You’re so good-looking and wise, my powerful leaders! When deference becomes flattery in employee–authority relations
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the relationship between employees’ deference to leaders’ authority and their upward ingratiatory behavior, which may be invigorated by two personal resources (dispositional greed and social cynicism) and two organizational resources (informational justice and forgiveness climate). Design/methodology/approach In this study survey data were collected among employees who work in the banking sector. Findings Strict adherence to leaders’ authority stimulates upward ingratiatory behavior, especially when employees (1) have a natural tendency to want more, (2) are cynical about people in power, (3) believe they have access to pertinent organizational information and (4) perceive their organization as forgiving of mistakes. Practical implications For human resource (HR) managers, this study points to the risk that employees’ willingness to comply blindly with the wishes of organizational leaders can escalate into excessive, inefficient levels of flattery. Several personal and organizational conditions make this risk particularly likely to materialize. Originality/value This study extends prior human resource management (HRM) research by revealing the conditional effects of an unexplored determinant of upward ingratiatory behavior, namely, an individual desire to obey organizational authorities unconditionally.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".