Business, Sauder School of
Management Information Systems, Division of
DSpace
UBCV
Zhang, Xiaoke
Master of Science in Business - MScB
University of British Columbia
The rising demand for online video content has fostered one of the fastest-growing markets as evidenced by the popularity of platforms like TikTok. Because video content is often difficult to create, platforms have attempted to leverage recent advancements in artificial intelligence (AI) to help creators with their video creation process. However, surprisingly little is known about the effects of AI on content creators’ productivity and creative patterns in this emerging market. Our paper investigates the adoption impact of AI-generated voice – a generative AI technology creating acoustic artifacts – on video creators by empirically analyzing a unique dataset of 4,021 creators and their 428,918 videos on TikTok. Utilizing multiple audio and video analytics algorithms, we detect the adoption of AI voice from the massive video data and generate rich measurements for each video to quantify its characteristics. We then estimate the effects of AI voice using a difference-in-differences model coupled with look-ahead propensity score matching. Our results suggest that the adoption of AI voice increases creators’ video production and that it induces creators to produce shorter videos with more negative words. Interestingly, creators produce more novel videos with less self-disclosure when using AI voice. We also find that AI-voice videos received less viewer engagement unintendedly. Our paper provides the first empirical evidence of how generative AI reshapes video content creation on online platforms, which provides important implications for creators, platforms, and policymakers in the digital economy.
HOW DOES AI-GENERATED VOICE AFFECT ONLINE VIDEO CREATION? EVIDENCE FROM TIKTOK by Xiaoke Zhang B.Mgt., Nanjing University, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN BUSINESS ADMINISTRATION in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Management Information Systems) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2023 © Xiaoke Zhang, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: How Does AI-Generated Voice Affect Online Video Creation? Evidence from TikTok submitted by Xiaoke Zhang in partial fulfillment of the requirements for the degree of Master of Science in Business Administration in Management Information Systems Examining Committee: Dr. Gene Moo Lee, Associate Professor, Accounting and Information Systems Division, Sauder School of Business, UBC Co-supervisor Dr. Mi Zhou, Assistant Professor, Accounting and Information Systems Division, Sauder School of Business, UBC Co-supervisor Dr. Baek Jung Kim, Assistant Professor, Marketing and Behavioral Science Division, Sauder School of Business, UBC Supervisory Committee Member Dr. Ning Nan, Associate Professor, Accounting and Information Systems Division, Sauder School of Business, UBC Additional Examiner iii Abstract The rising demand for online video content has fostered one of the fastest-growing markets as evidenced by the popularity of platforms like TikTok. Because video content is often difficult to create, platforms have attempted to leverage recent advancements in artificial intelligence (AI) to help creators with their video creation process. However, surprisingly little is known about the effects of AI on content creators’ productivity and creative patterns in this emerging market. Our paper investigates the adoption impact of AI-generated voice – a generative AI technology creating acoustic artifacts – on video creators by empirically analyzing a unique dataset of 4,021 creators and their 428,918 videos on TikTok. Utilizing multiple audio and video analytics algorithms, we detect the adoption of AI voice from the massive video data and generate rich measurements for each video to quantify its characteristics. We then estimate the effects of AI voice using a difference-in-differences model coupled with look-ahead propensity score matching. Our results suggest that the adoption of AI voice increases creators’ video production and that it induces creators to produce shorter videos with more negative words. Interestingly, creators produce more novel videos with less self-disclosure when using AI voice. We also find that AI-voice videos received less viewer engagement unintendedly. Our paper provides the first empirical evidence of how generative AI reshapes video content creation on online platforms, which provides important implications for creators, platforms, and policymakers in the digital economy. iv Lay Summary The advent of generative AI has been upending the traditional way of creation. This paper examines how AI-generated voice affects users’ creation patterns in online video platforms. Using a unique dataset from TikTok, we employ multiple machine-learning algorithms to detect the usage of AI voice in videos and measure video characteristics. We find that AI voice adoption leads to an increase in video production while inducing creators to make shorter, more novel videos with more negative words and less personal information. This paper provides the first empirical evidence of how generative AI reshapes video content creation on online platforms, which provides important implications for creators, platforms, and policymakers in the creator economy. v Preface This thesis is completed under the supervision of Prof. Gene Moo Lee and Prof. Mi Zhou. I initiated the research idea, and my supervisors guided me through the research design process. Andrea Ramazzina and Francesco Cognolato from Ensemble Data provided the API service for data collection. I collected the data, conducted the research, and drafted the manuscript. My supervisors helped me proofread and improve this paper. A shorter version of this paper was presented at: (i) INFORMS Workshop on Data Science in October 2022; (ii) UBC Sauder MIS seminar in November 2022; and (iii) Workshop on Information Technologies and Systems in December 2022 (presented by Prof. Gene Moo Lee). This paper is also scheduled to be presented at ISMS Marketing Science Conference in June 2023 (to be presented by Prof. Mi Zhou). vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................ ix List of Figures .................................................................................................................................x List of Abbreviations ................................................................................................................... xi Acknowledgments ....................................................................................................................... xii Chapter 1: Introduction ................................................................................................................1 Chapter 2: Literature Review .......................................................................................................8 2.1 Generative AI’s Impact on Content Creation ................................................................. 8 2.2 User-Generated Content.................................................................................................. 9 2.2.1 Image Motivation, Identity Disclosure, and the Disinhibition Effect ......................... 9 2.2.2 Efficacy and Technology-Augmented Video Creation ............................................. 11 2.3 Business Analytics with Multimodal Data ................................................................... 11 Chapter 3: Context, Data and Measurements ...........................................................................13 3.1 Research Context and Data Collection ......................................................................... 13 3.2 Detecting AI-Generated Voice to Determine the Treatment Variable ......................... 15 3.3 Outcome Variables ........................................................................................................ 16 3.3.1 Quantity and Quality ................................................................................................. 17 3.3.2 Novelty ...................................................................................................................... 17 vii 3.3.3 Identity Disclosure .................................................................................................... 18 3.3.4 Sentiment and Politeness .......................................................................................... 19 Chapter 4: Empirical Analysis ...................................................................................................22 4.1 Empirical Model ........................................................................................................... 22 4.1.1 Look-Ahead Propensity Score Matching .................................................................. 23 4.1.2 Model Specification .................................................................................................. 25 4.1.3 Summary Statistics .................................................................................................... 26 4.2 Main Results ................................................................................................................. 27 4.2.1 Effect on Quantity ..................................................................................................... 27 4.2.2 Effect on Quality ....................................................................................................... 28 4.2.3 Effect on Novelty ...................................................................................................... 30 4.2.4 Effect on Identity Disclosure, Sentiment, and Politeness ......................................... 31 4.3 Robustness Checks........................................................................................................ 33 4.3.1 Examination of Pre-Trends ....................................................................................... 33 4.3.2 Result Validation with Callaway and Sant’Anna DID Framework .......................... 34 4.3.3 Alternative Matching Procedure ............................................................................... 36 4.3.4 Alternative Measurements on Novelty ..................................................................... 36 Chapter 5: Additional Analysis ..................................................................................................38 5.1 Heterogeneous Effect .................................................................................................... 38 5.2 Effect on Consumer Engagement ................................................................................. 40 Chapter 6: Conclusions ...............................................................................................................42 6.1 Theoretical and Managerial Contributions ................................................................... 42 6.2 Limitations and Future Research .................................................................................. 44 viii References .....................................................................................................................................45 Appendices ....................................................................................................................................55 Appendix A Example of AI-Generated Voice on TikTok ........................................................ 55 Appendix B Textual and Visual Novelty Measures with Embeddings .................................... 56 B.1 Topic Embedding with GSDMM.............................................................................. 56 B.2 Video Embedding with Swin-Transformer ............................................................... 58 Appendix C Examples of Sentiment and Politeness Measurement .......................................... 60 Appendix D Sub-sample Matching for Creators with Clear Faces in Avatars ......................... 61 Appendix E DID Results with Coarsened Exact Matching ...................................................... 63 Appendix F Alternative Measures of Textual and Visual Novelty........................................... 65 F.1 Object Embeddings Example .................................................................................... 65 F.2 DID Results with Alternative Textual and Visual Novelty Measures ...................... 65 ix List of Tables Table 3.1 Summary of Main Variables ......................................................................................... 20 Table 4.1 Look-Ahead Propensity Score Matching Result .......................................................... 25 Table 4.2 Summary Statistics ....................................................................................................... 26 Table 4.3 The Effect of AI-Generated Voice Adoption on Video Quantity ................................. 28 Table 4.4 The Effect of AI-Generated Voice Adoption on Video Quality ................................... 29 Table 4.5 The Effect of AI-Generated Voice Adoption on Video Novelty .................................. 31 Table 4.6 The Effect of AI-Generated Voice Adoption on Identity Disclosure, Sentiment, and Politeness ...................................................................................................................................... 33 Table 4.7 Callaway and Sant’Anna Dynamic DID Effects .......................................................... 35 Table 5.1 Heterogeneous Effect of Experience ............................................................................ 39 Table 5.2 Heterogeneous Effect of Popularity .............................................................................. 40 Table 5.3 The Direct Effect of AI-Generated Voice Adoption on Consumer Engagement ......... 41 x List of Figures Figure 3.1 Multimodal Analysis for the Measurement of Treatment and Outcome Variables .... 21 Figure 4.1 Overall Effect, Direct Effect, and Spillover Effect ..................................................... 23 Figure 4.2 Look-Ahead Propensity Score Matching Example ..................................................... 