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Exploring a pseudo-regression model of transnational cooperation in science

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  • P. S. Nagpaul

Abstract

This paper reports the results of an empirical study on the impact of three proximity measures: geographical distance, thematic distance and socio-economic distance among the set of 45 scientifically most advanced countries on their cooperation network. In network data, individuals (viz. countries) are linked to one another and the relationships are nested and embedded in groups, with the result that statistical assumptions of independence underlying ordinary least squares regression are systematically violated. Hence, we have used a non-parametric regression procedure, Quadratic Assignment Procedure (QAP), for regressing the matrix of transnational cooperation on the matrices of three proximity measures: geographic proximity, thematic proximity and socio-economic proximity. The results indicate that all the three proximity measures have the expected negative effect on transnational cooperation. Geographic proximity has greater impact than the other proximity measures.

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  • P. S. Nagpaul, 2003. "Exploring a pseudo-regression model of transnational cooperation in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(3), pages 403-416, March.
    • Handle: RePEc:spr:scient:v:56:y:2003:i:3:d:10.1023_a:1022335021834
    DOI: 10.1023/A:1022335021834
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    1. Frank B. Baker & Lawrence J. Hubert, 1981. "The Analysis of Social Interaction Data," Sociological Methods & Research, , vol. 9(3), pages 339-361, February.
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    Cited by:

    1. Jiang, Ling (Alice) & Zhu, Nibing & Yang, Zhilin & Xu, Shen & Jun, Minjoon, 2018. "The relationships between distance factors and international collaborative research outcomes: A bibliometric examination," Journal of Informetrics, Elsevier, vol. 12(3), pages 618-630.
    2. Lv, Chengchao & Song, Jie & Lee, Chien-Chiang, 2022. "Can digital finance narrow the regional disparities in the quality of economic growth? Evidence from China," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 502-521.
    3. Cantner, Uwe & Rake, Bastian, 2014. "International research networks in pharmaceuticals: Structure and dynamics," Research Policy, Elsevier, vol. 43(2), pages 333-348.
    4. Galik, Christopher S., 2015. "Exploring the determinants of emerging bioenergy market participation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 107-116.
    5. Vieira, Elizabeth S. & Cerdeira, Jorge & Teixeira, Aurora A.C., 2022. "Which distance dimensions matter in international research collaboration? A cross-country analysis by scientific domain," Journal of Informetrics, Elsevier, vol. 16(2).
    6. Csomós, György, 2018. "Reprint of “A spatial scientometric analysis of the publication output of cities worldwide”," Journal of Informetrics, Elsevier, vol. 12(2), pages 547-566.
    7. Maisonobe, Marion & Eckert, Denis & Grossetti, Michel & Jégou, Laurent & Milard, Béatrice, 2016. "The world network of scientific collaborations between cities: domestic or international dynamics?," Journal of Informetrics, Elsevier, vol. 10(4), pages 1025-1036.
    8. Zhang, Ronda J. & Ye, Fred Y., 2020. "Measuring similarity for clarifying layer difference in multiplex ad hoc duplex information networks," Journal of Informetrics, Elsevier, vol. 14(1).
    9. Hennemann, Stefan & Rybski, Diego & Liefner, Ingo, 2012. "The myth of global science collaboration—Collaboration patterns in epistemic communities," Journal of Informetrics, Elsevier, vol. 6(2), pages 217-225.

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