Tag Archives: AI washing

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.

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.