Category Archives: Working Papers

Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation

Lee, Myunghwan, Victor Cui, Gene Moo Lee. “Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation”, AOM 2023

Given the importance of exploratory innovation in fostering firms’ sustainable competitive advantages, firms often depend on technological assets or inter-firm relationships to pursue exploration. Regarded as a general-purpose technology, deep learning (DL)-based artificial intelligence (AI) can be an exploratory innovation-seeking instrument for firms in searching unexplored resources and thereby broadening their boundary. Drawing on the theories of organizational learning and path dependence, we hypothesize the impact of a firm’s DL capabilities on exploratory innovation and how DL capabilities interact with conventional pathbreaking activities such as technical assets and inter-firm relationships. Our empirical investigations, based on a novel DL capabilities measure constructed from comprehensive datasets on AI conferences and patents, show that DL capabilities have positive impacts on exploratory innovation. The results also show that extant technological assets (i.e., structured data management capabilities) and inter-firm relationships remedy the constraints on a firm’s innovation-seeking behaviors and that these path-breaking activities negatively moderate the positive impact of DL capabilities on exploratory innovation. To our knowledge, this is the first large-scale empirical study to investigate how DL affects exploratory innovation, contributing to the emerging literature on AI and innovation.

AI Voice in Online Video Platforms: A Multimodal Perspective on Content Creation and Consumption

Zhang, Xiaoke, Mi Zhou, Gene Moo Lee “AI Voice in Online Video Platforms: A Multimodal Perspective on Content Creation and Consumption,” 3rd round R&R at MIS Quarterly.

  • Best Student Paper Nomination at CIST 2023; Best Paper Runner-Up Award at KrAIS Summer Workshop 2023
  • Presentations: INFORMS DS (2022), UBC (2022), WITS (2022), Yonsei (2023), POSTECH (2023), ISMS MKSC (2023), CSWIM (2023), KrAIS Summer (2023), Dalhousie (2023), CIST (2023), Temple (2024), Santa Clara U (2024), Wisconsin Milwaukee (2024)
  • Media coverage: [UBC News] [Global News]
  • API sponsored by Ensemble Data
  • SSRN version: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4676705
  • Previous title: How Does AI-Generated Voice Affect Online Video Creation? Evidence from TikTok

Major user-generated content (UGC) platforms like TikTok have introduced AI-generated voice to assist creators in complex multimodal video creation. AI voice in videos represents a novel form of partial AI assistance, where AI augments one specific modality (audio), whereas creators maintain control over other modalities (text and visuals). This study theorizes and empirically investigates the impacts of AI voice adoption on the creation, content characteristics, and consumption of videos on a video UGC platform. Using a unique dataset of 554,252 TikTok videos, we conduct multimodal analyses to detect AI voice adoption and quantify theoretically important video characteristics in different modalities. Using a stacked difference-in-differences model with propensity score matching, we find that AI voice adoption increases creators’ video production by 21.8%. While reducing audio novelty, it enhances textual and visual novelty by freeing creators’ cognitive resources. Moreover, the heterogeneity analysis reveals that AI voice boosts engagement for less-experienced creators but reduces it for experienced creators and those with established identities. We conduct additional analyses and online randomized experiments to demonstrate two key mechanisms underlying these effects: partial AI process augmentation and partial AI content substitution. This study contributes to the UGC and human-AI collaboration literature and provides practical insights for video creators and UGC platforms.

Ideas are Easy but Execution is Everything: Measuring the Impact of Stated AI Strategies and Capability on Firm Innovation Performance

Lee, Myunghwan, Gene Moo Lee (2022) “Ideas are Easy but Execution is Everything: Measuring the Impact of Stated AI Strategies and Capability on Firm Innovation Performance”, Work-in-Progress.

Contrary to the promise that AI will transform various industries, there are conflicting views on the impact of AI on firm performance. We argue that existing AI capability measures have two major limitations, limiting our understanding of the impact of AI in business. First, existing measures on AI capability do not distinguish between stated strategies and actual AI implementations. To distinguish stated AI strategy and actual AI capability, we collect various AI-related data sources, including AI conferences (e.g., NeurIPS, ICML, ICLR), patent filings (USPTO), inter-firm transactions related to AI adoption (FactSet), and AI strategies stated in 10-K annual reports. Second, while prior studies identified successful AI implementation factors (e.g., data integrity and intelligence augmentation) in a general context, little is known about the relationship between AI capabilities and in-depth innovation performance. We draw on the neo-institutional theory to articulate the firm-level AI strategies and construct a fine-grained AI capability measure that captures the unique characteristics of AI-strategy. Using our newly proposed AI capability measure and a novel dataset, we will study the impact of AI on firm innovation, contributing to the nascent literature on managing AI.

Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.com

Park, Jaecheol, Joy Wu, Arslan Aziz, Gene Moo Lee. “Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.com”, 3rd round R&R at Information Systems Research..

  • Presentations: UBC (2021), KrAIS (2021), WISE (2021), PACIS (2022), SCECR (2022), BU Platform (2022), CIST (2022), BIGS (2022)
  • Preliminary version in PACIS 2022 Proceedings
  • RAs: Minsuk Seo, Vibudh Singh

The rapid growth in e-commerce has led to a concomitant increase in consumers’ reliance on digital word-of-mouth to inform their choices. As such, there is an increasing incentive for sellers to solicit reviews for their products. The literature has examined the direct and indirect effects of incentivized reviews on subsequent organic reviews within consumers who received incentives. However, since incentivized reviews and reviewers are often only a small proportion of a review platform (only 1.2% in our sample), it is important to understand whether their presence and absence on the platform affect the organic reviews from other reviewers who have not received incentives, which are often in the majority. We theorize two underlying effects that incentivized reviews can generate on other organic reviews: the herding effect from imitating incentivized reviews and the disclosure effect from the increased trust or skepticism by explicit incentive disclosure statements. Those two effects make organic reviews either follow or deviate from incentivized reviews. Using Bidirectional Encoder Representations from Transformers (BERT) to identify incentivized reviews and a natural experiment caused by a policy change on Amazon.com in October 2016, we conduct difference-in-differences with propensity score matching analyses to identify the effects of banning incentivized reviews on organic reviews. Our results suggest the disclosure effects are salient: banning incentivized reviews has positive effects on organic reviews in terms of frequency, sentiment, length, image, and helpfulness. Moreover, we find that the presence of incentivized reviews has poisoned the well for organic reviews regardless of the incentivized review ratio and that the effect is heterogeneous to product quality uncertainty. Our findings contribute to the literature on online review and platform design and provide insights to platform managers.

VISAGE: Designing AI Artifacts for Dynamic Self-Presentation in Matching Platforms

Kwon, Soonjae, Gene Moo Lee, Dongwon Lee, Sung-Hyuk Park (2024) “VISAGE: Designing AI Artifacts for Dynamic Self-Presentation in Matching Platforms,” 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

Online matching platforms constrain users to static profiles, producing a mismatch between the idealized self a user presents and the heterogeneous preferences of potential partners. Drawing on self-discrepancy theory, we conceptualize this mismatch as an interpersonal gap between one’s presented self and what each partner desires to see, with AI serving as a mediator to help address it. Following the computational design science perspective, we propose VISAGE, an AI system comprising two artifacts grounded in distinct human-AI collaboration principles. The augmentation artifact selects optimal images from users’ existing assets, whereas the assemblage artifact generates new images tailored to individual partner preferences. Using large-scale operational data from a major online dating platform, we evaluate VISAGE at both the user and platform levels. At the user level, model-predicted ratings suggest that both artifacts improve attractiveness ratings. Relative effectiveness varies with partner-preference heterogeneity and user impression management skill, consistent with theoretical predictions from the human-AI collaboration literature. At the platform level, agent-based simulations suggest that VISAGE can enhance matching efficiency and reduce inequality in matching opportunities, although optimal deployment strategies depend on the platform’s recommendation algorithm. The theoretical contribution of this study is to foreground the interpersonal gap as a key source of matching inefficiency and to illustrate how AI can address it at scale. The design contribution lies in actionable design knowledge, including when to deploy augmentation versus assemblage artifacts based on user and partner characteristics and how to align user-facing AI features with backend algorithmic infrastructure.

Trustworthy Face? The Effect and Drivers of Comprehensive Trust in Online Job Market Platform

Kwon, Jun Bum, Donghyuk Shin, Gene Moo Lee, Jake An, Sam Hwang (2020) “Trustworthy Face? The Effect and Drivers of Comprehensive Trust in Online Job Market Platform”. Work-in-progress.

The abstract will appear here.

