| Knowledge is a powerful resource that enables organizations and employees to achieve faster learning and develop better decision-making processes. Knowledge management is a critical strategy for an organization to achieve a sustainable competitive advantage, and consequently succeed in today's knowledge-based economy. Knowledge management systems (KMS) are a class of information systems that manage, store and distribute knowledge. Knowledge is socially created and utilized; thus a socio-technical perspective is important for the deployment of KMS. The need for such socio-technical investigation has been stressed by several KMS and management researchers (Davenport and Prusak, 1998; O'Dell and Grayson, 1998; Alavi, 2000; Chauvel and Despres, 2002; Scholl et al., 2004). Limited empirical studies have focused on KMS usage (Kankanhalli and Tan, 2004). KMS involves two types of usage: knowledge utilization and contribution. There is a discrepancy between social and technical determinants of these types of KMS usage.; Consequently, the objective of this dissertation is to investigate the socio-technical determinants of knowledge management systems (KMS) usage, for repository KMS, based on two types of usage: knowledge contribution and utilization. The social factors are related to management support, trust and rewards. The technical factors are related to knowledge, system and service quality. The study data were collected through a questionnaire from employees in a petroleum company in Oman. The Partial Least Squares (PLS) methodology was utilized for the quantitative data analysis.; The study findings suggest that there are some differences between the socio-technical determinants of knowledge utilization and contribution. The determinants of knowledge utilization are knowledge quality, knowledge trust, management support, and utilization rewards; whereas the determinants of knowledge contribution are contribution rewards, management support and system storage level. The findings also indicate that knowledge utilization results in individual benefits such as improved performance and innovation, while knowledge contribution results in intangible, tangible and performance benefits at individual level. In addition, answers to open-ended questions were used to refine the results of the statistical analysis. The analysis indicates that there are different factors that predict the value of a system in terms of knowledge utilization and contribution. |
