شبکه فن آوری مبتنی بر ANP برای شناسایی فن آوری های هسته ای : یک مورد از فن آوری های مخابراتی

There have often been attempts to examine technological structure and linkage as a network. Network analysis has been mainly employed with various centrality measures to identify core technologies in a technology network. None of the existing centrality measures, however, can successfully capture indirect relationships in a network. To address this limitation, this study proposes a novel approach based on the analytic network process (ANP) to identification of core technologies in a technology network. Since the ANP is capable of measuring the relative importance that captures all the indirect interactions in a network, the derived “limit centrality” indicates the importance of a technology in terms of impacts on other technologies, taking all the direct and indirect influences into account. The proposed approach is expected to allow technology planners to understand current technological trends and advances by identifying core technologies based on limit centralities. Using patent citation data as proxy for interactions between technologies, a case study on telecommunication technologies is presented to illustrate the proposed approach.

Due to the intractable complexity and volatility of modern technologies, it becomes more important to photograph the overall structure and internal linkage of technology networks with the aim of grasping technological trends and advances. Identifying and assessing technological advances critical to the company’s competitive position is now recognized as a crucial activity for achieving and maintaining competitive positions in a rapidly evolving environment (EIRMA, 2000). Since technology systems are characterized by strong interdependence (Archibugi & Pianta, 1996), there have often been attempts to examine technological structure and linkage as a form of network (Shin and Park, 2007, Wartburg et al., 2005 and Yoon and Park, 2004). What is at the core of measuring technological interdependence or linkage is patent information (Kim, Suh, & Park, 2007). Patents have been the representative proxy for technology (Trajtenberg, Henderson, & Jaffe, 1997). A number of studies have been conducted to identify current technology structure and make a projection of technological future trends by using patent analysis (Archibugi and Pianta, 1996, Basberg, 1984, Basberg, 1987, Chen et al., 2005, Gangulli, 2004 and Grupp et al., 2003). Several measures have been employed for measuring technological linkage with patents, such as co-classification (Breschi et al., 1998 and Grupp, 1996), co-word (Courtial, Callon, & Sigogneau, 1993), and keyword vector similarity (Yoon & Park, 2004). Among those, citation analysis has been the most popular one in spite of controversial discussions about its validity. The underlying assumption is that there exists a technological linkage between the two patents if a patent cites another patent. Network analysis has often been used in conjunction with patent citation analysis with the aim of grasping the overall relationship and structure in a network. What is at the center of interest is to identify important or core technologies in a technology network (Shin & Park, 2007). As a quantitative measure of importance in a network, centrality measures can be used in network analysis. Among various measures, degree centrality has been implicitly deployed as an indicator of importance of technologies in the previous studies (Trajtenberg et al., 1997). However, it does not mirror indirect relationships despite the fact that indirect citations as well as direct citations play a crucial role in characterizing technology networks (Wartburg et al., 2005). There are other centrality measures that mirror indirect citations such as eigenvector centrality (Bonacich, 1972) and reachability out-degree (Wartburg et al., 2005). None of the measures, however, can successfully capture indirect relationships and produce meaningful results for identifying core technologies in a patent citation-based technology network. To address these limitations, this study proposes a novel approach based on the analytic network process (ANP) to identification of core technologies in a technology network. Since the ANP is capable of measuring the relative importance of technologies that captures all the indirect interactions in the technology network, the derived “limit centrality” can be used as an implicative centrality measure characterizing a technology network and showing core technologies in the network. The remainder of this paper is organized as follows. Section 2 deals with the previous studies on patent analysis and centrality measures in network analysis. The underlying methodology of the proposed approach, the ANP, is briefly introduced in Section 3. The proposed approach is explained and illustrated with a case study in Section 4. The paper ends with conclusions in Section 5.

The proposed approach measures the limit centralities of technologies with the aim of identification of core technologies in the technology network. A case study on the telecommunication technology network was presented to illustrate the proposed approach. After constructing the citation frequency matrix based on patent data collected from the USPTO, the ANP network model was constructed and local priority vectors were obtained. Forming and transforming the supermatrix led to converged priorities, limit centralities. The main contribution of this study is to apply the MCDM methodology, ANP, to a technology network. Since ANP captures the relative importance that mirrors all the direct and indirect interactions, the limit centrality measures importance of technologies in terms of impacts on other technologies in the technology network, taking indirect impacts or relationships into account, which is very difficult or tedious with the conventional centrality measures. The applicability of limit centrality is not limited to a technology network. For any type of social networks, the limit centrality can be used as an implicative centrality measure characterizing a network and showing core actors in the network. Nevertheless, this research is still subject to some limitations. Firstly, it cannot be used for undirectional networks where an edge has no direction and only represents the existence of a relationship between two nodes since relationships in a network of ANP must have directions depending on the influence between elements or clusters. Secondly, the influences among patent classes are measured by the absolute size of patent citations; thus, we cannot control the effect of the size of a class, that is, total number of patents in a class, on measuring the degree of impacts. The relative impact may be a more implicative measure depending on the context. It can be derived by dividing each column of the citation frequency matrix by the total number of patents in the corresponding class. Thirdly, the selected 13 patent classes as telecommunication technologies are by no means exhaustive. A more systematic procedure is required to select the target classes. These limitations could serve as fruitful avenues for future research. Applications of the proposed approach to a variety of networks can be a worthwhile area for future research. A dynamic analysis on the telecommunication network is also expected to provide useful information on the change of the network structure and technological trends.