Typologies of Service Supply Chain Resilience: A Multidimensional Analysis from China’s Regional Economies

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. While the study effectively employs fsQCA to identify resilience configurations, the manuscript would benefit from a more detailed explanation of how the causal asymmetry inherent in fsQCA aligns with the research objectives. Specifically, the authors should elaborate on why configurational analysis was prioritized over variable-based approaches (e.g., regression) for identifying resilience typologies, and how the fsQCA results complement the OLS findings. Additionally, discussing the selection of calibration thresholds for fuzzy sets (e.g., why quantiles at 0.95, 0.05, and 0.50 were chosen) and their impact on the configurational outcomes would enhance methodological transparency.

2. The study highlights significant regional disparities in service supply chain resilience but could strengthen the theoretical framework by explicitly linking these disparities to institutional and market contexts. For instance, the authors might explore how policy support (e.g., regional development strategies) or infrastructure endowments moderate the effectiveness of resilience configurations. Incorporating theories of regional economic development (e.g., core-periphery models) to explain why the cost-adaptive path prevails in eastern regions while tech-sustain paths are less prominent could deepen theoretical insights.

3. Although the 2023 data validate the cost-adaptive path, the manuscript underplays the temporal dynamics of resilience configurations. The authors should discuss why the cost-growth and tech-sustain paths lost statistical significance over time, considering factors like post-pandemic recovery trends or technological diffusion rates. Additionally, exploring whether the identified configurations represent stable typologies or context-dependent strategies (e.g., short-term vs. long-term resilience) would enhance the study’s practical implications for policy-makers.

Author Response

Comment 1: While the study effectively employs fsQCA to identify resilience configurations, the manuscript would benefit from a more detailed explanation of how the causal asymmetry inherent in fsQCA aligns with the research objectives. Specifically, the authors should elaborate on why configurational analysis was prioritized over variable-based approaches (e.g., regression) for identifying resilience typologies, and how the fsQCA results complement the OLS findings. Additionally, discussing the selection of calibration thresholds for fuzzy sets (e.g., why quantiles at 0.95, 0.05, and 0.50 were chosen) and their impact on the configurational outcomes would enhance methodological transparency.

 

Response: We sincerely thank the reviewer for this insightful comment. To address this, we have revised the manuscript as follows:

First, in Section 5.3 (originally Section 4.3), we provide a clearer justification for prioritizing fsQCA over traditional regression-based approaches. Given that service supply chain resilience arises from complex and conjunctural interactions among structural, relational, and behavioral conditions, fsQCA is better suited to capture multiple sufficient configurations and regional heterogeneity. This aligns with our objective of identifying typologies rather than estimating average effects.

Second, in Section 6.1 (originally Section 5.1) , we further elaborate on how fsQCA complements OLS regression. While the original version already provided a brief explanation of the rationale behind combining these methods, we have now enhanced this section to clarify the distinct roles each method plays.

Third, in Section 5.1 (originally Section 4.1) , we explain our selection of calibration thresholds for fuzzy set transformation. The 0.95 (full membership), 0.50 (crossover), and 0.05 (full non-membership) quantiles are consistent with established practices in fsQCA literature and reflect the distributional characteristics of our dataset. This choice ensures conceptual clarity and empirical differentiation across provinces.

All related modifications have been incorporated into Sections 5.1, 5.3, and 6.1 of the revised manuscript and are clearly marked for easy reference.

 

Comment 2: The study highlights significant regional disparities in service supply chain resilience but could strengthen the theoretical framework by explicitly linking these disparities to institutional and market contexts. For instance, the authors might explore how policy support (e.g., regional development strategies) or infrastructure endowments moderate the effectiveness of resilience configurations. Incorporating theories of regional economic development (e.g., core-periphery models) to explain why the cost-adaptive path prevails in eastern regions while tech-sustain paths are less prominent could deepen theoretical insights.

 

Response: We thank the reviewer for this insightful and constructive suggestion. In the revised manuscript, we have enhanced the theoretical explanation of regional path differentiation by embedding the discussion more explicitly in regional development contexts. For structural coherence, we made modest theoretical preparations in earlier sections, while reserving the full integration of regional development theory for Section 5.3 (originally Section 4.3).

In Section 5.3, the updated analysis of the Cost-Adaptive Path situates its prevalence in eastern China within the region’s institutional and infrastructural advantages, including high market accessibility, dense service ecosystems, and responsive governance. Conversely, the Cost-Growth Path is now discussed with greater emphasis on how limited institutional capacity and structural constraints in western regions necessitate capital-intensive but lower-efficiency strategies.