23 Figure 4.3 Relative-Time Model Results ...................................................................................... 34 xi List of Abbreviations AI Artificial Intelligence API Application Programming Interface BERT Bidirectional Encoder Representations from Transformers CEM Coarsened Exact Matching DID Difference in Differences GSDMM Gibbs Sampling Algorithm for the Dirichlet Multinomial Mixture Model LA-PSM Look-Ahead Propensity Score Matching LIWC Linguistic Inquiry and Word Count PSM Propensity Score Matching TTS Text-to-Speech UGC User-Generated Content xii Acknowledgments I would like to express my sincere gratitude to my supervisors Dr. Gene Moo Lee and Dr. Mi Zhou, for their invaluable guidance and support throughout my master’s program. Their expertise and encouragement were crucial in shaping and improving my research project. They provided constructive feedback and helped me to grow towards a qualified researcher. My great thanks also go to Dr. Baek Jung Kim for being my committee member and providing insightful suggestions for this research. I would like to thank all my fellow colleagues and friends in Sauder School of Business, for their inspiring ideas and companionship. They have made my academic life so enjoyable and fun. Last, special thanks for my parents, for their unconditional support and understanding on all my decisions, and for always standing with me throughout the journey.1 Chapter 1: Introduction Video is one of the most influential and fastest growing modalities in online platforms. Over the last decade, video content has gradually begun to dominate consumers’ online experience. According to recent statistics, 91% of online users watch videos every week and they spend more than 50% of their daily Internet time on videos (Kemp, 2022). Such high demand creates a massive market worth $473 billion (Fortune, 2022) and nurtures some of the world’s largest online platforms. For example, YouTube has 2.2 billion monthly active users and TikTok has 1 billion monthly active users (Wallaroo, 2022). The online video market continues to expand rapidly in recent years, with its value projected to reach $1,690 billion by 2029 (Fortune, 2022). With the increasing demand for online video content comes great pressure on the supply side. Video content is by nature more difficult to produce than traditional online content such as text. Creators need to shoot multiple scenes and edit them together while keeping consistency across different modalities (e.g., acoustic signals, visual signals). Moreover, professional devices like cameras and microphones are often needed to ensure content quality. Therefore, video creation has been the domain of professional users for a long time and their supply could barely meet the growing consumer needs. Major video-sharing platforms have been struggling to stimulate the production of high-quality and engaging video content. For instance, the amount of video uploaded to YouTube has stopped increasing since 2019 (Statista, 2020; YouTube, 2022). In 2021, the Chinese equivalent of YouTube, Bilibili, spent $423 million (27% of their yearly income) to retain creators and encourage video generation (Bilibili, 2021). Incorporating AI-powered tools into video creation platforms has emerged as a potential approach to alleviating supply pressure by facilitating creators in the production process and 2 making video creation more accessible to general users. To empower its creators, TikTok, the largest short video platform in the world, launched the AI-generated voice (henceforth be referred to as AI voice) – a new text-to-speech technology powered by deep generative networks – in December 2020 (Bain, 2020). With the AI voice, creators no longer need to create the videos with their own voice. Instead, they can type the words they want say in the video, and the generative AI will automatically generate a realistic voice to read out the text (see Appendix A for more details). Generative AI technologies, with the capabilities of creating new digital artifacts (e.g., text, image, audio, video) without significant human supervision (Goodfellow et al., 2020), are profoundly reshaping creative practice. For instance, they have been deployed in applications such as news reports (Longoni et al., 2022), drug discovery (Vanhaelen et al., 2020), and logo designs (Lysyakov & Viswanathan, 2022), which were previously thought to be unique human expert skills. However, little is known about the effects of generative AI on content creators’ productivity and creative patterns on online video platforms. Given the increasing importance of video content in consumers’ online experience, it is critical to understand how generative AI technologies will reshape online video platforms and creative industries more broadly. Our study uses the introduction of AI-generated voice on TikTok to empirically investigate this new phenomenon. To the best of our knowledge, our study is the first to empirically examine whether the adoption of AI voice can increase users’ video creation productivity, and if so, how it affects users’ creation patterns. User-generated content (UGC) platforms share a common concern about how to motivate users to create more content (Gallus, 2017; Goes et al., 2016). This is exacerbated for video platforms, because only a limited number of skillful creators can make 3 high-quality videos. Apart from quantity, platforms also need to inspire the production of high-quality (Pu et al., 2020), novel (Burtch et al., 2022; Wu & Zhu, 2022), and healthy (Lowry et al., 2016) content to ensure user engagement. However, it remains unclear if the adoption of generative AI will produce these desirable outcomes. On the one hand, AI voice frees creators from the burdensome work of voice recording, thus simplifying the video creation process. On the other hand, some factors may impede this positive effect of AI voice. For example, the synthesized AI voice will be incorporated into the final video, which may hamper creators’ image motivation. Image motivation refers to the tendency to behave in a way that looks good to others (Ariely et al., 2009), which is one of the primary drivers for users’ online content contribution (Gallus, 2017; Toubia & Stephen, 2013; Zhang & Zhu, 2011). AI voice will displace creators’ own voice, thus affecting their vocal identity and the overall image presented in their creative work. Hence, the effect of AI-voice on creators is generally ambiguous, and unravelling it requires empirical investigation. Our research aims to address this timely issue by conducting the first empirical study using real-world data collected from the largest video sharing platform worldwide. Specifically, our paper sheds light on this novel phenomenon for both practitioners and scholars by examining how the adoption of AI voice affects creators’ video creation productivity and creative patterns in various dimensions. Our research questions are as follows: • How does the adoption of AI voice affect creators’ productivity of video creation (as measured by video quantity)? • How does the adoption of AI voice affect creators’ creative patterns (as measured by quality, novelty, identity disclosure, sentiment, and politeness)? 4 To empirically answer these questions, we collect a unique dataset of 428,918 videos posted by 4,021 creators on TikTok between November 2020, and April 2021 using InfluencerHunter API. Utilizing state-of-the-art machine learning algorithms, we conduct multimodal analysis on the unstructured video data to detect the usage of AI voice in audios and capture video characteristics along various dimensions including content quality, novelty, identity disclosure, sentiment, and politeness. For the causal identification, we use a difference-in-differences (DID) model to estimate the effects of creators’ first-time adoption of the AI voice on their subsequent video creation patterns. We further examine its effects across two different types of videos – AI-voice videos (i.e., videos using AI voice) and human-voice videos (i.e., videos without using AI voice) – to elucidate (i) how AI voice adoption directly affects the characteristics of AI-voice videos produced by creators and (ii) how the effect spills over to creators’ subsequent human-voice videos. To alleviate the self-selection bias concern in AI voice adoption, we use look-ahead propensity score matching (LA-PSM) to create a matched sample to control for possible confounders before the DID estimation. The LA-PSM approach matches each treated creator with a control creator who has not adopted the treatment but will adopt it in the future based on various observables to ensure there is no systematic difference between the two groups after matching. The DID estimates show that the adoption of AI voice induces creators to produce substantially more videos. Moreover, the results from a time-varying model suggests that this positive effect on creators’ productivity is consistently significant throughout the five weeks after treatment. Interestingly, around 50% of the increase is attributed to human-voice videos, 5 indicating that AI voice adoption not only facilitates creators directly in the creation process but also motivates them to produce more videos even when they do not use AI voice in the video. We further investigate the impact of AI voice adoption on creators’ video creation patterns by examining different video characteristics. First, our results show that after adopting AI voice, creators have significantly reduced the length of their video. This change is mainly driven by the shorter AI-voice videos. This is likely because the AI voice displaced creators’ vocal identity in the video, which might make them less motivated to produce long videos. Moreover, as AI voice changes the speaking task to a writing task in the video creation process, this may leave users more time to deliberate and express in a briefer and more concise way (Berger et al., 2022; Melumad et al., 2019). To our surprise, the negative effect on content length also spills over to human-voice videos, suggesting that the adoption of AI voice might change users’ video creation behaviors and induce them to produce shorter videos even when they do not use AI voice. Second, for video novelty, our results suggest that creators use AI voice to make more novel videos, whereas they tend to make less novel videos using human-voice after the adoption. Third, our results indicate an increase in negative sentiments and a decrease in politeness in the videos after creators’ AI voice adoption. These effects are primarily due to the changes in AI-voice videos. We find that creators are more likely to hide their identities in AI-voice videos by talking less about themselves and showing up less often in the videos. This lowered identity disclosure causes the disinhibition effect (Lowry et al., 2016; Suler, 2004), inducing creators to express more negative feelings less politely. Alarmingly, we find that this effect also extends to videos in which creators do not use AI voice, even though their level of identity disclosure remains unchanged in those videos. Finally, we find that videos created with AI voice receive 6 substantially fewer views and likes from consumers, raising a potential concern that creators’ use of AI voice might hamper consumer engagement on the platforms. Our research makes important contributions to the nascent literature on AI and human connections (Caro et al., 2022). To begin with, our research is the first to study how generative AI affects creators’ online video creation. Prior literature primarily examines how predictive AI affects human’s decision making (Brynjolfsson et al., 2019; Dixon et al., 2021; Ge et al., 2021; Jussupow et al., 2021), whereas only few studies look at the role generative AI plays in online platforms by substituting human labors (Lysyakov & Viswanathan, 2022). Our research provides novel perspectives into this stream of literature by examining a novel phenomenon where AI voice is adopted to simplify creators’ online content creation process, and the generated voice artifact is incorporated into the final product. Second, our study addresses a new challenge in UGC in the video era. Because of the difficulty of video creation, efficacy becomes an important factor restricting users’ content generation. Platform strategies designed to increase image motivation (e.g., symbolic awards, hierarchical incentives, social endorsement) might have a limited effect in motivating video creation (Gallus, 2017; Goes et al., 2016; Qiu & Kumar, 2017). Our results indicate that AI tools can serve as an alternative promising approach in alleviating this problem. Finally, our study implements multimodal data analytics to operationalize important theoretical constructs (e.g., video quantity, novelty, identity disclosure) in a research context of video creation, which contributes to the nascent literature on leveraging deep learning for business analytics to generate novel managerial insights using unstructured data. The remainder of this paper is organized as follows. Section 2 reviews the literature. Section 3 describes the research context, data collection, and multimodal analysis process. In section 4, 7 we discuss the empirical design, present the main findings, and conduct robustness checks. Section 5 presents additional analysis on the heterogenous effect and the adoption of AI voice’s impact on consumer engagement. Section 6 concludes the paper with a discussion of managerial implications. 8 Chapter 2: Literature Review Our research is related to three research streams: (i) generative AI’s impact on content creation, (ii) user-generated content, and (iii) business analytics with multimodal data. In this section, we discuss each stream and how we contribute to the extant literature. 