An extension of the diffusion of innovation theory for business intelligence adoption: A maturity perspective on project management
Bibliographic record
Abstract
This study's objective is to analyze the factors that influence whether or not small and medium-sized enterprises (SMEs) use business intelligence. Based on an exhaustive assessment of the literature, the study offers a model dependent on the diffusion of innovation and augmented with factors expressing the idea of project management maturity (PMM). The research applied structural equation modelling (SEM) to examine data obtained from 112 Jordanian company workers. The findings showed that the adoption of business intelligence has a positive and significant relationship to the complexity, compatibility, and relative advantage of business intelligence; the level of project management maturity has a significant effect on the level of relative advantage, compatibility, and complexity; and the level of project management maturity is significantly associated with the change management and knowledge sharing practices in SMEs. However, we contend that further study has to be carried out, particularly in the context of developing nations, in order to get a comprehensive understanding of how different SMEs may effectively deploy and make use of business intelligence.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".