Digital empowerment, driven by advancements in digital technology, enhances the self-efficacy and coordination capabilities of enterprises within supply chains by leveraging digital platforms and tools (Fang and Liu, 2024; Sun et al., 2018). Its core function lies not only in the adoption of digital platforms and technical tools but also in promoting resource sharing and information exchange among organizations, thereby strengthening overall supply chain collaboration (Chang et al., 2025). Although prior studies have confirmed that digital empowerment positively influences supply chain responsiveness and resource integration in rural enterprises, its underlying mechanisms within rural supply chain contexts remain insufficiently explored. This study investigates these mechanisms through the three dimensions of structure, trust, and resource empowerment.

With the deepening of research, the concept of digital empowerment has been progressively expanded. Leong C. et al. (2015), Leong C. M. L. et al. (2015), and Leong et al. (2016) emphasized its role at the business ecosystem level, particularly in offering low-cost, high-efficiency support for small and medium-sized enterprises (SMEs) operating on e-commerce platforms (Min and Kim, 2021). Compared with traditional operational management, such platforms demonstrate distinct advantages in empowering rural SMEs through data sharing and market insight (Zhu et al., 2020). Sun et al. (2018) categorized digital empowerment into three dimensions: structural empowerment, psychological empowerment, and resource empowerment—corresponding, respectively, to the optimization of external institutional conditions, enhancement of collaborative trust, and reinforcement of resource integration capabilities. This classification provides a systematic framework for understanding how rural enterprises can enhance their capabilities and improve performance through digitalization. Therefore, structural empowerment, trust empowerment, and resource empowerment do not directly influence rural supply chain performance; rather, they exert their effects through supply chain collaboration by shaping coordination mechanisms, resource integration processes, and relational linkages among supply chain members. In this process, supply chain collaboration serves as a key transmission mechanism through which digital empowerment is transformed into performance outcomes and functions as a critical mediating link between digital empowerment and rural supply chain performance (Cao and Zhang, 2011). Moreover, digital empowerment and its synergistic effects do not operate in isolation but are conditioned by the external institutional environment (Mei et al., 2020). Accordingly, policy support is incorporated as a contextual institutional moderator to explain variations in the strength of different digital empowerment pathways. This study conceptualizes structural empowerment, trust empowerment, and resource empowerment as three digitalization-driven mechanisms of capability generation. These mechanisms, respectively, enable enterprises to sense changes in demand, seize opportunities through cross-stakeholder collaboration, and reconfigure resource allocation, thereby enhancing supply chain collaboration and performance (Li Z. et al., 2022; Li Z. B. et al., 2022; Teece, 2007). Dynamic capability theory thus provides the theoretical foundation for the three-dimensional empowerment framework adopted in the conceptual model and guides subsequent hypothesis development.

To further elucidate the impact mechanisms of digital empowerment on rural supply chains, this paper draws on dynamic capability theory (Teece, 2007). This theory posits that organizational capabilities consist of three core functions: sensing, seizing, and transforming. Empowerment, in this context, is not merely about delegation of authority, but also about capability creation (Li Z. et al., 2022; Li Z. B. et al., 2022). In rural digital transformation, enterprises utilize digital tools to rapidly sense market demands (Wu et al., 2022), integrate resources via digital platforms to optimize operations (Jiang et al., 2022), and continually adapt business models and organizational structures to address environmental uncertainty (Ellström et al., 2021). Furthermore, institutional theory highlights how formal and informal institutional arrangements constrain and guide firm behaviors (North, 1990; Scott, 1995), which is essential for explaining the external institutional foundations that enable digital empowerment in the digitalization of rural supply chains. As a result, digital empowerment enhances rural enterprises’ agility and responsiveness, ultimately promoting both coordinated rural supply chain optimization and performance improvement.

Supply chain collaboration (SCC) refers to the coordination and cooperation among multiple enterprises to achieve mutually beneficial objectives, particularly under conditions of resource constraints. Supply chain collaboration enhances firms’ capacity to access resources and improve market competitiveness (Simatupang and Sridharan, 2002). It involves not only the sharing of information and resources but also the alignment of interests and synchronization of decision-making processes to boost overall operational efficiency and profitability (Soosay and Hyland, 2015). As Cao and Zhang (2011) emphasized, Supply chain collaboration highlights the importance of mutual benefit and shared goals among independent entities within the supply chain. Given the increasing complexity of supply chain structures, collaboration has become an essential driver for improving responsiveness and operational performance (Acquah et al., 2021; Wu et al., 2014), and is recognized as a critical source of competitive advantage (Liao et al., 2017).

