Date of Award
Doctor of Philosophy
School of Business and Law
Dr Gregory Willson
Professor Sam (Songshan) Huang
Associate Professor Llandis Barratt-Pugh
Knowledge is widely recognised as the primary source of competitive advantage. Efficient transfer of knowledge within a tourism destination can improve the innovative capabilities and competitiveness of the destination. However, tourism scholarly research and industry practice have both been slow to import and apply knowledge management (KM) concepts.
This research explored the transfer of knowledge between tourism organisations in Western Australia (WA). A conceptual framework was developed based on social capital theory and KM constructs. The developed research framework comprised four dimensions of ‘structural’, ‘relational’, ‘organisational’ and ‘knowledge’ properties, which encompass the major antecedents of knowledge transfer.
Two types of data were collected in two stages within WA, and network analysis (NA) was central in the methodology and analyses of the data. In the first stage of the study, hyperlink data were collected, and the online web network of the destination was analysed. Tourism websites were considered online sources of information whose connections could affect the online visibility of the destination and its performance. A network of 1515 WA tourism websites was analysed via NA techniques. Results showed that the hyperlink network of the WA destination had a sparse, centralised and hierarchical structure. In addition, the websites tended to form communities based on their geographical locations. Public tourism organisations and information services played a central and significant role in the destination network.
For the main stage of data collection, an online questionnaire was developed based on the research conceptual framework. A total of 166 valid questionnaires were returned, which resulted in a network of 510 nodes (organisations) and 1054 ties (transfer of knowledge). A rich variety of network measures were used to analyse the topological properties of the network. The results indicated that the WA network has low connectivity and is highly centralised around public bodies. The network also showed very few reciprocal relationships and limited boundary spanners that connect the network to external sources of knowledge. In addition, results confirmed the similarity of the network characteristics with the virtual hyperlink network of the destination.
Structural analyses of the knowledge network cannot sufficiently explain the complex process of knowledge transfer. Thus, the last stage of the study proposed and applied a weighted diffusion model that used the major antecedents of knowledge transfer, including trust, tie strength, proximity, absorptive capacity and knowledge ambiguity. The diffusion model provided a quantitative tool to measure the efficiency of the knowledge transfer in the destination network. The result of the model for WA tourism provided evidence of the very low efficiency of knowledge transfer in this destination.
Overall, this study analysed the network of knowledge flow in WA and showed the destination network is not efficient enough and needs improvement. Network analysis provided a detailed map of knowledge flow, which destination management organisations can benefit from to improve their understanding of the network, its weaknesses and strengths, and to determine what actions will improve it. This study is among the very few to take a comprehensive approach to measure and quantify the efficiency of knowledge transfer within a tourism destination. In addition, this research provided a model for future research of how to explore and analyse the interorganisational transfer of knowledge within a tourism destination.
Access to Chapter 4 of this thesis is not available.
Raisi Varkani, H. (2019). Inter-organisational transfer of knowledge in tourism. https://ro.ecu.edu.au/theses/2214