Institutional Logics and Frame-Switching Ability: A Model of Entrepreneurial Insight
Bibliographic record
Abstract
To understand the cognitive processes of entrepreneurs is to shed light on the mechanisms of opportunity discovery and innovation. This paper presents a hierarchical model of entrepreneurial insight that combines individual experience with the seven-order typology of the institutional logics perspective and the functional mechanism of mental flexibility in cognitive science, frame switching ability. The main contribution of this paper is that frame switching ability is an important mediator in entrepreneurial cognitive processes that facilitates more novel idea generation. This aspect of individual cognitive functions has not been studied in prior strategy nor entrepreneurship literature to date. To test the conceptual model developed in this paper, this study uses the think-aloud protocol supported by survey methodology in a sample of environmental science entrepreneurs in a major western city in Canada. Specifically, the authors have conducted interviews with entrepreneurs, coded interview transcripts, designed surveys, validated creative exercises, and engaged subject-matter expert judges to support the interpretation of the transcripts. Preliminary exploratory and qualitative analysis support the study’s main hypotheses. The data also suggests that previous studies measuring entrepreneurial experience, a common independent variable, should consider both the depth and breadth of experience as well as differential influences of distinct types of experience with institutional logics.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| 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".