For the Technologically-Sustainable Path, we have further elaborated on the role of central provinces as transition zones—regions with growing innovation capacity and human capital but constrained by service accessibility and coordination frictions.

These theoretical enhancements link each configuration to distinct institutional and spatial-economic conditions, thereby strengthening the explanatory coherence of our regional resilience typologies. Relevant changes have been incorporated in Section 5.3 and highlighted in yellow in the manuscript.

 

Comment 3: Although the 2023 data validate the cost-adaptive path, the manuscript underplays the temporal dynamics of resilience configurations. The authors should discuss why the cost-growth and tech-sustain paths lost statistical significance over time, considering factors like post-pandemic recovery trends or technological diffusion rates. Additionally, exploring whether the identified configurations represent stable typologies or context-dependent strategies (e.g., short-term vs. long-term resilience) would enhance the study’s practical implications for policy-makers.

 

Response: We thank the reviewer for highlighting this critical issue. In the revised manuscript, we have significantly deepened the discussion of the temporal evolution of resilience configurations by analyzing their stability and transformation between 2021 and 2023.

In the revised manuscript, we have added a new section (Section 6.3) to discuss alternative developmental trajectories and transitional dynamics beyond the validated 2023 configuration. Rather than treating the Cost-Adaptive Path as a fixed outcome, we reinterpret it as a transitional state whose evolution diverged across regions. While some provinces showed signs of attempting to upgrade toward a technology-driven path, the Technologically-Sustainable configuration did not achieve statistical significance in 2023—likely due to institutional underdevelopment, limited digital infrastructure, or innovation capacity gaps. Meanwhile, the decline of the Cost-Growth Path suggests that reactive, investment-led resilience strategies are not sustainable under evolving conditions.

This new section also introduces a simplified “T-shaped” evolution model (Figure 3), which conceptualizes the Cost-Adaptive Path as a developmental pivot. From this point, regions may transition either upward (toward sustained technological resilience) or downward (toward structurally costly compensation models), depending on endogenous capabilities and policy support. These additions frame the identified configurations as context-dependent, path-sensitive, and dynamically unstable, offering a more nuanced perspective for long-term resilience planning.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript deals with a relevant issue: Supply Chain Resilience

In the Introduction the authors argue that resilience research "has gained momentum in manufacturing contexts or firm-level but few studies have addressed how resilience evolves in service ecosystems, particularly in geographically diverse and institutionally asymmetric economies."

The authors also argue that few assessments of supply chain resilience consider the combined effects of structural, relational, and behavioral dimensions, and to address these gaps they propose a multidimensional evaluation framework  (structure, relationship, and subject) for Service Supply Chain Resilience (SSCR) and apply it across 31 Chinese provinces.

In the section of Literature Review and Framework Development the authors present a bibliographical review to trace how the concept of service supply chain resilience (SSCR) has evolved over time and propose the "Structure-Relationship-Subject Framework"

Exploring data from the China Statistical Yearbook, China Science and Technology Statistical Yearbook, China Internet Development Report, and China Economic Census Yearbook and the  Ministry of Industry and the National Development and Reform Commission the authors used  Entropy Weighting, Fuzzy-set Qualitative Comparative Analysis, Necessary Condition Analysis, and OLS regression to characterize and analyze the three dominant indicators of resilience:  cost adaptive, cost growth, and technology driven on Chinese provincial-level panel data from 2017 to 2023, covering 31 regions across mainland China.

 The findings reveal a persistent regional disparities and demonstrate how context-specific strategies can shape service resilience under institutional and market variations and that no single condition guarantees resilience. Instead, three distinct paths emerge: cost-adaptive, cost-growth, and tech-sustain. The resultas also suggest that resilience is most robust when regions combine cost efficiency with industrial flexibility and institutional adaptability.

The Conclusions are pertinent and supported by the results.

Some points are worth revisiting:

1 - In Section 2.2. of Index System Construction, it seems pertinent to be a little more specific about how the Indicators of Cost deviation, Cost adaptation, Loss cost, Response time, and Turnaround Speed, Sector growth, Development flexibility and Resource coherence were collected.

2 - It the section of Conclusions, it seems pertinent to be a little more specific about the public policies and managerial implications that can be envisaged from the results.

