The impact of digital transformation on firm performance : three empirical studies based on panel data

Abstract

With the emergence of disruptive “ABCD” technologies represented by artificial intelligence, blockchain, cloud computing, and big data, the digital economy is reshaping the global economic landscape and emerging as a new driver of China’s economic growth. As significant micro-entities within economic activities, firms have undergone profound changes in their organizational structures, management paradigms, as well as the nature and format of their business activities amid the ongoing digital transformation. In practice, despite the widespread interest and active engagement of numerous firms in digital transformation, only a minority have achieved noteworthy results. Theoretically, scholars have yielded inconsistent findings regarding the relationship between digital transformation and firm performance, suggesting that digital transformation appears to be a dilemma for many firms. Therefore, it is imperative to unlock the “black box” of digital transformation and elucidate its impact on firm performance, as this holds both theoretical and practical significance. When confronted with decisions regarding digital transformation, firms often grapple with several key questions. First, should they allocate resources towards advancing digital transformation, and will it effectively enhance their performance? Second, which key areas should they prioritize in implementing digital transformation, and what core elements should be incorporated? Third, under what specific circumstances can digital transformation yield more pronounced performance improvements for firms? These inquiries are intimately linked to the motivation and commitment of firms towards digital transformation. A thorough exploration of these issues can not only deepen the theoretical understanding of the relationship between digital transformation and firm performance but also offer tailored managerial insights for firms. Building upon extensive dialogue with relevant literature, this thesis aims to complement existing research and address the challenges faced by firms in digital transformation. Drawing on theoretical frameworks such as the natural-resource-based view, upper echelons theory, attention-based view, and resource-based view, this thesis collects and synthesizes large-sample secondary data from multiple sources. Furthermore, it employs rigorous econometric empirical methods to conduct three studies that are logically interrelated. Study 1, proceeding from the dimension of digital technology, investigates how the use of digital technology influences firms’ environmental performance, and further examines the moderating effects of lean production and environmental leadership. Grounded in the natural-resource-based view, Study 1 utilizes panel data from 3308 A-share listed companies between 2007 and 2021. Employing fixed-effects regression models, the study conducts a comprehensive analysis of the data and performs a series of robustness checks to address potential endogeneity issues and ensure the consistency of research findings. The results of Study 1 unveil a significant positive impact of digital technology use on firms’ environmental performance. Additionally, it is found that lean production and environmental leadership play significant roles in moderating the relationship between digital technology use and firms’ environmental performance. Specifically, for firms that possess higher levels of lean production and environmental leadership, the positive impact of digital technology use on environmental performance is more pronounced. Study 2, proceeding from the dimension of digital talent, delves into the impact of the appointment of chief digital officers (CDOs) on firms’ financial performance, as well as the moderating effects of appointment mode (internal promotion versus external recruitment), scope of responsibilities (generalists overseeing overall digital transformation initiatives versus specialists focusing on specific domains), and board diversity. Drawing upon upper echelons theory and attention-based view, Study 2 utilizes panel data from 158 companies listed in the S&P 500 index spanning the period from 2002 to 2019. It employs a fixed-effects regression model to analyze the data and conducts robustness checks to mitigate potential endogeneity concerns and ensure the reliability of the research findings. The study reveals that appointing a CDO within the top management team significantly enhances a firm’s financial performance. Furthermore, “outsider” CDOs, “generalist” CDOs, and board diversity are associated with more pronounced improvements in financial performance for firms. Study 3, proceeding from the dimension of digital policy, explores the influence of intelligent manufacturing (IM) pilot policy on operational performance in manufacturing firms, while examining the moderating effects of internal operational resources (e.g., employee human capital quality and R&D intensity) and external operational environment (e.g., industry competition). Anchored in the resource-based view, Study 3 employs panel data from 1786 A-share listed manufacturing companies between 2010 and 2020. It adopts a staggered difference-in-differences method to analyze the data and conducts a series of robustness tests to ensure the consistency of research findings. Study 3 finds that the adoption of IM significantly enhances labor productivity in manufacturing firms. Moreover, firms with higher employee human capital quality, greater R&D intensity, and those operating in more competitive industries are inclined to achieve more substantial improvements in labor productivity through the adoption of IM. The theoretical contributions and innovations of this thesis are mainly manifested in the following aspects. First, it expands the perspective of empirical research on firms’ digital transformation and deepens the understanding of the role of non-technological factors (e.g., digital talents and digital policies) in the process of digital transformation. Second, it enhances the understanding of the relationship between different factors in digital transformation and multidimensional firm performance, providing new empirical evidence on the impact of these factors on firm performance and the moderating effects of contextual factors, thereby enriching the literature on the firm-level consequences of digital transformation in the field of operations management. Third, this thesis collects and synthesizes secondary data from multiple sources, employs various econometric methods to analyze the relevant data, and mitigates endogeneity issues as much as possible. Consequently, it enriches the methodological landscape of digital transformation studies to a certain extent.