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The motivations for knowledge transfer across borders: the diffusion of data envelopment analysis (DEA) methodology

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  • Mei Hsiu-Ching Ho

    (National Taiwan University of Science and Technology)

  • John S. Liu

    (National Taiwan University of Science and Technology)

Abstract

To facilitate technology development, people rely on quick and intensive knowledge interactions without barriers. However, when people need to transfer knowledge from one place to another, geographical distance is a critical barrier to overcome because tacit and invisible characteristics are embedded in certain knowledge and locations. This study explores how social and scientific resources embedded within persons can motivate personal knowledge-diffusion behaviors; that is, bridging resources between locations. To explain cross-border diffusion, this work analyzes knowledge dissemination of the data envelopment analysis (DEA) method. By collecting theoretical and application papers in DEA methodology from the Web of Science data set, this study analyzes the academic network consisting of 610 researchers and identifies author locations, research disciplines, and their mutual linkages to explain the importance of personal specific characteristics in cross-border diffusion. Regression models and network analysis show the advantages of personal research seniority and cross-disciplinary coordinating capabilities for researchers to diffuse knowledge from one region to another. The corresponding brokering capabilities accumulated within domestic area or adjacent nations are also helpful for specifically brokering resources of other farther places.

Suggested Citation

  • Mei Hsiu-Ching Ho & John S. Liu, 2013. "The motivations for knowledge transfer across borders: the diffusion of data envelopment analysis (DEA) methodology," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 397-421, January.
    • Handle: RePEc:spr:scient:v:94:y:2013:i:1:d:10.1007_s11192-012-0705-x
    DOI: 10.1007/s11192-012-0705-x
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    1. Kairui Zuo & Jiancheng Guan, 2017. "Measuring the R&D efficiency of regions by a parallel DEA game model," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 175-194, July.
    2. S. Lozano & L. Calzada-Infante & B. Adenso-Díaz & S. García, 2019. "Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 609-629, August.
    3. Mei Hsiu-Ching Ho & Vincent H. Lin & John S. Liu, 2014. "Exploring knowledge diffusion among nations: a study of core technologies in fuel cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 149-171, July.
    4. Shi‐Xiao Wang & Wen‐Min Lu & Shiu‐Wan Hung, 2020. "Improving innovation efficiency of emerging economies: The role of manufacturing," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(4), pages 503-519, June.
    5. José-Antonio Belso-Martínez & Manuel Expósito-Langa, 2015. "Persistence and extinction of brokerage roles in clusters: the role of status, former experiences and extra-cluster relationships," Papers in Evolutionary Economic Geography (PEEG) 1501, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Jan 2015.
    6. José Antonio Belso-Martínez & Manuel Expósito-Langa & Francisco Mas-Verdú & F. Xavier Molina-Morales, 2017. "Dynamics of Brokerage Positions in Clusters: Evidence from the Spanish Foodstuffs Industry," Sustainability, MDPI, vol. 9(2), pages 1-18, February.
    7. Le Song & Guilong Zhu & Xiao Yin, 2024. "Evaluating the wisdom of scholar crowds from the perspective of knowledge diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5103-5139, September.
    8. Jiancheng Guan & Kairui Zuo, 2014. "A cross-country comparison of innovation efficiency," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 541-575, August.

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