 

Author Response

Comment 1: In Section 2.2. of Index System Construction, it seems pertinent to be a little more specific about how the Indicators of Cost deviation, Cost adaptation, Loss cost, Response time, and Turnaround Speed, Sector growth, Development flexibility and Resource coherence were collected.

 

Response: We thank the reviewer for highlighting the need for greater clarity regarding the construction and data sourcing of key indicators. In response, we have added a detailed table (Table 3) in Section 3.1 (originally Section 2.1) titled “Selection and Measurement of Conditional Variables.” This table outlines the operational definitions, calculation formulas, data sources, and theoretical foundations for each indicator used in the analysis, including those specifically mentioned by the reviewer.

To ensure methodological transparency, we have also added a clarifying sentence in Section 3.1 directing readers to Table 3. All indicators were derived from official and publicly available data sources, primarily from national and provincial statistical yearbooks, as shown in the table. These revisions improve replicability and directly address the reviewer’s concern. All relevant additions have been highlighted in the revised manuscript.

 

Comment 2: It the section of Conclusions, it seems pertinent to be a little more specific about the public policies and managerial implications that can be envisaged from the results.

 

Response: We appreciate the reviewer’s insightful suggestion regarding the need to articulate more concrete public policy and managerial implications derived from our findings. In the revised manuscript, we have substantially expanded Section 7 (originally Section 6) by dividing the conclusions into two sub-sections: 7.1 Theoretical and Methodological Contributions and 7.2 Policy and Managerial Implications. This structural enhancement allows us to clearly distinguish the academic contributions from the practical recommendations.

Specifically, in Section 7.2, we now elaborate on how each of the three identified resilience configurations—Cost-Adaptive, Cost-Growth, and Technologically-Sustainable—entails distinct implications for regional development policy and firm-level decision-making. We have also explicitly noted that while only the Cost-Adaptive Path showed statistical significance in the OLS validation, the other two configurations remain relevant under specific regional conditions as identified through fsQCA. These clarifications aim to avoid overgeneralization and promote context-sensitive application of the results.

Public policy implications now include guidance for targeted investment strategies, digital infrastructure development, and institutional capacity building tailored to each pathway. From a managerial standpoint, we also provide differentiated recommendations for service enterprises to align their resilience strategies with the dominant configuration of their local environment. These additions are highlighted in yellow in the revised version and we believe they address the reviewer’s concern directly and meaningfully.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

 

The study addresses an important topic and offers a novel perspective by integrating a multidimensional SRS framework and applying a mix of empirical methods including entropy weighting, fsQCA, NCA, and OLS regression. Overall, the manuscript is well-structured and timely, particularly in the context of regional disparities in China’s service supply chain development. However, to further strengthen the academic rigor and practical value of the paper, I offer the following suggestions for revision:

1. While the paper touches upon the differences between service and manufacturing supply chains, the theoretical elaboration remains limited. 
2. The entropy weights appear somewhat evenly distributed, and some critical indicators have relatively low weight. I recommend verifying robustness via alternative weighting methods such as principal component analysis or expert-based weighting.
3. The fsQCA emphasizes configurational sufficiency and causal complexity, whereas OLS is based on linear correlation. The manuscript should better articulate the rationale for combining these methods and how their results complement each other without leading to contradictory interpretations.
4. Interpreting high-cost deviation as a sign of fiscal flexibility or institutional absorptive capacity seems counterintuitive. The authors are encouraged to provide empirical cases or theoretical references to justify this argument.
5. Explore the potential evolution or transition between resilience typologies. Are the three configurations static, or could they evolve over time? For example, could a region shift from “cost-growth” to “tech-sustain”? Introducing a conceptual discussion on path dependency or transformation mechanisms would enrich the analytical depth.

 

Author Response

Comment 1: While the paper touches upon the differences between service and manufacturing supply chains, the theoretical elaboration remains limited.

 

Response: We appreciate the reviewer’s constructive comment regarding the need for deeper theoretical elaboration on the distinctions between service and manufacturing supply chains. In the revised manuscript, we have substantially strengthened the conceptual foundation of our framework by introducing two new paragraphs and an expanded comparative table (Table 1).

