Research on the Mechanism and Identification of Key Influencing Elements for Releasing the Value of Data Elements in Smart Cities
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsDear Authors,
The topic the is very up to date, but I have the impression that the content of image 1 is poorly thought out, and the references provided do not contain sufficient arguments.
The following corrections should be done:
In subsection 1.1 – to change the title
In line 84 you wrote “From a microscopic perspective, we use literature research method to analyze …. “Table 1 does not contain all the references associated with the factors or presented in wrong way. It should be modified.
Check the references 18 and 19. I haven’t found the appropriate explanations for your arguments in these references.
Inappropriate experts selection or description: 30 persons: junior college student 20% Bachelor 33% Master and above? The lack of description of their work (topic) experience, level of knowledge, area of domain, position, experience of opinion-making. Why were these people considered as experts? It should be explained in paper.
It is better to name the “Total relationship matrix” instead of comprehensive.
References for the equations should be provided.
To precise the method explanation the method flowchart should be provided!
To change in Figure 2, Table 5 the title “centrality degree”
It is better to replace the contributions into the discussion or conclusion section.
You should add the research questions.
How the concept presents in terms of releasing of shared value of data in SCies? https://www3.weforum.org/docs/WEF_Unlocking_Shared_Value_Smart_City_Data_2022.pdf
Why was it not taken into account the dimensions of data value?
Data Valuation and Its Applications for Smart Cities, April 2023 DOI:10.1201/9781003399384_15 In book: Personal Data-Smart Cities: How cities can Utilise their Citizen’s Personal Data to Help them Become Climate Neutral (pp.217-243), Mihnea Tufis
It is better to write the “Fuzzy-DEMATEL method” instead of “Fuzzy-set DEMATEL method”.
There is a lack of Table 3,4 and 5 concrete results interpretation
Figure 1 Why you haven’t present the feedback arrows? The sources of Figure 1, which inspired you should be provided. The figure contain a lot of common unprecise information. The citations 29,30 and 31 are inappropriate for Figure 1 description as well as old. What are the sources of value release response, impacts… More explanations should be provided how the elements of each of 5 parts have been created! Why you exclude the “domain” in path: Data domain-data asset-data element?
Large discussion should be provided on the usage of the Dematel method and the research limitations should be discussed. The smart city is the city, which generates the large amount of data. Thus, why you haven’t apply the integrated big data approaches? For example an integrated DEMATEL-ANFIS approach, ISM-DEMATEL approach? Especially, in terms of [5] research. More arguments and explanations should be provided in paper.
More recent literature sources should be provided.
Best regards.
Author Response
Comment 1: In subsection 1.1 – to change the title
Response 1: Thank you for your suggestions. Based on your suggestions and that of other reviewers, we have reorganized the content of the introduction and removed the heading for Section 1.1.
Comment 2: In line 84 you wrote “From a microscopic perspective, we use literature research method to analyze …. “Table 1 does not contain all the references associated with the factors or presented in wrong way. It should be modified.
Response 2: Thank you for your suggestion, and we apologize for any inconvenience caused. Through our analysis of existing literature, we identified 47 influencing factors and categorized them into five dimensions. The dimensions, influencing factors, and their sources are all listed in Table 1. Each influencing factor has a corresponding literature source, though some factors may originate from the same reference. We have revised the presentation of Table 1 to ensure that reviewers and readers can clearly see the sources for each influencing factor. We apologize again for any confusion this may have caused.
Comment 3: Check the references 18 and 19. I haven’t found the appropriate explanations for your arguments in these references.
Response 3: Thank you for your suggestion. After reviewing references [18] and [19], we agree they do not fully support the content. We have replaced these references as follows:
[18] is now replaced with [19]: Liu, X., Heller, A., & Nielsen, P.S. (2017). CITIESData: a smart city data management framework. Knowledge and Information Systems, 53, 699–722. DOI: 10.1007/s10115-017-1051-3.
The referenced literature emphasizes that smart city data is diverse, complex, and often has quality issues, with various types, formats, and meanings that affect its consistency and usability. This complexity implies that smart city data originates as unprocessed, heterogeneous material that does not immediately lend itself to structured use for decision-making. Furthermore, the mention of platforms publishing data “without processing procedures” aligns with the description of smart city data as “original, unordered, and unprocessed.”
[19] is now replaced with [20]: Kaluarachchi, Y. (2022). Implementing Data-Driven Smart City Applications for Future Cities. Smart Cities, 5: 455-474. DOI 10.3390/smartcities5020025.
