Submit to this Journal Review for this Journal Propose a Special Issue

Article Menu

Open AccessArticle

Peer-Review Record

How Can Chatbots Help Companies to Improve the Customer Experience Offered to Their End Users/Customers in the Tourism Industry?

Tour. Hosp. 2025, 6(4), 207; https://doi.org/10.3390/tourhosp6040207

by Chrysa Agapitou¹, Athanasia Sabazioti¹, Petros Bouchoris², Maria-Theodora Folina³, Dimitris Folinas^4,* and George Tsaramiadis⁵

Reviewer 1:

Mithat Zeki Dinçer

Reviewer 2: Anonymous

Reviewer 3:

Mariya Zlatkova Stankova

Tour. Hosp. 2025, 6(4), 207; https://doi.org/10.3390/tourhosp6040207

Submission received: 1 August 2025 / Revised: 17 September 2025 / Accepted: 2 October 2025 / Published: 11 October 2025

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

To make it even more compelling, consider including one or two sentences to encourage translation of the critical negative relationship found for Propensity. Why might setting up schedules particularly discourage the selection of unused chatbot advances among Greek visitors?

The English could be improved to more clearly express the research.

Research at the sources of the last three years.

,Providing details about why Greek tourists were chosen for the study would strengthen the introduction.

No details about the significance of the study were included. It would be beneficial to emphasize its importance.

What gap in the literature does the study fill? This should be more clearly emphasized.

The introduction includes the data collection date range. This information would be better included in the method section.

Artificial intelligence, chatbots and customer experience

2.1. Customer Experience in the Tourism and Hospitality (T&H) Industry: Evolution and the role of AI / He added the following explanation below the title, which has no connection to the next paragraph. The first paragraph is about materials and methods. It was probably added here by mistake.

Author Response

Answers to Reviewer’s comments/suggestions - 1

(*) In red, your comments/suggestions / in (italic) blue, our answers

Comment 1.

Regarding the Introduction section:

To make it even more compelling, consider including one or two sentences to encourage translation of the critical negative relationship found for Propensity. Why might setting up schedules particularly discourage the selection of unused chatbot advances among Greek visitors?
Providing details about why Greek tourists were chosen for the study would strengthen the introduction.
No details about the significance of the study were included. It would be beneficial to emphasize its importance.
What gap in the literature does the study fill? This should be more clearly emphasized.
The introduction includes the data collection date range. This information would be better included in the method section.

Answer

We have revised the Introduction to also address the comments raised by the other reviewers, as follows:

“Artificial Intelligence (AI) has become a transformative force across industries, re-shaping business models and redefining customer engagement (Usman, 2024). In tourism, these changes are closely tied to broader developments in information and communication technologies (ICTs), which have steadily altered the way travelers plan, book, and experience their journeys. ICT-driven innovations such as online booking platforms, digital recommendation systems, and mobile applications have created new expectations for convenience, personalization, and real-time interaction (Venkatraman & Kurtkoti, 2024; Traversa, 2024). Within this evolving landscape, AI has emerged as a catalyst for further change, enhancing operational efficiency, enabling predictive analytics, and supporting tailored service delivery (Tussyadiah, 2020; Moilanen, 2023; Ma, 2024).

Among the most visible applications of AI in tourism are chatbots and virtual assistants. These tools streamline booking procedures, provide instant responses to customer queries, and generate personalized travel suggestions (Gretzel et al., 2015; Rather, 2024; Dutta, 2024). In addition, AI-powered analytics allow tourism providers to better understand customer preferences and behavior, fostering deeper insights into market trends and facilitating the creation of memorable travel experiences (Choi & Kim, 2024; Doğan & Niyet, 2024). Complementary tools such as automated translation systems and augmented or virtual reality applications further enhance accessibility, especially for international visitors (Ivanov & Webster, 2019). By simplifying processes and supporting adaptive decision-making, AI technologies are helping the tourism industry remain competitive in a rapidly evolving environment (Ma, 2024; Talukder & Muhsina, 2024).

This study focuses on the adoption of AI-based chatbots in the Greek tourism sector. Greece was selected as the research context due to its reliance on tourism as a key driver of the economy and its diverse tourist base, which makes it an ideal setting for examining technology acceptance patterns. Despite the increasing role of AI in global tourism, little empirical research has been conducted on how Greek tourists, in particular, perceive and adopt chatbot technologies. This creates a gap in the literature that this study seeks to address by examining the behavioral and perceptual factors influencing adoption.

The research draws on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model to analyze constructs such as performance expectancy, effort expectancy, social influence, habit, hedonic motivation, and perceived innovativeness. Through this lens, the study aims to assess not only the functional and emotional drivers of chatbot adoption but also the barriers that may hinder their acceptance. An especially noteworthy aspect concerns the negative role of habit (propensity): Greek tourists with well-established routines in planning and scheduling their trips may perceive chatbots as unnecessary or even disruptive. The effort required to change existing practices can discourage the use of chatbot technologies, reinforcing resistance to new digital tools.

