The Determinants of Entrepreneurs’ Business Performance in Rural South Jordan: Under the Mediating Role of Demographic Factors
Bibliographic record
Abstract
Although managing a business may seem straightforward, effectively navigating and adapting to environmental changes presents considerable challenges. In Jordan, rural entrepreneurs play a pivotal role in fostering economic development, driving prosperity, and generating employment opportunities that contribute significant value to the economy. This study aims to analyze the factors influencing the business performance of rural Jordanian entrepreneurs (BPRJE). These factors are categorized into three main areas: personal traits, the external business environment, and supportive elements. Additionally, this study investigates the direct and indirect effects of perceived demographic factors within this framework. To achieve the research objectives, data were collected using purposive sampling and a cross-sectional survey. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for data analysis. The results indicate that both the external business environment (EBE) and personal traits (PT) have a strong direct positive impact on BPRJE and an indirect influence through the mediating role of demographic factors (DF). However, supportive elements (SEL) were not significantly and positively correlated with BPRJE, either directly or through DF mediation. Furthermore, although perceived demographic factors influence BPRJE, they weaken the relationship between supportive elements and BPRJE. This study provides a comprehensive understanding of critical success factors and the role of demographic influences on the performance of rural Jordanian entrepreneurs. This underscores the importance of continued research on emerging entrepreneurial success factors and calls for comparative studies on rural entrepreneurship across different regional and global contexts to better address their unique challenges and opportunities.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".