研究目的
To disentangle the combined effects of innovation-capability and network embeddedness patterns on age, size and financial performance of organizations in the context of technological upgrading within the solar photovoltaics industry in China.
研究成果
The research identifies significant patterns in innovation capability and network embeddedness, showing that high-quality innovation combined with global integration and small-world networks leads to higher economic performance. Quality of innovation is more important than quantity or diversity. The findings support most hypotheses, indicating that network embeddedness moderates the impact of innovation on performance, with implications for managerial strategies and policy-making in clean energy technologies.
研究不足
The study does not involve the institutional framework of the innovation system or the dynamic dimension of network analysis over time. It relies on patent data as a proxy for innovation, which has known limitations, and the sample size is relatively small (37 actors).
1：Experimental Design and Method Selection:
The study uses a combination of patent, network, and cluster analysis to profile main actors in China's PV innovation system. It employs hierarchical cluster analysis (Ward's method with Euclidean distance) for innovation and network patterns, and multivariate analysis of variance (MANOVA) to test hypotheses.
2：Sample Selection and Data Sources:
Main actors are identified based on patent data from PATSTAT (worldwide patent statistical database) and market share data from sources like Yu et al. (2016), Roselund (2016), Mints (2014), Brown et al. (2015), and Shubbak (2017b). The sample includes 37 organizations.
3：List of Experimental Equipment and Materials:
Software tools include IBM SPSS Statistics version
4：0 for statistical analysis, Gephi 1 for social network analysis, and PATSTAT database for patent data. Experimental Procedures and Operational Workflow:
Steps include identification of main actors using patent and production data, calculation of innovation and network indicators (e.g., patent quantity, forward citations, degree centrality), cluster analysis to group actors, and concurrency matrix analysis with MANOVA to examine interactions and economic performance.
5：Data Analysis Methods:
Statistical methods include ANOVA, Tukey HSD post hoc tests, and MANOVA. Network analysis uses indicators like weighted degree, betweenness centrality, and clustering coefficient.
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