Entrepreneurs’ individual-level resources and social value creation goals
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a better understanding of how the interplay of individual-level resources and culture affects entrepreneurs’ propensity to adopt social value creation goals. Design/methodology/approach Using a sample of 12,685 entrepreneurs in 35 countries from the Global Entrepreneurship Monitor, it investigates the main effects of individual-level resources – measured as financial, human and social capital – on social value creation goals, as well as the moderating effects of the cultural context in which the respective entrepreneur is embedded, on the relationship between individual-level resources and social value creation goals. Findings Drawing on the resource-based perspective and Hofstede’s cultural values framework, the results offer empirical evidence that individual-level resources are relevant for predicting the extent to which entrepreneurs emphasise social goals for their business. Furthermore, culture influences the way entrepreneurs allocate their resources towards social value creation. Originality/value The study sheds new light on how entrepreneurs’ individual resources influence their willingness to create social value. Moreover, by focussing on the role of culture in the relationship between individual-level resources and social value creation goals, it contributes to social entrepreneurship literature, which has devoted little attention to the interplay of individual characteristics and culture.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".