Factors Affecting the Acceptance of Big Data Technology in Teaching among Higher Education Educators: An Empirical Investigation Using the UTAUT Model

Abstract

The era of big data has arrived along with rapid growth in the development of computer and communication technologies. This advancement of new technologies has brought about a new era of education. Personalized learning analysis and intelligent decision support based on big data technology have greatly improved education quality, optimized education management, and provided important support for realizing education modernization. Despite higher education institutions' growing interest in big data, research on the use of big data technology by teachers in higher education contexts is limited. Therefore, the purpose of this study was to examine the factors influencing higher education educators' intentions to use big data technology in teaching using the UTAUT model and to determine if there were statistical differences in higher education educators' intentions to use big data technologies in teaching based on age, gender, and teaching experience. Using simple random sampling technique, survey data were collected from 193 higher education educators in China's Yunnan Province using an online survey and analysed using structural equation modelling. The findings suggested that performance expectancy and facilitating conditions positively impact educators' behavioural intentions to use big data technology. However, in this study, the effects of effort expectancy and social influence on behavioural intention were not found to be statistically significant. Furthermore, the findings revealed that there were no significant differences in higher education educators' behavioural intentions to use big data technology in teaching based on gender, age, or teaching experience. Based on the findings, this study provides recommendations for university administrators and policy makers to motivate educators' behavioural intentions to use big data technology in teaching so that intentions eventually translate into actual usage behaviour. Future research can add models such as the PC utilization model, combine qualitative research such as interviews, and further expand the target population to other regions to compare the intention to use, level of use, and influencing factors of educators in different regions to make the findings more comprehensive.