Towards a model that explains knowledge sharing behaviour for complex tasks

© 2018 Dr. Marion ZalkIn organisations, knowledge sharing has been studied in many different contexts and settings, as it is an integral part of knowledge creation. This thesis presents and tests a model that explains motivations for knowledge sharing behaviour for complex tasks. The foundation of the research is a number of key organisational and motivational theories including social cognitive theory. Existing research in this area is focussed in 3 domains – the enabling and supportive role of technology; the organisation and its characteristics; and the individual and his/her attitudes and intentions to knowledge sharing. Scant research has considered the task at hand, the task about which individuals perform knowledge sharing and the impact of this task on the individual’s knowledge sharing behaviour. This research is centred on this task, from here on, referred to as ‘the task’ in this thesis. Since the focus of this research is on knowledge sharing behaviour related to complex tasks - it is the characteristic of complexity that forms the basis of the models that are tested in this research. In order to establish the task as the key factor in the performance of knowledge sharing behaviour, the research proposes 3 different models, each with a different variable as the key influencer in the performance of knowledge sharing behaviour. These variables are: i) the task about which knowledge is being shared, ii) the technology used for sharing knowledge, and iii) the actual task of performing knowledge sharing behaviour. These three behavioural models were tested empirically using an experimental research design involving 76 individuals performing a complex task. Data collected through surveys was analysed statistically using analysis of variance and process analysis to assess individual performance associated with knowledge sharing behaviour. Findings confirm that task self-efficacy and knowledge sharing self-efficacy indirectly influence the relationship between task complexity and the quality of knowledge shared. Findings also confirm that commitment to both the task itself and the knowledge sharing, in addition to self-efficacy, indirectly influence the relationship between task complexity and the understandability of knowledge shared. Finally, findings demonstrate that feedback on knowledge shared positively influences the choice of knowledge transfer mechanisms. In addition, qualitative analysis of the codified knowledge and the answers to the open-ended questions validated the study’s findings and provided richer insights into the empirical results. This research contributes to our understanding of the importance of a task and its influence on the quality and understandability of an individual’s knowledge sharing behaviour performance. Furthermore, it contributes to and extends the current literature on complex tasks. Outcomes of this research offer a new perspective on the importance of the role of the task at hand in knowledge sharing. Corresponding to this, there are important implications for the design of supporting technology and potential interventions for human resource management. In addition, this research has important implications for the organisation as it often relies on individual expertise associated with a complex task. This expertise may not be accessible, as it exists in geographically diverse locations. This may impact the execution of the complex task. In order to delve further into the relationship between a complex task and the performance of knowledge sharing behaviour, there is a clear need to consider other characteristics of the task about which knowledge is being shared, as this forms the basis for the individual’s choice of knowledge transfer mechanisms. Future studies should also consider feedback and how to integrate feedback into existing knowledge transfer mechanisms to optimise the sharing of knowledge as this feedback is a tool that can be useful in influencing the preference for knowledge transfer mechanisms