The reference emphasizes that smart cities generate vast and complex amounts of data through various urban activities, which are typically collected, stored, and systematically organized to improve service delivery, productivity, and decision-making. It also mentions that structured and processed data, accessible in electronic form, can significantly enhance economic, environmental, and social benefits, aligning with our study’s focus on data elements in smart cities.
I hope this clarifies the basis of my summary and how it reflects the essential points from the referenced literature. Thank you again for your insightful suggestions.
Comment 4: Inappropriate experts selection or description: 30 persons: junior college student 20% Bachelor 33% Master and above? The lack of description of their work (topic) experience, level of knowledge, area of domain, position, experience of opinion-making. Why were these people considered as experts? It should be explained in paper.
Response 4: Thank you for your valuable feedback on this manuscript. We carefully considered your comments and have refined and expanded the descriptions accordingly. Here are the detailed revisions we made:
(1) Added Criteria for Expert Selection: We included additional details on experts’ educational backgrounds, professional fields, and work experience to better demonstrate their relevant expertise in the smart city field.
(2) Provided More Detailed Expert Background Information: We expanded on the experts’ areas of specialization, years of experience, and specific roles in evaluating smart city data elements, enhancing readers’ understanding of the selection rationale.
(3) Explained the Geographic and Professional Distribution of Experts: We included the distribution of experts across regions and fields to highlight the broad representativeness of these 30 experts within the smart city domain.
We believe these modifications improve the transparency and scientific rigor of our study. Thank you once again for your helpful suggestions, which have contributed to strengthening the quality of this manuscript.
Comment 5: It is better to name the “Total relationship matrix” instead of comprehensive.
Response 5: Thank you for your suggestion. We have made the necessary revisions in the corresponding sections of the manuscript.
Comment 6: References for the equations should be provided.
Response 6: Thank you for your suggestion. In Section 5.3.1, we have added references [49] and [50] related to the method used in this manuscript. These two references employ the same research method as ours, and the formulas we used can be found in them.
Comment 7: To precise the method explanation the method flowchart should be provided!
Response 6: Thanks for your suggestion, we have added the method flow diagram in Section 5.1, which serves as the analysis framework for this article.
Comment 8: To change in Figure 2, Table 5 the title “centrality degree”
Response 8: Thank you for your advice. We have made changes in Figure 2 and Table 5.
Comment 9: It is better to replace the contributions into the discussion or conclusion section.
Response 9: Thank you for your suggestion.
We have merged Section 1.2 “Contributions” with Section 6 “Conclusion,” specifically within 6.1 “Theoretical Implications.”
The revised logic for Section 6.1 is as follows:
This section covers three main points: (1) expanding the application scope of DPSIR theory; (2) clarifying the mechanism of data element value release in smart cities within a broader social context, thereby strengthening the connection between data value release and the social environment; and (3) analyzing, from a micro perspective, the five dimensions and 47 factors influencing data value release in smart cities, and identifying 11 key factors. This provides specific guidance for fully realizing the potential value of data elements in smart cities.
Comment 10: You should add the research questions.
Response 10: Thank you for your comments. In the introduction section, we propose three research questions in this paper:
Q1: What is the mechanism for releasing the value of data elements in smart cities?
Q2: What are the factors influencing the release of data element value in smart cities based on the mechanism?
Q3: Which factors are the most critical among all those influencing the release of data element value in smart cities?
Comment 11: How the concept presents in terms of releasing of shared value of data in SCies? https://www3.weforum.org/docs/WEF_Unlocking_Shared_Value_Smart_City_Data_2022.pdf.Why was it not taken into account the dimensions of data value?
Response 11: Thank you for your suggestion. The white paper Unlocking Shared Value from Smart City Data highlights that releasing shared value from smart city data requires a robust data governance framework to ensure effective, secure, and ethical data sharing. The core approach focuses on establishing trust among multiple stakeholders, promoting cross-sector and cross-industry data sharing, and ensuring data flow between public and private sectors. This framework aims to use high-quality data to enhance public services, support decision-making, and drive sustainable social and economic benefits. The white paper outlines that shared value can be achieved through data governance and trust mechanisms, multi-stakeholder collaboration, improved data quality, and data-driven innovation.
Our study focuses on the mechanism of data element value release in smart cities and its key influencing factors, which relates to the theme of “releasing shared data value” in the white paper but differs in emphasis. While the white paper emphasizes governance frameworks and cross-sector collaboration for shared value, our study centers on the internal process of data element value release within the smart city environment, specifically analyzing and identifying key influencing factors to enhance efficiency at operational and policy levels.
