Allocation Efficiency of Public Sports Resources Based on the DEA Model in the Top 100 Economic Counties of China in Zhejiang Province
Round 1
Reviewer 1 Report
This is good paper with a strong introduction. The methodology looks good. But I have two suggestions for authors. 1) They should consider defining the term “mass sports” in the introduction. 2) They should consider separating the results from the discussions. I think doing it this way, authors can effectively discuss the major findings and support them with secondary literature.
The quality of English language is good. The paper may need minor editing.
Author Response
Dear reviewer,
Thank you for providing the detailed and constructive suggestions for our manuscript. We have carefully reviewed, addressed each comment below and made corresponding changes in the manuscript (marked up using the “Track Changes” function).
We hope you find the changes satisfactory.
Sincerely
Yijuan Lu
Comment 1: They should consider defining the term “mass sports” in the introduction.
Response
Thank you for constructive suggestions.
The revised manuscript adds a definition of "mass sports" to the introduction. (Line 45-50)
Comment 2: They should consider separating the results from the discussions. I think doing it this way, authors can effectively discuss the major findings and support them with secondary literature.
Thank you for constructive suggestions.
The revised manuscript presents the results and discussion separately.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Dear Authors,
I have a serious problem with your article. Since he “methodology/data” part is written cemplexy, I can not really find the research question in this scientific paper.
In my opinion, an introduction and discussion need to be written in totally different form. Article could be interesting e.g. by comparing various methods, but right now, for me, it looks like quite “political” paper rather than scientific. In the introduction I suppose to receive a background about scientific knowledge on public sport resources – what it is, did anyone conduct research in this topic, what they found, and, the most important, what new you research will bring in this area. But in the meaning of science. i.e.: what a researcher form USA/Africa/Europe could use to improve his research.
Please, rewrite the article in order to clearly state the scientific research question and properly adjust introduction and other parts to it.
Author Response
Dear reviewer,
Thank you for providing the detailed and constructive suggestions for our manuscript. We have carefully reviewed, addressed each comment below and made corresponding changes in the manuscript (marked up using the “Track Changes” function).
We hope you find the changes satisfactory.
Sincerely
Yijuan Lu
Comment 1: In my opinion, an introduction and discussion need to be written in totally different form. Article could be interesting e.g., by comparing various methods, but right now, for me, it looks like quite “political” paper rather than scientific. In the introduction I suppose to receive a background about scientific knowledge on public sport resources – what it is, did anyone conduct research in this topic, what they found, and, the most important, what new you research will bring in this area. But in the meaning of science. i.e.: what a researcher form USA/Africa/Europe could use to improve his research. Please, rewrite the article in order to clearly state the scientific research question and properly adjust introduction and other parts to it.
Response
We apologize for the confusing description and thank you very much for your valuable suggestions.
The revised manuscript adjusts the introduction section. The intellectual background of public sport resource allocation, the current status and limitations of existing research, and the main problem addressed in this study were added. The revised manuscript also condensed the methodological section as well as presented the results separately from the discussion.
Author Response File: Author Response.pdf
Reviewer 3 Report
Title: Allocation Efficiency of Public Sports Resources Based on DEA Model in the Top 100 Economic Counties of China in Zhejiang Province
This research takes the top 100 economic counties of China 1in Zhejiang Province as the research object, this study explores the allocation efficiency and influencing factors of public sports resources in the period of 2016 to 2020. However, I have a few major and minor concerns about this study which should be revised before its acceptance for publication. My detailed comments are as follows:
Minor Concerns.
Abstract: The abstract should sequence the objectives, techniques, and concise results. Revise accordingly. Check the sequence of keywords.
Introduction: I can see that the introduction section is lengthy. Exclude unnecessary information and be concise with the study’s research problem, gaps, objectives, and innovative contribution if possible. Usually, at the end of the introduction section, the authors describe the innovations and route of the study. Revise accordingly to put the introduction section in the appropriate shape. Further, include some recent references related to DEA applications in sports in the introduction section. The following reference will help you to find DEA papers in sports. https://doi.org/10.3390/su15043201
Results and Discussions
Add more citations in the interpretation of the results in sections 4.1 and 4.2. Further, elaborate on the results in detail.
Conclusion. Add limitations and future research ideas in the conclusion section. Give more policy implications for the sports resources efficiency of Zhejiang.
Avoid grammatical and typo errors, and revise the manuscripts for these concerns.
Carefully check and revise the table and figure numbers in the manuscript.
Add more citations on sports efficiency.
Major concern.
1. There are many estimation tools used to estimate efficiency and productivity growth. For example, stochastic frontier analysis (SFA) is a renowned parametric efficiency estimation technique. SFA is Frequently used for the efficiency evaluation of different units in diverse industries. My concern is why the authors choose data envelopment analysis-SBM, a linear programming technique, for agricultural water usage efficiency estimation in China. Does DEA-SBM have some superiority over SFA? If yes, discuss it in detail in the methodology section of the revised version. Explain in detail.
