Digital Intelligence and Decision Optimization in Healthcare Supply Chain Management: The Mediating Roles of Innovation Capability and Supply Chain Resilience

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Please see the attached file.

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript investigates the impact of digital intelligence on strategic decision optimization within healthcare supply chains, exploring the mediating roles of innovation capability and supply chain resilience (comprising absorptive, response, and restorative capabilities). The research aims to provide theoretical and practical insights for the sustainable development and optimization of healthcare supply chains.

The paper presents several strengths, but it also presents some weaknesses that should be addressed to enhance its clarity, depth, and impact and make it suitable for publication. These include:

• Relevance to Sustainability: While the paper claims a sustainability focus, the emphasis leans more heavily on resilience. Although related, the link between the two should be more explicitly developed to reinforce the sustainability dimension.
• Literature Review: The literature review would benefit from a subsection summarizing existing contributions (better if through a table) and clearly highlighting the research gap.
• Section 3 Structure: This section combines hypotheses and research methods; consider renaming or restructuring it for greater clarity and coherence.
• Practical Implications and Ethical Considerations: Although the abstract references practical guidance, the discussion section would benefit from more specific, actionable recommendations tailored to healthcare organizations. In particular, the paper should elaborate on how digital intelligence can be effectively implemented to strengthen different dimensions of supply chain resilience. Including concrete examples (e.g., tools, technologies, or implementation strategies) would significantly enhance the practical relevance of the findings for managers and policymakers. Additionally, a brief reflection on potential challenges related to digital intelligence adoption, such as data privacy concerns, cybersecurity risks, and organizational resistance, could offer a more balanced and realistic perspective, especially given the sensitivity of healthcare data.
• General Use of Acronyms: Acronyms should be defined once (not in the abstract) and not repeatedly throughout the text to improve readability.
• Generalizability of Data: While the sample size is adequate, data collection was exclusively from healthcare supply chain organizations in China. While this provides valuable insights into a specific context, the generalizability of findings to other geographical or economic contexts can be discussed more explicitly or introduced as a limitation.
• Temporal Aspect of Resilience: The temporal sequencing of absorptive, response, and restorative capabilities warrants deeper exploration, particularly in how digital intelligence dynamically supports each phase.
• Digital Intelligence Measures:  While the concept of digital intelligence is critical, the manuscript could further elaborate on how the measurement of digital intelligence specifically captures aspects relevant to healthcare supply chains, or if there are specific digital technologies emphasized.

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Title of the Paper: Digital Intelligence and Decision Optimization in Healthcare Supply Chain Management: The Mediating Roles of Innovation Capability and Supply Chain Resilience [Manuscript ID: sustainability-3711683]

The present research uses Resource Based View (RBV) and Dynamic Capabilities Theory (DCT) to connect digital intelligence and decision optimization in healthcare supply chain management. The research develops a conceptual model based on structural equation modeling (SEM) imbibing innovation capability and supply chain resilience (absorptive, response, and restorative capabilities), to know their direct and indeirectl influence.

Comments:

1. Please refer to the abstract: “Specifically, digital intelligence strengthens innovation capability, which in turn activates all three dimensions of resilience, and together these capabilities produce a synergistic effect that sustains decision improvement.” This may be suitably modified to include the inferences from the nonsuppurative hypothesis as well.
2. (a) A study by Zhang et al. (2025) revealed that artificial intelligence (AI) capabilities significantly contribute to green innovation (GI), while potential absorptive capacity (PAC) and realized absorptive capacity (RAC) serve as key mediators between AI capabilities and GI. Further, a study by Tian et al. (2022) revealed that AI technology reshapes decision-making. However, in the present study, the result of hypothesis H7 was rejected and shows the results another way for decision optimization.

(b) Another study by Belhadi et al. (2024) revealed that digital intelligence influences innovation capability and decision optimization in the health care supply chain. However, please refer to “Specifically, the indirect effect of DI --> IC --> DO was significantly negative (p < 0.001),” which revealed a negative relationship.

(i) Zhang, et al.,, (2025). Sustainable development with Artificial Intelligence: Examining the absorptive capacity pathways to green innovation. Journal of Environmental Management, 381, p.125219. (ii) Belhadi, et al., (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Annals of Operations Research, 333(2), pp.627-652.

 

3. Similarly, H1: Digital Intelligence has a positive effect on Decision Optimization.
4. Similarly, H4: Innovation Capability has a positive effect on Decision Optimization.
5. Decision optimization (DO) is abbreviated but not used further in the manuscript; similarly, several other phrases like Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), etc., are not used.
6. Table 6 of Path Analysis Results is not discussed in the manuscript.
7. The theoretical contribution looks very general. The authors should provide more concise and systematic applications of the present work or explain how digital intelligence (DI) combined with innovation capability (IC) improves supply chain resilience through absorption, responsive, and restorative capabilities towards decision optimization, giving industry examples.
8. The similarity of 23% seems to be on the higher side. 9. Please refer to “This study targeted managerial-level respondents within organizations that constitute the healthcare supply chain in China.” It looks very broad. More details, like designation and department, may be included in Table 2 of Demographic Characteristics of the Respondents.
9. Authors may refer the following research paper of Sustainability, MDPI on process innovation in SC.  Qureshi, K.M., et al., 2023. Sustainable manufacturing supply chain performance enhancement through technology utilization and process innovation in industry 4.0: a SEM-PLS approach. Sustainability, 15(21), p.15388.

Comments on the Quality of English Language

Writing issue/ Grammer

1. Please refer to line no. 43 'Personalized medicine..' should be 'Personalized medicine..'
2. Please refer to line no. 295 ‘DIn the highly uncertain environment…’, should be corrected.
3. Please refer to line no. 476 ‘…. . presented in <Table 1>., should be corrected similarity,
4. Pl check line no. 487 ‘…. are presented in <Table 2>, line no. 503’… presented in <Table 3>, Please refer to line no. 523’.presented in <Table 4 >, 536 ‘..presented in <Table 5>, Please refer to line no. 565 ‘… presented in <Table 7>.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Digital Intelligence and Decision Optimization in Healthcare Supply Chain Management: The Mediating Roles of Innovation Capability and Supply Chain Resilience

This is a well-structured and theoretically grounded study that offers valuable insights into the role of digital intelligence in decision optimization within healthcare supply chain management. However, there some flaws should be considered:

Abstract

The abstract lacks important information such as sample size and study approach , which are essential for readers to understand the scope and methodology of the research. Additionally, specific practical implications should be clearly addressed to enhance the paper’s relevance to practitioners.

The phrase “Our findings offer practical theoretical guidance” is ambiguous. Please clarify what is meant by “practical theoretical.” Do you mean that the findings provide both theoretical contributions and actionable recommendations for managers? If so, this should be explicitly stated.

Introduction

A pivotal flaw is that the relationship between Innovation Capability (IC) , Supply Chain Resilience (SCR) , and Decision Optimization (DO) is mentioned early but not clearly explained until much later in the paper. I recommend that the authors justify these relationships with supporting references and integrate them earlier in the introduction to build a stronger conceptual foundation.

Furthermore, more details are needed to clarify the distinction between Digital Intelligence (DI) and general digital technologies, as this is central to the novelty of the study.

In line 93, the author argues:

“Unlike previous studies that primarily emphasized IT systems or digital tools…”

This claim needs to be supported with references to prior studies that illustrate this limitation. Additionally, the statement:

“This study focuses on the technical characteristics of digital intelligence as a central feature of digital transformation”

is somewhat misleading, as there are existing studies that have already explored the technical characteristics of DI. The authors should position their contribution more clearly in relation to this body of work.

Moreover, the authors do not sufficiently discuss the limitations of prior studies beyond briefly noting that they are "qualitative or case-based." A deeper and more critical analysis of how existing frameworks fall short, particularly in terms of theoretical integration or practical application ,would help justify the research gap more effectively.

There appears to be an error in lines 182–183 , where "Senna et al. (2023)" is cited but seems misplaced or misformatted. Please check and correct accordingly.

In lines 82–85 , regarding the research objective: “to develop a theory-driven analytical framework that systematically explores how digital intelligence enhances decision optimization”

I recommend that the authors present this objective after outlining the research gap to strengthen its justification and make the flow of ideas more logical.

Finally, the introduction section needs to be rewritten with improved logical flow and storytelling style. This will enhance transitions between paragraphs and improve overall readability and coherence.

Literature Review

One major concern is how RBV and DCT specifically inform the hypothesized relationships. The authors should consider adding a dedicated theoretical foundation section that explains how each theory contributes to the model and links the constructs together.

Additionally, the focus on only three dimensions of supply chain resilience (absorptive, response, restorative) requires further justification. While these are valid components, many studies use a broader set of five dimensions of resilience (e.g., agility, adaptability). The authors should support their choice with academic arguments , not just general statements.

In line 142 , the statement:

“However, existing research largely treats digital intelligence as a technical tool, lacking structured modeling and theoretical interpretation…”

requires additional supporting references rather than being presented as a general observation.

