1. Introduction
Currently, the world is experiencing unprecedented changes at an accelerated pace. Crisis events with far-reaching impacts, such as pandemics, geopolitical conflicts, and escalating trade disputes, are occurring frequently. A VUCA environment characterized by volatility, uncertainty, complexity, and ambiguity has gradually become the norm, posing more disruption risks to global supply chains. A typical example is the COVID-19 pandemic in 2020, during which 94% of the Fortune 1000 companies experienced supply chain disruptions following the global public health crisis [
1]. In 2023, the United States successively established the “Indo-Pacific Economic Framework for Prosperity” supply chain agreement [
2] and formed the “White House Council on Supply Chain Resilience (SCR)” [
3]. Improving SCR has become the focus of a new round of corporate competition and great power games. SCR mainly represents the capacity of a supply chain to recover to a normal or even more efficient state after facing market risk disruptions [
4]. As the micro subjects of economic operation, enterprises undoubtedly face unprecedented supply chain disruption risks in the VUCA environment. Identifying effective means to enhance SCR is critical to achieving sustainable business development and a smooth global economic cycle.
Artificial intelligence (AI), as a revolutionary general-purpose technology, offers a new approach to shaping SCR in the era of the digital economy. AI mimics human intelligence, allowing computer systems to carry out tasks that resemble human thinking and decision-making processes [
5]. According to the 2025 Top 10 Supply Chain Trends Report released by the Association for Supply Chain Management, AI ranks first, becoming the most influential supply chain trend in 2025 [
6]. Supply chain management in the VUCA environment is increasingly characterized by diversification and complexity, leading to higher disruption risks and more urgent resilience-building needs. AI’s ability to think and act with both technological rationality and human sensibility can fulfill the need for SCR shaping [
7]. Through deep learning, automated decision-making, and visualization, AI has the potential to enhance different areas of the supply chain, such as demand forecasting, logistics, production, manufacturing, warehousing, and the design of supply chain networks [
8,
9,
10], driving the intelligent development of the supply chain. Studies related to the association between AI and SCR have primarily concentrated on the theoretical aspects, establishing research frameworks through a literature review [
11,
12] and interviews [
13]. From a quantitative perspective, only a few scholars have used questionnaire analysis and structural equation modeling to confirm the positive influence of AI on SCR [
14,
15]. Therefore, the existing research lacks a large-sample empirical test on the question of whether AI can enhance SCR.
Integrating AI into supply chain management is essentially a strategic change for the organization. The upper echelons theory emphasizes that the personal traits and psychological structure of executives affect firms’ strategic decisions and organizational performance [
16]. Many studies on CEO traits and strategic change are based on this theory, focusing on how traits like regulatory focus [
17], temporal focus [
18], and overconfidence [
19] impact strategic change. Other studies explore the role of CEO career variety [
20], international experience [
21], and prior board experience [
22] in shaping strategic change. However, the specific effects of CEO traits on the economic outcomes of firms adopting AI technology remain unclear. Unlike traditional innovation activities, supply chain intelligence centers on applying AI technologies to manage uncertainty and risk. The resilience and competitiveness shaped by a CEO’s sports experience can drive them to adopt AI technologies proactively. In practice, many successful AI company CEOs who have made a global impact have sports experience. For example, Meta’s CEO Mark Zuckerberg enjoys long-distance running and combat training, while Microsoft’s CEO Satya Nadella is a cricket enthusiast. From a theoretical perspective, sports experience—as an off-the-job activity—helps CEOs develop openness and emotional stability through physical training and teamwork [
23]. These qualities enable them to handle uncertainty, giving them an advantage in overcoming strategic inertia [
24]. CEOs with sports experience are often more open to ideological innovation and are more likely to encourage the adoption of AI technologies in supply chain management.
