger & Wrolstad, 2014; Minter, 2012; Rittenhouse). Candor is tempered by the norms of its industry that a firm operates in, as well as the legal (Harris & Rentfro, 2008) and regulatory requirements that are imposed on them (Franco, 2002; German, 2009; Lang & Lundholm, 1996). Findings by L.J. Rittenhouse, CEO of Rittenhouse Rankings, indicate companies that consistently promote candor generally benefit from superior market performance (Rittenhouse, 2013). L.J. Rittenhouse based her findings on her company’s annual Rittenhouse Candor Rankings Survey, which correlates measures of executive candor with stock price performance. Hence, the connection between candor and firm performance defined in the market has been well established. Conceptually, the idea of candor as a prevalent and important facet of financial reporting is shared here though somewhat different, whereas Rittenhouse (2013) focuses on the management team (Total Management Team Theory), EO within the EPIC framework is an organization-level construct. Rittenhouse (2013) has also associated candor attributes in the CEO’s grammatical and semantic attributes. For instance, deviations from clear candor is believed to be reflected in awkward grammar, the use of justification, cliches or jargon, hyperbole, and mixed or confused messaging (Rittenhouse, 2015). So far, there does not appear to be any form of text-based measurement tool such as a computer-assisted text analysis custom dictionary that is specifically designed for candor. 3.2. Strategic Posture in the EPIC Framework EO Theory asserts a positive relationship between EO and FP, which, in turn, is represented in one’s strategic posture. Strategic posture is viewed as a predictive quality and, in EO Theory, is depicted in two forms¾either low or high EO. Low EO suggests lower future FP. In such cases, firms would appear primarily focused on surviving and thereby deemed Business Oriented. When EO is high, this suggests higher future firm performance where firms are deemed Growth Oriented. That is the extent of strategic postures in EO Theory as little to no attention is placed on the predictive or strategic nature of entrepreneurial posture (EP), which is deemed to have little strategic importance. This gap in EO knowledge is addressed in the Entrepreneurial Posture in Communications (EPIC) framework depicted in Figure 3. Entrepreneurial Posture, as drawn from entrepreneurial rhetoric in practice, when combined with as assessment of candor may provide strategic insights in keeping with strategic posture. As depicted in the EPIC framework, candor is viewed as a state. In the absence of a specific measurement tool for Candor, this research seeks to explore the presence and absence of candor by inference in comparison of the EP to FP relationship across community of firms sharing similar messaging topics. In order to explore these concepts, we had to explore the EPIC framework 10 to detect patterns of rhetoric, those with clear candor and those without. Group differences, as shown in the EPIC framework, illustrate the possibility of candor differences. As a cross sectional analysis candor is presumed to be fixed at any given point in time. Clear candor is assumed to be the base state C=0, where, without any other influence, words contained in financial communications reflect a firm’s true nature (such that rhetoric measurement should be equivalent to psychometric measurement). In the EPIC framework, low EP combined with clear candor C=0, is viewed as a signal with the same predictive insights as low EO (Business Orientation) that provides the expectation of low future FP. High EP combined with clear candor C=0, has the same predictive insights as high EO (Growth Orientation) that provides high future FP. Non-clear candor occurs when one’s true nature is either exaggerated (C = -1) or diminished (C = +1). Firms exhibiting non-clear candor choose to strategically misrepresent EO behavioral qualities within financial communications. As such, they modify EO expression in financial communications, given some decisional process that is viewed to garner some benefit, and, therefore, has strategic intent. Negative candor (C = -1) is conceptualized in the EPIC framework when entrepreneurial statements are exaggerated and therefore express EP higher than their true entrepreneurial capacity, and thus over-represent their future firm performance (EP >EO), which eventually leads to low FP. The presence and existence of financial “promoters” has been recognized for many years (Wood, 1917). Hence, firms with negative candor are deemed Promoters Oriented in the EPIC framework. FIGURE 3. ENTREPRENEURIAL POSTURE IN COMMUNICATIONS (EPIC) CONCEPTUAL FRAMEWORK 11 Positive candor (C = +1) is conceptualized in the EPIC framework when entrepreneurial posturing statements are diminished thereby depicting lower EP than one’s planned entrepreneurial behavior, and thus under-represent future firm performance (EP <EO). The concept of strategically under-representing one’s capabilities yet attain higher FP has been depicted as stealth, which is commonly explored in competitive behaviors within Strategic Management (Chen & Hambrick, 1995). Firms exhibiting positive candor are, therefore, deemed Stealth Oriented in the EPIC framework. Stealth Oriented firms, though challenged in their lack of candor, can be viewed as positive given that, as far as business operations, they will achieve a high firm performance outcome. The EPIC framework assumes responsive self-awareness where firms adjust their entrepreneurial rhetoric based on internal expectations within a reasonable time frame. For companies listed on public stock exchanges, if any event is considered material to ongoing business operations, they must halt the trade of the company’s stock until the company issues a press release (TSX, 2020), usually the same day. In such circumstances, entrepreneurial messaging can be extremely responsive to internal company mechanisms. Established firms usually do not change on a day to day basis, hence, even annual-based statements such as Letters to Shareholders are adequately reflective. Regardless of the form or the frequency, firms provide consistent text based samples depicting their awareness and openness (Candor) with respect to inherent entrepreneurial processes (EO) relative to its future firm performance (FP) expectations as communicated within its entrepreneurial rhetoric (EP). In summary, the EPIC framework extends EO theory as a messaging construct introducing the conceptualization of candor and presenting two novel entrepreneurial postures: Promoter and Stealth Oriented. 4. RESEARCH QUESTIONS This research explores the nature of Entrepreneurial Posture (EP) as drawn from financial communications and its predictive nature with respect to future firm performance (FP). As noted in the literature background section, there is currently some debate as to whether the measurement tool applying custom EO dictionaries via computer-assisted text analysis (EO-CATA) measures the EO behavioral construct or something more in keeping with the EP conceptualization. This research explores this debate while seeking evidence in support of the purposed conceptualization of candor. Part I of this section asks, does EO, as measured by CATA, reflect the same predictive insights conceptualized as strategic posture in EO Theory? Part I presents numerous Entrepreneurial Orientation to Firm Performance (EO-FP) relationships, where the FP is defined using both accounting and market measures. This EO measurement approach draws on the technique pioneered by Short (2010). Jeremy Short (2010) provided theoretical and empirical support for 12 his pioneering CATA through an EO custom dictionaries technique, which he developed. Short’s findings suggest that this technique measures the EO behavioral construct, as denoted in EO Theory. Covin and Wales’ (2018) theoretical assessment of Short’s technique, however, is that it measures a form of EO more in keeping with EP. Part I of this research replicates Jeremy Short’s (2010) technique by seeking insights to research question (RQ1): Is EO being measured? Part II investigates whether the EO-FP relationship differ between communities of firms when segmented into clusters using a novel clustering technique that incorporates natural language processing (NLP) and machine learning, known as topic modeling, when applied to financial communications. This research introduces topic modeling as a novel contingency characteristic to the EO-FP relationship. This analysis explores whether different EO-FP patterns exist for various purposed strategic postures. It is posited that differences in the conceptual EO-FP models within settings shown by the EPIC framework may support the existence of candor, where some firms are clear in their messaging, whereas others are not. Groupings were explored because there is no known direct measure of Candor. The EPIC framework provides groupings of combinations that potentially illustrate inferred expression of posturing, candor and firm performance, which includes candor being zero (clear), perfectly negative one candor (-1) and perfectly positive one (+1). Candor differences will be shown by comparing results in these different EPIC framework combinations. The inferences will be made by using machine learning topic modelling (Canini, Shi, & Griffiths; Yin & Wang). The EPIC framework suggests groupings based upon high and low EO, posturing, candor and performance. We analyzed on the diagonal the situations where companies had clear candor where one expects a positive EO-FP relationship. Then we analyzed where companies had non-clear candor and varied their posturing statements to firm performance, and where we expected a negative EP-FP relationship. In order to get groupings based on similar messaging, we used machine learning topic modelling techniques believed to segment documents based on similarity of latent semantic expression, which in turn may isolate unique elements of text and grammar insightful to EO messaging. If so, firm segmentation based on messaging may infer a context as conceptualized in the entrepreneurial posturing in communications analytical model depicted in Figure 4. 13 FIGURE 4. ENTREPRENEURIAL POSTURE IN COMMUNICATIONS (EPIC) ANALYTICAL MODEL This research study focused on automated clustering techniques known as topic modeling, which have emerged from computer and data science disciplines. Topic modeling is capable of scanning a large set of documents (corpus), detecting word and phrase patterns within them, and automatically clustering word groups with similar expressions. Put simply, topic modeling reduces the heterogeneity of topics within a large collection of documents. The resulting segmentation is believed to associate documents based on similar latent semantic meaning (Newman et al., 2010). It is postulated that if topic modeling had no association to EO in messaging, then the resulting segmentation of firms would be random. If random, EO measurements should present little difference between before and after topic modeling. The same logic would also apply to the EO-FP relationship. After topic modeling, if the overall expression of EO subdimensions changed (reflected in the distribution found) and/or the nature of the EO-FP relationship changed, then topic modeling provides a novel contextual variable based on the topics found. The impact of topic modeling could, therefore, have the following possible outcomes: 1. No contextual impact: firms are segmented randomly, resulting in no change in the EO subdimension distribution. If this the case, then there is no evidence of Candor. 2. Contextual Impact: topic modeling segments firms related to EO, which changes the EO subdimension distribution amongst clusters. a. if groupings retain the same EO-FP relationship then, even though EO has been influenced by topic modeling, the EO-FP relationship has been preserved; hence, there is no inferred difference between EO and EP, and hence, no evidence of candor. 14 b. if groupings differ in the EO-FP relationship, this is an inferred difference between EO and EP, which suggests differences as conceptualized as candor. Groupings generated by automated topic modeling are interpreted, assuming EO-CATA measures the EP construct, where candor is clear and EP matches EO. If the modeled EP-FP relationship is significantly associated, this is viewed as only occurring when posturing equals strategic intentions. Drawing on EO Theory, the EO strategic postures, either Growth or Business Oriented, is identified when EO is positively associated to FP. Drawing on the purposed EPIC framework, new EO strategic postures of Promoter or Stealth Oriented are identified when EP is negatively associated to FP. A negative EP-FP relationship occurs when EP ≠ EO is deemed non-clear candor. Using this interpretation, if topics contained in entrepreneurial messaging captured elements related to strategic postures, one could, therefore, categorize communities as depicted in Figure 4. The research question here asks research question 2 (RQ2): Do modeled EP-FP relationships change due to topic modeling segmentation? This conceptualization is not a direct measure of candor but a means of inferring that candor could exist through different patterns found in the EO-FP relationships. Overall, this is viewed as a good first step in the systematic identification of candor. 5. RESEARCH METHODS 5.1. DATA Sources & Time Frame Letters to Shareholders (LTS) represent one of the most read components of annual reports, and it thereby holds significance to a firm, as well as to its investors. A lot of resources and effort go into one’s annual report. As the LTS will be read by the majority of the stakeholders, this is one more reason to ensure that the LTS is an accurate and proper reflection of the organization. LTS are traditionally written or at least reviewed by senior management, and the CEO in particular. The LTS is a personal reflection of the organization consisting of numerous elements, such as time, emotions, expectations, and facts. Therefore, the LTS is believed to hold unique ideographic insights into the potential orientations of the author or authors, and therefore, the organization, in addition to the nomothetic corporate facts, such as financial performance measures. The LTS is a true organizational-level document. The LTS is typically generated by the CEO. Yet, although the CEO may draft the document, the chances are it also gets edited through various contributors within the firm, such as accounting, communications, and legal departments. Given that this process may dilute the words, meanings, and intentions of the original author, it also serves to further model and shape the LTS as a truly organizational document. 15 The LTS is unique in its temporal perspective. Whereas financial reporting is very much geared to historical results, the LTS has a unique temporal perspective in that it typically addresses the previous year’s performance and indicates future goals, projections, and aspirations. The LTS is created in an environment where truth and facts are legally required. Each LTS is unique in style and content to the company. Yet, each company is governed by strict regulations and are held accountable for the content they publish. Even though the LTS is relatively informal in its nature, it is still a significant reflection on the company and its senior management. Hence, the time and care to have it both accurate in its detail and its nature seems appropriate. The LTS is an unstructured document that can take on many messages and many tones. With respect to measuring EO subdimensions, everything else represents noise. In computational techniques the context is derived primarily by the source. The ease of interpretation of the context depends on the homogeneity of the nature of the documents. The LTS provides a lot of context, but it must be extracted using techniques such as CATA, NLP, and Topic Modeling. Given the merits of the LTS, it has been well studied using both qualitative techniques and quantitative computational methods. Hence, it is the document of analysis for this research within the scope of detecting EO expressed with financial communications. In this research, all LTS text data is derived from the annual statements of companies publicly listed as a member of the Russell 2000 (R2K) or Canada’s Toronto Stock Exchange, the TSX. Full-length annual reports are available to download from various sources. The sources used in this research include EDGAR.com, SEDAR.com, and Annualreports.com. Text from the Letter to Shareholders (LTS) sections were extracted from and saved in an individual text file for each company. Accounting measures were available in the annual reports; however, for convenience and ease of access, all accounting measures, as well as market measures, were extracted using Bloomberg terminal. Bloomberg’s Excel integration enables one to set company names with encoded tags that draw individual accounting measures and market measures in real-time or for historic metrics for any given year. For this research, revenue and profit measures, plus 13 additional FP measures are drawn from various parts of the financial statement repertoire, including Income Statement, Balance Sheet, and Cash Flow statement metrics. For comparison purposes as per Short et al. (2010), the market measure is Tobin’s q. All EO measurements drawn from LTS documents are for the year 2012. The time frame for the financial, accounting, and market measures span six consecutive years, from 2010 to 2016. Hence, the initial time frame of this 16 research is pegged at 2012, which is sample year t=0 for the LTS. EO-FP relationships are, therefore, based on the EO2012 to FPt=2010 to 2016. Sample selection included selecting companies from both the Toronto Stock Exchanges as well as the Russel 2000, the TSX policy for whether firms must submit a LTS, and the completeness of their LTS document. The Annual Reports of 2,149 companies were randomly selected, and 525 Letters to Shareholders (documents) were manually extracted from their respective 2012 Annual Financial Report. In their annual report the companies may choose to include a Letter to Shareholder, which was conveyed during this process in interviews with Senior TSX executives. They noted that TSX has no recommendation on behalf of the exchange or securities commission for a company to provide an LTS. It is noteworthy to add that there is also no recommended topic nor format to any given LTS. The LTS is therefore a completely voluntarily and individually stylized document. Each document represents a single publicly listed company listed on either the Russel 2000 Index or the Canadian exchanges, TSX and TSXV. 5.2. Computer-Aided Text Analysis (CATA) Computer-Aided Text Analysis or CATA (Neuendorf, 2016) is “any instance of the application of computer algorithms to digitize text to produce some summarizing output (either data from multiple texts or data-based conclusions about a single text).” CATA programs can be designed with the goal of obtaining nomothetic information (e.g., data about messages such as newspaper articles) or idiographic information, (information about a single individual’s psychiatric state). A CATA dictionary is a set of words, phrases, parts of speech, or other word-based indicators (e.g., word length, number of syllables) that is used as the basis for a search of texts. Dictionaries built by the researcher are called custom dictionaries. A number of dictionaries are generally used in a single study, each serving as a measure of a different concept. 5.3. EO Dictionaries Short et al. (2010) provided the first comprehensive CATA protocol for the assessment of EO. The researchers generated six dictionaries: autonomy, innovativeness, proactiveness, competitive aggressiveness, risk-taking, and an additional inductive dictionary to represent the EO construct. Following Short et al.’s methodology, EO lexicons, shaped from deductive reasoning, were designed to target specific firm-level qualities of autonomy, competitive-aggressiveness, innovativeness, proactiveness, and risk-taking for the EO construct. In addition to the deductive process, a sixth measure of general EO characterization was produced via inductive methodology. 17 The complete EO lexicons are as follows: 5.3.1. Autonomy at-liberty, authority, authorization, autonomic, autonomous, autonomy, decontrol, deregulation, distinct, do-it-yourself, emancipation, free, free-thinking, freedom, independence, independent, liberty, license, on one’s-own, prerogative, self-directed, self-directing, self-direction, self-rule, self-ruling, separate, sovereign, sovereignty, unaffiliated, unattached, unconfined, unconnected, unfettered, unforced, ungoverned, unregulated 5.3.2. Competitive Aggressiveness achievement, aggressive, ambitious, antagonist, antagonistic, aspirant, battle, battler, capitalize, challenge, challenger, combat, combative, compete, competing, competition, competitive, competitor, competitive, conflicting, contend, contender, contentious, contest, contestant, cut-throat, defend, dog-eat-dog, enemy, engage, entrant, exploit, fierce, fight, fighter, foe, intense, intensified, intensive, jockey for position, joust, jouster, lock-horns, opponent, oppose, opposing, opposition, play against, ready to fight, rival, spar, strive, striving, struggle, tussle, vying, wrestle 5.3.3. Innovativeness ad-lib, adroit, adroitness, bright-idea, change, clever, cleverness, conceive, concoct, concoction, concoctive, conjure up, create, creation, creative, creativity, creator, discover, discoverer, discovery, dream, dream-up, envisage, envision, expert, form, formulation, frame, framer, free thinker, genesis, genius, gifted, hit upon, imagination, imaginative, imagine, improvise, ingenious, ingenuity, initiative, initiator, innovate, innovation, inspiration, inspired, invent, invented, invention, inventive, inventiveness, inventor, make up, master stroke, mastermind, metamorphose, metamorphosis, neoteric, neoterism, neoterize, new, new-wrinkle, novation, novel, novelty, original, originality, originate, origination, originative, originator, patent, radical, recast, recasting, resourceful, resourcefulness, restyle, restyling, revolutionize, see things, think up, trademark, vision, visionary, visualize 5.3.4. Proactiveness anticipate, envision, expect, exploration, exploratory, explore, forecast, foreglimpse, foreknow, foresee, foretell, forward-looking, inquire, inquiry, investigate, investigation, look-into, opportunity-seeking, proactive, probe, prospect, research, scrutinization, scrutiny, search, study, survey 5.3.5. Risk-taking adventuresome, adventurous, audacious, bet, bold, bold-spirited, brash, brave, chance, chancy, courageous, danger, dangerous, dare, daredevil, daring, dauntless, dicey, enterprising, fearless, gamble, gutsy, headlong, 18 incautious, intrepid, plunge, precarious, rash, reckless, risk, risky, stake, temerity, uncertain, venture, venturesome, wager 5.3.6. Inductively Derived Words Advanced, advantage, commercialization, customer-centric, customized, develop, developed, developing, development, developments, emerging, enterprise, enterprises, entrepreneurial, exposure, exposures, feature, features, founding, high-value, initiated, initiatives, innovations, innovative, introductions, launch, launched, leading, opportunities, opportunity, originated, outdoing, outthinking, patents, proprietary, prospects, prototyping, pursuing, risks, unique, ventures 5.4. EO-CATA Measurement When these lexicons are selected for a particular meaning within a highly specific context, then word frequency is deemed to be semantically meaningful. The higher the frequency of words matching a custom, human-coded dictionary aligned with a particular sample of text results in what is called a higher tokenized word-descriptive indicator. Using the Diction 7.0 software program, a text may obtain a variable score ranging from zero (if no dictionary words are counted) to a high numeric score (e.g., 55, if many words are counted). Research has found that it takes 500 words to get an accurate understanding of a person’s overall style or of an organization’s general language habits (Sydserff & Weetman, 2002). By default, DICTION provides a 500-word average for all long texts; for short texts, it extrapolates the data to a 500-word norm. This is referred to as the Insistence Score. The Insistence score is deemed a measure of the extent to which nouns or noun-derived adjectives are heavily repeated in a text. Thus, it becomes a measure of whether or not a text “stays on topic.” The unit of analysis for CATA variables with a custom EO dictionary measures the EO dimensional qualities of a particular message (Noel & Erskine, 2013). 5.5. EO-FP Relationship When EO is measured using CATA with custom EO dictionaries, the established technique for modeling EO association to firm performance is ordinary least squares regression (OLS). Using OLS, moderators such as firm size are scrutinized using hierarchical regression analysis with ANOVA test. Model quality is reflected in improvements in affect size, as measured by the coefficient of determination, denoted r^2, and interpreted as the proportion of the variance in the firm performance (the dependent variable) that is predictable from the EO measures (independent variable) (Allison, Davis, Short, & Webb, 2015; McKenny et al., 2016; McKenny, Short, Ketchen, Payne, & Moss, 2018; McKenny et al., 2013; Short et al., 2010). 19 5.6. Machine Learning Topic Modeling Machine learning topic modeling is one of the most commonly used unsupervised learning algorithms used in text data analysis. Clustering is the task of segmenting a collection of documents into partitions, where documents in the same group (cluster) are more similar to each other than those in other clusters. In topic modeling, a probabilistic model is used to determine a soft clustering, in which every document has a probability distribution over all the clusters, as opposed to hard clustering of documents. A machine learning topic model captures the intuition of topics within a mathematical framework. Such a framework allows one to examine a set of documents and discover, based on the statistics of the words in each document, what the topics might be and what topics are contained with each document. Each topic is represented as a probability distribution over words, and each document is expressed as probability distribution over topics. Thus, a topic is akin to a cluster, and the membership of a document to a topic is probabilistic. The topic cluster is strictly a categorical variable that provides no other information other than membership. The number of levels or clusters to the TP variable is determined by a scree plot of the measures contrasting topic perplexity vs. cohesiveness versus the number of latent topics. The number of latent topics (denoted as K) that minimizes the perplexity reduction and maximizes topic cohesiveness is deemed the ideal number of clusters. Machine learning topic models provide clusters that include context-specific information; however, this information is not explicitly provided and must be extracted. Information extraction is defined as the task of extracting information or facts from unstructured or semi-structured documents. In this research, information extraction adopts CATA analysis. One, this provides direct comparison to previous research, and two, CATA offers a human-coded process for semantic interpretation provided by custom EO dictionaries created by Short et al. (2010). In exploring for new clustering and topic modeling techniques to explore EO analysis, this research focused on an emerging machine learning technique using Bayesian analysis known as Latent Dirichlet Allocation (LDA) (Darling; de Groof & Xu; Eisenstein & Barzilay). LDA, a form of machine learning topic modeling, is showing promise in other disciplines such as in Finance (Feuerriegel, Ratku, & Neumann, 2016) and Healthcare (de Groof & Xu). Latent Dirichlet Allocation (LDA) is perhaps the most common machine learning topic model currently in use. LDA introduces sparse Dirichlet prior distributions over document-topic and topic-word distributions, encoding the intuition that documents cover a small number of topics, and that topics often use a small number of words (Banks, Woznyj, Wesslen, & Ross, 2018). LDA utilizes unsupervised learning methods, which are techniques trying to find hidden structure out of unlabeled data. Since it does not need any training phase, it can be applied to any text data without manual effort. 20 6. RESULTS Part I Part I of this research seeks to answer (RQ1: Is EO being measured?) exclusively using automated, computer assisted text analysis (CATA) with custom EO dictionaries. The analytical technique of linear regression modelling was chosen to analyze the EO-FP relationships as well as the influence of moderators such as firm size, industry and country is characterized for both total and percent change in market measure Tobin’s q, as well as accounting measures of revenue, profit, research and development expenditures (R&D Exp), Cash Flow from Financings, and Cash Flow from Operations. These measures are then compared within the EO-FP conceptual relationship for potential significant results in various timelines, up to two years prior and 3 years after the initial EO measurement. 6.1. Assessment of EO Drawn from Letters to Shareholders Results from one-sided t-test are shown in Table 1. One-sided t Test (mean >0) for EO Expression. One sided t-tests test whether the mean EO subdimension measures are greater than 0. The alternative hypothesis is that the EO sub-dimensions were found. The p value is significant, and hence, the null value is rejected. The conclusion, therefore, is that the CATA with custom EO dictionaries in measuring some form of EO in Letters to Shareholders. TABLE 1. ONE-SIDED T TEST (MEAN >0) FOR EO EXPRESSION df mean SD t value p value Autonomy df = 523 0.2565458 0.5523169 t = 10.633 p-value < 2.2e-16 alternative hypothesis: true mean is greater than 0 Competitive Aggressiveness df = 523 0.773187 1.16016 t = 15.256 p-value < 2.2e-16 alternative hypothesis: true mean is greater than 0 Risk-taking df = 523 0.287271 0.645803 t = 10.183 p-value < 2.2e-16 alternative hypothesis: true mean is greater than 0 Innovativeness df = 523 3.089256 2.416068 t = 29.269 p-value < 2.2e-16 alternative hypothesis: true mean is greater than 0 Proactiveness df = 523 1.055573 1.441531 t = 16.762 p-value < 2.2e-16 alternative hypothesis: true mean is greater than 0 Inductively Derived Words df = 523 4.136183 3.013426 t = 31.42 p-value < 2.2e-16 alternative hypothesis: true mean is greater than 0 6.2. EO Dimensionality Dimensionality of the EO construct is important given the theoretical and empirical support in the EO literature. An intercorrelation table reflecting both the bivariant correlations, as well as the significance of the dataset, is shown in Figure 5. If presented with very high correlations between dimensions one may consider collapsing dimensions measuring the same phenomena, whereas moderate correlation may have unidimensional affects. Given that little bivariant interaction is observed between the EO dimensions, this evidence suggests EO expression in this data set exhibit a multidimensional nature. 21 This is a significant result given the definition of strategic posture is most commonly associated with the unidimensional form, where strategic posture is represented by the sum EO subdimensions, typically risk-taking, innovativeness and proactiveness. Given this dataset and the CATA measurement technique exhibits multidimensional properties, strategic posture is viewed as a topology of EO subdimensions with the generally positive relationship between EO and FP. FIGURE 5. BIVARIANT CORRELATIONS AMONGST EO SUBDIMENSIONS Bottom left charts are scatter plots of instance values for respective EO subdimensions where the column heading is the instance on the y axis and the row heading is the instance on the x axis. The upper right is the calculated correlation between respective subdimensions. 6.3. Comparing EO Expression Across Exchanges Examining for external validity was done by segmenting the sample set by stock exchange, the Russell 2000 (R2K) and Toronto Stock Exchange (TSX), as was recommended by the EO measurement protocol outlined by Short et al (2010). Each EO subdimension represented on each exchange expressed a positive measure of EO, see Table 2. Short’s (2010) results exhibited one specific result where the R2K was significantly more proactive than the NYSE. Short argued that the R2K represents smaller, more growth-focused companies than the NYSE. This same argument is applied here. To evaluate mean differences between the samples, a one-way analysis of variance (ANOVA) was conducted. The results of this analysis revealed a significant difference between the R2K and TSX with respect to autonomy and proactiveness (see Table 3). Given the median size of a TSX company is similar to the median size of a R2K company, see Figure 6, it is unclear why TSX companies express more proactiveness or autonomy. This could simply be a cultural difference between Canadian and American word usage. 22 TABLE 2. T TEST COMPARISONS OF EO EXPRESSION BETWEEN THE RUSSEL 2000 AND THE TSX Evidence of Language Representing Entrepreneurial Orientation Dimensions in Shareholder Letters of Firms in TSX and Russell Index in 2012 Russell 2000® Sample TSX Sample N Mean SD tTest N Mean SD tTest Autonomy 333 0.21 0.44 8.58 * 189 0.34 0.70 6.77 * Competitive aggressiveness 333 0.73 1.19 11.15 * 189 0.85 1.10 10.70 * Innovativeness 333 3.21 2.56 22.93 * 189 2.87 2.13 18.59 * Proactiveness 333 0.88 1.11 14.40 * 189 1.37 1.85 10.23 * Risk-taking 333 0.28 0.61 8.45 * 189 0.30 0.71 5.79 * Inductively derived entrepreneurial orientation words 333 3.90 2.66 26.77 * 189 4.55 3.52 17.85 * Note: The results of this table were based on computer-aided text analysis entrepreneurial orientation values * p < .01. This replicates Short et al.’s (2010) “greater than zero t test” for EO signal detection. TABLE 3. T TEST COMPARING DIFFERENCE BETWEEN R2K AND TSX IN 2012 Two-sample t-test (2 tailed) comparing Russell vs TSX/TSXV Companies df t p Autonomy 520 2.6 <.01 means differ Competitive aggressiveness 520 1.143 0.254 not significantly different Innovativeness 520 1.545 0.123 not significantly different Proactiveness 520 3.769 <.01 means differ Risk-taking 520 0.339 0.735 not significantly different Inductively derived entrepreneurial orientation words 520 18.17 0.018 means differ FIGURE 6: COMPANY SIZE (NUMBER OF EMPLOYEES) FOR TSX AND RUSSEL LISTED FIRMS 050100150200100 1000 10000 100000 1000000FrequencyFirm Size (Number of Employees)Frequency by Firm Size for TSX and R2K CompaniesTSXRussel 2000 23 6.4. EO - Firm Performance Relationship Modeling The next important analytical results of Part 1 explored the EO-FP relationship measures for the entire sample population by analyzing with linear regression and testing regression modes for associations to various financial performance measures, not only for the current year but also for two years prior and four years after publicizing the LTS (in 2012). The financial measurements spanned 2010 through 2016. The rationale for regression analysis for EO measurements from 2012 concerning financial performance values in previous years is due, in part, to the interpretation of EPIC framework. EP is a messaging construct. This study therefore investigates whether EP correlates to previous FP accomplishments or does it correlate more with future firm performance. EO Theory states that having EO will, at some point in the future, translate into some form of firm performance. EO Theory is, therefore, not definitive on what exact firm performance measures are associated with EO, nor when this influence will occur. This research study captured the EO expressions within the 2012 Annual Reports and then set out to determine across numerous potential financial performance metrics which metric and when is correlated to EO Expression. 6.4.1. EO - FP (Tobin’s q) To assess one of the most frequently used market measures of Firm Performance (FP) in EO research, Tobin’s q, a ratio of a firm’s market value to its replacement value of a company, was investigated for its association to EO expression in Letters to Shareholders. The modeled relationships between EO and Tobin’s q are shown in Table 4. The EO literature suggests that firms that behave more entrepreneurially should have higher firm performance (FP). Firms with higher EO therefore should have higher FP values, even when adjusted for firm size. Results in the base year suggests Tobin’s q associates positively with proactiveness with the current year and increases (by beta weights) as time goes back. EO appears to have little association with Tobin’s q t+1, which is not in keeping with EO literature, (Short et al., 2010). EO Theory is generally presented with an assessment of EO today and its impact on FP in the future. Since EO literature has never explored EO as a messaging construct, there is no expected view on how EO expression is influenced by previous firm performance. These results suggest the influence of the previous year’s Tobin’s q could be interpreted as firms tend to express more proactiveness rhetoric because its share price has already gone up. 24 TABLE 4. REGRESSION ANALYSIS FOR TOBIN’S Q Year 2010 2011 2012 2013 2014 2015 Timeline -2 -1 0 +1 +2 +3 Tobin’s q (Intercept) 1.44 *** 1.38 *** 1.39 *** 1.69 *** 1.79 *** 1.93 *** Firm Size (Employees) -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 Autonomy 0.09 0.19 . 0.19 . 0.12 0.29 * 0.07 Competitive Aggressiveness 0.03 0.03 0.02 -0.02 0.00 -0.03 Risk-taking -0.13 -0.20 * -0.09 -0.07 -0.31 * -0.36 Innovativeness 0.01 0.01 0.03 0.03 0.02 0.01 Proactiveness 0.24 *** 0.18 *** 0.11 * -0.05 -0.03 -0.07 Multiple R-squared: 0.07 0.05 0.07 0.01 0.02 0.01 Adjusted R-squared: 0.06 0.04 0.06 0.00 0.01 0.00 df 517 517 517 517 517 517 F-statistic: 6.25 4.77 2.59 1.05 2.18 0.60 p-value: 2.40E-06 *** 9.58E-05 *** 0.01742 * 0.3902 0.04372 * 0.7279 6.4.2. EO - FP (% Tobin’s q Change) Next, the % change of Tobin’s q is investigated, which essentially investigates whether having a high EO leads to an increase in a company’s share price. Note that the EO to % change in FP relationship is modelled without moderation of firm size. In contrast to the overall value of Tobin’s q, the % change of Tobin’s q appears more responsive to EO expression (see Table 5). The findings suggest innovativeness has a very modest positive influence, whereas proactivity and risk have a negative influence on the change of Tobin’s q. As far as interpreting the strategic posture, there is a small yet significant and seemingly negative relationship between EO and FP. Though a negative EO-FP relationship has been noted in the EO literature it is usually associated with moderator influences (Chin et al., 2016). This regression is not moderated and as such, does not lend itself to tradition strategic posture interpretation where high EO will result in high FP. Given the unexpected result inconsistent with traditional EO interpretation, the EO - % Tobin’s q+1 negative relationship is an excellent candidate for interpretation by the EPIC framework, which is explored Part II. 25 TABLE 5. REGRESSION ANALYSIS FOR % TOBIN’S Q Year 2010 2011 2012 2013 2014 2015 Timeline -2 -1 0 +1 +2 +3 % Tobin q (Intercept) -0.03 0.11 ** 0.17 *** 0.20 * Autonomy -0.03 0.01 0.00 -0.05 Competitive Aggressiveness 0.01 -0.01 0.00 -0.01 . Risk-taking 0.03 . -0.04 -0.08 * -0.10 Innovativeness -0.01 * 0.02 * 0.01 0.01 Proactiveness 0.02 ** -0.04 *** -0.05 ** -0.06 Multiple R-squared: 0.03 0.04 0.03 0.01 Adjusted R-squared: 0.02 0.03 0.02 0.00 df 497 502 502 503 F-statistic: 3.30 3.93 3.13 1.05 p-value: 0.006104 ** 0.00168 ** 0.008541 ** 0.3904 6.4.3. EO - FP (Revenues) Revenues are the life blood of any organization. Modeling EO to size of revenues, accounting for firm size, asks do firms who express higher EO have higher revenues? Firm revenues appear to have a negative association to innovativeness, both historically and in the future (see Table 6). The size of revenues is clearly associated with the size of a company. Firms that espouse innovativeness and presumed to behave innovatively apparently find it challenging to translate innovation into revenues. This negative EO-FP association appears to continue to increase up to year +2, but by year +3, the negative impact is lower, though it is still negative. The conclusion is fairly straightforward: the more a company espouses innovativeness, the less likely it generates revenues, even when accounting for firm size. There is a significant yet negative EO-revenue relationship. Again, this result is not within EO Theory’s generally positive EO to FP relationship. 26 TABLE 6. REGRESSION ANALYSIS FOR REVENUES Year 2010 2011 2012 2013 2014 2015 Timeline -2 -1 0 +1 +2 +3 Revenues (Intercept) 690.41 *** 794.43 ** 806.54 ** 852.73 ** 1040.54 ** 777.26 ** Firm Size (Employees) 32.78 *** 63.55 *** 65.62 *** 84.54 *** 99.43 *** 184.19 *** Autonomy 169.04 86.75 50.40 11.71 51.73 -15.80 Competitive Aggressiveness -97.84 7.64 98.23 93.92 68.79 -9.76 Risk-taking -42.15 -132.63 -120.71 -199.07 -178.26 -154.64 Innovativeness -93.31 * -133.35 * -129.09 * -141.59 * -165.74 * -108.76 . Proactiveness -10.33 -6.42 3.05 20.95 5.92 -1.22 Multiple R-squared: 0.04 0.07 0.06 0.09 0.09 0.29 Adjusted R-squared: 0.03 0.06 0.05 0.08 0.08 0.28 df 517 517 517 517 517 517 F-statistic: 3.75 6.36 5.44 8.26 8.37 35.13 p-value: 0.001159 ** 1.81E-06 *** 1.79E-05 *** 1.49E-08 *** 1.13E-08 *** 2.20E-16 *** 6.4.4. EO - FP (% Revenue Change) Since growth is a key component of EO literature, this research investigates if high EO is an indicator of increasing revenues or % revenue change? Unlike EO to amount of revenues, a different revenue story emerges when EO is compared to % revenue change, another accounting measure of firm performance (see Table 7). EO expression at time 0 has no association with previous years. % revenue change in year +1 is negatively influenced by risk-taking and positively, albeit to a lesser extent, by innovativeness. Even more interesting is that % revenue change has significant and relatively high r^2 affect sizes for years +2 and +3, where both years have a positive association with proactiveness. The takeaway from this suggests risk-taking behavior has some immediate negative impacts on future revenue changes. Innovativeness has some immediate positive impact. Revenues of firms that express proactiveness are influenced positively, but it takes time to manifest. This observation appears central to entrepreneurship that innovation alone provides little commercial benefit. Some research, in fact, suggest the bilateral effect of proactive-innovativeness best serves commercial efforts (Linton & Kask, 2017). This perspective is outside of the field of EO research given that regression analysis investigating multilateral influences amongst EO subdimensions is uncommon in EO literature. The EO standard is to present each subdimension independently. Again, there is a significant yet negative EO-% revenue relationship with respect to revenue change in the year t+1. The EO-% revenue relationship, however, changes to positive in the t+2 year, and dramatically so in the t+3 year. These observations suggest EO expressed in the base year (2012) takes two, if not three 27 years, to manifest into revenue growth based on the degree of proactivity behavior (or expression) in the base year. TABLE 7. REGRESSION ANALYSIS FOR REVENUE CHANGE Year 2010 2011 2012 2013 2014 2015 Timeline -2 -1 0 +1 +2 +3 Revenue Growth (Intercept) -0.10 0.07 . 0.21 -1.47 Autonomy -0.04 0.00 0.20 -0.90 Competitive Aggressiveness 0.04 -0.02 0.08 0.13 Risk-taking -0.04 -0.12 * 0.25 -0.83 Innovativeness -0.01 0.02 * 0.05 0.03 Proactiveness 0.02 0.01 0.08 *** 2.86 *** Multiple R-squared: 0.02 0.09 0.17 0.18 Adjusted R-squared: -0.01 0.06 0.15 0.16 df 167 172 172 169 F-statistic: 0.81 3.24 7.26 7.42 p-value: 0.5423 0.007996 ** 3.40E-06 *** 2.56E-06 *** 6.4.5. EO - FP (Profit) One would think profit is an important facet to a company. Profit, however, may not be the driving force to a company looking to grow (Growth Oriented). However, profit would seem to be the main purpose for firms deemed Business Oriented. This research therefore investigated whether EO expression correlates to profits. Interestingly, EO expression appeared to have no association, either backward or forward, with respect to the accounting construct profit (see Table 8). The research results suggest there is no EO-FP relationship with respect to profit. This would seem somewhat surprising given that EO research has often used profit as an overall measurement of FP. Though perhaps when one considers that profit is a highly subjective metric often influenced by timing effects and discretional management decisions, perhaps this observation is not so unexpected. This observation, however, is inconsistent with general EO Theory. 28 TABLE 8. REGRESSION ANALYSIS FOR PROFITS Year 2010 2011 2012 2013 2014 2015 Timeline -2 -1 0 +1 +2 +3 Profit (Intercept) 221.30 * 269.27 * 286.74 ** 282.50 * 285.31 * 96.28 ** Firm Size (Employees) 27.35 *** 30.24 *** 30.78 *** 32.05 *** 33.30 *** 4.14 *** Autonomy 6.40 0.98 -1.82 -3.40 -4.95 84.65 Competitive Aggressiveness 51.80 72.32 70.62 67.68 67.26 40.56 Risk-taking -71.47 -84.60 -87.48 -93.00 -90.13 72.58 Innovativeness -33.61 . -46.72 . -38.52 -37.20 -31.84 19.46 Proactiveness 51.55 72.11 . 23.01 21.45 16.52 32.91 Multiple R-squared: 0.08 0.07 0.09 0.09 0.09 0.12 Adjusted R-squared: 0.07 0.06 0.07 0.08 0.08 0.11 df 517 517 517 517 517 517 F-statistic: 7.86 6.90 8.05 8.28 8.68 12.27 p-value: 4.08E-08 *** 4.65E-07 *** 2.55E-08 *** 1.41E-08 *** 5.16E-09 *** 6.26E-13 *** 6.4.6. EO - FP (% Profit Change) Since the amount of EO expression had no influence on the amount of total profit generated, the next analysis investigates whether the amount of EO influences the % profit change. There is little notable EO-FP relationship with respect to profit change, as seen in Table 9. Autonomy expression appears most associated, and negatively so, with the previous year’s (t-1) % profit change. EO Theory has no interpretation of the impact of a previous year’s FP on EO. From a messaging perspective, however, one could view this as when profits increase, a firm’s expression of autonomy decreases. 29 TABLE 9. REGRESSION ANALYSIS FOR % PROFIT CHANGE Year 2010 2011 2012 2013 2014 2015 Timeline -2 -1 0 +1 +2 +3 Profit Change (Intercept) -0.04 -0.02 0.22 ** -0.08 Autonomy -0.25 *** 0.05 -0.01 0.20 Competitive Aggressiveness 0.05 -0.02 -0.05 -0.42 Risk-taking -0.01 -0.01 -0.03 -0.02 Innovativeness -0.02 0.02 * 0.01 0.09 Proactiveness -0.01 -0.02 -0.04 0.03 Multiple R-squared: 0.04 0.02 0.01 0.01 Adjusted R-squared: 0.03 0.01 0.00 0.00 df 352 356 353 353 F-statistic: 3.15 1.64 0.74 0.70 p-value: 0.008575 ** 0.1493 0.5965 0.6268 6.4.7. EO - FP (R&D Expense) Since EO is connected with growth, which is commonly associated with innovation, this research investigates whether having higher amounts of EO leads to more investment into research, as measured by the amount spent in research and development. It is no surprise that firms expressing innovativeness have a strong and positive association with R&D expenditures (see Table 10). This association is also reflected both forward and backward in time. Hence, firms that invest in R&D appear to express innovativeness consistently from year to year to year. The results demonstrate a large, significant positive EO-FP relationship with respect to R&D expense. 30 TABLE 10. REGRESSION ANALYSIS FOR R&D EXPENDITURES Year 2010 2011 2012 2013 2014 2015 Timeline -2 -1 0 +1 +2 +3 R&D Exp (Intercept) 0.10 0.27 0.15 -0.08 -0.40 0.35 Firm Size (Employees) -0.07 -0.05 -0.06 -0.06 -0.08 0.02 Autonomy -1.62 -2.06 -2.50 -2.53 -2.28 -2.06 Competitive Aggressiveness 0.28 0.46 0.77 0.85 1.06 0.91 Risk-taking -1.43 -1.49 -1.37 -1.72 -2.11 -2.56 Innovativeness 2.07 *** 2.22 *** 2.31 *** 2.53 *** 2.92 *** 2.76 *** Proactiveness 0.74 0.96 1.09 1.25 1.12 1.33 Multiple R-squared: 0.06 0.06 0.06 0.07 0.07 0.06 Adjusted R-squared: 0.05 0.05 0.05 0.05 0.06 0.05 df 517 517 517 517 517 517 F-statistic: 5.64 5.38 5.61 6.06 6.72 5.53 p-value: 1.11E-05 *** 2.11E-05 *** 1.17E-05 *** 3.80E-06 *** 7.33E-07 *** 1.44E-05 *** 6.4.8. EO - FP (% R&D Expense) Further, we ask Does high EO lead to an increase in innovation R&D expenses? From the evidence presented in Table 11, competitive aggressiveness is positively associated with future increases in % R&D expenses. There is a small yet significant positive EO-FP relationship with respect to % R&D expense change, which is primarily related to a firm’s competitive aggressiveness and autonomy in later years (t+3). Whereas innovativeness expression was associated with the overall amount a firm spent on R&D, it would appear that competitive aggressiveness is associated with an increase in R&D spending. Competitive aggressiveness and autonomy are most often associated with EO with respect to new entry behaviour. Since R&D expenditure is the pursuit of innovation such as new products the association between %R&D change and these subdimensions is not unexpected. 31 TABLE 11. REGRESSION ANALYSIS FOR % CHANGE IN R&D EXPENDITURES Year 2010 2011 2012 2013 2014 2015 Timeline -2 -1 0 +1 +2 +3 R&D Exp Growth (Intercept) -0.20 0.03 -0.01 0.05 Autonomy 0.21 0.11 0.18 . 0.33 * Competitive Aggressiveness 0.03 0.04 0.15 ** 0.16 * Risk-taking -0.12 -0.04 -0.11 -0.11 Innovativeness 0.02 0.00 0.02 0.02 Proactiveness -0.12 0.02 0.01 0.04 Multiple R-squared: 0.05 0.03 0.12 0.10 Adjusted R-squared: 0.02 0.00 0.09 0.07 df 133 132 129 130 F-statistic: 1.52 0.91 3.63 3.03 p-value: 0.1875 0.4793 0.004218 ** 0.01285 * 6.4.9. EO - FP (Cash Flow from Financings) Cash flow from financings (CFF) reflects an accounting measure that represents cash that is typically injected into a firm to stimulate growth. As growth is an important firm performance outcome in EO Theory, it was theorized that EO expression may associate with future cash requirements in the form of financings. This next analysis therefore asks, do firms expressing higher amounts of EO have higher amounts of investment capital? As reflected in the results (see Table 12), there are no significant associations between EO and Cash Flow from Financings either forward or backward. It is tempting to assume that if companies needed cash that they would amp up their entrepreneurial posturing to lure additional investment, if so this would be reflected in CFF, (Mousa, Wales, & Harper, 2015). This, however, does not appear to be the case here since there is no significant EO-FP relationship with respect to cash flow from financing. 32 TABLE 12. REGRESSION ANALYSIS FOR CASH FLOW FROM FINANCINGS Year 2010 2011 2012 2013 2014 2015 Timeline -2 -1 0 +1 +2 +3 CF Finance (Intercept) -307.08 -211.83 -201.70 -252.00 -194.61 -340.54 Firm Size (Employees) 141.42 *** 139.69 *** 138.26 *** 123.92 *** 86.44 *** 143.16 *** Autonomy -98.26 -102.02 -117.78 -125.06 -64.15 -40.91 Competitive Aggressiveness 35.06 23.20 26.25 60.46 46.22 -5.77 Risk-taking 211.36 214.41 200.56 -59.56 217.04 435.25 Innovativeness -52.55 -73.62 -76.10 -22.99 -20.13 -30.37 Proactiveness 60.12 61.80 62.35 54.94 22.90 38.44 Multiple R-squared: 0.27 0.26 0.23 0.19 0.17 0.16 Adjusted R-squared: 0.27 0.25 0.22 0.18 0.16 0.15 df 517 517 517 517 517 517 F-statistic: 32.43 30.30 25.89 19.88 17.17 16.24 p-value: 2.20E-16 *** 2.20E-16 *** 2.20E-16 *** 2.20E-16 *** 2.20E-16 *** 2.20E-16 *** 6.4.10. EO - FP (% Cash Flow from Financings Change) If a company wanted investment capital, one could imagine they would be tempted to inflate their EO to attract more investment capital. This would be a scenario in keeping with Promoter Oriented firms. This next analysis looks to see if high EO is associated with an increase in investment as measured by Cash Flow from Financings. As is reflected in the previous CFF results (see Table 13), there are no significant associations between EO and % Cash Flow from Financings, either forward or backward. Interestingly, this may not for lack of trying for investment capital but from the lack of success. Savvy investors, though perhaps initially attracted by grandiose rhetoric, perform due diligence to justify an investment. 33 TABLE 13. REGRESSION ANALYSIS FOR % CHANGE IN CASH FLOW FROM FINANCINGS Year 2010 2011 2012 2013 2014 2015 Timeline -2 -1 0 +1 +2 +3 CF Finance Change (Intercept) 3.14 1.51 2.01 -1.80 Autonomy -0.93 -0.95 -1.40 1.17 Competitive Aggressiveness -0.27 0.89 0.42 -0.19 Risk-taking -0.44 -2.18 -1.54 0.21 Innovativeness -0.36 -0.50 -0.21 0.70 Proactiveness -0.45 -0.01 -0.28 -0.57 Multiple R-squared: 0.00 0.01 0.00 0.01 Adjusted R-squared: -0.01 0.00 -0.01 0.00 df 498 501 503 504 F-statistic: 0.09 1.02 0.21 1.09 p-value: 0.9933 0.4081 0.9596 0.3673 6.4.11. EO - FP (Cash Flow from Operations) Cash flow from operations (CFFO) is an accounting measure that is most often portrayed as the best measure to reflect a firm’s overall health with respect to ongoing operations. In contrast to profit, CFFO is considered a superior indicator of firm performance. As such, it is surprising that it is not commonly used to define the FP in EO research. This next analysis looks to answer if higher EO is associated with strong operational performance as measured by CFFO. As is reflected in the results (see Table 14), expression of innovativeness is negatively associated with CFFO (+1), whereas risk-taking (+2) is even more negatively associated. Given that CFFO is a measure of overall FP, it is not surprising that innovativeness that leads to higher R&D expenditures would negatively affect CFFO. Yet, one would expect revenues from R&D investment to increase in subsequent years as was seen in the % revenue change as shown back in Table 7. Clearly, the net effort does not appear to translate to positive CFFO. 34 TABLE 14. REGRESSION ANALYSIS FOR CASH FLOW FROM OPERATIONS Year 2010 2011 2012 2013 2014 2015 Timeline -2 -1 0 +1 +2 +3 CF Ops (Intercept) 142.31 219.43 ** 148.39 255.20 * 246.43 . 275.45 Firm Size (Employees) 18.25 *** 8.78 * 25.93 *** 18.92 *** 35.36 *** 64.95 *** Autonomy 44.06 25.83 14.45 -24.73 -79.08 -163.13 Competitive Aggressiveness -1.91 8.31 38.17 13.69 12.84 7.29 Risk-taking -62.93 -14.12 -28.66 6.31 -270.63 ** -199.49 Innovativeness -23.11 -25.02 -20.64 -47.55 * -40.60 -62.11 Proactiveness 8.25 -0.83 7.44 5.42 24.62 12.75 Multiple R-squared: 0.05 0.02 0.08 0.03 0.08 0.10 Adjusted R-squared: 0.04 0.00 0.07 0.02 0.07 0.09 df 517 517 517 517 517 517 F-statistic: 4.82 1.43 7.84 2.99 7.54 9.72 p-value: 8.54E-05 *** 0.2008 4.30E-08 *** 0.007027 ** 9.10E-08 *** 3.77E-10 *** 6.4.12. EO - FP (% Cash Flow from Operations) Next, we investigate whether higher EO influences future improvements in business operations. The overall % change of CFFO-given EO measures today appears not very telling (see Table 15). There is no significant EO-FP relationship with respect to % cash flows from operations change. TABLE 15. REGRESSION ANALYSIS FOR % CHANGE IN CASH FLOW FROM OPERATIONS Year 2010 2011 2012 2013 2014 2015 Timeline -2 -1 0 +1 +2 +3 CF Ops Change (Intercept) 0.25 -0.94 -0.31 -0.76 Autonomy 1.00 * 0.42 0.24 0.37 Competitive Aggressiveness 0.00 0.13 0.10 0.10 Risk-taking -0.27 -1.13 * 0.03 -0.77 Innovativeness -0.02 0.13 0.02 0.17 Proactiveness 0.04 0.10 -0.03 -0.02 Multiple R-squared: 0.01 0.02 0.00 0.01 Adjusted R-squared: 0.00 0.01 -0.01 0.00 df 501 507 507 508 F-statistic: 1.25 1.68 0.33 0.77 p-value: 0.2836 0.1368 0.8927 0.5751 Signify. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 35 6.5. Summary for Part I Part I addressed RQ1: Is EO being measured? As presented above, the EO measures and EO-FP relationship results are in keeping with EO Theory, as Short (2013) presented. Overall, the most significant EO-FP relationships occur for EO-FP (% Tobin’s q t+1) and for EO-FP (% Revenue Change t+2). Both of these observations have been noted in the EO literature (Lee & Chu, 2015). Yet, the EO-FP (% Tobin’s q+1) appears inconsistent with the traditional positive EO-FP relationship. Given there are some inconsistencies in the general interpretation of EO-FP results, this would suggest it premature to conclude that the CATA with the custom EO dictionaries technique measures EO directly. This finding may perhaps suggest that this technique measures EP, as theorized by Covin and Wales (2018), which is explored in Part II. Part II 6.6. Machine Learning Topic Modeling Results presented in Part I indicate some inconsistencies with EO Theory that Part II addresses. Part II explores whether EO-CATA may measure and be interpreted as entrepreneurial posture (EP) rather than EO, as has been suggested in EO literature. For Part II we utilize another text based analysis technique known as machine learning topic modelling in order to explore the groupings in the EPIC framework. Each topic model cluster, therefore, represents a group of companies expressing similar topics within a representative document, in this case, Letter to Shareholders. The associated wordlists via custom dictionaries are designed to quantify certain EO qualities. Essentially, this technique is being explored to determine if topic model groupings are sensitive to grammatical and/or textual elements related to candor since candor has been associated with grammatical and textual elements. For as sophisticated an analytic technique as machine learning topic modeling is, its application is rather simple. Only one decision is made, i.e., how many groupings are to be formed denoted as the k value. In determining the k value, topic modeling introduces the concept of perplexity. By increasing the number of topic clusters, k value, the overall perplexity of each cluster goes down (see Figure 7). Analyzing the LTS of the 525 companies in this research sample for perplexity versus the k number of clusters indicates that if one were to choose a k value of 5 topics, there are more than 1,400 latent semantic topics distributed between each topic. Essentially, the higher number of topics (k value), the lower the perplexity within each topic. Theoretically, one could achieve a very granular analysis of topic themes by choosing a relatively high k value. The goal, however, is to 36 reduce the perplexity of each topic with the lowest number (k value) of topic segmentations. FIGURE 7. PERPLEXITY OF LTS CORPUS An elegant technique to estimating the ideal number of topic modeling segments (choosing the optimal k value) is using models that contrast the degree of perplexity versus lexical cohesiveness. Lexical cohesion is the linkage within text that holds text together and gives it meaning. It is related to the broader concept of coherence. The often-sought ideal number of topics is when perplexity is minimized, and cohesiveness is maximized. The results of this analysis, as applied to this research’s data sets of 525 companies, are displayed in Figure 8. This analysis is interpreted as the optimal number of topics reflected in one’s k value, which is between 20 to 25. There is, however, no absolute perfect number of machine learning topic models as it is highly dependent on one’s analysis. For this research, for instance, a k value of 5 was chosen. Thus, the entire data set, consisting of Letters to Shareholders for each company, was segmented into five topic models or clusters. Given this, as Figure 8 illustrates, there remains a high degree of perplexity within each cluster. The rationale for choosing a k = 5 was primarily driven by the number of firms in the resulting clusters. The entire sample set consists of 525 firms. If firms were segmented by too many topic models, high k, then the subsequent statistical analysis would lose interpretive power given the smaller 37 number of companies per cluster and the empirically small EO-FP affect size common to EO analysis. FIGURE 8. CALCULATING NUMBER OF TOPICS TO MINIMIZE PERPLEXITY AND MAXIMIZE COHESIVENESS This analysis is computationally very intensive, requiring over 24 hours of processing time to complete using a MacBook Pro. When the entire corpus is segmented into five topic models and the top 10 words associated with each topic model are viewed (as shown in Table 16), the difference between cluster based on words only is, on a qualitative assessment, not very dramatic. The primary concern using a text-based form of clustering analysis of this nature is whether or not firms would be identified solely upon industry-related vernacular. Given a k value of 5, the allocation of firms based on industry classification relative to each topic model grouping does appear to indicate some bias towards certain industries, as shown in Table 17. The majority of information technology companies, for instance, are allocated to Topic 1, representing 40% of the companies in this cluster. The majority of the materials and energy companies are allocated to Topic 5, where materials make up 40%, and energy makes up 22% of the firms in this cluster. Clearly, topic modeling captures some elements associated to industry classification, but not exclusively so. Topic model classification appears contingent on industry classification, but this does not negate the possibility that topic modeling is also capturing unique semantic elements that span across industries. 38 TABLE 16. TOP 10 WORDS IN EACH TOPIC MODEL GROUPING Top 10 words in each topic model. Words have been stemmed and lemmatized, as is common in natural language processing. Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 [1,] product growth million bank product [2,] market busi year manag project [3,] new continu increas servic oper [4,] custom invest share year year [5,] busi year continu custom will [6,] year market improv compani compani [7,] revenu sharehold result client mine [8,] fiscal focus net will develop [9,] continu new rate financi increas [10,] technolog will per new well TABLE 17. % SECTOR REPRESENTATION IN EACH TOPIC MODEL GROUPING Sector Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 Consumer Discretionary 14%* 12% 12% 3% 0% Consumer Staples 3% 4% 4% 1% 0% Energy 1% 3% 1% 1% 22%** Financials 0% 12% 24% 27%** 1% Health Care 5% 2% 3% 13%** 0% Industrials 30%** 19% 13% 9% 6% Information Technology 40%** 3% 1% 4% 1% Materials 6% 4% 6% 1% 40%** Real Estate 0% 11% 4% 0% 0% Telecommunication Services 1% 1% 0% 1% 0% Utilities 1% 4% 1% 4% 4% * Percentage derived from the number of companies in sector / total number of companies in topic cluster ** High sector representation 6.7. EO Subdimensions Expressed in Each Topic Model Cluster Since the resulting five groupings were generated by the machine learning process, with no other interpretation, each group was simply numbered LDA1 through LDA5. One of the key observations in this research is the distributional analysis for each EO subdimension across each of topic model groupings. A permutation test of equality of each CATA EO measurement for each topic model was conducted, and the result, as shown in Table 18, shows that the application of topic modeling resulted in group differences across three of the five EO subdimensions: competitive-aggressiveness, innovativeness, and proactiveness. This observation suggests that topic modeling segments documents (i.e., companies) in some fashion related to EO. 39 TABLE 18. EO SUBDIMENSION EXPRESSION IN EACH TOPIC MODEL Univariate Kernel Density Estimates to Be Compared, Both Graphically and Formally, In A Permutation Test of Equality* A bootstrap hypothesis test of equality Bowman, A. W., & Azzalini, A. (1997). Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations (Vol. 18;18.;). Oxford: Clarendon Press. Comparing Each LDA Topic Autonomy Same Test of equal densities: p-value = 0.94 Competitive Aggressiveness Different Test of equal densities: p-value = 0 * Innovativeness Different Test of equal densities: p-value = 0 * Proactiveness Different Test of equal densities: p-value = 0 * Risk-taking Same Test of equal densities: p-value = 0.07 * This is a test whether the EO expressions are same or different across the five clusters. Significant p values indicate distribution of EO is expressed differently between each cluster. 6.8. EO-FP (Tobin’s q t+1 and % Tobin’s q t+1) in Each Topic Model EO literature, as well as the empirical results from Part I of this analysis, suggest that Tobin’s q has a modest EO-FP association. Each LDA topic cluster was therefore evaluated for its modeled EO-FP correlation between EO subdimensions and Tobin’s q t+1 (as the dependent variable), as shown in Table 19. In addition, each cluster was modeled for the EO-FP correlation between EO dimensions with % Tobin’s q+1 change, as shown in Table 20. Of the EO-FP correlations between EO with Tobin’s q+1, only LDA5 produced a significant model result. The LDA5 model result exhibits a positive but relatively small weighting from the influence of proactiveness. The overall interpretation of the model is that, when accounting for firm size as a moderator, there is a significant positive association between EO, primarily influenced by proactiveness, and FP with respect to overall Tobin’s q t+1 value. This community of firms is, therefore, deemed to be in keeping with a positive strategic posture. This result is in contrast to the result based on the entire sample population, which had a negative EO-Tobin’s q relationship. LDA1, LDA2, and LDA3 each have significant sub-dimensional variables within their models, but neither model was significant. Some of the differences in EO dimensional weightings between the groupings, however, are notable. Companies in LDA1, for instance, have a very high significant weighting with respect to autonomy. LDA2 companies have a very high significant weighting with respect to risk-taking. LDA3 had a positive correlation to competitive aggressiveness and a negative weighting with risk-taking. However, it is not worth reading too much into these values given that, overall, these models were not statistically significant. It is merely an observation that the EO weightings, and even the direction of weightings, changed between groups. The conclusion is that LDA1 40 through LDA4 represent a community of firms whose entrepreneurial posturing is not in keeping with any EO strategic posture. Only LDA5 presented a significant EO-FP relationship with respect to Tobin’s q+1. The adjusted r square value of the LDA5 model is impressive at .14, which is a significant increase from the EO-Tobin’s q t+1 without topic modeling, which was .00. The process of topic modeling alone, which segments companies into groupings based solely on the similarity of topic expression, independently isolated a group of firms where EO wordings expressed at time 0 correlate significantly and with a linear relationship to the following year’s Tobin’s q, as shown in Table 19. The impact of machine learning topic modeling with respect to EO to % Tobin’s q+1 change is even more dramatic, as shown in Table 20. LDA5 exhibits a positive association between innovativeness and % Tobin q+1, and a negative association between proactiveness and % Tobin q+1. The impact of topic modeling was high, relative to historic results common in EO literature, affecting the size r square equal to 0.21 and the adjusted r square of 0.1633. These results are illustrated in Figure 9 and Figure 10. These results are interpreted as the process of topic modeling isolated a community of firms engaged in similar topic messaging, where the messaging holds a positive EO-FP association in keeping with a positive strategic posture. The application of machine learning topic modelling alone appears to have isolated a community of firms displaying a linear relationship between EO expression and % change in Tobin’s q the following year. TABLE 19. REGRESSION ANALYSES FOR EO MEASURES AND TOBIN’S Q Tobin’s q t+1 : Regression Analyses for Entrepreneurial Orientation with Financial Performance TOBIN_Q_2013 ~ Size..Employee. + Innovativeness + Proactiveness + Risk.taking + autonomy + Competitive.aggressiveness LDA1 LDA2 LDA3 LDA4 LDA5 Tobin’s q t+1 (Intercept) 1.626746 *** 1.408505 *** 1.433283 *** 1.34555 *** 0.184263 *** Firm Size (Employees) -0.007717 -0.003948 0.01135 -0.01406 0.020702 Autonomy 0.765265 ** -0.136692 -0.056836 -0.01286 0.141255 Competitive Aggressiveness 0.034478 0.049436 0.232149 * -0.04925 0.116431 Risk-taking -0.504399 0.413261 ** -0.335531 * -0.20831 0.146366 Innovativeness -0.005928 -0.045533 -0.004074 0.12946 0.046974 Proactiveness 0.077961 0.095032 0.036339 0.15068 0.040468 *** Multiple R-squared: 0.07525 0.09977 0.09555 0.1025 0.1904 Adjusted R-squared: 0.03322 0.04351 0.03388 0.03516 0.1382 df 132 96 88 80 93 F-statistic: 1.79 1.773 1.549 1.522 3.646 p-value: 0.1058 0.1127 0.1716 0.1815 0.002705 ** Signify. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 41 TABLE 20. REGRESSION ANALYSES EO MEASURES TO % TOBIN Q CHANGE % Tobin’s q: Regression Analyses for Deductively Defined Entrepreneurial Orientation Measures with Financial Performance % TOBIN q t+1 ~ Innovativeness + Proactiveness + Risk.taking + autonomy + Competitive.aggressiveness LDA1 LDA2 LDA3 LDA4 LDA5 % Tobin’s q Change +1 (Intercept) 0.09362 0.1442125 0.240815 *** 0.1447264 -0.0928 Autonomy 0.0291 -0.0433081 0.004661 -0.0809696 0.04764 Competitive Aggressiveness -0.04028 0.0004342 -0.036655 -0.003444 -0.03189 Risk-taking -0.11659 -0.012927 -0.055903 -0.0220737 -0.03728 Innovativeness 0.01935 -0.0061072 -0.028368 * -0.0005263 0.09104 *** Proactiveness 0.02698 -0.0050595 0.003051 -0.0104567 -0.06986 ** Multiple R-squared: 0.06507 0.006304 0.08454 0.01118 0.2083 Adjusted R-squared: 0.02826 -0.04545 0.03311 -0.0522 0.1633 df 127 96 89 78 88 F-statistic: 1.768 0.1218 1.644 0.1764 4.631 p-value: 0.1241 0.9872 0.1567 0.9707 0.0008465 *** Signify. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 FIGURE 9. ACTUAL VS MODELED % CHANGE IN TOBIN’S Q T+1 42 FIGURE 10. MODELED R-SQUARE VALUES FOR EACH TOPIC CLUSTER The above figures demonstrated that the firms within the LDA5 community are well modeled, with an r square of 0.21. 6.9. Clear Candor vs Non-Clear Candor Firm Communities In Part II, we explore (RQ2): Does modeled EP-FP relationships change due to topic modeling segmentation? By the results presented, firms in LDA5 as segmented by topic modeling, suggest a unique community whose text-based entrepreneurial communications (EP) reflects a small positive linear association between EO at time 0 and % Tobin’s q+1 change. This suggests firms identified within LDA5 are communicating strategically with the intent to act entrepreneurially in keeping with the EO behavioural construct. Presuming that the EO-CATA is measuring the EP construct, then this result is theorized to only occur when EP approaches EO as depicted when firms possess clear candor (C=0) as proposed EPIC framework. The results found no topic model groupings to demonstrate a significant negative EP-FP relationship, as conceptualized by the EPIC framework, and therefore, no Promoter (C=-1) or Stealth (C=+1)-oriented communities were identified. Once the clear candor grouping was removed from the original population sample, the remaining firms exhibited no significant EP-FP relationship association. These firms appear to be communicating entrepreneurially yet without any form of strategic intent. Thus, this may suggest that only a small fraction of the firms in the original population were expressing entrepreneurially in keeping with entrepreneurial orientation. The vast majority of firms, therefore, had little to no interpretable strategic posture in their financial communications. This is an interesting and unexpected result. This observation would suggest yet another category of firms: firms that express no strategic posture in financial communications, where there is no predictive association with respect to the EP-FP relationship. 43 To illustrate this idea further, the LDA5 cluster was classified as Clear Candor, whilst the rest of the communities, LDA1 through LDA4, were aggregated into a second group deemed “Non-Clear” Candor. In comparing the two populations, Clear Candor versus Non-Clear Candor, the predicted vs actual % change Tobin’s q t+1 is illustrated for both communities in Figure 11. The respective modeled r square values are shown in Figure 12. The differences between linear models, before and after topic modeling segmentation, suggests differences between communities, as reflected in the EO expression and EO-FP relationship related to topics expressed in LTS and beyond simple industry vernacular. Observing the varying EO-FP relationship contingent on topic is considered reflective of differences in EP relative to EO, which is conceptualized as candor. Three-quarters of the clear candor firms are listed on Canadian exchanges, (see Figure 12). Given that the majority of the firms are listed on the Canadian exchange, TSX, it is tempting to infer that Canadian firms are more likely to exhibit clear candor in their financial communications than USA-listed firms, though more analysis would be required. Comparing the modeled regressions from part I and part II, as shown in Table 21, when the EO dimensions for the Clear Candor group are modeled in a linear regression with respect to % Tobin’s q+1, a significant degree of organization occurs, as reflected in the r^2 of 0.1633. The 408 remaining firms making up the non-clear candor group exhibit no significant EP-FP relationship. Essentially, the process of topic modeling appears to have “extracted” the primary population exhibiting the main EO - % Tobin’s q relationship demonstrated in Part I. Based on the empirical results from part II of this research, this would seem support Covin and Wales’ (2018) theoretical interpretations that measuring EO using CATA with custom EO dictionaries is most congruent with the conceptualization of entrepreneurial posture, EP. 44 FIGURE 11. MODEL PREDICTED VS ACTUAL % CHANGE TOBIN’S Q T+1 FIGURE 12. MODELED R SQUARE VALUES 45 FIGURE 13. PIE CHART FOR LDA5 FIRMS AND THEIR RESPECTIVE EXCHANGE TABLE 21. EO TO % TOBIN’S Q +1 YEAR Part I Part II EO (2012) to % Tobin’s q (2013) Total Non-Clear Candor Clear Candor 2013 2013 2013 (Intercept) 0.11 ** 0.143 *** -0.093 Autonomy 0.01 0.00 0.05 Competitive Aggressiveness -0.01 -0.014 -0.03 . Risk-taking -0.04 -0.034 -0.04 Innovativeness 0.02 * 0.0023 0.09 *** Proactiveness -0.04 *** -0.000 -0.07 ** Multiple R-squared: 0.04 0.0089 0.2083 Adjusted R-squared: 0.03 -0.003 0.1633 df 502 408 88 F-statistic: 3.93 0.736 4.631 p-value: 0.00168 ** 0.5968 0.0008465 *** 7. DISCUSSION 7.1. Does EO Measurement Using CATA Measure EO or EP? In part I of this research, a comprehensive study of individual EO subdimension expression, as well as the various EO to FP relationships across several time periods, extending both into the future and into the past, were analyzed using linear regression modeling. The overall impression of this research is that Short et al.’s (2010) pioneering EO measurement technique using CATA with custom EO 46 dictionaries measures properties are in keeping with the measurement of the EP construct. Given that EO is viewed as a resource, in keeping with the resource view of firm, it is not generally considered that previous firm performance results may somehow, and in some way, alter future EO levels. EO, when expressed and measured in text, however, could easily be viewed as influenced by previous successes or failures. This research, therefore, investigated whether EO expressed in financial communications was significantly associated with prior and future firm performance. It is interesting to note that entrepreneurial expression appears associated with prior firm performance results, such as Tobin’s q and % change in profits. Some entrepreneurial expression results, however, appear exclusively future based, such as % change in revenues. Again, this form of interpretation is only congruent with the interpretation of EO as a messaging construct, as denoted in the EPIC framework. In part II of this analysis, the EO CATA measurements are presumed to be EP as theorized by Covin and Wales (2018). The sample population was segmented into clusters via a form of machine learning known as topic modeling, believed to group companies based on similar latent semantic topics expressed within a document. This technique generated communities of firms differing in measured EO, as well as EP-FP relationships. One segmented community, in particular, appeared to capture firms engaged in a distribution of topics exhibiting a positive association between EP and the market-defined firm performance measure of % Tobin’s q. The positive association of the EP-FP relationship, in keeping with EO Theory, is interpreted as occurring only when EP approaches EO. This observation suggests the EO exhibited in Part I may be the result of a mixed sample population, where EO-CATA, despite generating EO measurements more in line with EP, is influenced by subpopulations where EP approaches EO. The combine approach of EO-CATA with machine learning topic modeling indicates that EO, derived by word dictionaries and measured at time = 0, linearly correlates with the percentage change in Tobin’s q manifesting one year later at time = +1. This EO-FP phenomenon appears to be in keeping with EO theory and the empirical evidence present in EO literature. The fact that the total sample population can be segmented in a manner where at least one of the segments generates a community organized in strategic posture interpretable as EO, in contrast to other communities interpreted as EP, is viewed as supporting evidence for the conceptualization deemed candor. The results from this research suggest EP can be derived automatically from financial documents using CATA with EO custom dictionaries. This research demonstrates that firm communities can be identified and characterized by EP to FP relationships. It was also demonstrated that at least one grouping within the total population was isolated solely by text-based applications of NLP and machine learning topic modeling, which appeared to independently capture an 47 EP-FP relationship in keeping with the EO-FP relationship, as characterized by EO Theory. This is interpreted as firms may self-identify future firm performance expectations based on EO words and contingent on the topic in financial communications. This observation is believed to suggest that both EP and EO can be captured by the completely automated mechanism solely on text. The set of topics the Clear Candor community of firms were engaged in were not found to be specific to any particular industry, nor the size of company. A large number of clear candor companies, however, are from the resources sector of materials and energy listed on the Canadian-based Toronto Stock Exchange. This may suggest that these industries lend themselves to well-managed companies, possessing both clear candor and good clarity in firm performance expectations. The conclusions drawn from the overall results are stated below: 1. CATA with custom EO dictionaries measures an EO construct reflects the conceptualization of EP as shown by the theme-based analysis (conclusion drawn from EO-FP relationship inconsistencies in part I). 2. EO-like characteristics assessed when measuring EO using CATA is possibly the result of mixed sample populations where a subsegment of the sample population exhibits EP near or equivalent to EO, which was shown by analysis where EP significantly related to FP in the future (conclusion drawn from differences in EO-%-Tobin’s q relationship both before and after topic modeling in Part II). 3. The identification of strategic posture, assessed by EO-CATA in combination with topic modeling (Part II), suggests textual or grammatical indicators differentiating EP from EO, and evidence of the EPIC framework, where varying entrepreneurial messaging was detected. This is viewed as supporting evidence that firm communities differing in clear candor, labeled business-oriented and growth-oriented, and non-clear candor, which spans non-strategic messaging and possibly strategic posturing, such as promoter-oriented and stealth-oriented. Thus, further research to develop specific measures of candor is recommended, such as word-lists. 7.2. Candor This research contributes to the study of Entrepreneurial Orientation (EO) through its Entrepreneurial Posture in Communications (EPIC) theoretical framework and its conceptualization of candor. The EPIC framework fosters an understanding of EP as encoded and decoded from financial communications, specifically, with regards to Letters to Shareholders (LTS) contained in Annual Reports issued by public companies. To empirically observe a strong EO-FP relationship using statistical methods, one must identify a community of firms that possesses clear candor, which are well managed (management’s FP expectations are in keeping with EO), and where the market can accurately reflect the management’s expectations, in a reasonable timeframe, most likely in industries where the firms express clarity to the investor community. Fortunately, the proposed two-step approach combining EO-CATA with a machine learning-based topic modeling appears to unveil a self-identifying factor, at a latent semantic level, interpretable 48 as a firm’s intent to act on EO expressed in financial communications, conceptualized as candor. 7.3. Entrepreneurial Orientation In this research, the predominant significant predictor variables in the modeled EO-FP regressions were proactiveness and innovativeness. Risk-taking did not appear to be a significant predictor variable when modeled, which is notable in the clear candor group’s EO to % Tobin’s q. It is unclear why this is the case. There is some speculation that the EO dictionary for risk-taking generated by Short et al. for companies in 2005 is not attuned to the vernacular of risk-taking for companies in 2012. Therefore, further fine-tuning of the risk-taking measurement is required. Autonomy and competitive aggressiveness did not appear to be significant predictor variables, at least for % Tobin’s q. Autonomy and competitive aggressiveness are qualities associated with the behavior of new entry. Since this research focused primarily on established public firms, the EO expression of autonomy and competitive aggressiveness, though displayed with some firm performance measures, was not prominent in the sample population to generate a significant EO-FP relationship. This result was expected. Only for r square comparison purposes with prior EO literatures were all five EO subdimensions included in the regression modeled results. Clearly, doing so inflated the r square value, which is the value most often noted in EO literature, but, in turn, was accounted for in the stated adjusted r square values. Lastly, to state that EO is being measured, there should be at least the three core subdimensions: risk-taking, innovativeness, and proactiveness, all of which contribute to the model. Otherwise, one is really only presenting either unique or bilateral interactions. Clearly, the linear modeling results in this research were not perfectly reflective of EO, as depicted in EO Theory, given not all subdimensions were found to be significantly relevant. This, however, is believed to be influenced by various factors. For instance, the N value of the segmented topic cluster of LDA5 was less than 100 companies, thus resulting in an underpowered sample for the smaller affect sizes contributed by risk-taking, autonomy, and competitive aggressiveness. 7.4. Firm Performance Since EO research does not provide a specific singular operationalized view of FP as a higher order construct, this research explored numerous FP measurements, both accounting and market-based. It is likely that all accounting-based single FP measurements investigated should be interpreted as EO mediators rather than the conceptualized higher-order FP construct in keeping with EO Theory. Tobin’s q, a market measure, is a measurement drawn from the collective actions born on the public stock exchanges. Tobin’s q is, therefore, inherently forward-looking and incorporates investor expectations. In this regard, Tobin’s q, in contrast to 49 accounting measures, appears to be the best single measure in keeping with the theoretically proposed nature of EO regarding the FP construct. As such, perhaps it is not that surprising that a moderately strong association exists between EO today and % change in Tobin’s q one year later. Again, one must highlight the distinction that EO and EP are fundamentally different constructs, each with their own unique relationship with FP. This research postulates that only in certain contexts, where EP and EO are similar, will EO positively associate with FP. It is the EO construct, as theorized in EO Theory, that supports the positive FP connection. As the relationship between EO and FP is unique and complicated, the same can be postulated for EP and FP. Given the similarities, and despite the substantial difference between EO and EP, this research serves to highlight the importance of clear and explicit definitions of constructs. A clear and concise definition and operationalization of the FP construct in EO Theory would appear to also be missing; therefore, this research highlights the need for much better understanding of the FP construct. 7.5. Entrepreneurial Posture (EP) Short et al. (2010) outlined the methodology for measuring the EO construct using CATA with custom EO dictionaries. At the time, the interpretation of the EO measurement was akin to EO. Covin and Wales (2018), however, took note of the CATA methodology, stating that the EO construct being measured is more in line with the conceptualization of entrepreneurial posture (Covin & Wales, 2018). The findings presented in this research appear to suggest Covin and Wales’ interpretation that CATA with custom EO dictionaries does indeed measure the EP form. The most recent definition of EP in EO literature is that EP is “fundamentally similar to EO in that the same component dimensions are assessed” (Covin & Wales, 2018). Clearly, this definition does not capture the entire essence of EP leaving numerous facets of EP to explore. This research outlines a possible and plausible theoretical Entrepreneurial Posture in Communications (EPIC) framework relating EP to EO. It further outlines novel strategic postures as a result of the EPIC framework. Though no examples of either Promoter or Stealth Oriented companies were empirically observed, it is still believed that varying expressions of candor were observed. If so, this suggests empirical support that EP, not EO, is derived from financial communications. Given this novel interpretation, the importance of further exploration into the nature of EP, its association with EO, and its association with FP is highlighted. With this insight, an emergent conceptual view is presented in Figure 14Figure 14. This model distinguishes EP as member of the EO family of constructs, where EP is viewed to be EO insights drawn from entrepreneurial rhetoric and is associated to the behavioral EO construct as influenced by some facet of Candor. 50 FIGURE 14: EMERGENT ENTREPRENEURIAL ORIENTATION / ENTREPRENEURIAL POSTURE MODEL 7.6. Entrepreneurial Posture as Predictor Strategic posture in EO Theory is interpreted as either high EO, growth-oriented, or low EO, which is deemed business-oriented. Entrepreneurial posturing in communications suggests that in addition to EO strategic postures, firms could strategically manipulate EO expression in financial communications. EO expressed in communications, EP, that is not necessarily aligned with the intent to act entrepreneurially, EO. Entrepreneurial strategic posture in communications can take on additional postures depending on candor. As conceptualized in the EPIC framework, firms possessing candor exhibit strategic postures, as portrayed in EO Theory. Whereas firms without clear candor could either overstate their EO, deemed Promoters, or understate their EO, deemed Stealth. As portrayed, in the EPIC framework, strategic posture can be interpreted from financial communications if one possesses insights into both EP and candor. In this research, EP is assessed using the EO-CATA technique pioneered by Jeremy Short (2010). Preliminary insights into expression of candor were drawn with the aid of topic modeling. Topic modeling was used to isolate unique textual and/or grammatical elements shared amongst a community of firms. Identification of EO-FP relationships attributed to EO between community groups is believed to have isolated firm communities exhibiting varying degrees of candor. This is viewed as the first step in the identification of unique textual elements associated with candor, as conceptualized in the EPIC framework. The evidence portrayed in this research suggests a mechanism, where companies adjust their expression of EO in communications in the form of EP. EP on its own provides little insight. With an assessment of candor however, EP may provide the predictive insights associated with EO since EO is generally attributed to a 51 positive association with firm performance. Even more fascinating is the possibility that the predictive nature of EO, hidden behind the mask of EP in financial communications, could be revealed in textual and/or grammatical attributes conceptualized as candor and subsequently unveiled using text based analysis such as natural language processing and topic modeling. If the evidence presented in this research is deemed by both the EO and topic modeling research communities to hold merit, then this could have significant implications to subsequent EO research, as well as implications to traditional investment screening and computational analysis. 