The Impact of Perception of Organizational Politics, Social Capital, and Power Centralization on Employee Turnover Intentions.
Bibliographic record
Abstract
ABSTRACT This study examines how perceptions of organizational politics (POP), social capital (SC), and centralization of power (CENT) influence employee turnover intentions (TOI) in nonprofit and public organizations in rural Northern Alberta. Grounded in social capital theory, the study explores how bonding, bridging, and linking forms of SC interact with organizational dynamics to shape employees’ decisions to stay or leave. Data were collected through a cross-sectional survey of 174 employees and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS. The findings indicate that POP is significantly associated with greater TOI. CENT partially mediates the relationship between POP and TOI, and when combined with SC, it does not sequentially mediate this relationship. Furthermore, POP, CENT, and SC collectively form a full mediation pathway, demonstrating the complex interplay among these constructs in influencing employee turnover. However, SC alone does not mediate the relationship between POP and TOI.By highlighting the conditional and interconnected nature of these relationships, this study challenges assumptions about the uniformly positive effects of social capital. These findings, derived from an underrepresented regional and organizational context, emphasize the importance of inclusive leadership, transparent decision-making, and coordinated teamwork in sustaining SC and reducing employee turnover. Keywords: organizational politics, social capital, centralization, turnover intention, PLS-SEM, nonprofit organizations, rural Alberta
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".