How does organizational support for innovation influence job crafting and knowledge sharing behaviors? A comparison between teleworkers and office workers
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».