Effects of Perceived Organizational Support and Leader Member Exchange on Individual Risk Taking
Bibliographic record
Abstract
People’s relationships with their organization and their managers have significant effects on their work and their workplace attitudes. In this paper we look at the relationship between perceived organizational support (POS), leader member exchange (LMX), and risk taking. Workers with high levels of POS and LMX have more organizational commitment, have lower absenteeism and turnover rates, and exhibit more citizenship behaviour. We hypothesize that the higher the level of POS and LMX the more likely a person will be to take constructive versus destructive risk. Constructive risk is defined as risk involved in innovation and creativity, and is therefore desirable for organizations. This paper also looks at the links between Perceived Organizational Support and Leader Member Exchange, and at which of these factors is more influential on risk taking. This will allow organizations to best allocate their time and energy on initiatives linked to raising POS and LMX to get the highest return. This paper reflects initial findings of three separate studies looking at POS, LMX and risk taking.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".