Author Archives: gene lee

Computational Design Science and AI Research in Information Systems

First published: December 4, 2025

In this post, I will discuss the current status of computational design science and AI research in the Information Systems community, focusing on the papers that I have recently handled as an editor in different IS journals (ISR, MISQ, DSS, I&M, etc.) and those on which I worked as an author. I will also share my thoughts on the future research agenda.

AI is the topic of our time. The reality is that many disciplines are trying to own the AI agenda in business schools (Caro et al. 2025). In my view, the IS community has a unique advantage to lead this AI agenda. To do that, we need to double down on our IS competitive advantage from a socio-technical perspective (Sarkar et al. 2019). While many neighboring disciplines (e.g., operations, marketing, strategy) can bring useful societal perspectives on the AI topic, I believe the IS research community has unique strengths in opening up the AI black box. I don’t think treating AI as one variable is enough. At the same time, if we solely focus on the technical aspects, the differentiation from computer science may not be clear. Therefore, our AI research in IS should focus on the consequential problems in business and society.

I had the great fortune to handle many IS papers that fall into this category. Since I started my AE role at ISR in 2024, I have accepted two papers (Xie et al. 2025; Gao et al. 2025). Xie et al. (2025) proposed a novel topic model approach to study the mental impact (e.g., suicidal thoughts) of short videos. The authors carefully analyzed the limitations of existing neural topic models (across IS and CS) and designed a novel topic model that can leverage a medical knowledge base. They then evaluated the artifact on two platforms and identified medically relevant topics from the short videos. Gao et al. (2025) investigated whether LLM-based social bots can create socializing values on social media platforms. They conducted extensive analyses on comments generated by LLM bots and found that the bot comment characteristics impact user engagement. Finally, the authors proposed a social bot targeting algorithm to optimize engagement and tested it with extensive simulations. Besides these two papers, there are more computational and AI papers in the pipeline.

Moving forward, I think our IS research community should lead the AI agenda in the business school and beyond. In the near future, I hope to see more research in the following areas:

  1. AI impact studies: As a general-purpose technology, AI is making changes across different industry sectors. I hope to see more AI impact studies, but in specific industry sectors such as online videos (Zhang et al. 2025a), journalism (Zhang et al. 2025b), online dating (Kwon et al. 2022), and more.
  2. Physical AI: Many AI papers are focusing on knowledge work in the digital space. With the advances of embodied AI, I hope the IS community can also study robots (Lee et al. 2025), drones, and rovers.
  3. Quantum AI: I predict that quantum computing will be the next computing paradigm. The IS community can conduct forward-looking research on the topics of quantum computing, quantum communication, and more (https://www3.fox.temple.edu/discover/events-conferences/from-qubits-to-business-value/).

References

Caro, Felipe, Jean-Edouard Colliard, Elena Katok, Axel Ockenfels, Nicolas Stier-Moses, Catherine Tucker, D. J. Wu (2025) Introduction to the Special Issue on the Human-Algorithm Connection. Management Science. https://doi.org/10.1287/mnsc.2023.intro.v72.n1 

Gao, Yang, Maggie Mengqing Zhang, Mikhail Lysyakov (2025) Does Social Bot Help Socialize? Evidence from a Microblogging Platform. Information Systems Research. https://doi.org/10.1287/isre.2024.1089

Kwon, Soonjae, Sunghyuk Park, Gene Moo Lee, Dongwon Lee (2022) Learning Faces to Predict Matching Probability in an Online Matching Platform. In Proceedings of International Conference on Information Systems. https://aisel.aisnet.org/icis2022/digital_commerce/digital_commerce/9/ 

Lee, Myunghwan, Lee, Gene Moo, Shin, Donghyuk, Cho, Wooje, Han, Sang Pil (2025) Service Robots and Workforce Transformation: Evidence from Restaurant Operations. SSRN Working Paper. http://dx.doi.org/10.2139/ssrn.5288874

