Entrepreneurial passion and venture profit: Examining the moderating effects of political connections and environmental dynamism in an emerging market
Bibliographic record
Abstract
This article analyses the contingent factors which influence the relationship between entrepreneurial passion and venture profit. While research on entrepreneurial passion is burgeoning, studies that analyse contingent factors and boundary conditions surrounding entrepreneurial passion theory are sparse. Moreover, we know very little about how the influence of entrepreneurial passion on venture outcomes might vary in emerging markets, typically characterised by higher levels of bureaucratic involvement and institutional deficiencies. We extend entrepreneurial passion theory by testing a contingent model that evaluates the influence of political connections and environmental dynamism on the relationship between entrepreneurial passion and venture profit. More specifically, we examine the role of passion on venture profit and the moderating impact of political connections and perceived environmental dynamism. Using time-lagged data from 231 small businesses in Ghana, we find that political connections amplify the potency of passion as a driver of venture profit. In addition, we find that this interaction is conditioned by environmental dynamism; specifically, the moderating effect of political connections on the relationship between entrepreneurial passion and venture profit is stronger when dynamism is high. Our fine-grained analysis increases the conceptual scope and generalisability of entrepreneurial passion to non-Western contexts.
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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 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".