Is it the end of the technology acceptance model in the era of generative artificial intelligence?
Open accessAbstract
Purpose: The technology acceptance model (TAM) is a widely used framework explaining why users accept new technologies. Still, its relevance is questioned because of evolving consumer behavior, demographics and technology. Contrary to a research paper or systematic literature review, the purpose of this critical reflection paper is to discuss TAM's relevance and limitations in hospitality and tourism research. Design/methodology/approach: This paper uses a critical reflective approach, enabling a comprehensive review and synthesis of recent academic literature on TAM. The critical evaluation encompasses its historical trajectory, evolutionary growth, identified limitations and, more specifically, its relevance in the context of hospitality and tourism research. Findings: TAM's limitations within the hospitality and tourism context revolve around its individual-centric perspective, limited scope, static nature, cultural applicability and reliance on self-reported measures. Research limitations/implications: To optimize TAM's efficacy, the authors propose several strategic recommendations. These include embedding TAM within the specific context of the industry, delving into TAM-driven artificial intelligence adoption, integrating industry-specific factors, acknowledging cultural nuances and using comprehensive research methods, such as mixed methods approach. It is imperative for researchers to critically assess TAM's suitability for their studies and be open to exploring alternative models or methods that can adeptly navigate the distinctive dynamics of the industry. Originality/value: This critical reflection paper prompts a profound exploration of technology adoption within the dynamic hospitality and tourism sector, makes insightful inquiries into TAM's future potential and presents recommendations.
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Recent discussions, like Mogaji et al.'s (2024) exploration of the Technology Acceptance Model in the generative AI era, indicate that established models may need revision to stay relevant.
“Recent discussions, like Mogaji et al.'s (2024) exploration of the Technology Acceptance Model-a derivative of TPB-in the generative AI era, indicate that established models may need revision to stay relevant.”
From Addressing student non-compliance in AI use declarations: implications for academic integrity and assessment in higher education (2025) · cites this paper as Mogaji et al. 2024
Cites
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Amankwah-Amoah, J., Abdalla, S., Mogaji, E., Elbanna, A., & Dwivedi, Y. K. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102759 · Cited by 305 · see citing works
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“…influences, and the impact of technology on individuals’ perceived ability to control and understand their behaviour. recent discussions, like Mogaji et al.’s (2024) exploration of the Technology Acceptance Model—a derivative of TPB—in the generative AI era, indicate that established models…”
Mahy, T., & Li, H. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2026.2623216 · Cited by 2 · see citing works
- Authors
- Mogaji, E., Viglia, G., Srivastava, P., & Dwivedi, Y. K.
- Affiliations
- Keele University (GB)
- University of Portsmouth (GB)
- Jaipuria Institute of Management (IN)
- Symbiosis International University (IN)
- Corresponding author
- Dwivedi, Yogesh K.
- Venue
- International Journal of Contemporary Hospitality Management
- Publisher
- Emerald
- Published
- 2024-01-18
- Volume
- 36
- Issue
- 10
- Pages
- 3324-3339
- ISSN
- 0959-6119 (ISSN-L)
- OpenAlex
- W4390953986
- Cited by
- 187
- References
- 7
- Citations by year
- 2026: 93 · 2025: 63 · 2024: 31
- FWCI
- 69.78
- Type
- journal article
- Language
- en
- Open access
- green
- cronfa.swan.ac.uk
- Licence
- Locations
- International Journal of Contemporary Hospitality Management (publishedVersion)
- Cronfa (Swansea University) (submittedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Keywords
- technology acceptance model, generative AI, technology adoption, hospitality industry, tourism industry, self-report measures, mixed methods
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