Category Archives: Working Papers

Architectural Innovation, Organizational Restructuring, and the Role of AI: Evidence from the Rise of Antibody-Drug Conjugates

Kwon, Angela Eunyoung, Jaecheok Park, Gene Moo Lee. “Architectural Innovation, Organizational Restructuring, and the Role of AI: Evidence from the Rise of Antibody-Drug Conjugates,” Work-in-progress.

  • Presentations: KrAIS (2026)

Architectural innovations reconfigure the linkages among existing components and thereby pose distinct challenges to established organizational structures. This study examines the rise of antibody-drug conjugates (ADCs) in oncology as a case of architectural innovation and investigates how this technological shift shapes organizational restructuring among pharmaceutical firms. Drawing on the theory of architectural innovation (Henderson & Clark,1990), we argue that ADCs disrupt established organizational routines and patterns of knowledge coordination, prompting firms engaged in oncology research to redesign internal roles, interfaces, and coordination mechanisms. We further theorize two moderators of firms’ adaptive effectiveness. First, AI capability functions as an architectural competence by facilitating cross-domain information processing and enabling knowledge recombination. Second, modality breadth provides absorptive capacity grounded in diverse drug development experience. This research-in-progress aims to contribute to the literature on architectural innovation and organizational restructuring by showing how firms adapt their internal structures to technological change, while also highlighting the emerging role of AI in firm-level knowledge coordination.

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 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. 

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.