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AI-Based Chatbots Adoption Model for Higher-Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach

Author

Listed:
  • Noor Irliana Mohd Rahim

    (Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Noorminshah A. Iahad

    (Department of Information Systems, Azman Hashim International Business School, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Ahmad Fadhil Yusof

    (Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Mohammed A. Al-Sharafi

    (Department of Information Systems, Azman Hashim International Business School, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
    Department of Business Analytics, Sunway University, Bandar Sunway 47500, Malaysia)

Abstract

Chatbot implementation for assisting customers as a virtual agent can be seen as a tool in helping an organisation to serve better customer service. Malaysia is among the countries forging ahead with the Fourth Industrial Revolution. One of the core technologies mentioned is adopting artificial intelligence tools such as chatbots. In the last few years, there has been a growing interest in AI-based chatbot adoption in the non-HEI context. However, most higher-education institutions (HEIs) are reported not ready to adopt AI-based chatbots as one of the solutions for virtual student services support. The research of chatbot adoption in the HEI context is still new and is a less explored and examined topic in the information systems domain. Moreover, most of the existing research regarding chatbot adoption in the HEI context focuses more on the benefit of chatbot usage and is not specialised in a student services solution perspective. Furthermore, most of the studies were not guided by the information systems (IS) theories. Therefore, this study aims to identify factors that influence the effectiveness of chatbot adoption in the HEI context by adapting the UTAUT2 model as the IS theory reference. A survey method was applied using the purposive sampling technique. For 3 months, data were collected online from 302 users of Malaysia’s HEI postgraduate students from various public and private universities. A two-stage analytical procedure (SEM-ANN) was used to validate the research model and assess the presented research hypotheses. This research reveals that perceived trust is influenced by interactivity, design, and ethics. Meanwhile, behavioural intention is influenced by perceived trust, performance expectancy, and habit towards the use of chatbot applications in the HEI context. Lastly, the findings of this study can be helpful to the HEI student services unit and can be a guide towards productivity and marketing strategy in serving the students better.

Suggested Citation

  • Noor Irliana Mohd Rahim & Noorminshah A. Iahad & Ahmad Fadhil Yusof & Mohammed A. Al-Sharafi, 2022. "AI-Based Chatbots Adoption Model for Higher-Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach," Sustainability, MDPI, vol. 14(19), pages 1-22, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12726-:d:935051
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    References listed on IDEAS

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    2. Şahin Gökçearslan & Elif Esiyok & Kemal Gurkan Kucukergin, 2025. "Understanding the intention to use artificial intelligence chatbots in education: The role of individual innovativeness and AI trust among university students," Journal of Computational Social Science, Springer, vol. 8(3), pages 1-23, August.
    3. Hana Yousuf & Mostafa Al-Emran & Mohammed A. Al-Sharafi & Khaled Shaalan, 2025. "Examining the factors influencing cybersecurity behaviors in m-payment contactless technologies using SEM–ANN approach," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 30(3), pages 1-22, September.
    4. Meennapa Rukhiran & Napasorn Phaokla & Paniti Netinant, 2022. "Adoption of Environmental Information Chatbot Services Based on the Internet of Educational Things in Smart Schools: Structural Equation Modeling Approach," Sustainability, MDPI, vol. 14(23), pages 1-32, November.
    5. Zericho R. Marak & Subhajit Pahari & Rishabh Shekhar & Ashish Tiwari, 2025. "Factors affecting chatbots in banking services: the UTAUT2 and innovation resistance theory perspective," Journal of Innovation and Entrepreneurship, Springer, vol. 14(1), pages 1-27, December.
    6. Wenjia Li & Huangyi Ding & Jingjing Gui & Qinghe Tang, 2024. "Patient acceptance of medical service robots in the medical intelligence era: an empirical study based on an extended AI device use acceptance model," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.

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