A Multi-Platform Online Data-Driven Diagnostic Approach for Macro-Level Sustainability of Homestays

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Please clarify the sentences...Specifically, it is unclear how many total homestays there are in China... „In 2021, the number and price of homestays increased, with listings exceeding 9.3 52 million [17]. By 2024, the industry reached a scale of 42.27 billion RMB [18], with 316,000 53 registered homestay enterprises [19], and high-end homestays comprising 30.9% of the 54 total [20]”.

The paper lacks a conclusion.

The reviewer suggests that the authors separate the part of the text from the "Discussion" section that refers to the limitations of the applied model and the need for further research, and move that part of the text to a new section - "Conclusion".

The "Conclusion" should also state the general conclusions of the research conducted within the framework of this paper.

Author Response

Please see the attachment. Thank you.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

I appreciate the opportunity to review this valuable study, which aims to develop a comprehensive evaluation framework that integrates digital and spatial data to assess the sustainability of digital popularity–driven consumption sectors, applying it to China’s homestay industry.

1. In the abstract, clearly identify the aim of the study and provide slightly more detail on methodology and practical application.
2. Give a brief about high-end homestays in the introduction section.
3. While the introduction notes deficiencies in existing studies, the gap could be articulated more sharply, distinguishing exactly how this study advances beyond prior macro-scale analyses.
4. At the end of the introduction section, identify how this research's findings can contribute to the existing knowledge in the field. More specifically, recognize the contribution of the study.
5. More details about homestays in general and Chinese homestays in particular are required.
6. Baidu data covers 2018–2024, while Douyin and Toutiao are only for 2024. This inconsistency could bias results. You might need to justify why only one year of data was available for two platforms.
7. Provide more detail on how the raw data were collected (API access, web scraping, third-party datasets?
8. Explain how it could be applied outside China, especially in contexts where Baidu/Douyin/Toutiao are not dominant.
9. Some results are very descriptive and could be made more concise. Long lists of statistics (e.g., vacancy rates, percentages, rankings) might be summarized in tables for clarity.
10. The study calculates multiple indices (SAI, industry scale, etc.), but the results sometimes present them in isolation. More explicit cross-analysis (e.g., does high social attention correlate with actual industry growth?) would enrich the findings.
11. Some parts of the discussion repeat the results rather than critically analyzing them. The section should focus more on interpretation and implications rather than summary.
12. The discussion is very China-centric. Adding a few international comparisons (e.g., other countries where homestays are important) could make the implications more generalizable.
13. The theoretical and practical implications of the study’s findings should be elaborated in greater detail, with particular emphasis on the practical implications.
14. The study’s limitations and directions for future research should be addressed in a dedicated section, with each separated for better clarity and emphasis.

Author Response

Please see the attachment. Thanks.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The paper titled "Macro-Level Sustainability Evaluation of Homestays Using Multi-Platform Digital Data” develops a macro-level evaluation model using multi-source online platform data to assess the sustainability of China's homestay industry, focusing on three key indices: industry scale, social attention, and regional typologies. It identifies spatiotemporal patterns, regional imbalances, and the role of digital platforms in shaping the industry’s growth, offering insights for policy and planning in sustainable tourism development. However, it presents some limitations. In particular:

The abstract does not mention any limitations, which omits important context about the study's weaknesses, such as reliance on digital data and the challenges in generalizing findings beyond China. This absence could lead readers to mistakenly assume the results are universally applicable, overlooking issues like data representativeness and bias from online platform

The study emphasizes the sustainable development of the homestay industry but doesn't fully explore the social and environmental challenges specific to rural or remote areas where these homestays are most concentrated. While it mentions the potential of homestays in poverty alleviation, the complexities related to unequal resource distribution and the lack of infrastructure in certain regions are not discussed in depth.

A literature review section is essential for providing context to the study by discussing prior work in the area, identifying gaps, and framing the research in terms of existing knowledge. Without a literature review, the Discussion section feels incomplete. It doesn’t explicitly tie the findings back to previous research or theories, which weakens the overall argument. The lack of a literature review may also leave readers questioning how this study contributes to or challenges the existing body of work, particularly on the intersection of online data and sustainable tourism.

Materials and Methods: The reliance on online data from platforms like Tianyancha and social media might not capture the full spectrum of homestay performance, particularly those outside digital reach. The methodology assumes that online presence directly correlates with actual sustainability or business performance, which may overlook more nuanced aspects such as local satisfaction or community impact.

The use of the Kriging method for spatial analysis and other statistical techniques like the Global Moran’s I Index may be limited by the assumption that the spatial patterns are stable across time, while in reality, these industries can be highly volatile due to market fluctuations or external economic shocks. Geographic-based data analysis, including the OLS and GWR models, assumes homogeneity in spatial relations, which might not fully capture the complex socio-economic dynamics affecting homestays in more diverse or marginalized regions.

While the findings indicate significant clustering in more urbanized regions, the study may not sufficiently consider the impact of urbanization on local cultures or the social fabric of rural communities. The analysis of regional differences in industry scale and social attention might not account for the different stages of industry maturity in various provinces, potentially oversimplifying the interpretation of results.

Discussion: The model proposed is quite rigid, offering typologies such as "externally dependent" or "outward spillover," but it might not be flexible enough to account for rapid changes in consumer behavior or unexpected economic shifts, such as the COVID-19 pandemic's impact on the homestay industry. The discussion around the overcapacity issue doesn't address the social and economic consequences for hosts and workers in the industry who may be disproportionately affected by market instability.

Conclusion: There is an assumption that the scale and density of homestay enterprises are sufficient indicators of sustainability, but this might overlook more qualitative aspects such as local governance, the quality of host-guest interactions, and environmental sustainability efforts.

