TMT Heterogeneity and Supply Chain Resilience: The Roles of Digital Transformation,Environmental Uncertainty and Innovation Quality

Abstract

Supply chain resilience (SCR) is increasingly important as firms face recurrent disruptions. This paper examines whether diversity in the top management team (TMT) improves firm-level SCR and why, using panel data for Chinese A-share firms (2011–2023) with controls for industry and year effects. We find that greater TMT heterogeneity is positively associated with SCR, and the baseline model explains of the variance in SCR. Mechanism tests indicate that heterogeneous TMTs promote digital transformation, which partially mediates the effect; environmental uncertainty attenuates the benefit, while higher innovation quality amplifies it. grounded in upper echelons theory (UET), the study shows that TMT heterogeneity has a specific impact on SCR, thereby applying the theory to the SCR context. The results are robust to endogeneity checks and alternative specifications, offering actionable implications for managers seeking resilience.

Keywords

supply chain resilience top management team environmental uncertainty enterprise innovation quality upper echelons theory

Introduction

From the COVID-19-induced global transportation disruptions to the Russia–Ukraine conflict, escalating geopolitical tensions, and the worldwide semiconductor crisis, global supply chains are confronting unprecedented challenges. Supply chain resilience (SCR) has thus emerged as a core capability for firms to sustain both competitiveness and survival. SCR not only reflects a firm’s ability to recover swiftly from disruptions or interruptions but also embodies its potential to reconfigure competitive advantages in highly uncertain environments (Li & Zobel, 2020; Ponomarov & Holcomb, 2009; Rice et al., 2003). To adapt to the growing volatility of the global economic and political landscape, as well as the intensifying competition and uncertainty of the contemporary business environment, the complexity of corporate supply chains has been continuously escalating (Um & Han, 2021). This complexity heightens the risk of disruption (Blackhurst et al., 2011; Li et al., 2020). Despite the widespread recognition of the importance of SCR, many firms continue to face challenges in its effective implementation (Hosseini et al., 2019). Environmental complexity often impairs TMTs’ judgment, limiting their ability to manage the firm in ways that enhance SCR (Albert & Cannella, 2008). In such a fragile business environment, assembling a diverse and resource-rich TMT is essential for improving strategic decision-making, mitigating supply chain risk, and strengthening resilience.

Existing research on the drivers of SCR has predominantly adopted an external perspective, focusing on inter-firm collaboration and relationship networks, and emphasizing coordination and resource sharing among supply chain node firms (Soni et al., 2014; Wamba et al., 2020; J. Zhou et al., 2024). This perspective largely overlooks the role of internal strategic decision-making in shaping SCR. In the face of supply chain disruptions, firms must not only rely on external collaboration but also reconfigure internal resources and rapidly adjust capabilities (Ambulkar et al., 2015). Existing research has shown that factors such as internal integration capability, continuity of business planning, and procurement strategies can facilitate the development of SCR (Azadegan et al., 2020; Mishra et al., 2016). However, whether it is internal corporate strategy or inter-firm collaboration, strategic decisions must be made by the firm’s decision-making core, TMT holds unique research value. Particularly in building SCR, enterprises need to possess the corresponding capabilities (Münch & Hartmann, 2023). Delays in sharing information and resources can significantly disrupt supply chain operations (Majumdar et al., 2020), highlighting the need for enhanced information management and collaborative capabilities (Law et al., 2021). It requires enterprises to actively develop and utilize resources such as information, enabling managers to govern the company in ways that enhance SCR (Antony et al., 2019), which places new demands on the TMT.

Grounded in upper echelons theory (UET; Hambrick & Mason, 1984), the TMT is regarded as the strategic decision-making core of the firm. Observable attributes of its members, such as age, gender, educational background, occupational experience, overseas exposure, and financial expertise, shape the cognitive frameworks and value systems that underpin decision-making. Differences in these observable attributes collectively form TMT heterogeneity, which ultimately determines a firm’s strategic choices and approaches to resource integration in times of crisis (Carlos Pinho & Sampaio De Sá, 2014). Especially for emerging economies like China, the competitive environment is characterized by high uncertainty (Wright et al., 2005), and the speed of strategic actions plays a more decisive role in determining a company’s market position (Chang & Park, 2012). To build a higher level of SCR, TMT needs to find management approaches that align with this goal (Antony et al., 2019). In this context, the diversity of the TMT may provide enterprises with a broader range of resources and perspectives, enhancing their ability to respond to external shocks (Y. Liu et al., 2018; H. Wang et al., 2022; Xi & Guo, 2024).

Existing research on TMT heterogeneity has predominantly examined performance-related outcomes rather than organizational resilience. They often adopt a static perspective and overlook the increasingly volatile and uncertain environments in which modern firms operate (Chen et al., 2020). As early as 2006, Seville et al. have emphasized that resilience is related to soft capabilities such as leadership, organizational culture, and management practices (Christopher et al., 2011; Seville et al., 2006; Soni et al., 2014). SCR is based on the organizational resilience, and still follows this view (Kamalahmadi & Parast, 2016). However, the link between TMT heterogeneity and SCR remains underexplored. Research has not systematically examined how variations in TMT composition influence the formation of SCR capabilities within firms. Moreover, prior studies that relate TMT to resilience tend to focus on managerial behavior (e.g. IT support, information sharing) rather than compositional heterogeneity (Mandal, 2021), leaving unexplored how cognitive diversity translates into resilience outcomes. This gap motivates the present study.

To address this gap, this study investigates the impact of TMT heterogeneity on SCR by using panel data for Chinese A-share firms (2011–2023) with controls for industry and year effects. Introduces environmental uncertainty and firm innovation quality as moderating variables, and examines digital transformation as a mediating mechanism. Specifically, this study aims to answer:

Q1: How does TMT heterogeneity affect SCR?

Q2: Do environmental uncertainty and innovation quality alter this relationship?

Q3: Does digital transformation mediate the effect of TMT heterogeneity on SCR?

To address these issues, this study demonstrating the specific impact of TMT heterogeneity on SCR and highlighting the importance of leadership composition in resilience formation. While prior studies have linked TMT heterogeneity to firm behavior and performance (Cui et al., 2019; H. Wang et al., 2022; Xi & Guo, 2024), they have not gone deep into the field of SCR. This study thus positions TMT heterogeneity as a key SCR driver. Importantly, SCR is not a singular or static metric, and research on its human capital dimension remains scarce (Carpenter et al., 2004). By conceptualizing TMT heterogeneity as a multidimensional construct, this study deepens the understanding of its impact on SCR. It also incorporates moderating variables (environmental uncertainty and innovation quality) and a mediating variable (digital transformation) to reveal the mechanisms underlying this relationship.

