The Effect of Entrepreneurial Competencies on Innovation: A Case Study of Professional Sport Clubs
Bibliographic record
Abstract
The aim of the present research is to evaluate the effect of entrepreneurial competencies on innovation in professional sport clubs. In a regression analysis, this paper uses a descriptive correlational field method. The statistical sample population of this research includes the staff and employees from five selected sport clubs, namely: Esteghlal, Perspolis, Naaft-e-Tehran, Sepahan and ZobAhan of Isfahan. For data collection, a standard questionnaire with 65 items was used to evaluate eight competencies: strategic, conceptual, opportunity, relationship, learning, personal, ethical, familyism; and three dimensions of innovation in goods/services, the process and execution. The validity through the convergent validity (AVE=0.56 and 0.84 respectively) and the reliability (0.95 and α=0.93; 0.91 and CR=0.95 respectively) were confirmed and were randomly distributed among the statistical sample population. The Smart PLS 2 software and the structural equations modeling (SEM) techniques were used. The obtained results of this research indicate that the there is a positive and significant relationship between the entrepreneurial competencies and innovation (t=13/14 and T-value >1/96). Moreover, there is a positive and significant relationship between all the variables of competencies (i.e. strategic, conceptual, opportunity, relationship, learning, personal, ethical, and familyism) and innovation. The 77% innovative change as fostered by entrepreneurial competencies is indicative of a strong impact of the entrepreneurial competencies on innovation & performance of enterprises. Contribution of this paper is measuring these competencies on innovation of professional sport clubs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".