Contingent value of director identification: The role of government directors in monitoring and resource provision in an emerging economy
Bibliographic record
Abstract
Research summary: Although previous studies have explored the value of government directors, less attention has been directed at the antecedents of government directors' engagement in value‐adding activities, such as managerial monitoring and resource provision. Drawing on social identity theory, we offer a novel model that specifies how a government director's dual identifications with the focal firm, and with the government individually and interactively affect his or her governance behavior. An investigation of government directors in China shows that their identification with the focal firm enhances monitoring and resource provision, while their identification with the government affects monitoring and resource provision differently. depending on the dominance of state ownership. The synergistic/substitutable effects between the two types of identification are contingent on state ownership and governance roles. Managerial summary: This study examines how a government director's dual identities—as a government official and as a board member of a focal firm affect his or her engagement in managerial monitoring and resource provision. Using data of Chinese listed firms, we find that government directors who strongly identify with the focal firm or with the government are highly motivated to fulfill their fiduciary obligations. However, the positive effects of their identification with the government differ between state‐owned enterprises ( SOEs ) and non‐ SOEs . The combination of the two identifications offers a further boost to monitoring in non‐ SOEs , and to resource provision in both SOEs and non‐ SOEs , but it acts as a disincentive to monitoring in SOEs . Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".