2. Inputs-output selection in DEA is a matter of great concern because it could impact the efficiency scores. Which studies do authors adopt for input and output selection? Please provide the reference. Further authors mentioned that “the number of people who regularly participate in physical exercise in the county (i.e., the total number of people who participate in physical exercise more than three times a week and exercise for more than 30 minutes each time) and the number of social organizations were selected as output indicators” how you collect this data and how it shows that it is the output of sports resources efficiency.
Minor English editing required.
Author Response
Dear reviewer,
Thank you for providing the detailed and constructive suggestions for our manuscript. We have carefully reviewed, addressed each comment below and made corresponding changes in the manuscript (marked up using the “Track Changes” function).
We hope you find the changes satisfactory.
Sincerely
Yijuan Lu
Comment 1: The abstract should sequence the objectives, techniques, and concise results. Revise accordingly. Check the sequence of keywords.
Response
Thank you for constructive suggestions.
Revision was made in the manuscript (line 11-36).
Comment 2: Introduction: I can see that the introduction section is lengthy. Exclude unnecessary information and be concise with the study’s research problem, gaps, objectives, and innovative contribution if possible. Usually, at the end of the introduction section, the authors describe the innovations and route of the study. Revise accordingly to put the introduction section in the appropriate shape. Further, include some recent references related to DEA applications in sports in the introduction section. The following reference will help you to find DEA papers in sports. https://doi.org/10.3390/su15043201
Response
Thank you for constructive suggestions.
The revised manuscript was rewritten with an introduction section (line 39-145).
Comment 3: Add more citations in the interpretation of the results in sections 4.1 and 4.2. Further, elaborate on the results in detail.
Response
Thank you for constructive suggestions.
The revised manuscript adds more citations.
Comment 4: Add limitations and future research ideas in the conclusion section. Give more policy implications for the sports resources efficiency of Zhejiang.
Response
Thank you for constructive suggestions.
The revised manuscript adds limitations and future research ideas in the discussion section to provide more policy insights into the efficiency of sports resource utilization in Zhejiang Province.
Although this study explores the efficiency of public sports resource allocation in China's top 100 economic counties through five consecutive years of data and draws some meaningful conclusions, the study still has limitations. This research only evaluates the efficiency level of the top 100 counties, which can be combined in the future from the perspective of the fairness of public sports resource allocation in the top 100 counties. Fairness evaluation mainly adopts the Gini coefficient method to draw the Lorenz curve and compare the fairness between different counties and cities according to the curvature of the curve. Firstly, the above data are standardized to eliminate the influence of the dimension between indicators, and then the entropy weight method is used to calculate the weights by weighting so as to obtain the comprehensive Gini coefficient value. Finally, the size of Gini coefficients of public sports in terms of people, money and materials between different regions based on population, economy and geographical area distribution were calculated separately.
The allocation of public sports resources is a systemic, complex and non-linear dynamic project. This study and the current study mostly focus on static and linear governmental role positioning, market mechanism innovation, collaborative governance, and external structure of supply paths, but do not make in-depth and specific analysis on the structure of specific internal resource elements. The development of public sports is the result of the coupling of multiple internal and external resource elements. Therefore, system simulation can be conducted on the basis of DEA efficiency decomposition combined with system dynamics modeling methods to draw causal feedback diagrams within the boundary of the system, and finally arrive at a combination strategy for the optimization path of public sports resources structure.
Comment 5: Avoid grammatical and typo errors, and revise the manuscripts for these concerns.
Response
Thank you for constructive suggestions.
We revised grammar and typos in the manuscript.
Comment 6: Carefully check and revise the table and figure numbers in the manuscript.
Response
Thank you for constructive suggestions.
The revised manuscript has been checked and revised for table numbers.
Comment 7: Add more citations on sports efficiency.
Response
Thank you for constructive suggestions.
Revision was made in the manuscript.
Comment 8: 1. There are many estimation tools used to estimate efficiency and productivity growth. For example, stochastic frontier analysis (SFA) is a renowned parametric efficiency estimation technique. SFA is Frequently used for the efficiency evaluation of different units in diverse industries. My concern is why the authors choose data envelopment analysis-SBM, a linear programming technique, for agricultural water usage efficiency estimation in China. Does DEA-SBM have some superiority over SFA? If yes, discuss it in detail in the methodology section of the revised version. Explain in detail.
Response
We apologize for the confusing description.
Revisions and additions were made in the 1. introduction section of the manuscript (line 80-93)
The scientific and reasonable measurement of the efficiency of resource allocation is to objectively quantify the material inputs and outputs in the form of a mathematical model, and the current methods of evaluating efficiency include parametric and nonparametric analysis. Non-parametric analysis method, also called non-parametric test, is a type of estimation method relative to parametric estimation. In nonparametric estimation, no assumptions are made about the underlying distribution, and the information from the random sample itself is mainly used to estimate the strengths and weaknesses of the quantity. Parametric analysis, as the name im-plies, is a method to estimate the unknown parameters in the overall distribution based on a random sample drawn from the overall population, commonly known as Least Squares Estimation, Stochastic Frontier Analysis (SFA), and Bayesian Estimation, through which the laws of development are revealed. Nonparametric estimation has the advantages of fewer assumptions, simple operation, and the ability to adapt to nominal and sequential scales. Such as Data Envelopment Analysis (DEA).