2.1 Generative AI’s Impact on Content Creation AI is an evolving frontier of computational advancement (Berente et al., 2021). Most studies in the management literature define AI as one or one set of predictive model(s) targeted at solving complex decision-making problems (Agrawal et al., 2018; Berente et al., 2021; Taddy, 2018). Scholars examine how AI affects human decision making (e.g., purchase, investment, diagnosis) and cognitive activities (Fügener et al., 2021; Krakowski et al., 2022), as well as how AI affects decision-related professions like investment consultant and managers (Dixon et al., 2021; Strich et al., 2021). Recent advances in generative AI (Goodfellow et al., 2020) have been moving the frontier forward. In contrast to predictive models, generative models can produce new digital artifacts like blogs, voices, and paintings, thus having the potential to revolutionize the traditional way of content creation (Brynjolfsson & Mitchell, 2017). Generative AI has profoundly reshaped the drug discovery industry (Lou & Wu, 2021; Vanhaelen et al., 2020; Walters & Murcko, 2020), and it is starting to make an impact on online platforms. There is scant literature studying the influence of generative AI on online content creation. One exception is Lysyakov and Viswanathan (2022), who find that the introduction of an AI logo designer drives lower-tier designers to leave a crowd-sourcing platform while forcing higher-tier designers to become involved in more complex designing tasks. Their study mainly focuses on the AI substitution 9 effect when AI and human compete against each other. It is still not clear how generative AI affects creation activities when AI and human coexist and work together such that creators use AI to partially replace certain tasks and outputs in their video production. Our study contributes to this nascent research stream by investigating the augmentative and supplementary role of AI voice. Like most AI technologies, AI voice simplifies the creation task and helps reduce creators’ cognitive load (Jain et al., 2021). Moreover, due to its generative nature, this technique creates new voice artifacts, which will be incorporated into the final video content, potentially altering creators’ perceptions of their own identities in their work (Leung et al., 2018; Strich et al., 2021). It remains unclear in the literature how this new technique may alter creators’ content creation patterns, which is an important question we aim to address in this study. 2.2 User-Generated Content 2.2.1 Image Motivation, Identity Disclosure, and the Disinhibition Effect User-generated content (UGC) is content created voluntarily by users that appears to be useful or entertaining to others (Krumm et al., 2008). Massive research efforts have been devoted to understanding the antecedents of user contribution (Ma & Agarwal, 2007; Wasko & Faraj, 2005) and how to motivate user creation (Chen et al., 2017; Gallus, 2017; Goes et al., 2016). While several mechanisms can explain why people create content online, Toubia and Stephen (2013) found that image motivation is the primary factor driving user contribution. This finding is consistent across different studies on knowledge-sharing communities (Ma & Agarwal, 2007; Zhang & Zhu, 2011), review platforms (Huang et al., 2017), and social media (Qiu & Kumar, 2017; Shriver et al., 2013). Image motivation refers to an individual’s tendency to be motivated partly by others’ perception (Ariely et al., 2009). This concept consists of two indispensable 10 aspects. First, as self-presentation theory (Schau & Gilly, 2003) indicates, humans have a natural desire to present their identities or self-image to others. Second, humans hope their self-image is perceived in a good way by their counterparts. Posting more high-quality content is regarded as the major approach to maintaining a good social image on UGC platforms (Huang et al., 2017; Zhang & Zhu, 2011). Moreover, creators tend to post positive content online (Melumad et al., 2019; Oh et al., 2022). Scholars have also shown that social endorsement can motivate creators to produce more content with higher quality (Qiu & Kumar, 2017) and greater creativity (Burtch et al., 2022). We argue that the image motivation can be moderated by the level of identity disclosure. In online platforms, users have the option to conceal their identities partially or completely, thus alleviating the concern that their online behaviors will hurt their images. They might do things that they would not do if their identity were known. This phenomenon is termed the disinhibition effect (Bapna et al., 2016; Suler, 2004). Disinhibition can be toxic or benign. While Lowry et al. (2016) suggest that the disinhibition effect exacerbates cyberbullying, some studies also show that it can foster creativity (Connolly et al., 1990; Jessup et al., 1990). On the other hand, the revelation of identity on the platform causes an inhibition effect, driving creators to post more positive content (Huang et al., 2017) of higher quality (Pu et al., 2020). In our research context, video creators not only disclose their identity through textual content but also through their vocal and facial features. The use of AI voice allows creators to substitute their vocal identity with the AI voice. On the one hand, displaced vocal identity might hamper users’ creation incentives. On the other hand, a lower level of identity disclosure can disinhibit creators, facilitating them to express themselves more openly and truly. Therefore, the effect of AI voice adoption on creators’ content creation cannot be easily predicted by existing theories. 11 Our study aims to address this gap by empirically analyzing how the adoption of AI voice affects creators’ identity disclosure level, content novelty, sentiment, and politeness in their videos. 2.2.2 Efficacy and Technology-Augmented Video Creation Extant studies on UGC primarily focus on textual content (Ma & Agarwal, 2007; Qiu & Kumar, 2017; Shriver et al., 2013), which requires relatively less creation effort than video content. Specifically, video generation is a complex task requiring careful design, the shooting of multiple scenes, and heavy editing with software. As a result, content creation in video format has been restricted to professional users for a long time. The emergence of AI voice offers the opportunity to simplify the job, thus making it more accessible to general users. Our study is among the first to look into whether and how this new technology affects users’ video creation productivity. Technology not only affects the volume of content creation, but also how users create (Melumad et al., 2020). Studies have shown that the use of mobile phones leads consumers to create more emotional content (Melumad et al., 2019) with increased self-disclosure (Melumad & Meyer, 2020). Melumad (2022) also finds that voice-assisted search raises users’ self-presentational concerns, leading them to exhibit greater self-enhancement when making search queries. In our research context, AI voice alters the original speaking task into a written task. As there are significant differences in content generated through different modalities (Berger et al., 2022), this change may affect the characteristics of videos created. Our research contributes to this line of literature by examining how AI-augmentation alters users’ video creation patterns. 2.3 Business Analytics with Multimodal Data A growing number of studies in the management literature employ advanced computational approaches (e.g., natural language processing, computer vision, deep neural networks) to analyze 12 unstructured data (e.g., text, audio, and video) in order to quantify their economic values and extract novel business insights. While many studies have analyzed how the characteristics of text data (Adamopoulos et al., 2018; Lee et al., 2018; Lee et al., 2020; Shi et al., 2016), image data (Hou et al., 2022; Kwon et al. 2022; Li & Xie, 2019; Liu et al., 2020; Shin et al., 2020; Q. Wang et al., 2018; Zhang et al., 2022), or audio data (Fong et al., 2022; Hwang et al., 2022) affect consumer behaviors and business outcomes on online platforms, few studies have exploited the potential of video data (Park et al., 2022; Zhang et al., 2022a, 2022b; Zhao et al., 2021; Zhou et al., 2021) in business research. Our research contributes to the nascent literature on business analytics with unstructured data by utilizing multiple state-of-the-art machine learning techniques to operationalize multimodal (textual, vocal, and visual) measures from videos and improve the interpretability of major UGC-related constructs (i.e., quality, novelty, identity disclosure). 13 Chapter 3: Context, Data and Measurements 3.1 Research Context and Data Collection The text-to-speech (TTS) technique converts existing text into speech. Before the advent of deep learning, concatenative TTS (i.e., creating speech by combining existing audio recordings) and parametric TTS (i.e., creating speech by operating a group of pre-defined parameters) had been adopted to develop speech synthesis systems (King, 2014). These systems relied heavily on existing audio recordings or known vocal attributes to generate new voices, thus suffering from problems such as low scalability and unnaturalness (Edgington, 1997). It was impossible to employ these systems to facilitate users’ creation activities because they could not reliably generate accurate speeches from users’ diverse input at scale, and the unnatural voice negatively affects the quality of the content, thus disengaging viewers (Schanke et al., 2021). Recent advancement in generative AI has greatly improved the performance of TTS systems (Ning et al., 2019). Utilizing deep neural networks to learn latent acoustic features, generative AI can produce stable, scalable, natural, and emotional voices with just a few hours of voice recordings (Oord et al., 2016). TTS techniques have since been increasingly used in different applications. On December 16th, 2020, TikTok launched its first AI-generated voice in English (Bain, 2020). This was the only AI voice TikTok provided until May 26th, 2021, when the platform replaced it with multiple female and male voices because of copyright issues.1 We choose the period between November 1st, 2020, and April 24th, 2021 for our empirical analysis. During the 1 On May 2021, Canadian voice actress Beverly Standing sued TikTok for using her voice to train the AI-voice without her consent. TikTok soon replaced this voice with other voices after the lawsuit. 14 observational period, TikTok was the first major video-sharing platform to implement the TTS function, making it an exogenous shock for all users on the platform. Using the API provided by InfluencerHunters,2 we draw a random sample of about 10,000 creators in TikTok whose videos appeared in the top 2,000 list of trending hashtags.3 For each creator, we extract their profile information and collect the metadata of all their historical videos. Each creator profile specifies the user id, avatar image, and whether the creator has commercial connections with companies. The video-level metadata includes posting time, region, textual description, video duration, user engagement (e.g., play count, like count), and whether the video is sponsored by a brand. Moreover, we downloaded the raw videos created by all creators in our sample for video analysis. To clean the data for empirical estimation, we apply the following filtering process: First, we only keep creators who posted at least one video during the observational period to ensure a basic level of activity. Second, since TikTok only provided an English AI voice at the time, we focus on creators in countries where English is the primary language.4 Third, we focus on creators who posted their first video before September 6th, 2020 (15 weeks before the AI voice introduction) so that they had at least some experience in video creation before the exposure to AI voice. Lastly, we exclude creators who have commercial connections or video sponsorship 2 See https://influencerhunters.com/ 3 The hashtags were selected from the following topics: education (#learning, #edutok, #knowledge), fun (#comedy, #prank, #1mincomedy), friendship (#friendship, #relationship, #internetfriends), health (#fitness, #health), food (#myrecipe, #healthfood), motivation (#motivational, #life, #decision), beauty (#beauty, #beautytips), and DIY (#diy, #craft). 4 Countries include the United States, Canada, Australia, New Zealand, the United Kingdom, and the Republic of Ireland. 15 (less than 1% of the entire sample). Our final sample consists of 4,021 creators with 428,918 videos between November 1st, 2020 and April 24th, 2021. 3.2 Detecting AI-Generated Voice to Determine the Treatment Variable Since creators could choose to adopt the AI voice any time after December 16th, 2020, the day when TikTok first launched the tool, we use creators’ first-time adoption of the AI voice as the treatment. It is challenging to construct the treatment variable, because one needs to detect the usage of the AI voice in massive video data, which is often packed with cluttering elements like background music, voice, noise, and silence. To address this challenge, we exploit multiple state-of-the-art algorithms for audio analysis. Figure 3.1(A) illustrates the process to detect videos with AI voice. After extracting the raw audio from videos, we use a pre-trained model Spleeter (Hennequin et al., 2020) to separate and remove background music from the audio. Then an end-to-end neural speaker segmentation model (Bredin & Laurent, 2021; Bredin et al., 2020) is utilized to detect the voice activities in the audio. This model splits the audio into sub-clips that contain voice activities, excluding clips of silence and noises. Finally, we apply the speaker verification algorithm ECAPA-TDNN (Desplanques et al., 2020) to detect the usage of the AI voice in each sub-clip. The algorithm takes in two raw voice waveforms and outputs a confidence score ranging from -1 to 1, indicating whether the two voices are from the same speaker. To guarantee the robustness of the AI voice detection, we compare each voice sub-clip from our sample with three audio samples generated by the AI voice, and only sub-clips passing 16 all three verifications are considered AI voices.5 The detection result shows that 2,225 out of the 4,021 creators (55.3%) had adopted the AI voice within four months of its release, indicating the prevalence of this AI tool. 3.3 Outcome Variables To investigate the effects of AI voice adoption on creators’ video creation productivity and creation patterns, we use a set of metrics to quantify the quantity, quality, novelty, identity disclosure level, sentiment, and politeness of videos. As illustrated in Figure 3.1(B), to operationalize these measurements of video content, we exploit multimodal data analysis to extract rich information from both textual and visual data. First, we use iFLYTEK’s6 Speech-to-Text service to convert audio files into text to generate variables in the textual modality. Then, we split the raw videos into sub-scenes containing similar frames using PySceneDetect7 and randomly sample one image from each scene. We employ multiple computer vision techniques to extract measurements from those images, thus expanding our ability to interpret visual content characteristics. To address the data sparsity issue, all variables are aggregated to the creator-week level. 