The significance of this study lies in its dual contribution. Academically, it extends the application of UTAUT2 by contextualizing it within tourism and by addressing the under-explored case of Greek tourists. Practically, the findings can help tourism stakeholders design chatbot systems that are user-friendly, innovative, and enjoyable, while minimizing obstacles related to usability and resistance to change. These insights are vital for companies aiming to enhance customer satisfaction and strengthen competitiveness through AI.

The remainder of this paper is structured as follows: Section 2 reviews the interaction between AI, chatbots, and customer experience. Section 3 presents the theoretical framework, outlining the UTAUT2 constructs relevant to this research. Section 4 details the research methodology, including data collection and analysis procedures. Section 5 discusses the findings, while Section 6 concludes with implications, recommendations, and directions for future research. “

Moreover, to incorporate the five (5) comments:

We have added an introductory paragraph to address the reviewer’s concern by discussing the negative relationship found for propensity. Specifically, we explain why established travel schedules may discourage Greek tourists from adopting new chatbot technologies.
We have explicitly stated that Greece was chosen as the research context due to tourism’s central role in its economy and its diverse tourist population, making it an appropriate case study.
We have added a paragraph highlighting both the academic significance (filling a research gap and extending the UTAUT2 model) and the practical significance (guiding tourism stakeholders).
We have emphasized that little empirical research exists on Greek tourists’ perceptions of chatbots, thereby clarifying the research gap this study addresses.
Finally, we have removed methodological details such as the data collection dates from the Introduction, noting that this information is more appropriately included in Section 4 (Methodology).

Comment 2.

Answer

That’s correct, the paragraph has been removed.

Comment 3.

The English could be improved to more clearly express the research.

Answer

We have conducted a thorough grammar and language review.

Comment 4.

Research at the sources of the last three years.

Answer

We have updated the list of references by adding the following sources:

Usman, M. (2024). Harnessing Artificial Intelligence: Redefining Industries and Economic Landscapes. https://doi.org/10.31224/4036
Venkatraman, D. P., & Kurtkoti, M. (2024). Artificial Intelligence in the Service Industry: Transforming Operations and Enhancing Customer Experience. Nanotechnology Perceptions, 198–201. https://doi.org/10.62441/nano-ntp.vi.3674
Traversa, F. (2024). Artificial Intelligence in Tourism. Elsevier BV. https://doi.org/10.1016/b978-0-443-13701-3.00344-3
Moilanen, T. (2023). Opportunities and Challenges of Artificial Intelligence in the Tourism Industry. Deleted Journal. https://doi.org/10.19080/gjtlh.2023.01.555553
Ma, S. (2024). Enhancing Tourists’ Satisfaction: Leveraging Artificial Intelligence in the Tourism Sector. Pacific International Journal, 7(3), 89–98. https://doi.org/10.55014/pij.v7i3.624
Choi, Y., & Kim, D. (2024). Artificial Intelligence in The Tourism Industry: Current Trends and Future Outlook. International Journal on Advanced Science, Engineering and Information Technology, 14(6), 1889–1895. https://doi.org/10.18517/ijaseit.14.6.20452
Talukder, M. B., & Muhsina, K. (2024). AI Assistants for Tour and Travel Operation. Advances in Business Strategy and Competitive Advantage Book Series, 292–320. https://doi.org/10.4018/979-8-3693-3498-0.ch013
Rather, R. A. (2024). AI-powered ChatGPT in the hospitality and tourism industry: benefits, challenges, theoretical framework, propositions and future research directions. Tourism Recreation Research. https://doi.org/10.1080/02508281.2023.2287799
Dutta, S. (2024). Chatbot Effectiveness in Enhancing Guest Communication: Insights from Secondary Data. Journal of Propulsion Technology, 45(04), 2137–2150. https://doi.org/10.52783/tjjpt.v45.i04.8497
Doğan, S., & Niyet, İ. Z. (2024). Artificial Intelligence (AI) in Tourism (pp. 3–21). Emerald (MCB UP). https://doi.org/10.1108/978-1-83753-970-320241001

Reviewer 2 Report

Comments and Suggestions for Authors

The paper addresses a timely and relevant topic within tourism studies. The growing influence of artificial intelligence on the tourism experience, particularly through the use of chatbots, is undeniable. In this context, the choice of topic and its general justification are appropriate and clearly presented.

However, a key issue is that the paper does not appear to focus substantively on tourism. Rather, it examines the general intention to use chatbots, with only one question in the survey (Question 27) relating specifically to travel. In its current form, the study misses an opportunity to make a meaningful contribution to tourism theory and practice, or to clarify how research on chatbot usage intentions in tourism settings could enhance our understanding of technology adoption in tourism contexts.

Also, the rationale and conclusions referring specifically to Greek tourism are unclear. I do not fully understand the argument that "The research explores the degree to which instrumental intentions and attitudes to- ward AI technologies encapsulated in the applications of chatbots contribute to the overall effect of tourism on Greek tourists’ travel" (lines 54–55). This purpose is too broad and needs to be specified more precisely. The paper would benefit from a clearer explanation of why Greek tourists were chosen as the focus group and what significance this context holds for the study.

The literature review is too general and does not lead to the formulation of hypotheses, which is essential in explanatory research aimed at understanding relationships between variables. As a result, the study feels fragmented and lacks depth in its theoretical grounding.

