Proposed framework for innovative business intelligence for competitive advantage in small, medium and micro-organisations in the North West province of South Africa
Bibliographic record
Abstract
Background: Innovative business intelligence enjoys popularity amongst mainly large organisations, particularly in the private sector. However, very limited studies have validated the impact of business intelligence in small, medium and micro-organisations (SMMEs), especially in a developing economy.Aim: To devise an innovative business intelligence as a competitive advantage model and to establish the impact of innovative business intelligence as a competitive advantage, as measured by success or growth and innovation in SMMEs in North West province, South Africa.Setting: Primary research was conducted amongst SMMEs owners or managers and employees in the North West province, South Africa.Methods: The final sample consisted of 12 SMME owners or managers and 394 other employees of SMMEs. The study used mixed methods and adopted a survey design. In-depth interviews and a structured questionnaire were used for data collection.Results: The study established that management support and internal environment commitment support the implementation of innovative business intelligence. Training and employee motivation are insignificant in enhancing innovative business intelligence as competitive advantage.Conclusion: Management of SMMEs must define clear goals and objectives, as well as support and provide education mechanisms for new technology advancement. The technology-acceptance model (TAM) of adoption is merely one of the key techniques SMMEs can employ to improve the implementation of business intelligence as a new technology instrument to promote quality decision-making and profitability.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".