Supply Chain Capability and Performance Under Environmental Uncertainty: The Mediating Role of Multidimensional Resilience

Abstract

Global supply chains face unprecedented challenges from geopolitical conflicts, climate change, economic volatility, and technological disruptions, highlighting the critical role of supply chain resilience as a core strategy for firms to maintain stability and competitive advantage. Grounded in the resource-based view and dynamic capability theory, this study examines how supply chain capability—that is, entrepreneurial leadership, collaborative capability, and digital transformation—enhances resilience, which mediates its impact on performance. Using structural equation modeling on survey data from Chinese firms, we find that resilience, comprising absorptive, reactive, and recovery capability, significantly mediates the relationship between supply chain capability and performance. Environmental uncertainty moderates this relationship, particularly in highly uncertain contexts, where resilience becomes a key driver of competitive advantage. Theoretically, this study extends dynamic capability theory by disaggregating resilience and exploring its mediating role. Practically, it emphasizes strengthening entrepreneurial leadership, collaborative capability, and digital transformation to improve resilience and performance in uncertain environments.

1. Introduction

Amid escalating geopolitical conflicts, climate change, economic volatility, and technological disruptions, global supply chains are facing unprecedented complexity. These challenges have revealed the vulnerabilities of traditional supply chain models. Particularly in the face of unforeseen events, many firms have demonstrated insufficient flexibility and adaptability [1]. This issue has become particularly evident in recent global crises—such as the pandemic and regional conflicts—highlighting the need for supply chains to transition from traditional, efficiency-driven models to more flexible, resilient, and sustainable management approaches. Furthermore, the ongoing development of globalization has further intensified the high interdependence of multinational supply chains. This has also increased their complexity. The dispersion of supply chain nodes across multiple countries and regions allows disruptions—such as logistical bottlenecks, shortages of critical raw materials, or policy changes—to trigger cascading effects. These effects impact global supply chain stability [2]. Concurrently, consumer demand is shifting towards greater personalization and diversification. The rapidly evolving market environment requires firms to enhance their responsiveness and customization capabilities. This trend amplifies the need for increased supply chain flexibility, agility, and sustainability [3]. As such, ensuring that a firm’s supply chain remains efficient and stable amidst uncertainty has become a critical strategy for maintaining long-term competitive advantage. By building more resilient supply chains, firms can not only better navigate rapidly changing external environments but also secure the long-term stability of global supply networks. This transformation is crucial for overcoming present challenges and shaping the next stage of global supply chain evolution. Furthermore, the ongoing development of globalization has further intensified the high interdependence of multinational supply chains.

Despite growing academic attention to supply chain resilience (SCR), studies on how to effectively build SCR capabilities remain limited. SCR is commonly defined as an organization’s ability to mitigate the adverse effects of disruptions and resume operations quickly through proactive planning and responses [4]. To achieve SCR, firms must flexibly integrate resources, core competencies, and business processes while dynamically adapting to contextual changes. Scholars have noted that definitions of SCR remain inconsistent, making it difficult to operationalize and leaving firms without clear guidance for implementation [5]. Future research should clarify the link between SCR and daily business operations to provide actionable guidance for practical implementation [6]. Previous studies have often treated SCR as a single-dimensional concept, overlooking its structural complexity. Even when researchers consider the multidimensional features of SCR, they mostly confine discussions to strategy formulation rather than comprehensively evaluating capabilities. There is still a lack of systematic research that comprehensively covers the various components of SCR. Disruptive events, such as lockdowns, supply chain interruptions, and global market volatility, have exposed firms’ weaknesses in managing complex environments, making the development of supply chain capability a central focus in operations management [7,8]. Drivers of key supply chain capability now include deeper collaboration, strategic sensitivity at the top level, embedded use of digital tools, and flexible restructuring of organizational processes [9,10]. Supply chain capability refers to a firm’s systemic capability in resource allocation, information integration, and strategic alignment aimed at improving adaptability, collaboration, and long-term competitiveness. This capability system has reshaped traditional supply chain models across industries, promoting a shift from reactive management to a resilience-oriented, proactive operational approach. Given the critical role of supply chain capability in managing uncertainty, recent studies have explored how firms can build systematic foundations to enhance their resilience in recovering swiftly and stabilizing performance during crises. Existing studies primarily focus on how specific technologies or tools enhance a firm’s ability to cope with disruptions [11]. However, enhancing SCR requires not only individual capabilities or technologies but also a systematic framework in which key capabilities synergize to address uncertainty. Research suggest s that supply chains with dynamic adjustment capabilities improve visibility, optimize structure and operations, and strengthen quality control, thereby enhancing overall resilience [12]. Highly adaptive supply chain capability enables flexible adjustments to structure and configuration, enhancing visibility, responsiveness, and coordination efficiency, thus achieving higher resilience while maintaining product quality [13,14]. Structures with dynamic capability adjustment mechanisms help supply chains cope with internal and external uncertainties, maintaining the functioning of key capabilities during disruptions and thereby strengthening overall resilience. However, empirical research on how supply chain capability enhances resilience remains scarce. Quantitative studies are necessary to elucidate the specific mechanisms by which various capabilities contribute to resilience management and to inform firms in their strategic planning and development of capabilities. Moreover, existing studies suggest that systematic development of supply chain capability helps to improve overall firm performance [15]. However, researchers know little about how supply chain performance evolves when firms develop multiple capabilities in parallel.

To address these research gaps, this study identifies the staged characteristics and core components of SCR. Theoretically, it integrates the resource-based view (RBV) and dynamic capability theory (DCT) to construct a mechanism model. This model aims to reveal how multiple capabilities indirectly enhance firm performance through different dimensions of resilience. Furthermore, the study examines how the relative importance of various capability types may vary when firms operate in high environmental uncertainty. Specifically, this study addresses the following research questions: 1. What are the core elements that constitute supply chain capability? 2. How does supply chain capability contribute to improvements in both resilience and performance? 3. In what way does SCR mediate the relationship between supply chain capability and performance?

