Designed entrepreneurial legitimacy: the case of a Swedish crowdfunding platform
Bibliographic record
Abstract
Digital entrepreneurs face the liability of newness, like any other entrepreneur. However, this liability of newness is manifested differently: it is mediated by an artefact, in this case a platform. This paper examines how a digital entrepreneur mediated by a platform can build legitimacy, something hitherto thought to be embedded within a social relationship (that is, one that a digital platform may be unable to have). Based on a qualitative research design, we develop the concept of “designed legitimacy”, and we point to how trust may not be enough to overcome the liability of newness. Rather, legitimacy is needed to attract users and resources, and thus for growth and success. We further highlight the means through which a platform may come to be seen as legitimate, namely by designing with legitimacy in mind: by using symbols in design, asymmetric legitimacy building, and sequential two-stage legitimacy building. We end the paper with propositions for further study and the implications of this research for digital entrepreneurship and platforms.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".