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

被引:60
|
作者
Rahim, Noor Irliana Mohd [1 ]
Iahad, Noorminshah A. [2 ]
Yusof, Ahmad Fadhil [1 ]
Al-Sharafi, Mohammed A. [2 ,3 ]
机构
[1] Univ Teknol Malaysia, Fac Comp, Johor Baharu 81310, Malaysia
[2] Univ Teknol Malaysia, Azman Hashim Int Business Sch, Dept Informat Syst, Johor Baharu 81310, Malaysia
[3] Sunway Univ, Dept Business Analyt, Bandar Sunway 47500, Malaysia
关键词
artificial intelligence; chatbot; higher-education institution; customer service; virtual assistance; CUSTOMER SATISFACTION; ACCEPTANCE; SERVICES; QUALITY; UTAUT2;
D O I
10.3390/su141912726
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:22
相关论文
共 10 条
  • [1] Adoption model for a hybrid SEM-neural network approach to education as a service
    Noawanit Songkram
    Suparoek Chootongchai
    [J]. Education and Information Technologies, 2022, 27 : 5857 - 5887
  • [2] Adoption model for a hybrid SEM-neural network approach to education as a service
    Songkram, Noawanit
    Chootongchai, Suparoek
    [J]. EDUCATION AND INFORMATION TECHNOLOGIES, 2022, 27 (05) : 5857 - 5887
  • [3] Does SMS advertising still have relevance to increase consumer purchase intention? A hybrid PLS-SEM-neural network modelling approach
    Sharma, Anshuman
    Dwivedi, Yogesh K.
    Arya, Vikas
    Siddiqui, Muhammad Qutubuddin
    [J]. COMPUTERS IN HUMAN BEHAVIOR, 2021, 124
  • [4] Predicting the antecedents of consumers' intention toward purchase of mutual funds: A hybrid PLS-SEM-neural network approach
    Mishra, Anand Kumar
    Bansal, Rohit
    Maurya, Prince Kumar
    Kar, Sanjay Kumar
    Bakshi, Palvinder Kaur
    [J]. INTERNATIONAL JOURNAL OF CONSUMER STUDIES, 2023, 47 (02) : 563 - 587
  • [5] Corporate Social Responsibility in Higher Education: A PLS-SEM Neural Network Approach
    Binsawad, Muhammad Hatim
    [J]. IEEE ACCESS, 2020, 8 : 29125 - 29131
  • [6] Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach
    Al-Sharafi, Mohammed A.
    Al-Emran, Mostafa
    Iranmanesh, Mohammad
    Al-Qaysi, Noor
    Iahad, Noorminshah A.
    Arpaci, Ibrahim
    [J]. INTERACTIVE LEARNING ENVIRONMENTS, 2023, 31 (10) : 7491 - 7510
  • [7] A new AI-based approach for automatic identification of tea leaf disease using deep neural network based on hybrid pooling
    Heng, Qidong
    Yu, Sibo
    Zhang, Yandong
    [J]. HELIYON, 2024, 10 (05)
  • [8] A game theory-based analysis of merchants' mobile payment adoption using hybrid SEM-neural network approach
    Naysary, Babak
    [J]. COMPETITIVENESS REVIEW, 2024, 34 (04) : 786 - 806
  • [9] Acceptance and Use of Cloud-Based Virtual Platforms by Higher Education Vocational School Students: Application of the UTAUT Model with a PLS-SEM Approach
    Sayginer, Can
    [J]. INNOEDUCA-INTERNATIONAL JOURNAL OF TECHNOLOGY AND EDUCATIONAL INNOVATION, 2023, 9 (02): : 24 - 38
  • [10] Economics students' behavioural intention and usage of ChatGPT in higher education: a hybrid structural equation modelling-artificial neural network approach
    Salifu, Iddrisu
    Arthur, Francis
    Arkorful, Valentina
    Nortey, Sharon Abam
    Osei-Yaw, Richard Solomon
    [J]. COGENT SOCIAL SCIENCES, 2024, 10 (01):