Sarker, Suprateek; Chatterjee, Sutirtha; Xiao, Xiao; and Elbanna, Amany. 2018. “The Sociotechnical Axis of Cohesion for the IS Discipline: Its Historical Legacy and its Continued Relevance,” MIS Quarterly, (43: 3) pp.695-719. https://doi.org/10.25300/MISQ/2019/13747

Xie, Jiaheng, Yidong Chai, Ruicheng Liang, Yang Liu, Daniel Dajun Zeng (2025) Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model. Information Systems Research. https://doi.org/10.1287/isre.2024.1071 

Zhang, Xiaoke, Zhou, Mi, Lee, Gene Moo (2025a) AI Voice in Online Video Platforms: A Multimodal Perspective on Content Creation and Consumption. SSRN Working Paper. http://dx.doi.org/10.2139/ssrn.4676705 

Zhang, Xiaoke, Lee, Myunghwan, Zhou, Mi, Lee, Gene Moo (2025b) Large Language Models in the Institutional Press: Investigating the Effects on News Production and Consumption. SSRN Working Paper. http://dx.doi.org/10.2139/ssrn.5357471

Jaecheol Park’s PhD Dissertation: Strategic Roles of AI and Mobile Management on Performance: Evidence from U.S. Public Firms

Jaecheol Park (2025) “Strategic Roles of AI and Mobile Management on Performance: Evidence from U.S. Public Firms”, Ph.D. Dissertation, University of British Columbia. https://jaecheol-park.github.io/

Committee: Gene Moo Lee (supervisor), J. Frank Li, Jiyong Park (Georgia), Jenny Li Zhang (Accounting), Dongwoo Yoon, Taha Havakhor (McGill)

The integration of Artificial Intelligence (AI) and mobile IT into the workplace is transforming how businesses operate. While both technologies are becoming increasingly prevalent and critical, there remains limited research on how firms can strategically manage these emerging technologies to achieve competitive advantage and enhance performance. This dissertation addresses this gap by focusing on AI as a representative of software-driven innovation and mobile IT as a representative of hardware-driven innovation. Together, it offers a comprehensive view of how firms can manage the dual dimensions of emerging digital technologies. It comprises two large-scale empirical studies on U.S. public firms, offering new theoretical and managerial insights into how firms can harness the power of these technologies to drive success.

The first chapter investigates the effect of AI strategic orientation on firm performance. By dissecting firms’ AI orientation into awareness, product, and process orientation across industries, the research deepens the understanding of AI’s strategic implications for firm performance. These findings provide new insights into how firms can strategically deploy AI to enhance performance and drive competitive advantage.

The second chapter explores mobile device management (MDM), a new IT infrastructure for managing mobile hardware used for work. Focusing on the pandemic, the research examines the impact of MDM on firm performance, underscoring its importance in supporting remote work. The study further demonstrates the moderating roles of external environmental munificence and internal IT capabilities of a firm. These findings inform how firms can strategically manage the usage of mobile devices for work purposes and present the business value of MDM.

The findings of the dissertation offer valuable insights for academics and industry practitioners seeking to understand and leverage these emerging technologies’ full potential. From an academic perspective, this dissertation contributes to the literature on the business value of IT and AI by empirically demonstrating the business value of AI strategies and mobile IT management. From an industry perspective, this dissertation provides actionable guidance for firms looking to leverage software- and hardware-based digital innovations to achieve strategic goals and drive success in the digital age.

How Does AI Change Drug Development? Evidence from Clinical Trial Phases and Drug Types

Kwon, Angela Eunyoung, Jaecheok Park, Gene Moo Lee. “How Does AI Change Drug Development? Evidence from Clinical Trial Phases and Drug Types,” Working Paper.

