An Examination of the Variables Affecting the Growth of the Tourist Sector in Guizhou Province

Round 1

Reviewer 1 Report

Thanks for looking into an interesting topic. However, this paper has severe weaknesses which must be addressed.

The concerns are listed below:

1    1. The relevance of Section 1 and Section 2 is questionable. The discussion related to Guizhou is important so that readers can understand why this province should be studied. However, these two sections do not explain explicitly the objectives of this paper. Also, relevant literature is not used.

2    2. The authors state that total number of tourists and number of travel agencies are the independent variables. Total tourism revenue is the dependent variable. In existing tourism demand literature, total number of tourists and total tourism revenue are the proxy for tourism demand. It is anticipated the both variables have a strong correlation. Therefore, the estimated equation is wrong from the theoretical perspective.

3   3. There is a lack of clarity on what is captured by total number of tourists and total tourism revenue. Does each capture domestic tourism, international tourism or both? Explanation must be provided so that the readers could understand the emphasis of this study.

4   4. This study has a sample with only 10 observations. It is too small for the results to be robust. Furthermore, it may have non-stationarity concern.

5   5. Since time series data is used, autocorrelation should be tested, rather than heteroscedasticity.

 

Author Response

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Paper Title” (ID: 1843977). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewer’s comments are as flowing: Responds to the reviewer’s comments:

Reviewer #1:

1) Response to comment: The relevance of Section 1 and Section 2 is questionable. The discussion related to Guizhou is important so that readers can understand why this province should be studied. However, these two sections do not explain explicitly the objectives of this paper. Also, relevant literature is not used.

Response: The first and second sections of the article have been reworked in the course of the revision process. The relevant discussion of your province has also been further elaborated, and the more authoritative current literature on the subject has been cited.

2) Response to comment: The authors state that total number of tourists and number of travel agencies are the independent variables. Total tourism revenue is the dependent variable. In existing tourism demand literature, total number of tourists and total tourism revenue are the proxy for tourism demand. It is anticipated the both variables have a strong correlation. Therefore, the estimated equation is wrong from the theoretical perspective. 

Response: The article was reworked to take tourism revenue in Guizhou province from 2006 to 2019 as the dependent variable. Nine factors, including railway mileage, road mileage, number of civil flights, number of travel agencies, the total number of tourists, disposable income of urban residents, disposable income of rural residents, employees in the tertiary industry and the scale of foreign direct investment, were selected as independent variables. As the number of variables chosen in this paper is large, the units between the variables are not uniform, and the magnitudes are different, it is not possible to analyse the data directly, so it is necessary to carry out standardisation and apply sum-of-squares normalisation to transform them into dimensionless data. Using summation normalisation, the formula for standardising the data is as follows. After normalisation, the data for the variables are shown in Table 2 in basic terms.

 

3) Response to comment:  There is a lack of clarity on what is captured by total number of tourists and total tourism revenue. Does each capture domestic tourism, international tourism or both? Explanation must be provided so that the readers could understand the emphasis of this study.

Response: As the re-revised article explains, tourism headcount data includes domestic and inbound tourism. Total tourism receipts include domestic tourism consumption receipts and foreign exchange tourism receipts. See lines 163-166 and 185-188 for details.

 

4) Response to comment: This study has a sample with only 10 observations. It is too small for the results to be robust. Furthermore, it may have non-stationarity concern.

Response: In the re-revision process, the data for the relevant studies were traced back from the beginning of 2016, when the files were available, to 2019. This paper takes tourism revenue in Guizhou Province from 2006 to 2019 as the dependent variable. Nine factors, including railway mileage, road mileage, number of civil flights, number of travel agencies, the total number of tourists, disposable income of urban residents, disposable income of rural residents, workers in the tertiary industry and the scale of foreign direct investment, were selected as independent variables.

5) Response to comment: Since time series data is used, autocorrelation should be tested, rather than heteroscedasticity.

Response: In the reworked article, the number of variables selected for this paper and the inconsistent units and different magnitudes between variables prevented direct data analysis. Standardisation was required, using summation and normalisation to convert to dimensionless data. Factor analysis was carried out first, using KMO and Bartlett's test.

Special thanks to you for your good comments.

We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked in red in revised paper.

We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions.

 

Reviewer 2 Report

 

It would be good to include a map of the geographical location of Guizhou Province in the work. May be at the beginning of work or in 2.1 Rich and profound intangible cultural heritage. This is necessary for a better understanding of the topic being discussed

Line120 – 121

'' Tourists in Guizhou may be expected to grow steadily in the next few years because of the province's wide range of ethnic and cultural tourism resources and attractions''.

Although the paper deals with the period 2010-2019, the authors should also comment on the Covid19 period and its impact on tourism development expectation

 It would be good to expand the concluding remarks with the problems arising during the Covid19 pandemic, in brief.

I recommend the authors to continue researching the impact of the pandemic and the post period on their research.

Author Response

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Paper Title” (ID: 1843977). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewer’s comments are as flowing: Responds to the reviewer’s comments:

Reviewer #2:

Response to comment: '' Tourists in Guizhou may be expected to grow steadily in the next few years because of the province's wide range of ethnic and cultural tourism resources and attractions''.Although the paper deals with the period 2010-2019, the authors should also comment on the Covid19 period and its impact on tourism development expectation It would be good to expand the concluding remarks with the problems arising during the Covid19 pandemic, in brief.I recommend the authors to continue researching the impact of the pandemic and the post period on their research.

Response: I have also further revised the paper concerning this issue. One of the implications of the covid-19 pandemic during this period and the corresponding solutions to be derived from this paper are discussed.

Special thanks to you for your good comments.

We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked in red in revised paper.

We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions.

 

Reviewer 3 Report

This paper should be improved:

- longer abstract with all results

- much more references

- more statistical analysis

are needed. I would like look through this paper after revision. 

