Research


Topic areas: AI strategy, Managing AI, Innovation, Firm performance




Developed from first-year summer paper at the University of British Columbia 


Presentation: INFORMS Accounting and Data Analytics Session (October 2022 / Indianapolis), KrAIS Workshop (December 2021 / Virtual), INFORMS Workshop on Data Science (DS) (November 2021 / Virtual), KrAIS Workshop, (December 2019 / Munich, Germany), INFORMS Conference on Information Systems and Technologies (CIST) (November 2019 / Seattle), UBC Sauder MIS Seminar Series, (November 2018 / Vancouver)


Abstract: In this research method paper, we propose a firm-specific strategic competitive positioning (SCP) measure to capture a firm’s unique competitive and strategic positioning. Using an unsupervised machine learning approach, we use structural holes, a concept in network theory, to develop and operationalize an SCP measure derived from a strategic similarity matrix of all existing U.S. publicly traded firms. This enables us to construct a robust firm-level measure of strategic competitive positioning with minimal human intervention. Our measure dynamically captures competitive positioning across different firms and years without using artificially bounded industry classification systems. We illustrate how the measure dynamically captures firm-level, industry-level, and cross-industry strategic changes. Then we demonstrate the effectiveness of our measure with a case study showing the subsequent performance imprinting effect of SCP at the time of initial public offering (IPO). The results show that our unsupervised SCP measure predicts post-IPO performance. This paper makes a significant methodological contribution to the information systems and strategic management literature by proposing a network theory-based approach to dynamically measure firm-level strategic competitive positioning. The measure can be easily applied to firm-specific, industry-level, and cross-industry research questions across a wide variety of fields and contexts.


Online presence: SCP_network_construction




Developed from third-year summer paper at the University of British Columbia 


Presentation: Academy of Management (CTO division) Conference (August 2023 / Boston), UBC Sauder MIS Seminar Series, (April 2022 / UBC), INFORMS Conference on Information Systems and Technologies (CIST) (October 2023 / Phoenix). 


Abstract: Firms often depend on technological assets or inter-firm relationships to pursue exploratory innovation. 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 concept of path dependencies, we hypothesize the impact of a firm’s DL capabilities on exploratory innovation and how DL capabilities interact with conventional path-breaking activities. Our empirical investigations, based on a novel DL capabilities measure constructed from AI conference and patent datasets, 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 (i.e., technology collaboration) 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.




Developed from second-year summer paper at the University of British Columbia 


Presentation: BIGS Conference (December 2022 / Virtual), INFORMS Workshop on Data Science (DS) (October 2022 / Indianapolis), UBC Sauder MIS Seminar Series, (February 2021 / Virtual), Workshop on Information Technologies and Systems (WITS) (December 2020 / Virtual), KrAIS Workshop, (December 2020 / Virtual). 


Abstract: Service providers, such as restaurants, have been adopting various robotics technologies to improve operational efficiency and increase customer satisfaction. AI Robotics technologies bring new restaurant experiences to customers by taking orders, cooking, and serving. While the impact of industrial robots has been well documented in the literature, little is known about the impact of customer-facing service robot adoption. To fill this gap, this work-in-progress study aims to analyze the impact of service robot adoption on restaurant service quality using 4,612 restaurants and their online customer reviews. We analyzed the treated effect of robot adoption using a difference-in-differences approach with propensity score and exact matching. Estimation results show that restaurant robot adoption has a positive impact on customer satisfaction, specifically on perceived food quality and perceived value. This study provides both academic and practical implications on the emerging AI robotics techniques.




Developed from third-year paper at the University of British Columbia


Presentation: INFORMS Workshop on Data Science (DS) (October 2022 / Indianapolis) 


Abstract: 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 three major limitations, limiting our understanding of the impact of AI in business. First, the definition of AI itself is still elusive in the IS and business literature. With the recognition that AI is a multifaceted problem-solving process different from traditional IT, we present a detailed AI classification scheme using various sources (e.g., PapersWithCode, HuggingFace, ACM). Second, existing measures on AI capability do not distinguish between stated strategies and actual AI implementations. To distinguish “AI washing” and actual AI capability, we collect various AI-related data sources, including AI conferences (e.g., NeurIPS, ICML, ICLR),  AI software projects (GitHub repositories), inter-firm transactions related to AI adoption (FactSet), and AI strategies stated in 10-K annual reports. Third, while prior studies identified successful AI implementation factors (e.g., data integrity and intelligence augmentation) in a general context, little is known about the fit of specific AI technologies and different types of tasks. We draw on the theory of task-technology fit to construct a fine-grained AI capability measure that captures the unique characteristics of different industries. Using our newly proposed AI capability measure and a novel dataset, we will study the impact of AI on firm innovation and market reaction, contributing to the nascent literature on managing AI.

 

Online presence: AI_classification 



Presentation: KrAIS Workshop (July 2023 / Seoul), Montreal Symposium on IS Research (May 2023 / Montreal)  (Manuscript preparation for submission to Academy of Management 2023)


Abstract: The use of mobile information technology (IT) has become increasingly vital for businesses, especially for remote and hybrid work during the COVID-19 pandemic, providing employees with accessibility, flexibility, and responsiveness. However, despite its growing significance, the business value of mobile device management and its role in establishing digital resilience during crises remain underexplored in the literature. To address this research gap, our study examines the effect of mobile device management on a firm’s resilience to external shocks. Using a proprietary dataset from a global mobile device management solution provider for public U.S. firms over the three-year period of 2019-2021, we find that firms with mobile device management have better financial performance during the pandemic, demonstrating greater resilience to the shock. Furthermore, we observe heterogeneous resilience effects across industries, with greater impacts in non-high-tech industries than in high-tech ones, and in manufacturing, retail, and service industries compared to others. Our findings are robust to various tests. This study contributes to the literature by emphasizing the crucial role of mobile device management in building digital resilience.