Service Robots and Workforce Transformation: Evidence from Restaurant Operations

Lee, Myunghwan, Gene Moo Lee, Donghyuk Shin, Wooje Cho, Sang-Pil Han (2025) “Service Robots and Workforce Transformation: Evidence from Restaurant Operations”, Working Paper.

  • Presented at WITS (2020), KrAIS (2020), UBC (2021), DS (2022)
  • Research assistants: Raymond Situ, Gallant Tang

The introduction of AI-powered service robots, those capable of order taking, table delivery, and busser support, is significantly altering the workflow dynamics within the restaurant industry, fundamentally reshaping operations. Although these robots hold considerable promise for enhancing customer experiences and operational efficiency, their integration can introduce complex and potentially unintended consequences. Successful integration demands a careful balance among customer acceptance, automation efficiency, and worker adaptation. Yet critical questions remain insufficiently explored, particularly how the adoption of robots affects the workforce structures. This study addresses this gap by theorizing and empirically examining the impact of robotic integration on the composition of labor, with emphasis on part-time workers, who represent a significant portion of the restaurant workforce. Increased automation may reduce the number of part-time positions, but among those who remain, service robots may augment their roles by supporting or replacing routine tasks, allowing workers to focus on higher-touch interactions. This dual effect—numerical displacement alongside functional augmentation— illustrates a nuanced form of inequality in which the benefits of automation accrue unevenly even within the same labor group. Such shifts could either exacerbate labor inequalities or create opportunities for workforce adaptation and upskilling. From a systematic analysis of operational and customer review data from 3,636 restaurants, our results uncover asymmetric and unintended consequences of robotic integration on labor costs, workforce distribution, and overall restaurant performance. By shedding light on the intersection of automation, workforce restructuring, and customer reception, our findings contribute to the nascent discourse on the digital transformation of retail operations. The insights offered have important implications for managers and policymakers navigating the evolving landscape of AI-driven automation in customer-facing industries.

What Fuels Growth? A Comparative Analysis of the Scaling Intensity of AI Start-ups

Schulte-Althoff, Matthias, Daniel Fuerstenau, Gene Moo Lee, Hannes Rothe, Robert Kauffman. “What Fuels Growth? A Comparative Analysis of the Scaling Intensity of AI Start-ups”. Working Paper. [ResearchGate]

  • Previous title: “A Scaling Perspective on AI startup”
  • Presented at HICSS 2021 (SITES mini-track), Copenhagen Business School 2021, FU Berlin 2021, University of Cologne 2021, University of Bremen 2021, Humboldt Institute for Internet and Society 2021, WITS 2022

We examine how firm revenue scales with labor for revenue-per-employee (RPE) and is moderated by firm-level AI investment. We compare AI start-ups, in which AI provides a competitive advantage, with digital platforms and service start-ups. We use propensity score matching to explain the scaling of start-ups and find evidence for sublinear scaling intensity for revenue as a function of labor. Our study suggests similar scaling intensities between AI and service start-ups, while platform start-ups produce higher scaling intensities. We show that an increase in employee counts is associated with major revenue increases for platform start-ups, while increases were modest for service and AI start-ups.

Corporate Social Network Analysis: A Deep Learning Approach

Cao, Rui, Gene Moo Lee, Hasan Cavusoglu. “Corporate Social Network Analysis: A Deep Learning Approach,” Working Paper.

Identifying inter-firm relationships is critical in understanding the industry landscape. However, due to the dynamic nature of such relationships, it is challenging to capture corporate social networks in a scalable and timely manner. To address this issue, this research develops a framework to build corporate social network representations by applying natural language processing (NLP) techniques on a corpus of 10-K filings, describing the reporting firms’ perceived relationships with other firms. Our framework uses named-entity recognition (NER) to locate the corporate names in the text, topic modeling to identify types of relationships included, and BERT to predict the type of relationship described in each sentence. To show the value of the network measures created by the proposed framework, we conduct two empirical analyses to see their impacts on firm performance. The first study shows that competition relationship and in-degree measurements on all relationship types have prediction power in estimating future earnings. The second study focuses on the difference between individual perspectives in an inter-firm social network. Such a difference is measured by the direction of mentions and is an indicator of a firm’s success in network governance. Receiving more mentions from other firms is a positive signal to network governance and it shows a significant positive correlation with firm performance next year.

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.