Author Response

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Paper Title” (ID: 1843977). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewer’s comments are as flowing: Responds to the reviewer’s comments:

Reviewer #3:

Response to comment: This paper should be improved:

- longer abstract with all results

- much more references

- more statistical analysis are needed. I would like look through this paper after revision. 

Response: The first and second sections of the article have been reworked in the course of the revision process. The relevant discussion of your province has also been further elaborated, and the more authoritative current literature on the subject has been cited.

The article was reworked to take tourism revenue in Guizhou province from 2006 to 2019 as the dependent variable. Nine factors, including railway mileage, road mileage, number of civil flights, number of travel agencies, the total number of tourists, disposable income of urban residents, disposable income of rural residents, employees in the tertiary industry and the scale of foreign direct investment, were selected as independent variables. As the number of variables chosen in this paper is large, the units between the variables are not uniform, and the magnitudes are different, it is not possible to analyse the data directly, so it is necessary to carry out standardisation and apply sum-of-squares normalisation to transform them into dimensionless data. Using summation normalisation, the formula for standardising the data is as follows. After normalisation, the data for the variables are shown in Table 2 in basic terms.

 

As the re-revised article explains, tourism headcount data includes domestic and inbound tourism. Total tourism receipts include domestic tourism consumption receipts and foreign exchange tourism receipts. See lines 163-166 and 185-188 for details.

In the re-revision process, the data for the relevant studies were traced back from the beginning of 2016, when the files were available, to 2019. This paper takes tourism revenue in Guizhou Province from 2006 to 2019 as the dependent variable. Nine factors, including railway mileage, road mileage, number of civil flights, number of travel agencies, the total number of tourists, disposable income of urban residents, disposable income of rural residents, workers in the tertiary industry and the scale of foreign direct investment, were selected as independent variables.

In the reworked article, the number of variables selected for this paper and the inconsistent units and different magnitudes between variables prevented direct data analysis. Standardisation was required, using summation and normalisation to convert to dimensionless data. Factor analysis was carried out first, using KMO and Bartlett's test.

Special thanks to you for your good comments.

We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked in red in revised paper.

We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions.

 

Reviewer 4 Report

Dear authors,

The abstract needs to be improved. Include motivation, objectives, theoretical framework, methodology, and main results briefly and concisely. It should be coherently structured in a single paragraph.

I think your paper's key objective is unclear: Researching the factors influencing the wetland tourism business is too generic. So in the introduction, you can try to answer some points of interest. 1. A practical introduction answers three questions: a. Who cares? What is the topic or research question, and why is it exciting and essential in theory and practice? b. What do we know, what don't we know, and so what? What central, unaddressed puzzle, controversy, or paradox does this study address, and why does it need to be addressed? c. What will we learn? How does your study fundamentally change, challenge, or advance scholars' understanding?

Introduction

This section is too short. The introduction should indicate not only the aims of the paper but also the current state of the art, the current limitations, the issues to be analyzed, and finally, a brief description of the paper's contents. Also, the contribution of each referenced article should be indicated with more precision and not only suggest that a specific paper deals with that issue or topic.

In your introduction, you do not discuss several important issues, such as the research gap of your study, the study's research question and objectives, and the study's contribution. Before you move to the literature review, these issues should be discussed, including the theoretical background, hypotheses or propositions' building, and the study's conceptual model.

The increase in overtourism and tourism seasonality have become global problems that need to be studied in virtually all countries. I suggest you include this point of view in your study, supported by the recent scientific literature I propose in the next section.

Theoretical framework

The theoretical background is not very fine. It is well structured. However, it should be improved. For example, some references are included without explaining why they have been included. At the same time, some more details of what is being done will be clarified. Also, the novelties of the methodology should be pointed out. In addition, more information should be provided on the procedure developed. The main thread of the research should also be highlighted.

You do not try to develop a theoretical background in your study, and your study's conceptual model is not discussed at all. You do not have a theoretical framework related to helping improve tourism. It would help if you searched the excellent literature on developing new procedures for tourism promotion to find a suitable model related to your findings and then develop and discuss the propositions of your research model.

It should also raise the situation's status quo: Why should tourism's economic impact be promoted and analysed? 

The increase in overtourism in several places of the world must appear in your introduction, and I suggest you include this point of view in your study supported by recent scientific literature, e.g.

Martínez, J. M. G., Martín, J. M. M., Soriano, D. E. R., & Fernández, J. A. S. (2021). Social Sustainability on Competitiveness in the Tourism Industry: Toward New Approach?. In Technological Innovation and International Competitiveness for Business Growth(pp. 141-164). Palgrave Macmillan, Cham.

Also, about sustainable tourism, this idea needs to be further supported by the scientific literature; remember, this is a "Sustainability" journal:

Martínez, J. M. G., Martín, J. M. M., Fernández, J. A. S., & Mogorrón-Guerrero, H. (2019). An analysis of the stability of rural tourism as a desired condition for sustainable tourism. Journal of Business Research, 100, 165-174.

Martín Martín, J. M., Guaita Martínez, J. M., Molina Moreno, V., & Sartal Rodríguez, A. (2019). An analysis of the tourist mobility in the island of Lanzarote: Car rental versus more sustainable transportation alternatives. Sustainability, 11(3), 739.

At the end of the conclusions, I recommend you introduce the role of the entrepreneur:

Martin, J. M. M., & Martinez, J. M. G. (2019). Entrepreneurs' attitudes toward seasonality in the tourism sector. International Journal of Entrepreneurial Behavior & Research.

Regarding the methodology, many indices measure the most relevant factors influencing the competitiveness of tourism, and you should name these in your study.