In the digital era, Supply chain collaboration has gained heightened strategic importance. The emergence of digital platforms and the widespread use of data analytics tools have significantly enhanced upstream and downstream interactions, facilitating improved supply–demand alignment and more effective resource integration (Ikram et al., 2018). Particularly within industrial agglomeration contexts, geographical proximity further amplifies the efficiency of collaboration among rural enterprises, leading to shorter response times and reduced communication costs (Merrell et al., 2022). Effective collaborative mechanisms not only improve the operational performance of rural supply chains but also increase their adaptability and competitiveness in dynamic market environments.

Although existing studies affirm the positive impact of supply chain collaboration on rural enterprises’ responsiveness and resource allocation, its mechanisms in the rural e-commerce context remain underexplored. This paper investigates how digital empowerment enhances the supply chain performance and competitiveness of rural enterprises through collaborative mechanisms. Central to this process is the development of dynamic capabilities—enterprises’ abilities to respond to environmental uncertainties by fostering coordination, engaging in interactive learning, and reorganizing resources through cooperative complementarities (Al-Omoush et al., 2023; Eslami et al., 2021; Yang and Ren, 2021). From an institutional perspective, improvements in the institutional environment reduce uncertainty and reinforce cooperative expectations among members of rural supply chains. Such enhancements provide more stable institutional support for the digital empowerment–driven collaboration mechanisms in rural supply chains (Liu et al., 2024).

Rural supply chain performance (SCP) encompasses multiple dimensions, including resource utilization, output effectiveness, and operational flexibility (Belhadi et al., 2024; Khan et al., 2009). SCP refers to improvements in operational outcomes achieved through collaborative engagement among supply chain members (Molnár et al., 2010; Odongo et al., 2016). Prior research highlights that SCP is influenced by various factors, with the interplay between internal and external collaboration recognized as a critical driver of enhanced performance (Zhong et al., 2023).

In the context of digital empowerment, technologies such as big data analytics, artificial intelligence, and digital performance management systems have been shown to optimize decision-making processes and enable supply chain transformation, thereby significantly improving e-commerce supply chain performance (Balakrishnan and Ramanathan, 2021). Furthermore, digital tools foster information sharing and collaborative mechanisms—particularly in supplier-customer interactions—which are essential for enhancing delivery speed, product quality, service responsiveness, and flexibility (Zhang et al., 2016). From the perspective of performance formation mechanisms, supply chain performance is not only reflected in outcome-based results but also depends on firms’ continuously enhanced capabilities in coordination, adaptation, and resource integration during collaborative processes. These capabilities, in turn, drive improvements in innovation, operational efficiency, and strategic collaborative performance (Zhan and Tan, 2020).

For rural e-commerce enterprises, SCP is typically assessed using five key indicators: profit growth, cost reduction, business expansion, product upgrading, and improved partnerships (Odongo et al., 2016; Abd Rahman et al., 2023; Zeng et al., 2018). Profit growth reflects the net income generated through business activities, while cost reduction refers to the minimization of operational and opportunity costs through management optimization and collaboration (Odongo et al., 2016). Business expansion is evident in increased product sales resulting from investments in digital platforms (Zeng et al., 2018). Product upgrades involve improvements in product structure, function, or value to meet evolving market demands. Improved partnerships are reflected in heightened trust and collaboration with upstream and downstream supply chain partners. Collectively, these indicators provide a comprehensive evaluation of performance within rural e-commerce supply chains.

Although existing literature emphasizes the roles of collaboration, capability development, and technology adoption in enhancing SCP, the mechanisms through which digital empowerment contributes to SCP improvement in rural contexts remain insufficiently examined. This study investigates how digital empowerment facilitates rural supply chain performance by fostering collaborative supply chain mechanisms.

