Building Sustainable Financial Capacity: How Supply Chain Digitalization Facilitates Credit Access by Adjustment Capability

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Thanks for inviting me to review the manuscript entitled "Building Sustainable Financial Capacity: How Supply Chain Digitalization Facilitates Credit Access by Adjustment Capability". This manuscript explores how supply chain digital transformation enhances Chinese manufacturing firms’ commercial credit financing through corporate adjustment capability. It further examines the moderating roles of external governance and internal endowments. The study addresses a timely topic amid global supply chain restructuring and digitalization, with a clear theoretical framework and rigorous empirical design. However, there are several critical gaps that exist, which require substantial revisions to strengthen its academic contribution and practical implications.

(1) The study invokes Dynamic Capabilities Theory and Agency Theory but fails to elaborate on how these theories explain why SCDT enhances adjustment capability. Please add a subsection in Section 2 to explain how Dynamic Capabilities Theory justifies SCDT’s role in enhancing resource reconfiguration and how Agency Theory explains SCDT’s impact on managerial optimism bias.

(2) The literature review on “SCDT → Adjustment Capability” is underdeveloped. It overlooks recent studies that could strengthen the rationale for the dual mediating mechanisms. Additionally, the review does not distinguish between “supply chain digitalization” and “firm-wide digitalization,” leading to ambiguity about the study’s scope. Please integrate recent studies on SCDT and corporate adjustment capability. And explicitly distinguish “supply chain digitalization” from “firm-wide digitalization” to clarify the study’s boundary.

(3) The study measures SCDT via textual analysis of MD&A sections using five dimensions but does not explicitly provide the keyword list. It also does not justify the weighting of these dimensions. Hence, please append the list of SCDT keywords and justify dimension weighting. 

(4) The CCF variable ignores industry heterogeneity. For example, capital-intensive manufacturing sectors may naturally have higher accounts payable, biasing cross-industry comparisons. No industry adjusted CCF measure is considered. Please revise CCF to an industry-adjusted measure to control for sectoral differences.

(5) In Table 1, the control variable “Lev (Asset-Liability Ratio)” is incorrectly described as “Log value of corporate total assets”. This inconsistency undermines the manuscript’s rigor. Please fix the “Lev” description in Table 1 to “Asset-Liability Ratio (Total Liabilities/Total Assets)” and ensure consistency with Section 3.2.4.

(6) While the IV (1984 post office density) addresses regional digitalization endogeneity, two gaps remain: first, the study does not rule out reverse causality—firms with better CCF may have more resources to invest in SCDT. No lagged independent variables or PSM are used to address this; second, the study treats SCDT as a single construct but ignores differences between digital technologies. Do these technologies have heterogeneous effects on CCF via adjustment capability? Please add lagged SCDT (t-1) as the independent variable in regression models and conduct PSM to match firms with high/low SCDT based on firm size, age, and ROE, then re-test the main effect. Moreover, please split SCDT into sub-dimensions and test their differential impacts on CCF.

(7) The study treats “operational adjustment capability” and “risk-bearing adjustment capability” as independent mediators but does not test their interaction effect. Please add an interaction term between “Stick” and “Resilience” in the mediation model to examine whether the two capabilities complement each other.

(8) The ESG moderating effect is tested as a single index, but its three dimensions may have distinct impacts. Similarly, “polluting enterprises” are defined using 2012 industry guidelines—no consideration of post-2012 environmental policies that may alter their risk profiles. Please split ESG into Environmental, Social, and Governance sub-indices to test their individual moderating effects. Some study is helpfu, such as 10.3390/su17030963. Please update the “polluting enterprise” definition to include post-2012 policies and test policy heterogeneity.

(9) The Discussion section (5.1) does not sufficiently contrast findings with prior work. For example, how does this study’s dual mediating mechanism differ from previous studies, which focuses on digitalization and trade credit? What makes the “adjustment capability” framework a novel contribution? In Section 5, please add a subsection titled “Comparisons with Prior Studies” to explicitly contrast findings with previous studies, highlighting the study’s incremental contribution.

