How Does AI Education Shape Sustainability Attitudes Among Generation Z? Evidence from Istanbul’s Move Toward Hospitality 5.0

Round 1

Reviewer 1 Report (Previous Reviewer 3)

Comments and Suggestions for Authors

I would like to thank you for giving me the opportunity to review this revised manuscript, which investigates the relationship between AI education and sustainability attitudes among Generation Z within the Hospitality 5.0 framework. The authors have demonstrated substantial improvements in both the conceptual and methodological dimensions of the paper, effectively addressing the issues raised in the previous review.

the manuscript now reflects strong conceptual clarity, methodological rigor, and practical relevance. It makes a valuable contribution to the emerging literature on digital transformation and sustainable education in the hospitality industry.

Author Response

We appreciate your new comments and are grateful for all the suggestions we've received so far

Reviewer 2 Report (Previous Reviewer 2)

Comments and Suggestions for Authors

This paper explores how AI education shapes sustainability attitudes among Generation Z, employing the emerging discourse of Hospitality 5.0. While this attempt has some basic significance, several issues in the structure and content need substantial revision.

 

1. Introduction

• The paper emphasizes Hospitality 5.0; however, this concept is not yet established in the academic community. Even the distinction between Industry 4.0 and 5.0 remains unsettled. Beginning the study with Hospitality 5.0 as a given premise reveals theoretical fragility. A clearer and more accessible explanation is required.
• Despite the vast body of literature on AI education and sustainability, the introduction cites only a few general studies to support the hypothesis that “AI education influences sustainability attitudes.” However, contextual differences in prior research (e.g., general education vs. tourism/hospitality industry contexts) are not sufficiently discussed.
• The introduction poses the general question, “How does AI education influence Generation Z’s sustainability attitudes?” Yet, it remains unclear what form of education is under consideration (formal, informal, online, etc.) and which dimensions of sustainability (environmental, social, economic) are being addressed. The scope and focus of the study therefore lack transparency.
• The claim that “this study will contribute significantly by showing the role of AI education in fostering sustainability attitudes” remains at the level of a self-evident statement. The fact that education shapes attitudes has already been repeatedly demonstrated in educational psychology and sustainability education research. Simply placing this in the frame of Hospitality 5.0 does not amount to a novel contribution. The study must clarify the scope, categories, and theoretical coherence of such interdisciplinary integration.

 

2. Background and Related Work

• The authors combine the Technology Acceptance Model (TAM) and the Value-Belief-Norm (VBN) theory, but do not provide a logical rationale for how these two frameworks are integrated. Simply asserting that “both technology adoption and value-based behavior are important” is insufficient. Their combined theoretical utility should be examined more rigorously.
• Alternative frameworks such as the Theory of Planned Behavior (TPB), Social Cognitive Theory (SCT), or Learning Motivation Theory may in fact be more appropriate for analyzing AI education and attitude change. The absence of such consideration raises questions about the validity of the theoretical choice.
• Statements such as “AI plays an important role in education” or “Generation Z is digitally adept” are already well-established in prior research. Reiterating them without deeper analysis leans toward descriptive rather than scholarly contribution. The section needs to be more concise and focus on critical insights.

 

5. Discussion & 6. Conclusion

• The relationship between AI education and sustainability attitudes is heavily dependent on cultural, institutional, and social contexts. Yet, the discussion fails to address how Istanbul’s specific local context may have shaped the results.
• The paper briefly mentions similarities with prior research but does not articulate the theoretical or empirical differences, nor how this study extends or challenges existing knowledge.
• Merely invoking Hospitality 5.0 does not constitute a new academic contribution. The discussion highlights this framework uncritically, without showing any substantive distinction from prior literature.
• Recommendations such as “AI education should include ethics and values” or “sustainability should be an important theme in education” are already widely acknowledged and add little novelty. Concrete strategies or policy interventions within the Hospitality 5.0 context are not provided.
• The conclusion asserts that Generation Z will support sustainability more strongly through AI education. However, social psychology research has long emphasized the attitude–behavior gap, showing that positive attitudes do not automatically translate into concrete behavior. This gap is not acknowledged, resulting in an overstated claim. A more nuanced theoretical articulation is needed.

 

• To enhance its scholarly value, the paper requires not a superficial conclusion, but a deeper theoretical refinement and a more grounded policy discussion. Best of luck in revising.