In response to the research questions, this study provides several key insights to advance the existing literature. First, it establishes a logical framework linking supply chain capability, resilience, and performance, clarifying how firm supply chain capability influences performance through resilience mechanisms. This enriches the theoretical understanding of how SCR is formed and leveraged. Second, this study conceptualizes SCR as a multidimensional construct comprising absorptive, reactive, and recovery capability, deepening the theoretical recognition of SCR as an integrated system of dynamic capability. Third, it identifies the mediating role of resilience in bridging supply chain capability and performance, offering new insights into the transformation pathways of capabilities. Beyond theoretical contributions, the findings provide practical guidance for managers seeking to build resilient supply chains that integrate digital transformation, collaboration, and adaptability, enabling firms to better cope with environmental uncertainty.

2. Literature Review and Research Hypotheses

2.1. Supply Chain Resilience

SCR refers to a firm’s ability to maintain operational continuity and quickly return to a stable state in the face of disruptions [16,17]. It has emerged as a central topic in supply chain management research under conditions of increasing uncertainty. The literature generally conceptualizes SCR across three temporal stages—pre-disruption, during disruption, and post-disruption—to define its core dimensions [18]. Current research on SCR often adopts a capability-oriented perspective, among other theoretical frameworks. From this view, researchers define SCR as the supply chain’s ability to absorb shocks, adapt operations, and reconfigure its processes in response to disturbances [19]. Enhancing SCR enables firms to proactively manage risks, mitigate interruptions, and sustain stable operations [20]. Unlike traditional notions of robustness, SCR emphasizes adaptability and recovery in the face of unknown risks. To strengthen resilience, researchers increasingly emphasize that firms should develop a diverse portfolio of capabilities, including structural flexibility, process agility, information transparency, and organizational collaboration. This perspective has gained broad acceptance in the academic community.

Early studies often characterized SCR as a unidimensional dynamic attribute. However, subsequent research has increasingly recognized that SCR should be conceptualized from a multidimensional perspective rather than within a single capability framework [21,22]. Achieving such multidimensional resilience structures requires the coordinated development of diverse resource bases and capability systems [23]. The expansion of capability dimensions has prompted scholars to move beyond narrow perspectives and gradually build multidimensional SCR frameworks. Most studies categorize SCR into two primary capability types: preventive capabilities, which involve proactive preparations before potential supply chain disruptions and responsive capabilities, which reflect a firm’s ability to handle risks promptly once disruptions occur [24]. Scholars applying the DCT framework further divide SCR into three fundamental dimensions: supply chain design, risk prevention, and emergency response. Researchers often divide SCR into three stages over the course of a disruption: firms enhance the absorptive capability to prepare beforehand, deploy the reactive capability to manage immediate impacts, and reinforce the recovery capability to restore operations afterward. Each dimension consists of several critical sub-capabilities. Some scholars also define SCR as comprising three functional components: preparedness, response, and recovery. Building on previous studies, this research develops an integrated SCR framework that incorporates RBV, DCT, and the stage-based nature of supply chain disruptions. It identifies three core components of SCR—absorptive, reactive, and recovery capability—and emphasizes that firms must strategically integrate resources to establish response mechanisms that correspond to each capability type [25,26].

To enhance their ability to manage risks, firms must actively reconfigure their manufacturing systems and supply chain operations. This urgency highlights the need to investigate the key mechanisms that influence SCR and the pathways through which it impacts firm performance. Scholars are systematically identifying the antecedents and consequences of SCR. Building on the RBV and information processing perspectives, existing studies have confirmed that risk management practices strengthen supply chain resilience [18]. According to the information processing theory framework, researchers have demonstrated that SCR positively impacts firms’ risk control capacity, market responsiveness, and financial outcomes [5]. Although firms generally regard resilience as a core capability, earlier studies often relied on single-dimensional definitions of resilience. While SCR comprises multiple dimensions, current research has not fully clarified the specific roles these dimensions play within the supply chain. In addition, researchers must further investigate how SCR bridges capability and performance to uncover its internal transmission mechanisms.

2.2. Integration of the Resource-Based View and Dynamic Capability Theory

RBV and DCT offer critical insights into how firms build competitive advantage in complex environments [27]. RBV emphasizes that firms can achieve sustained competitive advantage by identifying, integrating, and leveraging valuable and inimitable internal resources and capabilities [28], enabling long-term performance in relatively stable markets [29]. In the context of supply chain management, these resources encompass not only a firm’s internal technologies, human capital, and financial assets but also key elements embedded in external collaborative networks [28], which together form a comprehensive capability base for coordination and resource integration. However, while RBV highlights the importance of resource rarity and inimitability, it offers limited explanatory power regarding how firms reconfigure their resources in response to environmental turbulence. Prior studies have argued that RBV lacks sensitivity to contextual variations in resource utility and fails to explain why identical resource portfolios lead to different outcomes across environments. This limitation, often referred to as context insensitivity, raises concerns about the applicability of RBV under conditions of high uncertainty. To address these shortcomings, DCT has emerged as a theoretical extension of RBV. It underscores the importance of sensing, seizing, and reconfiguring resources as firms adapt to volatile environments and pursue strategic renewal [30,31]. DCT posits that firms with competitive resilience demonstrate strong adaptive capability, an absorptive knowledge base, and organizational innovation capability. These capabilities enable them to respond quickly and adjust their structures in the face of market volatility [29]. These mechanisms closely align with the essential features of supply chain resilience, particularly under large-scale disruptions, where dynamic capabilities have been shown to enhance firms’ responsiveness and continuity [24,32,33]. Building on this foundation, the present study refines the structure of supply chain capability by identifying three key components: entrepreneurial leadership, collaborative capability, and digital transformation capability. It incorporates SCR as a mediating variable to examine how resilience functions as a bridge between capability coordination and performance realization within dynamic supply chain environments. The theoretical model is shown in Figure 1.

