Examining the inverted U-shaped relationship between workload and innovative work behavior: The role of work engagement and mindfulness
Bibliographic record
Abstract
Is workload good or bad for employee innovation? Workload and innovative work behavior are widely studied research topics. However, the relationship between them is not well understood. As a result, there is a lack of evidence-based knowledge that could inform managers and organizations on how to boost workplace innovation in demanding work contexts. Building on the job demands–resources model, the present study posits that workload relates to innovative behavior through work engagement. Specifically, we argue that this indirect relationship exhibits an inverted U-shaped pattern in which workload is most likely to benefit innovative behavior when it is moderate. We further identify mindfulness as an important moderator that influences individuals’ ability to manage stress. In support of these predictions, three studies – a two-wave time-lagged study of 160 employees from various Canadian firms, a three-wave time-lagged study of 153 employees from US firms, and a two-wave panel study of 208 employees from US firms – found work engagement mediated the inverted U-shaped relationship between workload and innovative behavior. Moreover, when mindfulness was high, intermediate levels of workload were associated with increased innovative behavior through enhanced work engagement (Studies 1 and 2). We discuss the implications of these findings for theory and practice.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".