Bibliographic record
Abstract
This chapter describes the contribution of the operations management field to entrepreneurship and startup practices. As examples of unique operational innovations in startup settings, readers may recall well-documented news stories about two unicorns: the transportation firm Uber with its yield management strategies and the hospitality firm Airbnb with its quality management practices. It defines business startup operations as a configuration of resources and activities at nascent organizations that are geared to create, organize, and grow businesses based on the manufacturing of a product or delivery of a service. The chapter's organization and resulting contribution follow a two-part setup: what we know about startups and what we observe as emerging trends. It explores the body of knowledge in the OM-entrepreneurship interface regarding venturing creation and technology commercialization throughout the entrepreneurial value chain. To establish what is already known about startup operations, the chapter summarizes published review covering the 2001–2011 period and then augment this summary with publications from the 2011–2015 period.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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; both teacher heads agree on what is shown here.
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".