Time to adopt knowledge management applications: influences that affect individual decisions within a large information technology services organisation

There is growing consensus in business research and practice that knowledge is increasingly the driver of competitive advantage. This thesis focuses on one aspect of the issue by identifying factors that affect the adoption of Knowledge Management (KM) applications by individuals in an IT Services organisation. The study considers the adoption decision by individuals once senior management have decided to invest in IT enabled KM applications (KMA) and KM systems (KMS). -- In the thesis, a framework, the KM Spectrum, is developed that differentiates between the varying characteristics of KMAs and frames the research. The thesis identifies 32 potential success factors for KM adoption proposed in the reviewed literature. These factors are related to the disciplines of organisational science, diffusion theory and adoption models. -- The methods used in the research: secondary data study, interviews and the electronic survey, combined with the representativeness of the survey sample, triangulate to provide confidence in the empirical understanding of the factors that influenced the adoption of KM within the specific knowledge-based organisation. -- In developing the theoretically-informed view of the factors that affect individual adoption of KMAs the research concludes that studying KM adoption at an individual level and across multiple KMAs identifies influences on adoption masked by adoption research conducted at a KM system and/or organisational level. By studying KM adoption at an individual level this thesis finds that the adoption by individuals of KMAs is primarily a diffusion phenomenon and that the factors that influence KMA adoption vary with the type of KMA being adopted. The empirically identified factors that affect adoption at an individual level build to a staged model of KM adoption, called the enhanced KM adoption (EKMA) model. The EKMA model represents four phases of KM adoption and differential influences that apply across the adoption lifecycle. Additionally, the study provides some indications of further research topics and proposes a checklist to assist practitioners with the deployment of KMAs and KM systems.