THE EMERGENCE OF HEALTH TECHNOLOGY FIRMS THROUGH THEIR SENSEGIVING ACTIVITIES AND COMPETITIVE ACTIONS
Bibliographic record
Abstract
Few studies have examined the process by which health technology innovators must socially construct their firm and share their ideas with economic and health system actors. To fill this gap, we intended to provide insights into the differences characterising the health technology startup (HTS) among other startups and test a conceptual model by characterising press releases and media coverage emanating from five firms (three HTS and one well-established firm, and one non-health information technology). Using a multiple case study design, with three embedded units of analysis composed of the startups’ sensegiving intentions, its competitive actions and its strategic responses to pressures, we observed marked difference in the use of marketing and symbolic actions as well as recourse to prominent actors. Besides, health startups were the only ones relying on cognition rather than actors’ self-interest or moral judgments. There were also differences depending on the startup status and the number of actors resulting in different response patterns to pressures. The findings are paving the way to further research on innovators and actor’s inner thinking, which may contribute to shaping business development programs targeted specifically for health tech startups, and may help emerging entrepreneurs compare their evolution to health and non-health tech startups.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".