Specifically, we now clarify the contextual distinctiveness of service supply chains (SSCs) by highlighting their reliance on real-time interaction, relational coordination, and cognitive agility—features that render traditional, manufacturing-oriented resilience models insufficient. To support this argument, we introduce Table 1: Key Differences Between Manufacturing and Service Supply Chains, which outlines five fundamental dimensions and their respective implications for resilience modeling. Each entry is substantiated by recent literature and explicitly linked to the rationale for adopting a service-specific resilience approach.

Moreover, we emphasize that, unlike prior studies which treat these distinctions descriptively, our research explicitly integrates them into the Structure–Relationship–Subject (SRS) framework. As we now state in the revised manuscript:

"Unlike prior studies that merely list structural differences between service and manufacturing chains, this study explicitly integrates such distinctions into the theoretical backbone of the SRS framework. The core rationale is that service supply chains’ unique reliance on real-time interactions, relational coordination, and cognitive agility necessitates a redefinition of resilience not as inventory buffering but as systemic responsiveness, adaptive co-production, and digital orchestration."

These enhancements provide both theoretical justification and conceptual precision to our framework, ensuring that the model is tailored to the distinctive dynamics of service supply systems. The newly added content is highlighted in yellow in the revised manuscript, and we believe this directly addresses the reviewer’s concern in a substantive and meaningful way.

 

Comment 2: The entropy weights appear somewhat evenly distributed, and some critical indicators have relatively low weight. I recommend verifying robustness via alternative weighting methods such as principal component analysis or expert-based weighting.

 

Response: We thank the reviewer for this important suggestion regarding the potential uniformity of entropy weights and the need for robustness verification through alternative weighting methods. In the revised manuscript, we have added two new explanatory paragraphs to Section 3.2 (originally Section 2.2) and updated Table 5 to include an additional column presenting weights derived from Principal Component Analysis (PCA).

Specifically, we now acknowledge that the original dataset exhibited moderate variance across indicators and relatively uniform dispersion across provinces, which could cause the entropy method to produce evenly distributed weights and potentially underweight theoretically critical indicators. In response, we conducted a robustness check using PCA on standardized Z-score data. The weights derived from the first principal component revealed meaningful structural differentiation and showed partial convergence with entropy-based results, especially for key indicators such as cost adaptation and turnaround speed.

Moreover, we discuss notable discrepancies: while technological innovation and development flexibility received higher entropy weights, PCA placed greater emphasis on cost-related indicators. This divergence is explained by the methodological logic of the two approaches—entropy reflects informational entropy across regions, while PCA captures statistical variance and latent structure.

Despite these differences, we justify the continued use of the entropy method as the primary weighting strategy, citing its objectivity and suitability for measuring cross-regional heterogeneity. The PCA results thus serve as a robustness benchmark, and the revised content (including the additional PCA weight column in Table 5) is clearly highlighted in yellow in the manuscript. We hope this enhancement addresses the reviewer’s concern and strengthens the methodological rigor of the study.

 

Comment 3: The fsQCA emphasizes configurational sufficiency and causal complexity, whereas OLS is based on linear correlation. The manuscript should better articulate the rationale for combining these methods and how their results complement each other without leading to contradictory interpretations.

 

Response: We appreciate the reviewer’s insightful comment regarding the need to clarify the methodological rationale for combining fsQCA and OLS, and to explain how their results complement rather than contradict one another. In the revised manuscript, we have expanded Section 6.1 (Pathway Discussion) by adding two new explanatory paragraphs to articulate this integration in both conceptual and empirical terms.

Specifically, we now emphasize that the use of both configurational and variable-based methods reflects an intentional effort to bridge theoretical sufficiency (fsQCA) with empirical regularity (OLS). As stated in the revised text:

“While fsQCA reveals conjunctural and asymmetric causal paths tailored to specific regional contexts, OLS provides a symmetric and generalizable assessment of their empirical validity. ”

We also explain that fsQCA captures conjunctural causality and causal asymmetry, which is particularly appropriate for identifying region-specific pathways to service supply chain resilience. In contrast, OLS provides a symmetric, linear assessment of average effects across all cases, which is valuable for validating whether fsQCA-derived configurations exhibit empirical stability under new data conditions (2023).

To avoid misinterpretation, we further clarify that the partial divergence between fsQCA and OLS results should not be seen as contradiction, but as evidence of contextual and temporal dynamics. This distinction is crucial for accurately interpreting path effectiveness in a policy-relevant manner.

These additions are highlighted in yellow in the revised manuscript. We believe they directly address the reviewer’s concern and strengthen the methodological transparency and interpretive clarity of our mixed-method design.