5 To ensure the accuracy of the detection, we manually labelled 200 audio samples to test the model and decide the optimal threshold. The original model uses 0.25 as the threshold to decide whether the two inputs are from the same speaker (Desplanques et al., 2020). In our analysis, the threshold of 0.35 yields the highest F1 score. We also use other thresholds (e.g., 0.25, 0.3) to generate the treatment variable and the empirical results are consistent. 6 See: https://global.xfyun.cn/ 7 See: http://scenedetect.com/en/latest/ 17 3.3.1 Quantity and Quality We use the number of videos creator 𝑖𝑖 posted in week 𝑡𝑡 (i.e., 𝑉𝑉𝑖𝑖𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖) to measure content quantity. Meanwhile, we measure the quality of video content with length and complexity. Specifically, we use three variables to quantify content length for creator 𝑖𝑖 in week 𝑡𝑡: average video duration (i.e., 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑡𝑡𝑖𝑖𝑉𝑉𝑉𝑉𝑖𝑖𝑖𝑖), average word counts in transcripts (i.e., 𝑊𝑊𝑉𝑉𝐷𝐷𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖), and average scene counts (i.e., 𝑆𝑆𝑆𝑆𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖). Prior studies suggest that people attempt to deliver information in a succinct way when using more big words (Pennebaker & King, 1999; Pfeil et al., 2009). Following these studies, we measure textual complexity by the average proportion of big words (i.e., words longer than six letters) in transcripts (i.e., 𝐵𝐵𝑖𝑖𝑖𝑖𝑊𝑊𝑉𝑉𝐷𝐷𝑉𝑉𝑖𝑖𝑖𝑖𝑖𝑖). 3.3.2 Novelty We use self-novelty (Carmel et al., 2012), which refers to how novel the content is in comparison to the creators’ prior work, to measure users’ explorational behavior.8 To construct the measures, we follow Burtch et al. (2022) to convert the textual and visual content into vector embeddings and use cosine differences between content to represent novelty. For the textual content, we employ a short-text topic modeling technique called the Gibbs Sampling algorithm for the Dirichlet Multinomial Mixture Model (GSDMM) (Yin & Wang, 2014) to convert the text into topic vectors. We use this method because traditional topic modeling methods such as Latent Dirichlet Allocation (Blei et al., 2003) rely on the term co-occurrence to find topic patterns, thus not performing well on short text where words distribution is sparse (Murshed et al., 2022). For 8 There are two types of novelty (Carmel et al. 2012): self-novelty (compared to users’ previous work) and contemporary novelty (compared to other users). Since this study focuses on UGC, we concentrate on self-novelty. 18 visual embeddings, we apply an image transformer called swin-transformer (Liu et al., 2021) to turn each sampled image into a 1,024-dimensional vector. More details of the embeddings are described in Appendix B. The video vector is then calculated by taking the mean of all the image vectors in a single video. Finally, based on the generated textual and visual embeddings, we calculate the cosine difference between the vector of the focal content and the average vector of the creators’ previous three videos to construct the novelty measurements (i.e., 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖 and 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖). 3.3.3 Identity Disclosure In videos, creators can disclose their identity not only in language (Melumad & Meyer, 2020) but also through their vocal and visual features (e.g., voices and faces). While AI voice substitutes creators’ vocal identity in videos, it is not clear how it impacts creators’ identity disclosure in textual and visual modalities. To measure language-based identity disclosure, we calculate the average proportion of first-person pronouns (e.g., I, my, we, us) in transcripts (i.e., 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑉𝑉𝑆𝑆𝑉𝑉𝐷𝐷𝑉𝑉𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖) using Linguistic Inquiry and Word Count (LIWC) (Boyd et al., 2022) to quantify the extent to which creators refer to themselves (Melumad & Meyer, 2020). To measure the visual identity disclosure, we propose a novel metric based on face recognition techniques to determine whether the creators themselves appear in the videos (i.e., 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑉𝑉𝐷𝐷𝐷𝐷𝐷𝐷𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖). Assuming that the faces shown in user avatars are the creators’ faces, we compare all the faces detected in scene samples with the creator’s face using Dlib’s face encodings.9 If there is at least 9 See: http://dlib.net/imaging.html 19 one face match, we consider that the creator shows up in their videos. It is worth noting that not all creators reveal their facial identities in the avatars. To ensure the accuracy of the measurement, only creators with a clear face in the avatars are kept for face comparison. Therefore, 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑉𝑉𝐷𝐷𝐷𝐷𝐷𝐷𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖 is calculated on a sub-sample of 1,164 creators. 3.3.4 Sentiment and Politeness We create two variables to measure content sentiment: the average intensity of positive sentiment (i.e., 𝑃𝑃𝑉𝑉𝑖𝑖𝑖𝑖𝑡𝑡𝑖𝑖𝑇𝑇𝑉𝑉𝑖𝑖𝑖𝑖) and the average intensity of negative sentiment (i.e., 𝑇𝑇𝑉𝑉𝑖𝑖𝐷𝐷𝑡𝑡𝑖𝑖𝑇𝑇𝑉𝑉𝑖𝑖𝑖𝑖) in all video transcripts creator 𝑖𝑖 posted in week 𝑡𝑡. Recent advances in deep learning have significantly improved the accuracy and explanatory power of sentiment analysis tools (Frankel et al., 2022). We use roBERTa (Loureiro et al., 2022), a BERT model fine-tuned on 124 million tweets to predict sentiment, to analyze the transcripts. This model takes in the raw text and outputs a three-dimensional vector indicating its confidence in whether the text is positive, negative, or neutral. We use the confidence score of the text being positive and negative, respectively, to quantity the intensity of positive and negative sentiment. The politeness metric is applied to further evaluate the way sentiments are expressed (Hong et al., 2021). Based on the politeness corpus provided by LIWC, we calculate the percentage of polite words (e.g., hello, thanks, please, welcome) in the transcripts (i.e., 𝑃𝑃𝑉𝑉𝑇𝑇𝑖𝑖𝑡𝑡𝑉𝑉𝑖𝑖𝑖𝑖). Appendix C provides some examples of the measurement outcomes for sentiment and politeness. Table 3.1 summarizes our main dependent variables on video quantity, quality, novelty, identity disclosure, sentiment, and politeness. 20 Variables Description Value Quantity VideoCntit The number of videos creator i posted in week t. 𝑇𝑇 ≥ 0 Quality Durationit The average duration (in seconds) of all videos creator i posted in week t. [0, +∞) WordCntit The average number of words in transcripts of all videos creator i posted in week t. [0, +∞) BigWordsit The average proportion of words longer than six letters in all videos creator i posted in week t. [0, 100] SceneCntit The average number of scenes in all videos creator i posted in week t. [0, +∞) Novelty TextualNoveltyit The average cosine difference between the focal textual content embeddings and the creators’ previous three textual content embeddings for all videos creator i posted in week t. The textual embeddings are derived from the short-text topic modelling method. [0, 2] VisualNoveltyit The average cosine difference between the focal visual content embeddings and the creators’ previous three visual content embeddings for all videos creator i posted in week t. The visual embeddings are generated by swin-transformer. [0, 2] Identity Disclosure SelfReferenceit The average proportion of first-pronoun words (e.g., I, my, we, us) in transcripts of all videos creator i posted in week t. [0, 100] SelfAppearanceit The proportion of videos in which the creators reveal their facial identity among all videos creator i posted in week t. [0, 100] Sentiment & Politeness Positiveit The average intensity of positive sentiments in all videos creator i posted in week t. [0, 1] Negativeit The average intensity of negative sentiments in all videos creator i posted in week t. [0, 1] Politeit The average proportion of polite words (e.g., hello, hi, please) in transcripts of all videos creator i posted in week t. [0, 100] Table 3.1 Summary of Main Variables 21 PositiveNegativeSelfAppranceAI Voice VocalAI Samples Speaker VerificationECAPA-TDNN(Desplangues et al., 2020)Human Voice Background Music RemovalSpleeter (Hennquin et al., 2020)Voice Activity DetectionEESM (Bredin et al., 2020)NoiseSilenceBGMAI VoiceHuman VoiceLegendVisualScene DetectionpySceneDetectRandom SampleFace Recognition DlibImage EmbeddingSwin Transformer(Liu et al., 2021).........Image VectorAvatarTextualTXTText Statistics LIWC22(Boyd et al., 2022)Sentiment Analysis BERT - TweetEval(Barbieri et al., 2020)Short-Text Topic Model GSDMM(Yin & Wang, 2014).........Topic VectorSpeech TranscriptioniFLYTEKVisualNoveltyTextualNoveltySceneCntA. Treatment (AI-Voice Adoption) B. Outcome VariablesWordCntBigWordSelfReferencePolite Figure 3.1 Multimodal Analysis for the Measurement of Treatment and Outcome Variables 22 Chapter 4: Empirical Analysis 4.1 Empirical Model Our empirical strategy exploits a quasi-experiment design. Specifically, we use a DID approach (Angrist & Pischke, 2009) coupled with LA-PSM to investigate the effects of AI voice adoption on creators’ video creation patterns. DID evaluates causal impact by comparing how the changes in outcomes over time in the treatment group differ from those in the control group. In our research context, we use creators’ first-time adoption of the AI-generated voice as the treatment and estimate its effect using a panel over a nine-week time window (i.e., 4 weeks before treatment, and 5 weeks after treatment). After the first-time adoption of the AI voice, creators can choose whether to use the AI voice or not in each subsequent video. It is likely that creators’ content creation behaviors may differ significantly between videos in which they use the AI voice and those in which they decide not to use it. Therefore, we further examine the direct and spillover effect of the adoption to uncover the underlying mechanism (Qiao et al., 2020). As illustrated in Figure 4.1, We separate each treated creator’s videos in the post-treatment period into AI-voice videos and human-voice videos, and calculate the outcome variables for each type. Then we use the same DID models to estimate (i) how the AI-generated voice adoption directly affects subsequent AI-voice video creations and (ii) how the adoption effect spills over to the videos created using their original human voices. 23 Human-Voice VideoDirect EffectOverall EffectSpillover EffectFirst-Time Adoption of AI Voice Figure 4.1 Overall Effect, Direct Effect, and Spillover Effect Creator 1First-time Adoption Observation Time Window××Creator 2Creator 3Creator 4Creator 5Time Figure 4.2 Look-Ahead Propensity Score Matching Example 4.1.1 Look-Ahead Propensity Score Matching A major endogenous concern of our setting is that creators have self-selected themselves into the treatment by adopting the AI voice. Creator characteristics, such as activeness level and video creation skills, may affect creators’ adoption decisions. Following previous studies with similar settings (Kumar et al., 2022; C. Wang et al., 2018; Xu et al., 2017), we use LA-PSM to reduce potential selection bias by matching each creator in the treatment group with a similar creator in the control group based on many pre-treatment observables. Specifically, LA-PSM matches each 24 treated creator to a control creator that has not yet adopted AI voice but will adopt it soon. For a creator 𝑖𝑖 who adopts AI voice in week 𝑡𝑡, LA-PSM selects all creators that have not adopted the voice between week 𝑡𝑡 − 4 and week 𝑡𝑡 + 4 but adopt it sometime after week 𝑡𝑡 + 5, as the control candidates. For example, as illustrated in Figure 4.2, creators 2 and 3 are selected as the control candidates for creator 1, whereas creator 4 is excluded because of time window overlap, and creator 5 is excluded because they have never adopted the AI voice. In this way, selection bias can be further alleviated, since the creators in the treatment and control groups all have the tendency to adopt the AI voice. We estimate the propensity score by modeling the AI voice adoption probability as a function of the following covariates calculated with the videos posted in the pre-treatment period: (i) weekly video post count; (ii) aggregated mean values of the dependent variables for all videos; (iii) average viewer engagement measurements (i.e., log-transformed number of views and like ratios); (iv) average video file size in megabytes and the average number of distinct faces in all videos. Using 1-on-1 matching with a caliper distance of 0.05 without replacement, 1,896 treated creators and 1,896 control creators are matched in the final sample. In total, we obtain 34,128 creator-week observations in the panel for econometric analysis. Table 4.1 presents the matching results. After matching, the mean differences between the treatment and control groups are not statistically significant across all variables. Note that we run a separate set of analyses on the sub-sample of 1,164 creators with clear faces in their avatars to estimate the effect on self-appearance in video creation. Therefore, a separate matching procedure is executed on this sub-sample and the details are presented in Appendix D. 25 Before Matching After Matching Treated Control Diff. T-statistics Treated Control Diff. T-statistics VideoCnt_pre4 6.218 3.821 2.396 12.326*** 6.041 5.837 0.204 0.618 VideoCnt_pre3 6.245 3.862 2.382 12.649*** 6.074 5.819 0.256 0.820 VideoCnt_pre2 6.369 3.867 2.502 13.673*** 6.144 5.997 0.147 0.497 VideoCnt_pre1 6.346 3.880 2.466 13.528*** 6.141 6.065 0.076 0.257 Duration 21.394 19.830 1.564 4.548*** 21.468 21.119 0.349 0.877 WordCnt 44.436 39.451 4.985 5.066*** 44.436 43.705 0.731 0.601 SceneCnt 5.840 5.680 0.160 1.099 5.938 5.680 0.257 1.543 BigWord 6.962 5.949 1.013 8.828*** 6.994 7.171 -0.177 -1.217 TextualNovelty 0.491 0.427 0.063 8.468*** 0.494 0.485 0.009 1.008 VisualNovelty 0.453 0.417 0.036 6.515*** 0.454 0.445 0.009 1.547 SelfReference 7.01 5.998 1.010 9.394*** 7.078 7.007 0.070 0.553 SelfAppearance 0.342 0.279 0.063 6.303*** 0.349 0.342 0.007 0.509 DistinctFaceCnt 1.376 1.194 0.182 6.844*** 1.398 1.392 0.006 0.177 Positive 0.307 0.280 0.182 5.832*** 0.308 0.306 0.002 0.287 Negative 0.236 0.195 0.041 9.928*** 0.237 0.238 -0.001 -0.199 Polite 1.030 0.930 0.101 1.684+ 1.032 0.958 0.074 0.923 LogPlayCnt 9.708 8.65 1.059 9.866*** 9.728 9.681 0.047 0.413 LikeRatio 0.112 0.093 0.020 11.753*** 0.114 0.113 0.001 0.318 Note: *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1. Table 4.1 Look-Ahead Propensity Score Matching Result 4.1.2 Model Specification Our DID model specification for estimating the overall effect is as follows: 𝐷𝐷𝑉𝑉𝑖𝑖𝑖𝑖 = 𝛽𝛽 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 + 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 + 𝜆𝜆𝑖𝑖 + 𝜃𝜃𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 , �1� where 𝐷𝐷𝑉𝑉𝑖𝑖𝑖𝑖 is the outcome variable aggregated on all videos for creator 𝑖𝑖 in week 𝑡𝑡. 