Identifying determinants of big data adoption in the higher education sector using a multi-analytical SEM-ANN approach

Even though big data offers new opportunities to organizations, big data adoption (BDA) is still in the early stages of introduction, and its determinants remain unclear in many sectors. Therefore, this research intended to identify the determinants of BDA in the education sector. A theoretical model was developed based on the integration of the Technology−Organization−Environment (TOE) and Diffusion of Innovation (DOI) theories. The data was collected from 190 decision-makers in university campuses in Pakistan. A two-step structural equation modeling–artificial neural network (SEM-ANN) approach was employed to unveil the determinants of BDA and predict their levels of importance. The results obtained from the SEM showed that compatibility, IT infrastructure, management support, financial resources, security and privacy, and government guidelines were important determinants of BDA. Meanwhile, the ANN algorithm highlighted security and privacy as the most important predictors of BDA. The findings can assist higher education commissions, big data facilitators, and university managements in providing safe and successful BDA in university campuses.