The selection of the UTAUT model is appropriate and well justified. However, the analysis based on correlation and regression analyses is rather simple, largely due to the underdeveloped theoretical discussion. Similarly, the conclusions remain general and offer limited contribution to theory or practice.

To make a meaningful contribution to the field, the authors should clarify the tourism relevance of their research, specify their theoretical aims more precisely, formulate hypotheses based on the literature, go deeper in analysing the results, and ensure completeness in references.

Specific Remarks:
Line 39: “This approach based on data” – this phrase is vague;
Lines 105–106 overlook important historical communication tools such as telephones and traditional mail, they should be revised to reflect the broader range of historical communication methods;
Line 457 invites the classic “so what?” question.

Technical placeholders or notes seem to have remained in the body text and should be removed, especially lines 74–75 and 93–99.

Several references cited in the main text are missing from the reference list, including Zsarnóczky (2017), Zlatanov & Popesku (2019), Sanchez (2019), and Chiong et al. (2022), among others. This oversight should be corrected for proper scholarly practice.

Author Response

(*) In underlined red, your comments/suggestions / in (italic) blue, our answers

Comment 1

Answer

Thank you for your comment.

It is true that many questions (e.g., PE, EE, PI) are generic and not explicitly tied to tourism. However, at least 4 questions explicitly mention travel planning or using chatbots while traveling. Therefore, we believe that the study does not only test “general intention to use chatbots”; it does embed the analysis in a tourism context, though the link between findings and tourism theory/practice could indeed be strengthened in the discussion.

Moreover, the Abstract and Introduction clearly frame the study as focusing on Greek tourists’ adoption of chatbots in tourism. The survey instrument (Questions 8–38) includes constructs from UTAUT2 tailored to chatbots, with several items framed in a tourism context:

Q19: “My social circle supports the idea of using chatbots while traveling.”

Q23: “I feel good when using chatbots during travel planning.”

Q27: “I would feel strange if I didn’t use chatbots while planning travel.”

Thus, multiple questions (not just Q27) explicitly situate chatbot use in tourism or travel planning, not merely generic chatbot adoption.

Comment 2

Answer

We have revised the Introduction to also address the comments raised by the other reviewers, as follows:

Moreover, we have added the following paragraph in the last section to reinforce the rationale and context:

“This research contributes to tourism theory and practice by contextualizing technology adoption within the Greek tourism sector, a market where tourism plays an outsized role in economic and social life. The findings underscore that Greek tourists’ adoption of chatbots is driven not only by functional benefits but also by enjoyment and perceptions of innovation, while habitual reliance on traditional practices can act as a barrier. These insights are particularly relevant for Greek tourism providers, who must balance the integration of advanced digital solutions with the preservation of personalized, human-centered service that remains highly valued in the Greek cultural context. More broadly, by focusing on Greece, this study highlights how national tourism contexts shape the interplay between consumer attitudes and emerging technologies, offering a pathway for comparative studies across different destinations.”

Comment 3

We appreciate this observation and agree that the literature review would benefit from stronger theoretical grounding. In the revised version, we have refined the review to explicitly connect UTAUT2 constructs with prior tourism and technology adoption research. This allowed us to formulate clear hypotheses (e.g., that performance expectancy, hedonic motivation, and perceived innovativeness positively influence chatbot usage intention, while habit and inconvenience exert negative effects), thereby aligning the theoretical discussion more directly with the study’s objectives, adding them at the end of the Literature review section.

“Following is a set of hypotheses (H1-H7) that align with your UTAUT2 framework, your data, and the Greek tourism context:

H1: Performance Expectancy (PE) has a positive effect on Greek tourists’ intention to use chatbots in the tourism sector.

H2: Effort Expectancy (EE) has a positive effect on Greek tourists’ intention to use chatbots in the tourism sector.

H3: Social Influence (SI) positively influences Greek tourists’ intention to adopt chatbots in the tourism sector.

H4: Hedonic Motivation (HM) has a positive effect on Greek tourists’ intention to use chatbots in the tourism sector.

H5: Habit (HB) negatively affects Greek tourists’ intention to use chatbots in the tourism sector, as reliance on established routines may discourage adoption of new technologies.

H6: Perceived Innovativeness (PI) positively influences Greek tourists’ intention to use chatbots in the tourism sector.

H7: Attitude toward Self-Service Technologies (SSTs) positively influences Greek tourists’ intention to adopt chatbots in the tourism sector.

H8: Inconvenience (INC) has a negative effect on Greek tourists’ intention to use chatbots in the tourism sector (Venkatesh, Thong & Xu., 2012 for PE, EE, SI, HM, HB; Hernández, Jiménez & Martín, 2017 for PI; Dabholkar & Bagozzi, 2002 for SST attitude and inconvenience).”

Our initial aim was explanatory, but we acknowledge the importance of hypothesis-driven explanatory research. To address this, we strengthened the literature review by situating the UTAUT2 constructs in the specific context of Greek tourism and deriving hypotheses that reflect this setting.

Comment 4

Answer

Very good points.