2.3. Research Hypotheses

Supply chain capability reflects a firm’s proficiency in recognizing, coordinating, and efficiently leveraging diverse resources within and beyond the organization to enhance supply chain performance [34]. These capabilities equip firms to safeguard against potential risks arising from both internal operations and external environments, thereby strengthening supply chain resilience [35]. By improving information transparency and collaboration efficiency, supply chain capability enhances absorptive capability. Within digital transformation, companies leverage cutting-edge tools like IoT, big data, and AI to oversee supply chain activities in real time and rapidly detect emerging risks [36]. Studies have shown that the introduction of digital technologies enhances a firm’s risk perception, allowing for early detection of risks through data analysis [37]. Entrepreneurial leadership offers strategic direction and innovative approaches to respond to external shocks, ensuring agility and comprehensiveness in the absorptive process. Specifically, entrepreneurs guide firms to develop heightened sensitivity to risk through rapid decision making and innovation [38]. Collaborative capability fosters trust and stability within the supply chain network through information sharing and resource complementarity, strengthening both risk perception and absorptive capability [39]. Some scholars have noted that inter-organizational collaboration enhances the absorptive capability of supply chain networks, allowing firms to address external threats through resource sharing [40]. Drawing from these arguments, the subsequent hypothesis is proposed:

H1: 

Supply chain capability positively influences resilience-absorptive capability.

Digital transformation capability provides real-time data support, enabling firms to swiftly analyze the causes of disruptions and formulate appropriate response strategies [36]. Entrepreneurial leadership enables the rapid mobilization of resources and coordination of actions through flexible strategies and efficient decision-making processes, thereby minimizing the impact of disruptions [38]. Collaborative capability enhances connectivity among supply chain members, ensuring rapid collective responses during crises and fostering collective resilience to risks [39]. Drawing from these arguments, the subsequent hypothesis is proposed:

H2: 

Supply chain capability positively influences resilience-reactive capability.

Supply chain capability enhances recovery capability through optimized resource allocation and continuous improvement mechanisms. Digital transformation enhances supply chain resilience by supporting timely recovery from disruptions and sustaining efficient operations through smart resource coordination [41]. Entrepreneurial leadership contributes to the recovery process by injecting new growth momentum through innovative strategies and a long-term perspective [42]. Collaborative capability fosters mutual assistance and resource sharing among supply chain members, ensuring rapid recovery and stable operations after a crisis [43]. Drawing from these arguments, the subsequent hypothesis is proposed:

H3: 

Supply chain capability positively influences resilience-recovery capability.

Supply chain capability significantly contributes to improving supply chain performance. Digital transformation capability optimizes supply chain operations by leveraging advanced digital technologies to enhance information flow efficiency, reduce decision making delays, and improve flexibility and responsiveness. This ultimately leads to better overall performance [36]. Entrepreneurial leadership enables firms to maintain a competitive edge through innovative decision making and efficient resource allocation, strengthening supply chain resilience and adaptability [42]. Collaborative capability fosters closer cooperation among different stages of the supply chain, enhancing information sharing and resource integration. This enables firms to respond more quickly to market changes, reduce operational uncertainty, and enhance supply chain stability and performance [39]. Moreover, effective collaboration within the supply chain fosters trust among partners, promotes long-term relationships, and enhances overall effectiveness, enabling firms to capture a larger market share in competitive environments [44]. Drawing from these arguments, the subsequent hypothesis is proposed:

H4: 

Supply chain capability positively influences supply chain performance.

Leveraging insights from the resource-based perspective and dynamic capability framework, supply chain resilience is acknowledged as a critical yet limited asset. It enables businesses to maintain stability amid disruptions. Resilience not only supports the ongoing operation of supply chains but also ensures long-term performance sustainability and steady improvement by enhancing adaptability and flexibility [29]. Through its dimensions, such as absorptive, reactive, and recovery capability, resilience provides firms with the necessary support to handle uncertainty and disruptions, thus positively influencing performance [45].

Absorptive capability helps reduce the fluctuations associated with disruptive events, ensuring the stability of supply chain operations. Measures such as redundant inventories and diversified sourcing act as buffers, providing time to respond to unforeseen events and contributing to improved financial performance [46]. Moreover, supply chains characterized by strong absorptive capability and superior situational sensitivity can rapidly identify environmental shifts, enabling them to adopt adaptive strategies, mitigate potential risks of disruptions, and thereby enhance overall financial performance [47]. Drawing from these arguments, the subsequent hypothesis is proposed:

H5: 

Resilience-absorptive capability positively influences supply chain performance.

Reactive capability enables firms to swiftly formulate and execute risk management strategies in response to disruptions, playing a crucial role in adapting to market fluctuations. Studies suggest that supply chains possessing robust adaptive capability can efficiently reallocate resources and enhance the quality of products and services when faced with uncertain conditions, thereby strengthening market responsiveness and driving overall performance improvement [48]. Firms with higher reactive capability are more likely to establish both horizontal and vertical partnerships within their supply chain, building collaborative networks that reduce vulnerabilities and enhance performance [49,50]. Drawing from these arguments, the subsequent hypothesis is proposed:

H6: 

Resilience-reactive capability positively influences supply chain performance.

Improved recovery capability enables firms to quickly reorganize resources and restore operations, thereby minimizing losses and potential risks from supply chain disruptions and enhancing performance [51]. Greater recovery efficiency shortens disruption duration, reduces subsequent risks, and improves operational performance. Furthermore, recovery capability enables firms to rapidly introduce high-quality products and new services following disruptions, thereby enhancing market competitiveness and financial performance [52]. Drawing from these arguments, the subsequent hypothesis is proposed:

H7: 

Resilience-recovery capability positively influences supply chain performance.

Absorptive capability underpins supply chain resilience by enabling firms to recognize, obtain, and synthesize both organizational and external knowledge and assets to mitigate potential risks [46]. Specifically, strong absorptive capability, responsiveness, and decision making quality enhance the supply chain’s responsiveness and decision making quality during periods of uncertainty and disruptions. By optimizing information flow and resource allocation, it significantly enhances reactive capability [47]. Moreover, absorptive capability contributes to the enhancement of recovery capability by increasing organizational flexibility and agility, enabling firms to quickly recover and accelerate resource reallocation after disruptions [53]. Drawing from these arguments, the subsequent hypothesis is proposed:

H8: 

Resilience-absorptive capability positively influences reactive capability.

Reactive capability denotes a supply chain’s capability to swiftly formulate and execute response strategies in the face of disruptions or emerging risks [48]. Firms with strong reactive capability can promptly implement effective measures during disruptions, minimizing their impact on overall operations and ensuring that the supply chain enters the recovery phase quickly. Timely and effective responses not only reduce the firm’s losses but also create favorable conditions for the subsequent recovery process, improving recovery efficiency and outcomes [54]. Firms with higher reactive capability are better equipped to formulate recovery plans and coordinate resource allocation, ensuring smooth supply chain recovery. Drawing from these arguments, the subsequent hypothesis is proposed:

H9: 

Resilience-reactive capability positively influences recovery capability.

