Entrepreneurial Scripts and the New Transaction Commitment Mindset: Extending the Expert Information Processing Theory Approach to Entrepreneurial Cognition Research
Bibliographic record
Abstract
In this study, we extend the expert information processing theory approach to entrepreneurial cognition research through an empirical exploration of the new transaction commitment mindset among business people in Canada, Mexico, and the United States. Using analysis of covariance, multivariate analysis of variance, and hierarchical regression analysis of data from a cross–sectional sample of 417 respondents, our results provide a foundation for additional cross–level theory development, with related implications for increasing the practicality of expert information processing theory–based entrepreneurial cognition research. Specifically, this paper: (1) clarifies the nature of the relationship between entrepreneurial expert scripts and constructs that might represent an entrepreneurial mindset at the individual level of analysis; (2) identifies analogous relationships at the economy level of analysis, where the structure found at the individual level informs an economy–level problem; (3) presents a North American Free Trade Agreement–based illustration analysis to demonstrate the extent to which cognitive findings at the individual level can be used to explain economy–level phenomena; and (4) extrapolates from our analysis some of the ways in which script–based comparisons across country or culture can inform the more general task of making information processing–based comparisons among entrepreneurs across other contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".