  • Presentations: KrAIS (2025), CIST (2025), INFORMS (2025)

We examine how the AI capability of pharmaceutical firms affects drug development processes, with a particular focus on clinical trials. Clinical trials test the safety and efficacy of drug candidates on human subjects across four phases. While early phases (Phases I & II) assess preliminary safety and efficacy in smaller populations, later phases (Phases III & IV) investigate thorough effectiveness in larger populations. To examine the effect of the AI capability of firms on clinical trial outcomes (total trials, new trials, and retrials), we utilize job postings data and ClinicalTrials.gov. Our preliminary findings suggest that AI capability has significant effects on clinical trial outcomes in the early phases of clinical trials, but not in the later phases. Moreover, we find the heterogeneous effects across drug types, where the effect of AI capability on retrials is greater for biologics. This study contributes to the healthcare IS literature by empirically demonstrating the business value of AI in enhancing drug development processes. Our study also provides practical guidance on AI investment and implementation for pharmaceutical firms, regulatory agencies, and policymakers.

COMM 634 Empirical Research Methods in IS

March 19, 2025

COMM 634 is a graduate-level course on empirical research methods in Information Systems (IS) and related fields. We explore multiple empirical research methods including machine learning, econometrics, field/lab experiments, and simulation. Topically, we review the recent AI research in IS and related fields, covering three pillars of computational design science, the economics of IS, and organizational IS.

I would like to thank the guest speakers who shared their expertise and experience with us!

  1. Course introduction | Three Pillars of AI Research in IS
  2. Computational Design Science, Speaker: Donghyuk Shin (KAIST)
  3. Economics of AI and IS, Speaker: J. Frank Li (UBC)
  4. AI and IS Strategies, Speaker: Myunghwan Lee (CUHK)
  5. Economics of AI and IS, Speaker: Yoonseok Son (Notre Dame)
  6. Field Experiment, Speaker: Dongwon Lee (HKUST)
  7. Lab Experiment, Speaker: Joy Wu (UBC)
  8. Simulation, Speaker: Timo Sturm (TU Darmstadt)
  9. Sustainability, Speaker: Jiyong Park (Georgia)
  10. Generative AI

From Resistance to Collaboration: Labor Unions, AI Investment, and Firm Value

Park, Jiyong, Myunghwan Lee, Yoonseock Son, Gene Moo Lee. “From Resistance to Collaboration: Labor Unions, AI Investment, and Firm Value”, Under Review.

  • The first three authors equally contributed to the work.
  • Presentations: CIST (2024), WISE (2024), ISR-PDW (2025).
  • Nominated for Best Paper Award at WISE 2024

Despite ongoing debates about Artificial Intelligence (AI) and the future of work, little is known about how firms’ efforts to strengthen AI capabilities affect their workforce dynamics. As labor unions emerge as key stakeholders in the societal discourse on AI, we investigate how unionization influences firms’ AI investments and moderates their impact on firm value. Drawing on a novel dataset of U.S. public firms that integrates human capital-based measures of AI investment, unionization status, and a range of firm operations and employee perceptions, we employ a long-difference design to capture changes in AI investment and firm value. Our findings show that unionization constrains AI investments, particularly in firms with low perceived job security and operations more exposed to AI disruption. This resistance, however, also enables firms to align AI adoption with workforce interests. Consistent with this view, we find that unions can amplify the value-creating effects of AI, most evident in firms with stronger labor relations, reflected in active employee involvement and higher job security satisfaction during AI investments. Taken together, these findings highlight unions’ dual role: constraining AI investments at the adoption stage but enhancing returns during the value-creation stage. This study offers important insights into how firms can strategically manage AI adoption in the presence of an organized workforce.

Jaecheol Park’s PhD Proposal: Strategic Roles of AI and Mobile Management on Performance: Evidence from U.S. Public Firms

Jaecheol Park (2024) “Strategic Roles of AI and Mobile Management on Performance: Evidence from U.S. Public Firms”, Ph.D. Dissertation Proposal, University of British Columbia. https://jaecheol-park.github.io/

Supervisor: Gene Moo Lee

Supervisory Committee Members: J. Frank Li, Jiyong Park (Georgia)

The integration of emerging technologies such as Artificial Intelligence (AI) and mobile IT into the workplace is transforming how businesses operate. Despite the increasing prevalence and importance of AI and mobile IT, there is limited research on how firms can strategically manage these technologies to achieve competitive advantage and enhance performance. This dissertation consists of two large-scale empirical studies on U.S. public firms, aiming to provide new theoretical and managerial insights into how firms can harness the power of these technologies to drive success.