Research indicates that absorptive capability plays a supportive role in recovery capability. By proactively identifying and addressing external risks before disruptions occur, absorptive capability enables firms to accumulate the necessary knowledge and resources, providing a foundation for recovery after disruptions [55]. These preparations enable firms to promptly initiate recovery actions following a disruption, leveraging existing resources and information to expedite the recovery process and mitigate the negative impacts on supply chain operations [56]. Additionally, improved absorptive capability helps build more flexible management mechanisms within the organization, enhancing the resilience and adaptability of the recovery process following risk events. Drawing from these arguments, the subsequent hypothesis is proposed:

H10: 

Resilience-absorptive capability positively influences recovery capability.

Supply chain strengths play a crucial role in helping firms navigate external challenges and enhance resilience. These competencies stem from the seamless coordination of in-house assets and external partnerships. Resilience acts as an essential bridge connecting supply chain strategies with sustainable business performance by mitigating risks and navigating uncertainties. It ultimately safeguards supply chain continuity and operational stability. From a resource-based view, firms must develop diversified capabilities to enhance their resilience in complex environments, securing long-term competitiveness and sustainable performance [57]. Enhancing the robustness of supply chains effectively connects management strategies to performance improvements, increasing market responsiveness and strengthening competitive positioning [58].

Absorptive capability improves a firm’s crisis management ability and enhances supply chain performance by acquiring, integrating, and utilizing external knowledge. A supply chain’s reactive and absorptive capability in the face of external disruptions depends on efficient information flow and robust connectivity, strengthening its agility and resilience [59]. Additionally, a firm’s supply chain capability facilitates rapid recovery after major disruptions through recovery capability. This mechanism enhances supply chain resilience and performance by leveraging knowledge and learning from past experiences. Drawing on established theoretical foundations and empirical findings, absorptive, reactive, and recovery capability are identified as the core dimensions of resilience. These capacities are proposed as mediating mechanisms, helping to clarify the complex pathways through which supply chain capability influences performance. Drawing from these arguments, the subsequent hypothesis is proposed:

H1a: 

Resilience-absorptive capability mediates the positive relationship between supply chain capability and supply chain performance.

H2a: 

Resilience-reactive capability mediates the positive relationship between supply chain capability and supply chain performance.

H3a: 

Resilience-recovery capability mediates the positive relationship between supply chain capability and supply chain performance.

Supply chain capability influences performance through a combination of multidimensional resilience [57]. For example, the interaction between absorptive and reactive capability enhances the supply chain’s immediate response and knowledge transfer efficiency. The integration of absorptive and recovery capability ensures robust recovery post-crisis by combining knowledge integration with resource reorganization. Recovery and reactive capability jointly promote dynamic adaptation and long-term resilience through fast operational responses and system rebuilding [45]. Additionally, the integration of absorptive, reactive, and recovery capability provides a comprehensive solution for firms to address complex environments and crises [60]. The multidimensional combination enhances supply chain agility, adaptability, and robustness, driving performance optimization in uncertain environments. Drawing from these arguments, the subsequent hypothesis is proposed:

H1b: 

Supply chain capability enhances performance outcomes by reinforcing resilience-absorptive and reactive capacity.

H1c: 

Supply chain capability enhances performance outcomes by reinforcing resilience-absorptive and recovery capacity.

H3b: 

Supply chain capability enhances performance outcomes by reinforcing resilience-reactive and recovery capability.

H1d: 

Supply chain capability enhances performance outcomes by reinforcing resilience-absorptive, reactive, and recovery capability.

Resilience plays a pivotal role in enhancing firm performance, while environmental uncertainty, as a fundamental external factor, further modulates this relationship. In highly uncertain environments, firms encounter challenges such as market fluctuations, demand variability, and policy shifts, which intensify operational complexity and magnify the impact of resilience on performance [61]. Prior research suggests that environmental uncertainty reshapes supply chain performance dynamics by influencing supply chain integration, relationship quality, and risk management strategies [62]. Key drivers of environmental uncertainty—such as demand volatility, technological advancements, and supply instability—necessitate continuous adjustments in supply chain strategies and integration practices.

Under high-uncertainty conditions, supply chain agility is regarded as a crucial enabler of operational performance, enabling firms to mitigate external shocks through rapid responsiveness and flexible adaptation [47]. Furthermore, strengthening supply chain relationship quality plays an essential role in performance optimization, particularly by fostering collaborative networks that facilitate resource sharing and risk mitigation [28]. Drawing from these arguments, the subsequent hypothesis is proposed:

H11: 

Environmental uncertainty serves as a positive moderator in the connection between resilience-absorptive capability and supply chain performance.

H12: 

Environmental uncertainty serves as a positive moderator in the connection between resilience-reactive capability and supply chain performance.

H13: 

Environmental uncertainty serves as a positive moderator in the connection between resilience-recovery capability and supply chain performance.

3. Methodology

3.1. Questionnaire Management and Sampling Process

To ensure accurate measurement of the research model, this study adopted established and widely used validated scales, which were tailored to the research context. As most of the measurement items were from English-language sources and the respondents were professionals from Chinese enterprises, the English scales were translated and back-translated to ensure semantic accuracy [63,64,65]. To test the feasibility and applicability of the scales, a pilot study was conducted. The pilot sample, drawn from the target research group, allowed for adjustments to be made to the questionnaire’s wording, logic, and phrasing based on feedback from respondents. This improved the comprehensibility of the scales and ensured the effectiveness of data collection. After several rounds of revision, the questionnaire was finalized with a clear structure and accurate items (see Appendix A) [66,67,68,69,70,71,72,73,74,75].

3.2. Questionnaire Structure and Distribution

Structured into three main segments, the questionnaire begins with an overview of the research background and objectives, incorporating details on the survey’s theme and confidentiality commitment. It explicitly affirms the exclusive use of data for academic research while guaranteeing stringent protection of participants’ privacy, thereby building respondent trust and encouraging honest responses. The second section contains the measurement scales for the research variables, using a seven-point Likert scale. The third section collects basic demographic information from participants.