(10) Tables 5 and 6 have confusing column alignments. Some regression results (e.g., t-statistics in Table 7) lack consistent formatting, hindering readability. Please reformat Tables 5–8 to align column headers with regression results and ensure consistent labeling of t-statistics and fix missing N values.

Comments on the Quality of English Language

The Quality of English Language can be improved.

Author Response

We sincerely appreciate the reviewer for taking the time to provide such thorough and constructive comments. Their insightful feedback has significantly strengthened our paper. We have carefully considered all points and incorporated changes throughout the manuscript. Our responses to each comment are listed below.

Comments1: The study invokes Dynamic Capabilities Theory and Agency Theory but fails to elaborate on how these theories explain why SCDT enhances adjustment capability. Please add a subsection in Section 2 to explain how Dynamic Capabilities Theory justifies SCDT’s role in enhancing resource reconfiguration and how Agency Theory explains SCDT’s impact on managerial optimism bias.

Response1: In Section 2.2, we added Paragraphs 5–7 to clarify how dynamic capability theory and agency theory explain the mechanisms by which supply chain digitalization enhances firms’ adjustment capacity. As elaborated in the text, dynamic capability theory reveals that digital transformation strengthens organizational resilience to external risks by promoting dynamic adjustment capabilities and enabling the continuous optimization of resource allocation. Meanwhile, agency theory explains that digital transformation mitigates managers’ optimism bias through information-transparency-based governance mechanisms, thereby improving firms’ cost stickiness under enhanced operational management capacity. Together, dynamic capability theory and agency theory provide critical theoretical support for understanding how supply chain digitalization facilitates firms’ adjustment capacity.

Comments2: The literature review on “SCDT → Adjustment Capability” is underdeveloped. It overlooks recent studies that could strengthen the rationale for the dual mediating mechanisms. Additionally, the review does not distinguish between “supply chain digitalization” and “firm-wide digitalization,” leading to ambiguity about the study’s scope. Please integrate recent studies on SCDT and corporate adjustment capability. And explicitly distinguish “supply chain digitalization” from “firm-wide digitalization” to clarify the study’s boundary.

Response2: First, we enriched the literature review on the impact of supply chain digitalization on firms’ adjustment capacity by incorporating recent studies that support the dual mediation mechanism. These include relevant works published in the past three years, such as Shi et al. (2023), Chen and Xu (2023), Sharma et al. (2024), Al-Moaid and Almarhdi (2024), Li et al. (2024), and Zhang et al. (2025). Details are provided in the additional Paragraphs 5–7 of Section 2.2.

In addition, in Paragraph 4 of Section 2.1, we redefined the distinction between supply chain digitalization and enterprise-wide digitalization. Supply chain digitalization is thus understood as an interconnected business system enabled by modern digital technologies (Ishfaq et al., 2022). Unlike traditional enterprise-wide digitalization, which emphasizes systemic transformation within a single firm—integrating functional departments into a unified IT architecture and data governance framework to enhance overall operational efficiency and innovation capacity (Chen, 2024)—supply chain digitalization focuses on cross-organizational data and process coordination. It relies on the institutional foundations of trust mechanisms and contractual arrangements among supply chain partners to achieve information sharing and collaborative decision-making across suppliers, manufacturers, and logistics providers. While supply chain digital transformation depends on enterprise-level digitalization, external digital practices within supply chains further drive the upgrading of internal functional digitalization (Al Tera et al., 2024). The informational value generated by supply chain digitalization empowers dynamic processes across all stages of the supply chain, optimizing network structures, strengthening ecosystem collaboration, and enhancing the reliability of supply chain financing.

Comments3: The study measures SCDT via textual analysis of MD&A sections using five dimensions but does not explicitly provide the keyword list. It also does not justify the weighting of these dimensions. Hence, please append the list of SCDT keywords and justify dimension weighting. 

