Sufficiency and necessity of big data capabilities for decision performance in the public sector

Abstract

Purpose

This study aims to examine the necessity effects of big data analytics capabilities (BDAC) on decision-making performance (DMP), particularly in the public sector.

Design/methodology/approach

The authors used the combined methods of partial least square structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) to test the hypothesized relationships.

Findings

The findings show that the presence of all three BDAC (infrastructure, management and personnel) is significant and necessary to achieve higher levels of DMP. Specifically, the results revealed big data management capabilities to be of higher necessity to achieve the highest possible DMP. The findings provide public-sector practitioners with insights to support the development of their BDAC.

Originality/value

Time-sensitive domains such as the public sector require insight and quality decision-making to create public value and achieve competitive advantage. This study examined BDAC in light of the combined methods of (PLS-SEM) and NCA to test the hypothesized relationships in the public sector context.

Citation

Talib, S., Papastathopoulo, A. and Ahmad, S.Z. (2024), "Sufficiency and necessity of big data capabilities for decision performance in the public sector", Digital Policy, Regulation and Governance, Vol. 26 No. 1, pp. 18-37. https://doi.org/10.1108/DPRG-05-2023-0057