The choice of Chinese enterprises as the research focus is based on several reasons. Firstly, as a key hub in the global supply chain network, China offers a unique perspective due to its supply chain complexity and network effects. Chinese enterprises have gained extensive experience in handling diverse market demands and coordinating multinational supply chains, thus providing valuable, representative data. Secondly, China has made significant strides in recent years in digital transformation and logistics innovation [75]. These advancements are evident in the widespread application of supply chain technologies, as well as breakthroughs in areas such as smart logistics, blockchain technology, and real-time supply chain monitoring. These developments provide practical examples and data sources for studying how digital capabilities influence resilience. Furthermore, China’s multi-tiered supply chain structure—encompassing high-end manufacturing, low-cost supply chains, and regional logistics networks—offers diverse perspectives and validation opportunities for understanding resilience in different contexts and mechanisms.

Data was collected through an online questionnaire distributed via professional communities in supply chain management and logistics, company intranets, and relevant training and seminar activities. A random sampling approach was used to ensure the sample was both representative and widely distributed. The data collection period spanned from 14 to 31 October 2024, with a total of 800 questionnaires collected. To ensure completeness, all questions were mandatory, and a lottery incentive was introduced to encourage participation. To maintain data quality, strict screening was applied based on response time and answer consistency. If the completion time was below 120 s or exceeded 900 s, the responses were flagged as inattentive or rushed [76,77]. Additionally, questionnaires with excessively uniform or obviously inconsistent answers were excluded. After cleaning, 649 valid questionnaires were obtained, accounting for 81% of the total responses. A minimum sample size of 200 is recommended for structural equation modeling to ensure the stability and scientific validity of the analysis [78]. The 649 valid samples met the minimum requirement for analysis and provided statistical support for multivariate analysis.

3.3. Common Method Bias

This study conducted a common method bias test to enhance the data’s reliability and ensure its scientific rigor [79]. After collecting data, an exploratory factor analysis (EFA) was conducted to assess underlying structures, followed by a confirmatory factor analysis (CFA) to validate the measurement model and address potential biases in the dataset. Initially, EFA was conducted utilizing Harman’s single-factor test to identify potential common method bias [78]. The analysis indicated that the primary factor explained 25.95% of the total variance, remaining well below the 40% threshold. This suggests that common method bias is unlikely to pose a significant threat to the validity of the analytical model. Subsequently, CFA was performed to provide additional validation. All measurement variables were incorporated into a single-factor model, and its fit indices were then evaluated in comparison to those of the original measurement framework to assess model validity. The analysis demonstrated that the single-factor model exhibited significantly poorer fit indices (CMIN/DF = 14.356, CFI = 0.418, IFI = 0.420, RMSEA = 0.144, SRMR = 0.1591), deviating from acceptable thresholds when contrasted with the original measurement model (CMIN/DF = 1.540, CFI = 0.977, IFI = 0.977, RMSEA = 0.029, SRMR = 0.0287). This discrepancy indicates that the measurement items do not conform to a single-factor structure, further mitigating concerns regarding common method bias.

3.4. Demographic Characteristics

The respondents come from a wide array of industries, ensuring that the study’s findings capture diverse business environments and enhance the generalizability of the results. Differences between industries in their responses to external shocks and adjustments to supply chain strategies offer a multifaceted perspective for the study. In terms of job levels, the sample includes respondents from multiple management tiers, including general employees (35.9%), frontline staff (48.4%), middle management (10.2%), and senior executives (5.5%). This diversity ensures that the study captures a wide range of perspectives within companies, particularly regarding how supply chains are adjusted and risks are managed across different levels of management. Key stakeholders at both operational and strategic levels significantly influence the perception and enhancement of supply chain resilience. The sample includes companies from different ownership structures, such as state-owned, foreign-invested, private, and joint-venture firms. This diversity highlights the varying approaches to resilience building across different ownership types. Additionally, the sample represents companies of different sizes, ranging from small to large enterprises, based on employee count and annual revenue. These demographic characteristics suggest that the respondents not only have an in-depth understanding of overall business operations but also possess significant insight into supply chain management practices, particularly in emergency response, risk management, and long-term resilience development. By analyzing a diversified sample that spans various organizational levels, company sizes, and ownership types, this research enhances the understanding of critical determinants shaping supply chain resilience, while also delivering meaningful insights for advancing theory and informing practical applications. Participant demographics are shown in Figure 2.

4. Results and Discussion

4.1. Confirmatory Factor Analysis

The measurement model was tested empirically to assess the relationships between each measurement indicator and its corresponding latent construct. The study employed CFA to verify the internal consistency and construct validity of the measures. Convergent validity measures the degree of correlation between the measurement indicators within the same latent construct and is assessed through the average variance extracted (AVE) [79,80,81]. Following the guidelines of Fornell and Larcker (1981), an AVE score of at least 0.5 suggests that the construct accounts for over 50% of the variance among its corresponding indicators [82].

Table 1. Results of confirmatory factor analysis.

Construct		Item	Mean	SD	Factor Loading	Cronbach’s Alpha	AVE	CR
SCC	SCDT	SCDT1	3.98	1.809	0.808	0.913	0.678	0.913
		SCDT2	3.91	1.79	0.822
		SCDT3	3.99	1.825	0.821
		SCDT4	3.99	1.865	0.828
		SCDT5	3.96	1.866	0.839
	ENLC	ENLC1	3.7	1.843	0.843	0.956	0.733	0.956
		ENLC2	3.69	1.806	0.865
		ENLC3	3.69	1.784	0.844
		ENLC4	3.6	1.881	0.876
		ENLC5	3.84	1.893	0.779
		ENLC6	3.65	1.863	0.894
		ENLC7	3.68	1.805	0.878
		ENLC8	3.74	1.821	0.863
	COLC	COLC1	4.05	1.737	0.725	0.838	0.565	0.838
		COLC2	4.03	1.717	0.753
		COLC3	4.1	1.722	0.761
		COLC4	4.06	1.715	0.766
ABSC		ABSC1	3.97	1.817	0.736	0.838	0.565	0.839
		ABSC2	3.95	1.835	0.781
		ABSC3	4.02	1.8	0.74
		ABSC4	3.96	1.852	0.749
REAC		REAC1	4	1.806	0.733	0.831	0.551	0.831
		REAC2	3.97	1.834	0.772
		REAC3	4.04	1.778	0.718
		REAC4	3.98	1.842	0.746
RECC		RECC1	4.59	1.732	0.74	0.853	0.593	0.853
		RECC2	4.53	1.743	0.785
		RECC3	4.47	1.724	0.784
		RECC4	4.57	1.718	0.77
SCP		SCP1	5.09	1.547	0.756	0.837	0.563	0.837
		SCP2	4.99	1.449	0.716
		SCP3	5.03	1.552	0.762
		SCP4	5.04	1.528	0.765
EU		EU1	4.64	1.664	0.792	0.894	0.678	0.894
		EU2	4.59	1.718	0.823
		EU3	4.58	1.7	0.841
		EU4	4.6	1.71	0.836