Response3: First, in Section 3.2.1, we supplemented the keyword list for supply chain digitalization (see Table 1). Supply chain digitalization is measured across five dimensions—planning digitalization, procurement digitalization, production digitalization, sales digitalization, and logistics digitalization. These dimensions, established based on official guidelines, possess framework validity, authority, and general applicability. Moreover, the five dimensions are closely interrelated and all play critical roles in supply chains. Therefore, this study applies an equal-weighted aggregation approach, measuring the degree of corporate supply chain digitalization by the cumulative frequency ratio of supply chain digitalization-related keywords across the five dimensions.

Comments4: The CCF variable ignores industry heterogeneity. For example, capital-intensive manufacturing sectors may naturally have higher accounts payable, biasing cross-industry comparisons. No industry adjusted CCF measure is considered. Please revise CCF to an industry-adjusted measure to control for sectoral differences.

Response4: We have incorporated the industry-adjusted CCF into the robustness checks. As shown in Table 18, the regression results support the original conclusions.

Comments5: In Table 1, the control variable “Lev (Asset-Liability Ratio)” is incorrectly described as “Log value of corporate total assets”. This inconsistency undermines the manuscript’s rigor. Please fix the “Lev” description in Table 1 to “Asset-Liability Ratio (Total Liabilities/Total Assets)” and ensure consistency with Section 3.2.4.

Response5: Thank you for pointing out this issue. We have revised Lev to the debt-to-asset ratio (total liabilities / total assets), as shown in Table 2, and ensured consistency with the description in Section 3.2.4.

Comments6: While the IV (1984 post office density) addresses regional digitalization endogeneity, two gaps remain: first, the study does not rule out reverse causality—firms with better CCF may have more resources to invest in SCDT. No lagged independent variables or PSM are used to address this; second, the study treats SCDT as a single construct but ignores differences between digital technologies. Do these technologies have heterogeneous effects on CCF via adjustment capability? Please add lagged SCDT (t-1) as the independent variable in regression models and conduct PSM to match firms with high/low SCDT based on firm size, age, and ROE, then re-test the main effect. Moreover, please split SCDT into sub-dimensions and test their differential impacts on CCF.

Response6: First, we conducted propensity score matching based on firm size, firm age, and return on equity to mitigate the endogeneity of corporate digitalization, as shown in Table 15. Second, we performed regressions using lagged independent variables, as reported in Table 16. Both sets of endogeneity tests further reinforce our original findings.

In addition, we decomposed supply chain digitalization into its five dimensions, as presented in Table 20. Four of these dimensions remain consistent with the original conclusions, while marketing digitalization exhibits an inverse relationship with trade credit financing. As marketing digitalization advances, firms disclose information more comprehensively to supply chain partners, particularly to customer firms. Such a high degree of transparency may be unfavorable to customers’ credit granting decisions. The differentiated effect of marketing digitalization represents an interesting and complex research issue. In future research, we plan to investigate this phenomenon in greater depth, and we have incorporated it into the Limitations and Future Research section.

Comments7: The study treats “operational adjustment capability” and “risk-bearing adjustment capability” as independent mediators but does not test their interaction effect. Please add an interaction term between “Stick” and “Resilience” in the mediation model to examine whether the two capabilities complement each other.

Response7: We further examined the interaction effect between organizational resilience and cost stickiness, as reported in Table 8, and found partial synergies between them. On the one hand, organizational resilience amplifies the suppressive effect of digitalization on cost stickiness; that is, under high organizational resilience, digitalization more effectively reduces cost stickiness. On the other hand, the moderating effect of organizational resilience on the relationship between cost stickiness and trade credit financing is not significant. These regression results suggest that organizational resilience, as an internal adjustment capability, can better facilitate resource reallocation and process reengineering under the catalyst of digital transformation, thereby mitigating cost stickiness. At the same time, organizational resilience, as a latent adjustment capability, effectively addresses long-term external shocks; however, compared with cost stickiness—an explicit financial indicator—its short-term impact on external financiers is relatively limited.

Comments8: The ESG moderating effect is tested as a single index, but its three dimensions may have distinct impacts. Similarly, “polluting enterprises” are defined using 2012 industry guidelines—no consideration of post-2012 environmental policies that may alter their risk profiles. Please split ESG into Environmental, Social, and Governance sub-indices to test their individual moderating effects. Some study is helpfu, such as 10.3390/su17030963. Please update the “polluting enterprise” definition to include post-2012 policies and test policy heterogeneity.

