Research on the Construction Path of Circular Supply Chain with Multiple Subjects: Identification and Analysis of Key Driving Factors Based on Technology Cycle

Comments 1:

The abstract is too long and the structure is a bit unclear. It does not highlight the key points well. It is recommended to reorganize around "research questions-methods-key findings-theoretical/practical significance" and remove unnecessary details, leaving only the core ideas. In addition, acronyms like "CE" should be spelled out for the first time.

Response 1:

Thank you for pointing this out. We agree with this comment. Therefore, we have revised the abstract in a strict "research questions-methods-key findings-theoretical/practical significance" format, as shown below. In addition, we apologize for the error you pointed out that acronyms such as "CE" should be spelled out the first time they appear, so we have checked and revised the entire text to avoid this.

Page 1, lines 10-39

Before:

Circular supply chain (CSC) can achieve resource optimization and eco-efficiency in business strategy and environment, and its circular process involves many stakeholders. However, in the operation of CSC, there are interactions and interdependencies among multiple drivers. How to synergistically promote the active participation of different entities in CSC practices in the complex context of interacting factors is an important condition for moving towards low-carbon sustainable development and realizing the economic benefits as well as the competitive advantages of enterprises. Therefore, based on the technology cycle perspective (including recycling, remanufacturing, refurbishing, repairing, and reusing), this study uses text mining and bibliometrics to identify the drivers systematically. In addition, an assessment model is developed to demonstrate the prioritization of the factors and to construct a comprehensive framework for a set of CSC implementation pathways covering multiple subjects and multidimensional factor interaction mechanisms. The results emphasize that the most critical drivers are policies and regulations, resource use efficiency, and consumer awareness and attitudes, with contribution rates of 5.1%, 4.5%, and 4.5%, respectively. For different actors: 1. Improving product quality is an important path for suppliers and manufacturers to collaborate in implementing CSC, with a contribution rate of 30.8% from both parties. In this path, suppliers must develop durable products from the product design stage to ensure that manufacturers maintain high standards in the remanufacturing/recycling process. This positive cycle reinforces the cooperation between the two parties.2. Business agility contributes 18.3% to the synergistic practices of manufacturers and distributors. Cascading and reuse, which will enable manufacturers to produce new products from recycled materials through remanufacturing and make them reusable by distributors through repair or refurbishment at the end of the product's life, integrates both parties' relationship with the environment and improves business agility, with a contribution rate of 14.1%.3. Developing CE's skill sets and sharing of information are the optimal paths for collaborative suppliers, manufacturers, and distributors to successfully implement CSC, with a synergistic contribution rate of 14.6%. Its synergistic contribution is 14.6% and 12.5%. This helps to break down data silos and facilitate the integration of resources among the three parties, thereby enhancing the efficiency and performance of the entire supply chain.

Change to:

The cyclic process of the circular supply chain (CSC) involves many stakeholders, and how to synergistically promote the active participation of different entities in CSC practices in the complex context of interacting factors is an important condition for moving towards low-carbon sustainable development and realizing the economic benefits as well as the competitive advantages of enterprises. Therefore, based on the technology cycle perspective (recycling, remanufacturing, refurbishing, repairing, and reusing), this study combines text mining and bibliometrics to identify CSC drivers, establish a factor prioritization assessment model, and construct a comprehensive framework for a set of CSC implementation pathways covering multiple subjects and multidimensional factor interaction mechanisms. The results emphasize that the most critical drivers are policies and regulations, resource use efficiency, and consumer awareness and attitudes, with contribution rates of 5.1%, 4.5%, and 4.5%, respectively. On this basis, this paper explores the efficiency-enhancing path strategy for the synergistic implementation of CSC by multiple subjects from the perspectives of the four key subjects of CSC. It puts forward policy recommendations to promote the successful implementation of CSC at the level of mechanism construction and specific operation, to provide theoretical guidance for the cooperation of upstream and downstream subjects.

Comments 2:

Although the introduction and background sections cover the circular supply chain and technology cycle, they are relatively superficial. The theoretical contribution of the paper is not clearly stated. It would help to further clarify what is new in the paper about identifying multi-actor drivers and path construction, and how it differs from or complements previous studies.

Response 2:

Thank you very much for your comments and review. We agree with this comment. First of all, the first three paragraphs of the introduction section introduce the concepts of CE and CSC, and the value of research on the synergistic implementation of CSC by multiple subjects. However, we are not clear enough about why “the research value of categorizing the subjectivity of drivers based on the technology cycle perspective”. Therefore, we have developed a detailed explanation and deleted redundant text in the fourth paragraph. Secondly, we add the gaps in the existing research after the introduction, then draw out the differences between this paper and the previous studies, and finally express the theoretical and practical contributions of this paper clearly. This can be seen in the following.

Page 2-3, lines 71-105

Before:

In addition to focusing on the mechanisms of collaboration among CSC stakeholders, there is a need to identify the key influencing factors of different stakeholder subjects and to explore the interactions between these factors and how they affect the subjects in order to facilitate successful CSC practice. Although “drivers” are one of the driving functions of CSC and have received a lot of attention from researchers, there is no literature on the subject that identifies and categorizes the influences from the cross-sectional dimension of SC and explores in depth the dynamic interactions between these factors (Ayati et al., 2022). Furthermore, it is observed that the literature on the effective adoption of the strategic framework of circular supply chain management (CSCM) practices to promote overall firm competitiveness is still in its infancy (Lahane et al., 2020). Knowledge of the factors influencing SC transformation in the context of CE can be broadened by investigating the challenges of CSC and the associated managerial impacts brought about by the network of key players involved in different restorative business models (Zhang et al., 2021). As a result, we found a lack of research that promotes the implementation of CSCM from the perspectives of multi-actor synergy and multi-factor synergy. In addition, there is a need for research to develop intervention policies that are satisfactory to all subjects or CSC implementation pathways in which multiple subjects are actively involved.

