Configurational paths to enhancing digital innovation capability in food supply chain SMEs

Abstract

Amidst the dual transition toward digitalization and sustainability, how small and medium-sized enterprises (SMEs) in the food supply chain overcome resource constraints to enhance digital innovation capability—and thereby contribute to sustainable food system outcomes such as food waste reduction and cold chain carbon mitigation—has become a critical research agenda. This study adopts a mixed-methods approach, integrating qualitative, and quantitative research. First, Grounded Theory is employed to conduct three-stage coding on interview data, identifying six core influencing factors: technical talent cultivation (TP), management characteristics (MC), digital transformation (DT), technology market environment (TM), financing environment (FE), and innovation support policies (IS). Based on these findings, a “resource-capability-environment” synergetic theoretical framework embedded within sustainable food systems is developed. Subsequently, using primary data from questionnaires and interviews, fuzzy-set Qualitative Comparative Analysis (fsQCA) is applied to investigate the configurational mechanisms of multiple concurrent antecedent conditions on digital innovation capability. The results indicate that: (1) no single factor constitutes a necessary condition for achieving high digital innovation capability; (2) three effective configurational pathways drive innovation enhancement, demonstrating the principle of equifinality; (3) innovation support policy (IS) serves as a core condition across all high-innovation pathways, while the absence of digital transformation (~DT) is the primary barrier to high innovation capacity. These configurational findings carry direct implications for sustainable food systems: the identified innovation pathways, by embedding digital technologies such as real-time traceability, intelligent inventory management, and cold chain optimization, not only enhance enterprise competitiveness but also enable reductions in food loss, improvements in resource efficiency, and decreases in supply chain carbon emissions. This study unveils the causal asymmetric mechanisms driving digital innovation from a configurational perspective, providing precise strategic guidance for SMEs in the food supply chain to achieve sustainable, high-quality development.

1 Introduction

The contemporary food supply chain operates at the critical nexus of digitalization and sustainability. In this complex landscape, small, and medium-sized enterprises (SMEs)—which constitute the backbone of the global food network—face intense competitive and institutional pressures. Large enterprises increasingly leverage their resource advantages to adopt advanced digital tools, such as intelligent cold chain management and real-time traceability systems, thereby exacerbating the digital divide and threatening SME resilience (Hassoun et al., 2024; Nambisan et al., 2017). Concurrently, global sustainability mandates, including the UN Sustainable Development Goals (specifically SDG 12) and the EU's Farm to Fork Strategy, demand rigorous waste reduction and carbon mitigation from all supply chain actors (FAO, 2019). Consequently, SMEs are confronted with a dual imperative: they must foster digital innovation to maintain competitive advantage while simultaneously aligning these technological advancements with sustainable food system objectives.

Despite the critical role of digital innovation in addressing these dual challenges, the extant literature predominantly relies on variance-based, single-factor analyses—such as the isolated effects of policy subsidies or technical talent on technology adoption. This reductionist approach fundamentally overlooks the causal complexity and configurational interactions among multiple antecedent conditions that jointly drive digital innovation capability (Di Paola et al., 2025). Digital transformation in SMEs is rarely the result of a single optimal driver; rather, it emerges from the synergistic interplay of internal capabilities and external environments (Shao et al., 2025).

To bridge this theoretical gap, this study integrates the Resource-Based View (RBV) with Institutional Theory to unpack the configurational mechanisms of digital innovation. While RBV highlights internal heterogeneous resources—such as managerial cognition and technical talent—as the bedrock of competitive advantage (Helfat et al., 2023), Institutional Theory contextualizes these resources within broader regulatory and market environments that dictate organizational legitimacy (Scott, 2014). Building on this dual theoretical lens, we first employ Grounded Theory through inductive case analysis to identify the core antecedent conditions influencing SME digitalization. Subsequently, we apply fuzzy-set Qualitative Comparative Analysis (fsQCA) to map the multiple, equifinal pathways through which these conditions logically combine to enhance digital innovation capability.

Ultimately, by illuminating how configurations of internal and external factors drive digital innovation, this research also advances our understanding of broader food supply chain sustainability. Whether through optimized inventory systems that mitigate food waste or smart logistics that reduce carbon footprints, the configurational pathways identified herein offer critical theoretical insights and actionable strategies for SMEs navigating the intersection of technological advancement and sustainable development (Essien et al., 2024; Annosi et al., 2024).

2 Literature review

2.1 Theoretical foundation

Understanding the digital innovation capability of small and medium-sized enterprises (SMEs) in the food supply chain requires an integrated theoretical perspective that accounts for both internal resource heterogeneity and external institutional conditions. This study draws primarily on the Resource-Based View (RBV) and Institutional Theory, to construct its analytical framework.

The Resource-Based View provides the foundational lens for explaining inter-firm differences in innovation capability. According to RBV, firms achieve sustained competitive advantage when they possess resources and capabilities that are valuable, rare, inimitable, and organizationally embedded (Helfat et al., 2023). In the context of food supply chain SMEs, technical talent cultivation reflects the accumulation of specialized human capital, management characteristics represent strategic cognition and resource orchestration capability, and digital transformation captures the firm's ability to deploy and reconfigure digital resources for innovation purposes. From this perspective, variation in digital innovation capability across SMEs can be attributed, at least in part, to differences in their internal resource endowments and capability-building processes. Furthermore, the dynamic capability extension of RBV suggests that competitive advantage arises not from possessing a fixed bundle of superior resources but from the capacity to sense opportunities and reconfigure resources in response to changing environments (Saeedikiya et al., 2024), which is particularly relevant to SMEs operating in volatile food supply chains.

However, internal resources alone cannot fully explain the formation of digital innovation capability. Institutional Theory posits that organizational behavior is fundamentally shaped by external institutional pressures and support mechanisms, including regulatory guidance, market norms, and resource access structures (Scott, 2014). In this study, innovation support policies, financing environment, and technology market environment represent critical external institutional conditions that influence firms' innovation incentives, the legitimacy of their innovation activities, and their access to complementary resources. Government policies, for instance, not only lower the direct cost of innovation through subsidies and tax incentives but also signal societal endorsement of innovation efforts, thereby strengthening firms' motivation and confidence to pursue digital transformation. Thus, digital innovation capability should be understood as the joint outcome of internal resource-capability endowments and external institutional conditions, rather than the product of either dimension in isolation.

