The Impact of Owner-Managers’ Personality Traits on Their Small Hospitality Enterprise Performance in Saudi Arabia
Bibliographic record
Abstract
Governments in many countries have paid close attention to small enterprises because of their social and economic impacts. The role of the owner-manager in advancing the performance of their small business cannot be underestimated. The current study tests the influence of an owner-manager’s big five personality traits on the performance of their small enterprises. For this purpose, a pre-tested questionnaire was directed to owner-managers of small hospitality enterprises in Saudi Arabia. The results of SEM analysis, with AMOS, showed that high levels of both openness to experience and agreeableness of owner-managers have a significant positive impact on the performance of their small enterprises. However, a high level of neuroticism has a significant negative impact on the performance of their small enterprises. The results interestingly showed that high levels of both conscientiousness and extraversion among owner-managers have positive, but insignificant, impacts on the performance of their small enterprises. These two traits had a minor impact on the performance of small hospitality enterprises. Hence, managers of small hospitality enterprises in Saudi Arabia are required to have high levels of openness to experiences and agreeableness and low level of neuroticism to achieve significant organizational performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".