This paper aims to fill this research gap by developing a multi-subject construction pathway framework capable of coordinating the participation of many stakeholders in CSCM implementation from the perspective of the CE technology cycle. In addition, it shows the synergies between the key drivers in the classification and the relationship between multifactor interactions and different subjects, leading to the construction of a more optimized CSCM system. In summary, this study aims to achieve the following three objectives:

O1. To construct a set of CSC theoretical analysis framework covering the interaction mechanism of multiple subjects and multidimensional factors, to provide support for research and decision-making in the field of CSC, and to explore the mechanism of the role between multidimensional subjects and factors based on the perspective of technology cycle.

O2. An assessment model for multi-actor CSC influencing factors was developed to determine the importance of factors to help companies achieve their dual-carbon goals and ensure their economic development.

O3. A CSC construction path for the satisfaction of multiple subjects is proposed to enable the organization to strategically manage resources and reduce risks by increasing the participation motivation and collaboration synergy of each subject.

Change to:

In addition to focusing on the collaboration mechanism between CSC subjects, it is also necessary to identify the key influencing factors of different subjects and explore the interactions between these factors and how they affect the subjects to promote the successful practice of CSC. By analyzing the role of the factors and the response mechanism of the subjects, we can accurately design synergistic strategies and transform the theoretical feasibility into operability in practice (Zhang et al., 2021). Therefore, as one of the driving functions of CSC, “drivers” have received attention from many researchers. However, there is no literature on this topic that identifies and categorizes the influencing factors from the transversal dimension of CSC (Ayati et al., 2022) and explores the dynamic interactions among these factors in depth. Moreover, it is observed that the literature on the effective adoption of the strategic framework of circular supply chain management (CSCM) practices to promote overall firm competitiveness is still in its infancy (Lahane et al., 2020).

In summary, relevant studies still have the following limitations. (1) Existing literature mostly focuses on the vertical challenges (e.g., technical/policy barriers) of CSC implementation. However, it does not systematically identify and categorize the influencing factors from the horizontal dimension (suppliers, manufacturers, users, etc.), resulting in the subject-specific driving mechanism not being deconstructed. (2) Theoretical gaps in the dynamic interaction between factors. (3) Existing CSC practice frameworks are mostly conceptual proposals, lacking a systematic approach that integrates multi-subject collaboration mechanisms, dynamic factor weights, and competitiveness enhancement paths.

In this regard, this paper focuses on the following issues. (1) Systematically identifying and counting CSC drivers, and categorizing the factors regarding subject relevance through a technology cycle perspective. (2) Quantitatively assessing the priority of the driving factors and their differential impact on the enthusiasm of multiple subjects to participate. (3) Construct a multi-subject CSC pathway framework and provide specific multi-subject collaborative optimization pathways and strategies.

The main contributions of this paper are as follows. (1) Enriching the theoretical connotation of CSCM. From a multi-subject perspective, this paper identifies and categorizes the factors as subjects and explores in depth the dynamic interactions among these factors, which solves the problem of confusing factor-subject coupling effects in previous studies. (2) Expanding research methods. This paper constructs an assessment model to prioritize CSC driving factors, which helps identify the driving effects of factors on each subject, to better grasp the differences in the response of the same subject to different factors, and to drive the subject to actively participate in CSC practices to promote the realization of the dual-carbon goal. (3) This paper provides decision-making support on cooperation strategies for CSC participants. This paper fully grasps the correlation between the CSC technology cycle and the key subjects, analyzes the influence of the subjects on the CSC technology cycle, considers the dual-objective optimization strategy of maximizing the economic benefits and minimizing the wastes, and proposes the framework of the CSC pathway for the satisfaction of multiple subjects. It provides theoretical guidance and a decision-making basis for promoting the participation enthusiasm and collaboration synergy of each subject.

Comments 3:

The connection between research objective O2 (driver assessment model) and dual carbon goals is weak. As for O3 (proposing a multi-actor CSC path), most of the content is still conceptual or diagram-based, without specific implementation strategies. It is recommended to add empirical cases or practical methods to make the research findings more actionable and relevant.

Response 3:

Thank you for pointing this out. We agree with your comment. On page 2-3, lines 88-105 in the original text, the content of these 3 objectives did not clearly express what this study expects to get, so we chose to delete this paragraph and add a paragraph about the problem that is the focus of this paper to clearly express the purpose of this study. The specific modifications are shown below.

Secondly, in response to your question “The link between the research objective O2 (driver assessment model) and the dual-carbon target is weak”, we sincerely explain to you. This study quantifies the key factors and differentiated responses of the subjects through the establishment of a driver assessment model, to transform the dual-carbon target into specific action guidelines for each link of the CSC, and ultimately realize the paradigm leap from “passive emission reduction” to “active symbiosis”.

We are grateful for your comment “O3 (Proposed multi-actor CSC path), most of the content is still based on concepts or charts, and we suggest adding empirical cases or practical methods to make the research results more operable and relevant”, as this will improve the practical guidance of this study. We will add the specific content in the “Conclusion and Recommendation” part of this paper.