Response8: We further decomposed ESG into three dimensions for additional testing. As shown in Table 10, the moderating effect of the environmental dimension is more differentiated, while the moderating effects of the social and governance dimensions are less significant. Regarding the industry standards for polluting industries, on the one hand, these standards cover a wide range of 16 sub-industries within the manufacturing sector, which can be regarded as being equally affected by environmental protection policies. On the other hand, in the Chinese research context, this version of the heavily polluting enterprise standard has been continuously adopted to date and has been widely cited in the latest literature (Wu et al., 2024; Jia et al., 2025).

Comments9: The Discussion section (5.1) does not sufficiently contrast findings with prior work. For example, how does this study’s dual mediating mechanism differ from previous studies, which focuses on digitalization and trade credit? What makes the “adjustment capability” framework a novel contribution? In Section 5, please add a subsection titled “Comparisons with Prior Studies” to explicitly contrast findings with previous studies, highlighting the study’s incremental contribution.

Response9: In the final paragraph of Section 5.1 (Discussion), we added a summary of prior research and explicitly articulated the incremental contributions of this study. Specifically, the contributions are threefold. First, from the perspective of firms’ supply chain digital transformation, this study reveals its proactive role in trade credit financing. Second, it introduces the dual mediation mechanism of “cost stickiness and organizational resilience,” thereby extending the multiple pathways through which digitalization improves financing efficiency. Third, it develops a dual-dimensional moderating framework of “external governance and internal endowment,” uncovering the contextual dependence of the financing effects of digitalization. In addition, in the final paragraph of Section 1 (Introduction), we emphasized the innovative aspects of this study.

Comments10: Tables 5 and 6 have confusing column alignments. Some regression results (e.g., t-statistics in Table 7) lack consistent formatting, hindering readability. Please reformat Tables 5–8 to align column headers with regression results and ensure consistent labeling of t-statistics and fix missing N values.

Response10: Thank you for your reminder. We have adjusted the column widths of Table 5-6 (now Table 6-7) to align the headers with the data and refreshed the formatting of the regression results for consistency. The missing sample size is likely due to the table length causing the data to span multiple pages, with the sample size appearing on the next page. We will do our best to optimize the layout of the paper.

 

 

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors,

Your paper looks quite interesting and the statistical tools used are appropriate. However, I have a few issues/suggestions:

• Table 7: How were high ESG and low ESG companies segregated. You must explain it clearly in the text before this table or in the discussion section.
• Table 8: You have divided the table into Pollute companies, green companies, private companies and state companies. How did you segregate these companies?A green company could be either private or state owned. How can this table have four colums? In fact you had to divided it itno a grid: A. private: A1. green B1. polluting and B. state owned: B1. green B2. polluting. This implies that your analysis in this table is not correct, which affects the discussion and conclusions. Please redo table 8 and the discussion, conclusions and limitations based on the new data analysis.
• Your references are majorly outdated. Out of 74, only 27 are from 2021 and subsequente years. Please update the references, by adding a few new ones, without removing the old ones.  

Author Response

We are grateful to the reviewer for the valuable feedback. We have addressed all the comments and have revised the manuscript as detailed below.

Comments1: Table 7: How were high ESG and low ESG companies segregated. You must explain it clearly in the text before this table or in the discussion section.

Response1: We determined high-ESG and low-ESG companies using the median value of the listed company sample as the classification criterion. This was previously explained in the paper but may not have been clearly stated. The description has now been revised accordingly, specifically in the second sentence of the second paragraph of Section 4.5.1.

Comments2: Table 8: You have divided the table into Pollute companies, green companies, private companies and state companies. How did you segregate these companies?A green company could be either private or state owned. How can this table have four colums? In fact you had to divided it itno a grid: A. private: A1. green B1. polluting and B. state owned: B1. green B2. polluting. This implies that your analysis in this table is not correct, which affects the discussion and conclusions. Please redo table 8 and the discussion, conclusions and limitations based on the new data analysis.

