Honesty Is Not Always the Best Policy: The Role of Self-Esteem Based on Others’ Approval in Qualifying the Relationship Between Leader Transparency and Follower Voice
Bibliographic record
Abstract
In this article, we integrate social exchange theory with insights from contingent self-esteem to explain why leader transparency (LT) might not always be reciprocated by enhanced follower voice. We theorize that when leaders are transparent, they initiate a social process that offers the exchange of honesty by signaling that the work environment is psychologically safe enough for followers to express their opinions in return. Yet, for individuals whose self-esteem fragilely relies on the approval of others (i.e., self-esteem based on others’ approval), reciprocating transparent communication is more difficult because speaking up exposes their self-worth to the potential for rejection. We test our model at the individual and team level. In Study 1 (individual level), we find that LT is positively related to follower self-rated voice one-month later through enhanced follower psychological safety, but only when follower self-esteem based on others’ approval is low as opposed to high. In Study 2 (team level), we find that team LT is positively related to leader-rated team voice six-months later through team psychological safety; however, only when team level self-esteem based on others’ approval is low, but not high. These results underscore that leader transparency can be reciprocated with enhanced follower voice, but only when followers have secure and stable self-esteem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".