Perceived organizational politics and quitting plans: an examination of the buffering roles of relational and organizational resources
Bibliographic record
Abstract
Purpose The goal of this research is to examine the link between employees' beliefs that organizational decision-making processes are guided by self-serving behaviors and their own turnover intentions, as well as how this link may be buffered by four distinct resources, two that speak to the nature of peer exchanges (knowledge sharing and relationship informality) and two that capture critical aspects of the organizational environment (change climate and forgiveness climate). Design/methodology/approach Quantitative survey data were collected among 208 employees who work in the oil and gas sector in Mozambique. Findings The results indicate that employees' beliefs about dysfunctional political games stimulate their plans to quit. Yet this translation is less likely to occur to the extent that their peer relationships are marked by frequent and informal exchanges and that organizational leaders embrace change and forgiveness. Practical implications For organizations, these findings offer pertinent insights into different circumstances in which decision-related frustrations are less likely to escalate into quitting plans. In particular, such escalation can be avoided to the extent that employees feel supported by the frequency and informal nature of their communication with colleagues, as well as the extent to which organizational leaders encourage change and practice forgiveness. Originality/value This study adds to extant research by explicating four unexplored buffers that diminish the risk that frustrations with politicized decision-making translate into enhanced turnover intentions.
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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 | 0.002 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".