The Mind in the Middle: Taking Stock of Affect and Cognition Research in Entrepreneurship
Bibliographic record
Abstract
In spite of substantial advances, entrepreneurship research on affect and cognition remains characterized by a multiplicity of theoretical approaches, methods, variables and measures. Although this multiplicity affords a lot of richness, it also poses potential risks – from the lack of a coherent knowledge base to the dangers of an atomistic evolution, with minimum exchanges between ‘siloed’ groups of scholars, limited theoretical integration and increased chances of redundant repetitions without real advances in understanding. To help guard against these risks and in order to augment the impact and value‐adding contribution of future research, the six papers in this special issue analyse the progress made in entrepreneurship research on (1) situated cognition, (2) fear, (3) how affective dynamics influences entrepreneurship, (4) intuition, (5) opportunity evaluation and (6) entrepreneurial team cognition. This short introductory essay builds on the synthesis of the literature to summarize ‘the road travelled so far’. The six papers forming this special issue are then introduced and their respective focus and contributions are detailed. The authors conclude by reflecting on these papers’ implications, and offer a number of observations about future research and the ‘road ahead’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".