Individual and contextual determinants of innovative work behaviour: Proactive goal generation matters
Bibliographic record
Abstract
This study examines the extent to which proactive goal generation is a self‐regulatory mechanism that explains how individual and contextual factors influence employee innovative work behaviour. Specifically, it is proposed that learning goal orientation ( LGO ), psychological climate for innovation, and task variety positively and indirectly influence innovative behaviour via proactive goal generation activities, namely envisioning and planning. Additionally, LGO is expected to positively moderate the planning–innovation link. Based on data collected at two points in time on a sample of 107 employees from 12 small I talian enterprises, we found that envisioning and planning mediated the positive impact of LGO , psychological climate for innovation, and task variety on innovative behaviour. Furthermore, the relationship between planning and innovative behaviour was stronger when LGO was higher. Theoretical and managerial implications of these findings are discussed. Practitioner points Individual engagement in proactive goal‐regulatory activities is an important driver of workplace innovative behaviour. It is hence worthwhile for managers to provide employees with practical tools and guidelines to develop their proactive goal‐setting and goal‐planning skills. Learning goal orientation, psychological climate for innovation, and task variety indirectly shape innovative work behaviour by affecting envisioning and planning processes. In order to increase employees' motivation to engage in proactive goal generation tasks, managers should thus promote the development of a change‐oriented work environment, ensure an adequate amount of variety in the execution of work activities, and retain and develop a learning‐oriented workforce. Learning goal orientation is a boundary condition associated with planning's effects on innovative behaviour. Managers should hence emphasize skill development and the use of flexible approaches in the execution of plans and tasks.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".