Acceptance and success model for AI use in higher education: Development, instrument decomposition, and its triangulation testing

Subiyakto, A'ang, Huda, Muhammad Q., Hakiem, Nashrul, Suseno, Hendra B, Arifin, Viva, Azmi, Agus N., Sani, Asrul, Yuniarto, Dwi, Hartawan, Muhammad S., Suryatno, Agung, Muji, Muji, Kurniawan, Fachrul ORCID: https://orcid.org/0000-0002-3709-8764, Kusumawati, Ririen ORCID: https://orcid.org/0000-0001-6090-7219, Balogun, Naeem A. and Ahlan, Abd. Rahman (2025) Acceptance and success model for AI use in higher education: Development, instrument decomposition, and its triangulation testing. Journal of Applied Data Sciences (JADS), 6 (4). pp. 3046-3059. ISSN 2723-6471

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Abstract

Prior social computing studies described that the performance of technology products is about how the product use benefits the users, including Artificial Intelligence (AI). To have an impact, ensuring how AI is used is a prerequisite after the development. Furthermore, its use is also influenced by how users accept AI. This study aimed to develop an acceptance and success model of AI use in the higher education world from the user perspective, to decompose the model into its instrument level, and to test the validity and reliability of the research instrument. The researchers developed the model by adopting and combining the Technology Acceptance Model (TAM) and the Information System Success Model (ISSM) and adapting the proposed model in the context of AI use in higher education learning. The measurement items were derived from definitions of the variables and indicators of the model. The instrument was tested sequentially using triangulation methods. The quantitative testing was online survey with about 51 respondents and the qualitative one was interview involving five experts. This study may contribute methodologically as one of the guidance for novice scholars in similar works. It may relate to the clarity of the research procedure and the implementation of the mixed testing methods. Of course, the assumptions, samples, and data used in the study cannot be generalized for the other studies. Referring to the model development, the proposed model may not cover the other factors related to the ethical, cultural, and organizational barriers for adopting AI. These barriers may also affect its acceptance and success. Thus, the adoption of the factors related the barriers may also be interesting to study further.

Item Type:	Journal Article
Keywords:	AI; Acceptance And Success Model; Model Development; Instrument Decomposition; Instrument Testing; Triangulation Method
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing 08 INFORMATION AND COMPUTING SCIENCES > 0806 Information Systems 09 ENGINEERING > 0915 Interdisciplinary Engineering
Divisions:	Graduate Schools > Magister Programme > Graduate School of Informatics Engineering
Depositing User:	Ririen Kusumawati
Date Deposited:	08 Dec 2025 14:48

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