Adaptation of Big Data: an empirical investigation for sustainability of education

Abstract

The current study aims at developing a framework to assess big data use for education and encompassing the theoretical background of knowledge sharing and diffusion of innovations in the educational environment. This study hypothesizes that age and cultural diversity, and motivators can influence knowledge sharing, whereas the constructs of relative advantage, trialability, complexity, observability would impact innovations. Thus, innovations influence knowledge sharing and would be positively associated with behavioural intention to use big data and sustainability for education. This study utilized a version of knowledge sharing model and Diffusion of Innovations (DOI) theory as the study framework and implemented quantitative approach for data analysis by collecting 494 responses from university students who were elected using stratified random sampling technique. The data were processed using eleven factors to unveil and understand the predictors of big data use for education sustainability. The study adopts the quantitative approach and employs structure equation modelling (SEM) to data analysis. According to the study's findings, age and cultural diversity and motivators significantly determine knowledge management sharing, while relative advantage, trialability, complexity, and observability have a positive impact on innovations. The adoption of innovations, knowledge sharing, and big data are able to capture 78.9% of sustainability phenomenon on education. Further, the study concludes by reporting findings and implications for research and practitioners.