 

Response2: We further considered the cross-moderating effects based on the internal endowment matrix. As shown in Regression Table 12, private enterprises are more significantly influenced by market mechanisms, and green private companies can better obtain trade credit financing through digital transformation. In contrast, state-owned enterprises are more markedly affected by policy adjustments, with polluting state-owned enterprises more likely to receive policy support and invest more heavily in digitalization, thereby facilitating access to financing. Accordingly, we have also refined the Limitations and Future Research section.

 

Comments3: Your references are majorly outdated. Out of 74, only 27 are from 2021 and subsequente years. Please update the references, by adding a few new ones, without removing the old ones.  

Response3: We have reviewed the latest literature and enhanced the theoretical foundation by incorporating 17 recent publications from the past three years, as detailed in the reference list.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors analyze how Supply Chain Digital Transformation (SCDT) affects Commercial Credit
Financing (CCF), using Chinese manufacturing listed companies (A-share enterprises, 2013–2023) as the sample. It is an interesting article because corporate adjustment capability (cost stickiness and organizational resilience) is set as a mediating variable, and ESG, industry competition, ownership structure, and whether it is a polluting enterprise are tested as moderating variables. On the other hand, several improvements seem necessary for this article to be published.
▶ The path “Digitalization → financial access” has already been discussed in similar previous studies (FinTech/SCF literature). What is the difference of this research? The creativity of the study is not clear. Similar research, for example: 1) Guan, Y., Sun, N., Wu, S. J., & Sun, Y. (2025). Supply Chain Finance, Fintech Development, and Financing Efficiency of SMEs in China. Administrative Sciences, 15(3), 86. (There are more related studies available, and the authors are encouraged to refer to them) It is necessary to highlight what is different from these studies.
▶ “Adjustment capability” is limited to cost stickiness and organizational resilience, but isn’t this
too narrow? It seems theoretically more valid to consider strategic/dynamic aspects such as sensing the environment, opportunity, seizing, and resource reallocation, rather than limiting adjustment capability only to financial indicators. This is a newly published article. Please refer to it: Operating financial sustainability in the German car industry: a new approach. https://doi.org/10.1016/j.tranpol.2025.103791
▶ The SCDT indicator is keyword frequency-based, but do the authors think that the text-mining
method can fully reflect the actual digitalization level among companies? Text analysis has become
much more sophisticated. By using centrality, network, clustering, core–periphery analysis, etc., the
substantive influence of indicators can be derived and used as the SCDT indicator.
▶ You use “1984 post office density” as IV, but please present the F-statistic and Over-identification
test results. Otherwise, the validity of the IV must be doubted.
▶ China is a strong policy-executing country. In the research background, the context “digital
transformation is aligned with government strategies (e.g., Made in China 2025, Digital China)” is
well mentioned, but not reflected in the empirical model, so the possibility of endogeneity exists.
(For example, in industries with concentrated government policy, both digitalization level and
financial access may increase simultaneously → omitted variable bias). This needs to be resolved.
Please consider a policy-shock variable as a dummy variable.
▶ The effect of digitalization on financial access is more likely to appear with a certain time lag
rather than immediately. The absence of lagged regression is a limitation and should be discussed
in detail.
▶The literature review relies on certain authors (especially Ivanov, Hofmann), but since there are
many related studies on this topic, a more diversified literature review is needed.
▶Contribution should be described by dividing into theoretical, policy, and practical implications.

Author Response

We thank the reviewer for their time and insightful comments. These suggestions have been very helpful in improving the quality of our manuscript. We have revised the manuscript accordingly and point-by-point responses to the comments are provided below.

 

Comments1: The path “Digitalization → financial access” has already been discussed in similar previous studies (FinTech/SCF literature). What is the difference of this research? The creativity of the study is not clear. Similar research, for example: 1) Guan, Y., Sun, N., Wu, S. J., & Sun, Y. (2025). Supply Chain Finance, Fintech Development, and Financing Efficiency of SMEs in China. Administrative Sciences, 15(3), 86; 2) Liu, Z., Zhang, J., & Cao, L. (2025). The synergistic effect of digital transformation technology and supply chain finance: empirical evidence from 500 listed companies. Frontiers in Physics, 13, 1664273; 3) He, W., Zhang, Y., & Wang, M. (2024). Fintech, supply chain concentration and enterprise digitization: Evidence from chinese manufacturing listed companies. Finance Research Letters, 59, 104702; 4) Wan, Q., & Cui, J. (2024). Dynamic evolutionary game analysis of how fintech in banking mitigates risks in agricultural supply chain finance. arXiv preprint arXiv:2411.07604…..and so on. It is necessary to highlight what is different from these studies.

 

Response1: We have refined the research innovations of this paper, with specific improvements detailed in the last two paragraphs of Section 1 (Introduction). The incremental contributions of this study are mainly reflected in three aspects: First, in terms of research perspective, this study shifts the focus from the digital transformation of the financial supply side to the digital practices within firms' own supply chains, highlighting the proactive role of enterprises in commercial credit financing. Second, in terms of mechanism, this study innovatively proposes a dual mediating mechanism of "cost stickiness and organizational resilience," thereby moving beyond the prevailing logic that emphasizes information transparency alone. It reveals that digital transformation improves financing efficiency through multiple pathways, by enhancing both operational management capabilities and organizational management capabilities. Third, in terms of moderating effects, this study constructs a dual-dimensional framework of "external governance and internal endowments" to systematically examine the heterogeneous impacts of ESG performance, industry competition, ownership structure, and pollution attributes on the financing effects of digital transformation, thereby uncovering its contextual dependence.

 

 

Comments2: “Adjustment capability” is limited to cost stickiness and organizational resilience, but isn’t this too narrow? It seems theoretically more valid to consider strategic/dynamic aspects such as sensing the environment, opportunity, seizing, and resource reallocation, rather than limiting adjustment capability only to financial indicators. This is a newly published article. Please refer to it: Operating financial sustainability in the German car industry: a new approach. (https://doi.org/10.1016/j.tranpol.2025.103791)

Response2: We thank the reviewer for their constructive feedback. We fully agree on the necessity of examining "firm adjustment capability" from a strategic/dynamic dimension. It is important to clarify that this paper defines "firm adjustment capability" as cost stickiness (financial adjustment) and organizational resilience (organizational adjustment), which does not imply a neglect of strategic or dynamic capability elements. On the contrary, these indicators theoretically represent the manifestation of strategic dynamic capabilities (sensing–seizing–reconfiguring) at the financial and organizational levels. Furthermore, a firm’s ability to adjust its cost structure more swiftly during adversity, thereby improving cash flow and solvency, reflects the micro-level performance of strategic resource reallocation. Using cost stickiness as a financial metric for gauging a firm’s "dynamic factor adjustment capability" is an established empirical approach (Li et al., 2024).

Additionally, in the final three paragraphs of Section 2.2, we have revisited and expanded the literature review based on firm adjustment capability. Theoretically, we explicitly position "firm adjustment capability" as a micro-level manifestation of dynamic capabilities: digital transformation enhances a firm’s environmental sensing and opportunity seizing capacities and facilitates resource reallocation through organizational and process reconfiguration. These strategic dynamic capabilities are concretely reflected at the firm level as reduced cost stickiness and increased organizational resilience (i.e., adjustment capabilities at the financial and organizational levels), thereby promoting the improvement of trade credit and access to supply chain financing.

 

Comments3: The SCDT indicator is keyword frequency-based, but do the authors think that the text-mining method can fully reflect the actual digitalization level among companies? Text analysis has become much more sophisticated. By using centrality, network, clustering, core–periphery analysis, etc., the substantive influence of indicators can be derived and used as the SCDT indicator.