 

Author Response

We sincerely thank the reviewer for the detailed and constructive comments, which have significantly helped us to strengthen the theoretical, methodological, and contextual foundations of our paper. All suggestions have been carefully addressed through substantial revisions to the Introduction, Background and Related Work, Discussion, and Conclusion sections. Below is a detailed explanation of how each issue has been resolved.

 

1. Introduction

We acknowledge the reviewer’s observation that the concept of Hospitality 5.0 remains emergent within the academic community. To address this, we have added a new paragraph at the beginning of the Introduction, which clearly distinguishes between Industry 4.0 and Industry 5.0, emphasizing the human-centered, sustainable, and ethical aspects of the latter. We also clarified how Hospitality 5.0 extends these principles to the hospitality sector by merging technological innovation with social responsibility.

In response to the comment regarding the lack of contextual differentiation in prior studies, we have expanded the literature review in the Introduction to include sector-specific examples of AI education and sustainability in the hospitality and tourism domain. This addition highlights the unique characteristics of the hospitality context compared to general education settings.

To improve conceptual clarity, we have also defined the three forms of AI education (formal, informal, and experiential) and explicitly stated the three dimensions of sustainability (environmental, social, and economic) considered in this study. These clarifications enhance the transparency of the study’s scope and focus.

Finally, we revised the concluding part of the Introduction to strengthen the statement of novelty. The new text explains that our contribution lies in empirically linking AI education with sustainability attitudes within the emerging paradigm of Hospitality 5.0, thereby bridging the gap between technological literacy and value-based learning.

2. Background and Related Work

Following the reviewer’s recommendation, we have added a new integrative paragraph at the end of this section  to explain the theoretical rationale for combining the Technology Acceptance Model (TAM) and the Value–Belief–Norm (VBN) theory. The revised text clarifies that TAM captures the cognitive and behavioral mechanisms of technology adoption, whereas VBN explains the value-driven and moral motivations that support sustainability-oriented behavior. Their integration provides a holistic framework that connects technological readiness with ethical awareness, which is particularly relevant for Generation Z and Hospitality 5.0 education.

We have also acknowledged and briefly discussed alternative theoretical frameworks, the Theory of Planned Behavior (TPB), Social Cognitive Theory (SCT), and Learning Motivation Theory (LMT), to justify our theoretical choice. This addition demonstrates that while these models explain behavior formation, they lack the capacity to account for the simultaneous influence of technology and value internalization, which our study aims to capture.

In line with the reviewer’s advice, we also revised overly descriptive sentences (e.g., “AI plays an important role in education”) and replaced them with critical analysis, focusing on the interplay between digital competence, ethical reasoning, and sustainability-oriented learning outcomes.

 

5-6. Discussion and Conclusion

We appreciate the reviewer’s suggestion to contextualize the findings within Istanbul’s local socio-cultural setting. Accordingly, we have added a new subsection within the Discussion addressing how the socio-demographic and institutional context of Istanbul may have influenced the results. This section references recent empirical research on digitalization and education in Istanbul, noting that cultural values, policy priorities, and unequal digital access likely shaped participants’ sustainability perceptions.

We have also expanded the Discussion to include a theoretical reflection on the attitude, behavior gap, drawing from social psychology research. This addition acknowledges that positive attitudes toward sustainability do not necessarily translate into consistent behavioral engagement, providing a more balanced interpretation of the findings.

Furthermore, the revised Discussion emphasizes the originality of the Hospitality 5.0 framework not as a label but as an interdisciplinary bridge linking AI education with human-centered sustainability competencies.

The Conclusion section (page 14) has been rewritten to move beyond general recommendations. It now proposes context-specific strategies and policy implications for integrating sustainability into AI education within hospitality programs, such as embedding ethical simulations, real-world sustainability projects, and cross-sector partnerships with technology firms. These revisions ensure that the paper offers both theoretical advancement and practical applicability.

Author Response File: Author Response.docx

Reviewer 3 Report (Previous Reviewer 1)

Comments and Suggestions for Authors

Thank you so much to the author(s) for the great effort in revising this paper based on my previous comments. Everything looks much better than before. To further improve the quality of this paper, I propose my comments.