Thank you again for your insightful suggestion. Although the white paper’s focus differs from ours, it provides valuable insights that support our framework development and mechanism analysis and offers direction for future research in this series.
Comment 12: Data Valuation and Its Applications for Smart Cities, April 2023 DOI:10.1201/9781003399384_15 In book: Personal Data-Smart Cities: How cities can Utilise their Citizen’s Personal Data to Help them Become Climate Neutral (pp.217-243), Mihnea Tufis
Response 12: Thank you for your reminder. Upon reviewing the manuscript, we found that this book chapter is highly relevant to the third section of our literature review, “Value Assessment.” We have now incorporated it into the manuscript. Thank you again for your helpful suggestion.
Comment 13: It is better to write the “Fuzzy-DEMATEL method” instead of “Fuzzy-set DEMATEL method”.
Response 13: Thank you for your suggestion. We have revised the entire manuscript accordingly.
Comment 14: There is a lack of Table 3,4 and 5 concrete results interpretation
Response 14: Thank you very much for your valuable feedback. In response to your suggestion, we have added detailed interpretations for each table in the revised manuscript to assist readers in understanding the significance of these results and their implications for our research.
Specifically:
Table 3: We have added an interpretation explaining how the values in the direct impact matrix R represent the direct influence each factor exerts on others. Higher values indicate a stronger direct impact.
Table 4: We provided an interpretation for the comprehensive relationship matrix T, which includes both direct and indirect influences among factors. This matrix highlights the overall influence of each factor across the network, allowing us to prioritize those with the most extensive and far-reaching impact.
Table 5: We included an interpretation of the “four degrees” metrics—namely, influence degree, influenced degree, center degree, and cause degree—for each factor. These metrics and their rankings represent the overall impact of each factor, and we have clarified how they are used to identify the most influential factors, or the key influencing factors, in the value release process.
We are grateful for your guidance in improving the clarity of our manuscript.
Comment 15: Figure 1 Why you haven’t present the feedback arrows? The sources of Figure 1, which inspired you should be provided. The figure contain a lot of common unprecise information. The citations 29,30 and 31 are inappropriate for Figure 1 description as well as old. What are the sources of value release response, impacts… More explanations should be provided how the elements of each of 5 parts have been created! Why you exclude the “domain” in path: Data domain-data asset-data element?
Response 15:
(1) Figure 1 Why you haven’t present the feedback arrows?
Thank you for your valuable feedback on Figure 1. Regarding the issue of “feedback arrows,” the five components in Figure 1 (driver, pressure, state, impact, response) represent a sequential mechanism for the value release process of data elements in smart cities. According to the logical structure of DPSIR theory, these components are arranged in a causal sequence to clearly illustrate the flow of value release. Therefore, we believe that feedback arrows may not be necessary in Figure 1, as they could potentially disrupt the intended logical progression of this process.
(2) The sources of Figure 1, which inspired you should be provided. The figure contain a lot of common unprecise information.
As for the basis of Figure 1’s design, it is an analytical framework we developed independently, based on the principles of DPSIR theory. We integrated the general framework structure from existing literature with our specific research questions to create Figure 1.
(3) The citations 29,30 and 31 are inappropriate for Figure 1 description as well as old.
We apologize for the oversight. We have replaced these citations with more recent references. Thank you for pointing this out. The replaced references are [29][30].
(4) What are the sources of value release response, impacts… More explanations should be provided how the elements of each of 5 parts have been created!
Thank you for your suggestion! We have provided further detail in the manuscript on the origins and formation of each element within the DPSIR framework.
In the DPSIR framework, Value Release Drivers originate from macro social factors, including socio-economic activities, natural environmental conditions, cultural ideologies, and urban population. These factors drive the foundational needs for value release of data elements in smart cities, serving as the starting point for the entire value release process.
Value Release Pressures are composed of institutional and technological forces that directly influence data through stages of collection, management, and transformation, facilitating the shift from raw data to data resources and ultimately to data elements.
Value Release State reflects the current status and structure of data elements in smart cities, including data form, participating stakeholders, and intelligent service platforms linking data elements to users. These states dynamically change with the transformation and utilization of data throughout the value release process.
Value Release Impacts refer to the direct effects of data element value release on intelligent service design, stakeholder satisfaction, and the operational environment of smart cities. These impacts drive smart cities to adapt continuously to meet evolving demands.
