Doing Good, Feeling Good? Entrepreneurs’ Social Value Creation Beliefs and Work-Related Well-Being
Bibliographic record
Abstract
Abstract Entrepreneurs with social goals face various challenges; insights into how these entrepreneurs experience and appreciate their work remain a black box though. Drawing on identity, conservation of resources, and person–organization fit theories, this study examines how entrepreneurs’ social value creation beliefs relate to their work-related well-being (job satisfaction, work engagement, and lack of work burnout), as well as how this process might be influenced by social concerns with respect to the common good. Using data from the German Public Value Atlas 2015 and 2019 and the Swiss Public Value Atlas 2017, a three-study design analyzes three samples of entrepreneurs in Germany and Switzerland. Study 1 reveals that entrepreneurs report higher job satisfaction when they believe their organization creates social value. Study 2 indicates that these beliefs relate negatively to work burnout; entrepreneurs’ perceptions of having meaningful work mediate this relationship. Study 3 affirms and extends these results by showing that a sense of work meaningfulness mediates the relationship between social value creation beliefs and work engagement and that this mediating role is more prominent among entrepreneurs with strong social concerns. This investigation thus identifies a critical pathway—the extent to which entrepreneurs experience their work activities as important and personally meaningful—that connects social value creation beliefs with enhanced work-related well-being, as well as how this process might vary with a personal orientation that embraces the common good.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".