Driving digital transformation in banking: assessing the factors influencing business intelligence adoption
Purpose The current research investigates the factors affecting “Business Intelligence” (BI) adoption in Jordanian commercial banks by integrating the “Technology–Organization–Environment” (TOE) framework with the “Technology Acceptance Model” (TAM). Design/methodology/approach Survey data from 290 senior and mid-level IT professionals were analyzed to examine the effects of technological “relative advantage, complexity, compatibility,” organizational “readiness, top management support” and environmental “vendor support, competitive pressure” factors on BI adoption. Findings Results show that “compatibility, top management support, relative advantage, competitive pressure, and vendor support” significantly promote BI adoption, while complexity negatively affects “perceived usefulness” (PU) and “perceived ease of use.” Organizational readiness showed no significant impact. PU emerged as the strongest predictor of adoption intention. Originality/value By validating an integrated TAM–TOE model, the research paper highlights the importance of technological alignment, leadership support and intuitive BI design, providing practical insights for policymakers and banking professionals to enhance digital transformation and competitiveness.
Authors
- Amir A. Abdulmuhsin (ORCID: https://orcid.org/0000-0002-1383-8342)
- Ayman Wael Al‐Khatib (ORCID: https://orcid.org/0000-0002-8700-4738)
- Abeer F. Alkhwaldi (ORCID: https://orcid.org/0000-0002-3092-965X)
- Sayed Samer Ali Al Shami (ORCID: https://orcid.org/0000-0003-3090-1734)
- Khaled Aldarabah
- Ibrahim A. Abu-AlSondos
Institutions
- University of Jordan (JO)
- Dakota State University (US)
- Technical University of Malaysia Malacca (MY)
- University of Business and Technology (SA)
- Middle East University (JO)
- College of Business Administration (LV)
Publication Details
- Journal
- Journal of Enterprise Information Management
- Published
- 2026-07-17
- DOI
- https://doi.org/10.1108/jeim-03-2026-0356
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00