Model fit indices: CMINI/DF = 1.545, RESEA = 0.029, SRMR = 0.041, GFI = 0.935, NFI = 0.939, RFI = 0.932, IFI = 0.977, TLI = 0.975, CFI = 0.977.

summarizes the results of the confirmatory factor analysis, including factor loadings and model fit indices. All factor loadings surpassed 0.7, while AVE values exceeded 0.5, indicating that the model met the criteria for convergent validity. These results confirm that the measurement indicators effectively represent the underlying latent constructs. To further validate the measurement reliability, composite reliability and Cronbach’s α values were also calculated. All constructs exhibited composite reliability values greater than 0.7, demonstrating their internal consistency [79]. Furthermore, all Cronbach’s α values exceeded 0.75, demonstrating good internal consistency among the measurement items.

Discriminant validity measures the degree of differentiation between latent constructs, specifically whether each construct is distinct from others. The key criterion determines whether the AVE for each construct is greater than its maximum shared variance (MSV) with other constructs. According to Hair et al., when the AVE value is significantly higher than the MSV value, the model is considered to have no discriminant validity issues [79].

Table 2. Results of discriminant validity analysis.

	SDTC	ENLC	COLC	ABSC	REAC	RECC	SCP	EU
SDTC	0.823
ENLC	0.303	0.856
COLC	0.368	0.39	0.752
ABSC	0.114	0.131	0.04	0.752
REAC	0.198	0.169	0.161	0.329	0.742
RECC	0.249	0.3	0.239	0.218	0.306	0.77
SCP	0.312	0.386	0.296	0.25	0.447	0.516	0.75
EU	0.176	0.244	0.209	0.116	0.21	0.44	0.307	0.823

Note: Diagonal = √AVE, lower triangle = construct correlations.

provides the discriminant validity data, showing that the AVE values for all constructs are greater than their corresponding MSV values. This confirms that the measurement model effectively distinguishes between different constructs, validating the discriminant validity.

4.2. Structural Model Testing

The analysis employed AMOS 28.0 software to assess both model estimation and structural relationships. The results of the model fit test demonstrated that the measurement framework aligns well with the sample data, with key fit indices exceeding the recommended thresholds (CMIN/DF = 1.628, CFI = 0.976, IFI = 0.976, TLI = 0.974, RMSEA = 0.031) [82]. This shows that the measurement model accurately reflects the underlying structure of the data. To further test robustness, a multiple regression analysis was performed to evaluate the predictive power of the structural model. Variance inflation factor (VIF) tests were conducted using SPSS 27, and the VIF values for all variables ranged from 1.15 to 1.22, well below the critical threshold of 2. This confirms that there is no multicollinearity issue, and the model parameter estimates are reliable [83].

As shown in

Table 3. Results of hypothesis testing.

Hypotheses	Constructs	Standardized Estimate	S.E.	C.R.	p	Hypothesis Supported
H1	SCC → ABSC	0.164	0.293	4.754	***	Supported
H2	SCC → REAC	0.234	0.086	3.799	***	Supported
H3	SCC → RECC	0.348	0.089	5.247	***	Supported
H4	SCC → SCP	0.373	0.085	5.422	***	Supported
H5	ABSC → SCP	0.063	0.039	1.379	/	Not Supported
H6	REAC → SCP	0.228	0.042	4.847	***	Supported
H7	RECC → SCP	0.257	0.047	4.999	***	Supported
H8	ABSC → REAC	0.279	0.048	5.522	***	Supported
H9	REAC → RECC	0.124	0.048	2.485	*	Supported
H10	ABSC → RECC	0.207	0.046	4.161	***	Supported
Second-order constructs	SCDT	0.543	0.104	7.967	***	Supported
	ENLC	0.613	0.161	7.939	***	Supported
	COLC	0.592	0.098	7.939	***	Supported

Significance criterion: *** p < 0.001, ** p < 0.01, * p < 0.05.

, supply chain capability exerts a notable beneficial impact on absorptive (β = 0.164, p < 0.001), reactive (β = 0.234, p < 0.001), and recovery capability (β = 0.348, p < 0.001), thereby confirming hypotheses H1, H2, and H3. The results demonstrate a significant positive effect of supply chain capability on performance (β = 0.373, p < 0.001), providing empirical support for H4. The findings indicate that reactive (β = 0.228, p < 0.001) and recovery capability (β = 0.257, p < 0.001) contribute positively to performance, whereas absorptive capability (β = 0.063, p > 0.05) does not show a statistically meaningful effect. Accordingly, H5 is not supported, while H6 and H7 are supported. The results indicate that absorptive capability has a significant positive effect on reactive capability (β = 0.279; p < 0.001), reactive capability exerts a significant positive influence on recovery capability (β = 0.124; p < 0.05), and absorptive capability also contributes positively and significantly to recovery capability (β = 0.207; p < 0.001). Hypotheses H8, H9, and H10 are all supported. Figure 3 visually illustrates the testing outcomes of the hypotheses.

4.3. Mediating Effects

To explore the mediating function of resilience’s three dimensions in connecting supply chain capability with performance and to examine their combined effects, a bootstrap analysis with 5000 resamples was conducted using Amos 28.0. A 95% confidence interval was employed to test the mediation pathways. The findings suggest that reactive and recovery capability function as partial mediators, linking supply chain capability to organizational success, underscoring the crucial impact of resilience on performance improvement.