First, we have added the following paragraph after the “Figure 2. Chatbot Usage Intention analysis results” to enrich the statistical tests findings:

“Furthermore, the correlation and regression analyses provide further insight into the dynamics between UTAUT2 constructs and the intention to adopt chatbots in tourism. Performance expectancy shows the strongest positive effect, confirming that perceived usefulness remains the dominant factor in technology adoption. Hedonic motivation and perceived innovativeness also emerge as significant predictors, suggesting that enjoyment and novelty play key roles in shaping adoption behavior, consistent with prior UTAUT2 research. Interestingly, habit demonstrates a negative effect, indicating that entrenched user routines may act as a barrier to change, a finding that enriches the theoretical discussion by highlighting resistance mechanisms within consumer behavior. While effort expectancy and social influence show weaker or insignificant effects in the regression, their moderate correlations suggest indirect roles that could become more salient in different cultural or demographic contexts. Overall, these results emphasize the multifaceted nature of technology adoption, where both utilitarian and experiential drivers coexist with barriers such as routine and inconvenience.”

Second, the following paragraph has been added in the last section to add theory in the managerial implications:

“Based on these findings, several targeted recommendations can be made for both theory and practice. From a theoretical perspective, future studies should expand the UTAUT2 framework by incorporating constructs such as trust, perceived risk, and privacy concerns, which appear particularly relevant in the tourism context where sensitive data are shared. This would strengthen explanatory power and capture additional psychological barriers to chatbot adoption. From a practical standpoint, tourism providers should prioritize the development of user-friendly and intuitive chatbot interfaces that minimize learning effort, while integrating playful and personalized features to enhance hedonic motivation. Marketing strategies should highlight the innovative aspects of chatbot services and leverage social proof through testimonials and influencer engagement to overcome resistance to change. Additionally, training initiatives and transparent communication of data security policies can mitigate concerns related to habit and trust, ensuring wider acceptance and long-term engagement with chatbot technologies in tourism.”

Comment 5

Answer

We sincerely thank the reviewer for these constructive comments, which have helped us to improve the quality and focus of the paper. In the revised version, we have clarified the tourism relevance of our research by explicitly justifying the choice of Greek tourists as the focus group and explaining the significance of this context. We have also specified our theoretical aims more precisely and formulated clear hypotheses based on the UTAUT2 literature and its application to tourism. Furthermore, we have deepened the analysis of the correlation and regression results, highlighting the theoretical and practical implications of each construct. Finally, we have thoroughly checked the reference list to ensure completeness and corrected missing or inconsistent entries. We believe that these revisions directly address the reviewer’s concerns and substantially strengthen both the theoretical grounding and the contribution of our work to tourism research.

Comment 6

Specific Remarks:

Line 39: “This approach based on data” – this phrase is vague;

We have rephrased the sentence as follows:

“These tools streamline booking procedures, provide instant responses to customer queries, and generate personalized travel suggestions (Gretzel et al., 2015; Rather, 2024; Dutta, 2024).”

Lines 105–106 overlook important historical communication tools such as telephones and traditional mail, they should be revised to reflect the broader range of historical communication methods;

We have added the following paragraph to cover this comment/suggestion:

“The customer experience in the Tourism and Hospitality (T&H) industry has undergone profound changes over the years, shaped by historical developments and technological advancements. From the inception of organized travel, hospitality has consistently played a pivotal role in human interactions, emphasizing service quality and guest satisfaction. In earlier periods, communication tools such as face-to-face interaction, traditional mail, and telephones were central to shaping the guest experience and maintaining customer relationships, before being gradually complemented and later transformed by digital technologies (Buhalis & Law, 2008; Ivanov & Webster, 2019; Hoyer et al., 2020).”

Line 457 invites the classic “so what?” question.

We have added the following paragraph to cover this comment/suggestion:

“These findings underscore the necessity of systematically enhancing technological solutions, ensuring the development of user-centered interfaces, and integrating psychological and social considerations into strategies that facilitate the adoption of innovative technologies in tourism. In practice, this may involve investing in intuitive design features that minimize perceived effort, implementing training or awareness initiatives to overcome resistance to change, and leveraging social influence through peer recommendations and testimonials to encourage adoption.”

Technical placeholders or notes seem to have remained in the body text and should be removed, especially lines 74–75 and 93–99.

Done.

Done. All cited sources appear in the reference list.

Reviewer 3 Report

Comments and Suggestions for Authors

A topical article addressing an issue of importance to tourism businesses. I sharing and supporting the thoughts that this and other processes related to technological innovations are expected to be of interest to researchers due to the effect they have and are expected to increase on tourism businesses and tourists.

In this sense, I support the publication of the manuscript presenting comprehensive research on the effects of the application of chatbots in tourism (focusing on the perceptions and reactions to them by Greek tourists). Of course, thе research can be used also as a reference point due to its illustrative nature.

When reviewing the article, its applied focus made a positive impression on me. Specifically, I would highlight the attempts of the authors to reveal the aspects under the influence of which the use of chatbots is changing, on the one hand; on the other – the article outlines directions for promoting the use of Artificial Intelligence in tourism.

Against the background of the strengths of the study, some recommendations can be highlighted that do not diminish its importance, and would rather contribute to wider interest in it.