This study makes three theoretical contributions. First, it extends UET to the domain of SCR. By showing that heterogeneity is positively associated with SCR, we highlight that variation in managerial attributes not only shapes exploratory or growth-oriented outcomes, but it also matters for organizational stability under disruption, thereby broadening the scope of leadership research beyond performance measures. Second, we identify digital transformation as a capability-building mechanism rather than an exogenous correlate. Heterogeneous TMTs are more inclined to initiate and adopt digital practices, which improve information visibility, coordination efficiency, and response capacity within supply chains. In doing so, digital transformation channels managerial diversity into supply chain capability formation, refining current understandings of how leadership characteristics become embedded in organizational systems. Third, we demonstrate that the benefits of TMT heterogeneity are contingent on contextual conditions. Environmental uncertainty attenuates the positive association with SCR, while higher innovation quality strengthens it. These boundary effects help explain why empirical findings on TMT diversity have been mixed and show that its contribution to resilience is conditional on firms’ complementary capabilities and external environments.

Theoretical Analysis and Hypotheses

Upper Echelons Theory

UET, proposed by Hambrick and Mason, departs from classical strategic management approaches by explicitly recognizing bounded rationality in managerial decision-making. Rather than assuming fully rational actors, UET posits that executives interpret complex and uncertain environments through their own cognitive frames, which are shaped by personal experiences, values, and backgrounds. Observable demographic characteristics—such as gender, age, and educational attainment—therefore serve as proxies for these underlying cognitive orientations and systematically influence strategic choices and organizational outcomes. As a result, the composition of the TMT is central not only to strategy formulation but also to effective strategy implementation. This theory has been widely applied to examine how the personal characteristics of top executives influence strategic decision-making (Campayo-Sanchez et al., 2025). Given top managers’ substantial control over corporate decisions, the theory has also been frequently adopted in the context of corporate governance (Y. Liu et al., 2018).

Because top managers operate under bounded rationality, their strategic interpretations and decisions are shaped not by fully rational optimization but by accumulated experiences and socialization. As a result, heterogeneity within the TMT becomes a critical source of variation in strategic judgment, sensemaking, and organizational action. Prior studies therefore conceptualize TMT heterogeneity as a multidimensional construct, typically composed of demographic, experiential, and professional attributes that capture distinct cognitive and behavioral channels through which leadership teams influence firm outcomes.

Age and gender heterogeneity are among the most commonly examined dimensions, as they reflect generational differences in risk preferences, time horizons, leadership styles, and interpersonal orientation. Mixed-age teams integrate experiential knowledge with exploratory thinking, while gender diversity has been shown to broaden cognitive repertoires and foster more relational and collaborative decision-making processes, particularly under uncertainty (H. Wang et al., 2022; Y. Zhou et al., 2022). Beyond demographic attributes, heterogeneity in overseas experience, educational backgrounds, financial backgrounds, and occupational backgrounds reflects deeper variation in executives’ knowledge structures, experiential repertoires, and problem-framing logics. Upper Echelons Theory suggests that such observable characteristics proxy for differences in how top managers interpret complex information and evaluate strategic trade-offs under bounded rationality. Executives with overseas exposure bring diverse institutional logics and boundary-spanning perspectives, enhancing teams’ sensitivity to cross-border risks and systemic disruptions. Educational heterogeneity captures variation in analytical schemas and cognitive styles, enabling TMTs to assess supply chain disturbances from multiple technical and managerial viewpoints. Diversity in financial and functional backgrounds further expands the team’s capacity to balance resource allocation, risk management, and operational feasibility, which is critical when firms must rapidly reconfigure resources and coordinate responses across the supply network. Collectively, these forms of heterogeneity enrich the TMT’s cognitive complexity and experiential breadth, strengthening the dynamic capabilities and coordinated decision-making processes that underpin SCR. (Cui et al., 2019; Lyu et al., 2024; Sun et al., 2025).

Thus, these six dimensions represent well-established indicators of TMT heterogeneity in the Upper Echelons literature. They translate UET’s abstract notion of “cognitive bases” into observable and measurable constructs, capturing how leadership diversity shapes dynamic capability formation, social-capital activation, and coordinated action. As such, these dimensions provide a theoretically coherent and empirically grounded foundation for examining how TMT heterogeneity contributes to SCR, which depends on collective sensemaking, relational alignment, and flexible resource reconfiguration under conditions of disruption.

TMT Heterogeneity and SCR

Building upon UET, TMT heterogeneity reflects the degree of diversity in executives’ observable attributes (Harrison & Klein, 2007). In the context of SCR, this mechanism extends beyond individual ability: the composition of the TMT shapes how firms interpret supply shocks, mobilize resources, and coordinate actions within the supply network (Dong et al., 2025; Williams et al., 2017). TMT heterogeneity broadens informational variety and reduces decision myopia, leading to richer strategic option sets (Lopez-Cabrales et al., 2017; Van Knippenberg et al., 2004).

Unlike innovation or financial performance, SCR is a collective capability involving external relational coordination, boundary spanning, and cross-functional synchronization within supply networks (Aslam et al., 2020), requiring firms to possess the necessary resources and dynamic capabilities to manage disruptions (Golgeci & Ponomarov, 2013). After proposing the UET, Hambrick and Mason (1984) suggested that TMT attributes are a source of firms’ dynamic capabilities, the TMT enhances the organization’s dynamic capabilities through processes of integration and coordination (Hambrick & Mason, 1984). Heterogeneous TMTs are more likely to identify weak signals within upstream–downstream networks, challenge status-quo heuristics, and recombine resources into adaptive solutions. When a firm’s dynamic capabilities are assessed, factors such as knowledge acquisition and market-sensing orientation are considered critical components (Yin et al., 2024). These capabilities are therefore tightly linked to decision-makers, particularly their leadership styles (Lopez-Cabrales et al., 2017).

Beyond internal cognition, heterogeneity expands firms’ access to social capital, which comprises structural, relational, and cognitive dimensions (He et al., 2022; Nahapiet & Ghoshal, 1998). For supply chains, structural and relational capital strengthen interfirm ties and cooperation, which is a key to improving agility and robustness (Türkeş et al., 2024; Wieland & Wallenburg, 2013). Cognitive capital promotes shared visions and values among partners, reducing conflicts and fostering alignment in strategic decision-making (Villena et al., 2011). Moreover, in the process of building distinctive organizational advantages, social capital supports the creation and sharing of new intellectual capital (Nahapiet & Ghoshal, 1998). This shared capital is particularly effective when rooted in fluid, well-established relationships, enabling firms within the supply chain to access a broader range of resources. In times of crisis, these networks facilitate faster recovery through established trust and cooperation (Amelia et al., 2024; Doerfel et al., 2010). Based on this rationale, this study proposes the following hypothesis:

H1: TMT heterogeneity is positively associated with SCR.

