The Role of Business Entertainment in Economic Exchanges: A Governance Perspective and Propositions
Bibliographic record
Abstract
ABSTRACT While entertainment activities in private business settings (i.e., business entertainment) are widely seen all over the world, issues about their prevalence have remained unresolved in the literature. This study takes an institutional approach to elucidate (1) the governance role of business entertainment in economic exchanges, (2) the mechanism through which business entertainment plays this role, and (3) the conditions under which business entertainment plays a greater role to facilitate economic exchanges. Our starting point is that economic transactions are governed through a combination of market rules, legal restraints, and social norms. We argue that business entertainment plays a governance role by boosting the power of social norms to regulate the behaviors of economic actors. As such, business entertainment should be more prevalent under the conditions where social fabrics are dense but market and legal infrastructures are underdeveloped. This governance approach provides a common ground to accommodate the positive versus negative views on business entertainment advocated by two camps of researchers in management, economics, and sociology. It also offers useful guidelines for policymakers to regulate, and for executives to manage, this prevalent but often misunderstood business practice.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".