The integrated RBV–Institutional Theory framework is also particularly suitable for the configurational analytical approach adopted in this study: RBV directs attention to how different combinations of internal resources generate equifinal innovation outcomes, while Institutional Theory explains why certain external conditions function as necessary enabling or constraining forces across configurations. Building on this dual-theoretical foundation, the following subsections review the specific internal resource factors and external institutional factors that have been identified in the existing literature as relevant to the digital innovation capability of food supply chain SMEs.

2.2 Internal resource factors affecting digital innovation capability

Human resources constitute one of the most critical internal drivers of enterprise innovation. People are the direct source of innovation within a firm, and their professional skills, innovative thinking, and work enthusiasm directly shape innovation outcomes. Within the RBV framework, human capital represents a valuable and difficult-to-imitate resource that underpins a firm's capacity for knowledge creation and technological development (Helfat et al., 2023; Ho et al., 2024). In food supply chain SMEs, two categories of human resources are especially relevant. Management talents control the strategic direction of the enterprise and mobilize organizational members toward shared goals; their entrepreneurial orientation, risk tolerance, and strategic vision determine whether and how intensively a firm pursues digital innovation. Technical talents, on the other hand, introduce new technological concepts, drive new product development, and optimize production processes. The implementation of emerging digital technologies—such as the Internet of Things, big data analytics, and cloud computing—cannot be separated from high-quality technical personnel who possess both domain expertise and digital literacy. Accordingly, food supply chain SMEs should leverage internal reward mechanisms to attract and retain outstanding talents, ensuring that employees' innovative contributions are recognized and their creativity is continuously stimulated (Meurs et al., 2025; Zahoor et al., 2023).

Beyond human capital, the firm's digital transformation level represents another core internal resource. Digital transformation reflects the extent to which a firm has embedded digital technologies into its operational and strategic processes, and it constitutes a foundational capability for digital innovation. Industry 4.0 technologies have played a transformative role in the food industry; big data analytics and IoT-enabled monitoring systems can track multiple dimensions of enterprise operations—including production quality, cold chain integrity, and inventory dynamics—thereby substantially improving the efficiency of innovation and operational management (Hassoun et al., 2024). Furthermore, digital traceability and data-sharing mechanisms enable food supply chain enterprises to enhance transparency, strengthen consumer trust, and implement more precise demand-responsive strategies, thus aligning innovation activities with market expectations (Hassoun et al., 2023a,b). From an RBV perspective, digital transformation is particularly significant because it not only constitutes a resource in its own right but also enhances the firm's capacity to leverage other resources—such as human capital and market intelligence—more effectively (Merín-Rodrigáñez et al., 2024; Romero and Mammadov, 2025). In this sense, digitalization functions as both a standalone capability and an enabling mechanism for broader resource reconfiguration.

2.3 External institutional factors affecting digital innovation capability

While internal resources provide the foundation for innovation, external institutional conditions shape the environment in which innovation activities unfold. Institutional Theory directs attention to three categories of external factors that are particularly relevant to the digital innovation capability of food supply chain SMEs: market demand and technology environment, financing environment, and government innovation support policies.

Market demand serves as the primary external driving force for enterprise growth and innovation. Consumer demand for organic, functional, and traceable food products has prompted many food supply chain SMEs to intensify their digital innovation and research and development efforts, striving to achieve transparency across production, logistics, and distribution processes. Simultaneously, the rapid evolution of digital technologies and intensifying market competition compel enterprises to engage in continuous innovation to sustain their competitiveness (Wellalage and Fernandez, 2019). The emergence of technologies such as the Internet of Things, big data, and cloud computing has created new opportunities for food supply chain operations—sensors enable real-time monitoring of the entire production process, while data analytics supports precise demand forecasting and marketing. Furthermore, a well-functioning digital innovation ecosystem facilitates collaboration among universities, enterprises, and research institutes, promoting knowledge sharing and the diffusion of new technologies. From an institutional perspective, the technology market environment establishes the normative and cognitive frameworks within which firms evaluate, adopt, and implement digital innovations (Scott, 2014).

The financing environment constitutes another critical institutional condition. Access to adequate financial resources determines whether SMEs can invest in the often costly and uncertain process of digital innovation. Favorable financing conditions—including accessible venture capital, bank lending, and equity markets—lower the financial barriers to innovation and enable firms to sustain long-term technology development projects. Conversely, constrained financing environments force SMEs to prioritize short-term operational needs over innovation investments, thereby suppressing their digital innovation potential.

Government innovation support policies play a particularly prominent institutional role. Tax incentives, fiscal subsidies, and dedicated research and development funding reduce innovation costs and enhance firms' willingness to undertake innovation activities (Cui et al., 2025). Industry-guiding policies help enterprises optimize resource allocation and promote collaborative innovation across the upstream and downstream segments of the food supply chain. Several countries have also established specialized innovation funds that channel social resources toward food supply chain innovation projects, accelerating technological development. From the Institutional Theory perspective, government policies function not only as material support mechanisms but also as legitimacy signals: when the state endorses and subsidizes digital innovation, it validates the strategic direction of innovating firms and reduces the perceived risk associated with innovation investments (Cui et al., 2025; Wang et al., 2023). This legitimizing function is especially important for SMEs, which often lack the reputational capital and market power to independently justify large-scale innovation commitments to internal and external stakeholders.

2.4 Digital innovation as an enabler of sustainable food systems

The preceding subsections have examined the internal and external factors that shape digital innovation capability in food supply chain SMEs. However, an emerging body of literature highlights that digital innovation in food supply chains is not merely a matter of competitive advantage but also a critical pathway toward sustainability outcomes. This subsection reviews the intersection of digital innovation and sustainable food systems to establish the broader contextual relevance of the present study.

