Tag Archives: firm-level

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.

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.

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.

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. 

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.

Ideas are Easy but Execution is Everything: Measuring the Impact of Stated AI Strategies and Capability on Firm Innovation Performance

Lee, Myunghwan, Gene Moo Lee (2022) “Ideas are Easy but Execution is Everything: Measuring the Impact of Stated AI Strategies and Capability on Firm Innovation Performance”, Work-in-Progress.

Contrary to the promise that AI will transform various industries, there are conflicting views on the impact of AI on firm performance. We argue that existing AI capability measures have two major limitations, limiting our understanding of the impact of AI in business. First, existing measures on AI capability do not distinguish between stated strategies and actual AI implementations. To distinguish stated AI strategy and actual AI capability, we collect various AI-related data sources, including AI conferences (e.g., NeurIPS, ICML, ICLR), patent filings (USPTO), inter-firm transactions related to AI adoption (FactSet), and AI strategies stated in 10-K annual reports. Second, while prior studies identified successful AI implementation factors (e.g., data integrity and intelligence augmentation) in a general context, little is known about the relationship between AI capabilities and in-depth innovation performance. We draw on the neo-institutional theory to articulate the firm-level AI strategies and construct a fine-grained AI capability measure that captures the unique characteristics of AI-strategy. Using our newly proposed AI capability measure and a novel dataset, we will study the impact of AI on firm innovation, contributing to the nascent literature on managing AI.