An Analysis of the Use of Accounting Information by Portuguese SMEs
Bibliographic record
Abstract
Despite the significant economic contribution of small and medium-sized enterprises (SMEs), little is known about the extent to which they make use of accounting information (AI). Although AI is considered one of the main sources of information for SMEs, many continue to ignore its potential, considering that this information is only intended to meet tax obligations. The literature stresses the influence of several factors on AI usage. However, the conclusions of the studies are fragmented, contradictory, and not very enlightening. Following these studies, the purpose of this paper is to explore which characteristics of decision makers, companies, and accounting services influence the importance and use of AI in SMEs. Data were collected through an online questionnaire survey applied to Portuguese SMEs. The findings show that the decision makers’ level of education, as well as their educational background, influence the importance they attribute to AI. It has also been found that smaller companies and SMEs that use outsourced accounting services make the least use of AI. Therefore, in addition to providing empirical evidence on the importance and use of AI, a debate that has been mainly theoretical, and on the importance of SMEs in any economy, this paper aims to raise awareness of the need to further study the decision-making process in such firms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".