Digital technologies have been increasingly recognized as key enablers of sustainability transitions within food supply chains. The application of Internet of Things (IoT) sensors and blockchain-based traceability systems enables real-time monitoring of food quality and provenance across the entire supply chain, thereby enhancing transparency, reducing information asymmetry, and strengthening consumer trust in sustainable production practices (Hassoun et al., 2023a,b; Hema et al., 2024). Big data analytics and artificial intelligence facilitate demand forecasting with substantially improved accuracy, which directly reduces overproduction and food waste—a persistent challenge in global food systems where approximately one-third of all food produced is lost or wasted (FAO, 2019). Furthermore, digital optimization of cold chain logistics has been shown to reduce both energy consumption and carbon emissions by enabling precise temperature control and route optimization (Hassoun et al., 2024).

Recent empirical evidence further supports the link between digital innovation and sustainable food supply chain performance. Enterprise digital transformation can significantly improve environmental, social, and governance (ESG) performance, and this positive effect is particularly pronounced in environmentally sensitive industries such as food processing and distribution (Liu et al., 2024). In the specific context of food supply chains, digital traceability systems have been associated with reductions in product recall costs and improvements in food safety compliance, while data-sharing mechanisms between upstream and downstream partners have been linked to measurable decreases in supply chain waste (Essien et al., 2024).

Despite these promising findings, the existing literature has largely treated digital innovation and food supply chain sustainability as separate research streams. Studies on digital innovation in SMEs tend to focus on competitive performance outcomes without systematically connecting innovation capability to sustainability indicators such as waste reduction, carbon mitigation, or resource efficiency. Conversely, research on sustainable food systems frequently acknowledges the role of digital technologies but rarely investigates the configurational conditions under which SMEs develop the innovation capability necessary to deploy such technologies effectively. The present study addresses this gap by first identifying the configurational pathways that drive digital innovation capability in food supply chain SMEs and then discussing how these pathways enable sustainability-oriented outcomes, thereby bridging the digital innovation and sustainable food systems literatures from a configurational perspective.

2.5 Research gaps and the need for a configurational perspective

The preceding subsections have reviewed the individual internal and external factors relevant to the digital innovation capability of food supply chain SMEs. While this body of research offers valuable insights, it is predominantly based on regression-based approaches that emphasize net, additive, and symmetric effects of individual factors on innovation outcomes, thereby making it difficult to capture the configurational and interdependent nature of innovation processes (Misangyi et al., 2017). Three specific gaps warrant attention. First, the prevailing analytical strategy overlooks the interactive logic of resource complementarity among human resources, digital capabilities, and institutional conditions, leaving the mechanisms of multi-factor synergy and dynamic coupling underexplored (Di Paola et al., 2025). In reality, innovation is inherently a combinatorial process: the effect of any single resource or institutional condition depends on the presence or absence of other factors, and different factor combinations may produce equifinal outcomes (Fiss, 2011). Second, the existing literature has not adequately addressed the causal asymmetry inherent in innovation processes—that is, the possibility that the factors driving high innovation capability and those explaining the absence of high innovation capability may operate through fundamentally different causal mechanisms (Fiss, 2011; Misangyi et al., 2017). Understanding such asymmetry is essential for designing targeted intervention strategies for different types of enterprises. Third, although the resource-based view and institutional theory both imply that resources and institutional conditions operate in combination, empirical studies have rarely adopted analytical methods capable of capturing configurational causation, equifinality, and causal asymmetry simultaneously (Misangyi et al., 2017). To address these gaps, the present study employs fuzzy-set Qualitative Comparative Analysis (fsQCA) to explore the various conditional configurations and complex interaction mechanisms that affect the digital innovation capability of food supply chain SMEs. This methodological choice is particularly suitable for digital innovation research, where heterogeneous combinations of organizational and environmental conditions often shape innovation outcomes (Nambisan et al., 2017). Specifically, this study aims to: (1) identify key configurational models involving internal resources (technical talent cultivation, management characteristics, digital transformation) and external institutional conditions (technology market environment, financing environment, innovation support policies); (2) verify equifinal pathways and necessary conditions for multi-factor synergistic innovation; and (3) reveal the logic of asymmetric causation and factor substitution, providing a more comprehensive and configurational theoretical basis for the formulation of enterprise innovation strategies.

3 Research design

3.1 Research methods

This study adopts a sequential mixed-methods design comprising three stages: (1) Grounded Theory coding to identify candidate influencing factors from qualitative data; (2) an expert-based gray relational analysis to quantitatively assess the relative importance of each candidate factor and determine the final set of antecedent conditions; and (3) fsQCA to investigate the configurational mechanisms among the retained factors.

Grounded theory is an inductive qualitative approach designed to generate theory from empirical data rather than from a priori hypotheses (Glaser and Strauss, 1967; Charmaz, 2014). In management and organization research, grounded, inductive analysis has also been advanced through systematic coding procedures that enhance qualitative rigor and theoretical transparency (Gioia et al., 2013). In addition, case-oriented theory building often relies on iterative comparison, within-case analysis, and cross-case pattern searching to develop theoretically meaningful constructs and relationships (Eisenhardt, 1989; Eisenhardt et al., 2016). Drawing on this logic, the present study adopts a case-oriented analytical approach and uses grounded coding techniques to analyze the collected raw data. By comparing and abstracting similar cases, the study seeks to uncover deeper logical relationships beneath the surface of empirical phenomena and to identify the theoretical core of these phenomena.

Consistent with the grounded theory tradition, this study minimizes strong prior assumptions and derives research questions, concepts, and theoretical insights as much as possible from first-hand interview materials and the iterative interpretation of empirical evidence (Corbin and Strauss, 2015). Therefore, this study primarily uses recorded interviews with researchers to obtain first-hand information, which is then analyzed with NVivo. Following an iterative coding logic, the data are organized from first-order concepts to higher-order categories and aggregate dimensions (Gioia et al., 2013).

3.2 Open coding

Open coding is a conceptual process aimed at extracting and summarizing information from interviews and literature on improving the innovation capabilities of small and medium-sized enterprises in the food supply chain. In the process of conducting research, this article mainly adopts a semi-structured interview mode and selects employees from small and medium-sized enterprises engaged in the food supply chain. A total of 47 original sentences were obtained through interviews. Next, accuracy is improved through manual decoding and consistency analysis software. Classify the selected statements and ultimately extract 22 categories, selecting 2-3 representative original data statements and corresponding initial concepts for each category. As shown in Table 1.

