Abstract
In the paper, we apply small world complex network theory to analyze scientific research in the field of service innovation, and discover its research focuses. Our study considers the key words and subject categories of the publications as actors to map keyword co-occurrence network and subject category co-occurrence network, and compare them with their corresponding random binary networks to judge whether these complex networks have the characteristics of small world network, in order to find the hot issues in the field by the small world network analysis. We discuss the knowledge structure in the field through analyzing 437 papers that were searched from Web of Science database over the period 1992–2011. We find that case study, service industry, service quality, market orientation, new product development, and knowledge management were the most popular keywords of the field, and also show the dynamic development of the research focuses in recent 10 years. The researchers who made most contribution in a certain field are also found out. It is concluded that there were more researchers who did investigation about service innovation in the category of Business and Economics, Engineering, Public Administration, Operations Research and Management Science, and Computer Science than those in other categories. The study suggests a quantitative method to analyze trends of scientific research in a certain field, and presents some directions of research mainstream to the researchers who may be interested in the service innovation.
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Acknowledgments
The paper is a stage result of the proposed project “Measuring complex innovation networks of frontier sciences and emerging technologies” to National Natural Science Foundation of China (NSFC) in 2013 and is also supported by a NSFC’s project (No. 70932001). The authors are grateful for the editor’s and the reviewer’s valuable comments and suggestions that have led to the significant improvement of this article.
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Zhu, W., Guan, J. A bibliometric study of service innovation research: based on complex network analysis. Scientometrics 94, 1195–1216 (2013). https://doi.org/10.1007/s11192-012-0888-1
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DOI: https://doi.org/10.1007/s11192-012-0888-1