 

Comment 4: Interpreting high-cost deviation as a sign of fiscal flexibility or institutional absorptive capacity seems counterintuitive. The authors are encouraged to provide empirical cases or theoretical references to justify this argument.

 

Response: We thank the reviewer for this perceptive comment regarding the counterintuitive interpretation of high cost deviation. In the revised manuscript, we have expanded the discussion of the “Cost-Adaptive” configuration in Section 5.3 (originally Section 4.3) to include specific empirical examples and theoretical justification.

Specifically, we now point out that provinces such as Jiangsu and Guangdong, which exhibit relatively high cost deviation, are also regions with robust institutional capacity and fiscal reserves. In such cases, temporary cost spikes are often the result of deliberate resource mobilization strategies during disruptions—such as emergency procurement, platform expansion, or fast-track service delivery. These are not indicators of inefficiency, but rather manifestations of institutional absorptive capacity.

We further substantiate this interpretation by referencing recent empirical research on slack resources and organizational resilience, which shows that controlled fiscal flexibility can enhance responsiveness during crises. As now stated in the manuscript:

“These deviations are typically underpinned by strong fiscal reserves and administrative agility, enabling both governments and enterprises to absorb additional expenditures without jeopardizing service continuity.”

These additions (highlighted in yellow) aim to clarify the distinction between uncontrolled overspending and strategic fiscal adaptation, and to reposition cost deviation as a functional buffer in resilience-building, particularly in high-capacity regions. We hope this directly addresses the reviewer’s concern and enhances the interpretive credibility of our findings.

 

Comment 5: Explore the potential evolution or transition between resilience typologies. Are the three configurations static, or could they evolve over time? For example, could a region shift from “cost-growth” to “tech-sustain”? Introducing a conceptual discussion on path dependency or transformation mechanisms would enrich the analytical depth.

 

Response: We appreciate the reviewer’s insightful suggestion to explore the potential evolution and transitional dynamics among the identified resilience typologies. In response, we have significantly expanded the discussion on this topic in the revised manuscript by adding Section 6.3: Development Discussion, specifically addressing the evolutionary potential of resilience pathways.

In this new section, we empirically examine how the Cost-Adaptive configuration evolved spatially between 2021 and 2023. Our analysis reveals a notable geographical shift, with provinces aligned with the Cost-Adaptive path transitioning from primarily eastern coastal regions (2021) toward central and western provinces by 2023. While this empirical tracking is limited specifically to the Cost-Adaptive path due to the statistical insignificance of other configurations in 2023, it clearly demonstrates the dynamic nature of resilience configurations, highlighting that these typologies are not static but subject to significant spatial and temporal changes.

To better conceptualize the potential divergence and transitional risks identified through both empirical observations and theoretical reasoning, we introduce a “T-shaped evolution model” . This conceptual framework provides a simplified visual abstraction of possible regional transitions between resilience strategies. Specifically, the horizontal axis symbolizes a potential institutional and technological upgrading trajectory (Cost-Adaptive to Technologically-Sustainable), typically feasible for provinces with moderate reform capacity and developing digital infrastructure. In contrast, the vertical axis represents a fallback trajectory toward the Cost-Growth configuration, characterized by intensifying resource input without sufficient efficiency improvements or innovation returns.

Furthermore, we explicitly discuss the conditions under which horizontal upgrades or vertical regressions may occur, emphasizing the role of regional endowments, institutional capabilities, and policy contexts. Importantly, we highlight that the Cost-Adaptive configuration identified in 2021 is best viewed not as a permanent state but as a transitional pivot, capable of evolving either positively or negatively depending on regional dynamics and policy interventions.

From a policy perspective, recognizing this bifurcation offers valuable insights. Regions exhibiting transitional signals towards technology-oriented resilience should receive targeted institutional support, while regions showing signs of regression require early intervention to prevent long-term inefficiencies. These policy implications reinforce the practical value of our conceptual model in guiding region-specific resilience strategies.

We believe this comprehensive addition—both empirically grounded (Cost-Adaptive path analysis) and theoretically enriched (T-shaped evolution model)—fully addresses the reviewer’s comment by illustrating the dynamic, path-dependent nature of resilience typologies. The relevant additions and revisions are highlighted in yellow in the revised manuscript.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have invested considerable effort in addressing all of the issues raised in the previous round of review, which has significantly improved the quality of their paper. I therefore recommend acceptance of the paper in its current form.