Applying an Extended Theory of Planned Behavior for Sustaining a Landscape Restaurant
Round 1
Reviewer 1 Report
Dear authors
Thank you very much for the submission of the revised manuscript of yours.
This version indeed has been improved many aspects of the study. Nevertheless, in the last section of it there are no improvements regarding the theoretical and practical implications of this study. High ranked journal, like Sustainability, require published papers to have a sound theoretical contribution to the literature. It would be nice to add some comments on both sections of your manuscript, in order to further justify the novelty of your research.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
I don't think the authors have addressed this point. There is nothing really new in this proposed research. The theoretical framework needs to be developed.
The authors need to clearly justify why regression analysis was used and not Structural Equation Modelling (SEM).
It would be good to ask respondents who have never been to the restaurant as well as those who have been to the restaurant
Author Response
Response to Reviewer 2 Comments
Point 1: The authors need to clearly justify why regression analysis was used and not Structural Equation Modelling (SEM).
Response from the Author(s):
Thanks for the reviewer’s comment. We had done our best to cite the following literature to clearly justify why regression analysis was used.
On Page 5,
Researchers suggested that the regression model is better than the structure equation model (SEM) in exploratory studies [26, 27]. Bryne [28] indicated that each one of three SEM software - AMOS, PLS, and LISREL - differs in the way it treats missing data and offer many methods to users to handle incomplete data. Different software produces different types of fit indices. However, the regression analysis using the SPSS program is properly executed and easier to use [26, 27].
References
Nunkoo, R.; Gursoy, D., Residents’ support for tourism: An identity perspective. Annals of Tourism Research 2012, 39, (1), 243-268. Nunkoo, R.; Ramkissoon, H., Structural equation modelling and regression analysis in tourism research. Current Issues in Tourism 2012, 15, (8), 777-802. Byrne B. M., Structural Equation Modeling With AMOS: Basic Concept, Applications, and Programming. Mahwah, NJ: Lawrence Erlbaum Associates, 2001.Point 2: It would be good to ask respondents who have never been to the restaurant as well as those who have been to the restaurant.
Response from the Author(s):
Thanks for the reviewer’s comment. We had put the future research to ask respondents who have never been to a landscape restaurant and compare their results to those respondents who have been to landscape restaurants. Our response is in the following,
On Page 11,
This study does have limitations that should be taken into account by future studies on this topic. This paper could extend coverage to respondents who have never been to a landscape restaurant and compare their results to those respondents who have been to landscape restaurants.
Author Response File: Author Response.pdf
Reviewer 3 Report
I appreciate that the authors have responded to feedback but in providing a response they appear to have simply inserted two sections of text rather than considering these additions in the wider context of the paper. I would like to see a better integration of these ideas as they are seminal concepts which have far more relevance to the findings of this study than they are given credit for here.
Author Response
Please see the attachment
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Line 252 and 262: please double check the numbers are correct
Author Response
Response to Reviewer 2 Comments
Point 1: Line 252 and 262: please double check the numbers are correct.
Response from the Author(s):
Thanks for the reviewer’s comment. We had corrected the statement of the variance inflation factor (VIF) in the content to be consistent with the numbers in Table 4. We had also corrected the standardized coefficients in Figure 2 on page 8.
On Pages 7-8,
The variance inflation factor (VIF) examines the collinearity of independent variables, and they are between 1.189 and 2.002, or less than 10, indicating no collinearity problems.
