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    Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/102826


    Title: An actor-network perspective on evaluating the R&D linking efficiency of innovation ecosystems
    Authors: 洪秀婉;Chen, Ping-Chuan;Hung, Shiu-Wan
    Contributors: 管理學院企業管理學系
    Keywords: Actor-network theory;Efficiency;Innovation;Joining;Linking;Network data envelopment analysis;Networks;Operations research;Performance measurement;Policies;R&D efficiency;Research and development
    Date: 2016-11-01
    Issue Date: 2026-04-23 11:17:33 (UTC+8)
    Publisher: Elsevier Inc.;Elsevier Inc
    Abstract: 摘要: Research and development (R&D) is one of the key factors contributing to the economic growths in both advanced and developing countries. Implementing technological innovation strategies to accelerate the research and development has thus become one of the most important industrial policies for governments. The R&D performance is highly influenced by the complexities of interactions among actors in an innovation system. An evaluation model that incorporates the influence of linking activities is highly desired. This study employed the actor-network theory to construct a three-stage R&D production framework that emphasizes the linking activities among basic research stage, technology translation stage, and system development stage. In addition, the network data envelopment analysis (DEA) method was used to evaluate the relative R&D efficiency across the global twenty-five countries. The analysis results screened out specialized efficient country at each sub-process and further constructed the efficiency group for benchmark-learning. This study also pointed to the importance of the research institution for technology commercialization. The potential application of network DEA and actor-network theory approach in assessing the efficiency of R&D activities were also highlighted. •This study employed the actor-network theory for an innovation system.•This study constructed a three-stage transformation model.•The analysis results constructed the efficiency group for benchmark-learning.•This study highlighted the research institution for technology commercialization.
    出版者: Elsevier Inc
    出版日期: 2016-11-01
    出處: Technological forecasting & social change, 2016-11, Vol.112, p.303-312
    資源來源: Elsevier ScienceDirect Journals Complete - Autoholdings
    版權: 2016 Elsevier Inc.
    識別號: ISSN: 0040-1625
    識別號: EISSN: 1873-5509
    識別號: DOI: 10.1016/j.techfore.2016.09.016
    Appears in Collections:[Department of Business Administration ] journal & Dissertation

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