Contextual Factors and the Diffusion of MAIs in Manufacturing and Non-Manufacturing Sectors in Libya
Bibliographic record
Abstract
The diffusion of innovation theory has already addressed the major contextual factors hindering or facilitating the diffusion of management accounting innovations (MAIs) in organisations. However, the diffusion of MAIs in less developed countries (such as Libya) is still very low, and the contextual factors addressed by the diffusion of innovation seem to fail to explain the low diffusion. To address this important gap in the literature, this study used contingency theory and investigated the association between a variety of contextual (contingent and institutional) factors and the diffusion of MAIs in Libyan manufacturing and non-manufacturing organisations. Seven MAIs were chosen from the literature perceived to have higher popularity, namely, ABC, ABM, BSC, TC, life-cycle costing, benchmarking, and Kaizen. A questionnaire acted as the data collection instrument. Two hundred and fifty questionnaires were distributed, and one hundred and three useable questionnaires were returned. The results indicate that three factors were significantly associated with facilitating the adoption of MAIs in both sectors. They were using computer systems for MA purposes, top management support, and MA training programmes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".