Synthetic WOM? The Emergence of Generative Artificial Intelligence-Induced Recommendations DOI
Dušan Mladenović, Moein Beheshti, Tomaž Kolar

и другие.

Journal of Computer Information Systems, Год журнала: 2024, Номер unknown, С. 1 - 18

Опубликована: Окт. 25, 2024

This paper examines how Generative Artificial Intelligence (GAI) influences word-of-mouth (WOM) in travel and hospitality, focusing on synthetic WOM (syWOM). It explores GAI-driven reshapes traveler interactions decision-making an experience-centric industry. Using a literature review conceptual analysis approach1, this study the integration of GAI tools, such as ChatGPT, to enhance experiences. The presented highlights GAI's potential inducing syWOM its effects perceptions behaviors. Additionally, it addresses emerging role WOM, emphasizing need for further research impact planning engagement. presents fresh view interaction with travel, aiming inform future practical applications personalized

Язык: Английский

Risks and Benefits of Future Travel with Hyperloop: A Multi-Analytical Approach DOI
Sung‐Eun Kang

Journal of Travel Research, Год журнала: 2025, Номер unknown

Опубликована: Янв. 23, 2025

Despite significant government investment in Hyperloop transportation across various countries, research on its potential advantages and drawbacks from the tourist’s perspective remains scarce. Guided by prospect theory, this study examines perceptions behaviors of frequent ( n = 386) occasional 214) travelers regarding technology, using multiple analytical methods such as partial least squares structural equation modeling (PLS-SEM), multi-group analysis (MGA), fuzzy-set Qualitative Comparative Analysis (fsQCA), artificial neural networks (ANN). The explores how four perceived benefits (economic, environmental, socio-cultural, time-saving) risks (functional, physical, psychological, financial concerns) influence travelers’ intentions to use for tourism. PLS-SEM MGA analyses indicate that prioritize time-saving, while economic, socio-cultural benefits, functional decisions. Additionally, fsQCA findings identify distinct benefit-risk combinations affecting travel decisions, deep learning models highlight critical factors predicting adoption. This provides theoretical insights practical strategies integrating into tourism planning management.

Язык: Английский

Процитировано

1

Making tourism smart in the age of artificial intelligence DOI Creative Commons
C. Michael Hall, Chris Cooper

Current Issues in Tourism, Год журнала: 2025, Номер unknown, С. 1 - 5

Опубликована: Фев. 5, 2025

Язык: Английский

Процитировано

1

Digital happiness in tourism: towards a conceptual framework DOI
Hyo Dan Cho, Chulmo Koo, Namho Chung

и другие.

Current Issues in Tourism, Год журнала: 2025, Номер unknown, С. 1 - 10

Опубликована: Янв. 17, 2025

This research note scrutinises the ambivalent dimensions of digital technology within realm tourism, highlighting its complex interaction with physical engagement. By exploring existing literature on evolving landscape tourism experiences through an interdisciplinary approach, this paper recognises that current analysis use in often remains biased and narrow. Furthermore, despite ongoing debates surrounding well-being, discussions have been largely restricted to utility, emotional use, moderation resources. Drawing philosophical insights from Chinese classic I-Ching, which offers a holistic understanding happiness contemporary era domain, provides comprehensive theoretical blueprint for tourism. robust framework aims elucidate harmonious integration elements into landscape.

Язык: Английский

Процитировано

0

Embracing sustainable diversity climates in hospitality and tourism: insights from prospective employees on human–robot collaboration DOI
Myung Ja Kim, Stuart J. Barnes, Sung‐Eun Kang

и другие.

Journal of Sustainable Tourism, Год журнала: 2025, Номер unknown, С. 1 - 21

Опубликована: Фев. 11, 2025

As service robots increasingly collaborate with human employees in hospitality and tourism, understanding their complex interactions is crucial. This study explores prospective employees' perceptions of sustainable diversity climates involving human–robot collaboration using the Modified Model Goal-Directed Behavior (MMGB). A research model integrating attitude, subjective norms, expectancy, perceived behavioral control, desire was tested through an online survey 492 potential US employees. Generalized Linear Modeling fuzzy-set Qualitative Comparative Analysis were applied to find solutions for better climates. Results show that these factors influence sustainable-behavioral intentions,differing according groups development goals (SDGs). Three SDG (efficient allocation, fair distribution, scale) found have distinctiveness similarities. Our findings suggest communicating MMGB's initiatives, fostering innovation, tailoring recruitment strategies sustainability-oriented segments are key cultivating inclusive, future-oriented workplaces.

Язык: Английский

Процитировано

0

ChatGPT and Tourist Decision‐Making: An Accessibility–Diagnosticity Theory Perspective DOI Creative Commons
Dimitrios Stergiou, Athina Nella

International Journal of Tourism Research, Год журнала: 2024, Номер 26(5)

Опубликована: Сен. 1, 2024

ABSTRACT This paper investigates the role of ChatGPT in informing tourist decision‐making across different destination contexts, focusing particularly on accessibility and diagnosticity its recommendations. Specifically, we inform our analysis with tenets Accessibility–Diagnosticity Theory (ADT), to draw insights into ChatGPT's capabilities produce contextually relevant personalised travel content. Our findings reveal a sophisticated multi‐dimensional advisory approach characterised by three themes: ‘Tailored Engagement Accessibility’, ‘Diagnosticity Information’ ‘Contextual Variation Criteria Prioritisation.’ These themes their intersections highlight potential improve offering comprehensive user‐centric guidance. Based these findings, develop model dynamics tourism decision‐making, which illustrates how integrates insights, diagnostic relevance contextual adaptation, blueprint for leveraging artificial intelligence enhancing experience. We conclude discussion theoretical practical contributions study.

Язык: Английский

Процитировано

3

Good deeds deserve good outcomes: Leveraging generative artificial intelligence to reduce tourists' avoidance of ethical brands embracing stigmatized groups DOI
Linxiang Lv, Yongheng Liang, Siyun Chen

и другие.

Annals of Tourism Research, Год журнала: 2024, Номер 110, С. 103889 - 103889

Опубликована: Дек. 11, 2024

Язык: Английский

Процитировано

1

Synthetic WOM? The Emergence of Generative Artificial Intelligence-Induced Recommendations DOI
Dušan Mladenović, Moein Beheshti, Tomaž Kolar

и другие.

Journal of Computer Information Systems, Год журнала: 2024, Номер unknown, С. 1 - 18

Опубликована: Окт. 25, 2024

This paper examines how Generative Artificial Intelligence (GAI) influences word-of-mouth (WOM) in travel and hospitality, focusing on synthetic WOM (syWOM). It explores GAI-driven reshapes traveler interactions decision-making an experience-centric industry. Using a literature review conceptual analysis approach1, this study the integration of GAI tools, such as ChatGPT, to enhance experiences. The presented highlights GAI's potential inducing syWOM its effects perceptions behaviors. Additionally, it addresses emerging role WOM, emphasizing need for further research impact planning engagement. presents fresh view interaction with travel, aiming inform future practical applications personalized

Язык: Английский

Процитировано

0