Specifically, the direct influence of supply chain capability on performance is substantial, contributing 64.42% to the overall impact. Moreover, the mediating effects of reactive and recovery capability in resilience between supply chain capability and performance are significant, contributing 9.15% and 15.37%, respectively, to the total effect. This suggests that these two dimensions function autonomously while reinforcing the overall impact on operational outcomes. Furthermore, the aggregated intermediary role of absorptive, reactive, and recovery capability represents 35.58% of the total indirect effect in linking supply chain capability with performance. While the indirect effect is lower than the direct effect, the synergistic effect of the chain path and path dependency is empirically supported.

According to

Table 4. Mediation analysis results.

Hypotheses	Constructs	Standardized Estimate	Lower	Upper	p	Percentage of Effect (%)
	Direct effect
H4	SCC → SCP	0.373	0.258	0.492	**	64.421
	Indirect effect
H1a	SCC → ABSC → SCP	0.018	−0.01	0.047	/
H2a	SCC → REAC → SCP	0.053	0.029	0.089	***	9.154
H3a	SCC → RECC → SCP	0.089	0.031	0.096	**	15.371
H1b	SCC → ABSC → REAC → SCP	0.019	0.01	0.034	**	3.282
H1c	SCC → ABSC → RECC → SCP	0.016	0.007	0.03	***	2.763
H3b	SCC → REAC → RECC → SCP	0.007	0.002	0.017	**	1.209
H1d	SCC → ABSC → REAC → RECC → SCP	0.003	0.001	0.007	**	0.518
	Total indirect effect	0.206	0.126	0.235	***	35.579
	Total effect	0.579	0.486	0.664	**	100

Significance criterion: *** p < 0.001, ** p < 0.01, * p < 0.05.

, the chain mediation path analysis further shows that absorptive capability indirectly influences performance through reactive and recovery capability. This mechanism confirms the synergistic relationships between the dimensions and underscores the crucial role of reactive and recovery capability in transforming supply chain capability into performance outcomes [15].

4.4. Moderating Effects

A moderating effect analysis was carried out in SPSS 27 to explore the role of environmental uncertainty in shaping the connection between resilience dimensions and firm performance.

Table 5. Moderation effect analysis results.

Hypotheses	Variable	Coeff	SE	T	p-Value	LLCI	ULCI
H11	ABSC × EU → SCP	0.0372	0.0215	1.7324	0.0837	−0.005	0.0794
H12	REAC × EU → SCP	0.0667	0.0221	3.0184	0.0026	0.0233	0.1101
H13	RECC × EU → SCP	0.0891	0.0247	3.6078	0.0003	0.0406	0.1376

Note: × indicates that the variable was included in an interaction term with EU.

presents the moderation effect analysis results, and Figure 4 and Figure 5 visually depict the moderating effects of environmental uncertainty on the paths from reactive and recovery capabilities to supply chain performance. The findings indicate that under conditions of environmental uncertainty, the beneficial effects of reactive and recovery capability on performance are notably amplified. Specifically, in highly uncertain environments, firms can rapidly adjust strategies by strengthening reactive capability to better adapt to market fluctuations and external changes. At the same time, enhanced recovery capability allows firms to efficiently reorganize resources and optimize operational models, thereby improving performance. However, environmental uncertainty did not significantly moderate the relationship between absorptive capability and performance. This may be because although absorptive capability plays a crucial role in acquiring information and integrating external knowledge, in highly dynamic environments, relying solely on knowledge integration is insufficient to directly improve performance. This finding aligns with the view that absorptive capability is most effective when combined with other dynamic capabilities, such as reactive capability and resource reorganization, to effectively enhance performance [84]. Overall, this study provides support for hypotheses H11, H12, and H13, highlighting the impact of environmental uncertainty as a contextual factor on the mechanisms of resilience dimensions, particularly emphasizing the critical role of reactive and recovery capability in dynamic environments. Table 5 presents the results of the moderation effect analysis.

4.5. Discussion of Results

First, previous studies have widely recognized supply chain capability as a key driver of organizational resilience [15,47,85]. The empirical findings of this study further support this view by demonstrating that the impact of supply chain capability on recovery capability (β = 0.348, p < 0.001) is the strongest, followed by its impact on reactive capability (β = 0.234, p < 0.001), while the effect on absorptive capability (β = 0.164, p < 0.001) is relatively weaker. These results suggest that supply chain capability plays a crucial role in facilitating rapid recovery and effective responses to major disruptions.

Second, this study confirms the positive impact of supply chain capability on performance, consistent with prior research. Firms that integrate key competencies, such as supply chain digital transformation, entrepreneurial leadership capability, and collaborative capability, can enhance operational efficiency and cost control, thereby strengthening supply chain performance and competitiveness [36,42]. By improving information integration and resource mobilization, digital elements can be embedded into products and services, enhancing responsiveness and alignment with customer needs and boosting performance [44]. Multidimensional supply chain capabilities also enable the development of efficient omnichannel systems and flexible logistics, creating diverse revenue opportunities. As an important component of supply chain capability, collaboration enhances end-to-end connectivity and resource sharing, fostering a collaborative and open supply network that supports resilience and long-term profitability [39].

Third, unlike previous studies that emphasized the positive impact of absorptive capability on performance, this study finds no significant effect of absorptive capability in enhancing supply chain performance. One possible explanation for this is that although absorptive capability helps firms acquire and integrate external information, delays in information processing and inefficiencies in translating insights into concrete actions may reduce its direct contribution to performance [86]. Additionally, excessive reliance on redundancy or safety buffers can lead to resource waste and reduced flexibility, further suppressing performance improvements. This interpretation is consistent with prior research on the cost–resilience trade-off [87]. In contrast, both adaptive capability (β = 0.228, p < 0.001) and recovery capability (β = 0.257, p < 0.001) have significant positive effects on performance, with recovery capability exerting the most decisive influence. Recovery capability not only determines how quickly a firm can recover from disruptions but also serves as a critical factor in sustaining long-term supply chain stability [88]. Firms with high recovery capability can rapidly resume operations following external shocks such as supply chain failures or natural disasters, thereby minimizing economic losses and ensuring business continuity. Such rapid recovery and operational stability are crucial for enhancing overall performance outcomes [89].

