Training Needs Analysis and Course Design for Financial and Tax Management: Evidence from Chinese SME Owners in Thailand
Bibliographic record
Abstract
This study investigates the training needs for financial and tax management among Chinese small and medium-sized enterprise (SME) owners operating in Thailand. Through a mixed-methods approach combining questionnaire surveys from 40 Chinese SME entrepreneurs and semi-structured interviews with 10 selected participants, the research identifies critical areas requiring training support. The findings show that bank credit knowledge ranks as the highest training need (mean score 4.52), followed by financial management (4.14), VAT declaration and management (3.54), and corporate income tax (3.50). The study highlights specific challenges faced by Chinese SME owners in Thailand, including language and cultural barriers in bank communications, difficulties in accessing financing, and limitations of financial outsourcing services. Based on these findings, a comprehensive training program was designed, comprising three core modules: bank credit practice, financial management, and tax management practice. This research contributes to understanding the unique financial and tax management training needs of foreign SME owners operating in Thailand and provides practical implications for developing targeted training programs to enhance their business competencies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".