Tag Archives: AI

Designing for Designers: A Multimodal Hypergraph RAG System To Enhance Automotive Design

Zhang, Xiaoke, Angela Kwon, Mi Zhou, Gene Moo Lee “Designing for Designers: A Multimodal Hypergraph RAG System to Enhance Automotive Design,” Work-in-progress.

The growing adoption of large language models (LLMs) across industries highlights the need for domain-specific systems that leverage an organization’s proprietary knowledge. In the automotive sector, general-purpose LLMs often lack specialized expertise and may produce irrelevant or misleading outputs, hindering vehicle designers’ creative processes. To address these challenges, we partner with Kia Motor, a leading automotive manufacturer in South Korea, to develop a designer-oriented, multimodal hypergraph retrieval-augmented generation (RAG) framework for vehicle concept ideation. Our framework consists of two core components. First, we construct a custom hypergraph knowledge base that captures complex relationships between customer feedback (text modality) and design assets (visual modality). Second, we design an interactive chatbot interface that accepts both free-form text and image inputs, retrieves relevant subgraphs from the knowledge base, and generates contextually grounded responses. We plan to evaluate the prototype through randomized online experiments and user studies involving Kia’s design teams. This work will contribute to design science by proposing a scalable method for multimodal knowledge representation and demonstrating how interactive AI tools can support domain-specific creative exploration.

Client AI Adoption and Auditing: Evidence from Process- and Product-Oriented AI

Park, Jaecheol, Pauline Wu, Rajesh Vijayaraghavan, Gene Moo Lee. “Client AI Adoption and Auditing: Evidence from Process- and Product-Oriented AI”, Under Review.

  • Presentations: TBD.

Artificial Intelligence (AI) is transforming firms’ information production, operations, and business models, with important implications for financial reporting and external auditing. We examine how auditors respond to client AI adoption, focusing on audit pricing and audit outcomes. Using textual disclosures in Form 10-K filings, we construct a novel firm-year measure of client AI adoption and further decompose it into AI embedded in internal processes and AI embedded in products and services. Using U.S. public firm data from 2010 to 2022 and a long-difference research design, we find that client AI adoption improves reporting discipline but does not lead to systematic changes in audit fees, consistent with offsetting efficiency and risk effects. When we distinguish between types of AI adoption, however, we find opposing audit responses. Process-oriented AI adoption leads to lower audit fees and improved reporting discipline, consistent with audit efficiency gains. In contrast, product-oriented AI adoption increases reporting complexity and risk, leading auditors to increase monitoring and scrutiny. Consistent with increased monitoring and error detection, product-oriented AI adoption increases the likelihood of subsequent financial restatements but not material misstatements, suggesting improved detection rather than deterioration in reporting quality. Cross-sectional analyses show that these effects vary with client complexity, operating performance, governance, and auditor industry expertise. Overall, our findings indicate that client AI adoption reshapes how auditors allocate effort, assess risk, and deploy monitoring, highlighting how technological change alters the audit production process and the financial reporting environment.

Computational Design Science and AI Research in Information Systems

First published: December 4, 2025; Updated: April 26, 2026

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 offer useful societal perspectives on AI, I believe the IS research community has unique strengths in opening the AI black box. I don’t think treating AI as one variable is enough. At the same time, if we focus solely on the technical aspects, the distinction 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 the following papers.

  1. Xie et al. (2025) proposed a novel topic model approach to study the mental impact (e.g., suicidal thoughts) of short videos. After analyzing the limitations of existing neural topic models (across IS and CS), the authors designed a novel topic model that can leverage a medical knowledge base and evaluated it on two platforms.
  2. Gao et al. (2025) investigated whether LLM-based social bots can create socializing values on social media platforms. They analyzed comments generated by LLM bots and found that the bot comment characteristics impact user engagement.
  3. Wang et al. (2026) proposed a novel computational approach called a discrete and regularized deep learning (DRDL) method for multiview data-based dynamic financial risk prediction (DFRP). The proposed approach provides predictive insights into the dynamics of financial risk from entangled information from multiview data.
  4. Mousavi et al (2026) offered the first holistic comparison of four major approaches of psychometric text analysis: lexicons, custom-built machine learning models, fine-tuned masked language models, and large language models (LLMs). It also introduced a cognitive-affective prompting strategy for LLMs that emulates these human strengths, yielding performance gains beyond state-of-the-art prompting methods.