SFA is a well-known technique for estimating parametric efficiency and is often used to evaluate the efficiency of different units in different industries. However, the basic assumptions of the model of SFA are more complex and need to consider the specific form of the production function, the distribution of the technical inefficiency term, which directly leads to the difficulty of doing further model extensions. Because of the complex form of the density function of the synthetic error term ε, the corresponding likelihood function is even more complex, which brings a lot of computational difficulties to the parameter estimation, so it is difficult to further analyze the heteroskedasticity and other situations or to do further model extensions.
The main advantage of DEA is that it does not need to consider the specific form of the production frontier, only input-output data, the model is easy to do other forms of expansion, there are dozens of DEA models. DEA is a non-parametric estimation method, circumventing a variety of limitations of the parametric approach, do not need to set the parameters of the frontier production function and the specific form, do not need to consider the outline between indicators, and can be a linear programming approach to the model weights can be calculated in a linear programming manner, thus circumventing the interference of artificial subjective weighting. In addition, because the model assumptions of SFA are more complex, it requires more input and output data. If the input and output data do not meet the basic assumptions of the model, the skewness problem analyzed above will easily occur and eventually lead to the failure of the calculation. Based on the above analysis we chose DEA instead of SFA for our study.
Comment 9: Inputs-output selection in DEA is a matter of great concern because it could impact the efficiency scores. Which studies do authors adopt for input and output selection? Please provide the reference. Further authors mentioned that “the number of people who regularly participate in physical exercise in the county (i.e., the total number of people who participate in physical exercise more than three times a week and exercise for more than 30 minutes each time) and the number of social organizations were selected as output indicators” how you collect this data and how it shows that it is the output of sports resources efficiency.
Response
We apologize for the confusing description.
Revisions and additions were made in the 3.1 Selection of Input and Output Indicators section and 3.2 Data sources section of the manuscript.
(1) The responses to how the input and output indicators were selected for the study are as follows.
We agree with you that the rationality of efficiency evaluation indicators directly affects the scientific and objective nature of the evaluation results. Our study follows the empirical pre-selection (initial indicators) - Delphi method (round 1) - indicator screening - Delphi method (round 2) - Hierarchical analysis - Indicator assignment - Final indicator logical sequence and framework to select indicators.
First, based on the studies of Chen(2016)[1],Yu(2019)[2],Li(2017)[3],Zhu(2019)[4], and other researchers, an evaluation index system for the efficiency of county public sports resources allocation was initially constructed based on the principles of rationality, representativeness, measurability and data accessibility in the construction of efficiency indicators. Second, after two rounds of Delphi method with reference to 14 experts' suggestions, useless indicators were excluded and Friedman and Kendall coefficient tests were conducted. The results showed that: p<0.05 in Friedman test, rejecting the original hypothesis, indicating that there is a significant difference in the experts' assignment of the six evaluation indicators; the original hypothesis of Kendall coefficient is that there is no significant consistency among the experts' evaluation indicators, p<0.05 in Kendell test, therefore rejecting the original hypothesis, indicating that there is significant consistency in the experts' overall evaluation of the six evaluation indicators; according to the value of ω (0.45) indicates that the coordination of expert evaluations is good. Finally, DEA efficiency indicators of public sports resource allocation in the top 100 economic counties were derived.
(2) The responses to why the indicators were selected for the study and how the indicator data were obtained are presented below.
County sports social organizations, refers to the county government departments approved by law in the Civil Affairs Bureau or registered or filed in the Sports Bureau of the non-profit nature of sports social organizations, including specifically mass sports associations, mass sports foundations, sports private non-enterprise units. Therefore, the number of social organizations of directly from the statistical database of each county and city sports bureau. The number of people who regularly participate in physical activity is obtained from the annual report statistics of each county and city government or sports bureau.
To examine the efficiency of public sports resource allocation is to measure how limited human, financial, and organizational resources are invested in the development, construction, and allocation of public sports resources in order to produce what social and economic benefits. Considering the shortcomings of previous studies that focused only on economic efficiency but not on social justice efficiency, the output indicators of both social and economic benefits were selected to be examined in this study's output indicators. The output indicator of social efficiency is expressed in terms of the number of people who regularly participate in sports and exercise in the county, while the indicator of economic efficiency focuses on examining the level of public finance management and the degree of organization of mass sports activities, which is mainly measured from the tangible material level, here the number of county sports social organizations is measured.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
I have reviewed the manuscript again and it looks good. Authors have revised the manuscripts based on my previous comments.
It will need some editing.
Reviewer 2 Report
Authors significantly improved the article. It could be accepted in actual form.
Reviewer 3 Report
I am satisfied with revision