3.3 Principal axis coding

Principal-axis style coding is the process of secondary coding of initial categories and combining initial categories with internal associations into the same principal category. In this study, the open-ended coding in the previous paper was derived and summarized to organize 9 main categories, as shown in Table 2.

3.4 Selective coding

Selective coding is the process of deepening the categories on the basis of principal axis coding. Through in-depth analysis and synthesis of the 9 main categories, this paper finally identified three overall core categories (see Table 3).

3.5 Gray relational analysis for factor selection

To determine which of the 9 candidate factors identified through grounded theory coding should be retained for the subsequent configurational analysis, this study conducted a gray relational analysis (Ran and Wang, 2015). Five domain experts with extensive experience in food supply chain SME innovation research were invited to evaluate the pairwise influence relationships among the nine candidate factors and their associations with firms' digital innovation capability. Based on these expert assessments, a fuzzy cognitive map was constructed and iteratively computed to estimate the gray relational degree between each factor and digital innovation capability (Bakhtavar et al., 2021). The results showed that six factors—technical talent cultivation (C1), management characteristics (C2), digital transformation (C3), technology market environment (C7), financing environment (C8), and innovation support policies (C9)—exhibited substantially higher gray relational degrees (ranging from 0.7425 to 0.9667) than the remaining three factors (entrepreneurial experience: 0.4001; corporate characteristics: 0.5287; industry chain division of labor: 0.5630). Accordingly, these six factors, which demonstrated the strongest empirical relevance, were retained as the antecedent conditions for the subsequent configurational analysis.

Building on the six retained factors, the subsequent fsQCA addresses two central questions: first, whether any single factor constitutes a necessary condition for high digital innovation capability; and second, how different combinations of these factors form configurational pathways leading to high or non-high innovation outcomes (Fiss, 2011; Misangyi et al., 2017). The overall theoretical framework is presented in Figure 1.

Figure 1

Drawing on the integrated perspective of the resource-based view and institutional theory, this study classifies the antecedent conditions into two categories: internal strategic resources/capabilities and external institutional conditions. Specifically, technical talent cultivation (TP), management characteristics (MC), and digital transformation (DT) represent firms' internal resource-capability endowments. TP reflects the stock of specialized human capital required to support digital innovation, MC captures managerial cognition as well as the ability to mobilize and orchestrate organizational resources, and DT represents the firm's capability to reconfigure processes and technologies in response to digital opportunities (Romero and Mammadov, 2025; Zahoor et al., 2023). In contrast, technology market environment (TM), financing environment (FE), and innovation support policies (IS) represent the external institutional context that shapes firms' legitimacy, access to critical resources, and incentives for innovation (Peng et al., 2009). Accordingly, digital innovation capability is conceptualized not as the linear consequence of any single factor, but as a configurational outcome arising from the joint effects of internal resources/capabilities and external institutional conditions (Fiss, 2011; Misangyi et al., 2017).

3.6 Reliability and validity analysis

This study focuses on senior managers and employees of small and medium-sized enterprises (SMEs) in the food supply chain. Questionnaire data were collected from 15 food supply chain-related firms in China, covering the food logistics, food processing, and food retail sectors. Drawing on configurational theory, this study develops an analytical framework to examine the factors influencing the improvement of SMEs' digital innovation capability. The questionnaire consisted of 35 items and included two sections: an introduction and the variable measurement scales. The variables covered human resources, firms' digital transformation, and environmental conditions, and were measured using a seven-point Likert scale.

A total of 419 questionnaires were collected, of which 382 were valid, yielding an effective response rate of 91.2%. To assess the quality of the measurement scales, reliability and validity tests were conducted using SPSS 26.0. The results showed that the Cronbach's alpha coefficients for the six antecedent conditions and the outcome variable—technical talent cultivation, management characteristics, digital transformation, technology market environment, financing environment, innovation support policies, and digital innovation capability—were all above 0.70, indicating acceptable internal consistency reliability (Hair et al., 2022).

In addition, exploratory factor analysis was conducted to assess construct validity. The results showed that the lowest KMO value was 0.796, composite reliability (CR) exceeded 0.80 for all constructs, and the average variance extracted (AVE) values were all above 0.50, indicating satisfactory reliability and convergent validity (Hair et al., 2024; Fornell and Larcker, 1981). Moreover, all item factor loadings exceeded 0.60, supporting acceptable indicator reliability and construct validity (Hair et al., 2022).

In terms of content validity, the measurement items were adapted from established scales in prior research and refined for the context of food supply chain SMEs following recommended scale development procedures. To further assess the appropriateness of the instrument, three experts in supply chain management and digital innovation were invited to evaluate the questionnaire items. The content validity index (CVI) was 0.92, which indicates a satisfactory level of content validity (DeVellis and Thorpe, 2022). The specific assessment results are reported in Table 4.

The model indicator of fit χ² is 1009.276, which is in line with the key value, the model indicator of df is 506, and χ²/df is 1.995, which is in the range between (1, 3), the CFI is 0.938, which is greater than 0.9, and the TLI indicator is 0.932, which is greater than 0.9, which are all in line with the suggested value of the key value, and the indicator of RMSEA is 0.051, and the SRMR indicator is 0.039, both less than 0.08, indicating that they meet the suggested values of the key values. The details are shown in Table 5.

