Using Experience Sampling Methodology to Advance Entrepreneurship Theory and Research
Bibliographic record
Abstract
The authors propose the use of experience sampling methodology (ESM) as an innovative methodological approach to address critical questions in entrepreneurship research. ESM requires participants to provide reports of their thoughts, feelings, and behaviors at multiple times across situations as they happen in the natural environment. Thus, ESM allows researchers to capture dynamic person-by-situation interactions as well as between- and within-person processes, improve the ecological validity of results, and minimize retrospective biases. The authors provide a step-by-step description of how to design and implement ESM studies beginning with research design and ending with data analysis, and including issues of implementation such as time and resources needed, participant recruitment and orientation, signaling procedures, and the use of computerized devices and wireless technologies. The authors also describe a cell phone ESM protocol that enables researchers to monitor and interact with participants in real time, reduces costs, expedites data entry, and increases convenience. Finally, the authors discuss implications of ESMbased research for entrepreneurs, business incubators, and entrepreneurship educators.
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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 | 0.060 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| 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".