{"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":"2026-08-05T15:46:25","modified_gmt":"2026-08-05T22:46:25","slug":"working-papers","status":"publish","type":"page","link":"https:\/\/blogs.ubc.ca\/analyticsailab\/working-papers\/","title":{"rendered":"Working Papers"},"content":{"rendered":"

Our group studies how AI reshapes firms, labor, and institutions \u2014 raising productivity, output, and firm value while also eroding trust, displacing workers, and degrading information quality. Across auditing, journalism, clinical trials, and beyond, we show which outcome prevails hinges on governance and human\u2013AI collaboration: the efficiency\u2013integrity frontier of AI adoption<\/strong>.<\/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>.\u00a0<\/em>[SSRN<\/a>]<\/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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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>. [SSRN<\/a>]<\/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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\"\"Park, Jaecheol, Myunghwan Lee, J. Frank Li, Gene Moo Lee. Unpacking the AI Transformation: \u000bThe Impact of AI Strategies on Firm Performance from the Dynamic Capabilities Perspective<\/a><\/strong>. R&R (2nd round), Information Systems Research<\/strong>. <\/i>[SSRN<\/a>]<\/p>\n

[CIST ’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. VISAGE: Designing AI Artifacts for Dynamic Self-Presentation in Matching Platforms<\/strong><\/a>. Under Review<\/em>. [SSRN<\/a>]<\/p>\n

[DS ’21, WITS ’21, ICIS ’22, WITS ’24] #genAI #matching #onlinedating<\/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, Rajesh Vijayaraghavan, Gene Moo Lee. Client AI Adoption and Auditing: Evidence from Process- and Product-Oriented AI<\/a>. <\/strong>Working Paper.<\/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>. [SSRN<\/a>]<\/p>\n

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

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\"\"Kwon, Angela Eunyoung, Jaecheol 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, Victor Song, Hasan Cavusoglu, Li Zhi Ma, Gene Moo Lee. IT Risk and Stock Price Crashes<\/a><\/strong>. Under Review.<\/em><\/p>\n

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

 <\/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>. Working Paper<\/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, Matthias, 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 group studies how AI reshapes firms, labor, and institutions \u2014 raising productivity, output, and firm value while also eroding trust, displacing workers, and degrading information quality. Across auditing, journalism, clinical trials, and beyond, we show which outcome prevails hinges on governance and human\u2013AI collaboration: the efficiency\u2013integrity frontier of AI adoption. Zhang, Xiaoke, Mi Zhou, […]<\/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":200,"href":"https:\/\/blogs.ubc.ca\/analyticsailab\/wp-json\/wp\/v2\/pages\/301\/revisions"}],"predecessor-version":[{"id":1668,"href":"https:\/\/blogs.ubc.ca\/analyticsailab\/wp-json\/wp\/v2\/pages\/301\/revisions\/1668"}],"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}]}}