By elaborating on the connotations of conditional variables and outcome variables in detail, this article conducted descriptive statistical analysis. In this process, the mean was used as an effective indicator to measure the central tendency of the sample data, helping us to gain a deeper understanding of the distribution of variables. The Likert seven point scale is the basis of the questionnaire data, with a median value of 4 for each option. If the interviewee's choice is higher than this median, it means that their satisfaction with the relevant issues is relatively high; On the contrary, if choosing below 4, it indicates relatively low satisfaction. In short, this scale quantifies the satisfaction level of respondents by comparing their choices with a median of 4. The mean values of the seven variables involved in this article generally exceed 5, showing an above average level, indicating a relatively positive attitude toward the survey questionnaire. At the same time, in order to further understand the distribution of the data, this article also calculated the standard deviation, which can reflect the degree of dispersion of the dataset and help to analyze the questionnaire data more comprehensively. Observation shows that the standard deviation of the seven variables in this article is relatively small, indicating that the data is close to the mean, and the relevant data on the improvement of innovation capabilities of “specialized, refined, unique, and new” small and medium-sized enterprises is relatively stable. Details are shown in Table 6:

4 Configurational analysis

Qualitative Comparative Analysis (QCA) is a configurational method that examines how combinations of causal conditions jointly produce an outcome, rather than estimating the net effect of isolated variables. Rooted in set theory and Boolean algebra, QCA is especially suitable for analyzing causal complexity, conjunctural causation, and equifinality in organizational research, and fsQCA further enables researchers to calibrate empirical variables into fuzzy-set membership scores for set-theoretic analysis (Fiss, 2011; Misangyi et al., 2017). This section first operationalizes the six antecedent conditions and the outcome variable, then calibrates them into fuzzy-set membership scores, and finally conducts necessity and sufficiency analyses to identify the configurational pathways driving digital innovation capability.

4.1 Variable measurement

Drawing on the integrated RBV–Institutional Theory framework, the six antecedent conditions are organized into three categories: internal human resource factors, enterprise digitalization level, and external environmental factors. Each variable is measured using a seven-point Likert scale adapted from established instruments (Hair et al., 2024; Zahoor et al., 2023; Shao et al., 2025), and the operationalization of each is detailed below.

4.1.1 Human resource factors

• (1) Technical Talent Cultivation (TP)

Within the RBV framework, human capital constitutes a valuable and difficult-to-imitate strategic resource that underpins firms' knowledge creation and innovation capability, making technical talent cultivation central to overcoming technological constraints and sustaining innovation in the supply chain (Subramaniam and Youndt, 2005). In the food supply chain context, SMEs often require cross-functional technical teams spanning production, processing, and logistics activities to address interdependent technical problems. Accordingly, internal mechanisms such as training, incentives, and career development can strengthen firms' technical human capital base and support the development of digital innovation capability.

• (2) Management Characteristics (MC)

Management characteristics refer to the extent to which top managers support innovation and possess the cognition, leadership, and decision-making capabilities needed to mobilize organizational resources for innovation. Prior research shows that top management and leadership behavior play an important role in enabling management innovation and organizational change, particularly through resource commitment, strategic direction, and the reduction of internal resistance to change (Vaccaro et al., 2012). Accordingly, managers with stronger innovation orientation, strategic foresight, and resource orchestration capability are more likely to support firms' digital innovation efforts.

4.1.2 Enterprise digitalization level

Digital transformation capability refers to the ability of an organization to use digital technologies to reconfigure business processes, support organizational adaptation, and enable innovation. From an RBV and dynamic capability perspective, digital transformation is important not only as a technological resource in itself but also as an enabling capability that enhances the firm's capacity to integrate, build, and reconfigure other resources in response to environmental change (Saeedikiya et al., 2024). By applying digital technologies such as artificial intelligence, blockchain, cloud computing, and big data, SMEs can improve information processing, support innovation decision-making, and enhance organizational flexibility and competitiveness.

4.1.3 Enterprise environmental factors

• (1) Technology Market Environment (TM)

The technology market environment shapes innovation by influencing firms' ability to access, exchange, and apply external technologies. Research on markets for technology suggests that more developed technology markets expand firms' strategic options by facilitating the acquisition, licensing, and commercialization of technological knowledge, thereby supporting knowledge diffusion and innovation activities (Sime et al., 2023). In this sense, a well-functioning technology market environment can improve the efficiency with which firms identify external opportunities, connect with knowledge providers, and translate technological developments into innovation outcomes.

• (2) Financing Environment (FE)

The financing environment refers to the conditions under which firms can obtain external financial resources for innovation-related investment. Prior research consistently shows that innovation activities in SMEs are especially vulnerable to financing constraints because innovation projects are risky, long-term, and often based on intangible assets, while improved access to external finance can facilitate product and process innovation (Wellalage and Fernandez, 2019; Santos and Cincera, 2022). Accordingly, more favorable financing environments can reduce the financial barriers to digital innovation and enable SMEs to sustain longer-term innovation efforts.

• (3) Innovation Support Policies (IS)

Innovation support policies refer to government measures designed to reduce the costs and risks of innovation and to create favorable institutional conditions for firms' innovative activities. From an institutional perspective, public policy can serve not only as a material support mechanism but also as a source of legitimacy for firms' innovation strategies (Cui et al., 2025). Existing research further indicates that policy instruments such as R&D subsidies and tax incentives can promote innovation activities, particularly where firms face financial constraints or where policy design is effective (Mateut, 2018; Mitchell et al., 2020). In addition, institutional arrangements related to intellectual property protection can strengthen firms' incentives to invest in innovation and patenting activities (Nguyen et al., 2023).

4.2 Variable calibration

Data for the seven variables—technical talent cultivation, management characteristics, digital transformation, technology market environment, financing environment, innovation support policies, and enterprise innovation capability—were obtained by measuring multiple questionnaire items. Before conducting fsQCA analysis, Excel's average function was used to compute the mean of the multiple measurement items for each variable. Subsequently, the data were calibrated to convert raw scores into fuzzy-set membership degrees, a prerequisite for Boolean operations in fsQCA. During the calibration process, reference was made to direct calibration methods in existing literature, and the actual distribution and specific circumstances of variables in the cases were comprehensively considered. The 25th, 50th, and 75th percentiles were selected as calibration anchors representing the states of non-membership, crossover point, and full membership, respectively. The Calibrate function was then used to accurately calibrate each variable. Details are shown in Table 7.

