The necessity and sufficiency of big data analytics capabilities and its impact on public sector decision making performanc

Abstract

Despite the growing prevalence of curiosity, interest, and research regarding the influence and potential of big data, there exists a dearth of empirical investigations concerning the essential capabilities needed to attain elevated levels of decision-making performance (DMP) in both a general context and, more specifically, within the public sector. The generation of public value and the attainment of a competitive advantage in time-sensitive industries, such as the public sector, necessitate the acquisition of valuable insights and the capacity to carry out high-quality decision-making. This study employed a combination of the partial least square structural equation modeling (PLS-SEM) method and necessary condition analysis (NCA) to examine the hypothesized relationships between various big data analytics capabilities (BDAC) and DMP. Additionally, it assessed the indispensability of three specific BDAC components (management, personnel, and infrastructure) in attaining optimal levels of DMP, taking into account the dynamic capabilities theory. The data was obtained through the administration of an online survey to a sample of 145 individuals who hold decision-making positions across 32 distinct government institutions affiliated with the Dubai Government. The results of the study provided support for the hypothesized sufficiency and necessity links, indicating that the existence of all three big data analytics capabilities (management, staff, and infrastructure) is both substantial and essential in order to attain elevated levels of DMP. Furthermore, the Dubai Government's public sector entities required enhanced big data management capabilities in order to attain optimal.

Keywords Big Data Analytics, Business Knowledge, Artificial Intelligence, Dubai Government.