Moderating Role of Environmental Uncertainty

UET emphasizes the external environment as a key contextual factor shaping TMT characteristics and outcomes (Finkelstein & Hambrick, 1997). Environmental uncertainty refers to the dynamism, complexity, and unpredictability of the external environment (Miller & Friesen, 1983). This uncertainty modifies the TMT heterogeneity-SCR relationship by heightening cognitive diversity’s risks while diminishing its benefits.

On the one hand, uncertainty increases interpretive ambiguity. Under moderate uncertainty, this pluralism fosters exploration and interfirm cooperation (Koç et al., 2022). However, under high uncertainty, decision fragmentation escalates relational conflict and coordination failure, impairing collective judgment (Bin-Feng et al., 2024; Qian et al., 2013). When market signals conflict, cognitive diversity produces competing sensemaking frames rather than synergistic ones. In such settings, cognitive integration becomes costlier, weakening TMT heterogeneity’s resilience contribution (Prasad & Junni, 2017). On the other hand, uncertainty intensifies resource constraints. Firms facing sharp demand shocks and volatile inventories incur higher safety-stock costs, procurement premiums, and disruption losses (Bin-Feng et al., 2024). These pressures limit resources for resilience initiatives (Afshar Jahanshahi & Brem, 2020). Hence, even if heterogeneous teams generate sophisticated resilience solutions, resource bottlenecks block implementation, creating a gap between cognitive potential and strategic execution (Tang & Hull, 2012).

By hindering both the cognitive coordination needed to capitalize on TMT heterogeneity and the resource availability to implement resilience-focused strategies, environmental uncertainty systematically diminishes the positive relationship between TMT heterogeneity and SCR. Thus:

H2: Environmental uncertainty weakens the positive relationship between TMT heterogeneity and SCR.

Moderating Role of Enterprise Innovation Quality

Innovation is a key mechanism through which firms respond to market competition (X. Liu et al., 2020). Since the outbreak of COVID-19, innovation has been recognized as one of the most critical drivers of SCR (Ozdemir et al., 2022). UET links TMT characteristics (including heterogeneity) to innovation orientation (Stevens & Dimitriadis, 2004), but this orientation translates to resilience only via high-quality innovation. Innovation quality modifies the TMT heterogeneity-SCR link by improving how diverse TMT insights convert to actionable resilience outcomes.

Unlike resource-wasting low-quality innovation (Huang et al., 2022), high-quality innovation generates rich information during implementation (Hult et al., 2005). For heterogeneous TMTs, this acts as a “cognitive bridge,” structuring diverse perspectives into actionable knowledge (Simons et al., 1999). A TMT with operations, finance, and IT backgrounds, for example, contributes varied insights to supply chain flexibility initiatives; high-quality innovation integrates these into a cohesive strategy, avoiding fragmented ideas and enabling effective responses to dynamic challenges. Golgeci and Ponomarov (2013) reported that proactive and large-scale innovation efforts significantly improve SCR. Interestingly, their findings also suggest that under conditions of severe supply chain disruption, only substantial and comprehensive innovation initiatives enable firms to attain the desired level of resilience (Golgeci & Ponomarov, 2013). UET notes TMTs shape innovation orientation (Stevens & Dimitriadis, 2004), but high quality ensures this orientation avoids irrelevant initiatives. It acts as a “filter,” directing heterogeneous TMT insights to impactful resilience efforts (e.g. real-time risk systems over minor packaging tweaks), maximizing heterogeneity’s contribution to SCR.

On this basis, we treat firm innovation quality as a moderating variable and propose the following hypothesis:

H3: Firm innovation quality strengthens the positive relationship between TMT heterogeneity and SCR.

Mediating Role of Digital Transformation

Since the advent of Industry 4.0, digital transformation has emerged as a central theme in academic discourse, enabling firms to more effectively manage environmental uncertainty (Alcácer & Cruz-Machado, 2019). As a key tool for managing supply chain uncertainty. UET highlights TMT strategic posture as decisive for digital transformation (Zhu et al., 2024). TMT heterogeneity drives digital transformation by combining diverse expertise needed for this complex process (Wrede et al., 2020). Importantly, TMT perspectives on digital transformation are partially shaped by the individual attributes of team members (Peng & Jia, 2025). Owing to their diverse knowledge base and access to varied resources, highly heterogeneous TMTs are more likely to embrace novel development models (Gao et al., 2023), avoiding groupthink and recognizing digital tools’ supply chain value.

While digital transformation is not a strict prerequisite for achieving high SCR, it serves as a critical enabling factor (Akhtar et al., 2024). As digital transformation progresses, firms tend to gain greater agility (Chan et al., 2017; Tallon & Pinsonneault, 2011), improve demand–supply coordination (Ge & Bao, 2024), and enhance supply–demand stability (Yin & Ran, 2022), thereby strengthening SCR (J. Zhang et al., 2025). From a decision-making perspective, firms choose appropriate digital pathways on the basis of their size, level of digital maturity, and supply chain complexity (Yuan et al., 2024), which is a process largely influenced by the TMT. Therefore, firms with more heterogeneous TMTs are better positioned to implement digital transformation, which in turn enhances SCR in volatile environments. On this basis, this study introduces digital transformation as a mediating variable and proposes the following hypothesis:

H4: TMT heterogeneity enhances SCR by promoting digital transformation.

The theoretical model of this study is illustrated in Figure 1.

Figure 1.

Theoretical framework.

Research Methodology

Sample and Data

To empirically test our hypotheses, we selected A-share listed companies in China over the period from 2011 to 2023 as the research sample. First, China has become a global manufacturing hub with complex and extensive supply chain networks, which makes the issue of SCR particularly salient. Listed firms as key players in these networks are at the forefront of responding to supply chain disruptions through strategic and digital initiatives. Second, A-share listed companies are subject to strict corporate governance and disclosure regulations, providing reliable and standardized data on top management characteristics, innovation activities, and digital transformation practices. Panel data analysis was employed, controlling for year and industry effects to control for unobserved heterogeneity and mitigate potential biases arising from temporal and sectoral differences. The primary data sources include the CSMAR, Wind, and CNRDS databases, as well as publicly disclosed annual reports of listed firms. The dataset was subjected to the following preprocessing steps: (1) observations with missing values for core variables were excluded; (2) 1% and 99% tailing was performed for all continuous variables; (3) financial firms were excluded due to their distinct capital structures and regulatory environments; and (4) firms designated as ST or *ST were removed to avoid distortions caused by special treatment due to financial distress. All data processing and statistical analyses were conducted via STATA 15.1. After cleaning, the final sample comprises 20,120 firm-year observations for empirical testing.