Digital technologies have created new opportunities for economic development and social interaction in rural areas, particularly in the field of e-commerce (Freeman and Park, 2015). Their application facilitates online collaborative environments and business network activities, offering rural enterprises enhanced access to cooperation and knowledge exchange (Merrell et al., 2022). The use of digital tools to engage in online business networks also enables enterprises to rapidly establish credibility, thereby fostering cooperation and accelerating problem-solving (Quinton and Wilson, 2016; Tiwasing et al., 2022). Digital empowerment comprises three dimensions—resource empowerment, structural empowerment, and trust empowerment—each of which plays a vital role in promoting rural supply chain collaboration and performance. Digital empowerment comprises three dimensions—resource empowerment, structural empowerment, and trust empowerment—each exerting significant influence on the collaborative mechanisms and performance outcomes of rural supply chains. Meanwhile, policy support, as part of the external institutional environment, further strengthens the effects of digital empowerment by improving infrastructure, reducing environmental uncertainty, and expanding channels for resource access. Based on these insights, we develop the conceptual model illustrated in Figure 1. Drawing on dynamic capability theory and institutional theory, this study argues that digital empowerment strengthens rural supply chains’ sensing, seizing, and reconfiguring capabilities through three interrelated pathways: structural empowerment improves institutional and organizational conditions, trust empowerment reinforces relational governance, and resource empowerment enhances resource acquisition and restructuring capabilities. These capability enhancements are primarily translated into performance outcomes through supply chain collaboration as a key mediating mechanism. Policy support, as a contextual institutional condition, is introduced to explain differences in the strength of these empowerment pathways.

Based on the literature review and the conceptual framework described above, a survey tool was designed to measure digital empowerment, policy support, supply chain collaboration, and supply chain performance. The specific items are shown in Table 1.

Specifically, digital empowerment is measured through three dimensions: structural empowerment, trust empowerment, and resource empowerment, each of which contains 3, 3, and 4 measurement items, respectively. These items are adapted from the studies of Leong C. et al. (2015), Leong C. M. L. et al. (2015), and Lee and Koh (2001). The measurement of policy support includes policy planning, e-commerce talent training, infrastructure construction, and market supervision, referring to the studies of Dania et al. (2018) and Mei et al. (2020). The measurement of supply chain collaboration performance is based on the studies of Cao and Zhang (2012) and Acquah et al. (2021), covering four dimensions: information sharing, resource sharing, collaborative communication, and co-creation of knowledge. Supply chain performance is evaluated using five items, including profit growth, cost reduction, business expansion, product upgrades, and improved partnerships, which are adapted from the studies of Zeng et al. (2018) and Odongo et al. (2016).

To ensure theoretical consistency, this study draws primarily on dynamic capability theory to define and operationalize the constructs, allowing them to capture processes of capability formation and transformation. Institutional theory is additionally incorporated to explain the moderating role of policy support. Structural empowerment reflects enterprises’ perceptions of improvements in institutional and structural conditions, indicating their ease of access to information, opportunities, and market connections. Trust empowerment captures confidence and relational support under conditions of uncertainty, facilitating cross-stakeholder collaboration. Resource empowerment represents enterprises’ ability to share, control, and reconfigure key resources with partners. Supply chain collaboration characterizes cross-organizational coordination and joint knowledge activities, serving as a central mechanism through which empowerment is translated into performance outcomes. Performance indicators capture observable outcomes in rural e-commerce contexts, including profitability, cost efficiency, market expansion, and upgrading. Measures of policy support reflect enterprises’ perceptions of the external institutional environment and policy instrument provision; their role is to moderate the extent to which different empowerment mechanisms are transformed into collaborative behavior rather than to directly determine performance outcomes (North, 1990; Scott, 2014).

Respondents were asked to select a partner they were most familiar with and, based on the actual situation of inter-enterprise cooperation, indicate their agreement with the statements about the relationship between digital empowerment, policy support, supply chain collaboration, and supply chain performance. We used a 5-point Likert scale, where “1” means “completely disagree” and “5” means “completely agree.”

Before the formal survey, the study conducted a preliminary survey of 5 companies in a “Taobao Village.” After the managers filled out the questionnaire, the researchers discussed the survey questions with them face to face, clarified the meaning of some questions, and revised and optimized the items that caused ambiguity.

