Big data analytics adoption via lenses of Technology Acceptance Model: empirical study of higher education

Abstract

The goal of this study was to establish a model to quantify the adoption of big data in relation to education and to translate the adoption of big data in literature into the educational context. This study hypothesizes that encouraging situations, perceived risk, perceived usefulness, perceived ease of use influence the attitude of the students towards use and the intention to use behavior, in turn impacting the adoption of big data in education, this research used the Technology Acceptance Methodology (TAM) model. Through analyzing 282 university students, the present thesis followed quantitative data collection along with analysis procedures. Therefore, the responses of students were grouped into seven testing constructs and evaluated to understand their adoption influence. Accordingly, data were subsequently quantitatively analysed utilising Structure Equation Modelling (SEM). The findings revealed that facilitating conditions, perceived risk, perceived usefulness, perceived ease of use were important determinants of the attitude of students towards use and behavioral intention to use big data, and 71.2% of acceptance was also significant for the attitude of students towards use and behavioral intention to use big data.