Page 2-3, lines 88-105

Before:

This paper aims to fill this research gap by developing a multi-subject construction pathway framework capable of coordinating the participation of many stakeholders in CSCM implementation from the perspective of the CE technology cycle. In addition, it shows the synergies between the key drivers in the classification and the relationship between multifactor interactions and different subjects, leading to the construction of a more optimized CSCM system. In summary, this study aims to achieve the following three objectives:

O1. To construct a set of CSC theoretical analysis framework covering the interaction mechanism of multiple subjects and multidimensional factors, to provide support for research and decision-making in the field of CSC, and to explore the mechanism of the role between multidimensional subjects and factors based on the perspective of technology cycle.

O2. An assessment model for multi-actor CSC influencing factors was developed to determine the importance of factors to help companies achieve their dual-carbon goals and ensure their economic development.

O3. A CSC construction path for the satisfaction of multiple subjects is proposed to enable the organization to strategically manage resources and reduce risks by increasing the participation motivation and collaboration synergy of each subject.

Change to:

In this regard, this paper focuses on the following issues. (1) Systematically identifying and counting CSC drivers, and categorizing the factors regarding subject relevance through a technology cycle perspective. (2) Quantitatively assessing the priority of the driving factors and their differential impact on the enthusiasm of multiple subjects to participate. (3) Construct a multi-subject CSC pathway framework and provide specific multi-subject collaborative optimization pathways and strategies.

Comments 4:

The methods section lacks details on how the text mining was actually done - for example, whether any NLP tools or manual coding were used, how low-frequency items were handled, etc. The paper should also be more rigorous on technical points - for example, there is no mention of coding consistency checks (such as Cohen's Kappa), and in the construction of the M value, why are weights a and b both set to 50%? This should be justified, preferably with references.

Response 4:

Thank you for the thoughtful review and constructive feedback you provided. We agree with your suggestions.

First of all, regarding the lack of details on how text mining actually works in the methods section, we apologize for the omission of uploading a file containing the method processing. Therefore, we hereby carefully send you the review file. It consists of two Excel files, namely the “Document Inclusion and Exclusion Review Checklist” and the “Driver Analysis and Statistics Table”. It contains details of our specific review criteria, review process, text mining operations, classification statistics, etc. Please review them.

Your questions about the coding consistency check, for which we sincerely apologize and provide clarification and explanation. As a small research team, all of our researchers conducted face-to-face discussions and text reviews every day during the review period, and all the factors screened out by the text mining content were discussed thoroughly until a consensus was reached to ensure the reliability and validity of the findings. You have provided us with good ideas, and we will make coding consistency checking a part of our work in the subsequent research work, your suggestions are much appreciated!

In constructing the M-values, both weights a and b were set to 50%. It is because, this weight assignment uses subjective weight assignment, and there are only two indicators that can quantitatively evaluate each driver. And the two indicators are equally important and both are positive indicators. Therefore, it is more appropriate to choose equal weights in this paper. In addition, we are very grateful for your suggestion to add references, which makes the methodology of this study more rigorous. We have added references for both the selection of the two indicators and the combined weighting method. The details are as follows.

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Before:

To identify key driving factors or understand the importance of different driving factors, this study established an evaluation model consisting of three indicators. The first indicator is the frequency of the driver , which aims to demonstrate the prevalence of the driver based on the frequency of the factor appearing in all articles (counted only once per article). The second evaluation metric is the number of citations  of the literature with the driver, which represents the authority and importance of the driver, i.e., the average number of citations per year of the most highly cited literature among all the literature. By combining these two indicators, the importance of the driver is evaluated. In addition, a third evaluation metric is introduced, . Which is the combined weight of the drivers in the existing literature, to better quantify the importance of the drivers. All the drivers mined from the 46 papers,  and  were used to assess the importance of the drivers and rank them comprehensively (Table 1).

Change to:

After systematically identifying all CSC drivers, need to identify the key drivers or to recognize the importance of different drivers, so this study developed an evaluation model with three indicators.

The first indicator is the frequency of the driver , which aims to demonstrate the prevalence of the driver based on the frequency of the factor appearing in all articles (counted only once per article). The larger the value, the more important the driver(Jung & Lee, 2020). The second evaluation metric is the number of citations  of the literature with the driver, which represents the authority and importance of the driver, i.e., the average number of citations per year of the most highly cited literature among all the literature. Citation rate is the most representative, simple, standardized and objective measure of academic influence(Wallin, 2005). By combining these two indicators, the importance of the driver is evaluated. In addition, a third evaluation metric is introduced, . Which is the combined weight of the drivers in the existing literature, to better quantify the importance of the drivers (Tian et al., 2022). All the drivers mined from the 46 papers,  and  were used to assess the importance of the drivers and rank them comprehensively (Table 1).

Weight allocation note. Since these two indicators for the quantitative evaluation of drivers are equally important and both are positive indicators, it is more appropriate to choose an equal weight assignment in this paper.

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Add references:

Jung, H., & Lee, B. G. (2020). Research trends in text mining: Semantic network and main path analysis of selected journals. Expert Systems with Applications, 162, 113851. https://doi.org/10.1016/j.eswa.2020.113851

Wallin, J. A. (2005). Bibliometric methods: pitfalls and possibilities. Basic & clinical pharmacology & toxicology, 97(5), 261-275. https://doi.org/10.1111/j.1742-7843.2005.pto_139.x

Tian, M., Hu, Y. J., Wang, H., & Li, C. (2022). Regional allowance allocation in China based on equity and efficiency towards achieving the carbon neutrality target: A composite indicator approach. Journal of Cleaner Production, 342, 130914. https://doi.org/10.1016/j.jclepro.2022.130914

Comments 5:

The conclusion should be stronger. It mainly summarizes which actors are affected by which factors, but the recommendations for business managers or policymakers are still quite general. It would be better to link the findings with more targeted recommendations to improve practical relevance and decision-making value.