These are the following:

- Regarding the Abstract: I would recommend that the authors add a brief description of the aim and the used methodology, which are actually part of the text, but are not fully highlighted; Again, here, it is striking that the names of the studied factors are written in capital letters, which I find completely unnecessary. By the way, they are written in the same way in the main text too.

- In the Introduction, I would recommend that the authors pay attention to the context of changes in tourism under the influence of information and communication technologies (this focus is missing now) as effects and expectations, of course in the most general terms, so as not to "burden" the text.

- Regarding the next Part - 2. Artificial intelligence, chatbots and customer experience 2.1. Customer Experience in the Tourism and Hospitality (T&H) Industry, I have noticed the following text:

Evolution and the role of AI, it is assumed that it is oriented towards a review of the state and development of the issues to which the article is devoted. It is striking, however, that the part begins as follows: The Materials and Methods should be described with sufficient details to allow oth-95 ers to replicate and build on the published results. Please note that the publication of your 96 manuscript implicates that you must make all materials, data, computer code, and proto-97 cols associated with the publication available to readers. Please disclose at the submission 98 stage any restrictions on the availability of materials or information. New methods and 99 protocols should be described in detail while well-established methods can be briefly de-100 scribed and appropriately cited. - (lines 95-100 (101))

This text is completely irrelevant to the article and I can assume that it is left over from some previous review. The authors must remove it.

- I would recommend that the authors also pay attention to some terminological clarifications, such as the use of T&T or T&H sector. For example, in the Introduction they mention the Tourism and Travel sector, and in Part 2, they talk about the Tourism and Hospitality sector. It would be good to clarify this issue.

- I would recommend changing the name of tab. 1; At the moment it is mainly descriptive. An option could be: Systematization of research on …

- Part 3 actually presents the Methods used in the study. It is surprising why it is not named this way. I would recommend that the authors consider a change in its designation, because the text here do not refer to the theoretical framework, but rather present the methodological one. Part 3 could also be merged with part 4 by dividing it into two subparts. However, this is entirely up to the authors.

- I accept the chosen methodology as adequate and relevant to the author's idea.

- Regarding Part 5, I would recommend that the authors start with a brief summary and then list the first, second, etc.

- The conclusion in Part 6 cannot remain as it is. There is not convenient or typical to include a table in it. I would like to recommend that the authors add an additional section for discussion before the Conclusion.

I would also recommend that the authors review the References list once again and consider the way they note the sources cited in the text. It seems that at the moment there is some repetition of the authors' last names/their references in the text.

Author Response

(*) In underlined red, your comments/suggestions / in (italic) blue, our answers

Comment 1

Answer

Thank you for your comments.

Comment 2

Against the background of the strengths of the study, some recommendations can be highlighted that do not diminish its importance, and would rather contribute to wider interest in it.

These are the following:

Answer

Thank you for your comment. We have rewritten the Abstract as follows:

“This study examines the intention of Greek tourists who visit national touristic destinations to adopt Artificial Intelligence (AI) chatbots in the travel and tourism sector. Using the UTAUT2 model as a framework, data were collected through a closed-ended questionnaire and analyzed with correlation and regression methods to identify the main drivers and barriers to this adoption. Results show that specific factors such as performance expectancy, hedonic motivation, and perceived innovativeness significantly and positively influence chatbot usage, emphasizing the role of usefulness, enjoyment, and innovation in shaping user acceptance. Conversely, factors such as inconvenience, habit, and difficulty of use negatively affect adoption, indicating the importance of overcoming usability challenges and resistance to change. These findings highlight the need for the development of accessible and engaging chatbot systems and underscore the value of continuous technological improvements. The study concludes that adopting AI-driven solutions can help tourism providers personalize services, improve operational efficiency, and enhance customer satisfaction, fostering sustainable competitiveness in the sector.”

The Abstract now clearly presents the aims, methodology, and key findings. In addition, the factors are written in lowercase both in the Abstract and throughout the rest of the paper.

Comment 3

Answer

We have revised the Introduction to also address the comments raised by the other reviewers, as follows:

Moreover, we have added a first-paragraph discussion situating AI within the broader role of ICTs in tourism (online booking, recommendation systems, mobile apps) in order to highlight the general technological changes shaping tourism.

Comment 4

- Regarding the next Part - 2. Artificial intelligence, chatbots and customer experience 2.1. Customer Experience in the Tourism and Hospitality (T&H) Industry, I have noticed the following text:

This text is completely irrelevant to the article and I can assume that it is left over from some previous review. The authors must remove it.

Answer

That’s correct, the paragraph has been removed.

Comment 5

Answer

That’s correct. In all cases, the “Tourism and Hospitality sector” has been used.

Comment 6

- I would recommend changing the name of tab. 1; At the moment it is mainly descriptive. An option could be: Systematization of research on …

Answer

Thank you for your suggestion. We have changed the title accordingly.

Comment 7

Answer

We appreciate your suggestion and have revised the title accordingly. However, with respect to the merging, we consider that the present structure is more helpful for the reader.

Comment 8

- I accept the chosen methodology as adequate and relevant to the author's idea.

Answer

Thank you for your comment.

