Self-Awareness in Business Acumen as a Cognitive Bridge Between Accounting Proficiency and Financial Performance in Thai Community Enterprises
Bibliographic record
Abstract
This study investigates the mediating role of self-awareness within the broader framework of business acumen, emphasizing its connection to entrepreneurial accounting proficiency and financial performance in community enterprises across Thailand. The purpose is to advance theoretical understanding by integrating metacognition theory and the resource-based view (RBV), and to provide practical insights for strengthening grassroots entrepreneurship. Using survey data from 210 enterprises, a hybrid Structural Equation Modeling–Artificial Neural Network (SEM–ANN) approach is applied to capture both linear and nonlinear relationships among cognitive, technical, and financial variables. The results confirm that accounting proficiency has a significant and positive effect on self-awareness with value of 0.125. However, self-awareness does not exert a direct influence on financial performance. These findings suggest that self-awareness may function as a cognitive enabler, facilitating the translation of entrepreneurial skills into effective decision-making, rather than serving as an independent predictor of financial outcomes. Empirical patterns further reveal that commercial enterprises report higher self-awareness than service firms, unregistered enterprises show greater awareness than registered ones, and financially stable firms display lower awareness, suggesting complacency or overconfidence. In contrast, regular participation in training significantly enhances awareness, underscoring the role of continuous learning.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".