Research Impact: How Seemingly Innocuous Social Cues in a CEO Survey Can Lead to Change in Board of Director Network Ties
Bibliographic record
Abstract
This study extends earlier research suggesting that board network ties may reflect the strategic and/or political concerns of top managers by considering how the managerial objectives that drive the formation and maintenance of board interlock ties may be subject to social influence. The particular form of social influence examined in this study derives from the social network research process itself. Specifically, we draw from research on social information processing and the framing of information to suggest how the administration of social network surveys can influence managers’ perceptions about their relationship to directors and the potential benefits to be derived from director network ties, thus affecting their subsequent selection of board members in ways that change the firm’s board interlock ties.We also consider how this social influence effect may diffuse beyond the actual survey respondents to create a more pervasive influence on the actions of managers at other firms in the board interlock network. We test our theoretical argument with an original quasiexperiment in which CEOs are randomly assigned to different versions of a survey questionnaire that have the potential to prime different schemata about the possible benefits to be derived from board network ties. Beyond addressing the potential for social influence in the formation and maintenance of board network ties, our study also addresses the potential for unintended reactive measurement effects in social network research, wherein network surveys influence the very ties that they are designed to measure.
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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 | 0.027 | 0.220 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".