Determinants of Business Intelligence Systems Adoption in Developing Countries: An Empirical Analysis From Ghanaian Banks
Bibliographic record
Abstract
Keen competitions among banks to attract and maintain clients, together with issues such as risk management, and loss prevention are some of the common phenomena in the banking sector recently. As a result, Business Intelligence (BI) technologies which can be used to analyze and detect fraud, predict and understand the behavior of clients have come to the rescue of the banks. This study explores the factors that influence Ghanaian banks to adopt BI Systems and also determines the extent of its implementation. This was done with the development of a structural model through the lens of the Diffusion of Innovations Theory, Technology-Organization-Environment framework, and the Institutional Theory. A sample data from 130 Bank executives were subjected to partial least squares structural equation modeling (PLS-SEM). The results showed that technological factors (Relative Advantage and Complexity), organizational factors (Presence of Champion and Organizational Readiness), and environmental factors (Regulatory Body) account for BI Systems adoption in Ghanaian banks. Also, the analysis revealed that Ghanaian banks have reached a high level in terms of BI Systems implementation. This study contributes to enrich the Information Systems (IS) literature by identifying the contextual factors that organizations especially in sub-Saharan Africa (SSA) countries should focus on with their BI Systems implementation effort. Other implications are also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".