Seeing the whole: Configurational cognition and new venture resource mobilization
Bibliographic record
Abstract
Abstract Research Summary Research is scant on how multiple venture attributes combine as “whole packages” of signals (or cognitive configurations) in resource holders’ eyes, shaping a venture's ability to mobilize resources. Drawing on a qualitative comparative analysis of 1,395 crowdfunding campaigns, we identified different configurations of signals for high and low resource mobilization, theorizing abductively their underlying mechanisms through the analysis of case‐level qualitative data. Our results explain some past mixed findings, such as the contradictory effects of social value and entrepreneurial narratives, showing that these narratives can instead be successfully combined in the presence of signals of venture quality and community embeddedness. We show that there is no single best way to impress resource holders, but multiple recipes to holistically communicate a venture's value. Managerial Summary Analyzing Kickstarter crowdfunding campaigns, we examine how entrepreneurs combine four signals to raise money: 1) the venture's underlying quality; 2) social networks; 3) narratives; 4) embeddedness in the crowdfunding community. We identified four successful configurations of these signals (500% above the funding goal) and two failing configurations (4% of the funding goal). Narratives per se are not sufficient to mobilize resources, unless backed by signals of quality and community embeddedness. A simpler narrative is supported by cheaper quality signals (product images). More complex narratives (combining social value, entrepreneurial orientation, positive psychology) are supported by more costly signals (videos). Our results encourage entrepreneurs to look beyond “silver bullet” solutions and think holistically how to communicate their ventures as “whole packages”.
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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 | 0.005 | 0.020 |
| 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 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".