This study collected data from April to May 2024. The formal survey lasted for 2 months, aiming to obtain the opinions of upstream and downstream participants in the rural e-commerce supply chain. The survey subjects included government departments, e-commerce associations, e-commerce public service centers, suppliers (farmers), producers (processors), rural e-commerce retailers, operating companies, e-commerce platform companies, logistics companies, and related service providers (such as packaging, printing, training, etc.).

To ensure the smooth progress of the formal survey, the research team first conducted a pre-survey. 20 paper questionnaires were distributed to verify the scientificity and rationality of the questionnaire. Based on the feedback collected in the pre-survey, the research team discussed and revised, and finally determined the formal questionnaire. Before the survey, the researchers received special training, focusing on the business scenarios and questionnaire items of rural e-commerce supply chain collaboration to ensure that they could provide accurate interpretations to the respondents so that they could answer according to the actual situation of the enterprise.

The questionnaires were administered in Chinese, and all respondents were native speakers. For scale items adapted from English literature, a translation–backtranslation procedure was employed, conducted by two bilingual experts to ensure conceptual equivalence. Any discrepancies identified during backtranslation were discussed and resolved. To minimize respondent bias, all investigators received standardized training to maintain neutrality in interpreting questions and emphasized the principles of anonymity and the absence of correct answers (see Table 2).

After revising the questionnaire, the data collection work of the formal survey adopted a combination of on-site and online methods. On the one hand, the team members visited 10 “Taobao villages” in Zhejiang Province, including Bainiu Village, Beishan Village, Qingyanliu Village, Changdai Village, Jiangchuan Village, Xinze Village, Shenjia Village, Hanbu Village, Xi’ao Village and Shuangyi Village. These sample villages have a relatively mature e-commerce operation foundation and strong supply chain linkage characteristics, and the survey subjects are representative. The questionnaire was distributed through the online platform “Wenjuxing” and forwarded with the help of key informants (such as enterprise managers, platform leaders, government policymakers, etc.) established during the field investigation, covering more rural e-commerce enterprises of different types and development stages, and improving the diversity of the sample. On the other hand, to alleviate the selection bias that may be caused by exclusive dependence on a few sample villages, the research team further distributed online questionnaires in Taobao town where the sample village is located, and the nearby rural e-commerce industrial park based on the above-mentioned field visits, to expand the scope of sample sources and improve the external generalizability of the research conclusions (see Figure 2).

With the strong support of the e-commerce platform (Ali Research Institute), local governments, and e-commerce associations, a total of 400 questionnaires were distributed, 350 were collected, and the collection rate was 87.5%. After eliminating the questionnaires that did not understand the cooperative relationship, had incomplete information, or had obvious problems, 263 valid questionnaires were finally obtained, with an effective collection rate of 75.14%, which met the requirements of the subsequent model empirical analysis. The descriptive analysis of the sample characteristics showed that the sample distribution was reasonable and representative. The specific statistical results are shown in Table 3.

Based on the statistical survey results, the following conclusions can be drawn: The typical age of rural e-commerce practitioners is between 20 and 40 years old, which is related to the use of the Internet. Since there are certain barriers to entry in Taobao store operations, most managers have a high school degree or above. Although Taobao Village has been in operation for about fifteen years, most entrepreneurs have only opened online stores in the past 5 to 15 years, and they started inter-enterprise cooperation after the development of rural e-commerce.

Although the proportion of raw material suppliers and processing/manufacturing enterprises is relatively low, this distribution reflects the actual structure of rural e-commerce supply chains in “Taobao Villages,” where downstream e-commerce retailers and logistics providers serve as core entities. These villages typically exhibit “production–sales integration” in a family-based operational model, with farmers naturally assuming dual roles as both product suppliers and online retailers (AliResearch, 2019; Mei et al., 2020). Moreover, the sample spans “Taobao Villages” across different regions and developmental models, encompassing key actors such as suppliers, processing enterprises, e-commerce platforms, government agencies, and related service providers. This diversity ensures sufficient structural representation, thereby enhancing the external generalizability of the research findings.

This study evaluated the measurement model through three key aspects: internal consistency, indicator reliability, and convergent validity (Hair et al., 2020). The main results are presented in Table 3. First, item reliability was assessed using factor loading, with a recommended threshold of 0.7, indicating that the construct explained at least 50% of the variance and thus ensuring item reliability (Hair et al., 2019). Second, Cronbach’s alpha and composite reliability (CR) were employed to evaluate internal consistency. The results demonstrated that both Cronbach’s alpha and CR for all constructs exceeded the 0.7 threshold, meeting the required reliability standards (Hair et al., 2019). Additionally, convergent validity was assessed using the average variance extracted (AVE). The AVE values for all constructs surpassed the 0.50 threshold, indicating strong convergent validity of the model (Hair et al., 2019). Although the Average Variance Extracted (AVE) primarily assesses convergent validity, its square root is also used to evaluate discriminant validity between constructs according to the Fornell–Larcker criterion (Al-Omoush et al., 2023; Fornell and Larcker, 1981). Furthermore, the model exhibited good discriminant validity, as all factor loadings for their respective dimensions were higher than those for other dimensions, thereby further validating the measurement model.

In Table 4, we assessed the discriminant validity of each construct in the model using the Fornell-Larcker criterion. This criterion evaluates discriminant validity by comparing the average variance extracted (AVE) of each construct with its squared correlation coefficients with other constructs (Fornell and Larcker, 1981). The diagonal values in the table, highlighted in bold, represent the square root of the AVE for each construct and are contrasted with the correlations of the remaining constructs. According to this criterion, the square root of the AVE should exceed the correlation with other constructs, indicating significant differences among them. Thus, the model is considered to exhibit good discriminant validity. Furthermore, following the standard established by Chin (1998), the AVE value must exceed 0.5 to ensure convergent validity in reflective models. Additionally, according to Fornell and Larcker (1981), the square root of the AVE must be higher than the correlations between the construct and other latent variables, further reinforcing the discriminant validity of the model.

In the assessment of the measurement model, we further verified discriminant validity by employing the heterotrait-monotrait ratio (HTMT) as a supplementary evaluation method alongside the Fornell-Larcker criterion. Table 5 presents the HTMT results, which measure the correlation between different constructs, specifically assessing the average correlation between each construct and its items (Hair et al., 2019; Henseler et al., 2015). All HTMT values in this study were below the threshold of 0.85, indicating that the model exhibits good discriminant validity. The values reported in Table 5 align with literature recommendations (Hair et al., 2019; Henseler et al., 2015), further confirming that the constructs can be effectively distinguished statistically.

In evaluating the structural model, we employed the Stone-Geisser Q² test (Mitrega et al., 2017; Stone, 1974) to assess the model’s predictive relevance. This test calculates samples iteratively using the Monte Carlo algorithm (Ringle et al., 2015). A Q² value greater than zero indicates that the model possesses predictive capability (Hair et al., 2019). Specifically, the Q² value assesses the predictive relevance of the model for a particular dependent variable; a Q² greater than zero signifies predictive relevance, whereas a value less than or equal to zero indicates a lack of predictive relevance.

In this study, the overall model achieved a Q² value of 0.306, which aligns with established standards in the literature (Hair et al., 2019). A higher Q² value reflects stronger predictive ability. Additionally, the explained variance for the endogenous variables was as follows: supply chain collaboration (SCC, 0.456) and supply chain performance (SCP, 0.400). Table 6 presents the specific values of Q² and R², further elucidating the model’s explanatory and predictive power.

The results of the PLS path analysis for the research model are illustrated in Figure 3. The analysis reveals that digital empowerment accounts for 47.0% of the variance in supply chain collaboration. Furthermore, digital empowerment and supply chain collaboration jointly explain 40.9% of the variance in supply chain performance.

In Table 7, the standardized path coefficients of the hypotheses (H1–H4) are listed. The findings indicate that structural empowerment has a significant positive effect on both supply chain collaboration (H1a; β = 0.216, p = 0.001) and supply chain performance (H1b; β = 0.160, p = 0.016), with corresponding t-values exceeding 2. Trust empowerment significantly influences supply chain collaboration (H2a; β = 0.193, p = 0.002) and supply chain performance (H2b; β = 0.220, p = 0.001), with t-values greater than 3. Resource empowerment also exerts a significant impact on supply chain collaboration (H3a; β = 0.263, p = 0.000) and supply chain performance (H3b; β = 0.154, p = 0.015), both with t-values exceeding 2. In addition, supply chain collaboration significantly contributes to supply chain performance (H4; β = 0.288, p = 0.000), with a t-value above 3.

Following the procedure outlined by Preacher and Hayes (2008), the mediating role of supply chain collaboration was further assessed. The analysis confirms that supply chain collaboration significantly mediates the relationships between structural empowerment, trust empowerment, resource empowerment, and supply chain performance, thereby supporting hypotheses H5a, H5b, and H5c. The corresponding indirect effects are β = 0.062 (p = 0.017), β = 0.056 (p = 0.027), and β = 0.076 (p = 0.008), respectively, with all t-values greater than 2.

Moreover, the results demonstrate that policy support significantly moderates the relationships between structural empowerment and supply chain collaboration (H6a; β = 0.131, p = 0.025) and between resource empowerment and supply chain collaboration (H6c; β = 0.160, p = 0.006), with t-values exceeding 2 in both cases. However, the moderating effect of policy support between trust empowerment and supply chain collaboration is not significant (H6b; β = 0.016, p = 0.796), as the p-value exceeds 0.05 and the t-value is below 2. Detailed results are presented in Table 8.

This study develops and validates a theoretical model linking digital empowerment, collaboration mechanisms, and performance improvement, using “Taobao Villages” in Zhejiang Province as the empirical context. It elucidates how digital empowerment enhances supply chain collaboration through multi-dimensional empowerment mechanisms, thereby ultimately improving the performance of rural supply chains. The findings demonstrate that collaboration mechanisms serve as key mediators in this process. Furthermore, policy support strengthens structural and resource empowerment, although its moderating effect on trust empowerment appears limited. This research contributes theoretically by the advancing understanding of how digitalization enhances rural supply chain competitiveness and sustainability. Practically, it offers valuable guidance for rural managers and policymakers in designing digital strategies, strengthening collaborative mechanisms, and optimizing resource allocation to achieve improved supply chain performance.

First, digital empowerment significantly fosters supply chain collaboration and improves rural supply chain performance, consistent with prior research (Jiang et al., 2023; Li Z. et al., 2022; Li Z. B. et al., 2022). Structural empowerment enhances information-sharing infrastructure; trust empowerment strengthens cooperative intentions; and resource empowerment improves the efficiency of factor acquisition. These mechanisms broaden upstream and downstream collaboration, providing rural enterprises with advantages in customer responsiveness, cost control, and relationship management (Zhang et al., 2023). Thus, digital empowerment enhances collaborative capabilities by reducing information asymmetry and transaction friction, ultimately leading to stable improvements in supply chain performance.

Second, supply chain collaboration plays a significant mediating role between digital empowerment and rural supply chain performance. This finding underscores that collaborative mechanisms are critical for translating digital capabilities into measurable performance outcomes, aligning with studies on e-commerce cluster models (Yue, 2022). In “Taobao Villages,” digital infrastructure enables dispersed rural supply entities to establish stable collaborative networks, minimizing redundant investments and improving resource utilization. The effectiveness of these collaborative mechanisms explains why the influence of digital empowerment on performance emerges indirectly, gradually materializing through the collaborative process rather than instantaneously.

Finally, policy support significantly moderates certain empowerment pathways, though its influence on trust empowerment is limited. Results indicate that policy support strengthens structural and resource-based empowerment pathways, consistent with previous studies (Ramjaun et al., 2024). Government initiatives, including training, infrastructure investment, and policy incentives, provide exogenous capabilities for resource-constrained rural enterprises, thereby enhancing their collaborative engagement. However, trust empowerment primarily arises from long-term interactions and endogenous relationships among enterprises; consequently, policy support has only an indirect effect, and its moderating role is not significant. This suggests that the external institutional environment is more likely to influence structural and resource-based empowerment than to replace the endogenous trust-building mechanisms among rural enterprises.

This study makes several theoretical contributions to the field of rural e-commerce supply chain management (SCM). First, it addresses a significant gap in the literature by focusing on supply chain collaboration (SCC) in rural contexts, an area that has received limited scholarly attention. Existing studies often overlook the distinct characteristics and challenges of rural supply chains, particularly under the pressures of sustainable competition (Duan et al., 2022). By conducting empirical research in this setting, the study highlights the pivotal role of supply chain collaboration in rural environments and extends the applicability of SCM theory. The findings not only offer practical insights for developing countries with similar rural supply chain structures but also expose the fragmented and underdeveloped nature of SCM in these regions (Ramirez et al., 2021).

