A practical framework for assessing business intelligence competencies of enterprise systems using fuzzy ANP approach

Rouhani, Saeed ORCID: 0000-0002-4580-522X and Ravasan, Ahad Zare (2015) A practical framework for assessing business intelligence competencies of enterprise systems using fuzzy ANP approach. International Journal of Applied Decision Sciences, 8 (1). pp. 52-82. doi:10.1504/IJADS.2015.066559

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Abstract

As traditional concept in management, decision support had a remarkable role in competitiveness or survival of organisations and following, as modern impression, nowadays business intelligence (BI) has various applications in achieving desirable decision supports. Consequently, assessing BI competencies of enterprise systems can enable decision support in firms. This paper presents a practical framework for assessing the business intelligence capabilities of enterprise systems based on a set of novel factors and utilising fuzzy analytic network process (FANP). Through this, the construct of BI competency is decomposed into three main competency parts including ‘managerial’, ‘technical’ and ‘system enabler’ sub-goals, five main factors and 26 criteria. Using this framework, the BI competency level of enterprise systems can be determined which can help the decision makers to select the enterprise system that best suits organisations’ intelligence decision support needs. In order to validate the proposed model, it is applied to a real Iranian international offshore engineering and construction company in the oil industry to select and acquire ERP system. This research provides a complete frame (factors, criteria and procedures) for firms to assess their proposed software and systems in the field of BI competencies and functions.

Item Type: Article
Article Type: Article
Subjects: H Social Sciences > HF Commerce > HF5001 Business > HF5717 Business communication
Divisions: Schools and Research Institutes > School of Business and Technology > Engineering Technologies
Research Priority Areas: Applied Business & Technology
Depositing User: Marta Kemp
Date Deposited: 17 Jan 2020 12:36
Last Modified: 01 Jun 2020 16:03
URI: http://eprints.glos.ac.uk/id/eprint/7952

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