Measurements

Dependent Variable

While prior studies typically evaluate SCR by emphasizing resistance and recovery capabilities in response to external shocks (Qi et al., 2024). Building on this literature, the present study adopts a broader perspective that incorporates both external risk response and internal capability development. Specifically, SCR is conceptualized along three dimensions: absorptive capacity (pre-disruption), resistance capacity (during disruption), and recovery capacity (post-disruption).

Absorptive capacity reflects the ability of a supply chain acquire, assimilate, and apply new knowledge. Following the outcome-based approach in prior research, this dimension is proxied by R&D intensity, measured as the ratio of R&D expenditure to operating revenue. A higher ratio indicates stronger absorptive capacity.

Resistance capacity refers to the ability of a supply chain to maintain stable operations during disruptions. Consistent with Cull, this dimension is captured using two indicators. First, capital occupation is measured as the logarithm of the ratio of accounts receivable to operating revenue, where a lower value indicates stronger operational stability. Second, relationship stability is measured by the proportion of the top five customers that maintain continuous cooperation with the firm across consecutive years. A higher proportion suggests stronger resistance capacity.

Recovery capacity captures the ability to restore operations after a disruption. Drawing from Qi et al. (2024), we assess this dimension by examining the deviation between production and demand fluctuations.

Matchin g_{it} = \frac{Var (Productio n_{it})}{Var (Deman d_{it})}

(1)

where Production represents firm output and Demand is proxied by the cost of goods sold. A value of Matching >1 indicates larger deviations between upstream production and downstream demand, suggesting weaker recovery capability.

To further capture the recovery trajectory of firm performance after disruptions, we also estimate a performance expectation model and use the residual term as an additional indicator of recovery:

\begin{matrix} {Perfor m}_{it} = α_{0} + α_{1} Siz e_{it} + α_{2} LE V_{it} + α_{3} Growt h_{it} \\ + α_{4} Ag e_{it} + α_{5} Boar d_{it} + \sum Firm \\ + \sum Year + ε_{it} \end{matrix}

(2)

This study uses the entropy weight method to thoroughly assess the supply chain’s absorptive, resistance, and recovery capabilities, leading to a quantitative SCR index. The detailed calculation process is provided in Appendix 1.

Independent Variables

TMT heterogeneity primarily refers to the differences among TMT members in terms of personal characteristics (Carpenter et al., 2004). To measure heterogeneity in continuous variables—specifically, age, this research uses the coefficient of variation (the ratio of the standard deviation to the mean), as shown in Formula 3.

H_{age} = \frac{S}{\bar{X}}

(3)

Where S denotes the standard deviation, and $\bar{X}$ represents the average age of TMT members. A higher value of $H_{age}$ indicates a greater degree of age heterogeneity within the TMT.

For categorical variables such as gender, professional background, overseas experience, financial background, and educational background, the Herfindahl–Hirschman Index (HHI) is adopted, following Peter’s (1977) approach. The calculation is shown in Formula 4.

H = 1 - \sum_{i = 1}^{n} P_{i}^{2}

(4)

Where $P_{i}$ denotes the proportion of TMT members in category i relative to the total number of team members. The H value ranges from 0 to 1, where a higher value indicates a greater level of TMT heterogeneity. After the five categorical indicators are standardized, their average value is calculated to construct the composite TMT heterogeneity (TMTH) index.

Control Variables

Following the literature (Qi et al., 2024; J. Wang et al., 2023), this study includes the following control variables: board size, firm size, firm age, the asset-liability ratio, ownership concentration (measured by the shareholding ratio of the largest shareholder), return on total assets, gross profit margin, and the price-to-book ratio. The specific definitions and measurement methods of these variables are presented in Table 1.

Table 1.

Variable Definitions.

Variable category	Variable names	Variable symbols	Variable definition
Dependent variable	SCR	Resilience	A composite index constructed using the entropy-weight method based on three dimensions: absorption capacity, resistance capacity, and recovery capacity
Independent variable	TMT heterogeneity	TMTH	The average of six heterogeneity indicators
	Age heterogeneity	Hage	The coefficient of variation of TMT age
	Gender heterogeneity	Hgender	Initial variable pairs are assigned a value of 1 for males and 0 for females in TMT, and the HHI is calculated
	Heterogeneity of educational backgrounds	Heducation	The initial variable assigns the following values to TMT’s educational background: 1 for those with a college degree or below; Junior college: 2; Undergraduate: 3; Master’s degree: 4; Doctor: 5, and calculate the HHI
	Heterogeneity of occupational backgrounds	Hprofessional	The initial variables are assigned the following values to the educational background of TMT: 1 = production, 2 = research and development, 3 = design, 4 = human resources, 5 = management, 6 = market, 7 = finance, 8 = finance, 9 = legal, 10 = other or unclear direction, and the HHI is calculated
	Heterogeneity of overseas backgrounds	Hoverseas	The initial variables are assigned the following values to TMT’s educational background: 1 = overseas employment, 2 = overseas study, 3 = no overseas background, and the HHI is calculated
	Heterogeneity of financial Background	Hfinancial	The initial variables are assigned the following values to the educational background of TMT: 1 = regulatory department, 2 = policy bank, 3 = commercial bank, 4 = insurance company, 5 = securities company, 6 = fund management company, 7 = securities registration and settlement company, 8 = futures company, 9 = investment bank, 10 = trust company, 11 = investment management company, 12 = exchange, 13 = other, 14 = no financial background, and the HHI is calculated
Controlled variables	Board size	Board	Number of board members
	Enterprise size	Size	Take the natural logarithm after adding 1 to the total assets at the end of the period of the company
	Enterprise age	Age	Ln (year of investigation − year of company establishment)
	Financial Leverage	LEV	Total liabilities/total assets of the company
	Enterprise growth	Growth	Current year’s operating income/previous year’s operating income − 1
	Equity concentration	Top1	The ratio between the number of shares held by the first largest shareholder and the total number of shares of the company at the end of the period
	Return on Assets	ROA	Return on net assets
	Dual employment	Dual	Whether the chairperson and CEO are the same people

Note. HHI = Herfindahl–Hirschman Index.