Value Release Responses are actions taken to address the various impacts in the value release process. These responses include policy adjustments, resource allocation optimization, and technological updates, ultimately influencing the social environment supporting the value release of data elements in smart cities.
(5) Why you exclude the “domain” in path: Data domain-data asset-data element?
The term “domain,” as mentioned by the reviewer, typically refers in data management to a specific application area or thematic field, such as transportation, energy, or healthcare. These domains often involve distinct data types and processing requirements.
In our study and framework, the path “Data resource–Data asset–Data element” is designed to describe the process by which data in smart cities transitions from its raw state to an element with economic and practical value. In this path, data starts as raw resources collected from various sources, then undergoes processing and organization to become “data assets” with potential economic value, and ultimately transforms into data elements that can actively participate in value release activities. Our focus here is on the transformation pathway of data value, rather than on its specific application domains.
While “Data domain” indeed plays an important role in the practical application of data, we aimed to create a generalized framework for the value release mechanism that does not confine data to specific domains. Our research goal is to understand the value release mechanism of data elements on a macro level, rather than to differentiate across application areas. For this reason, “Data domain” was not included in this path.
In future studies that delve further into the value release mechanisms of data in different smart city domains, we may consider incorporating “Data domain” into the framework as a factor influencing the transformation of data assets. For now, our framework emphasizes the universal process of data value transformation.
Comment 16: Large discussion should be provided on the usage of the Dematel method and the research limitations should be discussed. The smart city is the city, which generates the large amount of data. Thus, why you haven’t apply the integrated big data approaches? For example an integrated DEMATEL-ANFIS approach, ISM-DEMATEL approach? Especially, in terms of [5] research. More arguments and explanations should be provided in paper.
Response 16: Thank you for your valuable feedback. We have provided a detailed explanation of our chosen research method in Section 5.
This study aims to analyze the mechanism of data element value release in smart cities, with a particular focus on the causal relationships between influencing factors. The release of data element value in smart cities is a complex process involving the interaction of multiple factors. The strength of the DEMATEL method lies in its ability to identify causal relationships in multi-factor systems and quantify interactions among factors. By constructing impact and relationship matrices, DEMATEL clearly shows the influence paths between factors, allowing us to understand the internal causal network. Therefore, DEMATEL is well-suited for multivariate causal analysis, helping to reveal key drivers and dependencies in the value release process, which aligns closely with our research objectives.
Regarding the integrated methods you suggested (such as DEMATEL-ANFIS and ISM-DEMATEL), while these approaches have proven effective in complex systems, they are primarily designed for big data analysis and nonlinear system modeling. For example, DEMATEL-ANFIS combines fuzzy logic and adaptive neural networks, making it suitable for system prediction or nonlinear analysis, while ISM-DEMATEL is commonly used in structural modeling to analyze hierarchical relationships between factors. In our study, however, our main objective is to understand and explain the causal structure of the data element value release mechanism within the DPSIR framework, rather than building predictive models or handling large-scale nonlinear data. Therefore, these integrated methods are not entirely aligned with our research focus and were not adopted.
While DEMATEL has advantages in causal analysis, we acknowledge its limitations. For instance, DEMATEL relies on expert judgment, which may introduce subjective bias. To mitigate this, we incorporated fuzzy set theory to process expert scores, reducing subjectivity and enhancing the reliability of our results. Fuzzy set theory also has unique advantages in handling uncertainty and imprecision, further stabilizing the outcomes of DEMATEL.
In summary, we adopted a combination of the DPSIR framework and the DEMATEL method to meet the requirements of analyzing the causal structure in data element value release, with fuzzy set theory applied to refine expert scoring. Future research could consider incorporating integrated methods into this framework to expand causal analysis depth and include nonlinear modeling capabilities.
Thank you again for your constructive suggestions.
Comment 17: More recent literature sources should be provided.
Response 17: Thank you for your helpful suggestion. We have reviewed and updated the literature in our manuscript, incorporating more recent sources to ensure the research reflects the latest developments in the field. We believe these additions strengthen the foundation of our manuscript and provide a more current context for our findings.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsReview Report - Research on the Mechanism and Identification of Key Influencing Elements for Releasing the Value of Data Element in Smart Cities
By Mo Hu, YunChao Zhang, Fan Sheng
The use of Fuzzy Set Theory-DEMATEL (Decision-Making Trial and Evaluation Laboratory) Method, which is a hybrid approach combining the flexibility of fuzzy set theory with the analytical power of DEMATEL is interesting and well presented in this article. This integrated combination is useful for addressing complex decision-making problems characterized by uncertainty and ambiguity of data use in smart city environments, providing both theoretical insights and real-world applications for improving the utility of data in urban management.
