Persuading managers to enact ideas in organizations: The role of voice message quality, peer endorsement, and peer opposition
Bibliographic record
Abstract
Summary We extend research on employee voice by examining what persuades managers to enact voice messages communicated on organizations' idea management platforms (i.e., software systems designed to gather, vet, and enact employee voice). Applying the elaboration likelihood model of persuasion, we propose that voice message quality affects managerial voice enactment via peer endorsement and that peer opposition qualifies the latter effect. Specifically, we argue that peers are more likely to endorse higher‐ versus lower‐quality voice messages because they are attentive recipients who are motivated to support higher‐quality voice. In turn, we argue that managers are influenced by image concerns and legitimacy inferred by social proof, and thus, they will enact voice messages with higher levels of peer endorsement, especially when combined with lower opposition. Results of our archival analysis of over 5000 voice messages communicated on five organizations' idea management platforms support our predictions, such that peer endorsement mediates the relationship between voice message quality and managerial voice enactment, and that this relationship is stronger under conditions of lower versus higher peer opposition. Altogether, our research illuminates how voice messages on idea management platforms are endorsed and ultimately enacted by organizational leaders.
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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 | 0.019 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".