Small- and medium-sized accounting firms’ learning processes regarding standards updates
Bibliographic record
Abstract
Small- and Medium-Sized Accounting Firms (SMAFs) are a separate category of small- and medium-sized entities (SMEs) that operate in a highly regulated environment. If they fail to comply with professional standards, their survival could be in jeopardy. The purpose of this study is to understand how SMAFs acquire and absorb standards updates. This is a qualitative field study centered on the main concepts of organizational learning. Based on interviews conducted with 36 professional members of 33 SMAFs, results show that SMAFs are heterogeneous in terms of this learning. Learning mechanisms (or tools) are presented for each of the main concepts of the learning process and by firm category (size), providing information about how learning occurs in these firms. This is complemented with comments about who (individual, group, organization) is doing what (actions) to acquire and disseminate knowledge, interpret information and store knowledge. Findings led to the identification of three SMAFs learning profiles: ‘highly proactive,’ ‘somewhat proactive’ and ‘slightly proactive.’ These findings contribute to the literature on SME organizational learning by showing different learning profiles. They also contribute to the accounting literature as they highlight differences in SMAFs instead of treating them as a uniform group.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".