Comment 9

- Regarding Part 5, I would recommend that the authors start with a brief summary and then list the first, second, etc.

Answer

We have added the following paragraph at the beginning of the 5^th section:

“Following the methodological framework presented in the preceding section, this part of the study reports the empirical results obtained from the survey of Greek tourists. The analysis focuses on assessing the reliability and validity of the measurement scales, examining the correlations among the UTAUT2 constructs, and evaluating their predictive power with respect to the intention to adopt AI-driven chatbots in the tourism and hospitality sector. By presenting the outcomes of reliability testing, correlation analysis, and regression modeling, this section provides a systematic account of the factors that facilitate or hinder chatbot adoption, thereby offering a foundation for the subsequent discussion and interpretation of results.”

Comment 10

Answer

We have presented the content of the Table in paragraph format as follows:

“The comparison of the present findings with similar studies highlights several important consistencies and insights regarding chatbot adoption in the tourism sector. Performance expectancy emerges as a decisive factor, consistent with the results of Melián-González, Gutiérrez-Taño, and Bulchand-Gidumal (2021). Users are more likely to adopt chatbots and travel applications when they perceive clear advantages such as enhanced navigation, personalized recommendations, and real-time information, which improve both efficiency and satisfaction (Topsakal & Çuhadar, 2024; Mishra et al., 2023). Similarly, social influence plays a significant role, as endorsements from friends, family, and online communities encourage adoption, while influencer marketing and user-generated content further enhance perceived value (Tran & Vu, 2024; Mishra et al., 2023).

Another key finding relates to hedonic motivation. The strong association between enjoyment and intention to use chatbots aligns with the work of Venkatesh et al. (2012) and Davis et al. (1992), who emphasized the importance of intrinsic pleasure in technology adoption. In tourism, the role of enjoyment in boosting engagement and willingness to adopt new digital tools has also been confirmed by Huang and Rust (2018). Perceived innovativeness is likewise consistent with earlier research, as individuals are more inclined to embrace technologies they regard as innovative and novel (Venkatesh et al., 2012; Hernández et al., 2017). In this respect, AI-powered chatbots are seen as valuable innovations that enhance personalization and user experience, reinforcing adoption (Huang & Rust, 2018).

Finally, the study underlines the role of attitude toward self-service technologies (SSTs). A positive outlook is shown to significantly encourage adoption, in line with Venkatesh et al. (2012) and Davis et al. (1992), who found that favorable perceptions of usefulness and ease of use strongly shape acceptance. Similarly, Hernández et al. (2017) noted that a welcoming mindset toward innovative tools in tourism fosters greater willingness to engage with them. Collectively, these findings confirm that the UTAUT framework remains a valuable lens for explaining AI chatbot adoption and for identifying the constructs that enhance customer interaction with travel services.”

Moreover, we changed the title of Section 6 to “6. Discussion and conclusion”.

Comment 11

Answer

Done. The reference list has been updated.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

EVERYTHİNG İS OK

Author Response

Thank you for your support.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have clearly made an effort to improve the manuscript, which I appreciate. Many of the comments and concerns I raised previously have been effectively addressed.

That said, the introduction still does not adequately justify the need for analysing the role of chatbots in improving customer experience specifically in the tourism context. Within the entire section, chatbots are only mentioned briefly in lines 40–43, and even then, they are grouped together with virtual assistants—technologically related but potentially distinct in terms of user experience. Moreover, this passage is supported by only three references, of which Gretzel et al. (2015) discusses smart tourism broadly, not chatbots in particular, and Rather (2024) refers specifically to ChatGPT. This is insufficient to justify the research gap. In the section titled “Chatbots on Facebook Messenger in the tourism sector” (line 164 onward), only one reference is provided to support the argument. This raises the question of whether the authors conducted a comprehensive literature review or whether the research gap concerning chatbot usage in tourism was not sufficiently well formulated. The reader is still left wondering: what makes tourists such distinct consumers that their intentions to use chatbots require specific investigation?

I must acknowledge that I initially overlooked some of the additional survey questions relating to travel. While they remain relatively few, the overall construct of the questionnaire is now more oriented toward tourism. If the introduction were strengthened to include a more robust justification for the relevance of chatbots in tourism, the study may offer a more compelling contribution to tourism studies.

I also appreciate that the authors have now formulated hypotheses. However, each hypothesis should clearly and logically follow from a preceding analysis of the relevant literature. Simply listing them at the end of the section appears as an oversimplification, and weakens the scholarly rigour of the article. The structure and coherence of the literature review section still require improvement.

The conclusion section has been strengthened through the addition of meaningful contributions to both theory and practice. Nonetheless, the question remains: what makes Greek tourists such a uniquely relevant consumer group in this context? This point continues to lack a convincing explanation, though it may indeed be difficult for the authors to justify more fully.

Finally, there are still several sources cited in the body of the manuscript that are missing from the reference list, for example, Sheffield (2016), Deng et al. (2023), and Tula et al. (2024). This is an issue of academic integrity, and the manuscript should be carefully reviewed for consistency between in-text citations and the reference list before the final version is submitted.