Martinez, J. M. G., Martín, J. M. M., & Fernández, J. A. S. (2020). Innovation in the measurement of tourism competitiveness. In Analyzing the relationship between innovation, value creation, and entrepreneurship(pp. 268-288). IGI Global.

The global and national economic situation is also relevant for boosting tourism.

Martínez, J. M. G., Martín, J. M. M., & Rey, M. D. S. O. (2020). An analysis of the changes in the seasonal patterns of tourist behavior during a process of economic recovery. Technological Forecasting and Social Change, 161, 120280.

Methodology

It will be necessary to justify why this methodology was selected and not others. An introduction to the method would be welcome. This introduction would help to understand better the steps presented in your study, which could be explained in more detail. For example, why did the authors use the web application to conduct the study? This section should be more exhaustive, especially the data in the tables. Despite the conciseness of this point, the

 

authors have been able to obtain results.

Conclusion:

As your findings are interesting, you can suggest a conceptual model based on them. This conceptual model can be tested in future research. However, you can at least discuss and develop some propositions based on the relationships of your conceptual model. This section will need to be expanded with the limitations of this work and future lines of research with more details that you have presented. Finally, it could be interesting to highlight the practical significance for organizational members of this study and make reference to policy prescriptions that derive from this analysis, as well as the implications for future research. I will encourage the authors to expand the agenda for future research.

References:

The methods section lacks the necessary support from suitable references. Overall, the authors make poor use of references without really helping the argument for their theory or research approach chosen. This article is not a literature review but rather a case study, and it should, on the one hand, increase the number of references, keeping the most relevant and up-to-date ones. It misses more references related to your topic from 2020-2021 and 2022.

I strongly recommend the following references to improve your paper:

Audretsch, D. B., Eichler, G. M., & Schwarz, E. J. (2022). Emerging needs of social innovators and social innovation ecosystems. International Entrepreneurship and Management Journal, 18(1), 217–254. https://doi.org/10.1007/s11365-021-00789-9

Belitski, M., Grigore, A. M., & Bratu, A. (2021). Political entrepreneurship: entrepreneurship ecosystem perspective. International Entrepreneurship and Management Journal, 17(4), 1973–2004. https://doi.org/10.1007/s11365-021-00750-w

Martín, J. M. M., & Fernández, J. A. S. (2022). The effects of technological improvements in the train network on tourism sustainability. An approach focused on seasonality. Sustainable Technology and Entrepreneurship, 1(1), 100005. https://doi.org/10.1016/j.stae.2022.100005

Puertas Medina, R. M., Martín Martín, J. M., Guaita Martínez, J. M., & Serdeira Azevedo, P. (2022). Analysis of the role of innovation and efficiency in coastal destinations affected by tourism seasonality. Journal of Innovation & Knowledge, 7(1), 100163. https://https://doi.org/10.1016/j.jik.2022.100163

 

Tiago, F., Gil, A., Stemberger, S., & Borges-Tiago, T. (2021). Digital sustainability communication in tourism. Journal of Innovation & Knowledge, 6(1), 27-34. https://10.1016/j.jik.2019.12.002

Author Response

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Paper Title” (ID: 1843977). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewer’s comments are as flowing: Responds to the reviewer’s comments:

Reviewer #4:

1) Response to comment: The abstract needs to be improved. Include motivation, objectives, theoretical framework, methodology, and main results briefly and concisely. It should be coherently structured in a single paragraph.

Response: Further improvements are made in the abstract section, which further elaborates on one of my overall motivational goals for the study. The theoretical framework approach and main results are presented, with some elaboration of the relevant measures I will take.

2) Response to comment: I think your paper's key objective is unclear: Researching the factors influencing the wetland tourism business is too generic. So in the introduction, you can try to answer some points of interest. 1. A practical introduction answers three questions: a. Who cares? What is the topic or research question, and why is it exciting and essential in theory and practice? b. What do we know, what don't we know, and so what? What central, unaddressed puzzle, controversy, or paradox does this study address, and why does it need to be addressed? c. What will we learn? How does your study fundamentally change, challenge, or advance scholars' understanding?

Response: In the article, I further elaborate on my objective to examine the past relevance of tourism in Guizhou province and provide the corresponding value of learning to break the dilemma now. As Guizhou is a province with a high tourism dependency level, tourism measures directly impact economic growth and improve people's living standards. The empirical study of the relevant data will enable the formulation of appropriate policies to address the problem and thus promote the development of the tourism economy.

3) Response to comment:  Problems with the introduction section.

Response: In the introductory section, I have restructured it to further elaborate on the importance of the relevant tourism economy. And perform the current state of Guizhou province and form an echo in section 2. The richness of Guizhou Province's tourism sources is described, offering great potential for developing its economy in later years.

This paper used tourism revenue in Guizhou Province from 2006 to 2019 as the dependent variable. Nine factors, including railway mileage, road mileage, number of civil flights, number of travel agencies, the total number of tourists, disposable income of urban residents, disposable income of rural residents, employees in the tertiary industry and the scale of foreign direct investment, were selected as independent variables.

4) Response to comment: Theoretical framework and methodological related issues.

Response: The overall research framework has been reworked. The current problem has also been analysed, and relevant solutions have been proposed. I have also read and used the references you provided comprehensively, which has helped me write my thesis.

5) Response to comment: Conclusions and references exist for related issues.

Response: I have read the section on conclusions and references carefully and made extensive revisions to allow the overall study to be further refined based on the relevant suggestions and authoritative literature you have provided. And the connections you have provided are cited in several places in the text.

Special thanks to you for your good comments.

We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked in red in revised paper.

We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions.

 

Round 2

Reviewer 1 Report

The authors have improved this paper in order to address my comments. However, earlier comments are yet to be addressed.

