Determinants of effective client entertainment in China: a transaction governance approach
Bibliographic record
Abstract
Purpose This paper aims to take an economic transaction governance approach to explore the determinants for the effectiveness of the social practice of client entertainment in facilitating business relationships in China. Design/methodology/approach The analysis is based on a broader theoretical framework which posits that exchange relationships are regulated through a combination of market, legal and social relational mechanisms and client entertainment plays a governance role by reinforcing social relational governance to regulate the behaviors of economic actors. Upon this framework, this study proposes that the social behavioral features of client entertainment affect the effectiveness of client entertainment in facilitating exchange relationships and that time moderates such effects. These hypotheses were tested on survey data collected from a sample of Chinese sales managers. Findings Empirical results indicate that the effectiveness of client entertainment in facilitating exchange relationships is associated with its social behavioral features that could reinforce social relational governance, including intensity (i.e. value and frequency) and format (i.e. intimacy and observability) of entertainment activities, and the time factor plays a moderating role. Practical implications This study can potentially help policymakers to regulate client entertainment, and business practitioners to manage entertainment spending, more effectively and efficiently without causing legal and ethical problems. Originality/value This is the first study that takes an economic transaction governance perspective to directly explore how the social practice of client entertainment plays a constructive role in China’s economic life and what factors affect its effectiveness in playing such a role. It offers guidelines for policymakers, business managers and future research to manage and study this 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".