Reviewing the Roles of AI-Integrated Technologies in Sustainable Supply Chain Management: Research Propositions and a Framework for Future Directions
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsIn my opinion, the title of the article does not completely cover the essence of the paper.
The authors have done serious work. At the same time, some sections should be revised and improved, since not all research questions are fully disclosed.
1) Figure 1 should be depicted as an algorithm
2) Figure 6 is difficult to understand
3) The description for figures 7 and 8 contradicts what is shown on them
4) Figure 9 is generally incomprehensible, as is the method by which the diagrams are constructed
5) the content of paragraph 4.3 does not interact well with the previous sections
The authors abuse generalizations and sometimes draw unfounded conclusions. The entire text needs to be re-read and corrected to remove redundancy of empty words and strengthen the evidentiary part of the analysis
Author Response
Comments 1: [In my opinion, the title of the article does not completely cover the essence of the paper.]
Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we revised the article’s title to “Reviewing the Roles of AI-integrated Technologies in Sustainable Supply Chain Management: Research Propositions and A Framework for Future Directions”, covering the essence of the paper.
Comments 2: […some sections should be revised and improved, since not all research questions are fully disclosed].
Response 2: We agree. Accordingly, we have revised the research questions and clarified all sections following the four research questions (RQs) (Page 3, Lines 116-120).
RQ1: What are the current trends concerning applied AI technology and SSCM?
This has been analyzed in section 4.1(bibliometric analysis) and discussed in section 5 (p. 17, Lines 661-668).
RQ2: What are the latent topics regarding AI technology and SSCM?
This has been analyzed in section 4.2(text analysis) and discussed in section 5 (p.17, Lines 669-679).
RQ 3: What AI-integrated technologies are involved in sustainable activities to address sustainable issues in supply chain processes?
This has been analyzed in section 4.4 (AI-integrated Technologies with Sustainable Issues, pp. 16-17, Lines 643-658) and discussed in section 5 (p.17, revised in Lines 683-685).
RQ4: What are future research directions in AI-integrated technologies and SSCM?
This has been addressed in the discussion section (pp. 17-18, revised in Lines 699-707).
Comments 3: [Figure 1 should be depicted as an algorithm]
Response 3: Thank you for pointing out this issue. Therefore, we revised the depiction: Figure 1 presents the PRISMA flow diagram as an algorithm, depicting the systematic process used to identify, screen, assess eligibility, and include studies in a literature review. Initially, 519 articles were identified from various databases based on criteria such as keyword retrieval, timeline (2000-2023), journal type (articles and reviews), and language (English). These articles were then screened, resulting in 267 assessed based on titles, keywords, and abstracts. Then, 228 full-text articles were evaluated for eligibility using domain and keyword inclusion/exclusion criteria. Ultimately, 170 studies met the criteria and were included in the synthetic analysis, ensuring a comprehensive and rigorous review of relevant literature (see pp.5, Lines 230- 238). In terms of the figure, the systematic review research paper needs to follow the PRISMA flow chart format, and Fig 1 was drawn according to that PRISMA protocol. (https://www.mdpi.com/journal/sustainability/instructions).
Comments 4: [Figure 6 is difficult to understand]
Response 4: Thank you very much for pointing out this issue. Thus, we have changed the figure to a table (see Table 3, p. 9) and revised the depiction (see pp.9, Line 366-371).
Comments 5: [The description for figures 7 and 8 contradicts what is shown on them]
Response 5: Thank you for pointing this out. The two figures were output from VOSviewer. The colors come with the software and are not adjusted. To avoid color contradictions, we just keep one figure of both. (see Figure 5 p. 10, Line 386-387).
Comment 6: [Figure 9 is generally incomprehensible, as is the method by which the diagrams are constructed]
Response 6: Thank you for pointing this out. Another reviewer suggested that this figure needs to be deleted and combined into a table. Thus, we deleted and combined it with Table 4 (pp.11-12). We have added topic names and revised the explanation of Table 4 in detail (see p.11, Lines 408-426).
Comment 7: [the content of paragraph 4.3 does not interact well with the previous sections]
Response 7: Thank you for pointing out this issue. We added an introduction at the beginning of section 4.3 to explain how these propositions interact with the emerging topics. This section follows section 4.2 to provide the propositions of AI in SSCM for future research directions (see page 12, Line 434-435). Besides, to understand the relations of topics, FREX, and Lift, we added more analysis in section 4.2 (see page 11, Line 408-430).
Thank you very much for all of your comments, and we are looking forward to your feedback.