The first chapter investigates the impact of mobile device management (MDM) on firm performance during the recent pandemic, highlighting the importance of MDM in digital resilience. Drawing on the resource-based view and a novel proprietary dataset from a global MDM provider for U.S. public firms, we find that firms with MDM have better financial performance during the pandemic, demonstrating greater resilience to the shock. Additionally, we explore the moderating role of external and internal factors, revealing that firms with high environmental munificence or those with low IT capabilities experience greater resilience effects from MDM. This study contributes to the work-from-home and hybrid work literature by emphasizing the business value of MDM and its crucial role in building digital resilience.

The second chapter investigates the effect of AI strategic orientation on firm performance with a dual lens on product and process orientation. We create a novel measure of AI orientation by employing a large language model to assess business descriptions in Form 10-K filings, and identify an increasing trend of AI disclosure among U.S. public firms. By dissecting firms’ AI disclosure into AI washing and AI (product and process) orientation, our long-difference analyses show that AI orientation significantly affects costs, sales, and market value but AI washing does not, showing the importance of strategic deployment of AI to create business value. Moreover, we find the heterogeneous effects between AI product and process orientation on performance. This study contributes to the recent AI management literature by providing the strategic role of AI orientation on firm performance.

The findings of the dissertation offer valuable insights for academics, practitioners, and policymakers seeking to understand and leverage these emerging technologies’ full potential. From an academic perspective, this dissertation contributes to the literature on the business value of IT and AI by empirically demonstrating the business value of MDM and AI strategies. From an industry perspective, this research provides actionable guidance for businesses looking to leverage the power of MDM and AI to achieve strategic goals and drive success in the digital age.

Large Language Models in the Institutional Press: Investigating the Effects on News Production and Consumption

Zhang, Xiaoke, Myunghwan Lee, Mi Zhou, Gene Moo Lee. “Large Language Models in the Institutional Press: Investigating the Effects on News Production and Consumption,” R&R, MIS Quarterly.

  • Presentations: UBC (2024), DS (2024), CIST (2024), BIGS (2024), JUSWIS (2025), UIUC (2025)
  • Industry partner: Muhayu

The rapid advancements of Large Language Models (LLMs) have introduced new opportunities and challenges for the institutional press. Utilizing a mixed-method approach, this paper combines two qualitative studies and two sets of large-scale quantitative studies to theorize and empirically examine how LLM assistance affects news production and consumption. We begin with open-ended surveys and interviews with 12 journalists to identify three key constructs central to the journalistic value–source quality, publication promptness, and reader engagement – and formulate our research questions. To empirically examine these dimensions, we compile a comprehensive dataset of 2,060,894 news articles sampled from 111 major South Korean media outlets. We collaborate with industry experts to fine-tune a Korean-language LLM detector to identify undisclosed LLM usage in the news corpus and leverage GPT-4.1 to label information sources in each article. Our event-level analysis reveals that while LLM assistance expedites news publication, it is associated with a reduction in both the number and quality of sources, as well as a decline in reader engagement. To further investigate the impact of LLM adoption on journalists’ long-term information sourcing behaviors, we conduct a journalist-level analysis using staggered Difference-in-Differences. Results reveal that journalists reduce the use of primary, unaffiliated, and contextual sources after LLM adoptions, and alarmingly, these negative effects are enlarging over time. Drawing on the information foraging theory and a second-wave of qualitative study with 32 journalists, we explore the underlying mechanisms and posit that the negative effects of LLM adoption on source quality are driven by a combination of LLM limitations and long-term shifts in journalists’ behaviors. We conclude by proposing actionable guidelines for the institutional press on combining technical solutions with organizational policies to mitigate the negative effects and facilitate the responsible integration of LLMs. Our findings contribute to the growing literature on the digital transformation of journalism.

