The manager and the accounting information system in small companies
Bibliographic record
Abstract
In very small organizations, the role of the manager in the choice and implementation of tools is predominant. In these entities, resources are scarce and accounting information systems are not very formalized. In this research work, we therefore sought to identify the typical profile of this manager and to understand his propensity to use accounting data. Several recent studies have highlighted the relevance of the concept of organizational bricolage to analyze the practices of small businesses. With this in mind, we have sought to explore the ways in which managers of small businesses use accounting information systems. For this, we opted for the qualitative research method based on semi-structured interviews with managers of small Tunisian companies. To conduct this study, we used a qualitative methodology. 36 companies were selected for study. The cross-site case study was favored because it maximizes generalization bias. Finally, the profile of the manager has an influence on the SIC and induces a type of MSE. The results of our research led to the conclusion that there are three types of small business leaders: survivalists, emerging and structured.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".