Tag Archives: individual-level

Generative AI and Creator Economy: Investigating the Effects of AI-Generated Voice on Online Video Creation

Zhang, Xiaoke, Mi Zhou, Gene Moo Lee Generative AI and Creator Economy: Investigating the Effects of AI-Generated Voice on Online Video Creation, R&R at Management Science.

Generative artificial intelligence (AI) has the potential to revolutionize the creative industry by reshaping the human creative process. We explore the potential of generative AI in the creator economy by investigating the effects of AI-generated voice adoption on creators’ productivity and creative patterns on TikTok, one of the world’s largest video-sharing platforms. Using a unique dataset of 554,252 videos from 4,691 TikTok creators, we conduct multimodal analyses of the video data to detect the adoption of AI voice and to quantify video characteristics. We then estimate the adoption effects using a stacked difference-in-differences model coupled with propensity score matching. Our results suggest that AI voice adoption significantly increases creator productivity. This effect is larger among less experienced or less popular creators, suggesting an equalizing effect of generative AI. Moreover, we find that the use of AI voice enhances video novelty across image, audio, and text modalities, especially among experienced creators, suggesting its role in reducing routine workload and fostering creative exploration. Lastly, our study also uncovers a disinhibition effect, where creators conceal their identities with the AI voice and exert more negative sentiments because of diminished social image concerns. Our paper provides the first empirical evidence of how generative AI reshapes online video creation.

Learning Faces to Predict Matching Probability in an Online Dating Market (ICIS 2022)

Kwon, Soonjae, Gene Moo Lee, Dongwon Lee, Sung-Hyuk Park (2024) “Digital Cupid: Empowering Generative AI for Fair and Efficient Matchmaking,” Working Paper.

  • Previous title: Learning Faces to Predict Matching Probability in an Online Dating Market
  • Presentations: DS (2021), AIMLBA (2021), WITS (2021), ICIS (2022)
  • Preliminary version in ICIS 2022 Proceedings
  • Based on an industry collaboration

With the increasing prevalence of online transactions, enhancing matching efficiency has emerged as a critical objective for most matching platforms. However, these efforts often lead to decreased fairness, making it challenging to balance these two elements. This study presents a novel generative AI-based approach to increase the platform’s efficiency and fairness simultaneously in the context of online dating. By developing a model that utilizes users’ multimodal features to predict individual preferences, we assess the impact of various matching algorithms on platform efficiency and fairness. Extensive simulations show that our fairness-aware algorithm significantly enhances both metrics, addressing conventional methods’ severe efficiency-fairness tradeoff issue. We also introduce a novel generative AI-based personalization technique that modifies users’ profile images in different directions according to their counterparts, further boosting efficiency without sacrificing fairness. Our matching framework can be applied to platforms with various objectives, contributing to all stakeholders in digital platforms.

When Does Congruence Matter for Pre-roll Video Ads? The Effect of Multimodal, Ad-Content Congruence on the Ad Completion

Park, Sungho, Gene Moo Lee, Donghyuk Shin, Sang-Pil Han. “When Does Congruence Matter for Pre-roll Video Ads? The Effect of Multimodal, Ad-Content Congruence on the Ad Completion, Working Paper [Last update: Jan 29, 2023]

  • Previous title: Targeting Pre-Roll Ads using Video Analytics
  • Funded by Sauder Exploratory Research Grant 2020
  • Presented at Southern Methodist University (2020), University of Washington (2020), INFORMS (2020), AIMLBA (2020), WITS (2020), HKUST (2021), Maryland (2021), American University (2021), National University of Singapore (2021), Arizona (2022), George Mason (2022), KAIST (2022), Hanyang (2022), Kyung Hee (2022), McGill (2022)
  • Research assistants: Raymond Situ, Miguel Valarao

Pre-roll video ads are gaining industry traction because the audience may be willing to watch an ad for a few seconds, if not the entire ad, before the desired content video is shown. Conversely, a popular skippable type of pre-roll video ads, which enables viewers to skip an ad in a few seconds, creates opportunity costs for advertisers and online video platforms when the ad is skipped. Against this backdrop, we employ a video analytics framework to extract multimodal features from ad and content videos, including auditory signals and thematic visual information, and probe into the effect of ad-content congruence at each modality using a random matching experiment conducted by a major video advertising platform. The present study challenges the widely held view that ads that match content are more likely to be viewed than those that do not, and investigates the conditions under which congruence may or may not work. Our results indicate that non-thematic auditory signal congruence between the ad and content is essential in explaining viewers’ ad completion, while thematic visual congruence is only effective if the viewer has sufficient attentional and cognitive capacity to recognize such congruence. The findings suggest that thematic videos demand more cognitive processing power than auditory signals for viewers to perceive ad-content congruence, leading to decreased ad viewing. Overall, these findings have significant theoretical and practical implications for understanding whether and when viewers construct congruence in the context of pre-roll video ads and how advertisers might target their pre-roll video ads successfully.