Regression Model

This study constructs the following regression model to examine the impact of TMT heterogeneity on SCR:

\begin{matrix} Resilienc e_{i, t} = & β_{0} + β_{1} TMTH + \sum β_{j} Controls \\ + \sum Year + \sum Industry + ε_{i, t} \end{matrix}

(5)

TMTH = \frac{\sum (H 1 ~ H 6)}{6}

(6)

Specifically, $Resilienc e_{i, t}$ represents the dependent variable, indicating the SCR of listed firm i in year t; TMTH is the independent variable, representing TMT heterogeneity of firm i in year t; Controls denotes the set of control variables; $\sum Year$ and $\sum Industry$ denote year and industry fixed effects, respectively; and ε_i,t is the random error term. H1∼H6 represent heterogeneity in age, gender, educational background, professional background, overseas background, and financial background, respectively.

Results

Explanation of Variable Measurements and Descriptive Statistics

The Table 2 presents the descriptive statistics, including sample size, mean, standard deviation, and maximum and minimum values. The maximum and minimum values of SCR are 0.558 and 0.294, respectively, with a mean of 0.467, indicating substantial differences in SCR across sample firms. Similarly, TMT heterogeneity ranges from a minimum of 0.294 to a maximum of 0.438, with a mean of 0.29, confirming significant variation in TMT heterogeneity among the sample firms.

Table 2.

Descriptive Results.

Variable	N	Mean	Std. Dev.	Min	Max
Resilience	20,120	0.467	0.073	0.294	0.558
TMTH	20,120	0.290	0.064	0.127	0.438
Board	20,120	2.090	0.193	1.386	2.890
Size	20,120	22.070	1.149	17.806	27.535
Age	20,120	2.885	0.340	0.693	4.025
LEV	20,120	0.395	0.199	0.008	2.128
Growth	20,120	0.300	0.810	−0.926	12.455
ROA	20,120	0.030	0.077	−0.578	0.220
Top1	20,120	0.311	0.138	0.076	0.758
Dual	20,120	0.356	0.479	0.000	1.000

The results of the Pearson correlation analysis indicate that the correlation coefficient in Table 3, denoted by |r|, between the variables is fundamentally <0.5. This suggests that there is no severe issue of multicollinearity, and the analysis successfully passes the validity test.

Table 3.

Correlation Analysis.

Variables	Resilience	TMTH	Board	Size	LEV	Age	Growth	ROA	Top1	Dual
Resilience	1.000
TMTH	0.059***	1.000
board	−0.051***	−0.054***	1.000
size	0.018***	−0.035***	0.229***	1.000
LEV	−0.053***	−0.070***	0.108***	0.443***	1.000
Age	0.032***	−0.030***	0.039***	0.162***	0.125***	1.000
Growth	−0.043***	−0.022***	0.030***	−0.031***	−0.000	−0.027***	1.000
ROA	0.039***	−0.007	0.048***	0.061***	−0.349***	−0.052***	0.018***	1.000
Top1	−0.014*	−0.066***	−0.018**	0.104***	−0.007	−0.062***	−0.013*	0.175***	1.000
Dual	0.060***	0.094***	−0.171***	−0.143***	−0.098***	−0.088***	−0.012***	0.001	−0.025***	1.000

Regression Results

Table 4 reports the baseline regression results. Model 1 includes only control variables, while Model 2 introduces TMT heterogeneity and adds industry and year fixed effects. The coefficient on TMT heterogeneity is positive and statistically significant (β = 0.027, t = 3.45), providing support for Hypothesis 1.

Table 4.

OLS Regression Results.

Variables	Resilience 1	Resilience 2
TMTH		0.027*** (3.45)
Board	−0.010*** (−3.68)	−0.010*** (−3.63)
Size	0.003*** (6.32)	0.003*** (6.27)
Age	−0.007*** (−4.12)	−0.007*** (−3.89)
LEV	−0.013*** (−3.86)	−0.012*** (−3.72)
Growth	−0.002*** (−3.25)	−0.002*** (−3.17)
Top1	0.000 (0.04)	0.001 (0.19)
ROA	0.017** (2.25)	0.017** (2.29)
Dual	0.005*** (4.98)	0.005*** (4.76)
yearFE	Yes	Yes
industryFE	Yes	Yes
Constant	0.387*** (25.72)	0.379*** (24.86)
Observations	20,120	20,120
R ²	0.080	0.081

t-Statistics are reported in parentheses. *, **, *** Statistical significance at the 10%, 5%, and 1% levels, respectively. All regressions include year and industry fixed effects.

Robustness Tests

Table 5 presents a series of robustness checks designed to verify the stability of the baseline results.

Table 5.

Robustness Tests.

Variables	Resilience 1	Resilience 2	Resilience 3	Resilience 4	Resilience 5
TMTH	0.027*** (3.45)	0.022*** (2.81)	0.019** (2.06)	0.022** (2.54)	0.033*** (4.23)
L.Resilience		0.374*** (42.78)
L2.Resilience			0.224*** (24.32)
Big4					0.005** (2.24)
Roe					−0.008 (−1.40)
Inv					0.127*** (19.54)
Rec					−0.038*** (−6.91)
Control	Yes	Yes	Yes	Yes	Yes
yearFE	Yes	Yes	Yes	Yes	Yes
industryFE	Yes	Yes	Yes	Yes	Yes
Constant	0.379*** (24.86)	0.251*** (13.90)	0.327*** (16.35)	0.377*** (23.30)	0.366*** (23.22)
Observations	20,120	15,988	12,540	17,171	20,120
R ²	0.080	0.209	0.120	0.078	0.102

t-Statistics are reported in parentheses. *, **, *** Statistical significance at the 10%, 5%, and 1% levels, respectively. All regressions include year and industry fixed effects.

Given that the presence of heteroskedasticity can invalidate the efficiency of OLS estimators, Model 1 corrects for heteroskedasticity to increase estimation efficiency. The findings continue to support the baseline results. To account for potential lagged effects, Models 2 and 3 incorporate first- and second-order lags of the dependent variable as additional controls. This adjustment improves the model’s ability to capture historical influences and partially mitigates omitted variable bias. The results from these models remain robust. Moreover, considering the potential influence of major external shocks in 2015 and 2020, Model 4 excludes data from these 2 years. The findings remain consistent, further reinforcing the reliability of the results. Finally, Model 5 incorporates additional control variables, such as Big Four auditor status, return on equity, the inventory ratio, and the accounts receivable ratio, following prior studies (Ge & Bao, 2024; Qi et al., 2024).

Endogeneity Analysis

To address potential endogeneity arising from omitted variables and reverse causality, this study adopts two approaches: the use of instrumental variables and lagged explanatory variables. First, to mitigate potential bias related to unobserved firm characteristics or simultaneity in TMT heterogeneity, this study employs the average TMT heterogeneity of other firms in the same region and year as an instrumental variable. This variable is correlated with the focal firm’s TMT heterogeneity due to regional and temporal commonalities, yet unlikely to directly affect the firm’s SCR performance, thus satisfying both relevance and exogeneity conditions (Bikhchandani et al., 1992; Manski, 1993). Second, considering the potential lagged effects of TMT heterogeneity, this study also uses its one-period lag to reduce contemporaneous endogeneity.

