Incentive Contract Design and Employee‐Initiated Innovation: Evidence from the Field*
Bibliographic record
Abstract
ABSTRACT This study examines how the design of incentive contracts for tasks defined as workers' official responsibilities (i.e., standard tasks) influences workers' propensity to engage in employee‐initiated innovation (EII). EII corresponds to innovation activities that are not formally assigned to workers but are nonetheless encouraged and considered to be important for the company's success. Like other extra‐role behaviors, EII is difficult to incentivize directly. Therefore, it is important to understand whether and how explicit incentive contracts designed for the workers' standard tasks may indirectly influence their EII activity. We use field data from a manufacturing company that uses a dedicated information system to track workers' EII idea submissions. We find theory‐consistent evidence that, compared to workers receiving fixed pay, employees rewarded for their standard tasks with variable compensation contracts exhibit a lower propensity to engage in EII. This result is concentrated among ideas benefiting other constituents and activities beyond the proponents' standard task (i.e., broad‐scope ideas). In contrast, we find no difference attributable to standard task incentive design in the proposal of innovation ideas narrowly focused on the proponent's standard task (i.e., narrow‐scope ideas). Our findings suggest that variable pay narrows employees' conceptual focus around the standard task and hinders employee engagement in broad‐scope innovation activities compared to fixed compensation contracts. We contribute to the literature on incentives for innovation by showing that standard task compensation contracts have spillover effects on EII behavior. We also contribute to the nascent literature on EII by showing that innovation types, defined based on their relation with the proponent's standard task, matter. Our results are relevant for practitioners in that managers relying on variable pay contracts to incentivize standard task performance should expect lower employee engagement in broad‐scope EII.
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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 | 0.034 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".