Empowerment Versus Pressure: The Paradoxical Effects of HPWS on Employees’ Helping Behaviors
Bibliographic record
Abstract
This study explores how HPWS might send mixed and contradictory effect powers (i.e., psychological empowerment versus performance pressure) toward employees’ helping behavior. Plus, we explore how line managers’ idiosyncratic HR implementation (HR I-deals) moderates these relationships. In that, I-deals foster the positive effects of HPWS on helping behavior through psychological empowerment and alleviate the harmful effect of HPWS on helping behavior via performance pressure. Collecting data from the banking sector in two waves of time (i.e., Time-lagged study), we found support for our theoretical predictions. We have found that though HPWS can encourage employees' helping behavior through psychological empowerment, it can concurrently discourage employees from helping others because HPWS is performance-oriented in nature and places immense pressure on employees for task accomplishment and achieving imminent operational targets. Our results support the duality of the effects of HPWS on employee discretionary behaviors. Plus, we demonstrated that line managers’ HR enactment behavior is crucial to fostering the positive side of HPWS while mitigating its harmful effects. This endeavor helps to bridge two disconnected research streams on HPWS and their impact on extra-role behaviors and resurfaces the critical role of line managers’ HR implementation behavior in enhancing the effectiveness of HPWS. We discuss implications for theory and practices and the avenues for future research.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".