Author Response

(*) In underlined red your comments/suggestions / in (italic) blue our answers

The authors have clearly made an effort to improve the manuscript, which I appreciate. Many of the comments and concerns I raised previously have been effectively addressed.

We thank once again the reviewer for these constructive comments, which have helped us to improve the quality and focus of the paper.

We have revised the Introduction section to also address the comments, by adding these paragraphs and supporting 9 relative references:

“Tourists differ significantly from general digital consumers, making their intentions to use chatbots a distinct area of investigation. First, tourism consumption is multi-stage and highly mobile, spanning pre-trip planning, on-site experiences, and post-trip reflections, often conducted under time pressure on smartphones. This makes tourists’ information needs and responsiveness expectations unique compared to static e-commerce contexts (Li, Wang, & Yu, 2016). Second, tourism decisions involve high uncertainty and perceived risk, as travelers deal with unfamiliar destinations, non-refundable bookings, and time-sensitive services, which increases the importance of trust, competence, and reliability in chatbot interactions (Tussyadiah, 2020). Third, tourists pursue both hedonic experiences, such as exploring attractions, and utilitarian tasks, such as booking tickets, meaning their adoption of chatbots depends on balancing emotional and functional needs (Chi, Denton, & Gursoy, 2020; Chi, Gursoy, & Chi, 2022).

In addition, the hospitality context is inherently interpersonal, meaning social presence, anthropomorphism, and trust play a stronger role in tourists’ willingness to adopt chatbots than in many other consumer sectors (Tussyadiah, Zach, & Wang, 2020; Xu, Li, & Kim, 2024). Situational factors also shape behavior, as studies show that conditions such as crowding can shift tourists’ preferences between human staff and automated service, influencing their readiness to use chatbots in real time (Li, Yen, Uysal, & Song, 2020). Moreover, because tourism involves diverse nationalities and cultures, cross-cultural differences in trust and acceptance strongly affect chatbot adoption, making global generalizations problematic (Lu et al., 2023). Psychological mechanisms further distinguish tourists from other consumers: for example, framing chatbots as “friends” versus “strangers” has been shown to influence adoption decisions in travel contexts (Scarpi, Pizzi, & Marzocchi, 2024). Finally, tourism-specific use cases (such as itinerary planning, in-destination navigation, and sustainability guidance) highlight the need for sector-specific models of chatbot adoption (Tussyadiah, 2020). Collectively, these factors illustrate why tourists are distinct consumers and why their intentions to use chatbots warrant targeted scholarly attention.”

We appreciate your comment.

We appreciate your comment. First, we add the following paragraph at the end of section 2.3:

“Thank you for your valuable comment. We trust that the revised Introduction now adequately addresses the unique characteristics of tourists as consumers and clarifies why their intentions to use chatbots merit specific scholarly investigation. By framing this distinction more explicitly, we aim to strengthen the theoretical foundation of our study and highlight its contribution to advancing research at the intersection of tourism and intelligent service technologies”, to introduce our new 2.4 Hypotheses formulation subsection that is added as follows:

“2.4 Hypotheses formulation

Previous researches highlight that chatbots and virtual assistants hold considerable potential for enhancing customer engagement, but their effectiveness depends on factors identified in established technology adoption models such as UTAUT2. For instance, studies consistently show that performance expectancy (the degree to which users believe that technology will improve task performance) is one of the strongest predictors of technology use in tourism contexts (Venkatesh et al., 2012; Melián-González et al., 2021). When tourists perceive chatbots as useful tools that simplify planning or improve access to services, they are more inclined to adopt them. Accordingly, we hypothesize: H1: Performance expectancy (PE) positively influences Greek tourists’ intention to use chatbots in the tourism sector.

In addition, effort expectancy, or the perceived ease of use of a system, is another key determinant of adoption (Venkatesh et al., 2012). While some studies suggest that user-friendly interfaces enhance adoption intentions (Verkijika, 2020), others indicate that complexity or lack of familiarity can hinder usage in tourism (Mishra et al., 2023). Based on this evidence, we propose: H2: Effort expectancy (EE) positively influences Greek tourists’ intention to use chatbots in the tourism sector. Social influence has also been shown to shape tourists’ technology use, especially in collectivist or highly connected cultural contexts (Chong et al., 2012; Tran & Vu, 2024). Peer recommendations, online reviews, and endorsements from family and friends can significantly impact whether travelers experiment with chatbots. Thus, we hypothesize: H3: Social influence (SI) positively influences Greek tourists’ intention to adopt chatbots in the tourism sector.

Moreover, the UTAUT2 model highlights hedonic motivation (the enjoyment or pleasure derived from technology) as a critical driver of consumer adoption. In tourism, enjoyment is particularly relevant since leisure activities are closely tied to experiential value (Chi et al., 2022; Van der Heijden, 2004). Therefore, we hypothesize: H4: Hedonic motivation (HM) positively influences Greek tourists’ intention to use chatbots in the tourism sector. Another important factor is habit. While prior studies emphasize that habitual use of digital tools can reinforce continued adoption (Limayem et al., 2007), in the tourism context, entrenched reliance on traditional practices may discourage consumers from experimenting with new tools such as chatbots (Huang & Rust, 2018). Thus, we test the following: H5: Habit (HB) negatively influences Greek tourists’ intention to use chatbots in the tourism sector.

