Multidimensional entrepreneurial intent: an internationally validated measurement approach
Bibliographic record
Abstract
Purpose Entrepreneurial intent (EI) is a foundational construct in theories of entrepreneurship. But three challenges currently threaten the author’s ability to accurately measure EI. First, previous measurement approaches have confounded EI with closely related but theoretically distinct constructs such as attitudes and beliefs about entrepreneurship. Second, they have treated EI as an “all-or-nothing” decision, without reflecting the step-wise commitment of the entrepreneuring process. And finally, much of past EI research has been done in Western developed countries without validation in a diverse international context in which unstated assumptions about the EI construct may not hold. The purpose of this paper is to report on the development of a new EI scale that addresses these issues. Design/methodology/approach Nested structural equation modelling is used to develop and validate a novel scale for measuring EI in international contexts, based on data from 998 respondents in eight countries. Findings A two-dimensional substructure to the EI construct is revealed as especially apparent in non-Western countries. Based on this, a new 11-item scale is proposed and validated. Research limitations/implications Previous studies utilizing the EI construct may be biased by its imprecise measurement and confounding by other constructs in the nomological net. The present study provides new insight into the nature of the EI construct and a novel instrument for measuring it without bias. The discovered two-dimensional structure for EI measurement may also have implications for theorists interested in antecedents and effects of EI. Practical implications Accurate measurement of EI is essential to developing and targeting policies to effect changes in national entrepreneurship. Previous measurements may therefore have contributed to misstatement of policy objectives and allocation of national resources. Originality/value This research provides a validated method of measuring EI without the serious confounds of previous scales, and that is robust to a wide range of international settings. It also provides new insight into a two-dimensional substructure to the EI construct that has not been observed in previous studies.
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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 | 0.034 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".