Name
Abstract | ||
|---|---|---|
Purpose - This paper aims to propose that knowledge management (KM) could be a way to nurture job satisfaction and examine how KM can increase individual employees' job satisfaction. Design/methodology/approach - A theoretical model concerning the connections between five facets of KM (knowledge acquisition, knowledge sharing, knowledge creation, knowledge codification and knowledge retention) and job satisfaction is proposed. It is then empirically tested with a structural equation modelling partial least squares analysis of a survey data set of 824 observations, collected from the members of a Finnish municipal organisation. Findings - Existence of KM processes in one's working environment is significantly linked with high job satisfaction. Especially intra-organisational knowledge sharing seems to be a key KM process, promoting satisfaction with one's job in most employee groups. Interestingly, significant knowledge-based promoters of job satisfaction differ as a function of job characteristics. Practical implications - KM has a strong impact on employee job satisfaction, and therefore, managers are advised to implement KM activities in their organisations, not only for the sake of improving knowledge worker performance but also for improving their well-being at work. Originality/value - This paper produces knowledge on a type of consequence of KM that has been largely unexplored in previous research, individual job satisfaction. Also, it promotes moving the KM literature to the next stage where the impact of KM practices is not explored as a "one size fits all" type of a phenomenon, but rather as a contingent and contextual issue. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1108/JKM-10-2015-0398 | JOURNAL OF KNOWLEDGE MANAGEMENT |
Keywords | Field | DocType |
Knowledge management,Job satisfaction | Job analysis,Knowledge sharing,Job design,Computer science,Knowledge management,Personnel psychology,Contextual performance,Job satisfaction,Job performance,Job attitude | Journal |
Volume | Issue | ISSN |
20.0 | 4.0 | 1367-3270 |
Citations | PageRank | References |
9 | 0.81 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aino Kianto | 1 | 93 | 5.55 |
| Mika Vanhala | 2 | 12 | 1.19 |
| Pia Heilmann | 3 | 9 | 0.81 |