Second, this study contributes to the extension of dynamic capability theory and institutional theory by proposing an explanatory framework of “digital empowerment–synergy mechanism–performance improvement.” It elucidates how the external institutional environment moderates the development of dynamic capabilities and the process of supply chain collaboration in rural enterprises through digital empowerment. Digital empowerment enhances rural enterprises’ abilities to capture production data, distribute benefits more equitably, and adopt new technologies across three dimensions: structural, trust-based, and resource-based empowerment. These improvements, in turn, strengthen supply chain synergy. Additionally, policy support, as an external moderating factor, further enhances supply chain performance by facilitating the synergy enabled by digital empowerment. This theoretical framework thus provides a robust foundation for understanding how rural enterprises can build dynamic capabilities and improve supply chain collaboration in the context of digital transformation.

First, implications for rural enterprise managers are discussed. The findings indicate that digital empowerment significantly enhances supply chain collaboration and performance, suggesting that rural enterprises should approach digitalization as a gradual and staged strategic process rather than a one-off investment. When promoting technologies such as high-speed networks, cloud computing, the Internet of Things, and data analytics, managers should carefully assess implementation costs, infrastructure readiness, and the alignment with existing digital skills, and adopt phased implementation strategies aligned with firm size and development stage. In contexts characterized by fragmented processes and resource constraints, digital tools should not aim for comprehensive technological substitution. Instead, they should be leveraged to incrementally improve information accessibility, strengthen inter-firm collaboration, and enhance operational visibility, thereby enabling steady improvements in rural supply chain performance.

Second, implications for rural supply chain practitioners and platform operators. Given that supply chain collaboration is central to performance gains driven by digital empowerment, practitioners should proactively strengthen coordination across nodes—for example, by sharing demand information and synchronizing inventory and logistics—to reduce operational uncertainty and enhance value co-creation efficiency. Platforms, as digital ecosystem hubs, should offer low-barrier digital tools, trust-building mechanisms, and collaborative systems that enable rural stakeholders to seamlessly integrate into digital supply chains and achieve performance improvements.

Finally, implications for policymakers. The results suggest that policy support reinforces the effects of structural and resource empowerment, underscoring the critical role of the institutional environment in promoting rural digitalization. Governments should continue investing in digital and logistics infrastructure and reduce the costs of digital transformation for enterprises through subsidies, tax incentives, and targeted training programs. Simultaneously, improving data governance and security mechanisms can enhance stakeholder trust in digital platforms. A supportive policy environment can effectively alleviate resource and capability constraints for rural enterprises, thereby systematically strengthening supply chain collaboration and performance.

First, this study is based on the context of “Taobao Villages” in Zhejiang Province, China, where the digital ecosystem and policy support mechanisms exhibit distinctive local characteristics. This may limit the generalizability of the findings to other institutional and cultural contexts. In particular, the mechanisms of policy support depend on the development stage of rural supply chain digitalization and the governmental role in China. In other countries or regions, such as Southeast Asia, South Asia, or Latin America, policy design, implementation logic, and social perceptions may differ. Future research should conduct cross-contextual validation across different countries and rural digitalization models to clarify boundary conditions.

Second, this study employs cross-sectional data, which cannot capture the dynamic interactions among digital empowerment, collaboration mechanisms, and rural supply chain performance. Given the ongoing adjustments in policy environments and digital infrastructure, the pathways of influence may vary over time. Future studies could adopt longitudinal designs or multi-wave data to capture the temporal evolution of policy interventions and digital collaboration capabilities.

Finally, this research focuses on digital empowerment, collaboration mechanisms, and policy support, but does not incorporate other potential influencing factors, such as relational governance, institutional voids, organizational culture (Mei et al., 2020), leadership (Zang et al., 2023), or resource sharing (Ye et al., 2020). Structural elements of rural supply chains and socio-cultural forces, such as community norms, also shape trust and collaborative mechanisms. Future studies should integrate a broader set of institutional and organizational behavior variables to provide a more comprehensive explanation of the mechanisms underlying rural supply chain collaboration and performance.