IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12726-d935051.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12726/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12726/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jiwang Yin & Xiaodong Qiu, 2021. "AI Technology and Online Purchase Intention: Structural Equation Model Based on Perceived Value," Sustainability, MDPI, vol. 13(10), pages 1-16, May.
    2. Adam, Martin & Wessel, Michael & Benlian, Alexander, 2020. "AI-based chatbots in customer service and their effects on user compliance," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 119304, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Liébana-Cabanillas, Francisco & Marinkovic, Veljko & Ramos de Luna, Iviane & Kalinic, Zoran, 2018. "Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 117-130.
    4. Yogesh K. Dwivedi & Nripendra P. Rana & Anand Jeyaraj & Marc Clement & Michael D. Williams, 2019. "Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model," Information Systems Frontiers, Springer, vol. 21(3), pages 719-734, June.
    5. Arpaci, Ibrahim & Karatas, Kasim & Kusci, Ismail & Al-Emran, Mostafa, 2022. "Understanding the social sustainability of the Metaverse by integrating UTAUT2 and big five personality traits: A hybrid SEM-ANN approach," Technology in Society, Elsevier, vol. 71(C).
    6. Dung Minh Nguyen & Yen-Ting Helena Chiu & Huy Duc Le, 2021. "Determinants of Continuance Intention towards Banks’ Chatbot Services in Vietnam: A Necessity for Sustainable Development," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    7. Mohammed A. Hajar & Ammar Ahmed Alkahtani & Daing Nasir Ibrahim & Mohammed A. Al-Sharafi & Gamal Alkawsi & Noorminshah A. Iahad & Mohd Ridzuan Darun & Sieh Kiong Tiong, 2022. "The Effect of Value Innovation in the Superior Performance and Sustainable Growth of Telecommunications Sector: Mediation Effect of Customer Satisfaction and Loyalty," Sustainability, MDPI, vol. 14(10), pages 1-20, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Al-Momani, Ala'a M. & Ramayah, T. & Al-Sharafi, Mohammed A., 2024. "Exploring the impact of cybersecurity on using electronic health records and their performance among healthcare professionals: A multi-analytical SEM-ANN approach," Technology in Society, Elsevier, vol. 77(C).
    2. 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," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Suhail, Faisal & Adel, Mouhand & Al-Emran, Mostafa & AlQudah, Adi Ahmad, 2024. "Are students ready for robots in higher education? Examining the adoption of robots by integrating UTAUT2 and TTF using a hybrid SEM-ANN approach," Technology in Society, Elsevier, vol. 77(C).
    2. Tang, Jia-Wei & Tsai, Pei-Hsuan, 2024. "Exploring critical determinants influencing businesses’ continuous usage of mobile payment in post-pandemic era: Based on the UTAUT2 perspective," Technology in Society, Elsevier, vol. 77(C).
    3. Alcántara-Pilar, Juan Miguel & Rodriguez-López, María Eugenia & Kalinić, Zoran & Liébana-Cabanillas, Francisco, 2024. "From likes to loyalty: Exploring the impact of influencer credibility on purchase intentions in TikTok," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    4. Francisco Liébana-Cabanillas & Nidhi Singh & Zoran Kalinic & Elena Carvajal-Trujillo, 2021. "Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: a multi-analytical approach," Information Technology and Management, Springer, vol. 22(2), pages 133-161, June.
    5. Haibei Chen & Xianglian Zhao, 2023. "Use intention of green financial security intelligence service based on UTAUT," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 10709-10742, October.
    6. Iyer, Pooja & Bright, Laura F., 2024. "Navigating a paradigm shift: Technology and user acceptance of big data and artificial intelligence among advertising and marketing practitioners," Journal of Business Research, Elsevier, vol. 180(C).
    7. Syed Imran Zaman & Sharfuddin Ahmed Khan & Sahar Qabool & Himanshu Gupta, 2023. "How digitalization in banking improve service supply chain resilience of e-commerce sector? a technological adoption model approach," Operations Management Research, Springer, vol. 16(2), pages 904-930, June.
    8. Cristopher Siegfried Kopplin, 2021. "Two heads are better than one: matchmaking tools in coworking spaces," Review of Managerial Science, Springer, vol. 15(4), pages 1045-1069, May.
    9. Leong, Lai-Ying & Hew, Teck-Soon & Ooi, Keng-Boon & Chong, Alain Yee-Loong, 2020. "Predicting the antecedents of trust in social commerce – A hybrid structural equation modeling with neural network approach," Journal of Business Research, Elsevier, vol. 110(C), pages 24-40.
    10. Chia-Chien Hsu & Brian Sandford & Chia-Ju Ling & Ching-Torng Lin, 2021. "Can the Unified Theory of Acceptance and Use of Technology (UTAUT) Help Explain Subjective Well-Being in Senior Citizens due to Gateball Participation?," IJERPH, MDPI, vol. 18(17), pages 1-15, August.
    11. Park, Gain & Park, YounJung & Lee, Seyoung, 2024. "Compliance-gaining in metaverse: A moderated parallel mediation model testing the interaction between legitimization of paltry favors technique and victim identification," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    12. Byung-Jik Kim & Julak Lee, 2024. "The mental health implications of artificial intelligence adoption: the crucial role of self-efficacy," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    13. Hiran, Kamal Kant & Dadhich, Manish, 2024. "Predicting the core determinants of cloud-edge computing adoption (CECA) for sustainable development in the higher education institutions of Africa: A high order SEM-ANN analytical approach," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    14. Mohammad (Behdad) Jamshidi & Arash Dehghaniyan Serej & Alireza Jamshidi & Omid Moztarzadeh, 2023. "The Meta-Metaverse: Ideation and Future Directions," Future Internet, MDPI, vol. 15(8), pages 1-31, July.
    15. Simona Sternad Zabukovšek & Samo Bobek & Uroš Zabukovšek & Zoran Kalinić & Polona Tominc, 2022. "Enhancing PLS-SEM-Enabled Research with ANN and IPMA: Research Study of Enterprise Resource Planning (ERP) Systems’ Acceptance Based on the Technology Acceptance Model (TAM)," Mathematics, MDPI, vol. 10(9), pages 1-28, April.
    16. Olivia Hornung & Stefan Smolnik, 2022. "AI invading the workplace: negative emotions towards the organizational use of personal virtual assistants," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 123-138, March.
    17. Tu, Gengyang & Faure, Corinne & Schleich, Joachim & Guetlein, Marie-Charlotte, 2021. "The heat is off! The role of technology attributes and individual attitudes in the diffusion of Smart thermostats – findings from a multi-country survey," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    18. Shahzad, Muhammad Farrukh & Xu, Shuo & An, Xin & Javed, Iqra, 2024. "Assessing the impact of AI-chatbot service quality on user e-brand loyalty through chatbot user trust, experience and electronic word of mouth," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    19. Jinmin Kim & Jaeyoung Kim, 2021. "An Integrated Analysis of Value-Based Adoption Model and Information Systems Success Model for PropTech Service Platform," Sustainability, MDPI, vol. 13(23), pages 1-17, November.
    20. Caroline Kopot & Brenda J. Cude, 2021. "Channel Depth or Consistency? A Study on Establishing a Sustainable Omnichannel Strategy for Fashion Department Store Retailers," Sustainability, MDPI, vol. 13(13), pages 1-16, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12726-:d:935051. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.