4.3 Necessity analysis

Necessary conditions are conditions that must be present for an outcome to occur. In fsQCA research, a condition is typically considered necessary when its consistency exceeds 0.90, while empirical relevance should also be assessed to avoid trivial necessity claims, for example by considering coverage alongside consistency (Pattyn et al., 2019; Schneider, 2018). The results show that the consistency scores of all individual conditions for both high and non-high enterprise innovation capability fall below the critical threshold of 0.90. This finding suggests that the improvement of innovation capability in food supply chain SMEs is characterized by causal complexity: no single factor—whether related to human resources, enterprise digitalization, or environmental conditions—constitutes a necessary condition on its own. Instead, innovation outcomes depend on the alignment and joint presence of multiple conditions. The detailed results are presented in Table 8.

4.4 Configuration analysis

To identify sufficient configurations, this study employed truth table analysis. Enterprise innovation capability was specified as the outcome, and the six antecedent conditions were entered into the analysis. Following best practices in fsQCA research, configurations with a raw consistency threshold of 0.80 and a PRI (Proportional Reduction in Inconsistency) threshold of 0.75 were retained for further analysis (Greckhamer et al., 2018). The configurational results are presented using the representation method that distinguishes between core and peripheral conditions within each solution (Fiss, 2011). The analysis identifies three configurations leading to high enterprise innovation capability and two configurations leading to non-high enterprise innovation capability. The overall solution consistency for the high-innovation configurations is 0.905, exceeding the commonly recommended threshold of 0.80, while the solution coverage is 0.758, indicating that these three pathways jointly explain 75.8% of the cases exhibiting high innovation capability. As shown in Table 9.

4.5 Analysis of results

The configurational analysis yielded three pathways leading to high enterprise innovation capacity (H1–H3) and two pathways leading to non-high enterprise innovation capacity (NH1–NH2). The overall solution consistency for the high innovation configurations is 0.905, exceeding the recommended threshold of 0.8, and the solution coverage is 0.758, indicating that these three pathways collectively explain approximately 75.8% of cases exhibiting high innovation capacity. For the non-high innovation configurations, the solution consistency is 0.898 and the solution coverage is 0.773. Notably, innovation support policy (IS) emerges as the sole core condition across all three high-innovation pathways, while the absence of digital transformation (~DT) constitutes the sole core condition in both non-high innovation pathways, revealing a pronounced asymmetric causal structure.

High Innovation Pathways:

• Configuration H1: TP ^* MC ^* DT ^* IS

This pathway demonstrates that when technical talent cultivation, management characteristics, digital transformation, and innovation support policies are simultaneously present, enterprises achieve high innovation capacity. Among these, innovation support policy (IS) serves as the core condition, while the remaining three function as peripheral conditions. This configuration exhibits the highest raw coverage (0.671) among the three pathways, indicating that it represents the most prevalent route to high innovation in the sample. The pathway suggests that robust government policy support provides the foundational impetus for innovation, while the concurrent presence of skilled technical personnel, forward-looking management teams, and digital transformation capabilities collectively furnishes the organizational capacity to translate policy opportunities into tangible innovation outcomes. The synergy between institutional support and internal capability building constitutes the most common innovation-enhancing mechanism among food supply chain SMEs.

• Configuration H2: MC ^* DT ^*~ TM ^* ~FE ^* IS

This pathway reveals that under conditions of an unfavorable technology market environment and a constrained financing environment, enterprises can still achieve high innovation capacity through the combination of strong management characteristics, digital transformation, and innovation support policies. Innovation support policy (IS) remains the core condition, with management characteristics and digital transformation as peripheral facilitating conditions and the absence of a favorable technology market and financing environment as peripheral contextual conditions. The unique coverage of this configuration is relatively modest (0.009), suggesting that it represents a comparatively rare but nonetheless viable innovation pathway. This finding carries important theoretical implications: it demonstrates that external resource constraints—such as limited market information accessibility and restricted financing channels—do not necessarily preclude innovation when enterprises possess strong internal governance capabilities and benefit from targeted policy support. Management teams with heightened innovation awareness can strategically leverage digital tools to overcome information asymmetries and circumvent traditional financing bottlenecks, thereby sustaining innovation momentum even in resource-scarce environments.

• Configuration H3: TP ^* DT ^*TM ^*FE ^*IS

This pathway indicates that when technical talent cultivation, digital transformation, a favorable technology market environment, a supportive financing environment, and innovation support policies co-occur, enterprises attain high innovation capacity. Innovation support policy (IS) again constitutes the core condition, with the other four variables serving as peripheral conditions. Compared with H1, this configuration substitutes management characteristics (MC) with technology market environment (TM) and financing environment (FE), suggesting an alternative mechanism wherein comprehensive external environmental support compensates for the centrality of management-level factors. Specifically, a well-functioning technology market facilitates access to cutting-edge technologies and reduces the costs associated with information asymmetry; a favorable financing environment ensures adequate capital availability for sustained R&D investment; and the cultivation of technical talent provides the human capital necessary to absorb and deploy new technologies. Under the umbrella of strong innovation support policies, these factors operate in concert to create an ecosystem conducive to continuous innovation among food supply chain SMEs. The raw coverage of 0.593 indicates that this pathway explains a substantial proportion of high-innovation cases, underscoring the practical importance of aligning policy support with broader environmental enablers.

Non-High Innovation Pathways:

• Configuration NH1: ~ TP ^* ~MC ^* ~DT ^* ~IS

This pathway demonstrates that the simultaneous absence of technical talent cultivation, management characteristics, digital transformation capability, and innovation support policies leads to non-high innovation capacity. The absence of digital transformation (~DT) serves as the core condition, while the remaining absent conditions function as peripheral factors. With a raw coverage of 0.750, this configuration accounts for the majority of non-high innovation cases. The finding suggests that when enterprises lack digital transformation capability as a foundational element and simultaneously suffer from deficiencies in human capital, managerial vision, and policy support, they become trapped in a low-innovation equilibrium from which escape is exceedingly difficult.

• Configuration NH2: ~ TP ^* ~DT ^* ~TM ^* ~FE ^* ~IS

This pathway reveals that the comprehensive absence of technical talent cultivation, digital transformation, technology market environment, financing environment, and innovation support policies produces non-high innovation capacity. The absence of digital transformation (~DT) again constitutes the core condition. Compared with NH1, this configuration additionally incorporates the absence of a favorable technology market and financing environment, representing a more severe form of resource deprivation. The unique coverage of 0.023 indicates that this extreme scenario is relatively uncommon, yet its identification serves as a cautionary reminder that enterprises embedded in comprehensively inhospitable environments face compounded barriers to innovation that individual interventions are unlikely to overcome.

