Guidelines for the development of small and medium enterprises into high-value markets
Bibliographic record
Abstract
This study aimed to explore the approaches for transforming Small and Medium Enterprises (SMEs) into high-value markets, focusing on constructing a structural equation model of guidelines for the development of small and medium enterprises into high-value markets. The conceptual research framework consisted of one exogenous latent variable, the Value Discovery element, combined with the four endogenous latent variables, Value Development, Value Communication, Value Distribution, and Value Experiences. The study utilized mixed-methodology research, including qualitative research through in-depth interviews with nine experts, a focus group discussion with 11 successful entrepreneurs, and quantitative research through a survey of 500 SMEs in Thailand. The results revealed that SMEs operating in high-value markets are divided into tangible and intangible products, representing 250 respondents from each group. The five elements of the guidelines for the development of small and medium enterprises into high-value markets were found to be of high importance both overall and in each element. The developed SEM model was consistent with the empirical data of five elements with 27 observed variable factors. Furthermore, the hypothesis test results for analyzing the causal influence between latent variables in the SEM model revealed that all six hypotheses were supported at the 0.001 level of statistical significance. The value of the study is beneficial for SMEs who target to do business in high-value markets and can improve their competitiveness, develop unique value propositions, and provide better customer experiences.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".