Beyond the core UTAUT2 constructs, scholars highlight the importance of perceived innovativeness. Tourists are often motivated by novelty, and technologies perceived as cutting-edge are more likely to be adopted (Hernández et al., 2017; Lu et al., 2023). There-fore, we hypothesize: H6: Perceived innovativeness (PI) positively influences Greek tourists’ intention to use chatbots in the tourism sector. Relatedly, prior research shows that attitudes toward self-service technologies (SSTs) significantly shape consumer acceptance in tourism and hospitality (Dabholkar & Bagozzi, 2002). Tourists with favorable attitudes toward digital service platforms are more inclined to extend this acceptance to chatbots. Hence, we propose: H7: Attitude toward self-service technologies (SSTs) positively influences Greek tourists’ intention to adopt chatbots in the tourism sector.

Finally, negative experiences with digital tools, such as inconvenience or difficulties in expressing queries, can deter adoption (Robertson et al., 2016; Hill et al., 2015). Since usability remains a major concern in chatbot applications (Tussyadiah, 2020), we hypothesize: H8: Inconvenience (INC) negatively influences Greek tourists’ intention to use chatbots in the tourism sector.”

Very good points. We added this paragraph to the last section:

“An additional consideration concerns the relevance of the Greek context. Greece is one of the most tourism-dependent economies in Europe, with tourism contributing over one-fifth of GDP and employment (World Travel & Tourism Council [WTTC], 2023). This structural reliance creates heightened sensitivity to innovations that affect tourist satisfaction and service efficiency. Greek tourists, in particular, represent a compelling case because they combine high travel frequency within national destinations with strong cultural preferences for interpersonal, human-centered service encounters (Trihas & Konstantarou, 2018). This creates a unique tension: while the tourism ecosystem requires efficiency gains through digital tools such as chatbots, Greek tourists may display greater resistance to substituting human interaction with automation compared to international visitors. Moreover, the demographic profile of Greek tourists, characterized by high levels of education, frequent travel, and diverse age representation, offers a distinctive testing ground for technology acceptance theories (Gretzel, Sigala, Xiang, & Koo, 2015). Investigating chatbot adoption in this group therefore provides insights not only into national consumer behavior but also into how deeply rooted cultural values and routines interact with global technological trends. By situating our findings in this context, the study underscores why Greek tourists constitute a uniquely relevant group for scholarly and practical investigation of chatbot adoption.

Done, once again thank you very much for your constructive comments.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

I accept the corrections. The authors have considered the notes and recommendations I made.

Author Response

Thank you for your support.

Author Response File: Author Response.pdf

Round 3

Reviewer 2 Report

Comments and Suggestions for Authors

The revision provided by the authors has made the manuscript more consistent, precise, and overall more solid.

I appreciate and value the enhancement of the introduction. However, I am concerned that the first part of my earlier comment regarding the insufficient justification of the chatbot literature was not fully addressed. If the authors chose not to include references directly related to chatbot usage in tourism due to the scarcity of such literature, this could in fact be framed as a central part of the research gap. Now that the authors have clarified the distinctiveness of tourists as consumers, it would be both logical and beneficial to explicitly highlight this gap in the literature. Doing so would enhance the clarity and relevance of the study’s contribution.

The addition of a dedicated hypothesis formulation section also strengthens the logical structure of the manuscript.

The conclusions have been improved by a clearer explanation of the Greek context, which helps clarify its relevance to the study.

Finally, I appreciate the correction and completion of the reference list, which brings the manuscript closer to academic publishing standards.

Author Response

Thank you very much for your insightful comment regarding the need to strengthen the justification of the literature review on chatbot usage in tourism. We fully agree with your observation and have revised the manuscript accordingly.

Specifically, we have explicitly emphasized the scarcity of research directly addressing chatbot adoption in tourism. While prior work has examined AI and automation broadly within tourism and hospitality (e.g., Gretzel et al., 2015; Tussyadiah, 2020), only a few studies directly focus on tourists’ perceptions and adoption of chatbots (e.g., Melián-González, Gutiérrez-Taño, & Bulchand-Gidumal, 2021; Scarpi, Pizzi, & Marzocchi, 2024). We now clearly state that this scarcity constitutes a central part of the research gap that our study addresses.

Furthermore, we highlight that tourists are distinct from other digital consumers due to the multi-stage nature of tourism experiences, heightened uncertainty and risk, and the interplay of hedonic and utilitarian needs (Chi, Denton, & Gursoy, 2020; Chi, Gursoy, & Chi, 2022; Tussyadiah, Zach, & Wang, 2020). This distinctiveness further underscores the importance of focusing on tourists’ behavioral intentions regarding chatbots.

We have added new bridging text in the Introduction and reinforced this point in the Discussion/Conclusion, ensuring that the research gap is now explicitly stated at both the beginning and end of the paper.

Article Menu

How Can Chatbots Help Companies to Improve the Customer Experience Offered to Their End Users/Customers in the Tourism Industry?

Further Information

Guidelines

MDPI Initiatives

Follow MDPI