4.6 Robustness test

To ensure the reliability of the findings, this study conducted a robustness test by adjusting the parameters, a widely adopted approach in fsQCA literature (Di Paola et al., 2025). Specifically, the consistency threshold for the truth table was increased from the initial 0.80 to 0.85. The results of this re-analysis yielded configurations that were fundamentally identical to the main analysis presented in Table 9, with no changes in the core or peripheral conditions constituting the pathways. This consistency confirms that the configurational findings of this study are highly robust and not overly sensitive to minor adjustments in threshold specifications.

5 Results and discussion

5.1 Research findings

Based on the statistical results of the comparative analysis and combined with the reality of the improvement of innovation ability of food supply chain SMEs, this paper proposes a grouping model of factors influencing the improvement of innovation ability of food supply chain SMEs, as shown in Figure 2:

Figure 2

5.1.1 Mechanism of individual influences in factor allocation

• (1) Innovation support policies constitute the core driving force

The configurational analysis reveals that innovation support policies (IS) serve as the sole core condition across all three high-innovation configurations (H1–H3), appearing universally as a large-circle core element. This finding underscores the irreplaceable foundational role of policy support in stimulating digital innovation among food supply chain SMEs. Particularly when combined with digital transformation (DT)—which appears as a peripheral condition in all three pathways—the synergy between policy guidance and digital capability enhancement generates a pronounced dual-wheel driving effect, confirming the complementary relationship between institutional enablement and technological empowerment (Cui et al., 2025; Wang et al., 2023).

• (2) Management characteristics and technical talent form differentiated peripheral pathways

Although neither management characteristics (MC) nor technical talent cultivation (TP) emerges as a core condition in the parsimonious solution, their differentiated peripheral roles across configurations reveal important substitution dynamics. Specifically, in H1, the co-presence of TP and MC alongside DT and IS creates an internally driven innovation pathway emphasizing comprehensive organizational capacity. In contrast, H2 demonstrates that MC combined with DT and IS can sustain innovation even under unfavorable technology market and financing conditions (~TM · ~FE), suggesting that managerial cognitive advantages can partially compensate for external resource deficits. Meanwhile, H3 shows that TP can replace MC when supported by favorable external environments (TM·FE), indicating that human capital accumulation and environmental enablement exhibit functional substitutability (Fiss, 2011; Ho et al., 2024).

• (3) Financing environment is characterized by non-essentiality and contextual contingency

The financing environment (FE) displays a distinctly contingent role: it appears as a peripheral presence in H3 but as a peripheral absence in H2. This asymmetric pattern confirms that financing is neither necessary nor sufficient for high innovation. When digital transformation, management characteristics, and innovation support policies form an effective synergy (as in H2), they can generate an alternative compensation mechanism for financing constraints, revealing its auxiliary rather than decisive role. This finding challenges the conventional assumption that financial resources are indispensable for SME innovation and highlights the potential of non-financial factor combinations to overcome capital barriers (Meurs et al., 2025).

5.1.2 Grouping characteristics of multiple driving factors

Previous studies have explored the innovation of SMEs in the food supply chain through case studies or large-sample regression, but these approaches face inherent limitations: case studies are constrained in generalizability due to limited sample sizes, while regression-based methods quantify the net effects of individual factors but struggle to capture the nonlinear synergistic mechanisms among multiple concurrent conditions. This study introduces configurational thinking through fsQCA, which offers distinct methodological advantages: first, it enhances explanatory validity by accommodating the design logic of “overall homogeneity and intra-group heterogeneity;” second, it deconstructs the synergistic and substitutive effects of factors from a systemic perspective, thereby transcending the traditional linear analysis paradigm (Pappas and Woodside, 2021). This method is particularly suitable for cross-case studies involving multiple concurrent causality, and its analytical flexibility in handling varying numbers of variables and sample sizes significantly broadens the applicability boundary of empirical research (Di Paola et al., 2025).

5.2 Discussion

First, innovation capacity improvement is rooted in multi-factor synergy, with innovation support policies as the pivotal anchor. Although existing studies have verified the significant influence of individual factors on innovation through regression analysis, our configurational analysis demonstrates that six factors—technical talent cultivation, management characteristics, digital transformation, technology market environment, financing environment, and innovation support policies—must operate in combinatorial form to drive innovation enhancement. No single factor can independently produce high innovation capacity, which confirms the inherent synergistic nature of innovation activities (Fiss, 2011; Di Paola et al., 2025). Notably, the emergence of IS as the universal core condition across all three high-innovation pathways reveals that policy support functions not merely as an external enabler but as a structural catalyst that activates the synergistic potential of other factors. This finding extends Institutional Theory by showing that government innovation support does more than confer external legitimacy on firms' innovative endeavors (Cui et al., 2025); it operates as a configurational linchpin that shapes the internal resource landscape, amplifying the combinatorial effects of talent, technology, and managerial attention in ways that no single resource can replicate alone. In the context of food supply chains, this catalytic function carries a sustainability dimension: when IS stimulates the synergy of digital transformation and technical talent, the resulting innovations—such as IoT-enabled traceability, AI-driven demand forecasting, and cold chain optimization—simultaneously enhance enterprise competitiveness and reduce food loss and carbon emissions, generating what may be termed an “innovation–sustainability double dividend”—a synergistic outcome in which the configurational enhancement of innovation capability simultaneously yields sustainability gains (Hema et al., 2024). In other words, institutional support enhances not only the availability of critical resources but also the legitimacy and strategic coherence of innovation activities, thereby enabling the synergistic mechanism identified above and steering its outcomes toward sustainability-aligned directions within the food supply chain.

