Business Intelligence Adoption and Implementation Risk in SMEs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Business Intelligence – BI systems are increasingly accessible to small and medium-sized enterprises (SMEs). Like all Information Systems (IS), their implementation is very risky by nature. Several scholars underscore that IS risk management is more effective when initiated earlier in the system life cycle, as early as at the adoption. The objective of this research is to describe and understand the process of BI adoption in SMEs focusing on the management of implementation risk of from the adoption stage using an interpretive holistic single-case study of a small manufacturing firm in Tunisia in Africa that successfully adopted a BI system. Consistent with previous research, the study shows that in order to manage the implementation risk during the adoption stage, SMEs can proceed in a way that is more efficient for them that is rather intuitive, informal and unstructured, which is, however, explicitly based on an architecture of principles, policies and practices. The main limitation of the study is related to the qualitative single case study design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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