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.

尽管高等教育机构 (HEI) 内对大数据的兴趣日益浓厚，但在 HEI 背景下对大数据分析能力的研究仍然有限。本研究的主要目标是更好地了解利用大数据分析能力进行数据驱动的决策，以实现马来西亚高等教育机构的更好表现。尽管高等教育机构 (HEI) 对大数据的兴趣日益浓厚，但在 HEI 背景下对大数据分析能力的研究却相当有限。本研究的主要目标是更好地了解利用大数据分析能力进行数据驱动的决策，以实现马来西亚高等教育机构的更好表现。本研究通过结合信息处理理论和基于资源的视图理论验证了一个集成模型。与现有文献不同，本研究提出的方法涉及涉及偏最小二乘结构方程建模和演化的人工智能（称为深度学习（人工神经网络））的双阶段分析。深度 ANN 架构的应用可以为所提出的模型预测 83% 的准确率。此外，从大数据分析能力和数据驱动决策之间的关系来看，数据驱动决策对高校绩效的结果具有重要意义。结果表明，数据驱动的决策可以在大数据分析能力与高校绩效之间的关系中发挥积极作用。理论上，