Unpacking the Complexities of Financial Well-being Among Entrepreneurs and Employees: It’s More Than the Money!
Bibliographic record
Abstract
This study investigates how employment type (entrepreneurship vs paid employment) and individual characteristics (demographics and career motivations) jointly influence financial well-being (FWB) in Trinidad and Tobago. It moves beyond traditional income-based indicators, adopting a subjective, contextualised approach to assess FWB for individuals in the abovementioned setting. A survey was administered to a sample comprising full-time entrepreneurs, full-time paid employees and hybrid entrepreneurs ( n = 364). Full-time entrepreneurs reported significantly higher levels of FWB than paid employees. However, hybrid entrepreneurs—who simultaneously engaged in entrepreneurship and paid employment—did not report significantly higher FWB than wage earners. This suggests that the intensity of entrepreneurial engagement plays a crucial role in shaping an individual’s FWB. Employment type interacted with other demographic variables to shape FWB, reiterating the complexity and multidimensionality of FWB. Intrinsic motivations for choosing one’s career path (passion and self-efficacy) were stronger determinants of FWB than extrinsic factors (financial motivations). The study introduces nuanced perspectives on subjective well-being theory and the theory of planned behaviour, which, to date, remain underexplored in mainstream entrepreneurship and FWB literature. Additionally, its findings underscore the importance of critically assessing individual motivations prior to entrepreneurial entry, thus offering valuable practical implications for aspiring entrepreneurs and policymakers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".