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 Multi-Agent Multi-Representational AI System to Enhance Automotive Design

Zhang, Xiaoke, Angela Kwon, Mi Zhou, Gene Moo Lee “Designing for Designers: A Multi-Agent Multi-Representational AI System to Enhance Automotive Design,” Work-in-progress.

Organizations increasingly seek to use large language models (LLMs) to support knowledge-intensive work. However, effective deployment requires systems that ground LLM reasoning in heterogeneous, domain-specific knowledge. In collaboration with the vehicle design team of a major automotive manufacturer, we develop Design Insight Atlas, a multi-agent, multi-representational retrieval-augmented generation (RAG) AI system for automotive design intelligence. The system grounds LLM responses in three complementary knowledge representations: structured vehicle specifications for factual analysis, automotive news for temporal and market intelligence, and a knowledge graph for relational reasoning. A central designer assistant orchestrates a news retrieval tool, a data analysis agent, and a knowledge graph analysis agent, integrating their outputs into unified, evidence-grounded responses. We evaluate the system using 160 designer-oriented questions across eight task categories and four backbone LLMs. Design Insight Atlas achieves an average overall win rate of 89.2% against vanilla LLM baselines and consistently improves comprehensiveness, accuracy, and actionability across models and question types. Our study demonstrates how multi-representational knowledge grounding and multi-agent retrieval can enhance LLM support for domain-specific organizational knowledge work.

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.

Balance by Machine Redirection? The Role of Machine Learning Investments in Organizations’ Innovation Search and Long-Term Survival

Lee, Myunghwan, Timo Sturm, Gene Moo Lee, “Balance by Machine Redirection? The Role of Machine Learning Investments in Organizations’ Innovation Search and Long-Term Survival”, 2nd round R&R at MIS Quarterly.

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

Organizations’ long-term survival depends on their ability to balance innovation search between explorative and exploitative innovation outputs. Machine learning (ML) investments can reshape this balance by enabling organizations to develop capabilities that generate innovation opportunities from data beyond those readily accessible through human-led innovation. Yet, it remains theoretically unclear whether ML helps organizations move toward greater balance or reinforces their innovation imbalances. We theorize that ML investments operate as a context-dependent directing mechanism: how they affect innovation search depends on organizations’ prior innovation tendencies and the types of ML capabilities they develop. We test this theory using a novel organization-year-level measure of ML investments in a longitudinal sample of 2,916 organizations. Combining panel regressions, long-difference analyses, survival models, and interviews with ML practitioners, we find that ML investments are associated with greater innovation balance by strengthening organizations’ underrepresented innovation tendencies: they redirect exploitation-oriented organizations toward explorative innovations and exploration-oriented ones toward exploitative innovations. The shift toward explorative innovations is strongest for organizations with unsupervised capabilities, and our survival and mediation analyses provide suggestive evidence that greater innovation balance may be linked to lower organizational failure risk. Our study extends research on IT-enabled innovation by showing that ML can become an additional source of innovation whose direction depends on organizations’ prior innovation search. In doing so, it offers a context-dependent view of ML-enabled innovation and explains how ML investments can help organizations counteract path-dependent innovation imbalances.

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,” R&R, Information Systems Research.

  • 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 postings) 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 costs, 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.