This study employs two-stage least squares (2SLS) estimation to address potential endogeneity concerns. Given that the instrumental variable is constructed at the industry-year level using a leave-one-out peer average, standard errors in the 2SLS regressions are clustered at the industry-year level to account for potential within-group correlation and to ensure inference is consistent with the instrument structure. The first-stage results are reported in Table 6 (Model 1). The instrumental variable is strongly and positively associated with TMT heterogeneity (b = 0.793, t = 149.61). The first-stage F-statistic equals 22,382.22, which is substantially higher than the conventional threshold of 10, indicating strong instrument relevance. In addition, the Kleibergen–Paap rk Wald F statistic (LM statistic) equals 142.29, rejecting the null hypothesis of under-identification and confirming that the model passes both the under-identification and weak-instrument tests. The second-stage results are presented in Table 6 (Model 2). The coefficient on TMT heterogeneity remains positive and statistically significant (b = 0.031, t = 3.61), consistent with the baseline findings.

Table 6.

Endogeneity Analysis.

Variables	TMTH 1	Resilience 2	Resilience 3
TMTH		0.030*** (2.76)
L.TMTH			0.031 (3.61)
IvTMTH	0.793*** (149.61)
Control	Yes	Yes	Yes
yearFE	Yes	Yes	Yes
industryFE	Yes	Yes	Yes
Constant	0.058*** (6.86)	0.390*** (22.41)	0.397*** (21.62)
Observations	16,473	16,473	15,988
R ²		0.080	0.074
F	22,382.22***
LM	142.29***

t-Statistics are reported in parentheses. *, **, *** Statistical significance at the 10%, 5%, and 1% levels, respectively. All regressions include year and industry fixed effects.

In addition to the instrumental variable approach, we further adopt a lagged independent variable specification as an alternative strategy to mitigate potential reverse causality concerns (Model 3). Specifically, we replace the contemporaneous TMT heterogeneity variable with its one-period lag and re-estimate the model. This approach is theoretically justified because changes in TMT composition are unlikely to instantaneously affect SCR; rather, strategic decisions and organizational adjustments typically unfold over time. Using the lagged variable helps ensure temporal precedence, thereby strengthening the causal interpretation and reducing simultaneity bias. The regression results show that lagged TMT heterogeneity remains positively and statistically significantly associated with SCR (b = 0.031, t = 3.61). The magnitude and significance are consistent with the baseline findings, suggesting that the positive effect of TMT heterogeneity on SCR is not driven by reverse causality or contemporaneous feedback effects.

Instrumental variable and lagged variable strategies yield results consistent with the OLS baseline, reducing concerns that reverse causality or omitted time-varying confounders drive the main finding.

Moderating Role

This study examines the moderating effects of environmental uncertainty and overall innovation quality at the firm level. Since environmental uncertainty stemming from fluctuations in the external environment ultimately leads to volatility in a firm’s core business activities, such as sales revenue (Bergh & Lawless, 1998), performance volatility is often used as a proxy for environmental uncertainty in empirical research (Cheng & Kesner, 1997). Drawing on prior studies, this research adopts the standard deviation of sales revenue over the past 5 years, adjusted by industry, as a measure of environmental uncertainty (Ghosh & Olsen, 2009). Specifically, environmental uncertainty is calculated via the coefficient of variation of a firm’s industry-adjusted sales revenue. The calculation model is as follows:

Sale = φ_{0} + φ_{1} Year + ε

(7)

The environmental uncertainty was measured via a two-step procedure. First, each firm’s annual sales revenue over the preceding 5 years is regressed on a time variable (Year, coded from 1 to 5) via ordinary least squares (OLS). The resulting residuals capture abnormal fluctuations in sales. Second, the coefficient of variation, calculated as the standard deviation of these residuals divided by their mean, is used to construct a firm-level (unadjusted) measure of environmental uncertainty. To obtain an industry-adjusted indicator, the unadjusted value for each firm is normalized by dividing it by the median value of firms within the same industry and year. Higher adjusted values reflect greater exposure to environmental uncertainty. This normalized metric is used as a moderating variable in the regression analyses, with the results presented in Table 7.

\begin{matrix} Resilienc e_{i, t} = & β_{0} + β_{1} TMTH + β_{2} EU + β_{3} TMTH \\ * EU + \sum β_{j} Controls + \sum Year \\ + \sum Industry + ε_{i, t} \end{matrix}

(8)

The regression result of Model 1 shows that environmental uncertainty weakens the effect of TMT heterogeneity on firm SCR, and the effect is statistically significant at the 1% level (β = −0.022, t = −2.65), thereby confirming Hypothesis 2.

Table 7.

The Moderating Effect.

Variables	Resilience 1	Resilience 2
TMTH	0.026*** (2.94)	0.030*** (3.44)
Environment	−0.006*** (−9.11)
c.TMTH#c.Environment	−0.022*** (−2.65)
Innovation		0.004*** (7.24)
c.TMTH#c.Innovation		0.013*** (2.61)
Control	Yes	Yes
yearFE	Yes	Yes
industryFE	Yes	Yes
Constant	0.406*** (17.74)	0.467*** (22.93)
Observations	14,854	15,694
R ²	0.093	0.085

t-Statistics are reported in parentheses. *, **, *** Statistical significance at the 10%, 5%, and 1% levels, respectively. All regressions include year and industry fixed effects.

Following the approach of He and Tian (2013), this study measures both the total and average number of patent citations. Specifically, the natural logarithm of one plus the total number of forward citations of patents filed by the firm in the following year is used to represent the firm’s overall innovation quality. To test Hypothesis 3, a regression model is constructed using this variable, and the regression results are presented in Model 2.

\begin{matrix} Resilienc e_{i, t} = & β_{0} + β_{1} TMTH + β_{2} IV + β_{3} TMTH \\ * IV + \sum β_{j} Controls + \sum Year \\ + \sum Industry + ε_{i, t} \end{matrix}

(9)

The regression result of Model 2 shows that the firm’s overall innovation quality s strengthens the effect of TMT heterogeneity on firm SCR, and the effect is statistically significant at the 1% level (β = 0.013, t = 2.61), thereby confirming Hypothesis 3.

