Exploring the Theory of Employee Planned Behavior: Job Satisfaction as a Key to Organizational Performance
Bibliographic record
Abstract
This article introduces a significant advancement with the "Theory of Employee Planned Behavior" (TEPB), a novel extension of the well-established Theory of Planned Behavior (TPB). The TEPB uniquely positions job satisfaction as a central determinant in driving organizational performance. Using data from county-level government institutions in the United States, this research offers a nuanced exploration into how employee satisfaction influences organizational commitment and citizenship behaviors, which, in turn, substantially impact organizational performance. Our approach utilizes a significant dataset involving 372 dyads across hierarchical levels in government institutions. Through the application of Structural Equation Modeling (SEM), we rigorously validate the TEPB model. The results highlight a significant relationship where enhanced job satisfaction leads to stronger organizational commitment. This heightened commitment further fosters organizational citizenship behaviors, crucial in achieving superior organizational performance. This work notably extends the TPB model by integrating organizational performance as a consequential outcome. It also provides empirical evidence of the direct relationship between job satisfaction and organizational performance, specifically in the context of government institutions. Such findings are invaluable for organizational executives and policymakers in recognizing the paramount importance of employee satisfaction for organizational success. Overall, the TEPB model presented in this study offers a holistic and practical framework for organizations seeking to understand and effectively manage employee behavior. By focusing on job satisfaction, organizations can foster a more committed and proactive workforce, significantly improving performance and efficiency.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".