Besides these 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

Mousavi, Reza, Brent Kitchens, Abbie Griffith Oliver, Ahmed Abbasi (2026). From Lexicons to Large Language Models: A Holistic Evaluation of Psychometric Text Analysis in Social Science Research. Information Systems Research. https://doi.org/10.1287/isre.2024.1143

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

Wang, Zhao, Wanliu Che, Cuiqing Jiang, Huimin Zhao (2026). Leveraging Multiview Data Through Discrete and Regularized Deep Learning for Dynamic Financial Risk Prediction. Information Systems Research. https://doi.org/10.1287/isre.2024.1417

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), UBC (2025), WISE (2025)

We examine how pharmaceutical firms’ AI capabilities influence drug development outcomes, focusing on clinical trials. Clinical trials progress through three phases that differ in regulatory scrutiny and evidentiary requirements. We measure firm-level AI capabilities using job postings and clinical trial outcomes using the number of trials initiated across phases. We find no significant overall effect of AI capabilities on clinical trial activity. However, this average relationship masks meaningful heterogeneity. AI capabilities are associated with increases in incremental innovation (refinement trials) but not radical innovation (new trials). These effects are stronger for biologics, where market incentives are high, than for small-molecule drugs, where learning hurdles are relatively low. AI capabilities also matter more in early-phase trials, where regulatory barriers are lower, and have no detectable influence in Phase III. This study contributes to the healthcare IS literature by identifying the nuanced and context-dependent business value of AI in drug development. It also offers practical guidance for pharmaceutical firms and policymakers on where AI investments are most likely to enhance R&D productivity.

Labor Unions and AI Investment: How Workforce Institutions Shape AI Investments and Firm Value

Park, Jiyong, Myunghwan Lee, Yoonseock Son, Gene Moo Lee. “Labor Unions and AI Investment: How Workforce Institutions Shape AI Investments 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 growing debates about Artificial Intelligence (AI) and the future of work, little is known about how firms’ efforts to build AI capabilities interact with workforce dynamics. Unlike prior information technologies, AI’s capacity to automate nonroutine and cognitive tasks creates qualitatively different workforce concerns, making union responses to AI theoretically and empirically distinct. As labor unions become key stakeholders in discussions on AI, we examine how unionization influences firms’ AI investments and moderates their impact on firm value. Using a novel dataset of U.S. public firms that integrates human capital–based measures of AI investment, unionization status, and operational and employee-perception indicators, we employ a long-difference design to capture changes in AI investments and firm value following unionization. Our findings show that unionization is associated with cautious AI investments, especially in firms with lower perceived job security and operations more exposed to AI disruption. Rather than reflecting simple resistance to technology, this pattern suggests that unions introduce additional organizational scrutiny in decisions about AI investments when workforce concerns are salient. At the same time, union presence can strengthen the value generated from AI investments once they are implemented. We find that unions amplify the positive relationship between AI investments and firm value, particularly in firms with stronger labor relations, reflected in active employee involvement and higher job-security satisfaction. Taken together, these findings highlight unions’ dual role: tempering and disciplining AI investments at the adoption stage while enhancing downstream value realization through more deliberate and workforce-aligned implementation.

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 Information Sourcing and News Production

Zhang, Xiaoke, Myunghwan Lee, Mi Zhou, Gene Moo Lee. “Large Language Models in the Institutional Press: Investigating the Effects on Information Sourcing and News Production,” 3rd round R&R, MIS Quarterly.

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

Large language models (LLMs) are transforming journalism by directly entering journalistic workflows, introducing new opportunities and challenges for the institutional press. This study investigates how LLM assistance affects journalists’ information sourcing in news production using a mixed-method approach. We begin with a qualitative study of 43 journalists to identify and theorize how LLM assistance affects three core journalistic values: publication promptness, information source quantity, and information source originality. We then compile a large-scale dataset of 1,073,742 news articles from 111 South Korean news outlets and collaborate with industry experts to detect undisclosed LLM-assisted articles. Our event-level analysis shows that LLM assistance accelerates publication but reduces the number of information sources used in news articles, with a larger decline in primary sources than in secondary sources. Heterogeneity analyses and a randomized experiment suggest that this reduction is driven by two mechanisms: an LLM generation mechanism that narrows the set of retrieved and represented sources, and a metacognitive regulation mechanism that reduces journalists’ active search and evaluation. We further show that these effects extend beyond individual articles. A journalist-level difference-in-differences analysis indicates that LLM adoption leads to persistent reductions in source usage over time. Our findings offer practical implications for LLM system design, newsroom practices, and institutional disclosure policy.

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”, 2nd round R&R at MIS Quarterly.

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

With growing computational power and the availability of large-scale data, machine learning (ML) has emerged as a new important driver of organizational learning, yet our understanding of ML’s precise role remains conflicted. To help unpack ML’s role, we examine how ML investments shift organizations’ learning toward exploitation versus exploration and how these shifts influence organizational performance and survival. Drawing on data from 3,383 publicly listed U.S. organizations from 2005 to 2019, our findings suggest that increased ML use generally tends to shift organizations towards exploration. This ML-induced learning tendency mediates the positive relationship between ML investments and organizational survival, with effects particularly pronounced among non-IT organizations with established exploitative tendencies. We further find that ML acts as a catalyst for context-dependent balancing: in stable environments, ML nudges exploitative organizations toward greater exploration, whereas in dynamic environments, ML tempers explorative organizations by reinforcing exploitation. This study provides the first large-scale empirical evidence on how ML reshapes organizational learning and its organizational impacts, contributing new empirical insights to the largely theoretical and contested discourse to help further rethink 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.