* If EFA were not a statistical method that functions as a data reduction technique to uncover underlying patterns in a large set of variables by grouping them into a smaller number of "latent" factors, I strongly recommend that the procedure of EFA be removed from this paper.

* In Figure 1, is it possible to display R-squared values as well?

* In the measurement section, the author(s) have not mentioned how the measurement items are evaluated. Are you going to use five-point or seven-point Likert scales or something else?

Author Response

• Hakemimizin dikkatli gözlemini takdir ediyoruz. Keşifsel faktör analizi (AFA), veri indirgeme aracı olarak değil, doğrulayıcı faktör analizi (DFA) yapılmadan önce teorik olarak önceden tanımlanmış yapıların boyutsal yapısını ve iç tutarlılığını doğrulamak için tanısal bir adım olarak kullanıldığı için makalede bilerek korunmuştur. Bu yaklaşım, yapıların teorik olarak desteklendiği ancak ampirik doğrulama gerektirdiği durumlarda AFA'nın ön doğrulama aşaması olarak uygulanabileceğini vurgulayan Hair vd. (2019) ve Kline'ın (2017) metodolojik önerileriyle uyumludur. Bu amacın açıklığı, olası yanlış yorumlamaları önlemek için gözden geçirilmiş makalede güçlendirilmiştir.
• R² değeri endojen yapı olarak hesaplanmış ve Şekil 1'e eklenmiştir.
• Ölçüm bölümü, tüm maddelerin 1 = “kesinlikle katılmıyorum” ile 5 = “kesinlikle katılıyorum” arasında değişen beş puanlık Likert ölçeği kullanılarak değerlendirildiğini açıklığa kavuşturmak için güncellendi.

Author Response File: Author Response.docx

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The topic of this paper is quite interesting. The theoretical framework of this paper looks fine. Also, the flow looks good. I do believe that this paper have some potential contributions to the hotel industry. To further improve the quality of this paper, please revise the current manuscript based on my comments below.

• In the introduction section, I cannot relate the research problems closely with the research objective of this paper.
• I just do not understand why the research questions are proposed as well in addition to the research objective of this paper.
• There is too much information in Sections 2.1, 2.2, and 2.3. If possible, try to cut it down.
• I cannot relate the literature review with all of the proposed hypotheses.
• I just do not understand why exploratory factor analysis is conducted. The author(s) would like to extract new variables or something else?
• Is common method bias conducted in this paper?
• In Table 4, is it possible to see VIF, skewness, and kurtosis values as well?
• I do not see any data results related to measurement models and structural models (e.g., chi-square, degrees of freedom, CFI, IFI, GFI, AGFI, SRMR, RMSEA, and so on).
• Is it possible to display a result model after the data analysis?
• The theoretical and practical implication section should be expanded more since this section looks a little superficial.
• English proofreading is highly required since there are several grammatical and technical errors throughout this paper.

Reviewer 2 Report

Comments and Suggestions for Authors

This paper deals with the possible use of AI technology in the promising field of smart tourism, but it needs to be improved in terms of academic consistency and theoretical contribution. Especially with the use of new technical terms from a new perspective, the following attention is required. Be careful not to rashly define and spread.

 

• Although it was stated in the title and abstract that "AI-based personalization" is an exploratory analysis of how to improve the tourist experience at smart tourist destinations, the actual focus of the paper is focused on tourist technology acceptance and satisfaction analysis. Describe it more closely to the purpose of AI education than to discuss how it can enhance the tourist experience.

 

• It does not specifically define or typify AI-based 'personalization (personalization)', but is replacing it with general smart tourism technology, clouding the core concepts of research. Investigate more prior research perspectives from an economic/management/social/ethical/engineering perspective and link AI-based personalization in a large context that may include the tourism sector, and link it to smart tourism.

 

• Like the above points, key terms such as 'AI-driven personalization', 'smart destinations', and 'enhanced experience' remain in technical descriptions without theoretical sophistication. Extend the theory after the definition of the underlying term has been made.

 

 

• Give specific information on how certain cities are satisfied with represent key educational and tourism-hospitality. It is also questionable why quota sampling method, ensuring representativeness by gender, age and and educational background were implemented. Solve the question about the sampling choice.

