How does organizational support for innovation influence job crafting and knowledge sharing behaviors? A comparison between teleworkers and office workers
Bibliographic record
Abstract
Purpose Scholars highlight the lack of research that explains the mechanisms leading to knowledge sharing, which appears complex and involves many variables. The primary aim of this study is to investigate the direct effect of organizational support for innovation on job crafting behaviors and knowledge sharing. The second objective is to assess the mediating role of job crafting in the relationship between organizational support for innovation and knowledge sharing. The third aim is to compare the direct effects of organizational support for innovation on job crafting behaviors and knowledge sharing between teleworkers and office workers. Design/methodology/approach Based on an empirical study involving 193 teleworkers and 191 office workers, the structural equation modeling method was employed to examine the direct and indirect effects of organizational support for innovation on knowledge sharing via job crafting behaviors. The comparison between teleworkers and office workers was investigated using a multigroup approach in AMOS software. This research is grounded in the conservation of resources theory and social exchange theory to elucidate these relationships. Findings The results indicate that organizational support for innovation has a positive influence on job crafting activities, manifested by the increase in structural and social resources, as well as the amplification of work-related challenges. The results also indicate that organizational support for innovation directly promotes knowledge sharing behavior and indirectly through job crafting. Furthermore, the findings reveal that these effects on job crafting and knowledge sharing are stronger among teleworkers compared to office workers. Research limitations/implications The study has limitations. Its cross-sectional design does not establish causality, potentially leading to common method variance. However, after implementing many procedural and performing statistical tests, common method variance was not significant in this research. Replicating the study longitudinally would be valuable. Additionally, considering personality traits and technology characteristics in job crafting behaviors would be beneficial. Lastly, the study focuses only on accountants and predates COVID-19, which may impact its findings and generalizability. Practical implications The study’s findings underscore the practical significance of supporting innovation and fostering job crafting to enhance knowledge sharing, particularly for remote workers. It highlights that the extent of employees’ engagement in job crafting depends on the level of innovation support provided in their workplace. To mitigate potential negative outcomes such as increased absenteeism, reduced productivity and retention challenges, organizations could benefit from training supervisors to prioritize and encourage job crafting and knowledge sharing behaviors among employees, especially in telework settings. Ensuring alignment between organizational messaging and managerial attitudes is crucial. Without autonomy or flexibility for job crafting, the positive effects of organizational innovation support may be limited. Originality/value This study contributes to the literature by demonstrating that job crafting behaviors serves as mechanisms between organizational support for innovation and knowledge sharing. The findings further advance the literature by revealing three psychological and motivational processes that may explain this relationship, particularly when comparing teleworkers to office workers. Our results reveal that the effect of organizational support for innovation on job crafting and knowledge sharing is stronger among workers who telework compared to office workers. This advances the theory of conservation of resources, especially the significance of resource gains, particularly in contexts where employees need resources, such as in telework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".