Effects of Chinese Firms’ Innovation on New Energy Vehicles Purchases
Round 1
Reviewer 1 Report
Page 2. The authors need to use the latest data to describe the market share of new energy vehicle sale in China. In China in 2022, the sales condition of new energy vehicle in China is far different from that in 2018.
The refence style is a mixed style with both MDPI style and APA style, please check this problem and unify it.
The source of online questionnaire is not detailed described. Which platform did authors recruit? When was the survey conducted? What are the standards of selecting respondents? And their intention of purchasing new energy vehicles.
The English language of the scale items need further edit.
'***' In table 4 and Table 6 refers to what should be added as a Note
The term used to represent the collection of items should be unified as 'dimension' or 'construct'.
Discussion part lacks relation to existing literature.
Maybe it's better to place the discussion section before the conclusion section.
In addition, the authors are suggested to separately list the theoretical and practical contributions in the conclusion section.
Overall, the literature referenced in this article is a little out of date.
Author Response
Dear Reviewer,
Above all, thank you for your detailed comment. Your comments have been a great help in developing my thesis. I modified my thesis according to your comments. The red color of the attached paper is the correction according to your comment, and many of the opinions of other reviewers are also reflected.
Below is my brief answer to your comment. Please see the attached file for detailed modification.
---------------------------------------------------------------------------------------
The authors need to use the latest data to describe the market share of new energy vehicle sale in China. In China in 2022, the sales condition of new energy vehicle in China is far different from that in 2018. ==> We have found the latest data and added it to this paper.
The refence style is a mixed style with both MDPI style and APA style, please check this problem and unify it.==> We have revised.
The source of online questionnaire is not detailed described. Which platform did authors recruit? When was the survey conducted? What are the standards of selecting respondents? And their intention of purchasing new energy vehicles. ==> We have revised.
The English language of the scale items need further edit.
'***' In table 4 and Table 6 refers to what should be added as a Note.==> We have revised.
The term used to represent the collection of items should be unified as 'dimension' or 'construct'.==> We have revised.
Discussion part lacks relation to existing literature.
==> In the discussion part, I use the existing NEV firms’ cases in China to explain the reasons for the assumptions which were not supported in the original model, and thank you for your review comments.
Maybe it's better to place the discussion section before the conclusion section.
==> We appreciate your review comments and have revised it in conjunction with other reviewers' comments.
In addition, the authors are suggested to separately list the theoretical and practical contributions in the conclusion section.==> We have revised.
Overall, the literature referenced in this article is a little out of date.==> We have revised and added some literature.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Dear Authors
The issues of consumer behavior and perceived risk has been my main scientific interest for years. That is why I am very happy to have the opportunity to read your very interesting article on this topic.
The manuscript entitled “Effects of Chinese Firms’ Innovation on New Energy Vehicles Purchases” is very well-written and has a research character. The Authors should be appreciated for the research reliability and methods used. The strong points of this article are also its layout and the clarity of the presented contents.
In my opinion, the Authors should expand subsection 3.1. Data Collection, explaining why the sample size was a total of 616 respondents. How do these numbers relate to the total number of consumers in China? What measurement error should be expected? Why did the authors decide to use the online survey technique, since the many disadvantages of this technique are known? What, after all, decided in favor of choosing such a technique?
It is essential to provide a statistical source confirming that the data are representative of the entire population. What source of demographic data do the authors mean?
I would separate the "Discussion" section from the "Conclusions" section and include more references to research by other authors. Alternatively, the name of this section could be written as "Discussion and conclusions".
The references are quite poor but closely related to the topic of the article. Taking advantage of the fact that both Authors and me are passionate about the problem of consumer behavior and the risk perceived by them, I would like to draw their attention to two works that can enrich the literature review, as well as the discussion of the results. Here they are:
Maciejewski, G. Consumers Towards Sustainable Food Consumption, Perceived risk in purchasing decisions of the polish consumers – Model-based approach, Journal of Economics & Management 2012, 8, 37-52. Available online: https://www.ue.katowice.pl/fileadmin/_migrated/content_uploads/3_Maciejewski_Perceived_risk_in_purchasing.pdf 1.
Maciejewski, G., Lesznik, D. Consumers Towards the Goals of Sustainable Development: Attitudes and Typology. Sustainability 2022; 14 (17):10558. https://doi.org/10.3390/su141710558.
I hope that the indicated remarks will help the Authors to improve their text so that the work will be published. Good luck!
Author Response
Dear Reviewer,
Above all, thank you for your detailed comment. Your comments have been a great help in developing my paper. I modified my paper according to your comments. The red color of the attached paper is the correction according to your comment, and many of the opinions of other reviewers are also reflected.
Below is my brief answer to your comment. Please see the attached file for detailed modification.
----------------------------------------------------------------------------------------
The issues of consumer behavior and perceived risk has been my main scientific interest for years. That is why I am very happy to have the opportunity to read your very interesting article on this topic.
The manuscript entitled “Effects of Chinese Firms’ Innovation on New Energy Vehicles Purchases” is very well-written and has a research character. The Authors should be appreciated for the research reliability and methods used. The strong points of this article are also its layout and the clarity of the presented contents.