 

• The conclusion is summarized as a technological optimism that 'AI-based personalization technology can improve the tourist experience'. Currently, AI-based personalization is hard to say that sufficient physical testing has been done, and ethical and security issues also exist. Suggest an alternative to a negative view like this. Also, clearly present how the tourist experience before and after AI-based personalization has changed and improved.

 

• Policy proposals and practical applications lack concreteness. Present important practical issues such as ethical and personal information protection of national and global AI technology with policy and technical discussions.

 

 

I hope it will help the completion of the study.

 

• Although it was stated in the title and abstract that "AI-based personalization" is an exploratory analysis of how to improve the tourist experience at smart tourist destinations, the actual focus of the paper is focused on tourist technology acceptance and satisfaction analysis. Describe it more closely to the purpose of AI education than to discuss how it can enhance the tourist experience.

 

• It does not specifically define or typify AI-based 'personalization (personalization)', but is replacing it with general smart tourism technology, clouding the core concepts of research. Investigate more prior research perspectives from an economic/management/social/ethical/engineering perspective and link AI-based personalization in a large context that may include the tourism sector, and link it to smart tourism.

 

• Like the above points, key terms such as 'AI-driven personalization', 'smart destinations', and 'enhanced experience' remain in technical descriptions without theoretical sophistication. Extend the theory after the definition of the underlying term has been made.

 

 

• Give specific information on how certain cities are satisfied with represent key educational and tourism-hospitality. It is also questionable why quota sampling method, ensuring representativeness by gender, age and and educational background were implemented. Solve the question about the sampling choice.

 

• The conclusion is summarized as a technological optimism that 'AI-based personalization technology can improve the tourist experience'. Currently, AI-based personalization is hard to say that sufficient physical testing has been done, and ethical and security issues also exist. Suggest an alternative to a negative view like this. Also, clearly present how the tourist experience before and after AI-based personalization has changed and improved.

 

• Policy proposals and practical applications lack concreteness. Present important practical issues such as ethical and personal information protection of national and global AI technology with policy and technical discussions.

 

 

I hope it will help the completion of the study.

Reviewer 3 Report

Comments and Suggestions for Authors

I would like to thank you for giving me the chance to review this manuscript, which dwells upon an interconnectedness between AI education and sustainability attitudes among the generation Z members within the domains of the hospitality industry. It is an extremely relevant and timely topic due to the increased penetration of digital technologies into sustainable business practice. Nonetheless, although the research provides a very interesting discussion, and employs a large data set, the ready-made manuscript does have an array of conceptual, methodological, and analytical weaknesses that should be remedied to strengthen its academic merit and practical value. The list of main comments identified in the manuscript is provided below:

• The manuscript deals with a topical and up-to-date issue, but the originality of the theoretical contribution aspires to reach higher. The article repeats familiar constructs (AI knowledge, application, attitudes, sustainability) without providing substantial theoretical advancement.
• The review is very extensive but fails to provide meaningful synthesis. Most of it is descriptive and discursive to the point of being repetitive rather than analytic. Several references are recent, yet the discussion does not sufficiently engage with competing perspectives or gaps in empirical evidence.

• Intensive use of secondary sources and citing of sources in large lists without critical analysis lowers the academic quality.
• Research questions are well notified yet too general and expected.

• The first hypothesis (H1) is stated in the form of a null hypothesis, which is not typical of such a study.
• The sampling size (N=823) is satisfactory, but the nature of sampling (quota sampling) would lead to bias and that fact is not discussed critically.
• Self-reported data comes with the risk of social desirability bias; such a problem is recognized but not addressed to methodological design.
• Lack of detail on how formal vs. informal education was operationalized. Were there minimum criteria for inclusion in these categories?

• The technical description of residual analysis (Python, sklearn) is excessive, and the lack of explanation of why such step was needed or how it contributes to the theoretical value is seen.

• Model fit of CFA and SEM is very excellent but the argument of 100% variance explained in the model is quite dodgy and must have been a forced solution of factors, which must be clarified.

• Strong relationships can be explained by the structural model, however path coefficients have limited interpretation in a practical context.

• The results are explicated in detail, but the discussion section mainly repeats numerical results rather than offers high-quality theoretical interpretation.
• Practical recommendations are too broad (e.g. state of introducing ethics into AI courses) and do not offer specifics on how to proceed in curriculum development or policy.

• Limitations are reported properly, however future research directions are generic and cannot be associated with the gaps identified.