Employing data from China, this paper investigates the following questions: (1) Does AI affect SCR, and how? (2) Does CEOs’ sports experience moderate the association between AI and SCR? Our study began by performing panel data regression to explore how AI applications impact SCR. Then, our study constructed the interaction term to examine the moderating effect of CEOs’ sports experience. To explore specific mechanisms, this paper discusses the mediating role of operational efficiency optimization, information, and knowledge spillover. Heterogeneity analysis was also conducted based on the corporate human capital, industry characteristics, and digital infrastructure. Moreover, our research further examined the diffusion effect of AI in the supply chain network.
The primary contributions of our research are outlined as follows.
First, our study confirms the positive role of AI in shaping SCR through large-sample empirical research, providing strong evidence for how AI affects SCR. Currently, studies examining the impact of AI on SCR mainly adopt qualitative interview methods [
13] and empirical survey methods [
14,
15], lacking large-sample empirical tests. On the one hand, our study employed the entropy method to construct the indicator of SCR from the dimensions of supply chain resistance and recovery. On the other hand, this study employed machine learning and text analysis methods to construct AI indicators for companies, establishing the groundwork for future empirical research on AI and supply chains. Additionally, our findings open up the “black box” of the mechanism. This paper overcomes the limitations of existing research on the indirect effects of AI on SCR [
25], revealing the internal mechanisms by which AI influences SCR, including operational efficiency optimization, information spillover, and knowledge spillover, thereby deepening the understanding of the relationship between AI and SCR.
Second, this paper identifies the positive moderating effect of the CEOs’ sports experience on the association between AI and SCR, enriching the research regarding the influence of CEOs’ off-the-job experience on corporate performance. Existing research on the economic consequences of AI at the firm level overlooks the impact of human cognitive factors on the application process of AI technology [
26]. Although the upper echelons theory emphasizes that the characteristics of top managers influence organizational performance, its framework has insufficient attention to CEOs’ off-the-job experience [
27]. In practice, despite differences among successful global CEOs in various aspects, they exhibit common sports-oriented leadership traits. CEOs with early sports experiences help develop a positive attitude and confidence in teamwork and self-management, build psychological resilience, and cultivate flexible psychological adaptability [
28]. These traits align with the demands of VUCA environments on leaders. CEOs’ sports experience shapes their positive personality traits and improves their cognitive understanding of AI, which further influences the value creation of AI applications in the supply chain. Our research explores the moderating role of the CEOs’ sports experience, which not only expands the application scope of the upper echelons theory but also provides a new research perspective on the economic consequences of firms’ adoption of AI technologies.
Third, our research uncovers the bidirectional diffusion effect of the core enterprises’ AI applications on the AI capabilities of upstream and downstream supply chain enterprises. Existing research on the economic effects of corporate AI applications has mainly focused on their impact on the core enterprise itself, overlooking the potential external effects of AI applications. Our research firstly constructs matched sample data of companies and their suppliers, as well as companies and their customers, on a 1:1 basis. Then, our study uses AI-adopting companies as the diffusion source and finds that they have a spillover effect on the AI capabilities of upstream and downstream supply chain enterprises. This paper identifies more positive outcomes of AI in empowering SCR, enriching the study of the spillover mechanisms of core enterprises’ AI applications in the supply chain, and expanding the boundaries of research on the economic consequences of AI.
5. Further Analysis
Rogers proposed the innovation diffusion effect, defined as the process by which innovation spreads among members of a social network through specific channels [
95]. The supply chain is a vital component of the social network, where enterprises are closely connected and exchange resources such as information with upstream and downstream companies, thus creating the transmission and linkage effects of the supply chain [
96]. As digital technology advances, the spillover effects of information and knowledge during AI application among supply chain companies have deepened. The innovation experience and knowledge of companies using AI technology become the starting point of the supply chain diffusion effect [
97], resulting in the spread of AI across upstream and downstream enterprises in the supply chain.