The authors have revised Section 1 and Section 2. There is an improvement in each of these sections. However, these two sections are yet to provide sufficient theoretical foundations that are relevant to the topic of this paper. The authors should provide literature review that explain the appropriate tourism demand for Guizhou. Without such theoretical foundations, an estimated empirical model is just a “data mining” activity.

The authors are yet to address my concern on the use of total number of tourists and total tourism revenue as variables in the estimation of tourism demand. Please refer to equation 1. The dependent variable is R (gross tourism receipts) and one of the independent variable is P total tourism arrivals. Both R and P should be strongly correlated based on my concern which I raised earlier (both are the proxies for tourism demand).

I have raised my concern related to sample size. The authors have improved the sample size. Now this study has annual data from 2006 to 2019. But the sample size is still very small, only 13 observations. Equation (1) has 9 independent variables. Definitely, the results of this equation is not robust. Even if later transformation has been carried out, there are 3 independent variables (please refer to equation 4), suggesting that degree of freedom is only 9.

Note that equation 4 is estimated with time series data under limited number of observations. Therefore, this equation may face autocorrelation and non-stationarity issues. Am I correct that DW reported to Table 8 refers to Durbin-Watson test? If yes, this value is smaller than 1, it seems to suggests that there is a presence of positive autocorrelation. If this is the case, the result of hypothesis testing is not reliable.

F1, F2 and F3 are created. What is measured by each? The authors should clearly explained each of these.

Author Response

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Paper Title” (ID: 1843977). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewer’s comments are as flowing: Responds to the reviewer’s comments:

Reviewer #1:

1.Response to comment: The authors have revised Section 1 and Section 2. There is an improvement in each of these sections. However, these two sections are yet to provide sufficient theoretical foundations that are relevant to the topic of this paper. The authors should provide literature review that explain the appropriate tourism demand for Guizhou. Without such theoretical foundations, an estimated empirical model is just a “data mining” activity. 

Response: Thank you for your comments. In response to your suggestions, the article has made the following changes.

As can be seen from the discussion above, the features of the tourist sector have a significant correlation with the industry's size and complexity.  There are six critical components of tourist development: food, lodging, transportation, travel, shopping, and entertainment.  The "six elements of tourism" are the tourist industry's fundamental components and material circumstances, in addition to more forgiving factors like services, workers, and innovation [10].  Therefore, concentrating on these characteristics alone is insufficient if a place achieves sustained and dependable tourist growth [11].  What elements, therefore, affect the growth of the tourist industry, and do various aspects affect the tourism economy?  These issues may be appropriately analysed and investigated to identify the significant drivers of the tourist economy, which is crucial for developing the sector's transformational potential and transformation and upgradation.  Researchers have already begun to look at and analyse these difficulties.  In earlier research, academics have tended to emphasise the significance of transportation infrastructure in fostering the growth of the tourist industry, seeing transportation infrastructure as a crucial component determining tourism [12].  Road mileage and civil aviation aircraft traffic are two elements that have a direct influence on tourism [13, 14].

Additionally, it is well known that one of the critical determinants of the economic growth of tourism is the level of consumption of the local populace in the province where the tourist destination is located [15]. The amount of consumption in a location may be determined by looking at the disposable income of both urban and rural populations [16]. From the body of current research, experts have concentrated more on how physical factors, such as tourist infrastructure and resources, affect the growth of the tourism industry [17]. Since much of the literature focuses on a single location or city, the findings may not be broad, and there is a dearth of studies on the influence of soft variables like innovation and practitioners on the growth of the tourist sector. This essay will use Guizhou Province's entire tourist industry growth as its study topic. Furthermore, we will research which variables significantly contribute to the growth of the tourist industry in Guizhou Province using factor analysis and regression analysis.

2. Response to comment: The authors are yet to address my concern on the use of total number of tourists and total tourism revenue as variables in the estimation of tourism demand. Please refer to equation 1. The dependent variable is R (gross tourism receipts) and one of the independent variable is P total tourism arrivals. Both R and P should be strongly correlated based on my concern which I raised earlier (both are the proxies for tourism demand).

Response: To address the current academic concerns about total tourism arrivals and total tourism receipts related to tourism demand. Firstly, a one-to-one regression model is constructed to analyse the relationship between total tourist arrivals and tourism receipts. Secondly, the regression analysis of the total number of tourists with this article's remaining seven independent variables verifies that the total number of tourists does not correlate with the other seven independent variables. This, in turn, ensures that the theoretical framework constructed in the article can proceed and complete the testing and construction of the model.

1) A one-to-one regression model is constructed to analyse the relationship between the total number of tourists and the total tourism revenue.

This is example 1 of an equation:

Ln R=β0 +β1LnP+μt

(1)

Where R is used to denote total tourism revenue, P denotes the total number of tourists μt is a random disturbance term. The detailed analysis of the data is shown below (Table 3).

Table 3. Results of linear regression analysis (n=14).

	Non-standardized coefficients		Normalized coefficients	t	p	VIF	R 2	Adjust R 2	F
	B	Standard error	Beta
C	-338.867	114.147	-	-2.969	0.012**	-	0.994	0.994	F (1,12)=2115.770,p=0.000
P	0.105	0.002	0.997	45.997	0.000***	1.000
Dependent variable.：R
D-W value：1.2
* p<0.1 ** p<0.05 *** p<0.01

From the above table (Table 3), it can be seen that the linear regression analysis was carried out with P as the independent variable and R as the dependent variable, and from the above table, it can be seen that the model formula is: R=-338.867 + 0.105*P, and the model R-squared value is 0.994, which means that P can explain 99.4% of the causes of change in R. The D-W value is 1.2 (greater than 1), which satisfies the independence condition. The F-test of the model found that the model passed the F-test (F=2115.770, p=0.000<0.05), which means that P must have an impact relationship on R. The final specific analysis shows that the regression coefficient value of P is 0.105 (t=45.997, p=0.000<0.01), which means that P will have a significant positive impact relationship on R. The final analysis shows that: the regression coefficient value of P is 0.105 (t=45.997, p=0.000<0.01), which means that P will have a significant positive impact on R. The summary analysis shows that: all of P will have a significant favourable influence on R.

