The Bribery Paradox in Transition Economies and the Enactment of ‘New Normal’ Business Environments
Bibliographic record
Abstract
Abstract We develop a novel, sense‐making perspective on corruption in transition economies. Prior research has focused on understanding why some entrepreneurs are more likely to pay bribes than others. It typically assumes that paying bribes will lead to an intended – albeit unfair – competitive advantage. We challenge this assumption and uncover a bribery paradox: drawing upon sense‐making logic, we argue that beyond gaining an immediate benefit from bribing, entrepreneurs who frequently pay bribes may in the longer run be enacting a ‘new normal’ business environment perceived as high in obstacles, especially in transition countries. As sense making is grounded in identity construction and one’s social context, we argue that owners of family firms will be especially vulnerable to the dangers of perceiving greater obstacles over time and enacting an obstacle‐ridden ‘new normal’ business environment. We find empirical support for our framework on a sample of 310 privately held small and medium‐sized enterprises (SMEs) from 22 transition economies.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".