Our previous analysis reveals that the application of AI technology enhances SCR through information and knowledge spillover mechanisms between companies in the supply chain. This raises the following question: Will the spillover effect of AI in the supply chain lead to a diffusion effect on the AI capabilities of upstream and downstream companies? To address this question, our research further develops a 1:1 matched database of supply chain companies, collecting 497 sets of enterprise–supplier annual samples and 565 sets of enterprise-customer annual samples. Equation (8) is used to test the diffusion effect of AI technology applications in the supply chain.
where refers to the application of AI technology in upstream supplier companies (S_AI-X) and downstream customer companies in the supply chain (C_AI-X). Controls represent the corresponding control variables for upstream and downstream companies, including core enterprise Controls, upstream supplier S_Controls, and downstream customer C_Controls.
Table 12 shows the empirical results of examining the diffusion effect of AI in the supply chain. The coefficients of
S_AI-X and
C_AI-X are both significantly positive, suggesting that the application of AI technology by core enterprises boosts the AI capabilities of upstream suppliers and downstream customers. The results above indicate that corporate AI adoption has a positive diffusion effect across supply chain networks, triggering an overall improvement in the intelligence level of the supply chain system.
6. Discussion
In an environment where the characteristics of VUCA are increasingly visible, supply chains urgently need to accelerate their own changes to cope with all kinds of complex and severe risks and challenges. How to use AI to enhance SCR has rapidly attracted extensive attention from the academic community. Scholars have studied the use of AI techniques for system development and algorithm modelling, and provided application strategies in demand forecasting, risk management, transportation, supplier selection, and inventory management. There are also studies that systematically sort out how AI technology impacts different aspects of SCR through a literature review and build a theoretical framework of AI-SCR [
11,
98]. Despite the comprehensive exploration of AI’s positive impact on shaping SCR in the existing literature, much of the research predominantly relies on theoretical analyses [
11,
98] and survey-based approaches [
15,
99]. In contrast, this study seeks to offer more timely and empirical evidence through large-scale testing. Specifically, an SCR index for enterprises is developed using the entropy weighting method, encompassing both the resistance and recovery capacity dimensions. Additionally, machine learning and text analysis techniques are applied to construct an AI index, which highlights the beneficial influence of AI applications on SCR. Further, this study elaborates on the operational mechanisms involved, such as optimization of operational efficiency, information spillover, and knowledge spillover. This research not only contributes to advancing the theoretical framework regarding AI’s role in shaping SCR but also provides a practical and quantitative model for future theoretical inquiries.
In addition, this paper analyzes how CEO characteristics influence the process of AI technology application. For traditional enterprises, the application of AI technology to supply chain management is a strategic change that requires top managers to carry out top-level design and resource allocation optimization. This paper overcomes the shortcomings of previous studies of the micro-level economic consequences of AI technology application, which ignore managerial characteristics [
26], and explores the moderating role played by CEOs’ sporting experiences. Practice has demonstrated that the sports experiences of many well-known entrepreneurs have a profound impact on the level of corporate AI technology adoption [
100]. Examining the moderating role of CEOs’ sports experience can make up for the shortcomings of the existing upper echelons theory framework, which pays insufficient attention to the economic consequences of CEOs’ off-the-job experience.
Finally, most of the existing studies on the economic effects of AI applications focus on the impact of AI on the firms themselves, ignoring the possible external effects of AI applications [
101,
102]. Studies have been conducted to identify groups of enterprises with vertical networks, and to analyze the spillover phenomenon of digital transformation by intercepting the vertical correlation between enterprises [
103,
104]. Artificial intelligence technology, as a kind of digital technology, also has high synergy and strong externality characteristics. This paper confirms the diffusion effect of enterprise AI technology applications upstream and downstream of the supply chain, which enriches the research on the factors influencing the level of AI technology.
7. Conclusions and Implications
7.1. Conclusions
This paper discusses the role of AI in shaping SCR through large-sample empirical examination. Based on dynamic capability and information processing theory, our study reveals that AI application significantly improves SCR, and this conclusion remains valid after addressing endogeneity issues. Moreover, the CEOs’ sports experience strengthens the positive impact of AI on SCR.
