Technology Acceptance Model in the Hospitality Industry: Applications, Limitations, Criticisms, and Directions for Theoretical Extension

Abstract

The accelerated adoption of emerging technologies in hospitality and tourism has intensified the need for strong explanatory frameworks to understand user acceptance and effective implementation. The Technology Acceptance Model (TAM) is one of the most frequently applied models for interpreting technology adoption behavior in hospitality contexts. However, their continued dominance has generated debate about their scope, assumptions, and practical utility. This study reviewed the literature to examine how TAM has been used in the hospitality industry and to synthesize its documented limitations and criticisms. In addition, it sought to identify recommended theoretical enhancements to improve TAM’s explanatory and predictive power for contemporary and emerging technologies in hospitality. This study employed a literature-based design, using secondary sources from peer-reviewed journals, published articles, and reports. Relevant materials were retrieved from databases and scholarly platforms, including Emerald Insight, ScienceDirect, ResearchGate, Academia, and SAGE. The data were analyzed to identify the dominant application areas, patterns of use within hospitality settings, and recurring critiques of the model. This review indicates that TAM, initially developed by Davis (1989), has been widely applied as a psychological model to explain the acceptance of information technologies across diverse domains, including e-learning, social media, web commerce, entertainment, healthcare, mobile commerce, chatbot adoption, and the adult use of new technologies. Within hospitality, the TAM has been used to investigate the acceptance of customer feedback systems in upscale hotels, biometric systems, customer perceptions of chatbots, hotel tablet applications, and intranet use in restaurant operations. Despite its extensive use, the literature highlights persistent limitations, including a limited capacity to capture subjective, context-dependent behaviour, limited applicability to small- and medium-sized enterprises in some settings, and a tendency toward weak practical guidance for implementation. Critiques further emphasize that TAM often underrepresents external determinants, such as demographic characteristics, social influence, organizational context, and human factors, and may overlook latent psychological or personality-related influences by emphasizing PU and ease of use as the primary predictors. Overall, the TAM remains a foundational framework for studying technology acceptance in hospitality; however, its explanatory limitations restrict its relevance in complex adoption environments and rapidly evolving technologies. The literature consistently recommends extending the TAM by integrating external and contextual variables to improve its predictive validity and better align it with real-world conditions. Future research on hospitality technology should employ extended TAM formulations that incorporate organizational, social, and individual-level determinants (e.g., demographics, social influence, organizational environment, and human factors). Such extensions are essential when examining emerging technologies, such as artificial intelligence, augmented/virtual reality, cloud computing, blockchain, IoT applications, and chatbots. These findings support a shift from reliance on core TAM constructs to context-sensitive models that explain heterogeneous adoption patterns in hospitality organizations. In practice, TAM-informed evaluation can guide human resource strategies, especially staff training and change management, and support implementation monitoring through adoption metrics, customer satisfaction indicators, and operational efficiency outcomes.