Integrating Entrepreneurial Competency and Ethical Conduct in Accounting Education: Evidence from Thai Professional Practices
Bibliographic record
Abstract
In today’s dynamic business environment, accounting entrepreneurs must demonstrate both entrepreneurial competencies and strong ethical conduct to achieve sustainable success. This study examines how these two factors influence the performance of small accounting practices in Thailand. Employing a convergent parallel mixed-methods design, the research integrates quantitative data from 400 accounting firm owners and qualitative insights from 10 semi-structured interviews. Guided by an integrated conceptual model, the study tested three hypotheses: (H1) the direct effects of entrepreneurial competency and ethical conduct on practice success; (H2) the mediating role of entrepreneurial competency in translating professional skills into performance; and (H3) a sequential mediation path from professional skills through ethical conduct and entrepreneurial competency to success. Quantitative results confirmed that both entrepreneurial competency and ethical conduct significantly predicted practice success (p < .01). Mediation analysis supported H2 and H3, showing that entrepreneurial competency acts as a key mechanism linking technical and ethical qualities to performance outcomes. The qualitative findings enriched these results, revealing how successful practitioners emphasize integrity, transparent client relationships, adaptability, and continuous learning in practice. The study highlights the interdependence between ethics and entrepreneurship and underscores the need for accounting education and professional development to integrate these competencies. Implications are discussed for curriculum reform, continuing professional education, and policy alignment in Thailand’s accounting sector. By fostering ethically grounded entrepreneurial mindsets, stakeholders can better equip accounting professionals to navigate complexity and drive sustainable growth.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".