Response 5:

Thank you very much for your comment and review, we agree with your comment which enable this paper to be a valid reference for various stakeholders. Therefore, based on our findings, we have added recommendations for business managers and policymakers in the conclusion section to successfully promote the implementation of CSC and active collaboration among multiple stakeholders. The specific modifications are as follows.

Page 22, after line 613

Add content:

For business managers, CSC closed-loop capabilities should be built to maximize value. (1) Cross-entity technology synergy. For the product quality contribution rate (30.8%) to establish a supplier-manufacturer synergy strategy, enterprises, and suppliers to form a product modularization design alliance to share detachable components to reduce the cost of recycling. And build a reverse logistics network with distributors to share logistics costs. (2) Green value integration. Utilizing the contribution rate of competitive pressure (18%) to promote upstream and downstream joint green certification, and jointly create industry standards. It has also set up a “trade-in” closed-loop marketing area to increase the recycling rate. (3) Data-driven governance. By deploying a blockchain traceability system to track carbon footprints in real-time, we can ensure that the actions of each subject can be traced, and accelerate the transition of CSC from “fuzzy promises” to “data contracts”.

For policymakers, the dynamic regulation of policies should be strengthened. (1) Optimize the financial incentive mechanism. For remanufacturing and recycling technologies, implement stepwise tax credits and utilization rate-linked subsidies. (2) Strengthen the technology-market double-binding. Gradually implement product modularization design certification (requiring a ≥70% detachable rate of key components), and make it a precondition for government procurement and export licensing. (3) Build a cross-regional quota trading market. Allow manufacturers to sell remanufacturing carbon emission reductions to energy-consuming industries to form a closed-loop incentive.

Comments 6:

The structure and flow between paragraphs need to be improved. The lack of smooth transitions between sections 4.1, 4.2, and 4.3 makes the reading experience feel a bit disjointed. Try adding short linking sentences at the beginning of each section to explain where the argument is going. For example, explain why a multi-actor CSC framework is needed at the beginning of section 4.3. The subheadings in 4.2 might also help improve clarity. Also, the connection between figures and the main text should be made clearer—avoid showing figures without clear explanation in the text.

Response 6:

Thank you very much for your comments and review. We agree with your comment that “the structure and logic between paragraphs needs to be strengthened.” Therefore, we have carefully revised the text by adding short, clear connecting sentences between sections 4.1, 4.2, and 4.3 to explain the direction of the argument and strengthen the transition between the upper and lower sections.

In addition, your suggestion to “strengthen the connection between the diagrams and the descriptions in the text” is much appreciated and makes the content of this paper more credible. We have carefully reviewed the diagrams and their corresponding textual content, have found some deficiencies, and made the following changes.

1. In the methodology section of Section 3, we deleted Figure 4 to avoid redundancy because the specific process of the literature review was described in detail in Section 3.1.2.

2. Since the information shown in Figure 5 has been described in detail on pages 9-10, lines 270-304 of the original text, we have added the significance of Figure 5 on page 9, after line 269.

3. On page 8, lines 260-261 of the original text, an explanation of what is expressed in Table 2 is provided. The details are as follows.

Page 8-9, lines 255-265

Before:

4.1. Categorization of drivers

Fifty-nine drivers were identified based on the statistics of the 46 selected papers. The 47 drivers were retained by removing duplicate expressions and factors with the least correlation with CSC. Based on the characteristics of the drivers, these 47 factors were categorized into 10 groups: social, policy, organizational, economic, environment, supply chain, technology, information, infrastructure, and Circular Design and Services (see Table 2 for a detailed explanation).

Fig. 5 illustrates these 10 categories, with each dot representing a CSC driver discussed in an article. The specific location of each dot is based on the year of publication and the article's average annual citation rate.

Change to:

4.1. Categorization of drivers

Organizational voluntary participation in CSCM practices is related to the drivers of CSC (Levering & Vos, 2019). Therefore, to coordinate the participation of many stakeholders in CSCM implementation, this study cuts through the technology cycle perspective and content analyzes 46 pieces of literature to count and categorize all drivers. A foundation is laid for subsequent driver prioritization assessment. Fifty-nine drivers were identified based on the statistics of the 46 selected papers. The 47 drivers were retained by removing duplicate expressions and factors with the least correlation with CSC. Based on the characteristics of the drivers, these 47 factors were categorized into 10 groups: social, policy, organizational, economic, environment, supply chain, technology, information, infrastructure, and Circular Design and Services. Table 2 explains in detail the meaning of the various types of CSC drivers in the technology cycle.

Fig. 4 illustrates these 10 categories, with each dot representing a CSC driver discussed in an article. The specific location of each dot is based on the year of publication and the article's average annual citation rate. A high frequency of discussion means that the drivers in this literature have received widespread attention in the academic community and may be considered core drivers. A high citation rate, on the other hand, indicates that this literature is authoritative and influential in the field, providing key insights into the drivers.

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Before:

4.2. Statistical ranking of drivers

Based on Section 4.1, categorization results were obtained. The results identify the drivers in the CSC technology cycle that motivate various stakeholders to implement CSCM: suppliers (recycling), manufacturers (remanufacturing), distributors (refurbishment), and users (repair and reuse). Among these, the circular design and services is the main driver in the recycling, remanufacturing, and refurbishment phases. In addition, the other nine categories of factors are present throughout the cycle, but their roles and focus differ at each stage. Table 3 was obtained by evaluating and analyzing the indicators for each type of driver using the evaluation model.

