Big data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: A multi-analytical SEM & ANN perspective.

Despite the growing interest towards big data within higher education institutions (HEI), research on big data analytics capability within the HEI context is somewhat limited. This study's main objective is to have a better understanding of the utilisation of big data analytics capability for data-driven decision-making to achieve better performance from Malaysian HEIs. Despite the growing interest towards big data within higher education institutions (HEI), research on big data analytics capability within the HEI context is rather limited. This study's main objective is to have a better understanding of the utilisation of big data analytics capability for data-driven decision-making to achieve better performance from Malaysian HEIs. This study validates an integrative model by combining information processing theory and resource-based view theory. Unlike extant literature, this study proposed methodology involving dual-stage analysis involving of Partial Least Squares Structural Equation Modelling and evolving Artificial Intelligence named deep learning (Artificial Neural Network) were performed. The application of deep ANN architecture can predict 83% of accuracy for the proposed model. Besides, the outcome of data-driven decision making from the relationship between big data analytic capability and data-driven decision making towards the performance of HEIs has significant findings. Results revealed that data-driven decision making could positively play an essential role in the relationship between big data analytic capability and performance of HEIs. Theoretically, the newly integrated theoretical model that incorporates information processing theory and resource-based view provides useful guidelines to HEI's about the crucial capabilities and resources that must be put into place to reap the benefits associated with big data implementations in the wake of Industry Revolution 4.0.