{"id":301,"date":"2022-06-10T19:19:59","date_gmt":"2022-06-11T02:19:59","guid":{"rendered":"https:\/\/blogs.ubc.ca\/analyticsailab\/?page_id=301"},"modified":"2026-04-24T11:58:13","modified_gmt":"2026-04-24T18:58:13","slug":"working-papers","status":"publish","type":"page","link":"https:\/\/blogs.ubc.ca\/analyticsailab\/working-papers\/","title":{"rendered":"Working Papers"},"content":{"rendered":"

Our research group examines how AI reshapes firms, labor, and institutions, examining the dual forces of efficiency gains<\/b><\/span> and integrity risks<\/b><\/span>. Across contexts such as auditing, journalism, video platforms, clinical trials, restaurants, labor unions, and firm strategy, we show that AI adoption is not monolithic: its impacts depend on whether it is product- or process-oriented, partial or full, embodied or disembodied. Using empirical designs such as causal inference, multimodal analysis, and field interviews, we find that AI boosts productivity, output, and firm value, but can erode trust, displace workers, and compromise information quality unless mediated by governance, institutional oversight, and strategic orientation. Collectively, our work advances our understanding of the efficiency\u2013integrity frontier of AI adoption<\/b><\/span>, demonstrating that value realization requires complementary investments, institutional safeguards, and human\u2013AI collaboration mechanisms.<\/p>\n

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Zhang, Xiaoke, Mi Zhou, Gene Moo Lee. <\/span>AI Voice in Online Video Platforms: A Multimodal Perspective on Content Creation and Consumption<\/strong><\/a>. R&R (3rd round), MIS Quarterly<\/strong>.<\/em><\/span><\/p>\n

[DS ’22, WITS ’22, KrAIS ’23, CSWIM ’23, KrAIS ’23, CIST ’23] [API Sponsored by Ensemble Data<\/a>] <\/span>#AI #video #creativity #TTS #tiktok<\/span><\/p>\n

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Zhang, Xiaoke, Myunghwan Lee, Mi Zhou, Gene Moo Lee. Large Language Models in the Institutional Press: Investigating the Effects on News Production and Consumption<\/a>.<\/strong> R&R (3rd round), MIS Quarterly<\/strong>.<\/span><\/em><\/p>\n

[DS ’24, CIST ’24, BIGS ’24] #ai #journalism #llm<\/p>\n

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Park, Jaecheol, Joy Wu, Arslan Aziz, Gene Moo Lee. Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.com<\/strong><\/a>. R&R (3rd round), Information Systems Research<\/strong><\/em>.<\/p>\n

[WISE ’21, PACIS ’22, SCECR ’22, BU Platform ’22, CIST ’22, BIGS ’22] #onlinereviews #incentives #platform #amazon<\/p>\n

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\"\"Lee, Myunghwan, Timo Sturm, Gene Moo Lee. Exploring the Influence of Machine Learning on Organizational Learning: An Empirical Analysis of Publicly Listed Organizations<\/a><\/strong>. R&R (2nd round), MIS Quarterly<\/strong>.<\/em><\/span><\/p>\n

[JUSWIS ’24, KrAIS ’24] #ai #org-learning #exploration #performance<\/p>\n

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\"\"Song, Victor, Hasan Cavusoglu, Jaecheol Park, Li Zhi Ma, Gene Moo Lee. IT Risk and Stock Price Crashes<\/a><\/strong>. R&R (2nd round), Journal of Management Information Systems<\/strong>.<\/em><\/p>\n

[HICSS ’20] #itrisk #cybersecurity<\/p>\n

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\"\"Park, Jiyong, Myunghwan Lee, Yoonseock Son, Gene Moo Lee. Labor Unions and AI Investment: How Workforce Institutions Shape AI Investments and Firm Value<\/a><\/strong>. Under Review.<\/i><\/p>\n

[CIST ’24, BIGS’24, WISE ’24, ISR-PDW’25] #ai #labor #unionization #value<\/p>\n

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Park, Jaecheol, Pauline Wu, Jaecheol Park, Rajesh Vijayaraghavan, Gene Moo Lee. Client AI Adoption and Auditing: Evidence from Process- and Product-Oriented AI<\/a>. <\/strong>Under Review.<\/em><\/p>\n

#AI #audit #quality #product #process<\/p>\n

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\"\"Lee, Myunghwan, Gene Moo Lee, Donghyuk Shin, Wooje Cho, Sang-Pil Han. Service Robots and Workforce Transformation: Evidence from Restaurant Operations<\/a><\/strong>. Working Paper<\/em>.<\/p>\n

[WITS ’20, KrAIS ’20, DS ’22, BIGS ’22] #AI #servicerobots #restaurants<\/p>\n

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\"\"Park, Jaecheol, Myunghwan Lee, J. Frank Li, Gene Moo Lee. Unpacking the AI Transformation: \u000bThe Impact of AI Strategies on Firm Performance \u000bfrom the Dynamic Capabilities Perspective<\/a><\/strong>.<\/i><\/p>\n

[CIST ’24, INFORMS ’24, KrAIS ’24, BIGS ’24, ISR-PDW’25] #ai #strategy #product #process #value<\/p>\n

