Increasing OCB: the influence of commitment, organizational support and justice
Bibliographic record
Abstract
Purpose – The purpose of this paper is to describe the development of a coherent conceptual framework that could guide research that enhances our understanding of the factors that influence extra-role workplace behaviors and work performance in health care. In health-care settings, work performance is dependent upon worker’s extra-role behaviors. Design/methodology/approach – The authors draw upon theory and current research in the field of organizational behavior and work motivation to explain the relationships between extra-role behaviors (ERBs), commitment, perceived organizational support (POS) and justice. These behaviors are related to a number of factors, including one’s affective commitment, POS and organizational justice. The influence of most of these concepts on work outcomes has been established in disparate studies, but their precedence in terms of influencing extra-role behaviors is not well understood. Findings – An augmented framework is produced, incorporating concepts of relevance to work motivation and work attitudes. Propositions, predicated on research evidence, are offered. Research limitations/implications – Spontaneous, emotional and/or reflexive behaviors are not accounted for in the conceptual framework. Practical implications – By adjusting interaction with employees, managers can bring about positive effects, facilitating constructive ERBs, which can improve work performance and productivity, patient safety, care quality and enable cost savings. Originality/value – This paper offers a novel comprehensive framework based upon a comprehensive literature review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".