Business intelligence effectiveness and corporate performance management: an empirical analysis

Richards, Gregory; Yeoh, William; Chong, Alain Yee Loong; Popovi?, Ale�

Abstract

Business intelligence (BI) technologies have received much attention from both academics and practitioners, and the emerging field of business analytics (BA) is beginning to generate academic research. However, the impact of BI and the relative importance of BA on corporate performance management (CPM) have not yet been investigated. To address this gap, we modeled a CPM framework based on the Integrative model of IT business value and on information processing theory. Data were collected from a global survey of senior managers in 337 companies. Findings suggest that the more effective the BI implementation, the more effective the CPM-related planning and analytic practices. BI effectiveness is strongly related to BA, planning and to measurement. In contrast, BA effectiveness is strongly related to planning but less so to measurement. The study suggests that although both BI and BA contribute to corporate management practices, the information needs are different based on the level of uncertainty versus ambiguity characteristic of the management practice.

Citation

Richards, G., Yeoh, W., Chong, A. Y. L., & Popovič, A. (2017). Business intelligence effectiveness and corporate performance management: an empirical analysis. Journal of Computer Information Systems, 59(2), 188-196. https://doi.org/10.1080/08874417.2017.1334244

• Journal Article Type: Article
• Acceptance Date: Jan 1, 2017
• Online Publication Date: Jul 31, 2017
• Publication Date: Jul 31, 2017
• Deposit Date: May 21, 2018
• Publicly Available Date: May 21, 2018
• Journal: Journal of Computer Information Systems
• Print ISSN: 0887-4417
• Electronic ISSN: 2380-2057
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 59
• Issue: 2
• Pages: 188-196
• DOI: https://doi.org/10.1080/08874417.2017.1334244
• Keywords: Business intelligence; corporate performance management; empirical study
• Public URL: https://nottingham-repository.worktribe.com/output/875169
• Publisher URL: https://www.tandfonline.com/doi/figure/10.1080/08874417.2017.1334244?scroll=top&needAccess=true
• Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Computer Information Systems on 31 July 2017, available online: http://www.tandfonline.com/10.1080/08874417.2017.1334244
• Contract Date: May 21, 2018