Innovating While Imitating: The Case of Microentrepreneurs in Contexts of Poverty
Bibliographic record
Abstract
In contexts of poverty, most entrepreneurs create ventures by imitating – but not duplicating – businesses that already exist. While doing this, evidence is clear that some degree of incremental innovation occurs. Unfortunately, there is little understanding of where this innovation comes from. Given that innovation is widely held as critical for entrepreneurs in contexts of poverty, we undertook a qualitative inductive study of 26 Ghanaian entrepreneurs. Across 11 years of data collection, we inquired into the 81 ventures they had collectively started. We unexpectedly found that innovation was shaped primarily by the source of knowledge – rather than resources, connections, or customer access – on which entrepreneurs relied to understand the basic ‘template’ underpinning a venture. Here, five distinct sources of knowledge led to five distinct types of innovation. Moreover, across their careers entrepreneurs tended to progress towards source of knowledge associated with greater degrees of innovation. From these findings we reconceptualize the importance of knowledge in contexts of poverty, contribute to the growing understanding of entrepreneurship as heterogeneous, and make practical recommendations.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.023 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".