Response3: The use of keyword frequency-based methods to measure the degree of corporate digital transformation has gained widespread acceptance in academia, indicating that it is a mature and extensively validated approach for assessing digitalization levels. Recent literature in the past few years (e.g., Li et al., 2023; Zareie et al., 2024; Ren et al., 2023) has commonly adopted this method, utilizing the frequency of keywords in corporate annual reports as a proxy for digitalization intensity to examine its impact on corporate performance and organizational resilience. This demonstrates the direct applicability of the metric in empirical analyses. Zou et al. (2024) systematically reviewed digitalization measurement methods and also highlighted text mining-based keyword frequency as a commonly used and effective approach in existing studies. We appreciate the reviewer’s suggestion regarding advanced methods such as network analysis and centrality measures. We will continue to actively learn and master these cutting-edge methodologies and strengthen their exploration in future research. This point has been incorporated into Section 5.5 (Limitations and Future Research).

 

Comments4: You use “1984 post office density” as IV, but please present the F-statistic and Over-identification test results. Otherwise, the validity of the IV must be doubted.

 

Response4: We thank the reviewer for their valuable feedback. Regarding your request for the results of the over-identification test, we would like to provide the following clarification:  Since our instrumental variable model is just-identified—with one endogenous variable (supply chain digital transformation, SCDT) corresponding to one instrumental variable (1984 post office density, post)—the degrees of freedom for the over-identification test are zero. As a result, it is not feasible to perform an over-identification test for the instrumental variable. In the first paragraph of Section 4.6 and in Table 13 of the paper, we report an F-statistic greater than 10, indicating that the instrumental variable is correlated with the endogenous variable and does not qualify as a weak instrument. Furthermore, as shown in Table 13, this study also reports the Anderson LM statistic with a p-value < 0.01, confirming a significant correlation between the instrumental variable and the endogenous variable.

 

Comments5: China is a strong policy-executing country. In the research background, the context “digital transformation is aligned with government strategies (e.g., Made in China 2025, Digital China)” is well mentioned, but not reflected in the empirical model, so the possibility of endogeneity exists. (For example, in industries with concentrated government policy, both digitalization level and financial access may increase simultaneously → omitted variable bias). This needs to be resolved. Please consider a policy-shock variable as a dummy variable.

 

Response5: Thank you for your suggestion. We have incorporated the impact of the supply chain digitalization pilot policy shock on trade credit financing in Section 4.6. As shown in Table 14, the coefficients of the interaction terms (Policy, Policy2019, Policy2020) demonstrate significantly positive correlations, indicating that the effect of supply chain digital transformation on corporate trade credit financing is both positively promotive and sustainable.

 

Comments6: The effect of digitalization on financial access is more likely to appear with a certain time lag rather than immediately. The absence of lagged regression is a limitation and should be discussed in detail.

 

Response6: We incorporated a one-period lagged term of the independent variable to mitigate endogeneity concerns in the study. The regression results, as shown in Table 16, support the original research conclusions and indicate that the impact of digitalization on financing acquisition exhibits a time-lag effect.

 

Comments7: The literature review relies on certain authors (especially Ivanov, Hofmann), but since there are many related studies on this topic, a more diversified literature review is needed.

 

Response7: In the final three paragraphs of Section 2.2, we have comprehensively revisited and expanded the literature review, strengthening the theoretical foundations by incorporating sources related to dynamic capability theory and agency theory. This includes references to scholars such as Teece, Sharma, Zhang, Shi, and Al-Moaid, as well as contributions from Cachon, Li, Chen, and others, thereby diversifying the literature sources and further solidifying the theoretical basis of this study.

 

Comments8: Contribution should be described by dividing into theoretical, policy, and practical implications.

 

Response8: We have thoroughly revisited the original research contributions and reorganized them into three dimensions: theoretical implications, policy insights, and practical value, as detailed in Sections 5.2 to 5.4.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors,

I feel your paper looks good enough now.

Reviewer 3 Report

Comments and Suggestions for Authors

I think the authors have done a great job in addressing the earlier comments.

The revised manuscript is now much clearer in terms of its theoretical positioning and contributions.

I especially appreciate the stronger articulation of the dual mediating mechanism and the additional robustness checks. One small point is that the discussion of “adjustment capability” could still be broadened in future work to cover strategic sensing and seizing aspects, beyond cost and resilience.

Similarly, the measurement of digitalization might be enhanced with more advanced text-mining techniques in future studies.

But these are relatively minor limitations—the current version is well-prepared and I lean toward acceptance.