Reviewer 2 Report
Comments and Suggestions for AuthorsI found the paper interesting.
I think that the authors should more clearly presenting the steps considered into the analysis and the used keywords. - e.g. it is known that the search conducted into WoS returns different results if singular forma are used - namely the plural forms do not necessarily include the singular forms - this is the reason one uses * when conducting the search. I can provide an example here: if you search for "supply chains" in the returned results it is very possible that you won't have the papers that contain the "supply chain" results. Furthermore, you will have papers that speak about supply in a phrase and about chains (in general) in another. Please consider carefully the research terms. My suggestion for this particular case will be to use the "supply_chain*" format.
Moreover, a search with the following keywords "artificial intelligence" provides different results than the search in which the keywords are written with underscore: "artificial_intelligence". Thus, please better present the keywords you have used.
Furthermore, it is not advisable to use acronyms when performing searchers as they might mean different elements in different fields - e.g. AI can stand for "artificial intelligence" but can also stand for a component in a questionnaire that the author calls "attitude influence".
Please eliminate the acronyms from the search are reconsider the analysis.
Also, please better stated why you have considered the review papers along with the articles for the analysis. It is known that these two types of papers are quite exclusive and the number of citations is highly dependent of the type of paper. Thus, why not considering conference proceedings and articles instead? Please better argue in the body of the paper.
The remainder of the paper is in line with what one should expect from a bibliometric paper.
I still have some observations of the results interpretation: please add more insight - e.g. please consider arguing in the paper why a certain source has been preferred by the authors in this field or why a particular country/university scores better in terms of published papers or received citations that others in the top?
Limitations should be added.
Author Response
Comments 1: [Please consider carefully the research terms. My suggestion for this particular case will be to use the "supply_chain*" format.]
Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we revised the search Strings (see p.5, Line 225 and p.6, Table 2).
Comments 2: [a search with the following keywords "artificial intelligence" provides different results than the search in which the keywords are written with underscore: “artificial_intelligence”. Thus, please better present the keywords you have used.].
Response 2: Thank you for pointing this out. We agree with this comment. Therefore, we revised the search Strings (see p. 5, Line 224, and p.6. Table 2).
Comment 3: [..it is not advisable to use acronyms when performing searchers as they might mean different elements in different fields - e.g. AI can stand for "artificial intelligence" but can also stand for a component in a questionnaire that the author calls "attitude influence". Please eliminate the acronyms from the search are reconsider the analysis.]
Response 3: Thank you for pointing out this issue. We revised the search Strings and eliminated the acronyms (see p.6. Table 2). We confirmed that all the keywords of AI stand for artificial intelligence among our selected 170 articles.
Comments 4: [please better stated why you have considered the review papers along with the articles for the analysis. It is known that these two types of papers are quite exclusive and the number of citations is highly dependent of the type of paper. Thus, why not considering conference proceedings and articles instead? Please better argue in the body of the paper.]
Response 4: Thank you very much for pointing out this issue. Extensive systematic literature review papers focus only on empirical studies because of the rigorous peer-review system, but we added review papers as a supplement to fill the current research gap of AI in SSCM. Conference papers were excluded because of our research breadth. We revised this and explained in the manuscript (see p. 5, section 3.1, Line 209-213).
Comments 5: [I still have some observations of the interpretation o: please add more insight - e.g. please consider arguing in the paper why a certain source has been preferred by the authors in this field or why a particular country/university scores better in terms of published papers or received citations that others in the top?]
Response 5: Thank you for pointing this out. We agree; therefore, we added the analysis on p. 9, lines 335-345.
Comment 6: [Limitations should be added]
Response 6: Thank you for pointing this out. We have revised the title of section 6 to “Conclusion and limitations” (see pp 19-20, Line 743-758).
Thank you very much for all of your comments, and we are looking forward to your feedback.
Reviewer 3 Report
Comments and Suggestions for AuthorsI hope that the following comments will improve the paper. They are divided into a more general part and a more specific part for each of the sections.
General:
- The article deals with two very topical issues (supply chain and IA), which makes it very interesting for the reader. Congratulations on the choice.
- The abstract seems to be sufficient: If a better way can be found, highlight the key findings and their importance within this study. Also explain the importance of the study and how it was done. It was lacking to be a bit more suggestive in the final part.
- You suggest 10 emerging themes related to CS and AI. What were the criteria for the specific selection of these 10 themes? It may be described in the paper, but I don't see it clearly defined.
- Check the spelling of the full text again.
Specifically:
1. Introduction:
- When talking about "academia has played a crucial role in promoting AI innovation re-81 search in areas relevant to supply chains and sustainability", it would be necessary to specify some studies and some concrete solutions. The whole paragraph (81-98) remains rather general and here it is important to be able to explain with concrete cases what has been proposed in the field of SSCM and AI.
2. Theoretical background
- 2.3 Sustainable Supply Chain Management: The previous two points are well explained and very clear. This point, which is the key point of the article, falls short of the theoretical framework. In particular, the concept, how it has evolved over time and its current importance should be explained in more detail. Line: 165
3. Methodology
- Check the methodology figure, there are spelling mistakes (e.g. inlusion or exlusion). Figure 1.
4.1 Bibliometric analysis
4.1.1 Trend of articles published
- The methodology states that the review of articles will continue until the end of 2023, but the first sentence of this section states that it will continue until the end of 2024 (which has not yet been completed).
- I am not able to see the relationship of the SSCM to the accounting, can you clarify exactly what issues are being referred to? Line: 264
- Figure 4 does not add value to the document. It is too easy to explain something that is already said in the text. It is suggested that it be deleted.
- Figure 5. Can't it be presented in a more attractive format? There are already many ways to make this type of graphic more visual and interesting for the reader. For example, Figure 5 and Table 3 could be combined into a single graph or figure. It is not necessary to include a graph and a table for the concept you want to convey.
- In Figure 6, I would remove the bar graph. The table is clear enough to explain the concepts and the graph does not provide any further ideas. Graphs and pictures are an excellent way of conveying information visually, but they should not be overused.
- If we only read the contents of Table 4, the information is confusing and the reader is unlikely to come to the same conclusion as the authors. There is an opportunity in the text (lines 357-363) to better explain what the technology was designed to do and what the results were.
- What is the scientific basis for propositions 3-4? Articles [99] and [24]? Lines 394-406. Something similar applies to proposals 13-14.
- The propositions presented refer to generic SCs. Except for proposition 19, which refers to the Indian case. This does not follow the explanatory line of the whole paper.
5. Discussion and proposed research framework
- In Figure 10, I don't quite understand how to interpret the last box (Activities in SSCM). Can you explain this in more detail?
6. Conclusions
- The conclusions should summarise the most important findings without repeating the data and emphasise how the results of the bibliometric search answer the three research questions.
- Furthermore, these results should be related to existing knowledge in the field of IA and SSC.
- It is important to highlight in the conclusions the practical applications of the paper: how the results can be applied in practice.
- Future recommendations. In addition to the limitations that have been indicated, suggest specific directions for future research.
Author Response
Comments 1: [The abstract seems to be sufficient: If a better way can be found, highlight the key findings and their importance within this study. Also explain the importance of the study and how it was done. It was lacking to be a bit more suggestive in the final part.]
Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we added the findings in the abstract: (p. 1, Line 21-24).
Comments 2: [You suggest 10 emerging themes related to CS and AI. What were the criteria for the specific selection of these 10 themes? It may be described in the paper, but I don't see it clearly defined.]
Response 2: Thank you for pointing this out. We agree with this comment. Therefore, we added the process and explanation of the ten topic names. As this issue is in line with another reviewer’s comment, we revised section 3.4 a lot. (Please see p.7, Line 281-300).
Comment 3: [Introduction- When talking about "academia has played a crucial role in promoting AI innovation re-81 search in areas relevant to supply chains and sustainability", it would be necessary to specify some studies and some concrete solutions. The whole paragraph (81-98) remains rather general and here it is important to be able to explain with concrete cases what has been proposed in the field of SSCM and AI.]
Response 3: Thank you for pointing out this issue. We agree; accordingly, we revised the introduction section and added examples (see p. 2, Lines 80-89).
Comments 4: [Theoretical background-2.3 …This point, which is the key point of the article, falls short of the theoretical framework. In particular, the concept, how it has evolved over time and its current importance should be explained in more detail. Line: 165]
Response 4: Thank you very much for pointing out this issue. We have revised this in the manuscript. Please see p. 4, section 2.3, Line 175-181; 187-199).
Comments 5: [ Methodology- Check the methodology figure, there are spelling mistakes (e.g. inlusion or exlusion). Figure 1.
Response 5: Thank you very much for pointing out this issue. We have revised this in the figure 1. (see p.6).
Comment 6: [4.1 Bibliometric analysis 4.1.1 Trend of articles published-The methodology states that the review of articles will continue until the end of 2023, but the first sentence of this section states that it will continue until the end of 2024 (which has not yet been completed).
Response 6: Thank you for pointing this out. I am sorry for this mistake. (Please see p. 7, section 4.1.1, Line 305).
Comment 7: [I am not able to see the relationship of the SSCM to the accounting, can you clarify exactly what issues are being referred to? Line: 264]
Response 7: Thank you for pointing this out. This subject category is from the Scopus statistics. We revised the depiction of Figure 3. (see p.8, Line 327).
Comment 8: [Figure 4 does not add value to the document. It is too easy to explain something that is already said in the text. It is suggested that it be deleted.]
Response 8: Thank you for pointing this out. We have deleted Figure 4.
Comment 9: [Figure 5. Can't it be presented in a more attractive format? There are already many ways to make this type of graphic more visual and interesting for the reader. For example, Figure 5 and Table 3 could be combined into a single graph or figure. It is not necessary to include a graph and a table for the concept you want to convey.]
Response 9: Thank you for pointing this out. We agree that the figure and table can combined into one figure (Figure 4). We revised it to a dot chart to visualize it more fashionably.
Comment 10: [In Figure 6, I would remove the bar graph. The table is clear enough to explain the concepts and the graph does not provide any further ideas. Graphs and pictures are an excellent way of conveying information visually, but they should not be overused.]
Response 10: Thank you for pointing this out. We have deleted this figure. The topic names generated from keywords are in Table 4. (see p.11)
Comment 11: [If we only read the contents of Table 4, the information is confusing and the reader is unlikely to come to the same conclusion as the authors. There is an opportunity in the text (lines 357-363) to better explain what the technology was designed to do and what the results were].
Response 11: Thank you for pointing this out. We agree, and therefore, we revised the analysis and explanations in section 4.2. (please see p.11, section 4.2, Lines 408-418). Another reviewer also suggested the interactions with Table 4 and Table 5; thus, we revised section 4.4, and specifically explained in this section. (please see pp.16-17, Lines 642-657).
Comment 12: [What is the scientific basis for propositions 3-4? Articles [99] and [24]? Lines 394-406. Something similar applies to proposals 13-14.]
Response 12: Thank you for pointing this out. We revised topic 2’s label name to “Optimizing Global SSCM through AI-integrated Big Data”. (see page 12, Line 452). The propositions are accordingly revised: The widespread application of AI and big data in SCM, particularly in decision support systems processes and analyzing large volumes of data, improves decision-making efficiency and accuracy [101]. Driven by these technologies, SSCM needs to consider uncertainties from the external environment to respond to the demands of the global market. Therefore, real-time data analysis and predictive models can facilitate sustainable activities in the supply chain (e.g., [26,102,103]). For example, Neethirajan (2023) [103] highlights the transformative potential of AI-integrated technologies in improving the efficiency and sustainability of the dairy livestock export industry through enhanced traceability and real-time monitoring. It also addresses the associated challenges and ethical considerations, providing a strategic framework for successfully integrating these technologies into long-distance livestock transportation [103]. Thus, based on the above discussion, we make the following propositions:
Proposition 3: Applying AI and big data to facilitate SSCM with precise decision-making of global supply chains.
Proposition 4: Globalization and sustainability requirements drive the innovative application of AI-integrated big data and digital technologies in SCM.
Comment 13: [The propositions presented refer to generic SCs. Except for Proposition 19, which refers to the Indian case. This does not follow the explanatory line of the whole paper.]
Response 13: Thank you for pointing this out. We have revised this. Please see p.16, Line 625-627).
Comment 14: [Discussion and proposed research framework- Figure 10, I don’t quite understand how to interpret the last box (Activities in SSCM). Can you explain this in more detail?
Response 14: Thank you for pointing this out. To make it clear, we revised the title of the last (right) box (see p. 19, Figure 6), and we revised the explanation (Please see pp.17-18, Line 699-707). It includes ten latent topics of SSCM, with 11 sustainable activities previously shown in Table 6. The AI-integrated technologies play significant roles in addressing the issues of traditional supply chains and enabling sustainable activities in plan, sourcing, making, delivering, returning, and enabling processes based on SCOR. SSCM leverages AI-integrated technologies and algorithms to address sustainable issues through sustainable activities. Therefore, in this framework, the left and right boxes rotate dynamically with the dynamic sustainability in supply chains, and the emerging future directions would inform the agenda and may adjust dynamically.
Comment 15: [Conclusions- The conclusions should summarise the most important findings without repeating the data and emphasise how the results of the bibliometric search answer the three research questions. Furthermore, these results should be related to existing knowledge in the field of IA and SSC.]
Response 15: Thank you for your comments. We agree and, therefore, revised the conclusions. Please see p.19, section 6, Line 712-742. The results of the bibliometric search answer the research questions in the discussion section.
Comment 16: [It is important to highlight in the conclusions the practical applications of the paper: how the results can be applied in practice].
Response 16: Thank you for your comments. Based on our results, we added some practical applications. Please see p.19, section 6, Line 732-742.
Comment 17. [Future recommendations. In addition to the limitations that have been indicated, suggest specific directions for future research].
Response 17: Thank you for pointing this out. We revised the paragraph on limitations and added future research based on each limitation. Please see pp. 19-20, Line 743-758.
Thank you very much for all of your comments, and we are looking forward to your feedback.
Reviewer 4 Report
Comments and Suggestions for AuthorsThe work is devoted to a current topic - finding ways to sustainably manage supply chains using artificial intelligence through bibliometric analysis and latent Dirichlet allocation modeling.
The article provides an overview of research on supply chain optimization and examines the key factors affecting their functioning. Researchers have noted the increasing role of social and environmental factors, which is truly important. It is worth noting that, based on bibliometric analysis and modeling, a significant number of articles from the Scopus database were analyzed. The article is written in accessible language, the research methodology is described in detail, the results are analyzed and conclusions are presented. At the same time, several recommendations can be made aimed at improving the quality of the article.
1) It is recommended to justify how the resulting research topics will help solve problems specific to supply chains. Especially in terms of solving environmental and social problems that were identified by the authors.
2) If possible, it would be useful to compare the data obtained as part of the study with the results of similar studies performed by other authors and justify the uniqueness of the proposed methodology using the results obtained as an example.
Comments on the Quality of English LanguageThat's good enough.
Author Response
Comments 1: [It is recommended to justify how the resulting research topics will help solve problems specific to supply chains. Especially in terms of solving environmental and social problems that were identified by the authors.]
Response 1: Thank you for pointing this out. We agree with this comment. Therefore, we added section 4.4 to clearly show how to specify topics and propositions to address sustainable issues using Tables 4 and 5. Please see the detailed revision on pp. 16-17, Line 634-657).
Comments 2: [If possible, it would be useful to compare the data obtained as part of the study with the results of similar studies performed by other authors and justify the uniqueness of the proposed methodology using the results obtained as an example.]
Response 2: Thank you for pointing this out. The topic name has changed according to another reviewer’s suggestion; thus, this review aims not to focus on LDA modeling but to explore gaps and future studies related to AI in SSCM based on SCOR’s six elements. We agree with your suggestions and added the rationale in the introduction part. Please see p. 3, Lines 104-110.
Thank you very much for all of your comments, and we are looking forward to your feedback.
Reviewer 5 Report
Comments and Suggestions for AuthorsExplanation of the LDA Method:
Current content: The description of the LDA method is very general and does not provide details about the technique or how it was applied in the text analysis.
Suggested change: Add a detailed explanation of the LDA method, how it works, and why it was chosen for the text analysis.
Implementation in R:
Current content: "Programmed by the R package" indicates that it was used for implementation, but it does not specify which package or provide details about the implementation.
Suggested change: Clarify which R package was used (e.g., topicmodels or ldatuning) and why. Also, briefly describe how the analysis was conducted using this tool.
LDA Results:
Current content: The LDA results are presented very generally, without a detailed explanation of how they were interpreted.
Suggested change: Provide a detailed description of the LDA results, including how the number of topics was determined, how the individual issues were interpreted, and how these results influenced the research propositions.
Comments on the Quality of English Language"LDA" should be spelt out as "Latent Dirichlet Allocation" when first introduced.
"FREX" and "Lift" should be explained when first mentioned.
The sentence "This generative method of LDA is first introduced by" needs a citation, but the structure should be clarified.
Missing commas in lists, e.g., "sustainable issues, activities, and ten emerging topics".
"Combining sustainable issues, activities, and ten emerging topics, we propose an AI-enabled SSCM framework for scholars and practitioners, informing a future research agenda." This sentence could be clarified: "By combining sustainable issues, activities, and ten emerging topics, we propose an AI-enabled SSCM framework for scholars and practitioners to inform future research agendas."
"In contrast, weak co-occurrence of keywords such as construction industry is the outermost point." should be "In contrast, the weak co-occurrence of keywords such as 'construction industry' is at the outermost point."
Ensure consistent use of terms such as "supply chain management" and "SSCM" after the first use.
The format of references should be consistent. For example, use either "Journal of Cleaner Production" or "J Clean Prod" consistently.
Clarity: Some sentences are complex and could be simplified. For instance, "This generative method of LDA is first introduced by." could be "The LDA method, first introduced by, was applied."
Acronyms like SCOR should be defined when first used.
Author Response
Comments 1: [Explanation of the LDA Method: Current content: The description of the LDA method is very general and does not provide details about the technique or how it was applied in the text analysis. Suggested change: Add a detailed explanation of the LDA method, how it works, and why it was chosen for the text analysis.]
Response 1: Thank you for pointing this out. The topic name has changed to “Reviewing the Roles of AI-integrated Technologies in Sustainable Supply Chain Management: Research Propositions and A Framework for Future Directions”, according to another reviewer’s suggestion; thus, this review aims not to focus on LDA modeling but to explore gaps and future studies related to AI in SSCM based on SCOR’s six elements. However, we agree with your suggestions and added detailed explanations in the methodology section. Please see p.7, Lines 267-280.
Comments 2: [Implementation in R: Current content: "Programmed by the R package" indicates that it was used for implementation, but it does not specify which package or provide details about the implementation. Suggested change: Clarify which R package was used (e.g., topicmodels or ldatuning) and why. Also, briefly describe how the analysis was conducted using this tool].
Response 2: Thank you for pointing this out. The specific R package we used has been introduced in the manuscript. Please see section 4.2 (Text Analysis using LDA Modeling), pp.10-11, Lines 396-407)
Comments 3: [LDA Results: Current content: The LDA results are presented very generally, without a detailed explanation of how they were interpreted. Suggested change: Provide a detailed description of the LDA results, including how the number of topics was determined, how the individual issues were interpreted, and how these results influenced the research propositions.]
Response 3: Thank you for pointing this out. We agree. Regarding the determined number of topics (k=10), we revised this in section 3.4. Please see p.7, Lines 281-300.
Comments 4: ["LDA" should be spelt out as "Latent Dirichlet Allocation" when first introduced.]
Response 4: Thank you for pointing this out. Please see p.6, section 3.4, Line 266.
Comment 5: ["FREX" and "Lift" should be explained when first mentioned]
Response 5: Thank you for pointing this out. We agree and have revised this in section 3.4. Please see p. 7, Lines 295-300.
Comment 6: [The sentence "This generative method of LDA is first introduced by" needs a citation, but the structure should be clarified.]
Response 6: Thank you for pointing this out. We have added the author’s name in the texts. Please see p. 7, section 3.4, Line 267.
Comment 7: Missing commas in lists, e.g., "sustainable issues, activities, and ten emerging topics".
Response 7: Thank you for pointing this out. This sentence has been deleted because, according to other reviewers’ comments, this part has been revised extensively.
Comment 8: [“In contrast, weak co-occurrence of keywords such as construction industry is the outermost point." should be "In contrast, the weak co-occurrence of keywords such as 'construction industry' is at the outermost point.
Response 8: Thank you for pointing this out. This sentence has been revised. Please see p. 10, Line 386-387.
Comment 9: [Ensure consistent use of terms such as "supply chain management" and "SSCM" after the first use.]
Response 9: Thank you for pointing this out. We have revised all these issues in the manuscript.
Comment 10: [The format of references should be consistent. For example, use either "Journal of Cleaner Production" or "J Clean Prod" consistently.]
Response 10: Thank you for pointing this out. We have revised all these issues in the section Reference.
Comment 11: [Some sentences are complex and could be simplified. For instance, "This generative method of LDA is first introduced by." could be "The LDA method, first introduced by, was applied."]
Response 11: Thank you for pointing this out. We have revised the issue in p.7, Line 267.
Comment 12: [Acronyms like SCOR should be defined when first used.]
Response 12: Thank you for pointing this out. We have revised this in p.4, Lines 151-157.
Thank you very much for all of your comments, and we are looking forward to your feedback.
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsI thank the authors for all the work employed during the revision of the manuscript and for considering the previous round of reviews. Much appreciated.
I have no further comments.
Reviewer 3 Report
Comments and Suggestions for AuthorsCongratulations on your work.
I think you have dealt with the suggestions in a very concise way and this makes the paper substantially better.
For my part, although some points could be improved, the paper is ready for publication.
I hope the paper will be successful in such a new field as AI and its link to SCM.