Figure 2 illustrates the moderating role of firm innovation quality in the relationship between TMT heterogeneity and SCR. The left panel presents the simple-slope visualization, while the right panel reports the marginal effects of TMT heterogeneity across the full range of innovation quality together with the Johnson–Neyman (J–N) significance region. The simple-slope plot indicates that the relationship between TMT heterogeneity and SCR varies across levels of innovation quality. Specifically, when innovation quality is relatively high, the slope between TMT heterogeneity and SCR becomes more positive, whereas under low levels of innovation quality the relationship appears relatively weak. This pattern suggests that higher innovation quality strengthens the positive association between TMT heterogeneity and SCR. The Johnson–Neyman analysis further examines the conditional effect across the full range of innovation quality. As shown in the right panel of Figure 2, the marginal effect of TMT heterogeneity on SCR increases as innovation quality rises. The Johnson–Neyman critical value occurs at approximately Innovation = 1.38. When innovation quality exceeds this threshold, the marginal effect becomes statistically significant at the 5% level, whereas below this value the effect is not statistically distinguishable from zero. These results indicate that the positive relationship between TMT heterogeneity and SCR emerges primarily when firms exhibit sufficiently high levels of innovation quality.

Figure 2.

Moderating effect of firm innovation quality on the relationship between TMT heterogeneity and SCR.

Figure 3 presents the moderating effect of environmental uncertainty on the relationship between TMT heterogeneity and SCR. The left panel displays the simple-slope visualization, while the right panel reports the marginal effects with confidence intervals and the Johnson–Neyman significance boundaries. The simple-slope plot shows that the association between TMT heterogeneity and SCR weakens as environmental uncertainty increases. When environmental uncertainty is relatively low, the relationship between TMT heterogeneity and SCR appears positive. However, as environmental uncertainty rises, the slope gradually declines and becomes negative, suggesting that greater managerial heterogeneity may be associated with lower levels of SCR under highly uncertain environments. The Johnson–Neyman analysis provides a more precise assessment of this conditional relationship. As illustrated in the right panel of Figure 3, the marginal effect of TMT heterogeneity on SCR decreases as environmental uncertainty increases. The Johnson–Neyman threshold occurs at approximately Environmental Uncertainty = 0.33, indicating that the effect is statistically significant only when environmental uncertainty falls below this level. As environmental uncertainty increases beyond this point, the confidence intervals overlap with zero, suggesting that the relationship between TMT heterogeneity and SCR is no longer statistically significant.

Figure 3.

Moderating effect of environmental uncertainty on the relationship between TMT heterogeneity and SCR.

The Mediating Role of Digital Transformation

The relationship between TMT heterogeneity and SCR is not purely direct; there may exist other underlying mechanisms. To further explore the potential pathway through which TMT heterogeneity influences SCR, this study introduces enterprise digital transformation as a mediating variable. Drawing on the literature, the level of digital transformation is measured by the frequency of relevant keywords extracted from corporate annual reports (G. Liu & Wang, 2023; H. Zhang et al., 2024). To reduce the data scale and alleviate heteroscedasticity, the keyword frequencies are transformed via the natural logarithm of the count plus one. For moderation analysis, the digital transformation variable is mean-centered before constructing the interaction term. Please refer to Appendix 2 for specific keyword selection.

Following the methodology proposed by Dell (2010), the effectiveness of the mediating effect is examined by assessing the influence of TMT heterogeneity on the mediating variable (Dell, 2010). The first column of Table 8 shows that TMT heterogeneity is significantly positively associated with enterprise digital transformation (β = 0.278, t = 4.13). To further clarify temporal ordering and strengthen causal interpretation in the panel setting, we additionally employ a one-period lag of TMT heterogeneity. As shown in Table 8 (Model 2), lagged TMT heterogeneity remains positively and significantly associated with digital transformation (b = 0.289, t = 3.93). Notably, the lagged effect remains statistically significant, which is theoretically reasonable given that digital transformation decisions typically require time for planning, resource allocation, and organizational adjustment. This temporal specification ensures that changes in TMT composition precede observed changes in digital transformation, thereby reinforcing the causal ordering of the mediation mechanism.

Table 8.

Regression Results for the Mediating Effect of Digital Transformation.

Variables	Enterprise digital transformation 1	Enterprise digital transformation 2
TMTH	0.278*** (4.13)
L.TMTH		0.289*** (3.93)
Control	Yes	Yes
yearFE	Yes	Yes
industryFE	Yes	Yes
Constant	−0.377** (−2.35)	−0.402** (−2.47)
Observations	20,095	15,980
R ²	0.729	0.586

t-Statistics are reported in parentheses. *, **, *** Statistical significance at the 10%, 5%, and 1% levels, respectively. All regressions include year and industry fixed effects.

Digital transformation in turn contributes to enhanced SCR, consistent with a partial mediation pathway, thus confirming Hypothesis 4. We employed a nonparametric bootstrap mediation test with 1,000 replications in Table 9. The indirect effect of TMT heterogeneity on SCR via digital transformation is positive and statistically significant (ind_eff = 0.0104, p < .001; 95% CI = [0.00833, 0.01245]). The direct effect remains significant (dir_eff = 0.0444, p < .001; 95% CI = [0.02846, 0.06030]), suggesting a partial mediation pattern. These results demonstrate that while TMT heterogeneity enhances SCR through digital transformation, it also exerts a direct influence independent of digital capabilities. Importantly, when the lagged TMT specification is employed, the mediation effect remains robust. The bootstrap results based on the lagged model show that the indirect effect is 0.00778 (z = 7.45, p < .001; 95% CI = [0.00573, 0.00982]), with the confidence interval excluding zero. The direct effect also remains positive and statistically significant (0.04674, p < .001), again supporting a partial mediation pattern.

Table 9.

The Bootstrap Test.

Mediator Model	Coeff.	Bootstrap Std. Err.	95% CI (lower)	95% CI (upper)
Indirect effect of TMTH	0.010*** (9.88)	0.001	0.008	0.012
Direct effect of TMTH	0.044*** (5.46)	0.008	0.028	0.060
Indirect effect of L.TMTH	0.008*** (7.45)	0.001	0.006	0.010
Direct effect of L.TMTH	0.047*** (5.28)	0.009	0.029	0.064

t-Statistics are reported in parentheses. *, **, *** Statistical significance at the 10%, 5%, and 1% levels, respectively. All regressions include year and industry fixed effects.

Heterogeneity Analysis

The preceding analysis examines the impact of TMT heterogeneity on SCR via the full sample. However, this effect may differ across firms with varying characteristics. To explore this, the study conducts a heterogeneity analysis based on firms’ geographic locations, assessing whether regional differences influence the relationship between TMT heterogeneity and SCR. The corresponding regression results are presented in Table 10.

Table 10.

