Category Archives: Working Papers

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.

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.

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,” 3rd round 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”, 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. 

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.

The Effect of Mobile Device Management on Work-from-home Productivity: Insights from U.S. Public Firms

Park, Jaecheol, Myunghwan Lee, Gene Moo Lee “The Effect of Mobile Device Management on Work-from-home Productivity: Insights from U.S. Public Firms”, Work-in-Progress.

  • Presentations: UBC 2023, MSISR 2023, KrAIS 2023, WeB 2023, AOM 2024
  • Best Paper Nomination at WeB 2023
  • RA: Chaeyoon Kim

The use of mobile IT, providing employees with accessibility, flexibility, and connectivity, has become increasingly vital for businesses, especially for work-from-home during the COVID-19 pandemic. However, despite its prevalence and importance in the industry, the business value of mobile device management (MDM) and its role in establishing digital resilience remain underexplored in the literature. To address this research gap, our study examines the effect of MDM on a firm’s resilience to the pandemic. Drawing on the resource-based view (RBV), 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. Furthermore, we observe heterogeneous effects across industries that firms in industry sectors demanding greater mobility have a greater resilience effect from MDM. This study contributes to the information systems literature by emphasizing the business value of MDM and its crucial role in building digital resilience.

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.