The Problems and Management Strategy of Local Convenience Stores for Business Survival in Violent Situations in Lower-South Thailand
Bibliographic record
Abstract
Violence in the lower south of Thailand has happened for more than 8 years, and it has affected the lives of local people; there is economic decline and many businesses are falling. The objective of this study is to analyze the management problems of convenience stores in lower-south Thailand and to study the management strategies of convenience stores for survival in this violent situation. We conducted in-depth interviews with 55 local owners of convenience stores in the towns of the Pattani, Yala, and Narathiwat provinces. We selected the sample by convenience sampling and purposive sampling technique. To analyze the data, we conducted content analysis and descriptive analysis. Violence is a big issue that is contributing to many problems for convenience stores, such as increases in the cost of goods, inability to transfer the business to the next generation, customer decline, and the divide between Chinese Buddhists and Muslims. In addition, franchises such as 7-Eleven have affected local businesses. We found seven management strategies that traders use to help their businesses survive: 1) reduce working hours, 2) proceed carefully in violent situations, 3) classify customers, 4) employ Muslims to work in convenience stores, 5) offer price promotions, 6) set up another business to reduce business risk, and 7) practice self-sufficiency by investing only in necessities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".