Influence of knowledge sharing, innovation passion and absorptive capacity on innovation behaviour in China
Bibliographic record
Abstract
Purpose This paper aims to address the question of what can significantly impact employees' IB and how employees' IB may be effectively stimulated by investigating key factors such as employees' knowledge sharing, innovation passion, absorptive capacity and risk-taking behaviour on workplace innovation. The moderating role of risk-taking behaviour on the link between absorptive capacity and innovation behaviour is also investigated. Design/methodology/approach Based on the principles of social exchange theory, the study design explores the complex relationship among knowledge sharing, innovation passion, absorptive capacity and risk-taking vis-à-vis employees' innovation behaviour within a unified analysis framework. Methodologically, employees in the information technology industry in China were surveyed via a questionnaire instrument, with a total of 318 valid questionnaires being collected online. Following a reliability and validity test of the questionnaire, the Smart PLS was used to verify the research model. Findings Statistically significant results reported were as follows: (1) employees' innovation behaviour is positively impacted by knowledge sharing, innovation passion and absorptive capacity; (2) employees' innovation behaviour is negatively impacted by risk-taking behaviour; (3) knowledge sharing is positively impacted by innovation passion; (4) absorptive capacity is positively impacted by innovation passion; and (5) risk-taking behaviour regulates the relationship between absorptive capacity and innovation behaviour. Research limitations/implications Owing to limited research resources, 318 front-line employees were surveyed via an online questionnaire vis-à-vis the sampling method only, specifically taking knowledge sharing, innovation passion, absorptive capacity and risk-taking behaviour as antecedent variables with implications on how employees' innovation behaviour may be stimulated. Originality/value The mechanism of augmenting employees' innovation behaviour is chiefly explained from the perspective of innovation passion and risk-taking behaviour, which are conducive towards promoting employees' willingness to improve knowledge sharing and innovation behaviour. The social exchange theory is used as a basis to form an integrated model for the research, contributing to a cumulative theoretical perspective for future work on the impact of innovation passion and risk-taking behaviour on innovation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".