Managing success factors in entrepreneurial ventures: a behavioral approach
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the links between entrepreneurial behavior and success factors in a developing country context. Design/methodology/approach A case study approach was selected to analyze real‐life situations in order to gain an insight about entrepreneurial cognition and action related to success factors. Drawing from the behavioral theory of entrepreneurship, this paper presents a conceptual model which shows that entrepreneurial cognitions about success factors may lead toward entrepreneurial actions. The data were collected through face‐to‐face interviews. Three entrepreneurs were asked to outline responses to identified success factors such as start‐up planning, managing risk, learning, networking, managing human resource, and managing finances. Findings The results suggest that many behavioral patterns exhibited by the case study entrepreneurs were similar to entrepreneurs' behavior in more developed regions. The similarities include: preparation of business plan, ability cognition for start‐up planning, overconfidence and representativeness heuristics for managing risk, obtaining professional outsider assistance for learning, developing business relationships with suppliers for networking and favorable credit policies, and employing owner‐related and delaying‐payment methods of bootstrapping for managing finances. Originality/value For the first time in Pakistan this study explores entrepreneurial cognition and action in managing success factors. The findings of the research will potentially help practitioners and policy makers in nurturing entrepreneurial initiatives in a developing country context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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 teacher head, 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".