The Development of a Causal Relationship Factor Model Affecting Entrepreneurial Behavior for Sustainable Development
Bibliographic record
Abstract
The objective of this article is to develop a causal relationship factor model affecting entrepreneurial behavior for sustainable development using a mixed-methods research approach that integrated both quantitative and qualitative methodologies. The quantitative phase involved testing the causal relationships affecting entrepreneurial behavior by collecting data through questionnaires from 327 university students in Bangkok. This was followed by a qualitative phase, which included conducting in-depth interviews with 17 university students in Bangkok. The quantitative research results indicated that a causal relationship factor model affecting entrepreneurial behavior for sustainable development was congruent with the empirical data (Chi-Square = 48.017, df = 35, p = .070, CMIN/DF = 1.372, RMSEA = .034, CFI = .975, GFI = .975, AGFI = .945, NFI = .918, RMR = .013). Structural Equation Modeling (SEM) has allowed us to examine the hypothesized relationships among the independent, mediators, and dependent variables. The empirical result, a testament to the robustness of our research, illustrates that entrepreneurship education had a positive relationship with and influenced both entrepreneurial mindset and orientation. Similarly, entrepreneurial orientation demonstrated a positive relationship with and influenced entrepreneurial intention, and entrepreneurial intention had a positive relationship with and influenced entrepreneurial behavior. The qualitative research results indicated that entrepreneurship education is essential for sustainable entrepreneurship, influencing the entrepreneur’s mindset, orientation, intention, and behavior. Overall, the findings from the qualitative research effectively explained, confirmed, and expanded upon the quantitative research results, providing greater clarity and completeness to the study’s outcomes.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".