2) The total number of tourists was analysed by direct regression with the remaining seven independent variables.

This is example 2 of an equation:

Ln P =β0 +β1LnA+β2LnT+β3LnM+β4LnD+β5LnI+β6LnE+β7LnF+μt

(2)

Where P denotes the total number of tourists, A denotes the number of travel agencies, T denotes road route mileage, M denotes civilian flight traffic, D denotes urban residents per capita disposable income, I denotes rural residents' per capita net income, E denotes tertiary industry employees, F denotes the scale of foreign direct investment, and μt is a random disturbance term (Table 4).

Table 4. Results of linear regression analysis (n=14).

	Non-standardized coefficients		Normalized coefficients	t	p	VIF	R 2	Adjust R 2	F
	B	Standard error	Beta
C	-74611.107	69907.184	-	-1.067	0.327	-	0.986	0.971	F (7,6)=62.338,p=0.000
A	46.191	77.167	0.119	0.599	0.571	17.517
T	-0.996	0.310	-0.849	-3.210	0.018**	30.955
M	0.266	0.213	0.579	1.247	0.259	95.387
D	5.650	5.150	1.318	1.097	0.315	638.615
I	-21.108	12.782	-1.753	-1.651	0.150	498.341
E	550.058	308.327	1.523	1.784	0.125	322.597
F	-0.001	0.031	-0.002	-0.021	0.984	5.396
Dependent variable.：P
D-W value：1.637
* p<0.1 ** p<0.05 *** p<0.01

From the above table (Table 4), it can be seen that linear regression analysis was conducted with A, T, M, D, I, E, and F as the independent variables and P as the dependent variable. From the above table, it can be seen that the model equation is: P = -74611.107 + 46.191*A-0.996*T + 0.266*M + 5.650*D-21.108*I + 550.058*E-0.001*F. The model R-squared value was 0.986, implying that A, T, M, D, I, E, and F explained 98.6% of the variation in P. The D-W value was 1.637(greater than 1), satisfying the independence condition. The model passed the F-test (F=62.338, p=0.000<0.05), which means that at least one of A, T, M, D, I, E, or F would affect P. In addition, the test for multiple cointegrations of the model found that the VIF value in the model was greater than 10, which means that there is a cointegration problem, which can be solved by using ridge regression. It is also recommended to check for closely correlated independent variables, eliminate those that are closely correlated, and re-run the analysis. The final specific analysis shows that.

The regression coefficient value of A is 46.191 (t=0.599, p=0.571>0.05), implying that A does not influence the relationship of P.

The regression coefficient value of T is -0.996 (t=-3.210, p=0.018<0.05), implying that T will have a significant negative influence relationship on P.

The regression coefficient value of M is 0.266 (t=1.247, p=0.259>0.05), implying that M does not have a meaningful relationship with P.

The regression coefficient value for D was 5.650 (t=1.097, p=0.315>0.05), implying that D does not influence the relationship for P.

The regression coefficient value for I is -21.108 (t=-1.651, p=0.150>0.05), implying that I does not have a meaningful relationship with P.

The regression coefficient value for E is 550.058 (t=1.784, p=0.125>0.05), implying that E does not influence the relationship for P.

The regression coefficient value for F is -0.001 (t=-0.021, p=0.984>0.05), implying that F does not influence the relationship for P.

Summing up the analysis, it can be seen that T will have a significant negative influence relationship with P. However, A, M, D, I, E, and F do not affect P.

3) Summary of interference test

The above two parts of the test show that total tourism headcount explains 99.4% of the variation in total tourism income, and the regression coefficient value of p is 0.105 (t=45.997, p=0.000<0.01), implying that total tourism headcount will have a significant favourable influence relationship on total tourism income (Table 3). Secondly, there is no direct relationship between total tourism numbers and the other seven variables; if there is, it is negative, which will not interfere with the following experiment (Table 4). Moreover, the two tests' D-W values (all greater than 1) satisfy the independence condition. Therefore, the relationship between total tourism revenue and eight factors, including road mileage, number of civil flights, number of travel agencies, the total number of tourists, disposable income of urban residents, disposable income of rural residents, workers in the tertiary sector, and size of foreign direct investment, will be explored in depth below.

3.Response to comment: I have raised my concern related to sample size. The authors have improved the sample size. Now this study has annual data from 2006 to 2019. But the sample size is still very small, only 13 observations. Equation (1) has 9 independent variables. Definitely, the results of this equation is not robust. Even if later transformation has been carried out, there are 3 independent variables (please refer to equation 4), suggesting that degree of freedom is only 9.

 Response: As shown in Table (Table 2).

1) For R, the t-statistic for the ADF test of this time series data is 7.051, with a p-value of 1.000 and critical values of -5.500, -4.072 and -3.493 for 1%, 5% and 10% respectively. p=1.000>0.1, the original hypothesis cannot be rejected, and the series is not smooth. The series was subjected to first-order difference and then an ADF test. The result of the ADF test on the data after first-order difference shows that p=0.982>0.1, the original hypothesis cannot be rejected, and the series is not smooth, so the series is subjected to second-order difference and then the ADF test. The result of the ADF test after second-order differencing shows that p=0.041<0.05, there is more than 95% certainty of rejecting the original hypothesis, and the series is smooth.

