Strategic Invisibility in Emerging Economies: How Entrepreneurs Navigate Corruption
Bibliographic record
Abstract
Entrepreneurship is vital for economic development in emerging economies. Yet entrepreneurs in these contexts continue to face unique challenges such as widespread opportunism fueled by weak institutions and high levels of corruption. While previous studies explored how entrepreneurs navigate these environments, it remains unclear how they strategically manage their visibility to mitigate threats. Using a qualitative phenomenological approach rooted in the entrepreneurship-as-practice perspective and Bourdieu’s theory of practice, we conducted extensive fieldwork in North Africa, shadowing an entrepreneur we refer to as Casper. Our study uncovers three key strategies—concealing, diminishing, and obfuscating—that collectively form a process we theorize as strategic invisibility work. Our theoretical model provides new insights into how entrepreneurs navigate weak institutional environments by strategically managing their visibility to reduce the risk of opportunism. Theoretically, we challenge the Western-centric view that visibility is universally essential for success, showing instead that invisibility can be a strategic advantage. Our findings contribute to the entrepreneurship literature by offering a novel process model of strategic invisibility, expanding our understanding of how entrepreneurs adapt and thrive in highly uncertain and opportunistic contexts.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".