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 is a dummy variable which equals 1 if a treated creator 𝑖𝑖 has already adopted AI voice in week 𝑡𝑡 and 0 otherwise. The corresponding matched creator will have the same values for this binary variable. 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 is a binary variable which equals 1 if creator 𝑖𝑖 is in the treatment group and 0 otherwise. 𝜆𝜆𝑖𝑖 and 𝜃𝜃𝑖𝑖 denote the time fixed effect and individual fixed effect, respectively. 𝜀𝜀𝑖𝑖𝑖𝑖 is the error term. To uncover the underlying mechanisms, we further estimate the direct effect on AI-voice videos and the spillover effect on human-voice videos. Specifically, we replace the original dependent variables for the treatment group in the post-treatment period with the variables 26 aggregated just on AI-voice videos or just on human-voice videos (i.e., 𝐷𝐷𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴, 𝐷𝐷𝑉𝑉𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻) for two additional DID estimations. 4.1.3 Summary Statistics Table 4.2 presents the summary statistics. On average, creators produce 6 videos per week, the average duration of which is 23.5 seconds. In each video, the creators speak approximately 49.81 words and the average scene count is 6.34. The average percentage of big words is 7.76%. The textual and visual novelty measures share similar statistics. Regarding the identity disclosure measures, the average proportion of first-pronoun words in transcripts is 7.42%, while creators show up their faces in the videos around 71.22% of the time. Lastly, the average for positive sentiment is higher than that for negative sentiment. This is consistent with prior studies which suggest that social media content is positively biased (Melumad et al., 2019; Oh et al., 2022). The average proportion of polite words in transcripts is 1%. Variables N Mean Std Min Max VideoCntit 34,128 6.09 9.50 0 306 Durationit 26,698 23.50 12.81 1.61 106.65 WordCntit 26,698 49.81 42.33 0 301 SceneCntit 26,698 6.34 5.89 1 67 BigWordit 26,698 7.76 5.06 0 71.43 TextualNoveltyit 26,698 0.50 0.29 0 1 VisualNoveltyit 26,698 0.48 0.14 0 1.04 SelfReferenceit 26,698 7.42 4.43 0 50 SelfAppearanceit 14,650 71.22 36.39 0 100 Positiveit 26,698 0.33 0.20 0.004 0.99 Negativeit 26,698 0.25 0.19 0.001 0.96 Politeit 26,698 1.03 3.32 0 100 Note: The number of observations for most variables except VideoCntit is less than the total observation number. This is because our observations are aggregated at the creator-week level. If the creator did not create any video in a specific week, the video count equals 0, but other video properties values cannot be computed. SelfAppearanceit has fewer observations, because this variable is generated on a sub-sample of 1,164 creators with clear appearance in avatars. Table 4.2 Summary Statistics 27 4.2 Main Results 4.2.1 Effect on Quantity Table 4.3 reports the DID estimation results on the effects of the AI voice adoption on video quantity. Because the number of videos created per week is a count variable, we use a Poisson model and a negative binomial model to estimate the treatment effect. According to the Poisson regression results, after adopting the AI voice feature, creators post 18.9% (𝑉𝑉0.173 − 1) more videos per week (column 1 in Table 4.3) and the result is consistent with the negative binomial model (column 2 in Table 4.3). This suggests that the adoption of AI voice significantly enhances creators’ productivity level. To further investigate whether the adoption of AI voice substitutes or complements creators’ video generation without the AI voice, we estimate the spillover effect of the adoption on human-voice videos. Column 3 in Table 4.3 shows that creators in the treatment group generate 8.8% (𝑉𝑉0.085 − 1) more videos using human voice, and negative binomial model yields consistent estimates (column 4 in Table 4.3). This demonstrates that creators make videos featuring the AI voice as a complement to those featuring human voices. Notably, around 50% of the increase in video quantity is due to the increase in human-voice videos, suggesting that this AI tool not only facilitates creators by simplifying the task in creating AI-voice videos, but also substantially increases creators’ production of human-voice videos. 28 (1) (2) (3) (4) Overall Effect - 𝑉𝑉𝑖𝑖𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 Spillover Effect - 𝑉𝑉𝑖𝑖𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 Model Poisson Negative Binomial Poisson Negative Binomial 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.173*** 0.200*** 0.085*** 0.090*** (0.024) (0.019) (0.024) (0.019) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.028 0.034* -0.014 -0.025+ (0.017) (0.015) (0.018) (0.015) N 34128 34128 34128 34128 Adjusted R2 0.620 0.235 0.626 0.239 Creator FE Yes Yes Yes Yes Week FE Yes Yes Yes Yes Note: *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1. Table 4.3 The Effect of AI-Generated Voice Adoption on Video Quantity 4.2.2 Effect on Quality Table 4.4 reports the DID estimates of the effect on video quality. Results suggest that the adoption of AI voice drives creators to produce shorter videos. The overall video and transcript length dropped significantly after adoption (see columns 1-2 in Table 4.4. Panel A). On average, the videos by the treated creators are 0.84 seconds shorter and contain 1.88 fewer words than those from the control group. There is also a marginal drop in scene count (0.21). Despite the drop in content length, the proportion of big words increase significantly by 0.86% (column 4 in Table 4.4. Panel A). For the direct effects on AI-voice videos, columns 1-3 in Table 4.4. Panel B indicate that treated creators produce significantly shorter videos when using AI voice (i.e., 3.7 seconds shorter, with 14.42 fewer words and 0.65 fewer scenes). There are two plausible explanations behind such effects. On one hand, the AI voice feature downgrades the speaking task to a writing task in the video creation process. Writing allows users to express themselves in more concise languages due to more deliberation time (Berger et al., 2022): a significant 3.44% increase in the use of big words (column 4 in Table 4.4. Panel B) may indicate that the treated creators can effectively deliver their message with shorter content. On the other hand, the displacement of 29 users’ vocal identities reduces their image motivation. Creators have fewer incentives to devote efforts and make long content in pursuit of a good social image (Pu et al., 2020). Interestingly, the decrease in video length and the increase in big words significantly spills over to human-voice videos (Table 4.4. Panel C), although the magnitudes are smaller. The videos created by the treatment group are on average 0.67 seconds shorter than those by the control group. The percentage of big words rises by 0.36%. These findings imply that the use of AI voice may alter creators’ video production patterns and induce them to produce more succinct videos even when they do not use the AI voice feature. (1) (2) (3) (4) Panel A: Overall Effect DV 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑡𝑡𝑖𝑖𝑉𝑉𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑊𝑊𝑉𝑉𝐷𝐷𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑆𝑆𝑆𝑆𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝐵𝐵𝑖𝑖𝑖𝑖𝑊𝑊𝑉𝑉𝐷𝐷𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.840*** -1.883** -0.205+ 0.863*** (0.224) (0.710) (0.107) (0.116) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.385+ 0.550 0.174 -0.005 (0.229) (0.726) (0.114) (0.130) N 26698 26698 26698 26698 Adjusted R2 0.601 0.634 0.562 0.241 Creator FE Yes Yes Yes Yes Week FE Yes Yes Yes Yes Panel B: Direct Effect on AI-Voice Videos DV 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑡𝑡𝑖𝑖𝑉𝑉𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑊𝑊𝑉𝑉𝐷𝐷𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑆𝑆𝑆𝑆𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝐵𝐵𝑖𝑖𝑖𝑖𝑊𝑊𝑉𝑉𝐷𝐷𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -3.700*** -14.420*** -0.654*** 3.442*** (0.328) (1.000) (0.155) (0.170) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.317 1.009 0.123 -0.105 (0.259) (0.797) (0.134) (0.153) N 22366 22366 22366 22366 Adjusted R2 0.537 0.581 0.494 0.233 Creator FE Yes Yes Yes Yes Week FE Yes Yes Yes Yes Panel C: Spillover Effect on Human-Voice Videos DV 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑡𝑡𝑖𝑖𝑉𝑉𝑉𝑉𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑊𝑊𝑉𝑉𝐷𝐷𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑆𝑆𝑆𝑆𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝐵𝐵𝑖𝑖𝑖𝑖𝑊𝑊𝑉𝑉𝐷𝐷𝑉𝑉𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.668** -0.819 -0.197+ 0.356** (0.227) (0.723) (0.108) (0.115) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.584* 1.322+ 0.242* -0.305* (0.233) (0.746) (0.115) (0.130) N 26236 26236 26236 26236 Adjusted R2 0.602 0.633 0.568 0.252 Creator FE Yes Yes Yes Yes Week FE Yes Yes Yes Yes Note: p *** < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1. Table 4.4 The Effect of AI-Generated Voice Adoption on Video Quality 30 4.2.3 Effect on Novelty Next, we examine the effects of AI voice adoption on the novelty of videos. Our results show that the visual novelty decreases significantly after the adoption of AI voice (see column 2 in Table 4.5. Panel A). The coefficient for textual novelty is also negative, though not significant. We further investigate the possible reasons for this change by analyzing the direct and spillover effects respectively. As shown in Panel B of Table 4.5, the textual novelty and visual novelty increase significantly in AI-voice videos, which may indicate that creators produce more novel and explorative content when using AI-voice in their videos. There are two plausible explanations. First, with the help of AI voice, creators can shoot the videos in more diverse scenarios without worrying about how to record their voices while shooting. Second, the use of AI voice can help creators hide their identities, motivating them to explore new ideas (Connolly et al., 1990; Jessup et al., 1990). On the contrary, creators in the treatment group become less novel and more exploitative when making videos using human voice (see Table 4.5. Panel C): the visual novelty decreases significantly, and the decrease in the textual novelty is marginally significant. Moreover, these negative effects in human-voice videos offset the positive effects in AI-voice videos, driving the overall effects downwards. These results may indicate that AI tools can empower creators to explore novel ideas, whereas creators may exclusively rely on AI tools when exploring new ideas and become less creative when not using those tools. 