Business intelligence tool selection for data management units in the banking sector

Abstract

Purpose This paper aims to provide a concrete roadmap for selecting the business intelligence (BI) tool that best suits the common interests of data units operating in the banking sector while addressing conflicting needs. Design/methodology/approach This study employs a novel hybrid model to address uncertainties in expert judgments and conflicts between criteria. This model combines interval-valued intuitionistic fuzzy (IVIF)-based entropy, which quantifies uncertainty, the Criteria Importance Through Intercriteria Correlation (CRITIC) method, which correlates criterion weights, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which ranks alternatives based on their proximity to the ideal solution. In addition, sensitivity analyses are performed by varying employee group weights, the method-combination a coefficient, and criteria weights, and by using other methods. Findings The results show that two employee groups prefer the same alternative tool, while the other group prefers a different tool. When the expectations of the three groups are considered together, one alternative ranks first in both scenarios because of its strength in analytical and predictive capabilities, and governance strength. Originality/value This study contributes to the literature by addressing the problem of selecting a business intelligence tool not only through technical criteria but also by evaluating different user profiles with conflicting expectations simultaneously. In addition, it presents a unique decision-support framework by offering an original model integrating IVIF-based entropy, CRITIC, and TOPSIS methods.