Research


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




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




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.




Abstract: 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.




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 



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.




Abstract: We contribute to the literature on the role of machine learning (ML) in organizational learning by examining two key learning tendencies: exploitation and exploration. We analyze the effect of ML investments on organizations’ learning tendency, which in turn influences firm performance and survival. Our findings suggest that ML primarily shifts organizations towards exploration and that ML-induced learning tendency fully mediates the positive relationship between ML investments and organization survival. Notably, we find that non-IT organizations with exploitative tendencies can effectively shift towards exploration through ML investments. To our knowledge, this study provides the first large-scale empirical insights into ML’s impact on organizations’ learning tendency and performance outcomes, offering valuable insights for rethinking organizational learning in the age of ML.




Abstract: As a general-purpose technology, Artificial Intelligence (AI) has profoundly transformed the human labor landscape with its capability to perform complex tasks. Despite ongoing debates about AI’s role in the future of work, there remains a limited understanding of how a firm’s endeavors to strengthen its AI capabilities interweave with its workforce. Reflecting on the ongoing role of labor unions in responding to technological advancements, we investigate the impact of unionization on AI investments and their subsequent effects on firm value. Utilizing datasets on labor elections and AI-skilled labor, we construct measures for unionization and AI investment. Based on a regression discontinuity design framework, our results indicate that while AI investments lead to an improvement in firm value, unionization decreases AI investments in the following 5 years, implying that labor unions have an indirect negative impact on firm value by hampering AI investments. We also provide suggestive evidence of the competition between firms and labor unions in resource allocation between AI investments and employment as a potential mechanism through which labor unions may hinder AI investments. This research advances our understanding of corporate AI strategies by recognizing labor unions as a crucial stakeholder, enriching the discourse on the literature on the business value of AI and IT.




Abstract: The advancement of digital technologies has transformed the news industry, with large language models (LLMs) introducing new opportunities and challenges for the institutional press. Utilizing a unique setting where two major news institutions in South Korea introduced LLM-based news writing assistants, this study investigates how LLM assistance affects journalists’ news production and readers’ engagement using a mixed-method approach. We first conduct interviews and surveys with journalists to identify key constructs in journalism and develop a research model on the emerging phenomenon. We then empirically examine the impacts of LLM assistance on news production and engagement using a unique dataset of 941 LLM-assisted articles and 7,230 human-written articles on identical events. Notably, we develop a novel framework using GPT-4o to extract information sources from a large number of news articles. Our results show that LLM assistance reduces the diversity and uniqueness of information sources in news articles but increases publication promptness. Furthermore, LLM-assisted news is associated with decreased reader engagement, a trend exacerbated by reduced source diversity and uniqueness despite faster publication. This study contributes to the understanding of generative AI’s role in journalism and provides implications for the institutional press navigating the integration of AI technologies in journalistic practices.




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.