Through the integration of DPSIR (Drivers, Pressure, State, Impact, and Response) theory and fuzzy set theory-DEMATEL, the study identifies 11 critical factors that influence data value release. By providing theoretical insights and real-world applications for improving data usefulness in urban management, this research makes a substantial contribution to our understanding of data consumption in smart city contexts.
The manuscript is well-structured and generally clear, and the literature review is comprehensive and covers essential aspects of data value in smart cities.
The following are some minor observations:
1. The use of acronyms such as DEMATEL, DPSIR, and others must be defined when first used in the text, because not all readers of this prestigious journal may know them, and it would facilitate the understanding of the text. I admit that I'm not used to some acronyms either, so I had to look them up.
2. Switching from triangular to a trapezoidal fuzzy approach could bring several potential advantages for the article's analysis. Triangular functions provide sharp transitions due to their single peak, which may not accurately reflect gradual changes in the influence of various factors. Trapezoidal functions generally offer a more flexible representation of uncertainty compared to triangular functions, as they allow for a plateau. Namely, a range of maximum membership values instead of a single peak. Trapezoidal functions allow for a gentler transition between full and partial membership levels.
3. A more comprehensive assessment of the findings' practical applications is required, particularly about how these characteristics might influence operational and policy decisions in smart cities.
In conclusion, the manuscript provides a valuable framework for the identification of key influencing elements for releasing the value of data elements in smart cities. Addressing comments would improve the manuscript, whether by stating observations for future research, and increase its impact in the field of smart city management.
Best regards,
The reviewer.
Author Response
Comment 1: The use of acronyms such as DEMATEL, DPSIR, and others must be defined when first used in the text, because not all readers of this prestigious journal may know them, and it would facilitate the understanding of the text. I admit that I’m not used to some acronyms either, so I had to look them up.
Response 1: Thank you for your suggestion. We have made revisions to the manuscript to clarify abbreviations at their first appearance. We apologize for any confusion caused and appreciate your valuable feedback once again.
Comment 2: Switching from triangular to a trapezoidal fuzzy approach could bring several potential advantages for the article’s analysis. Triangular functions provide sharp transitions due to their single peak, which may not accurately reflect gradual changes in the influence of various factors. Trapezoidal functions generally offer a more flexible representation of uncertainty compared to triangular functions, as they allow for a plateau. Namely, a range of maximum membership values instead of a single peak. Trapezoidal functions allow for a gentler transition between full and partial membership levels.
Response 2: Thank you for your insightful suggestion. Our study focuses on analyzing the influence mechanism of data element value release in smart cities, specifically by identifying and quantifying causal relationships among multiple factors using a fuzzy set-DEMATEL approach. Given this research objective, we selected the triangular fuzzy approach as it aligns well with our goal of capturing the primary influence paths and intensity levels in a clear and interpretable manner.
The triangular fuzzy approach is particularly suitable for this type of foundational causal analysis, as it provides a straightforward representation of uncertainty with a single peak that simplifies calculations and interpretation. This characteristic is advantageous in our study because it allows us to clearly identify and prioritize the key factors affecting data element value release without introducing additional complexity. Triangular functions allow for efficient processing of expert evaluations and facilitate a streamlined comparison of factor influence, which is critical to our goal of determining the relative importance of each factor in a practical and accessible way.
Furthermore, the triangular fuzzy approach’s sharp transitions are adequate for the current study, where we focus on capturing distinct levels of influence rather than modeling gradual or overlapping changes. This method has proven to be effective in studies like ours, where clarity and computational simplicity are prioritized to draw actionable insights.
That said, your suggestion has inspired us to consider the trapezoidal fuzzy approach in future research, as it offers additional flexibility in representing uncertainty, particularly when capturing gradual transitions or ranges of maximum influence. We plan to incorporate and compare the trapezoidal approach in subsequent studies to further validate and refine our findings.
Thank you once again for your valuable feedback, which has provided us with a promising direction for future exploration.
Comment 3: A more comprehensive assessment of the findings’ practical applications is required, particularly about how these characteristics might influence operational and policy decisions in smart cities.
Response 3: Thank you for your suggestion, which has been very helpful in improving our manuscript. In Section 5.4.2, we have provided a more detailed evaluation of the practical applications of our findings, specifically addressing how each key influencing factor supports the process of releasing the value of data elements in smart cities.
