Knowledge Sharing Types as Predictors of Job Performance Mediated by Problem-Solving Self-Efficacy in the Information System Integration Service Industry

Knowledge sharing is an essential approach to creative problem solving in technology firms, but few studies have considered the information system integration service industry. To address this gap, drawing on the micro-ecology theory, we developed a research model to explore the mediating role of four types of knowledge sharing (i.e., automatic response, rational reflection, ridiculed reflection, and stolen reflection) in the relationship between problem solving self-efficacy (PSSE) and IT workers’ job performance. Data were collected from 307 System Integration IT workers by using the snowball sampling method via a Google questionnaire. Structural equation modeling was used to test the hypotheses of the relationships between the variables. The results showed that PSSE can positively predict four knowledge sharing types; except for stolen reflection, the others can positively predict job performance. The implication of this study is that automatic response systems and rational reflection systems in knowledge sharing can enhance job performance, supported by PSSE. It is hoped that managers can generate System Integration workers’ rational reflection to effectively evoke knowledge sharing.