2) For A, the t-statistic of the ADF test for the time series data is -0.621, with a p-value of 0.978 and critical values of -5.118, -3.918 and -3.411 for 1%, 5% and 10% respectively. p=0.978>0.1, the original hypothesis cannot be rejected, and the series is not stationary. The series was subjected to first-order difference and then an ADF test. The results of the ADF test on the data after first-order differencing showed that p=0.012<0.05, there is more than 95% certainty that the original hypothesis is rejected, and the series is smooth at this point.

3) For P, the t-statistic for the ADF test of the time series data was 9.272, with a p-value of 1.000 and critical values of -5.500, -4.072 and -3.493 for 1%, 5% and 10%, respectively. p=1.000>0.1, the original hypothesis cannot be rejected, and the series is not smooth. The series was subjected to first-order difference and then an ADF test. The results of the ADF test on the data after first-order differencing showed that p=0.124>0.1, the original hypothesis could not be rejected, and the series was not stationary, so the series was subjected to second-order differencing and then the ADF test. The result of the ADF test after second-order differencing shows that p=0.021<0.05, there is more than 95% certainty of rejecting the original hypothesis, and the series is smooth now.

4) For T, the t-statistic of the ADF test for the time series data is -1.646, with a p-value of 0.774 and critical values of -5.118, -3.918 and -3.411 for 1%, 5% and 10% respectively. p=0.774>0.1, the original hypothesis cannot be rejected, and the series is not stable. The series was subjected to first-order difference and then an ADF test. The results of the ADF test on the data after first order differencing showed that p=0.568>0.1, the original hypothesis could not be rejected, and the series was not stationary. The series was subjected to second-order differencing and then an ADF test. The result of the ADF test on the data after second-order differencing shows that p=0.001<0.01, there is more than 99% certainty that the original hypothesis is rejected, and the series is smooth at this point.

5) For M, the t-statistic for the ADF test of this time series data is -0.684, with a p-value of 0.974 and critical values of -5.118, -3.918 and -3.411 for 1%, 5% and 10% respectively.

With p=0.974>0.1, the original hypothesis cannot be rejected, and the series is not smooth. The series was subjected to first-order difference and then an ADF test.

The ADF test result of the data after the first order difference shows p=0.001<0.01, there is a higher than 99% certainty of rejecting the original hypothesis, and the series is smooth now.

6) For D, the t-statistic of the ADF test for the time series data is 14.647, with a p-value of 1.000 and critical values of -5.500, -4.072 and -3.493 for 1%, 5% and 10%, respectively. p=1.000>0.1, the original hypothesis cannot be rejected, and the series is not smooth. The series was subjected to first-order difference and then an ADF test. The results of the ADF test on the data after first-order differencing showed that p=0.055<0.1, there is more than 90% certainty that the original hypothesis is rejected, and the series is smooth at this point. The ADF test result for the second order differential data shows that p=0.000<0.01, with more than 99% certainty of rejecting the original hypothesis, and the series is stable at this point.

7) For I, the t-statistic for the ADF test of the time series data is -1.348, with a p-value of 0.876 and critical values of -4.884, -3.822 and -3.359 for 1%, 5% and 10% respectively. p=0.876>0.1, the original hypothesis cannot be rejected, and the series is not smooth. The series was subjected to first-order difference and then an ADF test. The results of the ADF test on the data after first-order differencing showed that p=0.137>0.1, the original hypothesis could not be rejected, and the series was not stationary, so the series was subjected to second-order differencing and then the ADF test. The ADF test result of the data after second-order differencing shows p=0.006<0.01. There is more than 99% certainty of rejecting the original hypothesis, and the series is smooth now.

8) For E, the t-statistic of the ADF test for the time series data is -0.364, with a p-value of 0.988 and critical values of -5.118, -3.918 and -3.411 for 1%, 5% and 10% respectively. p=0.988>0.1, the original hypothesis cannot be rejected, and the series is not smooth. The series was subjected to first-order difference and then an ADF test. The ADF test result of the data after the first-order difference shows p=0.000<0.01; there is a higher than 99% certainty of rejecting the original hypothesis, and the series is smooth at this time.

9) For F, the t-statistic for the ADF test for this time series data is -3.515, with a p-value of 0.038 and critical values of -5.118, -3.918 and -3.411 for 1%, 5% and 10% respectively.

With p=0.038<0.05, there is higher than 95% certainty that the original hypothesis is rejected, and the series is smooth at this point.

Table 2. ADF test table.

Name	Differential order	t	p	The threshold value
				1%	5%	10%
Gross tourism receipts (R)	0	7.051	1.000	-5.500	-4.072	-3.493
	1	-0.531	0.982	-4.988	-3.865	-3.383
	2	-3.489	0.041	-5.797	-4.189	-3.555
	Lags based=4
The number of travel agencies (A)	0	-0.621	0.978	-5.118	-3.918	-3.411
	1	-3.916	0.012	-5.118	-3.918	-3.411
	Lags based=2
Total tourism arrivals (P)	0	9.272	1.000	-5.500	-4.072	-3.493
	1	-3.029	0.124	-5.118	-3.918	-3.411
	2	-3.725	0.021	-5.797	-4.189	-3.555
	Lags based=4
Road route mileage (T)	0	-1.646	0.774	-5.118	-3.918	-3.411
	1	-2.061	0.568	-5.500	-4.072	-3.493
	2	-4.600	0.001	-5.283	-3.985	-3.447
	Lags based=2
Civil flight flow (M)	0	-0.684	0.974	-5.118	-3.918	-3.411
	1	-4.736	0.001	-5.118	-3.918	-3.411
	Lags based=2
Disposable income per urban resident (D)	0	14.647	1.000	-5.500	-4.072	-3.493
	1	-3.375	0.055	-5.500	-4.072	-3.493
	2	-8.410	0.000	-5.797	-4.189	-3.555
	Lags based=4
Net income per capita of rural residents (I)	0	-1.348	0.876	-4.884	-3.822	-3.359
	1	-2.984	0.137	-4.988	-3.865	-3.383
	2	-4.136	0.006	-5.118	-3.918	-3.411
	Lags based=0
Tertiary sector employees (E)	0	-0.364	0.988	-5.118	-3.918	-3.411
	1	-5.578	0.000	-5.118	-3.918	-3.411
	Lags based=2
The scale of foreign direct investment (F)	0	-3.515	0.038	-5.118	-3.918	-3.411
	Lags based=2

4.Response to comment: Note that equation 4 is estimated with time series data under limited number of observations. Therefore, this equation may face autocorrelation and non-stationarity issues. Am I correct that DW reported to Table 8 refers to Durbin-Watson test? If yes, this value is smaller than 1, it seems to suggests that there is a presence of positive autocorrelation. If this is the case, the result of hypothesis testing is not reliable.