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\"\"Kwon, Soonjae, Gene Moo Lee, Dongwon Lee, Sunghyuk Park. Seeing the Unseen: The Effects of Implicit Representation in an Online Dating Platform<\/strong><\/a>. Preparing for Journal Submission<\/em>.<\/p>\n

[DS ’21, WITS ’21, ICIS ’22, WITS ’24] #genAI #matching #onlinedating<\/p>\n

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\"\"Kwon, Angela Eunyoung, Jaecheok Park, Gene Moo Lee. How Does AI Change Drug Development? Evidence from Clinical Trial Phases and Drug Types<\/a>.<\/b><\/p>\n

[KrAIS ’25, CIST ’25, INFORMS ’25, WISE ’25] #AI #clinicaltrials #drugdevelopment<\/p>\n

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\"\"Zhang, Xiaoke, Angela Kwon, Mi Zhou, Gene Moo Lee. Designing for Designers:\u000bA Multimodal Hypergraph RAG System To Enhance Automotive Design<\/strong><\/a>.<\/p>\n

[INFORMS ’25] #AI #GenAI #car #design<\/p>\n

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\"\"Park, Jaecheol, Myunghwan Lee, Gene Moo Lee. The Effect of Mobile Device Management on Work-from-home Productivity: Insights from U.S. Public Firms<\/a><\/strong>. Preparing for Journal Submission<\/em>.<\/p>\n

[MSISR ’23, KrAIS ’23, WeB ’23, BIGS ’23, AOM ’24] #mobile #resilience #productivity<\/p>\n

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Inactive working papers<\/strong><\/p>\n

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Lee, Myunghwan, Victor Cui, Gene Moo Lee (2023) Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation<\/a><\/strong>. [AOM ’23, CIST ’23]<\/p>\n

Lee, Myunghwan, Gene Moo Lee (2022) <\/span>Ideas are Easy but Execution is Everything: Measuring the Impact of Stated AI Strategies and Capability on Firm Innovation Performance<\/a><\/strong>. <\/span><\/span>[DS ’22]<\/p>\n

Schulte-Althoff, Matthais, Daniel F\u00fcrstenau, Gene Moo Lee, Hannes Rothes, Robert Kauffman (2022) <\/span>What Fuels Growth? A Comparative Analysis of the Scaling Intensity of AI Start-ups <\/a><\/strong>[HICSS ’21, WITS ’22]<\/p>\n

Cao, Rui, Gene Moo Lee, Hasan Cavusoglu (2021)\u00a0Corporate Social Network Analysis: A Deep Learning Approach<\/a><\/strong>. [WITS ’20, DS ’21] [Research demo site<\/a>]<\/p>\n

Park, Sungho, Gene Moo Lee, Donghyuk Shin, Sang-Pil Han (2022) When Does Congruence Matter for Pre-roll Video Ads? The Effect of Multimodal, Ad-Content Congruence on the Ad Completion<\/strong><\/a>. <\/em>[INFORMS ’20, AIMLBA ’20, WITS ’20]<\/p>\n

Schulte-Althoff, Matthias, Kai Schewina, Gene Moo Lee, Daniel<\/span>\u00a0<\/strong>F\u00fcrstenau (2021)\u00a0<\/span>On the Heterogeneity of Startup Tech Stacks<\/a><\/strong>. <\/em>[HICSS ’21]<\/p>\n

Koh, Yumi, Gea M. Lee, Gene Moo Lee (2023) Price Competition and Active or Inactive Consumer Search<\/a><\/strong>. <\/em>[APIOC ’19, EARIE ’23]<\/p>\n

Bera, Debalina, Gene Moo Lee, Dan J. Kim (2024) Anatomy of Phishing Tactics and Susceptibility: An Investigation of the Dynamics of Phishing Tactics and Contextual Traits in Susceptibility<\/span><\/strong><\/a>.<\/p>\n

Lee, Gene Moo, James Naughton, Xin Zheng, Dexin Zhou (2020) Predicting Litigation Risk via Machine Learning<\/a><\/strong>. [CFMA ’19] [Litigation risk score data 1996-2015<\/a>]<\/p>\n

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Disclaimer: Some of the images on the page are generated by DALL-E 2<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Our research group examines how AI reshapes firms, labor, and institutions, examining the dual forces of efficiency gains and integrity risks. Across contexts such as auditing, journalism, video platforms, clinical trials, restaurants, labor unions, and firm strategy, we show that AI adoption is not monolithic: its impacts depend on whether it is product- or process-oriented, […]<\/p>\n","protected":false},"author":51140,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-301","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/blogs.ubc.ca\/analyticsailab\/wp-json\/wp\/v2\/pages\/301","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.ubc.ca\/analyticsailab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/blogs.ubc.ca\/analyticsailab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/analyticsailab\/wp-json\/wp\/v2\/users\/51140"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/analyticsailab\/wp-json\/wp\/v2\/comments?post=301"}],"version-history":[{"count":189,"href":"https:\/\/blogs.ubc.ca\/analyticsailab\/wp-json\/wp\/v2\/pages\/301\/revisions"}],"predecessor-version":[{"id":1539,"href":"https:\/\/blogs.ubc.ca\/analyticsailab\/wp-json\/wp\/v2\/pages\/301\/revisions\/1539"}],"wp:attachment":[{"href":"https:\/\/blogs.ubc.ca\/analyticsailab\/wp-json\/wp\/v2\/media?parent=301"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}