In my opinion, the Authors should expand subsection 3.1. Data Collection, explaining why the sample size was a total of 616 respondents. How do these numbers relate to the total number of consumers in China? What measurement error should be expected? Why did the authors decide to use the online survey technique, since the many disadvantages of this technique are known? What, after all, decided in favor of choosing such a technique? ==> We have revised.
It is essential to provide a statistical source confirming that the data are representative of the entire population. What source of demographic data do the authors mean? ==> We have revised.
I would separate the "Discussion" section from the "Conclusions" section and include more references to research by other authors. Alternatively, the name of this section could be written as "Discussion and conclusions". ==> We have revised.
The references are quite poor but closely related to the topic of the article. Taking advantage of the fact that both Authors and me are passionate about the problem of consumer behavior and the risk perceived by them, I would like to draw their attention to two works that can enrich the literature review, as well as the discussion of the results. Here they are:
Maciejewski, G. Consumers Towards Sustainable Food Consumption, Perceived risk in purchasing decisions of the polish consumers – Model-based approach, Journal of Economics & Management 2012, 8, 37-52. Available online: https://www.ue.katowice.pl/fileadmin/_migrated/content_uploads/3_Maciejewski_Perceived_risk_in_purchasing.pdf 1.
Maciejewski, G., Lesznik, D. Consumers Towards the Goals of Sustainable Development: Attitudes and Typology. Sustainability 2022; 14 (17):10558. https://doi.org/10.3390/su141710558. ==> We have added.
Author Response File: Author Response.pdf
Reviewer 3 Report
This is an excellent quality paper.
Author Response
Dear Reviewer,
Most of all, thank you for giving me a good evaluation of my paper. However, I have upgraded the paper according to the comments of other reviewers, and I am sending you a revised paper as well. I hope you like the revised thesis more.
Thank you!
Author Response File: Author Response.pdf
Reviewer 4 Report
Dear Authors,
The paper is well written. I could follow the presentation easily and understood the main objectives. However, the methodological approach did not convince me. In the following I will formulate my main concerns:
The models in tables 7 and 8 differ only with respect to the variables PV and PR. Why are they not both presented simultaneously in one model? Your conceptualization in Figure 3 suggests that perceived value and perceived risk act together on purchase intention. I would therefore want to see PV and PR in one model. I generally recommend the following procedure: You should first estimate a model that contains only control variables. Then a model with controls and PV, PR and CIC. Then you can formulate two models in which you additionally test the moderations. For all models, you should specify the R-squared and, for example, the Akaike information criterion. In this way, the reader can see what additional explanatory contribution the individual variables make and how they act together on the prediction of purchase intention.
Furthermore, on page 17, you write, that income levels have a major impact on consumer behavior. I agree with this statement. However, when looking at your estimation results, I noticed that the variable monthly income is not statistically significant. How do you explain this?
I would also like to suggest splitting the discussion and conclusion into two separate sections. Especially since I missed a short summary of the main results and findings at the end of the paper. Also, the discussion should focus more on the fact that direct effects and moderation effects have different estimated signs. This suggests that there are opposing effects on purchase intention that overlap each other. It would therefore be useful to present the moderation effects in a graph in order to make it easier for the reader to understand your reasoning. You should also provide more plausible explanations for these results.
Some additional comments of minor importance that should be considered before publication:
- Please read the manuscript carefully. I noticed minor formatting errors in a few places, for example, a double space on page 1 (line 40).
- In the text, double references are made with round and square brackets. I do not know the exact formatting instructions of the journal. However, I assume that references in square brackets are sufficient.
- The significance levels of the F statistics in Tables 7 and 8 should be reported.
- In the limitations, you write, "We do not consider consumer demographic characteristics [...]." Please clarify. Because from my point of view, the most important demographic factors are already considered in the study.
I hope my comments can help improve the article. I look forward to your revision.
Author Response
Dear Reviewer,
Above all, thank you for your detailed comment. Your comments have been a great help in developing my paper. I modified my paper according to your comments. The red color of the attached paper is the correction according to your comment, and many of the opinions of other reviewers are also reflected.
Below is my brief answer to your comment. Please see the attached file for detailed modification.
----------------------------------------------------------------------------------------
The models in tables 7 and 8 differ only with respect to the variables PV and PR. Why are they not both presented simultaneously in one model? Your conceptualization in Figure 3 suggests that perceived value and perceived risk act together on purchase intention. I would therefore want to see PV and PR in one model. I generally recommend the following procedure: You should first estimate a model that contains only control variables. Then a model with controls and PV, PR and CIC. Then you can formulate two models in which you additionally test the moderations. For all models, you should specify the R-squared and, for example, the Akaike information criterion. In this way, the reader can see what additional explanatory contribution the individual variables make and how they act together on the prediction of purchase intention.
Furthermore, on page 17, you write, that income levels have a major impact on consumer behavior. I agree with this statement. However, when looking at your estimation results, I noticed that the variable monthly income is not statistically significant. How do you explain this?
==> Explanation: The purpose of our model is to put variables that are related to purchase intention into the model, and determine the final factor that has a significant impact on purchase intention through modeling. From the modeling results, compared with other factor, the effect of income on purchase factor is relatively insignificant or slightly significant
I would also like to suggest splitting the discussion and conclusion into two separate sections. Especially since I missed a short summary of the main results and findings at the end of the paper. Also, the discussion should focus more on the fact that direct effects and moderation effects have different estimated signs. This suggests that there are opposing effects on purchase intention that overlap each other. It would therefore be useful to present the moderation effects in a graph in order to make it easier for the reader to understand your reasoning. You should also provide more plausible explanations for these results.
Some additional comments of minor importance that should be considered before publication:
- Please read the manuscript carefully. I noticed minor formatting errors in a few places, for example, a double space on page 1 (line 40).
==> We have revised.
- In the text, double references are made with round and square brackets. I do not know the exact formatting instructions of the journal. However, I assume that references in square brackets are sufficient.
==> We have found it, and revised.
- The significance levels of the F statistics in Tables 7 and 8 should be reported. ==> We have revised.
- In the limitations, you write, "We do not consider consumer demographic characteristics [...]." Please clarify. Because from my point of view, the most important demographic factors are already considered in the study.
==> Thank you for your review comments, and we have found, and deleted the content.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
The source of online questionnaire is still not detailed described, but now it is more acceptable.
Discussion part still lacks relation to existing literature, the authors should find more references to support their results.
Author Response
Point 1: The source of online questionnaire is still not detailed described, but now it is more acceptable.
Response 1: Thank you so much for your comments. I have added an explanation of the questionnaire process in this paper.
Point 2: Discussion part still lacks relation to existing literature, the authors should find more references to support their results.
Response 2: I sincerely appreciate your comment. Above all, it is reasonable to point out the supplementation of previous research, and I fully agree with your opinion. While I was doing research, I studied more existing literature, but there were my faults that could not be written down in the paper, so your comments were a great help in improving my paper.
I had to spend a little more time because I had to supplement the research I had studied in the past. Sorry for taking a lot of time to revise the paper. In the second revision, the parts that have been corrected according to the opinions of you and other reviewers are also marked in red. Please refer to it.
Reviewer 4 Report
Dear authors,
Thank you very much for the changes. The revisions have already noticeably improved the paper. However, I still find the methodological approach in the regression models unconvincing. My main concerns are:
Model 1 in Tables 7, 8, and 9 has a non-significant F-statistic. Thus, the F-test suggests that the model as a whole lacks explanatory power. The results of the F-test are also consistent with the reported R-squared, which is remarkably small at only 3 percent. Why do your control variables provide such a small data fit?
In model 2, the R-squared increases to relatively high values between 37 and 46 percent. This can be attributed not only to the inclusion of the additional regressors PV, PR, and CIC, but also to the moderation term. My concern now is that the goodness of fit of the models was artificially induced by the built-in interaction. To address my concerns, it would be useful to present a different model with the controls, PV, PR, and CIC, but without the moderation. Again, please show the R-squared appropriately so that the change in data fit can be understood. An information criterion (e.g., the AIC) would also be useful. By the way, I already mentioned both as important points in the first review.
Overall, the results of the estimates seem to be very sensitive to changes in the models. This is often an indication that important model assumptions are being violated. Have you looked at diagnostic statistics? Are there any anomalies anywhere? Perhaps you can provide the diagnostic statistics as supplementary material?
Thank you and good luck with further improvements.
Author Response
Point 1: Model 1 in Tables 7, 8, and 9 has a non-significant F-statistic. Thus, the F-test suggests that the model as a whole lacks explanatory power. The results of the F-test are also consistent with the reported R-squared, which is remarkably small at only 3 percent. Why do your control variables provide such a small data fit?
Response 1:
Thank you very much for your comments.
Since the data is randomly sampled, the p values of gender, age, and monthly income are all large than 0.05, but they are all relatively small (less than 0.3). In order to obtain the true influence relationship between the focal variables, it is necessary to control for the influence of gender, age, education level, and monthly income on purchase intention in the model below. For this, I can add some more data and table 1 in the paper.
Point 2: In model 2, the R-squared increases to relatively high values between 37 and 46 percent. This can be attributed not only to the inclusion of the additional regressors PV, PR, and CIC, but also to the moderation term. My concern now is that the goodness of fit of the models was artificially induced by the built-in interaction. To address my concerns, it would be useful to present a different model with the controls, PV, PR, and CIC, but without the moderation. Again, please show the R-squared appropriately so that the change in data fit can be understood. An information criterion (e.g., the AIC) would also be useful.
Response 2:
I really appreciate your opinions and comments. I respect your opinion and have corrected it according to your comment. Based on your comments, I have supplemented the Table 8, Table 9, and Table 10, adding two more models, and the AIC fitting index. After following your opinion, my revised paper is much improved. I sincerely thank you again .
In the second revision, the parts that have been corrected according to the opinions of you and other reviewers are also marked in red. Please refer to it.
