Business Intelligence Effectiveness and Corporate Performance Management: An Empirical Analysis. Issue 2 (4th March 2019)
- Record Type:
- Journal Article
- Title:
- Business Intelligence Effectiveness and Corporate Performance Management: An Empirical Analysis. Issue 2 (4th March 2019)
- Main Title:
- Business Intelligence Effectiveness and Corporate Performance Management: An Empirical Analysis
- Authors:
- Richards, Gregory
Yeoh, William
Chong, Alain Yee Loong
Popovič, Aleš - Abstract:
- 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.
- Is Part Of:
- Journal of computer information systems. Volume 59:Issue 2(2019)
- Journal:
- Journal of computer information systems
- Issue:
- Volume 59:Issue 2(2019)
- Issue Display:
- Volume 59, Issue 2 (2019)
- Year:
- 2019
- Volume:
- 59
- Issue:
- 2
- Issue Sort Value:
- 2019-0059-0002-0000
- Page Start:
- 188
- Page End:
- 196
- Publication Date:
- 2019-03-04
- Subjects:
- Business intelligence -- corporate performance management -- empirical study
Electronic data processing -- Study and teaching -- Periodicals
658.4038011 - Journal URLs:
- http://www.tandfonline.com/ ↗
- DOI:
- 10.1080/08874417.2017.1334244 ↗
- Languages:
- English
- ISSNs:
- 0887-4417
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4963.730000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 12295.xml