 Response: In response to your suggestions above, I have re-examined and re-tested the data, excluding the variable rail line mileage as it was found to be autocorrelated with the dependent variable. Therefore a re-regression analysis of the eight variables resulted in the following table, which shows that the d w value is more significant than one and satisfies the independence condition. See Table 10 for details.

	Non-standardized coefficients		Normalized coefficients	t	p	VIF	R 2	Adjust R 2	F
	B	Standard error	Beta
C	0.071	0.003	-	25.747	0.000***	-	0.985	0.981	F (3,10)=221.321,p=0.000
F1	0.035	0.003	0.462	11.989	0.000***	1.000
F2	0.060	0.003	0.805	20.886	0.000***	1.000
F3	0.026	0.003	0.353	9.166	0.000***	1.000
Dependent variable.：SN_ R
D-W value：1.181
* p<0.1 ** p<0.05 *** p<0.01

5.Response to comment: F1, F2 and F3 are created. What is measured by each? The authors should clearly explained each of these.

Response: In response to your comments, here are the changes made and the factors provided in the relevant article to arrive at the relevant factors.

Table 7. variance Interpretation Rate Table.

Factor number	Feature root			Rotational forward difference interpretation rate			Rotational rear difference interpretation rate
	Feature root	Variance interpretation rate (%)	Cumulative rate (%)	Feature root	Variance interpretation rate (%)	Cumulative rate (%)	Feature root	Variance interpretation rate (%)	Cumulative rate (%)
1	7.424	92.800	92.800	7.424	92.800	92.800	3.276	40.953	40.953
2	0.312	3.896	96.696	0.312	3.896	96.696	2.570	32.122	73.075
3	0.187	2.343	99.039	0.187	2.343	99.039	2.077	25.964	99.039
4	0.054	0.675	99.713	-	-	-	-	-	-
5	0.016	0.196	99.909	-	-	-	-	-	-
6	0.005	0.063	99.972	-	-	-	-	-	-
7	0.002	0.019	99.991	-	-	-	-	-	-
8	0.001	0.009	100.000	-	-	-	-	-	-

The table above analyses the factor extraction (Table 7) and the amount of information extracted from the factors.  From the table above, we can see that a total of three factors were extracted from the factor analysis, and the variance explained by the rotation of these three factors was 40.953%, 32.122% and 25.964%, respectively, and the cumulative variance explained by the rotation was 99.039%.

Table 8. Factor load factor after rotation.

Name	Factor loading coefficient			Commonality (common factor variance)
	Factor 1	Factor 2	Factor 3
SN_M	0.550	0.674	0.483	0.989
SN_T	0.798	0.366	0.462	0.984
SN_A	0.821	0.467	0.280	0.970
SN_P	0.471	0.785	0.395	0.994
SN_D	0.679	0.566	0.465	0.997
SN_I	0.654	0.582	0.480	0.996
SN_E	0.653	0.596	0.465	0.997
SN_F	0.357	0.365	0.857	0.995
Note: Where figures in the table are coloured: blue indicates that the absolute value of the loading coefficient is greater than 0.4 and red indicates that the commonality (common factor variance) is less than 0.4.

The data in this study were rotated using the maximum variance rotation method (varimax) to find the correspondence between the factors and the study items. The table above shows how well the factors extracted information from the study items and the correspondence between the factors and the study items, from which it can be seen that all the study items have a commonality value above 0.4, which means that there is a strong correlation between the study items and the factors and that the factors can extract information effectively. After ensuring that the factors could extract most of the information from the research items, the correspondence between the factors and the research items was then analysed (an absolute value of factor loading coefficient greater than 0.4 means that there is a correspondence between the item and the factor).

Table 8 shows that the two road mileage (T) and civil flights (M) converge on the first common factor, and according to the characteristics of these three variables, the first common factor can be named the infrastructure influence factor.  The number of travel agencies (A) and the total number of tourists (P) converge on the second common factor, and according to the characteristics of these two variables, the second common factor can be named the influence factor of tourism flow.  The four variables of urban disposable income per capita (D), rural net income per capita (I), tertiary industry employees (E) and foreign direct investment (F) converge on the third common factor, and according to the characteristics of these two variables, the third common factor can be named as the investment and consumption influence factor.  After extracting the three common factors, it is necessary to consider the linear relationship between each common factor and the variables, which can be obtained from the component score coefficient matrix, as shown in Table 9.

Once the three common factors have been extracted, it is necessary to consider the linear relationship between each common factor and the variables, which can be obtained from the matrix of component scoring coefficients, as shown in Table 9.

[Tips]

1: A research item corresponds to more than one factor. At this time should be combined with professional knowledge to determine the specific attribution of that factor.

2: If a research item does not correspond to a factor, consider deleting the research item.

3: a factor and a research item do not correspond, in which case a reduction of one factor may be considered

4: If there is no correspondence between a research item and a factor, consider deleting the research item.