On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data (ISR 2021)

Kwark, Young*, Gene Moo Lee*, Paul A. Pavlou*, Liangfei Qiu* (2021) On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data. Information Systems Research 32(3): 895-913. (* equal contribution)

  • Data awarded by Wharton Consumer Analytics Initiative
  • Presented in WCBI (Snowbird, UT 2015), KMIS (Busan, Korea 2016), Minnesota (2016), ICIS (Dublin, Ireland 2016), Boston Univ. (2017), HEC Paris (2017), and Korea Univ. (2018)
  • An earlier version was published in ICIS 2016
  • Research assistants: Bolat Khojayev, Raymond Situ

We study the spillover effects of the online reviews of other covisited products on the purchases of a focal product using clickstream data from a large retailer. The proposed spillover effects are moderated by (a) whether the related (covisited) products are complementary or substitutive, (b) the choice of media channel (mobile or personal computer (PC)) used, (c) whether the related products are from the same or a different brand, (d) consumer experience, and (e) the variance of the review ratings. To identify complementary and substitutive products, we develop supervised machine-learning models based on product characteristics, such as product category and brand, and novel text-based similarity measures. We train and validate the machine-learning models using product pair labels from Amazon Mechanical Turk. Our results show that the mean rating of substitutive (complementary) products has a negative (positive) effect on purchasing of the focal product. Interestingly, the magnitude of the spillover effects of the mean ratings of covisited (substitutive and complementary) products is significantly larger than the effects on the focal product, especially for complementary products. The spillover effect of ratings is stronger for consumers who use mobile devices versus PCs. We find the negative effect of the mean ratings of substitutive products across different brands on purchasing of a focal product to be significantly higher than within the same brand. Lastly, the effect of the mean ratings is stronger for less experienced consumers and for ratings with lower variance. We discuss implications on leveraging the spillover effect of the online product reviews of related products to encourage online purchases.

A Friend Like Me: Modeling Network Formation in a Location-Based Social Network (JMIS 2016)

Lee, Gene Moo*, Liangfei Qiu*, Andrew B. Whinston* (2016) A Friend Like Me: Modeling Network Formation in a Location-Based Social Network, Journal of Management Information Systems 33(4), pp. 1008-1033. (* equal contribution)

  • Best Paper Nomination at HICSS 2016
  • Presented in WITS (Auckland, New Zealand 2014), and WISE (Auckland, New Zeland 2014), HICSS (Kauai, HI 2016)
  • Dissertation Paper #2

This article studies the strategic network formation in a location-based social network. We build an empirical model of social link creation that incorporates individual characteristics and pairwise user similarities. Specifically, we define four user proximity measures from biography, geography, mobility, and short messages. To construct proximity from unstructured text information, we build topic models using Latent Dirichlet Allocation. Using Gowalla data with 385,306 users, 3 million locations, and 35 million check-in records, we empirically estimate the model to find evidence on the homophily effect on network formation. To cope with possible endogeneity issues, we use exogenous weather shocks as our instrumental variables and find the empirical results are robust: network formation decisions are significantly affected by our proximity measures.

Strategic Network Formation in a Location-Based Social Network: A Topic Modeling Approach (HICSS 2016)

Lee, G. M., Qiu, L., Whinston, A. B. (2016). Strategic Network Formation in a Location-Based Social Network: A Topic Modeling ApproachProceedings of Hawaii International Conference on System Sciences (HICSS 2016), Kauai, Hawaii. Nominated for Best Paper Award

This paper studies strategic network formation in a location-based social network. We build a structural model of social link creation that incorporates individual characteristics and pairwise user similarities. Specifically, we define four user proximity measures from biography, geography, mobility, and short messages. To construct proximity from unstructured text information, we build topic models using latent Dirichlet allocation. Using Gowalla data with 385,306 users, three million locations, and 35 million check-in records, we empirically estimate the structural model to find evidence on the homophily effect in network formation.