Similarly, in line 193 :

“Although some studies acknowledge the multi-stage nature of resilience, there remains a lack of in-depth integration between resilience and DCT.”

This assertion also needs to be supported with references to show that such integration is indeed lacking in the literature.

There is also a repetition of Section 2.3.2 , titled “Absorptive Capability” , which appears to be a mistake. It seems the authors intended to refer to response capability instead.

The hypotheses development section is weak due to insufficient critique of the existing literature and limited supporting evidence from prior studies. The literature review must better establish the hypotheses and ensure that references are linked specifically to the healthcare supply chain context.

Methods

The methods section is generally well-written. The high response rate enhances the reliability of the findings. The results are clearly presented and supported by appropriate statistical analysis.

Discussions

While the discussion of findings is generally good, it would benefit from a more structured and in-depth analysis of each finding individually. Each hypothesis result should be discussed separately, including why certain paths were significant or not, and how they align or contrast with previous studies.

Implications

The practical implications need refinement. Currently, the recommendations are generic and lack the depth necessary to fully demonstrate the significance and applicability of the research. The authors should tailor implications based on study findings and stakeholder roles.

For instance, healthcare supply chains involve multiple stakeholders (e.g., hospitals, pharmaceutical companies, logistics providers, regulators), yet the recommendations do not differentiate among them or address potential conflicts or coordination barriers. Providing tailored guidance for different actors in the supply chain would significantly enhance the paper's impact.

Additionally, the implications focus heavily on technology adoption and operational tools , with little mention of organizational or strategic considerations such as change management, leadership, or governance structures.

The proposed solutions (e.g., API-based system integration, intelligent replenishment models) may require significant investment and technical expertise, particularly in resource-constrained or emerging economies. The authors should consider discussing cost-benefit trade-offs , feasibility, and policy supports for low-income settings.

 

There also Some citations are numbered sequentially (e.g., [1], [2]), while others include author-year formats (e.g., “IQVIA Institute, 2024” Zhao et al. (2023)…etc.). Ensure all references follow the required citation format consistently.

There are also grammatical mistakes like (Din in line 295).

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The paper can be accepted after minor revision on the editing and language.

Author Response

Comments: The paper can be accepted after minor revision on the editing and language.

Response: Thank you sincerely for your kind evaluation and thoughtful suggestion regarding the language and formatting of the manuscript.

In response to your comment that the paper requires minor revisions in editing and language, I have carefully reviewed the manuscript and made the following improvements:

1.I revised the first sentence of the Abstract from “…supply disruptions, high risk, and the imperative of sustainable resource optimization.” to “…unpredictable supply disruptions pose high risks and create an imperative for sustainable resource optimization,” in order to enhance clarity and precision.

2.I corrected the expression “On one hand / On the other hand” by adding the definite article to conform to proper usage, changing it to “On the one hand … on the other hand …” for improved grammatical accuracy.

3.I removed the terminal periods from all table captions (e.g., “Table 4. Exploratory Factor Analysis Results”), following standard journal formatting practices.

4.In Table 5, I revised the column header “Unstd.” to “Estimate” to align with standard terminology preferred by most academic journals.

5.I have revised all instances of “decision making” used as a compound adjective by adding hyphenation (i.e., “decision-making”) to ensure grammatical correctness.

Reviewer 3 Report

Comments and Suggestions for Authors

Digital Intelligence and Decision Optimization in Healthcare Supply Chain Management: The Mediating Roles of Innovation Capability and Supply Chain Resilience
Manuscript ID: sustainability-3711683
Thank you for the updated manuscript.
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(a) The number following the keywords is not in line with MDPI format hence may be removed. 
(b) Please refer to Table 2 of Variable Measurement; please check sources. As per MDPI format, the reference number is in line, and names may be removed, for instance Lee et al. (2023) [81]; remove: Lee et al. (2023).

Author Response

Comments: 
(a) The number following the keywords is not in line with MDPI format hence may be removed. 
(b) Please refer to Table 2 of Variable Measurement; please check sources. As per MDPI format, the reference number is in line, and names may be removed, for instance Lee et al. (2023) [81]; remove: Lee et al. (2023).

Response: Thank you very much for your careful review and constructive suggestions. 
(a) As per your advice, I have removed the number following the keywords to align with the MDPI formatting requirements.
(b) I have reviewed the reference citations in Table 2 and revised them accordingly. Specifically, I removed the author names (e.g., “Lee et al. (2023)”) and retained only the inline reference numbers (e.g., “[81]”) as required by the MDPI style guide.