If factor analysis is used to condense information, then the 'component score coefficient matrix' table is ignored. If factor analysis is used to calculate weights, the relationship equation between the factors and the study items (based on standardised data to create a relationship expression) is created using the 'component score coefficient matrix (Table 9), as shown in the formula below (5).

This is example 3 of an equation:

F1=-0.346*SN_M+0.898*SN_T+0.876*SN_A-0.704*SN_P+0.200*SN_D+0.096*SN_I+0.076*SN_E-0.455*SN_F F2=0.661*SN_M-0.922*SN_T-0.353*SN_A+1.346*SN_P+0.036*SN_D+0.125*SN_I+0.198*SN_E-0.577*SN_F F3=-0.044*SN_M+0.150*SN_T-0.478*SN_A-0.374*SN_P-0.032*SN_D-0.000*SN_I-0.057*SN_E+1.488*SN_F

(5)

 

 

Figure 6. Gravel diagram.

When the line suddenly becomes smooth, the number of factors from steep to smooth is the reference number of factors extracted. The rubble diagram only assists in the decision making of the number of factors, and the actual study is more based on professional knowledge, combined with the situation of the correspondence between the factors and the study items, and the comprehensive weighing judgment to arrive at the number of factors (Figure 6).

We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked in red in revised paper.

We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions.

Special thanks to you for your good comments.

 

Reviewer 3 Report

It is critical that the quality of written English is edited to the highest standard. Please make sure that English is clear, accurate and concise, and that the flow of ideas and arguments is logical and avoids unnecessary repetition. An experienced native English speaker colleague or a professional copy-editing service for academic articles can help you.

General recommendations:

TIMING REFERENCES - PLEASE BE SPECIFIC (WHEN EXACTLY ?! state the dates ) OR DELETE
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Author Response

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Paper Title” (ID: 1843977). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval.

I have re-edited the relevant language in the article and used your suggestions. Thank you very much for making my article more complete.

We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper.

We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions. Special thanks to you for your good comments.

Reviewer 4 Report

Many thanks to the authors for the new manuscript, which reflects the suggestions for improving the work presented.

 

Congratulations. Kind regards.

Author Response

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Paper Title” (ID: 1843977). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches.

We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions.

Special thanks to you for your good comments.

Round 3

Reviewer 1 Report

Dear Authors,

Thanks for responding to my concerns. However, the responses lead to further methodological mistakes. Let me focus on key points so that the authors can manage my concerns better.

First, equation (1) should not be estimated because as stated in the earlier review, both R and P are the proxies for tourism demand. Likely, they will have high correlation. Please refer to existing literature to understand the meaning of same proxy for tourism demand.

VIF is used to measure the extent of multicollinearity problem. It is not a test for cointegration. Note that if there is a concern related to spurious regression. We will begin with the application of suitable unit test on each variable. With the confirmation that each variable has a unit root, we then perform a suitable cointegration test. However, for the current sample size, both unit root and cointegration tests cannot be performed because of low power.

The authors are yet to respond to my concern related to sample size if equation (2) has to be estimated.

I guess the authors do not understand autocorrelation. Autocorrelation is related to disturbance terms. It has nothing to do with the correlation among independent variables. Currently, the provided results suggested that there is a presence of autocorrelation. This means that results from hypothesis testing are not reliable. This must be addressed so that the results can be accepted. 

Please rectify these mistakes.   

 

Author Response

Dear Editors and Reviewers:

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Paper Title” (ID: 1843977). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the responds to the reviewer’s comments are as flowing: Responds to the reviewer’s comments:

Reviewer #1:

Exploring: tourism revenue and tourism visitor numbers.

Response:  Through the ADF test of the time series data for the variables of total tourism revenue (R), number of travel agencies (A), the total number of tourists (P), road route mileage (T), number of civil flights (M), per capita disposable income of urban residents (D), per capita net income of rural residents (I), employees in the tertiary industry (E) and scale of foreign direct investment (F), the data are all serially smooth Further analysis can be conducted. It also shows no relevant sequence anomalies in the sample, as seen in Table 2.The relationship between tourism income and the number of tourists will be explored below.

1) Literature support.

Firstly, in Lu Liu's 'The impact of tourism numbers on domestic tourism income in China, the authors focus on the impact of domestic tourism numbers on domestic tourism income in China by developing a one-dimensional linear regression model [36]. The author also introduces the dynamic process of the development of the number of domestic tourists and domestic tourism income in China, followed by the determination of the quantitative relationship between these variables using the Eviews software system to determine the linear regression function from the data information; and then conducts statistical tests on the credibility of the model and determines the significance of the variables from the relevant variables [36]. Based on the conclusions drawn, countermeasures and suggestions for improving China's domestic tourism revenue are proposed to achieve a smooth growth of domestic tourism revenue [36].

Based on this author's well-documented view, the number of tourist visitors is included in the tourism revenue impact variable and empirically studied in this paper.

2) A brief description of the relationship between the two.

Tourism is an activity that requires the movement of people and consumption across regions, and the essential thing in this process is the participation of people, in short, the number of tourists, which plays a crucial role in this process [37]. It was evident that without the participation of people and tourists, a region could not generate the so-called tourism income. Without the involvement of tourists in tourism, the various functions would not be able to perform their work and utility, they would not be able to function accordingly, and they would not be able to generate direct economic benefits, i.e. tourism revenue, as most scholars have openly argued and thought [38]. If the one-sided relationship between tourism visitor numbers and tourism revenue is too much, the so-called pseudo-debate between the two will fall into an infinite cycle of 'chicken producing eggs and eggs producing chickens', which is not conducive to the depth of tourism research and is not in line with conventional thinking.

Therefore, this paper insists on including the number of tourists in the variables that affect tourism revenue for empirical analysis.

Special thanks to you for your good comments.

We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked in red in revised paper.

We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions.