The interactive effect of organizational identification and reward type on reward valuation
Bibliographic record
Abstract
Abstract Recent management trends highlight two techniques firms use to motivate employee effort: (1) fostering employees' organizational identification (OI) and (2) offering employees tangible rewards such as gift cards instead of cash rewards. We use three studies to examine how OI affects employees' reward valuation and how such effects differ depending on the reward type. Study 1 is an experiment, demonstrating that increasing OI increases the emphasis participants place on a reward's symbolic value, which then increases the total value of the reward—to a larger extent when the reward is tangible than when it is cash. Study 2 is an experiment, providing evidence that Study 1 results are robust to using a tangible reward that is not socially consumed, that is selected either by the firm or by the employee, and that is either a good or poor fit with the employee's personal preference. Finally, Study 3 is a survey, asking respondents about actual rewards they received from their current employer and capturing their actual OI with their current employer. Results in Study 3 are inferentially similar to those in Study 1 and Study 2, albeit stronger for rewards of smaller monetary value. Collectively, these results highlight the particular benefit of strong OI on how employees value tangible rewards relative to cash rewards, which should be of interest to incentive system designers.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".