Antecedent Factors and their Influence on the Young Entrepreneur’s Future Sustainable Intentions
Bibliographic record
Abstract
This exploratory study aims to understand the antecedent factors (sustainable/entrepreneurial orientations, self-determination motivation, and contextualities) that may influence a future young entrepreneur’s intentions in developing sustainable enterprise practices (SEP). Adopting humanistic values from a service-dominant logic framework, we evaluate how a future young entrepreneur’s mindset can be shaped in adopting sustainable enterprise best practices on ethical and moral altruistic decision-making. Based on the self-determination theory (SDT), we looked at the autonomous and self-regulatory motivation factors as key influencers on the individual decision-making process, and on how he/she weights contextual complexities in deciding to pursue doing good for the wellbeing of the organization and the community or preferring opportunistic self-interested rewarding goals. Combining online surveys and in-depth interviews in Canada and in China, it was found that intrinsic motivation factors influence the development of sustainable entrepreneurial orientations (SO) and sustainable enterprise practices (SEP). Although contextual and cultural factors moderate the effects of intrinsic motivations, one should capitalize on learned behaviors. As suggested through the in-depth interviews with experienced entrepreneurs, future young decision-makers should be expressly taught about ethical, social, and environmental issues to develop SEP intentions and potential future sustainable entrepreneurial behaviors.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".