While the paper includes recent references (e.g., studies from 2022 and 2023), the lack of a broader historical perspective in the literature might limit the depth of understanding regarding the evolution of the homestay industry. It would have been helpful to include more foundational studies on sustainable tourism and homestay models. Many references focus on data from China, making the references less diverse and regionally limited. This focus might skew the scholarship's broader applicability, as the study might overlook important global perspectives on sustainable tourism and homestays, which are increasingly significant in both developed and developing countries. While the study uses advanced methods such as Kriging and GWR, there is little acknowledgment of alternative methods or debates in spatial analysis or tourism sustainability that could have enriched the methodological discussion.

Despite the originality of the paper’s approaches, the study lacks a strong theoretical grounding in established tourism sustainability frameworks. The model is innovative but may lack robustness in its connection to prior theoretical contributions, leaving the theoretical context underdeveloped.

The generalizability of the findings beyond China is a potential limitation. While the study offers insights into the Chinese market, it doesn't fully address whether this framework can be applied to other global tourism markets. Future research could extend this model to different cultural and geographic contexts to strengthen its contribution to global scholarship in tourism and sustainability.

The paper doesn’t engage deeply with opposing views or challenges in the existing literature. It could have critiqued the methodology or theoretical assumptions more thoroughly.

By relying heavily on online data from Chinese platforms, the study may lack cultural and economic diversity in its conclusions, limiting its global relevance. This narrow focus could affect its scholarly impact in regions where online platforms and digital data aren't as dominant or applicable.

Although the paper touches on potential future studies (e.g., artificial intelligence, big data mining), it doesn't sufficiently explore how these areas could reshape the current model or methodology. Expanding on these could provide more clarity on the future trajectory of homestay sustainability research.

Author Response

Please see the attachment. Thanks.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for your efforts.

Best regards

Author Response

We sincerely thank you for your excellent suggestions and for the opportunity to improve our work.

We wish you all the best.

Reviewer 3 Report

Comments and Suggestions for Authors

I appreciate the considerable effort the authors have put into addressing my concerns. They have clearly undertaken substantial revisions, which makes this a stronger and clearer paper than the original submission. The abstract and conclusion now acknowledge limitations, a dedicated literature review has been added, and the theoretical grounding has been improved. The methodology has also been clarified, with explicit recognition of its limitations and suggestions for future enhancements. Overall, these revisions represent a meaningful improvement.

That said, some concerns remain. The scope of sustainability in the model is still confined to scale and social attention as proxies. While suitable for a digital-era macro model, this framing omits critical qualitative aspects of sustainability such as environmental pressures, cultural impacts, governance, and community well-being. These omissions are acknowledged in the discussion but not substantively addressed through new data. The added discussion on rural–urban divides and infrastructure gaps is helpful, yet the paper continues to operate almost entirely at a macro and digital level, leaving out the lived experiences of hosts, workers, and communities.

The authors also position their model as transferable to other regions, but its empirical testing is limited to China. Without applying it in other contexts, claims of global applicability remain speculative. Finally, the methods, while adequate for exploratory spatial analysis, are more descriptive than explanatory, and the lack of ground-truth validation through surveys or field data weakens the robustness of the findings.

In conclusion, the paper is publishable, but only if it is framed and understood as a macro-level, digital-data–driven diagnostic model rather than a comprehensive sustainability assessment. Its contribution lies in methodological innovation at the intersection of online data and spatial analysis, but its limits in addressing the broader, multidimensional nature of sustainability should be made clear.

Author Response

Thank you very much for giving us the opportunity to revise our manuscript and for providing two rounds of highly insightful and patient review comments. Thanks to your valuable suggestions, our paper has been significantly improved. In response to the latest comments, we have made the following revisions:

Firstly, throughout the title and the entire text, we have replaced “evaluation” with “diagnosis” to more accurately reflect the precise positioning of the model. For instance, the title has been changed from “A Multi-Platform Online Data-Driven Approach for Macro-Level Sustainability Evaluation of Homestays” to “A Multi-Platform Online Data-Driven Diagnostic Approach for Macro-Level Sustainability of Homestays”. Similarly, phrases such as “macro-level evaluation model” have been revised to “macro-level diagnostic model”, and terms including “assessment” and “evaluation” have been consistently replaced with “diagnosis” or related forms. All the corresponding revisions have been highlighted in red font in the re-submitted files. As these revisions are scattered throughout the manuscript, we kindly refer you to the revised version for details.

Secondly, within the study limitations section, the constraints of this research are reiterated and explicitly stated. We particularly appreciate your precise appraisement of these limitations and the value of the study, which enhances the rigour of our work and provides highly valuable direction and insights for our future continued focus on this topic.

(Please see the revision in the manuscript on page 24~25）                                                                                 

This multi-platform online data-driven diagnostic model represents a methodological innovation at the intersection of online data and spatial analysis. However, it still has limitations in addressing the broader, multidimensional aspects of sustainability, which also presents opportunities for future research.

... Although the discussion section of this study has suggested several alternative data sources for regions outside China, the differences in data quality and acquisition costs cannot be overlooked. (...) Moreover, the empirical testing in this research is limited to China, leading the global applicability remain speculative. (...)

... Secondly, while the model emphasises macro-scale validity, it sacrifices precision and explanatory power at the micro scale, which leading it only confined to scale and digital level.(...) Conversely, the current sustainability paradigm does not encompass the critical qualitative aspects of sustainability such as environmental pressures, cultural impacts, governance, and community well-being.(...)

... Finally, the methods, while adequate for exploratory spatial analysis, are more descriptive than explanatory, and the lack of ground-truth validation through surveys or field data weakens the robustness of the findings. (...)

Thank you once again for your invaluable guidance.

Wish you all the best.