31 (1) (2) Panel A: Overall Effect DV 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.009 -0.007* (0.006) (0.003) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.004 0.009** (0.006) (0.003) N 26698 26698 Adjusted R2 0.480 0.572 Creator FE Yes Yes Week FE Yes Yes Panel B: Direct Effect on AI-Voice Videos DV 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.026*** 0.018*** (0.008) (0.004) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.007 0.007* (0.003) (0.003) N 22366 22366 Adjusted R2 0.439 0.528 Creator FE Yes Yes Week FE Yes Yes Panel C: Spillover Effect on Human-Voice Videos DV 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.012+ -0.009** (0.006) (0.003) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.001 0.006* (0.006) (0.003) N 26236 26236 Adjusted R2 0.477 0.572 Creator FE Yes Yes Week FE Yes Yes Note: *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1. Table 4.5 The Effect of AI-Generated Voice Adoption on Video Novelty 4.2.4 Effect on Identity Disclosure, Sentiment, and Politeness The estimation results for identity disclosure level, sentiment, and politeness are reported in Table 4.6. As shown in Table 4.6. Panel A, while the adoption of AI voice does not significantly change creators’ overall identity disclosure level, there are significant impacts on sentiment and politeness: after the AI voice adoption, the creators use substantially more negative, less positive, and less polite words in their videos. We further examine the direct effect and spillover effect to explore the underlying mechanisms. Table 4.6. Panel B shows that creators disclose their identities less frequently 32 when creating videos with AI voice. Specifically, with their vocal identities displaced by AI, creators talk less about themselves (i.e., a 0.61% decrease in the percentage of self-referential words) and show up their faces less often in videos (a 5.6% decrease in self-appearance) as well. Columns 3 – 5 in Table 4.6. Panel B show that the videos created with the AI voice contain more negative sentiments and less polite expressions. These results together suggest a disinhibition effect (Suler, 2004): creators hide their identities in different modalities when using the AI voice feature, thus disinhibiting themselves from the concerns that their behaviors will hamper their social images. The literature suggests that the disinhibition effect drives people to express feelings that they would not have expressed if their identities were revealed to others (Lowry et al., 2016; Pu et al., 2020), often leading to the expression of negative and aggressive feelings. Interestingly, this tendency spills over to creators’ videos without AI voice features as well. As shown in Table 4.6. Panel C, the adoption of AI voice has a significant negative effect on the sentiment and politeness in human-voice videos, even though there is no significant impact on the identity disclosure level. Prior studies suggest that people have incentives to reveal negative feelings to release inner stress and anxiety (Stiles et al., 1992). Disclosing more negative sentiments with AI voice may alleviates creators’ self-presentational concern of such revelation (Kowalski, 1996) and they might continue to do so even without the use of AI-generated voice. Overall, the disinhibition effect on AI-voice videos and the spillover effect to human-voice videos together result in a significant increase in negative sentiment and a significant decrease in politeness in creators’ video production. 33 Identity Disclosure Sentiment Politeness (1) (2) (3) (4) (5) Panel A: Overall Effect DV 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑉𝑉𝑆𝑆𝑉𝑉𝐷𝐷𝑉𝑉𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑉𝑉𝐷𝐷𝐷𝐷𝐷𝐷𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑃𝑃𝑉𝑉𝑖𝑖𝑖𝑖𝑡𝑡𝑇𝑇𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑇𝑇𝑉𝑉𝑖𝑖𝐷𝐷𝑡𝑡𝑖𝑖𝑇𝑇𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑃𝑃𝑉𝑉𝑇𝑇𝑖𝑖𝑡𝑡𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.016 -0.476 -0.016*** 0.010* -0.290*** (0.105) (0.792) (0.005) (0.004) (0.077) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.131 -0.177 0.000 -0.005 -0.032 (0.117) (0.861) (0.005) (0.005) (0.085) N 26698 14650 26698 26698 26698 Adjusted R2 0.151 0.640 0.206 0.206 0.075 Creator FE Yes Yes Yes Yes Yes Week FE Yes Yes Yes Yes Yes Panel B: Direct Effect on AI-Voice Videos DV 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑉𝑉𝑆𝑆𝑉𝑉𝐷𝐷𝑉𝑉𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑉𝑉𝐷𝐷𝐷𝐷𝐷𝐷𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑃𝑃𝑉𝑉𝑖𝑖𝑖𝑖𝑡𝑡𝑇𝑇𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑇𝑇𝑉𝑉𝑖𝑖𝐷𝐷𝑡𝑡𝑖𝑖𝑇𝑇𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑃𝑃𝑉𝑉𝑇𝑇𝑖𝑖𝑡𝑡𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.609*** -5.570*** -0.052*** 0.033*** -0.607*** (0.149) (1.246) (0.007) (0.007) (0.080) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.084 1.054 -0.003 -0.005 -0.005 (0.135) (1.009) (0.006) (0.006) (0.093) N 22366 12219 22366 22366 22366 Adjusted R2 0.129 0.567 0.185 0.173 0.081 Creator FE Yes Yes Yes Yes Yes Week FE Yes Yes Yes Yes Yes Panel C: Spillover Effect on Human-Voice Videos DV 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑉𝑉𝑆𝑆𝑉𝑉𝐷𝐷𝑉𝑉𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑉𝑉𝐷𝐷𝐷𝐷𝐷𝐷𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑃𝑃𝑉𝑉𝑖𝑖𝑖𝑖𝑡𝑡𝑇𝑇𝑉𝑉𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑇𝑇𝑉𝑉𝑖𝑖𝐷𝐷𝑡𝑡𝑖𝑖𝑇𝑇𝑉𝑉𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑃𝑃𝑉𝑉𝑇𝑇𝑖𝑖𝑡𝑡𝑉𝑉𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.133 0.431 -0.010* 0.005 -0.240** (0.105) (0.784) (0.005) (0.004) (0.078) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.033 0.065 0.006 -0.009+ -0.003 (0.119) (0.868) (0.005) (0.005) (0.088) N 26236 14229 26236 26236 26236 Adjusted R2 0.157 0.646 0.209 0.211 0.076 Creator FE Yes Yes Yes Yes Yes Week FE Yes Yes Yes Yes Yes Note: *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1. SelfAppearanceit is regressed on the matched sub-sample of creators who show a clear face in their avatars. Table 4.6 The Effect of AI-Generated Voice Adoption on Identity Disclosure, Sentiment, and Politeness 4.3 Robustness Checks 4.3.1 Examination of Pre-Trends The key assumption underlying the DID estimation for causal inference is that the treatment group and control group evolve in a similar way prior to the treatment. Following the literature (Wu & Zhu, 2022; Zhou et al., 2020), we employ the relative-time model to examine the trends of the differences between the treatment and control groups in the pre-treatment period. Specifically, we replace the interaction term 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 in equation (1) with a set of 34 interaction terms between the relevant week dummies and the treatment dummy ∑ 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖𝑖𝑖=4𝑖𝑖=−4 × 𝑊𝑊𝑉𝑉𝑉𝑉𝑊𝑊𝑖𝑖𝑖𝑖 and repeat the DID estimation for each dependent variable. Figure 4.3 illustrates the results. None of the coefficients in the pre-treatment period are significant, suggesting that the parallel trend assumption holds in our analysis. Note: The confidence interval is drawn on a 95% significance level. The first week before the treatment week is used as the baseline. Figure 4.3 Relative-Time Model Results 4.3.2 Result Validation with Callaway and Sant’Anna DID Framework In this research context, creators adopted the AI-voice at different times, forming a staggered DID setting. Current literature (Baker et al., 2022) has raised concerns regarding the potential for 35 heterogeneity treatment effects over time to introduce bias in estimating the average treatment effect on the treated (ATT). Goodman-Bacon (2021) proved that the two-way fixed effects (TWFE) estimator for DID is a weighted mean of all two-group/two-period estimators, including the estimates calculated by comparing: (i) the treated group with the control group; (ii) the earlier-treated group with the later-treated group; (iii) the later-treated group with the earlier-treated group. The third comparison can bias the estimation if the treatment effect varies across time. To further validate our empirical model, we analyze our main outcome variable – 𝑆𝑆𝑉𝑉𝑖𝑖𝑡𝑡𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 with the DID estimator proposed by Callaway and Sant’Anna (2021). This framework mainly relies on the disaggregated causal parameter – group-time average treatment effect to estimate the treatment effect. We utilize the R package “did” developed by Callaway and Sant’Anna to calculate the effect. The results shown in Table 4.7 are consistent with our main analysis even though the effect size is slightly smaller. After the adoption of AI voice, creators creator around 0.786 more videos per week, and the positive effect lasts for at least 4 weeks. Effect ATT(SE) Dynamic-aggregated effect 0.786*** (0.159) Dynamic effects -4 0.013 (0.133) -3 0.135 (0.123) -2 0.222 (0.123) -1 0.083 (0.126) 0 1.801*** (0.130) 1 0.801*** (0.178) 2 0.624*** (0.184) 3 0.441* (0.186) 4 0.261 (0.220) Note: *** p < 0.001, ** p < 0.01, * p < 0.05. Table 4.7 Callaway and Sant’Anna Dynamic DID Effects for 𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒊𝒊𝒑𝒑𝑨𝑨𝑨𝑨𝑨𝑨 36 4.3.3 Alternative Matching Procedure To corroborate the results, we use coarsened exact matching (CEM) as an alternative matching method to address potential self-selection bias (Iacus et al., 2012). Different from PSM, which integrates multiple variables into one single propensity score to account for possible tendency to receive treatment, CEM allows researchers to match and retain groups according to multiple attributes. Targeted at approximating a fully blocked randomized experimental design, CEM ensures that the potential bias caused by unobserved variables is eliminated on average across each homogeneous block. We match our treatment group with a control group using CEM and run the DID analysis again to make sure our inference is robust to possible unobserved selection bias. The variables used for matching and the DID results are reported in Appendix E, which show qualitatively consistent results. Given these results, we are confident that our estimation is robust to possible bias induced by matching. 4.3.4 Alternative Measurements on Novelty In our main analysis, we use two approaches (i.e., short-text topic modelling and swin-transformer) to quantify creators’ exploratory behavior in the textual and visual dimensions. As a robustness check, we leverage alternative methods to extract content novelty. For the textual content, we restrict the number of topics for the GSDMM algorithm to 10 to generate the topic embeddings. For the visual content, we employ Amazon Rekognition API10 to detect the objects in video frames. This service can detect 2,587 object types in the video frames and provides the confidence scores for each object detected. We convert the detection results for each video into a 10 See: https://aws.amazon.com/free/machine-learning/?nc1=h_ls 37 2,587-dimensional vector to represent the visual content. Two alternative novelty measurements – 𝑇𝑇𝑉𝑉𝑆𝑆𝑖𝑖𝑆𝑆10𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖 and 𝑂𝑂𝑂𝑂𝑂𝑂𝑉𝑉𝑆𝑆𝑡𝑡𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖 – are calculated based on these two embeddings. Furthermore, all previous measures are based on the cosine difference between the focal video and the creators’ previous three videos. We further check the sensitivity of this estimation by comparing the focal video to the creator’s previous one video or previous five videos (i.e., 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇1𝑖𝑖𝑖𝑖, 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇1𝑖𝑖𝑖𝑖, 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇5𝑖𝑖𝑖𝑖, 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇5𝑖𝑖𝑖𝑖). Appendix F reports the details of the embedding generation process and presents the DID results with alternative measurements. The estimation results suggest that the effect of AI voice adoption on content novelty is consistent across different embeddings and with different reference sets. 38 Chapter 5: Additional Analysis 5.1 Heterogeneous Effect Our main results and robustness checks suggest that AI-generated voice adoption significantly increases creators’ productivity and alters their creation patterns (i.e., video quality, novelty, identity disclosure, sentiment, politeness). In this section, we further conduct heterogenous effect analyses to uncover the underlying mechanisms. First, video creation is difficult, so users’ efficacy in production is a crucial factor in their decision of whether and how to generate content. AI voice simplifies the creation task, thus motivating people to produce. If this mechanism stands, it is expected that the positive effect of AI voice adoption on video quantity is more salient for less-experienced users who have more difficulty making videos. To test this mechanism, we divide the creators into two experience levels based on the number of videos they posted before treatment. The dummy variable 𝐼𝐼𝑉𝑉𝑉𝑉𝑇𝑇𝑆𝑆𝑉𝑉𝐷𝐷𝑖𝑖𝑉𝑉𝑉𝑉𝑆𝑆𝑉𝑉𝑉𝑉𝑖𝑖 equals 1 if creator 𝑖𝑖’s number of posts before treatment is below the median of the whole sample, and 0 otherwise. We examine the heterogeneity in user experience by adding a three-way interaction term 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 × 𝐼𝐼𝑉𝑉𝑉𝑉𝑇𝑇𝑆𝑆𝑉𝑉𝐷𝐷𝑖𝑖𝑉𝑉𝑉𝑉𝑆𝑆𝑉𝑉𝑉𝑉𝑖𝑖 in the DID specification. Table 5.1 presents the result. The overall effect in column 1 indicates that inexperienced users create significantly more videos than experienced users after the adoption of AI voice. Moreover, the spillover effect to their human-voice videos is also more salient for inexperienced creators as shown in column 2. This implies that the adoption of AI voice has a larger impact on inexperienced users in motivating them to create more videos and become more engaged in the creation process. 39 (1) (2) Overall Effect - 𝑉𝑉𝑖𝑖𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 Spillover Effect - 𝑉𝑉𝑖𝑖𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 Model Negative Binomial Negative Binomial 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 × 𝐼𝐼𝑉𝑉𝑉𝑉𝑇𝑇𝑆𝑆𝑉𝑉𝐷𝐷𝑖𝑖𝑉𝑉𝑉𝑉𝑆𝑆𝑉𝑉𝑉𝑉𝑖𝑖 0.244*** 0.157*** (0.046) (0.047) 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.127*** 0.045* (0.020) (0.21) N 33543 33264 Adjusted R2 0.231 0.231 Creator FE Yes Yes Week FE Yes Yes Note: *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1. We include all lower-order interactions and base variables in the analysis, but do not report them here for simplicity. Table 5.1 Heterogeneous Effect of Experience Second, creators post on video-sharing platforms to present their self-identity and maintain a good social image. AI voice will substitute users’ vocal identity and reduce their image motivation. If this mechanism holds, we would expect that more popular or well-known creators have more concerns regarding their identities being replaced by AI. They might have fewer incentives to create videos with AI voice, and even if they do, they will try to protect their own identities in the videos. To test this, we split the creators into a popular group and a less-popular group based on the average number of likes received for each of their videos prior to treatment. The dummy variable 𝑃𝑃𝑉𝑉𝑆𝑆𝐷𝐷𝑇𝑇𝐷𝐷𝐷𝐷𝑖𝑖 equals 1 if creator 𝑖𝑖’s average number of likes is above the median of the whole sample and 0 otherwise. Table 5.2 reports the heterogeneous effect for popularity. As shown in column 1, the positive effect on video quantity is smaller for popular creators after the adoption of AI voice. Moreover, although popular creators also appear less frequently in their AI-voice videos (column 3), they do tend to refer to themselves and talk about themselves more frequently in those videos (column 2) to retain their identity. 40 (1) (2) (3) DV Overall Effect 𝑉𝑉𝑖𝑖𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 Direct Effect 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑉𝑉𝑆𝑆𝑉𝑉𝐷𝐷𝑉𝑉𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 Direct Effect 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑉𝑉𝐷𝐷𝐷𝐷𝐷𝐷𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 Model Negative Binomial OLS OLS 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑆𝑆𝐷𝐷𝑇𝑇𝐷𝐷𝐷𝐷𝑖𝑖 -0.113** 1.209*** -0.046+ (0.294) (0.297) (0.025) 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.253*** -1.192*** -0.032+ (0.030) (0.207) (0.017) N 33543 22366 12219 Adjusted R2 0.229 0.131 0.567 Creator FE Yes Yes Yes Week FE Yes Yes Yes Note: *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1. We include all lower-order interactions and base variables in the analysis, but do not report them here for simplicity. Table 5.2 Heterogeneous Effect of Popularity 5.2 Effect on Consumer Engagement Our main analysis focuses on the supply side by investigating generative AI’s impact on content creators. It is also important to understand its effect on the demand side to shed light on how it affects consumer engagement on the platform. Therefore, we conduct an additional analysis to examine how the use of AI voice in videos affects consumer engagement by looking at the direct effect in the DID specification on consumers’ views and likes for the videos (i.e., 𝑉𝑉𝑖𝑖𝑉𝑉𝑉𝑉𝑖𝑖𝑖𝑖 and 𝐿𝐿𝑖𝑖𝑊𝑊𝑉𝑉𝑖𝑖𝑖𝑖). As shown in Table 5.3, the use of AI voice in videos leads to significantly fewer consumer views and likes. This suggests that creators’ AI-voice videos received lower consumer engagement. However, we caution that it is hard to tell whether this reduced consumer engagement is caused by the use of AI-voice only, or the changes in other video properties induced by creators’ AI use as shown in our various analyses (e.g., shorter length, more explorational content with AI voice, more negative sentiment, and fewer polite words). Our results suggest that AI voice adoption significantly increases creators’ productivity and affects their video creation patterns, whereas these changes may lead to potential negative outcomes on the consumer side. 41 (1) (2) DV log (𝑉𝑉𝑖𝑖𝑉𝑉𝑉𝑉 + 1)𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 log (𝐿𝐿𝑖𝑖𝑊𝑊𝑉𝑉 + 1)𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.230*** -0.284*** (0.040) (0.044) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.030 -0.048 (0.037) (0.041) N 22366 22366 Adjusted R2 0.781 0.772 Creator FE Yes Yes Week FE Yes Yes Note: *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1. Creator fixed effect and week fixed effect are controlled. Table 5.3 The Direct Effect of AI-Generated Voice Adoption on Consumer Engagement 42 Chapter 6: Conclusions As video is increasingly dominating consumers’ online experience, how to supply abundant and engaging video content has become an important managerial question for the success of video-sharing platforms. Our research provides the first empirical evidence on the connection between human and AI in this emerging market by examining the effect of AI-generated voice adoption on the creation of user-generated videos. We find that the adoption of AI-generated voice can motivate creators to generate significantly more videos. However, the videos produced are of shorter length, less novelty, and contain more negative sentiment and fewer polite words. Interestingly, we also find that creators explore novel content with less identity disclosure in their AI-voice videos. Additional heterogeneous effect analysis suggests that two possible underlying mechanisms might induce these changes. While AI-generated voice simplifies the video creation task partially and enhances creators’ efficacy in producing videos, it also hampers their image motivation by displacing creators’ vocal identity in the video. Moreover, we find that the use of AI-generated voice might unintendedly results in lower consumer engagement. 6.1 Theoretical and Managerial Contributions This study makes important contributions to the management literature on AI and human connections. First, this research contributes to the nascent literature on the impact of generative AI on creator economy (Lysyakov & Viswanathan, 2022), extending prior studies mostly focusing on how predictive AI affects human decision-making processes (Brynjolfsson et al., 2019; Dixon et al., 2021; Fügener et al., 2021, 2022; Ge et al., 2021; Jussupow et al., 2021). Our study examines a novel phenomenon where generative AI is employed to assist video creation by humans. Our results show that the generative AI voice has a significant impact on creators’ video 43 creation patterns. While it helps simplify the video creation process and empower creators, the generated speech is directly incorporated into the creators’ final video, thus affecting creators’ image motivation. Second, our research addresses a new challenge in UGC studies. Previous work has primarily concentrated on textual UGC (Burtch et al., 2022; Huang et al., 2017; Qiu & Kumar, 2017), where content generation is relatively easy. With the prevalence of video content, users’ efficacy becomes another factor restricting content creation. Previous approaches designed to increase image motivation, like symbolic awards (Gallus, 2017), hierarchical incentives (Goes et al., 2016), and social endorsement (Qiu & Kumar, 2017), might have limited effects in motivating users to generate videos. Our study shows that the AI-generated voice induces creators to generate more videos, suggesting that AI can be an alternative and promising approach to solving this critical challenge in video platforms. Meanwhile, we find that AI voice might cause an inverse effect on online platforms. Creators tend to hide their identities with AI voices to exert negative emotions, indicating decreased image motivation. This finding relates to the literature on the dark side of AI, highlighting the imperative to investigate viable approaches for preserving the image motivation and self-presentation concerns of users engaged in collaborative creation with generative AI tools. Third, our study extends the stream of literature on unstructured data analytics (Hwang et al., 2022; Kwon et al. 2022; Lee et al., 2018; Park et al., 2022; Shin et al., 2020; Zhang et al., 2022; Zhou et al., 2021) by operationalizing important theoretical constructs (e.g., content quality, novelty, and identity disclosure) in a new research context of video creation. 44 Managerially, our study provides important insights for platform managers to build a sustainable creator ecosystem. With the rapid advancements of AI algorithms, particularly the untapped power of generative AI in business contexts, managers should consider leveraging AI-based tools to stimulate creators’ video production, as our research demonstrates that the adoption of AI-generated voice effectively increases creators’ video creation productivity. Nevertheless, our results also caution that creators’ usage of AI tools may lead to unintended consequences, such as negative impact on content quality, novelty, sentiment, and politeness. These novel results warrant more attention from platforms and policy makers to design better strategies for the prosperity of the digital economy. 6.2 Limitations and Future Research While we believe that our study represents an important advancement in understanding human-AI connection in the digital economy using unstructured video data, there are a few limitations to this research. First, our empirical analysis focuses on the introduction of the first AI-generated female voice on video sharing platforms. It remains unclear if creators may react differently to the introduction of other types of AI-generated voice. Future studies can extend our analysis to a time when different types of AI voices are available in the platform to investigate potential heterogeneous effects. 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Information Systems Research, 31(1), 176-197. 55 Appendices Appendix A Example of AI-Generated Voice on TikTok Figure A1 illustrates the basic procedure for using TikTok AI-generated voice when creating videos. First, in the TikTok app, creators need to type down words they hope to say on the screen. Second, creators should click on the text and choose “text-to-speech” from the toolbar. The speech generated by AI will be added to the video. To get a better understanding of the procedure, please see the following video instruction: https://www.youtube.com/watch?v=g7EpaXd7Hok. Figure A1 Example of How to Use TikTok AI-Generated Voice 56 Appendix B Textual and Visual Novelty Measures with Embeddings To understand how the adoption of AI-generated voice affects the creativity of the video creators, we measure the novelty of videos in textual and visual dimensions. For the text side, we apply short-text topic modeling because the text descriptions of the videos are usually short. The visual novelty is measured with the video embeddings constructed with a pre-trained transformer-based model. B.1 Topic Embedding with GSDMM In order to comprehensively capture the textual dimension of the videos, we combine the transcripts and video descriptions as the documents. Because the concatenated documents are still short, we use short-text topic modeling with GSDMM (Yin & Wang, 2014). This model takes in the distinct words in each document and clusters the documents into different topics. Setting the model parameter 𝛼𝛼 = 0.1 and 𝛽𝛽 = 0.1 based on the authors’ suggestions, the number of topics automatically converge to 30 after 12 rounds of iteration. Table A1 presents the top 10 keywords for the 30 topics. With a qualitative examination, we find the topics are capturing the topics of our sample videos well. Since the GSDMM model is well evaluated in the original paper, we do not perform further quantitative evaluation. We then convert each document from a video into a 30-dimensional topic embedding and compute the cosine distance between each focal document with the creator’s previous three video documents to generate the 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖 measurement. In Appendix 5, we also conduct robustness check by using different number of topics and by comparing a focal video with different number of previous videos. We find our empirical results are robust. 57 Topic Top 10 Keywords Topic 0 dog, dogsoftiktok, puppy, love, cute, pet, baby, life, cat, guy Topic 1 fitness, workout, gym, love, weightloss, work, motivation, guy, weight, body Topic 2 makeup, beauty, skin, love, eye, product, hair, skincare, color, pretty Topic 3 gaming, gamer, comedy, funny, twitch, game, lol, warzone, streamer, giveaway Topic 4 comedy, funny, love, man, fuck, girl, god, guy, fucking, bitch Topic 5 football, soccer, sport, ronaldo, messi, shoutoutot, neymar, nfl, szn, futbol Topic 6 witchtok, babywitch, divination, tarot, tarotreading, jameshalfaday, farmer, johndeere, farm, trump Topic 7 asmr, satisfying, oddlysatisfying, asmrsounds, diy, asmrvideo, lip, beauty, makeup, skincare Topic 8 christmas, rain, santa, snow, room, heaven, holiday, love, panda, year Topic 9 learnontiktok, health, doctor, plasticsurgery, tiktokpartner, body, surgery, skin, medical,guy Topic 10 food, guy, love, eat, recipe, add, today, foodie, cheese, cooking Topic 11 lgbt, love, gay, wlw, lgbtq, marvel, bi, feel, site, girl Topic 12 cheer, gym, cheerleader, tumble, flip, allstarcheer, stunt, stunting, cheerleading, park Topic 13 love, funny, asian, momsoftiktok, song, parati, music, comedy, man, mom Topic 14 love, life, feel, god, christian, person, jesus, feeling, happy, spiritual Topic 15 love, girl, baby, friend, god, dance, man, transition, happy, feel Topic 16 guy, thought, today, fact, talk, reptile, king, animal, tick, tiktokhunter Topic 17 diy, guy, love, work, video, car, clean, link, bio, start Topic 18 check, dance, cut, party, big, boy, night, bus, young, band Topic 19 vital, recital, rock, tricky, chicken, drinking, turkey, hair, girly, curly Topic 20 guy, video, talk, love, life, feel, year, friend, comment, work Topic 21 weeb, roadto, anime, onepiece, wanokuniarc, naruto, luffy, aot, man, god Topic 22 bobby, green, tea, ross, db, bitch, marly, entrance, barbie, bos Topic 23 strong, life, meet, mi, fa, ng, nice, mucho, custo, dinner Topic 24 harrystyles, onedirection, louistomlinson, niallhoran, liampayne, zaynmalik, love, tpwk, larrystylinson, fineline Topic 25 tiktokchallenge, dance, tiktokcreators, blacktiktok, sashaandre, victoriaandre, viiral, shinaandre, shit, deep Topic 26 love, guy, cute, video, today, favorite, kind, super, pretty, color Topic 27 parati, soyjuanjoselv, mexicangal, la, love, rundeal, eljuanjo, party, lartino, man Topic 28 love, guy, comedy, funny, today, girl, man, mom, god, friend Topic 29 biden, politics, trump, news, election, democrat, talk, yahoonews, president, covid Table A1 Top 10 Keywords for the 30 Topics 58 B.2 Video Embedding with Swin-Transformer Next, we use visual information of the videos to measure the visual novelty of videos. Swin-transformer is a state-of-the-art image transformer model that is widely adopted as the backbone architecture for object detection, image classification, and image-based search engines (Liu et al., 2021). This model takes in the raw image and converts it into a 1,024-dimensional vector representation. It learns the pixel-level features as well as the latent semantics in images. To measure the visual similarity of a pair of images, we calculate cosine similarity of their respective image vectors. For a qualitative evaluation, Figure A2 shows three randomly selected images from our video samples and their five most similar images according to the cosine similarity scores based on the swin-transformer model. We can find that this transformer performs well in recognizing the semantics and key objects in images. Because the original paper shows the effectiveness of the transformer model, we do not perform additional evaluation. Finally, we calculate the 𝐼𝐼𝐼𝐼𝐷𝐷𝑖𝑖𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖 of a creator’s video by comparing the cosine difference between its vector representation against those from the previous videos posted by the same creator. 59 Figure A2 The Five Most Similar Images Calculated with Swin-Transformer 60 Appendix C Examples of Sentiment and Politeness Measurement Table A2 presents three examples of the sentiment and politeness measurements. For the sentiment, the examples prove that the roBERTa algorithm can perform well on sentiment detection by capturing the latent meanings in languages. Even though sentence 1 and sentence 2 does not contain any sentiment-related words (e.g., happy, sad, cry), the algorithm measures the positive and negative sentiment level accurately. If the expression is neutral, both values (i.e., positive, and negative) are low. The level of politeness is calculated with the proportion of polite words (e.g., hi, hello, please, good morning) in transcripts. Sentence 1 has the highest politeness level because 2 out of 7 words in the text are polite words. Sentence 3 has the lowest level of politeness because it contains no polite words. Examples in Transcripts Positive Negative Polite 1. Hi everyone, welcome to my new home! 0.903 0.002 0.286 2. It is such a rainy day. Please let me stay indoors. 0.025 0.799 0.091 3. I am heading to the parking lot. 0.090 0.021 0.000 Note: The underlined text is the words matched to the LIWC “politeness” dictionary. Table A2 Example of Sentiment and Politeness Measurements Outputs 61 Appendix D Sub-sample Matching for Creators with Clear Faces in Avatars For the empirical analysis of the identity disclosure measure, we need to use video samples from the creators with clear face images in their profiles. We use the API service provided by Face++11 to detect the faces in the creator’s avatars and analyze their attributes. We define creators with a clear face as those: (i) showing a human face in their avatars, (ii) not wearing dark glass, and (iii) whose faces are not occluded because of face angles or other accessories. Figure A3 provides some examples to illustrate our criteria. With the face analysis, we found that 1,164 out of the 2,225 treated creators have a clear face in their avatars. We generate the outcome variable 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑉𝑉𝐷𝐷𝐷𝐷𝐷𝐷𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖 on these creators to examine how the adoption of AI-generated voice affects creators’ identity disclosure. We use the same variables mentioned in Section 4.1.1 to conduct look-ahead propensity score matching. The matching results are presented in Table A3. The matching significantly decreased the difference between the treatment group and the control group across all variables. Although the estimate of VideoCnt_pre2 is still statistically significant, the overall results show that the gaps between the two groups are significantly decreased after the matching. 11 https://www.faceplusplus.com/ 62 Figure A3 Face Examples Before Matching After Matching Treated Control Diff. T-statistics Treated Control Diff. T-statistics VideoCnt_pre4 6.332 4.053 2.278 7.578*** 6.009 5.527 0.481 1.267 VideoCnt_pre3 6.314 4.046 2.268 8.032*** 6.087 5.437 0.650 1.743+ VideoCnt_pre2 6.296 4.009 2.288 8.565*** 6.112 5.423 0.689 2.012* VideoCnt_pre1 6.332 3.986 2.346 8.947*** 6.104 5.466 0.638 1.901+ Duration 21.988 20.855 1.134 2.359* 21.987 21.596 0.391 0.725 WordCnt 47.772 44.879 2.893 2.020* 47.714 47.250 0.464 0.276 SceneCnt 6.113 6.074 0.039 0.184 6.190 6.039 0.152 0.615 BigWord 7.261 6.396 0.865 5.461*** 7.254 7.315 -0.061 -0.320 TextualNovelty 0.504 0.456 0.049 4.806*** 0.507 0.513 -0.006 -0.544 VisualNovelty 0.447 0.419 0.029 3.914*** 0.449 0.440 0.009 1.148 SelfReference 7.167 6.230 0.937 6.508*** 7.198 7.186 0.012 0.069 SelfAppearance 0.648 0.561 0.087 6.407*** 0.646 0.670 -0.024 -1.501 DistinctFaceCnt 1.488 1.316 0.172 4.841*** 1.495 1.471 0.024 0.580 Positive 0.310 0.285 0.025 3.815*** 0.313 0.308 0.004 0.524 Negative 0.244 0.201 0.043 7.553*** 0.243 0.250 -0.007 -0.975 Polite 0.959 0.851 0.109 1.407 0.969 0.951 0.018 0.174 LogPlayCnt 9.905 8.804 1.101 7.493*** 9.908 9.837 0.071 0.457 LikeRatio 0.111 0.094 0.017 7.371*** 0.111 0.111 -0.158 0.875 Note: *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1. Table A3 Look-Ahead Propensity Score Matching Result for the Subsample of Creators with Clear Faces 63 Appendix E DID Results with Coarsened Exact Matching As a robustness check to our main results using the PSM-DID approach, we also conduct the DID analysis using coarsened exact matching (CEM). We use the following variables for CEM: • The number of active weeks (posting videos) in the pre-treatment period. • The number of videos posted in the pre-treatment period. • The average duration of videos in the pre-treatment period. • The creator’s tenure before adopting the AI-generated voice. • The average number of views for the videos posted in the pre-treatment period. The DID results based on the CEM are reported in Tables A4-A6. We find the DID-CERM results are qualitatively similar to the main results. Specifically, after the AI-voice adoption, the creators post more videos, but the posted videos have shorter durations, fewer words, fewer scenes, and more big words. Also, the textual novelty decreases after the adoption, while the visual novelty does not change significantly. Finally, the creators use less self-referential, less positive, and less polite words in the videos. Quantity Quality (1) (2) (3) (4) (5) (6) DV 𝑉𝑉𝑖𝑖𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 (Poisson) 𝑉𝑉𝑖𝑖𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 (NB) 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑡𝑡𝑖𝑖𝑉𝑉𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 (OLS) 𝑊𝑊𝑉𝑉𝐷𝐷𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 (OLS) 𝑆𝑆𝑆𝑆𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 (OLS) 𝐵𝐵𝑖𝑖𝑖𝑖𝑊𝑊𝑉𝑉𝐷𝐷𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 (OLS) 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.154*** 0.165*** -0.828*** -1.849** -0.172+ 0.677*** (0.020) (0.018) (0.202) (0.636) (0.096) (0.110) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.033** 0.036*** 0.208 -0.243 0.077 0.059 (0.011) (0.012) (0.171) (0.527) (0.077) (0.100) N 74016 74016 48909 48909 48909 48909 Adjusted R2 0.550 0.238 0.554 0.593 0.562 0.217 Creator FE Yes Yes Yes Yes Yes Yes Week FE Yes Yes Yes Yes Yes Yes Note: *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1. Table A4 The Overall Effect of AI-Generated Voice Adoption on Video Quantity and Quality (CEM-DID) 64 (1) (2) DV 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.017** 0.000 (0.005) (0.003) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.011* 0.005* (0.005) (0.002) N 48909 48909 Adjusted R2 0.456 0.570 Creator FE Yes Yes Week FE Yes Yes Note: *** p < 0.001, ** p <0.01, * p < 0.05, + p < 0.1. Table A5 The Overall Effect of AI-Generated Voice Adoption on Video Novelty (CEM-DID) Identity Disclosure Sentiment & Politeness (1) (2) (3) (4) (5) DV 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑉𝑉𝑆𝑆𝑉𝑉𝐷𝐷𝑉𝑉𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑆𝑆𝑉𝑉𝑇𝑇𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑉𝑉𝐷𝐷𝐷𝐷𝐷𝐷𝑉𝑉𝑆𝑆𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑃𝑃𝑉𝑉𝑖𝑖𝑖𝑖𝑡𝑡𝑇𝑇𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑇𝑇𝑉𝑉𝑖𝑖𝐷𝐷𝑡𝑡𝑖𝑖𝑇𝑇𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑃𝑃𝑉𝑉𝑇𝑇𝑖𝑖𝑡𝑡𝑉𝑉𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.187* -0.001 -0.016*** 0.005 -0.145* (0.093) (0.007) (0.004) (0.004) (0.069) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.197* -0.003 -0.003 0.002 -0.077 (0.087) (0.007) (0.004) (0.004) (0.072) N 48909 25221 48909 48909 48909 Adjusted R2 0.138 0.618 0.183 0.183 0.065 Creator FE Yes Yes Yes Yes Yes Week FE Yes Yes Yes Yes Yes Note: *** p < 0.001, ** p <0.01, * p < 0.05, + p < 0.1. SelfAppearanceit is regressed on the matched sub-sample of creators who have a clear face in their avatar. Table A6 The Overall Effect of AI-Generated Voice Adoption on Identity Disclosure, Sentiment, and Politeness (CEM-DID) 65 Appendix F Alternative Measures of Textual and Visual Novelty F.1 Object Embeddings Example As a robustness check, we employ another approach to generate image embeddings to ensure the robustness of the empirical results. We first exploit the Amazon Rekognition service to detect the objects in images. This algorithm detects 2,587 common objects and provides the confidence score of the detection. We convert each image into a 2,587-dimension vector embedding with each dimension representing one object. If the object is detected in the image, we fill in the confidence score in the corresponding dimension. Otherwise, the same dimension is left as 0. Figure A4 illustrates the detailed procedure. Then we use cosine distance scores of the vector embeddings to calculate the visual novelty. 0.76Mushroom-0.92Knife-0.71Hand-0.83Pan-0.95Pot-0.500.50 0.95 0.83 0.71 ... 0.92Mushroom0.92Pan0.760 0 0 ... 0.92Pot Pan Hand Knife MushroomPot Hand Knife MushroomPan Figure A4 Object Embeddings Example F.2 DID Results with Alternative Textual and Visual Novelty Measures Now we report the DID estimation results with alternative textual and visual novelty measures. In Table A7, we use a 10-topic GSDMM model (instead of 30 topics in the main analysis) to construct 𝑇𝑇𝑉𝑉𝑆𝑆𝑖𝑖𝑆𝑆10𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖 measure and use the object detection results from Amazon 66 Rekognition services to construct 𝑂𝑂𝑂𝑂𝑂𝑂𝑉𝑉𝑆𝑆𝑡𝑡𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖 as an alternative visual novelty measure. The results show that the majority of the coefficients are consistent with the main analysis. It is worth noting that the negative overall and spillover effects on textual novelty (i.e., 𝑇𝑇𝑉𝑉𝑆𝑆𝑖𝑖𝑆𝑆10𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 and 𝑇𝑇𝑉𝑉𝑆𝑆𝑖𝑖𝑆𝑆10𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻) becomes significant with 10-topic GSDMM embeddings. The estimates of 𝑂𝑂𝑂𝑂𝑂𝑂𝑉𝑉𝑆𝑆𝑡𝑡𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 and 𝑂𝑂𝑂𝑂𝑂𝑂𝑉𝑉𝑆𝑆𝑡𝑡𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 are not statistically significant, but the direction of the effect remains the same. (1) (2) Panel A: Overall Effect DV 𝑇𝑇𝑉𝑉𝑆𝑆𝑖𝑖𝑆𝑆10𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑂𝑂𝑂𝑂𝑂𝑂𝑉𝑉𝑆𝑆𝑡𝑡𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.013* -0.002 (0.006) (0.004) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.008 0.008* (0.006) (0.004) N 26698 26698 Adjusted R2 0.428 0.544 Panel B: Direct Effect on AI-Voice Videos DV 𝑇𝑇𝑉𝑉𝑆𝑆𝑖𝑖𝑆𝑆10𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑂𝑂𝑂𝑂𝑂𝑂𝑉𝑉𝑆𝑆𝑡𝑡𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.019* 0.021*** (0.008) (0.005) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.006 0.007 (0.007) (0.004) N 22366 22366 Adjusted R2 0.385 0.495 Panel C: Spillover Effect on Human-Voice Videos DV 𝑇𝑇𝑉𝑉𝑆𝑆𝑖𝑖𝑆𝑆10𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑂𝑂𝑂𝑂𝑂𝑂𝑉𝑉𝑆𝑆𝑡𝑡𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.016** -0.005 (0.006) (0.004) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.004 0.006+ (0.006) (0.004) N 26236 26236 Adjusted R2 0.425 0.546 Note: *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1. Creator fixed effect and week fixed effect are included. Table A7 DID Estimation Results on Novelty Using Alternative Embeddings Table A8 reports the DID estimation results using different reference sets. Specifically, when comparing a focal video to the previous videos from the same creator, we use previous one and five previous videos (instead of three as in the main analysis) to construct 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇1𝑖𝑖𝑖𝑖, 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇1𝑖𝑖𝑖𝑖, 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇5𝑖𝑖𝑖𝑖and 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇5𝑖𝑖𝑖𝑖 measures. The results show that 67 all coefficients using alternative references (i.e., previous 1 video, previous 5 videos) are constantly significant in the same directions as well. Given these results, we are confident that our empirical results on the textual and video novelty are robust. (1) (2) (3) (4) Panel A: Overall Effect DV 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇1𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇1𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇5𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇5𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴𝐴𝐴 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.010 -0.007* -0.008 -0.006* (0.007) (0.003) (0.006) (0.003) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.000 0.010** 0.005 0.007* (0.008) (0.003) (0.006) (0.003) N 26698 26698 26698 26698 Adjusted R2 0.401 0.511 0.562 0.241 Panel B: Direct Effect on AI-Voice Videos DV 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇1𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇1𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇5𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇5𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 0.026*** 0.015*** 0.024** 0.019*** (0.009) (0.004) (0.008) (0.004) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.003 0.007+ 0.008 0.006+ (0.009) (0.004) (0.007) (0.003) N 22366 22366 22366 22366 Adjusted R2 0.365 0.472 0.445 0.523 Panel C: Spillover Effect on Human-Voice Videos DV 𝑇𝑇𝑉𝑉𝑇𝑇𝑆𝑆𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇1𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇1𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑇𝑇𝑉𝑉𝑇𝑇𝑆𝑆𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇5𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑉𝑉𝑖𝑖𝑖𝑖𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝑉𝑉𝑇𝑇𝑉𝑉𝑇𝑇𝑡𝑡𝑇𝑇5𝑖𝑖𝑖𝑖𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑇𝑇𝐷𝐷𝑉𝑉𝐷𝐷𝑡𝑡𝑖𝑖 × 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.014+ -0.009** -0.012* -0.009** (0.007) (0.003) (0.006) (0.003) 𝑃𝑃𝑉𝑉𝑖𝑖𝑡𝑡𝑖𝑖𝑖𝑖 -0.005 0.008* -0.001 0.004 (0.008) (0.003) (0.006) (0.003) N 26236 26236 26236 26236 Adjusted R2 0.401 0.477 0.487 0.575 Note: *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1. Creator fixed effect and week fixed effect are included. Table A8 DID Estimation Results on Novelty Using Alternative Reference Sets
Thesis/Dissertation
eng
Business Administration - Management Information Systems
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Lee, Gene Moo
Zhou, Mi
How does AI-generated voice affect online video creation? : evidence from TikTok
Text