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This study aims to examine the variables that influence learners’ acceptance of chat generative pre-trained transformer (ChatGPT) through the theoretical synthesis of variables in the field of behavioral science. It uses the use and gratifications theory in conjunction with variables related to the information system (IS), as proposed by the Delone and McLean IS success model.

This quantitative research collected data from 679 undergraduate students using stratified random sampling. A two-staged structural equation modeling (SEM)-neural network approach was used to analyze the data, with SEM used to study the factors influencing the intention to use ChatGPT. Additionally, an artificial neural network approach was used to confirm the results obtained through SEM.

The two-staged SEM-neural network approach yielded robust and consistent analysis results, indicating that the variable “System quality (SYQ)” has the highest influence, followed by “Cognitive need (CN),” “Information Quality (INQ),” “Social need (SN)” and “Affective need (AN)” in descending order of importance.

The results obtained from integrating the behavioral variables with IS variables will provide guidance to various organizations, such as the Ministry of Education, universities and educators, in the application of artificial intelligence technology in learning. They should prioritize the quality aspect of the system and the technological infrastructure that supports the use of ChatGPT for learning. Additionally, they should prepare learners to be ready in various dimensions, including knowledge, emotions and social aspects.

This study presents challenges in implementing artificial intelligence technology in learning, which educational institutions must embrace to keep up with the global technological trends. The educational sector should integrate artificial intelligence into the curriculum planning, teaching methods and learner assessment processes from the outset.

The purpose of this study was to investigate the factors that would influence the intention to use social banking during the coronavirus disease 2019 (COVID-19) pandemic. This study integrated two theories, namely the integrated technology acceptance model (TAM), which focused on the acceptance of technology by consumers, and electronic word of mouth (eWOM), which focused on consumer behavior. This study also applied the significant variables in the context of Thailand, which were trust and perceived risk.

A quantitative research method was applied by collecting data from 411 consumers during the COVID-19 pandemic in Thailand. A combined multi-analytic approach of a structural equation model (SEM)-neural network was used to analyze the data. In the first step, the SEM was used to determine the important factors that affected the adoption of social banking. In the second step, a neural network model was used to prioritize the important factors to confirm the results of the SEM method in step 1.

The empirical results of the data analysis using the SEM method showed that the perceived ease of use, perceived usefulness and trust were the most significant determinants of adopting social banking. This was consistent with the neural network method of the important factors.

The results of this research could initiate issues that should be developed for the continued use of online banking among consumers in the context of developing countries, such as Thailand.

This research model provided guidelines for the effective development of mobile banking applications for use on mobile devices. The results of this research made strong theoretical contributions to the existing literature on online banking and offered procedures and information to the relevant sectors.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2022-0709

This study aims to study the adoption of online learning in higher education through the perspective of the readiness of the following factors: self-directed learning (SDL), motivation for learning (ML), online communication self-efficacy (OCE) and learner control (LC). This was an empirical study in the context of developing countries, specifically Thailand.

This research applied a quantitative study method by collecting data from 605 higher education students in autonomous government institutions. The data analysis applied a structural equation model (SEM) to identify the significant determinants that affected the adoption of online learning. Moreover, this study applied a neural network model to examine the findings from the SEM.

From the data analysis using the SEM and neural network model, the results matched each other. The results of the empirical study were firm and supported that the readiness factors of students had statistical significance in the following order: SDL, OCE, LC and ML.

The study results showed an operational perspective to be prepared for online teaching, both for the related department of the Ministry of Education to support the infrastructure for online learning and for universities and instructors to create learning conditions and design teaching processes consistently with the online learning context.

Since the learning management in the 21st century is focused on student-centred learning, the empirical results obtained from this study presented the view of learners’ readiness that would influence the acceptance of online learning. In addition, this research presented the challenges and opportunities of online instruction during the COVID-19 pandemic.