The Role of the Perceived Justice in the Relationship between Human Resource Management Practices and Knowledge Sharing: A Study of Malaysian Universities Lecturers
Notice bibliographique
Résumé
Knowledge sharing is one of the important factors in knowledge-based industries due to the fact that knowledge sharing can result in knowledge creation (Noanaka, Umemoto, & Seonoo, 1996) and innovation (Kamsak & Bulutlar, 2010; Wang & Wang, 2012). Thus, by considering that innovation can impact different dimensions of performance (financial and non-financial), (Cohen, 2010), increasing knowledge sharing is one of the necessities in any type of company. Knowledge sharing can be relevant to individual tendency for donating and collecting knowledge (Van Den Hoof & De Ridder, 2004) Different researches have been conducted about influential factors on knowledge sharing. Such researches have emphasized on environmental factors, motivational factors, individual factors, etc. (Wang & Noe, 2010; Li & Poon, 2011) that among such factors, many different theories such as social capital, resource-based review (RBV) and social exchange can be observed. Through narrowing these factors according to available concepts in human resource management (HRM) and organizational behavior, HRM practices have key role on increasing knowledge sharing. HRM practices have strategic contribution (Lengnick-Hall, Lengnick-Hall, Andrade, & Drake, 2009) as well as previous researches (Huselid, 1995; Wright, Gardner, & Moynihan, 2003; Chen & Huang, 2009) regarding RBV theory have the capability to impact employee behavior in case of sharing knowledge. Fong, Ooi, Tan, and Lee (2011), studied impact of HRM practices (training, staffing, performance appraisal, team work and reward) on knowledge sharing as gap of previous researches. Results demonstrated that HRM practices have a key role on improving knowledge sharing. If we consider talent as one of the knowledge resources or as a scope for dependent researches to impact of talent on sharing knowledge, so according to conducted research by Gelen et al. (2013) role of justice will be highlighted in case of relationship between HRM practices and knowledge sharing. It will result to the fact that existed interaction between equity theory (Pritchard, 1969) and underlying theories of researches will become important. There was little focus about this subject in previous researches. However, Khanmohammdi (2014) also emphasized on moderating role of justice in case of relationship between IT, management support, training and reward and knowledge sharing. Universities of Malaysia currently attempt to improve their position in global ranking. One of the helpful aspects for improving scientific level of universities is utilizing of high potential of lecturers. In this regard, improving knowledge sharing level among lecturers results in science creation (Khanomohammadi, 2014). Through considering lecturers as talents and also importance of knowledge sharing among them, it is necessary to clarify role of HRM practices and justice. Hence, this research aims to examine what is the relationship between HRM practices, perceived justice, and knowledge sharing in the Malaysian universities.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».