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Article
Peer-Review Record

Big Data Management Capabilities and Green Innovation: A Dynamic Capabilities View

Sustainability 2023, 15(19), 14637; https://doi.org/10.3390/su151914637
by Hongyi Mao and Jiang Lu *
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Reviewer 3:
Sustainability 2023, 15(19), 14637; https://doi.org/10.3390/su151914637
Submission received: 25 August 2023 / Revised: 15 September 2023 / Accepted: 27 September 2023 / Published: 9 October 2023
(This article belongs to the Special Issue Economic Transition and Green Development)

Round 1

Reviewer 1 Report

Hello authors,

I greatly enjoyed reading your article, congratulations on that! It's a highly relevant topic, and there are just a few things that could be adjusted to enhance it further. Here are some considerations for improving your study:

Title:

Consider simplifying the title for greater clarity.

Abstract:

Include a brief mention of the methodology used for data collection and analysis in the abstract.

Introduction:

In the final paragraph of the introduction, consider incorporating an outline of the article's structure, providing readers with an overview of what to expect in the upcoming sections.

Conclusion:

Initiate your conclusion by clearly stating the primary objective of your work and assessing whether this objective has been successfully accomplished.

Following this, offer a concise summary of the actual results that your study has revealed.

These suggestions should help enhance the clarity and effectiveness of your article. Once again, congratulations on your work, and thank you for contributing to this important field of research.

Best regards.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

I wish to thank for the opportunity to review this interesting paper. It is well written, and it deals with an important and highly topical theme, i.e. how big data management capabilities (BDMCs) could drive green innovation. The authors suggest that BDMCs can impact green innovation only through interaction with other capabilities, particularly green dynamic capabilities (DCs). In other words, BDMCs’ impact on green innovation is diminished without green DCs. The research emphasizes the significance of developing both BDMCs and green DCs as a means of achieving green innovation. Another interesting finding is that the effects of BDMCs on green DCs increase under turbulent circumstances. As turbulence constantly increases in the world, the value of BDMCs and green DCs will increase in the future.

The empirical data is collected from 266 Chinese manufacturing enterprises. Although I am not an expert of the methodologies the authors use, the methods are logically explained, and the argumentation of the authors seems very convincing. The authors explain their model and all its elements in detail. The results are thoroughly explained.

I tried to find at least some issues that could be improved in the manuscript, but I find it hard. If anything, the authors could explain why they did not include in their model green value co-creation and green practices, even though they refer to Yousaf (2021) who found the mediating role of them between green DCs and green innovation.

I think this interesting paper makes a very nice contribution to the journal.

The English language is good, and I only suggest spell check.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

I thank the authors for the opportunity to review an actuality study assessing the linkage and impact of management big data practices and green innovation in manufacturing enterprises.

The manuscript is well structured. The authors defined the goal, described the context of the study, and formulated the research hypotheses.

A deeply developed theoretical section allows you to understand the theoretical foundations on which the authors built their reasoning and research model.

The authors used in the study mathematical and statistical methods for assessing the responses of 266 manufacturing companies according to the hypotheses of the study. All claimed research methods are evidence-based and suitable for testing hypotheses. Figures and tables demonstrate the key points of the study, allow you to adequately evaluate the results of statistical analysis.

The conclusions of the manuscript are consistent with the evidence and arguments presented.

Highly appreciating the quality of the submitted manuscript, I recommend that the authors include in the introduction and abstract information that the study is based on the analysis of the results of the survey (and not on the basis of the analysis of quantitative indicators of enterprises activity). The results of the study are the basis for moving on to the next stage - the analysis of quantitative indicators of the activities of enterprises within the framework of this research topic, which may be one of the areas of research in the future.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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