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http://hdl.handle.net/10397/113679
Title: | Exploring the drivers of hospitality and tourism customer loyalty for generative artificial intelligence (AI) : a multi-analytic approach | Authors: | Fakfare, P Manosuthi, N Lee, JS Han, H Jin, M |
Issue Date: | 2025 | Source: | Current issues in tourism, Published online: 09 May 2025, Latest Articles, https://doi.org/10.1080/13683500.2025.2500726 | Abstract: | Generative artificial intelligence (AI) is transforming the hospitality and tourism industry by offering innovative solutions to enhance customer experiences when planning holidays. The drivers of customer loyalty towards generative AI have not been thoroughly examined despite its potential. This study applies a multi-analytic approach, which incorporates both sufficiency and necessity logics in order to investigate the relationships among the risk variables, such as the cognitive and emotional responses in regards to shaping loyalty. We examine the impact of performance, financial, time, psychological, and privacy risks using the theory of planned behaviour and emotional variables by utilising the cognitive-emotional-behavioural framework. We also explore the moderating role of customer innovativeness and employ a fuzzy-set qualitative comparative analysis (fsQCA) in order to uncover causal recipes for loyalty. The findings reveal that cognitive and emotional variables, such as perceived behavioural control and emotional well-being are important and necessary conditions for customer loyalty to manifest, and customer innovativeness has an interaction effect on these relationships. This study offers valuable insights for academia and practitioners in regards to leveraging generative AI technologies in order to enhance the customer experience in the hospitality and tourism sector. | Keywords: | Curiosity Customer innovativeness Customer loyalty for generative artificial intelligence Emotion Multi-analytic approach Risk variables |
Publisher: | Routledge | Journal: | Current issues in tourism | ISSN: | 1368-3500 | EISSN: | 1747-7603 | DOI: | 10.1080/13683500.2025.2500726 |
Appears in Collections: | Journal/Magazine Article |
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