IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Big Data Analytics Capability and Governmental Performance: An Empirical Examination

Big Data Analytics Capability and Governmental Performance: An Empirical Examination
View Sample PDF
Author(s): Guido Ongena (HU University of Applied Sciences, Utrecht, The Netherlands)and Arjen Davids (Municipality of Berg en Dal, The Netherlands)
Copyright: 2023
Volume: 19
Issue: 1
Pages: 18
Source title: International Journal of Electronic Government Research (IJEGR)
Editor(s)-in-Chief: Nripendra P. Rana (Qatar University, Qatar)
DOI: 10.4018/IJEGR.321638

Purchase

View Big Data Analytics Capability and Governmental Performance: An Empirical Examination on the publisher's website for pricing and purchasing information.

Abstract

Although governments are investing heavily in big data analytics, reports show mixed results in terms of performance. Whilst big data analytics capability provided a valuable lens in business and seems useful for the public sector, there is little knowledge of its relationship with governmental performance. This study aims to explain how big data analytics capability led to governmental performance. Using a survey research methodology, an integrated conceptual model is proposed highlighting a comprehensive set of big data analytics resources influencing governmental performance. The conceptual model was developed based on prior literature. Using a PLS-SEM approach, the results strongly support the posited hypotheses. Big data analytics capability has a strong impact on governmental efficiency, effectiveness, and fairness. The findings of this paper confirmed the imperative role of big data analytics capability in governmental performance in the public sector, which earlier studies found in the private sector. This study also validated measures of governmental performance.

Related Content

Xiaodi Jiang, Yuanyuan Guo, Peng Dong. © 2024. 25 pages.
Rishi Kant Kumar, Adeeba Hoor, Sudhir K. Jain, Rana Singh, Kumod Kumar, Prashant Kumar, Apurva Chamaria. © 2024. 25 pages.
. © 2024.
. © 2024.
Rui Pedro Lourenço. © 2023. 19 pages.
Edna Dias Canedo, Ian Nery Bandeira, Larissa Pereira Gonçalves, Alessandra de Vasconcelos Sales, Fábio Mendonça, Cláudio Azevedo Costa, Rafael T. de Sousa Jr.. © 2023. 20 pages.
Mohamad Amin Alomar. © 2023. 22 pages.
Body Bottom