Change to:

4.2. Statistical ranking of drivers

After systematically counting and clarifying the drivers and their classification system, the relative importance of the factors needs to be further assessed to guide the optimization path of the CSC's multi-subject collaborative practice. Based on Section 4.1, the drivers in the CSC technology cycle that motivate different stakeholders to implement CSCM were obtained: suppliers (recycling), manufacturers (remanufacturing), distributors (refurbishment), and users (repair and reuse). Among these, the circular design and services is the main driver in the recycling, remanufacturing, and refurbishment phases. In addition, the other nine categories of factors are present throughout the cycle, but their roles and focus differ at each stage. Therefore, the evaluation model was applied in section 4.2 to evaluate and analyze the indicators of each type of driver (Table 3) in order to determine the importance of the drivers.

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Before:

4.3. key drivers and path construction of CSC from a multiple subject perspective

Change to:

4.3. key drivers and path construction of CSC from a multiple subject perspective

After identifying and statistically ranking all the drivers, this paper extracts the key drivers under different subjects in this section. Given the heterogeneous effects of drivers on subjects, it is also necessary to construct a multi-subjects CSC pathway framework to facilitate the transformation of “factor-driven” to “multi-subject synergistic action”.

Response to Reviewer 2 Comments

1. Summary

Thank you very much for your encouraging comments and through review, which have significantly helped us improve our manuscript. Please find the detailed responses below and the corresponding revisions in the re-submitted files.

2. Point-by-point response to Comments and Suggestions for Authors

Comments 1:

What hypothesis was raised, because you set a lot of tasks, but what did you expect?

Response 1:

Thank you for pointing this out. We agree with you. On page 2-3, lines 88-105 in the original text, the content of these 3 objectives did not clearly express what this study expects to get, so we chose to delete this paragraph and add a paragraph about the problem that is the focus of this paper to clearly express the specific results that this study expects to get. The revision is shown below.

Page 2-3, lines 88-105

Before:

This paper aims to fill this research gap by developing a multi-subject construction pathway framework capable of coordinating the participation of many stakeholders in CSCM implementation from the perspective of the CE technology cycle. In addition, it shows the synergies between the key drivers in the classification and the relationship between multifactor interactions and different subjects, leading to the construction of a more optimized CSCM system. In summary, this study aims to achieve the following three objectives:

O1. To construct a set of CSC theoretical analysis framework covering the interaction mechanism of multiple subjects and multidimensional factors, to provide support for research and decision-making in the field of CSC, and to explore the mechanism of the role between multidimensional subjects and factors based on the perspective of technology cycle.

O2. An assessment model for multi-actor CSC influencing factors was developed to determine the importance of factors to help companies achieve their dual-carbon goals and ensure their economic development.

O3. A CSC construction path for the satisfaction of multiple subjects is proposed to enable the organization to strategically manage resources and reduce risks by increasing the participation motivation and collaboration synergy of each subject.

Change to:

In this regard, this paper focuses on the following issues. (1) Systematically identifying and counting CSC drivers, and categorizing the factors regarding subject relevance through a technology cycle perspective. (2) Quantitatively assessing the priority of the driving factors and their differential impact on the enthusiasm of multiple subjects to participate. (3) Construct a multi-subject CSC pathway framework and provide specific multi-subject collaborative optimization pathways and strategies.

Comments 2:

As the authors claim, drivers received a lot of attention from researchers, but it is not clear why they chose to study them?

Response 2:

Thank you very much for your comments and review, and we agree with this revision. First, we sincerely explain and justify this issue. Drivers have received a lot of attention from researchers because they can directly or indirectly influence subjects' participation in CSC practices, and identifying and prioritizing these factors can help companies develop effective strategies to promote the achievement of dual-carbon goals. We have made content additions and deleted redundant textual content in the 4th paragraph of the introduction, as shown below.

Page 2, lines 71-78

Before:

In addition to focusing on the mechanisms of collaboration among CSC stakeholders, there is a need to identify the key influencing factors of different stakeholder subjects and to explore the interactions between these factors and how they affect the subjects in order to facilitate successful CSC practice. Although “drivers” are one of the driving functions of CSC and have received a lot of attention from researchers, there is no literature on the subject that identifies and categorizes the influences from the cross-sectional dimension of SC and explores in depth the dynamic interactions between these factors (Ayati et al., 2022).

Change to:

In addition to focusing on the collaboration mechanism between CSC subjects, it is also necessary to identify the key influencing factors of different subjects and explore the interactions between these factors and how they affect the subjects to promote the successful practice of CSC. By analyzing the role of the factors and the response mechanism of the subjects, we can accurately design synergistic strategies and transform the theoretical feasibility into operability in practice (Zhang et al., 2021). Therefore, as one of the driving functions of CSC, “drivers” have received attention from many researchers. However, there is no literature on this topic that identifies and categorizes the influencing factors from the transversal dimension of CSC (Ayati et al., 2022) and explores the dynamic interactions among these factors in depth.

Comments 3:

Table 2 needs to be supplemented. Drivers were selected according to factors, but you did not indicate by what methods those criteria were studied in the analyzed literature.

Response 3:

Thank you for the thoughtful review and constructive feedback you provided. We agree with your comment.

First of all, we are very sorry for the omission of uploading the file containing the details of the driver mining and screening operations, and again we sincerely apologize to you for our oversight that led to your confusion about the selection of the drivers. Therefore, we hereby carefully send you the review file. It consists of two Excel files, namely “Document Inclusion and Exclusion Review Checklist” and “Driver Analysis and Statistics Table”. They contain details of our review criteria, review process, text mining operations, classification statistics, etc. Please review them.

Since the core meaning of Table 2 is to explain the meaning of the 10 categories of drivers screened, the research methods in the literature are not described in detail in the table. The specific factor screening criteria you can view in these two Excel files we provided, thank you very much!

