The importance of innovation networks in health information system transformation has been recognised in research. It has been agreed that better-organised innovation networks can be related to better results of patient information system transformation. However, current research do not know much about how those innovation networks are organised, especially the structure of teamwork and information exchange in innovation networks. Thus, this study aims to improve the understanding about how innovation networks are organised and the influences on innovation results. Based on innovation network theory, this study develops and integrates three aspects, network dynamics, network structure and network influence, to explore innovation networks in patient information system transformation. Network dynamics represent complex interactions among people in the process of innovation; network structures show each person's roles and connections in the network; and and network influences link network structures to patient information system upgrade outcomes. Following this theoretical framework, this study answers three research questions: 1) what are the network patterns appearing frequently in network dynamics? 2) What are the patterns of the network structures? 3) To what extent innovation networks can influence the innovation outcomes? The data are collected form four patient record transformation projects in China. This study adopts network analysis method to demonstrate the fabrics of collaborations among the participants in innovation and quantify the regular network patterns and structures. Then, this study uses network regression modelling to explore the relations between innovation networks and innovation outcomes. This study contributes to innovation network research and by presenting 1) the patterns of innovation network dynamics. It demonstrates various patterns of innovation networks in each innovation stages; 2) the innovation network structures. This study identifies five types of brokers and two structures co-existing in the innovation network; 3) network influence. This study suggests that network structures significantly influence the outcomes.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:755002 |
| Date | January 2017 |
| Creators | Liang, Liang |
| Publisher | Kingston University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.kingston.ac.uk/41967/ |
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