Furthermore, SCR serves as a chain-mediating mechanism linking supply chain capability to performance, with its three dimensions working in a complementary manner. Supply chain reactive capability helps firms quickly adjust strategies and reallocate resources amid disruptions, improving flexibility and operational efficiency [15]. Reducing delays and stabilizing operations enhances short-term responsiveness in dynamic environments [90]. Supply chain recovery capability further reinforces resilience by enabling rapid resumption of operations and minimizing losses, which supports supply chain continuity and long-term performance [91,92]. Supply chain absorptive capability contributes by improving knowledge acquisition and integration, thereby supporting the effectiveness of reactive and recovery mechanisms. However, this study finds that absorptive capability does not significantly mediate the link between capability and performance. One possible explanation is that while transparency and redundancy rely on absorptive capacity, the associated costs of system investment and maintenance may outweigh the benefits, weakening their mediating effect.

Finally, the second-order model highlights entrepreneurial leadership as a key driver of supply chain capability (β = 0.613, p < 0.001), aligning with RBV and DCT in emphasizing the leader’s role in resource allocation and capability formation [93]. Firms with strong entrepreneurial leadership often achieve better performance and competitive advantage through supply chain innovation and resilience enhancement [94]. Moreover, leadership fosters dynamic learning, enabling integrated development of capabilities and adaptability in volatile markets [95].

5. Conclusions

This study investigates how supply chain capability enhances organizational resilience and performance under external disruptions. The findings deepen our understanding of supply chain resilience and offer practical insights for improving managerial effectiveness and performance in uncertain environments.

5.1. Theoretical Contributions

First, this study systematically conceptualizes and extends the multidimensional structure of supply chain resilience. Unlike prior research that treats resilience as a single construct or addresses its dimensions only in theoretical terms, this study emphasizes the dynamic interplay among absorptive, reactive, and recovery capability. Integrating key elements develops a comprehensive framework that illustrates how these components collectively enhance resilience.

Second, this study integrates the RBV and DCT to construct a novel conceptual framework for analyzing how supply chain capability influences performance through resilience. While the RBV emphasizes internal resource optimization, DCT focuses on proactive adaptation to external changes [29]. The proposed framework reveals the connective role of resilience between these two theoretical perspectives, clarifies its chain-mediated linkage between capability and performance, and advances the theoretical paradigm of supply chain management from static resource allocation to dynamic capability systems.

Third, this study extends the theoretical boundaries of resilience research by incorporating an external environmental perspective. Previous studies have predominantly focused on internal resilience capability, paying limited attention to the influence of dynamic external contexts [4]. By introducing environmental uncertainty into the resilience framework, this study offers a perspective grounded in responsiveness to environmental shifts, thereby deepening the theoretical understanding of contextual variables in supply chain management. It reinforces the integration of external environmental factors into resilience theory, shifting the analytical lens beyond intra-organizational mechanisms toward a more open and context-sensitive theoretical space.

5.2. Implications for Management

First, this study demonstrates that the integrated functioning of absorptive, reactive, and recovery capabilities enables firms to respond effectively to external disruptions and enhance supply chain performance. The empirical results support the view that redundancy should be considered a strategic reserve for crisis scenarios rather than idle inventory [96]. Neglecting its role within the resilience system may reduce resource allocation efficiency and constrain long-term performance improvement. Therefore, firms should improve the flexible deployment of redundant resources to strengthen their capacity for effective crisis response.

Second, improving the alignment between supply chain resilience and performance depends on the integrated functioning of entrepreneurial leadership, collaborative capability, and digital transformation capability. Firms can establish cross-functional emergency teams, adopt blockchain to enhance transparency and coordination [36], and deploy AI, big data, and digital twins to build simulation-based virtual supply chains. These technologies support disruption prediction, strategy testing, and dynamic resource allocation, improving foresight and recovery [96,97,98]. As simulation-based methods, they not only provide operational flexibility but also serve as critical tools in resilience-oriented digital transformation, bridging predictive analytics with strategic decision making. To fully leverage these capabilities, firms should invest in digital talent and ensure the alignment of digital infrastructure with organizational workflows [71,98,99].

Third, given the moderating role of environmental uncertainty, firms should proactively establish collaborative mechanisms with upstream and downstream partners [100], co-developing early warning systems and contingency plans for timely risk response. Promoting network diversification and benefit sharing can enhance structural resilience and ensure the long-term sustainability of the supply chain [101].

5.3. Limitations and Future Research

This study focuses on the mechanisms of SCR in the context of Chinese firms, capturing how organizations in emerging economies respond to high levels of uncertainty under specific institutional and market conditions. While these findings offer valuable insights, the generalizability of the conclusions to other economies or institutional environments requires further validation. In regions such as Europe and North America—where institutional frameworks are more mature and market structures are more stable—the mechanisms underlying SCR may vary due to differences in governance logic, risk response strategies, and policy support systems. These variations could lead to heterogeneous resilience patterns across different contexts. The current study does not systematically compare institutional regimes or incorporate international experiences. Future research is encouraged to include cross-national samples and conduct contextualized comparative analyses. Integrating international literature and multi-country data could enhance the theoretical robustness of the framework and improve the external validity of the findings.

Taking a firm-level view, this research assesses the contribution of supply chain capability to both resilience and performance; however, it does not incorporate sustainability-related dimensions, such as environmental or social resilience. This omission represents a theoretical limitation, particularly in the context of ongoing global efforts toward green transition. Recent studies have emphasized the intrinsic connection between supply chain resilience and sustainability, suggesting that the two may involve either synergistic effects or trade-offs, depending on structural and contextual factors. Future research could investigate how firms strike a balance between efficiency, resilience, and sustainability when faced with limited resources or competing objectives. It is also worth examining whether practices such as green procurement, circular economy initiatives, and responsible outsourcing can serve as drivers for enhancing multidimensional resilience. To advance theoretical development, future studies are encouraged to integrate environmental and social sustainability into resilience frameworks and to systematically investigate how these sustainability dimensions interact with traditional components of supply chain resilience.