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Comments 4:

It is incomprehensible, there is no logical transition between sections and subsections. How do sections 3.1 relate to 3.2? They need to be linked. Also, throughout section 4, there needs to be a logical connection between the sections, because they are now “hanging in the air”.

Response 4:

Thank you very much for the thoughtful review and constructive feedback you provided. We agree with your suggestions.

First, we sincerely apologize to you for the lack of logical transitions between sections and subsections in this paper. Next, we sincerely explain to you the link between sections 3.1 and 3.2. Section 3.1 describes the review process and screening methodology for text mining of CSC drivers, a link that identifies all drivers and categorizes them for analysis. Based on this, section 3.2 quantifies the factors counted in section 3.1 to determine the importance of the different drivers, so in this section, the paper presents the methodology of the constructed driver evaluation model.

We have strengthened the link between sections 3.1 and 3.2 to your comments. In addition, short, clear connecting sentences have been added between sections 4.1, 4.2, and 4.3 to explain the direction of the argument and to strengthen the transition between the upper and lower sections. This is specified below.

Page 5, lines 174

Before:

3.1. Text mining of drivers based on technology cycles

Change to:

3.1. Text mining of drivers based on technology cycles
In order to drive multiple subjects to actively participate in CSC implementation, it is first necessary to systematically identify all CSC drivers, and this paper chooses literature review and text mining methods in this segment. The specific steps are as follows.

Page 8-9, lines 255-265

Before:

4.1. Categorization of drivers

Fifty-nine drivers were identified based on the statistics of the 46 selected papers. The 47 drivers were retained by removing duplicate expressions and factors with the least correlation with CSC. Based on the characteristics of the drivers, these 47 factors were categorized into 10 groups: social, policy, organizational, economic, environment, supply chain, technology, information, infrastructure, and Circular Design and Services (see Table 2 for a detailed explanation).

Fig. 5 illustrates these 10 categories, with each dot representing a CSC driver discussed in an article. The specific location of each dot is based on the year of publication and the article's average annual citation rate.

Change to:

4.1. Categorization of drivers

Organizational voluntary participation in CSCM practices is related to the drivers of CSC (Levering & Vos, 2019). Therefore, to coordinate the participation of many stakeholders in CSCM implementation, this study cuts through the technology cycle perspective and content analyzes 46 pieces of literature to count and categorize all drivers. A foundation is laid for subsequent driver prioritization assessment. Fifty-nine drivers were identified based on the statistics of the 46 selected papers. The 47 drivers were retained by removing duplicate expressions and factors with the least correlation with CSC. Based on the characteristics of the drivers, these 47 factors were categorized into 10 groups: social, policy, organizational, economic, environment, supply chain, technology, information, infrastructure, and Circular Design and Services. Table 2 explains in detail the meaning of the various types of CSC drivers in the technology cycle.

Fig. 4 illustrates these 10 categories, with each dot representing a CSC driver discussed in an article. The specific location of each dot is based on the year of publication and the article's average annual citation rate. A high frequency of discussion means that the drivers in this literature have received widespread attention in the academic community and may be considered core drivers. A high citation rate, on the other hand, indicates that this literature is authoritative and influential in the field, providing key insights into the drivers.

Page 11, lines 307-315

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4.2. Statistical ranking of drivers

Based on Section 4.1, categorization results were obtained. The results identify the drivers in the CSC technology cycle that motivate various stakeholders to implement CSCM: suppliers (recycling), manufacturers (remanufacturing), distributors (refurbishment), and users (repair and reuse). Among these, the circular design and services is the main driver in the recycling, remanufacturing, and refurbishment phases. In addition, the other nine categories of factors are present throughout the cycle, but their roles and focus differ at each stage. Table 3 was obtained by evaluating and analyzing the indicators for each type of driver using the evaluation model.

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4.2. Statistical ranking of drivers

After systematically counting and clarifying the drivers and their classification system, the relative importance of the factors needs to be further assessed to guide the optimization path of the CSC's multi-subject collaborative practice. Based on Section 4.1, the drivers in the CSC technology cycle that motivate different stakeholders to implement CSCM were obtained: suppliers (recycling), manufacturers (remanufacturing), distributors (refurbishment), and users (repair and reuse). Among these, the circular design and services is the main driver in the recycling, remanufacturing, and refurbishment phases. In addition, the other nine categories of factors are present throughout the cycle, but their roles and focus differ at each stage. Therefore, the evaluation model was applied in section 4.2 to evaluate and analyze the indicators of each type of driver (Table 3) in order to determine the importance of the drivers.

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4.3. key drivers and path construction of CSC from a multiple subject perspective

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4.3. key drivers and path construction of CSC from a multiple subject perspective

After identifying and statistically ranking all the drivers, this paper extracts the key drivers under different subjects in this section. Given the heterogeneous effects of drivers on subjects, it is also necessary to construct a multi-subjects CSC pathway framework to facilitate the transformation of “factor-driven” to “multi-subject synergistic action”.

Comments 5:

The sections were analyzed quite interestingly, but paid articles were not included in the study. To state that there is a lack or no literature analyzed based only on free sources is unfounded.

Response 5:

Dear reviewer, we appreciate your review and attention to the rigor of literature screening. We would like to sincerely apologize for this query and explain the situation.

