Intention to Adopt AI-Powered Online Service Among Tourism and Hospitality Companies

Yi-Hui Ho, Syed Shah Alam, Mohammad Masukujjaman, Chieh-Yu Lin, Samiha Susmit, Sumaiya Susmit

Source Title: International Journal of Technology and Human Interaction (IJTHI)18(1)

ISSN: 1548-3908|EISSN: 1548-3916|EISBN13: 9781799893646|DOI: 10.4018/IJTHI.299357

MLA

Ho, Yi-Hui, et al. "Intention to Adopt AI-Powered Online Service Among Tourism and Hospitality Companies." IJTHI vol.18, no.1 2022: pp.1-19. http://doi.org/10.4018/IJTHI.299357

APA

Ho, Y., Alam, S. S., Masukujjaman, M., Lin, C., Susmit, S., & Susmit, S. (2022). Intention to Adopt AI-Powered Online Service Among Tourism and Hospitality Companies. International Journal of Technology and Human Interaction (IJTHI), 18(1), 1-19. http://doi.org/10.4018/IJTHI.299357

Chicago

Ho, Yi-Hui, et al. "Intention to Adopt AI-Powered Online Service Among Tourism and Hospitality Companies," International Journal of Technology and Human Interaction (IJTHI) 18, no.1: 1-19. http://doi.org/10.4018/IJTHI.299357

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Abstract

This study examines the factors affecting the adoption of artificial intelligence (AI) powered online service in the tourism and hospitality sector through an extension of the technology acceptance model (TAM) by including self-efficacy, subjective norms, technological knowledge, and perceived cost in the analysis. Data was collected from 336 respondents in Malaysia’s tourism and hospitality industry using the questionnaire survey. The empirical results confirmed that perceived usefulness, ease of use, attitude, cost, and technology knowledge significantly affected behavioral intention. Self-efficacy, perceived ease of use, and perceived usefulness affected the attitude towards AI. Attitude mediated the relationship between perceived ease of use and behavioral intention as well as the relationship between perceived usefulness and behavioral intention. This study contributes to enhancing AI's understanding of the tourism and hospitality industry context. This study also improves TAM by proposing a comprehensive model with cognitive external and technology-specific factors.