FORECASTING TECHNOLOGY ACCEPTANCE IN TOURISM AND HOSPITALITY: LESSONS FROM AKMOLA REGION IN KAZAKHSTAN
Dyussekeyeva Y. Kadyrbekova D. Issakov Y. Ageleuova A. Zhaksybekova D. Gajić T.
2025Editura Universitatii din Oradea
Geojournal of Tourism and Geosites
2025#61Issue 31519 - 1530 pp.
This study investigates the factors that shape the adoption and implementation of digital technologies in the tourism and hospitality sector of the Akmola region in Kazakhstan, employing an extended version of the Technology Acceptance Model (TAM). Focusing on a developing market context, the research analyses how users perceive the usefulness and ease of use of technology, how their attitudes and behavioural intentions influence their decisions, and how social support contributes to the overall process of technology acceptance. The study addresses the growing need for digital transformation in tourism, especially in regions with significant natural and cultural resources but underdeveloped infrastructure. The research included both tourists and local residents, allowing for a comparative analysis of user perceptions across different groups. Data were collected using a structured questionnaire and analysed through advanced statistical techniques, including structural equation modeling and multi-group analysis. To enhance the robustness of the findings, a machine learning model based on a neural network architecture was applied to predict users’ intention to adopt digital technologies. Findings indicate that users are more likely to adopt tourism technologies when they perceive them as beneficial and easy to use. Tourists tend to prioritise the functional benefits of technology, whereas local residents place greater value on its simplicity and usability. While attitudes and behavioural intentions consistently influence technology adoption, social support plays a secondary yet meaningful role. The neural network model confirmed the reliability of the theoretical framework, offering a high degree of predictive accuracy. This study contributes to the theoretical refinement of TAM in tourism research and provides practical guidelines for designing user-oriented digital strategies. The results are especially relevant for policymakers and tourism managers in developing regions who seek to balance innovation with local context and user readiness. The integration of machine learning with behavioural modeling further underscores the potential for data-driven decision-making in tourism development. These findings pave the way for future research into personalised digital solutions that enhance tourist experiences while supporting sustainable regional growth. Based on the comprehensive analysis, this study highlights the strategic importance of tailoring digital solutions to different user groups, particularly in regions with contrasting levels of digital readiness. It also demonstrates the potential of combining behavioural models with machine learning to enhance the accuracy of predictive insights. Future research should explore longitudinal effects and integrate qualitative approaches to better understand the evolving dynamics of technology adoption in tourism.
digital transformation , hospitality , Kazakhstan , technology adoption , tourism , users perception
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Kazakh Academy of Sports and Tourism, Tourism Faculty, Tourism and Services Department, Almaty, Kazakhstan
Abai Kazakh National Pedagogical University, Faculty of Natural Sciences and Geography, Department of Geography and Ecology, Almaty, Kazakhstan
Geographical Institute “Jovan Cvijić”, Serbian Academy of Sciences and Arts, Belgrade, Serbia
Faculty of Organizational Studies Eduka, University Business Academy in Novi Sad, Belgrade, Serbia
Kazakh Academy of Sports and Tourism
Abai Kazakh National Pedagogical University
Geographical Institute “Jovan Cvijić”
Faculty of Organizational Studies Eduka
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