First, this study strictly followed the pre-established literature screening criteria (including topic relevance, study methodology, time of publication, etc.), and the objective limitations (financial constraints) to exclude paid literature were clearly labeled. All 46 included papers were obtained through open-access platforms and institutional subscription resources to ensure data reproducibility. We provide a complete list of literature with open-source data and welcome academic reproduction and expansion. This treatment is in line with the principle of transparency in systematic reviews (PRISMA). In addition, during the literature review process, all of our researchers compared the abstracts of the paid literature with the abstracts and keywords sections of the included literature and confirmed that they were highly relevant to the core themes such as “drivers”, “technology cycles”, “multi-subject synergies”, and so on. and “multi-subject synergies” and other core themes. This ensured that the exclusion of paid literature did not introduce research bias.

We sincerely apologize for this, as this study was limited by funding and was unable to access some of the paid literature, and our research team will expand our data sources in the future.

Comments 1:

The introduction could better highlight the research gap and novelty.

Response 1:

Thank you very much for your comments and review. We agree with this comment. Therefore, we have revised the introduction section by removing redundancies and supplementing the gaps and lacunae in existing research in the section following the introduction, followed by citing the differences between this paper and previous studies and concluding with a clear expression of the paper's theoretical and practical contributions to highlight the novelty of the paper. This can be seen specifically in the following.

Page 2, lines 71-87

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In addition to focusing on the mechanisms of collaboration among CSC stakeholders, there is a need to identify the key influencing factors of different stakeholder subjects and to explore the interactions between these factors and how they affect the subjects in order to facilitate successful CSC practice. Although “drivers” are one of the driving functions of CSC and have received a lot of attention from researchers, there is no literature on the subject that identifies and categorizes the influences from the cross-sectional dimension of SC and explores in depth the dynamic interactions between these factors (Ayati et al., 2022). Furthermore, it is observed that the literature on the effective adoption of the strategic framework of circular supply chain management (CSCM) practices to promote overall firm competitiveness is still in its infancy (Lahane et al., 2020). Knowledge of the factors influencing SC transformation in the context of CE can be broadened by investigating the challenges of CSC and the associated managerial impacts brought about by the network of key players involved in different restorative business models (Zhang et al., 2021). As a result, we found a lack of research that promotes the implementation of CSCM from the perspectives of multi-actor synergy and multi-factor synergy. In addition, there is a need for research to develop intervention policies that are satisfactory to all subjects or CSC implementation pathways in which multiple subjects are actively involved.

This paper aims to fill this research gap by developing a multi-subject construction pathway framework capable of coordinating the participation of many stakeholders in CSCM implementation from the perspective of the CE technology cycle. In addition, it shows the synergies between the key drivers in the classification and the relationship between multifactor interactions and different subjects, leading to the construction of a more optimized CSCM system. In summary, this study aims to achieve the following three objectives:

O1. To construct a set of CSC theoretical analysis framework covering the interaction mechanism of multiple subjects and multidimensional factors, to provide support for research and decision-making in the field of CSC, and to explore the mechanism of the role between multidimensional subjects and factors based on the perspective of technology cycle.

O2. An assessment model for multi-actor CSC influencing factors was developed to determine the importance of factors to help companies achieve their dual-carbon goals and ensure their economic development.

O3. A CSC construction path for the satisfaction of multiple subjects is proposed to enable the organization to strategically manage resources and reduce risks by increasing the participation motivation and collaboration synergy of each subject.

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In addition to focusing on the collaboration mechanism between CSC subjects, it is also necessary to identify the key influencing factors of different subjects and explore the interactions between these factors and how they affect the subjects to promote the successful practice of CSC. By analyzing the role of the factors and the response mechanism of the subjects, we can accurately design synergistic strategies and transform the theoretical feasibility into operability in practice (Zhang et al., 2021). Therefore, as one of the driving functions of CSC, “drivers” have received attention from many researchers. However, there is no literature on this topic that identifies and categorizes the influencing factors from the transversal dimension of CSC (Ayati et al., 2022) and explores the dynamic interactions among these factors in depth. Moreover, it is observed that the literature on the effective adoption of the strategic framework of circular supply chain management (CSCM) practices to promote overall firm competitiveness is still in its infancy (Lahane et al., 2020).

In summary, relevant studies still have the following limitations. (1) Existing literature mostly focuses on the vertical challenges (e.g., technical/policy barriers) of CSC implementation. However, it does not systematically identify and categorize the influencing factors from the horizontal dimension (suppliers, manufacturers, users, etc.), resulting in the subject-specific driving mechanism not being deconstructed. (2) Theoretical gaps in the dynamic interaction between factors. (3) Existing CSC practice frameworks are mostly conceptual proposals, lacking a systematic approach that integrates multi-subject collaboration mechanisms, dynamic factor weights, and competitiveness enhancement paths.

In this regard, this paper focuses on the following issues. (1) Systematically identifying and counting CSC drivers, and categorizing the factors regarding subject relevance through a technology cycle perspective. (2) Quantitatively assessing the priority of the driving factors and their differential impact on the enthusiasm of multiple subjects to participate. (3) Construct a multi-subject CSC pathway framework and provide specific multi-subject collaborative optimization pathways and strategies.

Comments 2:

Methodology sections need clearer justification of the chosen methods and simplified visuals, while results and tables should avoid redundancy.

Response 2:

Thank you for the thoughtful review and constructive feedback you provided. We agree with your suggestions. Therefore, we have added the reasons for the indicator selection in the methodology section and added relevant references, which makes the methodology of this study more rigorous. We have also added references to the selection of the composite weighting methodology and explained the values of the indicator weights. This is specified below.

In addition, your suggestion to simplify the visualization is strongly agreed and the full text has been carefully revised. In the methodology section, we have deleted Figure 4 to avoid redundancy as the specific process of the literature review has been described in detail in Section 3.1.2.