In terms of the specific channel, mechanism examination shows that AI can drive companies to enhance SCR by improving operational efficiency and promoting information and knowledge spillover in the supply chain. Heterogeneity analysis reveals that such a positive impact is more pronounced in firms with high-skilled labor forces, firms with high heterogeneity of the executive team’s human capital, high-tech industries, and areas with strong digital infrastructure.
In addition, our research successfully identifies the diffusion effect of AI applications in the supply chain network. AI application of core enterprises not only enhances SCR but also boosts the AI capabilities of upstream suppliers and downstream customers.
7.2. Managerial Implications
Our research has several managerial implications for regulators and enterprises. For regulators, this paper reveals that AI application improves SCR. The better the digital technology facilities in the area where the enterprise is located, the stronger the positive effect that AI technology may exert. The above findings indicate that the government should first guide companies to solidify their strategic direction towards intelligence. The government can implement incentive policies at the firm level, providing financial subsidies, tax reductions, and other support for AI innovation and application, guiding companies to seize the opportunities presented by the new round of technological revolution and industrial change. Secondly, the government should accelerate the creation of a “hard environment” to provide fertile ground for AI adoption. Specifically, the government can improve digital infrastructure construction, including building high-speed, stable internet networks and secure, reliable data centers, establishing cloud computing platforms to provide computation, storage, and application services for companies, and promoting the application of IoT technology in logistics and warehousing.
For companies, apart from the vital role of AI in shaping SCR, our results also indicate that CEOs’ sports experience, the heterogeneity of senior executive teams, and high human capital can strengthen the positive association between AI and SCR. AI application has a diffusion effect on the upstream and downstream enterprises of the supply chain. In a VUCA environment, managers must deeply understand the close relationship between digital intelligence transformation and SCR development, formulate reasonable AI application strategies, and leverage AI technology to enhance crisis response capabilities in supply chain management. CEOs’ sports experience can be an important consideration for the board of directors when selecting a CEO. In addition to the usual academic background and work experience, the board of directors can try to focus on the CEOs’ sports experience to assess personal traits such as openness. In addition, companies should build a diverse executive team. Here, companies form their executive team based on the principles of differentiation and diversity, introducing senior managers with varied backgrounds to inject vitality into corporate AI applications. Companies can also adjust their workforce skill structure to better leverage the positive effects of AI. Companies should actively recruit talent with expertise in AI and regularly provide employees with AI-related skills training. Finally, companies should capitalize on the supply chain diffusion effects of AI. Companies should actively build collaborative relationships for digital intelligence across the upstream and downstream supply chain, integrating internal and external resources, data, and services to achieve full-chain upgrades and ecological development of the intelligent supply chain.
7.3. Limitations and Prospects
This study has several limitations that warrant further investigation.
First, this study did not fully capture the technological characteristics of AI when measuring this variable, as it lacked a more detailed classification of AI technologies. AI can be categorized into logical AI and machine learning, with the former emphasizing propositional reasoning and the latter utilizing data-driven learning for prediction and decision-making. Future research could attempt to classify AI technologies more comprehensively and examine how different AI applications influence SCR in varying ways.
Second, in exploring the mechanisms through which AI impacts SCR, our study only considers the CEO’s sports experience as a moderating variable at the individual level, without delving into other executive attributes or traits. Consequently, future research may explore additional factors at the executive level, such as the background in information technology or academic expertise, and investigate their influence on the microeconomic outcomes of AI applications.
Finally, while AI is a key technological factor influencing SCR, other organizational and environmental factors also play crucial roles. Future research should explore these additional dimensions to provide a more comprehensive understanding of SCR. By broadening the focus beyond technology to include organizational and environmental aspects, a more holistic perspective can be developed, offering practical guidance for businesses navigating the increasingly complex and volatile global supply chain landscape.
