Determinants of Business Intelligence Systems Adoption in Developing Countries: An Empirical Analysis From Ghanaian Banks
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it