Page 7, lines 232-243

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To identify key driving factors or understand the importance of different driving factors, this study established an evaluation model consisting of three indicators. The first indicator is the frequency of the driver , which aims to demonstrate the prevalence of the driver based on the frequency of the factor appearing in all articles (counted only once per article). The second evaluation metric is the number of citations  of the literature with the driver, which represents the authority and importance of the driver, i.e., the average number of citations per year of the most highly cited literature among all the literature. By combining these two indicators, the importance of the driver is evaluated. In addition, a third evaluation metric is introduced, . Which is the combined weight of the drivers in the existing literature, to better quantify the importance of the drivers. All the drivers mined from the 46 papers,  and  were used to assess the importance of the drivers and rank them comprehensively (Table 1).

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After systematically identifying all CSC drivers, need to identify the key drivers or to recognize the importance of different drivers, so this study developed an evaluation model with three indicators.

The first indicator is the frequency of the driver , which aims to demonstrate the prevalence of the driver based on the frequency of the factor appearing in all articles (counted only once per article). The larger the value, the more important the driver(Jung & Lee, 2020). The second evaluation metric is the number of citations  of the literature with the driver, which represents the authority and importance of the driver, i.e., the average number of citations per year of the most highly cited literature among all the literature. Citation rate is the most representative, simple, standardized and objective measure of academic influence(Wallin, 2005). By combining these two indicators, the importance of the driver is evaluated. In addition, a third evaluation metric is introduced, . Which is the combined weight of the drivers in the existing literature, to better quantify the importance of the drivers (Tian et al., 2022). All the drivers mined from the 46 papers,  and  were used to assess the importance of the drivers and rank them comprehensively (Table 1).

Weight allocation note. Since these two indicators for the quantitative evaluation of drivers are equally important and both are positive indicators, it is more appropriate to choose an equal weight assignment in this paper.

Add references:

Jung, H., & Lee, B. G. (2020). Research trends in text mining: Semantic network and main path analysis of selected journals. Expert Systems with Applications, 162, 113851. https://doi.org/10.1016/j.eswa.2020.113851

Wallin, J. A. (2005). Bibliometric methods: pitfalls and possibilities. Basic & clinical pharmacology & toxicology, 97(5), 261-275. https://doi.org/10.1111/j.1742-7843.2005.pto_139.x

Tian, M., Hu, Y. J., Wang, H., & Li, C. (2022). Regional allowance allocation in China based on equity and efficiency towards achieving the carbon neutrality target: A composite indicator approach. Journal of Cleaner Production, 342, 130914. https://doi.org/10.1016/j.jclepro.2022.130914

Comments 3:

Figure 5 could benefit from more text.

Response 3:

Thank you very much for your review and comment. We agree with your comment. First, we sincerely explain to you the significance of Figure 5. The drivers of this study were extracted from the 46 core literatures retrieved, and the specific information of these 46 literatures has been included in the appendix section of the article, Tables A.1. To visualize the most discussed factor categories and highly cited literature data from the 46 studies, we plotted Figure 5. this shows the key driver categories. Next, on pages 9-10, lines 270-304 of the original article, we provide a detailed description of the information shown in Figure 5. We have carefully added content based on your comments. Because the information shown in Figure 5 was described in detail on pages 9-10, lines 270-304 of the original text, we have added the significance of the value of Figure 5 on page 9, line 269 of the original text, which follows.

Page 9, lines 263-269

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Fig. 4 illustrates these 10 categories, with each dot representing a CSC driver discussed in an article. The specific location of each dot is based on the year of publication and the article's average annual citation rate.

As depicted in Fig. 4, the most discussed categories of drivers among the 46 studies were social, governmental, organizational, and economic. The highly cited literature is then concentrated on the years 2018 to 2020. These results illustrate the importance and authority of these drivers. Specifically, they are as follows.

Change to:

Fig. 4 illustrates these 10 categories, with each dot representing a CSC driver discussed in an article. The specific location of each dot is based on the year of publication and the article's average annual citation rate. A high frequency of discussion means that the drivers in this literature have received widespread attention in the academic community and may be considered core drivers. A high citation rate, on the other hand, indicates that this literature is authoritative and influential in the field, providing key insights into the drivers.

As depicted in Fig. 4, the most discussed categories of drivers among the 46 studies were social, governmental, organizational, and economic. The highly cited literature is then concentrated on the years 2018 to 2020. These results illustrate the importance and authority of these drivers. Specifically, they are as follows.

Comments 4:

The paper should clearly state its theoretical and practical contributions, especially regarding multi-actor collaboration.

Response 4:

Thank you for pointing this out and we agree with you. Therefore, we have added and clearly explained the theoretical and practical contributions of this paper, especially in terms of multisubjective synergies, in the section following the introduction. This is shown below.

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The main contributions of this paper are as follows. (1) Enriching the theoretical connotation of CSCM. From a multi-subject perspective, this paper identifies and categorizes the factors as subjects and explores in depth the dynamic interactions among these factors, which solves the problem of confusing factor-subject coupling effects in previous studies. (2) Expanding research methods. This paper constructs an assessment model to prioritize CSC driving factors, which helps identify the driving effects of factors on each subject, to better grasp the differences in the response of the same subject to different factors, and to drive the subject to actively participate in CSC practices to promote the realization of the dual-carbon goal. (3) This paper provides decision-making support on cooperation strategies for CSC participants. This paper fully grasps the correlation between the CSC technology cycle and the key subjects, analyzes the influence of the subjects on the CSC technology cycle, considers the dual-objective optimization strategy of maximizing the economic benefits and minimizing the wastes, and proposes the framework of the CSC pathway for the satisfaction of multiple subjects. It provides theoretical guidance and a decision-making basis for promoting the participation enthusiasm and collaboration synergy of each subject.