Top-level design (S6) sets a unified strategic framework for smart city development, with government regulatory structures (S7) overseeing data security and compliance. Expert participation (S14) provides the scientific foundation needed for informed planning and implementation, while collaborative stakeholder relationships (S17) improve resource allocation and efficiency.
At the data level, data acquisition (P1) and computing technology (P4) support real-time data management and analysis, ensuring effective decision-making. Data privacy protection (P9) builds citizen trust and encourages engagement, while the data transaction system (P7) transforms data into economic assets, supporting sustainability.
The self-transformation capacity of smart cities (R6) enables adaptability and innovation, while the design and provision of data products and services (I1) create direct value for citizens and businesses. Socio-economic level (D1) underpins resource distribution and technology development, forming a foundation for smart city operations.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsAfter reviewing the article, I found that it is methodologically well presented in form and content. Each section fulfills the expected purpose. The contribution is clear, identifying key factors that influence releasing the value of data element in smart cities.
I found minor details in the document. In particular, on line 130, three aspects are presented and the third is "Value assessment". The aspects are described and I assume that on line 132, it should be "Value assessment" instead of Value Release (which is presented on line 118).
Author Response
Comment 1: I found minor details in the document. In particular, on line 130, three aspects are presented and the third is “Value assessment”. The aspects are described and I assume that on line 132, it should be “Value assessment” instead of Value Release (which is presented on line 118).
Response 1: Thank you for your suggestion and for your positive feedback on this manuscript. In line 132, “Value Release” is indeed the correct term.
The original text in line 132 reads: “In view of this, this paper will try to analyze the mechanism and influencing factors of the value release of data element in smart cities based on the DPSIR theory after defining the concept of data element value release in smart cities…”This statement intends to convey that, after reviewing the existing literature, this study seeks to examine the mechanism, influencing factors, and key factors in the process of value release for smart city data elements, hence the use of the term “Value Release.” As you noted, “Value Assessment” is part of the literature review section. In this section, we reviewed previous studies on smart city data elements, organizing them into three dimensions: value creation, value release, and value assessment, which provide a solid foundation for our research.
In summary, line 132 should indeed read “Value Release.” Thank you again for your valuable input.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsDear Authors,
Thank you, I accept most of corrections. Just some suggestions: the information about whether and which answers to the research questions have been found should be presented in the conclusion section. Equations: may be a,b,c letter will be better to minimize as indexes or use the numbers 1,2,3? Like here: (equations 19-21) and use letters l and r for left and right values. https://onlinelibrary.wiley.com/doi/10.1155/2018/3696457
Best Regards.
Author Response
Comment 1: The information about whether and which answers to the research questions have been found should be presented in the conclusion section.
Response 1: Thank you very much for your constructive suggestions. In the revised conclusion section, I have explicitly stated that all three research questions have been addressed and provided a clear summary of the corresponding answers to each question. Specifically:
(1) For Research Question 1 (What is the mechanism for releasing the value of data elements in smart cities?), I have elaborated on the mechanism based on the DPSIR framework, highlighting the dynamic pathways and evolution logic.
(2) For Research Question 2 (What are the factors influencing the release of data element value in smart cities based on the mechanism?), I identified a comprehensive set of 47 influencing factors across five dimensions.
(3) For Research Question 3 (Which factors are the most critical among all those influencing the release of data element value in smart cities?), I described how 11 key factors were extracted using the fuzzy-DEMATEL method and classified their attributes to reveal their interrelationships.
These revisions aim to ensure that the research questions are clearly addressed and the findings are effectively communicated to the readers. Thank you once again for your insightful suggestion.
Comment 2: Equations: may be a, b, c letter will be better to minimize as indexes or use the numbers 1,2,3? Like here: (equations 19-21) and use letters l and r for left and right values. https://onlinelibrary.wiley.com/doi/10.1155/2018/3696457
Response 2: Thank you very much for your valuable feedback. We have carefully revised the manuscript to address this issue. In the revised manuscript:
(1) The index letters a, b and c have been replaced with the numerical indices 1, 2 and 3 throughout the equations to improve clarity and consistency with standard practices.
(2) For left and right values, the notation has been updated to use l (for left) and r (for right), as suggested, ensuring that the equations are more intuitive and aligned with the format presented in the referenced article.
These changes ensure that the mathematical framework is more reader-friendly, systematic, and easier to interpret. Thank you for bringing this to my attention and for providing the reference, which was highly useful in guiding these improvements.
Author Response File: 
Author Response.docx