Second, there exist multiple equifinal innovation paths with asymmetric causal structures. The configurational analysis identifies three high-innovation pathways (H1–H3) and two non-high-innovation pathways (NH1–NH2), revealing that innovation enhancement exhibits the characteristic of equifinality—different configurations of conditions lead to the same outcome (Ragin, 2009). More importantly, the comparison between high and non-high innovation configurations confirms pronounced causal asymmetry: the core condition driving high innovation is the presence of innovation support policies (IS), whereas the core condition underlying non-high innovation is the absence of digital transformation (~DT). These are neither mirror images nor simple negations of each other, substantiating the theoretical premise that promoting factors and inhibiting factors operate through distinct causal mechanisms (Greckhamer et al., 2018). This asymmetry is particularly consequential for sustainable food systems: digital transformation not only serves as a peripheral facilitator in all high-innovation paths but also constitutes the most critical barrier when absent, suggesting that digitalization functions as a threshold condition whose deficiency triggers cascading innovation failures—and, by extension, forecloses the sustainability benefits that digital technologies deliver, including reduced food waste through intelligent inventory management and lower carbon emissions through optimized logistics (Hassoun et al., 2024). From a Resource-Based View, this asymmetry further reveals that digital transformation constitutes a threshold capability for food supply chain SMEs—without it, firms cannot effectively convert external institutional support into innovation outcomes, regardless of other resource endowments (Saeedikiya et al., 2024). The fact that the absence of a single internal capability, rather than the absence of policy support, defines the non-high-innovation configurations underscores that absorptive capacity for digital technologies is a necessary, though not sufficient, precondition for translating institutional advantages into tangible innovation performance (Zahoor et al., 2023).

Third, there exists a dynamic structural balance among factors through complementarity and substitution. Although the ideal configuration of all factors being simultaneously present can optimize innovation performance, such a condition is prohibitively demanding for enterprises to achieve in practice. Our configurational analysis reveals that factors realize structural balance through complementarity, substitution, and reinforcement. For example, management characteristics and technology market environment exhibit a substitution effect: in H2, strong management characteristics compensate for the absence of favorable market and financing environments, whereas in H3, favorable external environments (TM·FE) substitute for the absence of management characteristics. This substitutive relationship provides strategic space for enterprises with “inherent defects” in certain factors, enabling them to select appropriate factor combinations based on their resource endowments and construct differentiated paths for innovation enhancement (Fiss, 2011; Escoz Barragan and Becker, 2025). Such differentiation also extends to the sustainability mechanisms embedded in each pathway: the internally driven configuration (H1) tends to foster proactive sustainability initiatives—such as managerial investment in circular supply chain models—whereas the externally supported configuration (H3) achieves sustainability gains primarily through ecosystem-level mechanisms such as shared logistics platforms and industry-wide digital food safety standards, suggesting that effective sustainability interventions must be tailored to the configurational profile of target enterprises. This observation resonates with the dynamic capability view (Saeedikiya et al., 2024), which posits that competitive advantage arises not from possessing a fixed set of superior resources but from the firm's ability to reconfigure and orchestrate available resources in response to internal constraints and external contingencies. The equifinal substitutability documented here—between managerial agency and environmental munificence—provides configurational evidence that food supply chain SMEs can achieve comparable innovation outcomes through alternative resource orchestration paths rather than converging on a single optimal configuration, thereby enriching the theoretical understanding of how dynamic capabilities manifest in resource-constrained contexts. Preliminary evidence from the sample corroborates this interpretation: descriptive analysis of the survey data indicates that enterprises classified in the high digital innovation capability group reported an average 28% improvement in inventory turnover efficiency compared to their low-innovation counterparts—a gain that directly reduces waste from overstocking and food expiration—while their digitally optimized logistics operations were associated with reductions in cold chain energy consumption as reported by respondents, indicating that the configurational innovation pathways identified above function simultaneously as enabling mechanisms for broader food supply chain sustainability (Hassoun et al., 2024).

6 Limitations and future outlook

6.1 Limitations

While this study constructs a rigorous configurational framework using grounded theory and fsQCA, it is subject to several limitations that present opportunities for future research.

First, This study collected data through questionnaires, but this method has a potential limitation: respondents may have varying levels of cognition and understanding of the research content. Such differences may affect the accuracy and reliability of the data to a certain extent, thereby exerting a negative impact on the validity of the overall research conclusions. To improve data quality, future studies could consider adopting more diverse data collection methods or providing more detailed explanations of the research content to respondents. Second, while fsQCA mitigates the limitations of traditional net-effects analysis, the calibration process involves researcher discretion. Although robustness tests (such as adjusting consistency thresholds) were conducted to ensure validity, the reliance on qualitative anchor setting leaves room for methodological refinement (Pappas and Woodside, 2021).

6.2 Future outlook

To address these limitations and advance the field, future research should proceed in the following directions:

In terms of expanding research coverage, efforts should be made to improve the effective sampling capability for food supply chain SMEs, enabling the sample to cover more provinces and regions. Research channels and scope should be expanded appropriately through methods such as offline field surveys to gain a more comprehensive understanding of the actual situation of enterprises. This will further enhance the generalizability and universality of research conclusions, providing a more reliable basis for relevant policy formulation and practical operations.

To further enhance the scientific rigor of the research and the quality of conclusions, it is necessary to continuously improve variable measurement methods to ensure the accuracy and reliability of data acquisition. Specifically, the measurement forms of specific variables can be optimized, and more objective and compelling methods can be adopted for data collection. In addition, incorporating secondary data as a basis for analysis and reasoning is also an important approach to improving data quality. Through these measures, the quality bottleneck in data acquisition can be effectively broken through, promoting the simultaneous improvement of the research's scientific rigor and the quality of conclusions.

Finally, given that the configurational paths identified in this study have demonstrated the critical role of digital transformation in enhancing innovation capability, future research could further explore how these digital innovation configurations translate into measurable supply chain sustainability outcomes. For instance, conducting longitudinal studies to track changes in food loss rates and cold chain carbon footprints before and after enterprises implement configurational innovation strategies can more intuitively verify the sustainability benefits revealed in this study. Such research would extend the present framework from identifying the conditions for innovation capability formation to quantifying the resulting sustainability gains, thereby strengthening the bridge between digital innovation research and sustainable food systems science (Liao et al., 2024).

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