Exploring the Influence of Machine Learning on Organizational Learning: An Empirical Analysis of Publicly Listed Organizations

Lee, Myunghwan, Timo Sturm, Gene Moo Lee “Exploring the Influence of Machine Learning on Organizational Learning: An Empirical Analysis of Publicly Listed Organizations”, Work-in-Progress.

  • Presentations: JUSWIS 2024, KrAIS Summer 2024
  • Best Short Paper Award at KrAIS Summer Workshop 2024.

We contribute to the literature on the role of machine learning (ML) in organizational learning by examining two key learning tendencies: exploitation and exploration. We analyze the effect of ML investments on organizations’ learning tendency, which in turn influences firm performance and survival. Our findings suggest that ML primarily shifts organizations towards exploration and that ML-induced learning tendency fully mediates the positive relationship between ML investments and organization survival. Notably, we find that non-IT organizations with exploitative tendencies can effectively shift towards exploration through ML investments. To our knowledge, this study provides the first large-scale empirical insights into ML’s impact on organizations’ learning tendency and performance outcomes, offering valuable insights for rethinking organizational learning in the age of ML.

Unpacking AI Transformation: The Impact of AI Strategies on Firm Performance from the Dynamic Capabilities Perspective

Park, Jaecheol, Myunghwan Lee, J. Frank Li, Gene Moo Lee “Unpacking AI Transformation: The Impact of AI Strategies on Firm Performance from the Dynamic Capabilities Perspective,” Work-in-Progress.

  • Presentations: UBC (2024), CIST (2024), INFORMS (2024), SNU (2024), UMass (2024), BIGS (2024), KrAIS (2024), CityU Hong Kong (2025), NTU (2025), AIM (2025), ISR-PDW (2025)
  • Best Paper Award at BIGS 2024
  • Best Student Paper Award at KrAIS 2024

Artificial intelligence (AI) technologies hold great potential for large-scale economic impact. Aligned with this trend, recent studies explore the adoption impact of AI technologies on firm performance. However, they predominantly measure firms’ AI capabilities with input (e.g., labor/job posting) or output (e.g., patents), neglecting to consider the strategic direction toward AI in business operations and value creation. In this paper, we empirically examine how firms’ AI strategic orientation affects firm performance from the dynamic capabilities perspective. We create a novel firm-year AI strategic orientation measure by employing a large language model to analyze business descriptions in Form 10-K filings and identify an increasing trend and changing status of AI strategies among U.S. public firms. Our long-difference analysis shows that AI strategic orientation is associated with greater operating cost, capital expenditure, and market value but not sales, showing the importance of strategic direction toward AI to create business value. By further dissecting firms’ AI strategic orientation into AI awareness, AI product orientation, and AI process orientation, we find that AI awareness is generally not related to performance, that AI product orientation is associated with short-term increased operating expenses and long-term market value, and that AI process orientation is associated with long-term increased costs and sales. Moreover, we find the negative moderating effect of environmental dynamism on AI process orientation. This study contributes to the recent AI strategy and management literature by providing the strategic role of AI orientation on firm performance. 

Anatomy of Phishing Tactics and Susceptibility

Bera, Debalina, Gene Moo Lee, Dan J. Kim “Anatomy of Phishing Tactics and Susceptibility: An Investigation of the Dynamics of Phishing Tactics and Contextual Traits in Susceptibility,” Working Paper.

Phishing is a deceptive tactic to create a front of apparent credibility to fraudulently acquire sensitive personal or financial information from an unsuspecting user or espionage system by infiltrating malware or crimeware. Despite automated technological solutions and training interventions, recent phishing statistics show that specifically few phishing tactics are increasing users’ phishing susceptibility (PS). Further, assessing the moderating role of phishing contextual traits in the relationship between phishing tactics and PS indicates the importance of their trait differences. Based on theoretical postulation, employing a sequential mixed method design, and using two sets of data (simulated phishing penetration testing results and scenario-based experiments), we examine the effect of phishing tactics along with the moderating role of individual phishing contextual traits on PS. This study extends the theoretical boundary relevant to phishing tactics and provides practical guidance to identify the most dangerous phishing tactics that increase PS and phishing contextual traits that help to combat phishing attacks.