7.7. Implications to Investing This research may also have implications to the investment model. Traditionally, the onus would be on the entrepreneur and a very small circle of investors to get a company to a sustainable (i.e., profitable) level as quickly as possible. All subsequent growth (investment) would be self-financed from surpluses generated out of firm operations, a process known as boot strapping (Auken & Neeley, 1996; Carter & Van Auken, 2005; Malmström, 2014). In today’s hypercompetitive global environment, the luxury of time and the ability to bootstrap one’s growth plans is increasingly unrealistic. Advancements in technology, changing consumer preferences catering to global markets, alongside changes in foreign direct investment and an evolution in financing early stage companies (e.g., angel investors or venture capital market), has seen the emergence of a new, idealized form of company known by many names, such as Unicorns (Ray, 2015), Gazelles (Aldrich & Ruef, 2018), Cheshire Cats (Kenney & Zysman, 2019), and Born Globals (Andersson et al., 2015; Knight & Liesch, 2016) to name a few. The success of these firms is primarily supported by investment. The investment decisional process spans an assessment of companies based upon historical financial performance, market opportunity, and an assessment of management’s ability to execute (Chemmanur et al., 2009). Of these parameters, research evidence clearly indicates one of the most important parameters is the assessment of management (Huang & Pearce, 2015). The assessment of management, however, is particularly challenging and is often based on intuition and gut feeling, and is frequently incorrect (Levie & Gimmon, 2008). Given the importance of conducting management assessments, one of the most active segments of entrepreneurial investment research is directed towards the assessment of management’s ability to “grow a company.” Significant empirical evidence in the EO literature demonstrates that firms possessing EO traits also possess a competitive advantage over their rivals, and will, therefore, tend to outperform their competition. EO research suggests that EO is an antecedent of firm growth, as reflected in a firm’s strategic posture. This research, therefore, sought the predictive insights associated with strategic posture via text analytical 52 technique of CATA, with custom EO dictionaries pioneered by Jeremy Short and partners (2010). Through the literature review and empirical observation generated in this research, it appears that EO-CATA measurement is more in keeping with EP than EO. As EO holds the predictive quality (i.e., strategic posture), the goal was to derive strategic posture from EP measurement. Since very little is known about the EP construct and its association with FP, this research postulates a novel theoretical Entrepreneurial Posture in Communications (EPIC) framework to explore the relationship between EP and EO. As defined in this research, the difference between EO and EP is conceptualized as candor. Although there is no known measure for candor, given the research conducted in other disciplines, it is speculated that candor has textual and grammatical elements associated with it (Rittenhouse, 2013). This research explored a protocol where EO assessment is derived from the measurement of the EP construct in combination with Natural Language Processing, Latent Dirichlet Allocation and Gibb’s Sampling. This assessment protocol combined with the insights of EPIC framework resulted in the identification of a community of firms exhibiting a significant linear regression model with an affect size of r^2 = .17, one of the highest cited EO-FP affect sizes using computational methods to date. In the pursuit of a strategic posture drawn from financial communications using automated text analysis, the suggested two-step protocol detailed in this research is therefore believed to derive predictive insights similar to EO strategic posture via the assessment of both EP and candor. This technique may therefore serve to provide quantitative insights into management quality assessment previously absent in traditional investment analysis. 7.8. Automatic Computational Analysis in EO Research Advancements in automated content analysis techniques hold intriguing opportunities to garner greater theoretical insights and to possibly automate the process of EO data collection and analysis (de Groof & Xu, 2017). Several examples of this approach are in the academic literature. It should be noted that this form of research is still in its early days, and there are numerous gaps in understanding within the field of EO research and, in particular, in the detection and interpretation of EO using text analysis. Early research has already been directed towards the EO analysis of financial communications via computerized content analysis, such as computer-assisted text analysis or CATA (McKenny et al., 2016; McKenny et al., 2018; McKenny et al., 2013). The most active researchers on this topic have been Short and McKenny (2016), McKenny et al. (2013), and Short et al. (2010). Their work has laid the fundamental groundwork in this area. This research has benefited greatly from their protocols, dictionaries, and more. 53 Surprisingly few, however, have joined their efforts, as reflected in the number of publications. Using a Google Scholar search combining “text analysis” with “entrepreneurial orientation” generated 453 results. Of these, only the Short and McKenny papers are relevant to text analysis. The same key word search using Proquest generated 128 results, with only five references relevant to the text analysis of EO and international business (Belderbos et al., 2017). Based on literary research and supported by the empirical evidence in this research, it became clear that automated text analysis using EO-CATA measured EP rather than EO. Based on the proposed theoretic EPIC framework, one can draw a strategic posture assessment from EP if one had insights to firm candor. Since no known direct measure of Candor is noted in EO literature, this research investigated various text-based techniques that explore possible Candor expression in financial literature. Ultimately, this research adopted the emerging computational techniques of natural language processing (NLP) and topic modeling, which have demonstrated advantages in other disciplines (Feuerriegel et al., 2016; Kanungsukkasem & Leelanupab, 2019). Topic modeling is a sophisticated clustering technique that effectively reduces the heterogeneity of topics (entrepreneurial messages in financial communications) expressed within a population of documents (Letters to Shareholders) by latent sematic association (Blei, Ng, & Jordan, 2003). One of the most popular forms of topic modeling is the unsupervised Bayesian machine learning approach of Latent Dirichlet Allocation (Blei et al., 2003) with Gibbs Sampling (Darling, 2011), which has been demonstrated to be advantageous in business disciplines such as finance (Feuerriegel et al., 2016; Kanungsukkasem & Leelanupab, 2019). Literary searches for “topic modeling” combined with “CATA” generated an interesting result where researchers who have used LDA followed up with CATA measurements applied to healthcare text analysis (James et al., 2017). Searching the academic literature using the combined key words of “latent dirichlet” and “entrepreneurial orientation” resulted in zero relevant papers. It is quite evident that the proposed technique using LDA with Gibbs sampling, combined with CATA custom EO dictionaries, appears to be a completely unique approach within the scope of EO research. The standard data science approach to follow topic modeling is known as Support Vector Machines (SVM), which is a supervised method of predicting categories (Boser, Guyon, & Vapnik). SVM is basically trained on a set of known cluster categories and then uses a predictive algorithm to categorize new clusters (Drucker, Wu, & Vapnik, 1999). In this research study, we have fundamentally set out an approach to apply theoretical knowledge, such as EO Theory, that could possibly augment the SVM technique. This research thereby ushers in a new era of computational analysis for entrepreneurship research to further explore the nature of EO theory and suggests the possibility that candor is present in financial communications. 54 7.9. Limitations & Suggestions for Further Research From an EO perspective, utilizing publicly-listed companies represented an excellent sample market given readily available access to financial communications representing a fairly good cross-section of firms across the business landscape, reflecting various industries, stages, ages, and entrepreneurial orientation qualities. It does, however, exclude many forms of businesses, such as self-employed and family-run firms, as well as government-owned and private-equity backed firms. This diminishes the comprehensiveness of the business landscape and thereby reduces the generalizability of these findings. This research does, however, cover a significant facet of the business landscape, and particularly related public companies, thus providing insights into the core nature of EP, EO and the EO-FP relationship, as well as the introduction and influence of candor in financial communications. Company size based on number of employees has some companies larger on the TSX than on the Russel, however, the median firm size of TSX and R2K are remarkably similar. This may reflect a cultural bias as a source of limitation when comparing results across exchanges, which one could expect when comparing different regions of the world. The most challenging limitation in the study was the need to infer candor state. In an ideal setting, a direct candor measurement tool would have been available such as a CATA custom Candor dictionary. Given that such a tool did not exist the ultimate goal would be to create one. There were, however, many research objectives in this research endeavour. Hence, the use of machine learning and the analytical model to infer candor was adopted as an initial step to gain confidence that candor as conceptualized in the EPIC Framework does in fact exist. Given the results of this research, the next logical step would be to pursue the creation a CATA custom Candor dictionary. With a more explicit and defined measurement tool of Candor, subsequent investigation would explore more detail facets of candor such as whether a firm’s candor change over time. 8. CONCLUSIONS Whether public or private, large or small, companies communicate their entrepreneurial intentions through financial communications. Through the analysis of financial communications, investors seek to identify the strategic posture of an organization. Ideally, it would be beneficial to separate those firms possessing entrepreneurial orientation, amongst a proverbial sea of entrepreneurial posers whose words will not translate into entrepreneurial actions, and subsequently, not manifest into value-creating firm performance. Within this context, this research proposes the transformation of EO into an entrepreneurial messaging construct, thereby introducing the concept of Candor. 55 This research’s primary contribution to the field of EO research is the conceptual framework of entrepreneurial posturing in communications (EPIC). EPIC is unique to the field of EO research as it bridges the resource-based view of firms with management’s communication of expectations via messaging. With messaging, investors gain predictive expectations that could manifest in market measures. Predictive expectations similar to strategic posture may be generated with the combined assessment of both EP and candor. Although further research is required, preliminary research (not presented here) has already identified unique textual elements between clear candor and non-clear candor groups. These textual elements share qualitative elements associated with candor. It was with the merit of machine learning topic modeling that these subtle textual elements were derived. The second most important contribution of this research is the unique and seemingly first demonstration of machine learning topic modeling applied to EO. This research uses natural language processing, text analysis, and computational linguistics. This technique, tailored to entrepreneurial messaging, is supported, not by affective state nor subjective information of the sender, but by substantive theoretic reasoning and empirical evidence borne from EO Theory. 56 9. BIBLIOGRAPHY Al-Swidi, A. K., & Mahmood, R. (2011). How does organizational culture shape the relationship between entrepreneurial orientation and the organizational performance of banks? European Journal of Social Sciences, 20(1), 28-46. Allison, T. H., Davis, B. C., Short, J. C., & Webb, J. W. (2015). Crowdfunding in a Prosocial Microlending Environment: Examining the Role of Intrinsic Versus Extrinsic Cues. 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Thesis/Dissertation
eng
Interdisciplinary Studies
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Entrepreneurial posture in communications : entrepreneurial orientation (EO) analysis in financial communications with computer-assisted text analysis combined with EO custom dictionaries, natural language processing, and machine learning
Text