Heterogeneity Analysis.

Variables	Region
Variables	West	East	Mid
TMTH	−0.022 (−0.77)	0.041*** (4.44)	−0.031 (−1.44)
Control	Yes	Yes	Yes
yearFE	Yes	Yes	Yes
industryFE	Yes	Yes	Yes
Constant	0.390*** (8.99)	0.391*** (21.04)	0.324*** (9.12)
Observations	2,036	14,992	3,010
R ²	0.151	0.072	0.136

t-Statistics are reported in parentheses. *, **, *** Statistical significance at the 10%, 5%, and 1% levels, respectively. All regressions include year and industry fixed effects.

In eastern China, TMT heterogeneity is significantly and positively associated with SCR (β = 0.041, t = 4.44), whereas in the central and western regions, the regression coefficients are negative and statistically insignificant. The eastern region benefits from advanced infrastructure and efficient information flows, which enable firms to access external resources more readily. Under such conditions, the cognitive and experiential diversity of TMT members can be more effectively utilized to increase SCR. In contrast, firms in the central and western regions face limitations due to less developed infrastructure and slower information dissemination, which restricts their ability to leverage external resources. Even with a heterogeneous TMT, the benefits of diversity may be constrained by resource scarcity. Furthermore, firms in these regions may adhere more closely to traditional management practices, where diversity in TMT could reduce decision-making efficiency or exacerbate internal conflict. Given the relatively lower level of market competition in these areas, firms may also be less responsive to environmental changes, thereby weakening the influence of TMT heterogeneity on SCR. Therefore, although TMT heterogeneity exerts a significant influence on SCR, this effect is only statistically evident in Eastern China.

Conclusion, Implications and Limitations

Conclusion

Using panel data from Chinese A-share listed firms (2011–2023), this study investigates the effect of TMT heterogeneity on SCR and offers several key findings. First, the findings consistently indicate that TMT heterogeneity contributes positively to SCR. Second, the relationship between TMT heterogeneity and SCR is context dependent. Environmental uncertainty weakens the positive link, high innovation quality strengthens the effect.

Practical Implications

This study provides several practical insights for improving SCR, especially in emerging economies exposed to uncertainty. First, TMT heterogeneity can support resilience by expanding cognitive perspectives. Firms facing frequent supply–demand fluctuations and regulatory risks may benefit from leadership teams that differ in education, role experience, and international exposure. Such diversity helps firms balance short-term operational needs and long-term resilience goals. Second, digital transformation is an important channel through which heterogeneous TMTs enhance SCR. Firms should view digital capability as a leadership competency rather than an IT function. When appointing executives, digital literacy should be considered alongside financial or operational expertise. Cross-functional digital units and flexible budgets for transformation initiatives can reduce delays and avoid siloed decision-making. Third, firms in less digitally mature regions often face talent constraints. In these cases, internal managers with cross-functional experience can be developed as resilience leaders. Programs such as executive digital training, rotational assignments, or partnerships with digital innovation centers can help build this capability. Knowledge transfer from digitally advanced regions to lagging regions within the same country is also a viable approach. Finally, our findings suggest that SCR is not only a supply chain coordination problem. It is closely linked to leadership composition, organizational learning, and governance. Building a TMT, that is, both diverse and digitally competent is therefore not a human resources issue, but a strategic decision that supports resilience in uncertain environments.

Limitations

Despite its contributions, this study has several limitations that suggest directions for future research. First, our sample is limited to publicly listed Chinese firms, which may differ from private or multinational enterprises in their governance structures and transformation incentives. Future studies could extend the analysis to firms in other Asia-Pacific economies to validate the cross-national generalizability of our findings. Second, this study emphasizes digital transformation as an important mechanism linking TMT heterogeneity to SCR. However, other organizational mechanisms may also play a role in shaping this relationship. For instance, future studies could explore how inter-organizational collaboration, supply network structure, or strategic flexibility influence the extent to which heterogeneous management teams contribute to SCR. Third, the present study focuses on the overall level of TMT heterogeneity by constructing a composite index. While this approach captures the comprehensive diversity of TMTs, it does not fully distinguish the potentially different roles of heterogeneity across specific dimensions (e.g. demographic, functional, or experiential diversity). Future research could examine the heterogeneous effects of different types of TMT diversity to provide a more nuanced understanding of how managerial heterogeneity influences SCR.

Data sharing does not apply to this article as no datasets were generated or analyzed during the current study.

Footnotes

Appendix 1

Appendix 2

Data and information	Digital technologies	Platform and Internet	Intelligent systems
Data center	Data mining	Internet platform	Artificial intelligence
Data governance	Data science	Internet ecosystem	Machine learning
Data network	Digitalization	Internet business	Cloud computing
Data platform	Digital control	Internet strategy	Blockchain
Big data	Digital marketing	Industrial Internet	Internet of Things
Cloud services	Digital technology	Industrial cloud	Smart manufacturing
Cloud platform	Digital communication	Internet solutions	Smart logistics
Information system	Digital network	E-commerce	Smart factory
Information integration	Digital infrastructure	O2O	Smart management
Information sharing	Digital terminal	B2B	Intelligent production
Information center	Virtualization	B2C	Intelligent control
Information network	Simulation manufacturing	C2C	Intelligent monitoring
IT ecosystem	Industrial communication	C2B	Intelligent equipment
Industrial information	Industrial intelligence	Online and offline integration	Intelligent warehousing
ERP/production execution system	Smart technology	Internet thinking	Automated production
Lifecycle management	Smart systems	Internet application	Automated monitoring
Integrated solutions	Smart terminal	Internet model	Integrated control
Integrated system	Smart mobility	Internet marketing	Integrated management
Industrial Internet of Things	Smart connectivity	Internet +	Industrial smartization
Future factory	Smart network	Digital economy	Industrial integration

ORCID iD

Yi Lu

Ethical Considerations

This study uses publicly available secondary data from A-share listed companies in China covering the period 2011–2023. The primary data sources include the CSMAR, Wind, and CNRDS databases, as well as publicly disclosed annual reports of listed firms. No human participants or personally identifiable information are involved. According to relevant Chinese laws and institutional guidelines, studies based solely on publicly available data do not require ethical approval. This constitutes the ethical statement for this study.

Author Contributions

Conceptualization, Ruyu Zhang; methodology, Ruyu Zhang; software, Ruyu Zhang and Qinghao Zheng; validation, Ruyu Zhang; formal analysis, Ruyu Zhang and Qian Yang; resources, Yi Lu; writing—original draft preparation, Ruyu Zhang. All authors have read and agreed to the published version of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Major Program of National Social Science Foundation of China (grant no. 23&ZD051).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author.

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