What entrepreneurial skillsets support responsible value creation in health and social care? A mixed methods study
Bibliographic record
Abstract
Abstract Although various scholars underscore the importance of innovating responsibly in view of today's societal challenges, less attention has been paid to the entrepreneurial skillset, that is, the range of individual skills and organizational capabilities, that innovation‐based organizations mobilize to deliver new responsible products and services. This paper thus explores the relationships between the entrepreneurial skillsets of 16 Canadian and Brazilian for‐profit and not‐for‐profit organizations producing Responsible Innovations in Health (RIH) and their degree of responsibility. Our mixed methods study includes interviews with entrepreneurs ( n = 23) and fieldnotes as well as quantitative results from the RIH Assessment Tool. Our findings identify four skillset orientations—Technical, Technical + Business, Social, and Social + Business—that not only reflect (co)founders' training and entrepreneurial motivations but also a proclivity toward the consolidation of either “Technical” or “Social” skills and capabilities. Such consolidation is made possible by recruiting high‐level executives with diverse backgrounds or by tapping on external knowledge sources (e.g., boards of directors, incubators, or volunteers). As five enterprises had no formal business skills, patterns associated to their overall RIH score (ranging from 1 to 5) reveal intriguing results. Organizations with a Social + Business skillset have a slightly lower RIH score (4.1) than those with a Social skillet (4.4) and those with a Technical + Business skillset have a slightly higher score (3.5) than those with a Technical skillset (3.0). The presence of business skills thus appears to mediate the relationship between entrepreneurial skillsets and the degree of responsibility, which may be linked to the distinct roles of ordinary (“doing things right”) and dynamic capabilities (“doing the right things”). These exploratory findings have scholarly and practical implications. First, the tensions and synergies characterizing responsible value creation should be approached by examining the complementary skills and capabilities that need to be assembled and consolidated. Second, the eight cases with a Social + Business skillset clarify the unique capabilities needed to produce highly responsible health innovations. Third, entrepreneurs with a scientific or engineering background should recognize that a Technical skillset is not enough. Fourth, recognizing that “falling in love with the cause” of RIH is not sufficient, investors and boards of directors should adequately support responsible entrepreneurs towards the proper orchestration of skills and capabilities that can reconcile economic and social goals.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| 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".