The Effect of Individual’s Technological Belief and Usage on their Absorptive Capacity towards their Learning Behaviour in the Learning Environment

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While hard to define, knowledge is critical for organisational success. Organisations who know this spend a significant amount of resources to manage it. In organisations, there are three common barriers to knowledge transfer which are causal ambiguity, relation between knowledge holder and recipient, and recipient’s Absorptive CAPacity (ACAP). Horizontal organizational structures appear to be an effective solution to causal ambiguity and relation between knowledge holder and recipient because they allow knowledge to flow across organisational silos. The recipient’s ACAP is defined as a dynamic capability to absorb knowledge. While the content and its context are important for knowledge transfer, technology can also enhance learning. This study addressed the gap in knowledge by examining the role of Technology Readiness (TR), Tools for Knowledge Sources (TKS), Social Influences (SI) and Social Networks (SN) in an individual's ACAP towards learning behaviour. The research addresses the following research question. What is the effect of an individual's technological belief and use on their capability to absorb knowledge towards their technological learning behaviour? A research model is proposed to empirically test the relationship between TR and individual's ACAP towards learning behaviour. As beliefs affect behaviour, the TRI measures an individual's propensity towards technology. Other antecedents such as TKS, SI and SN may also have an effect on an individual’s ACAP. To assess behaviour under control of an individual the Individual Work Performance (IWP) was adapted towards learning behaviours. To test the presence of a causal effect, this study applied a quantitative approach to confirm theories. A survey was conducted at the University of Technology Sydney (UTS). An online questionnaire was used to collect data from students. A total of 199 participated in the survey. This questionnaire borrowed items from other academic peer reviewed journals. The data were analysed using the Partial Least Square (PLS)-Structural Equation Modelling (SEM) approach. The PLS approach had many benefits such as being flexible and the use of formative and reflective indicators. A High order Construct (HC) allowed abstraction in the research model. The disjoint two-stage approach enabled further validation of the measurement model in its second stage. The results generated mixed findings. Among the different hypotheses enumerated, the individual’s technological belief in Optimism and Innovation, and the SN had a significantly very weak effect on individual ACAP which in turn had a significantly weak effect on their learning behaviour. This study highlighted the need to assess individual ACAP and learning behaviour.
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