The University of Southampton
University of Southampton Institutional Repository

Predicting future new technology trade relations among regional comprehensive economic partnership member countries - based on a link prediction method

Predicting future new technology trade relations among regional comprehensive economic partnership member countries - based on a link prediction method
Predicting future new technology trade relations among regional comprehensive economic partnership member countries - based on a link prediction method

Based on 2009-2018 technical trade data, this article uses link prediction methods to explore RCEP's potential technological innovation cooperation and trade relationship rules from the perspective of physical topology networks and national node attributes, and to predict the possible RCEP technical trade links that may be established in the future. The research results show that: 1) the number of national trading partners in a node country has a positive impact on the potential links of its technology trade; 2) countries with technology trade cooperation will be more likely to conduct technology transactions in the future; 3) there will be more technology trade between RCEP and EU countries in the future; 4) ASEAN countries and enterprises have weak capacity to establish new technological trade relations. Priority should be given to expanding technology trade with other RCEP enterprises to enhance ASEAN's international technology trading capacity.

RCEP, international technology trade, link prediction, social network, technological innovation cooperation
0267-5730
133-164
Qi, Xinli
900aec4c-d5dd-49fb-9cc9-edde0378fe50
Zhao, Changping
b96dd9b8-f264-4724-ae75-79925700b4b6
Gong, Yu
86c8d37a-744d-46ab-8b43-18447ccaf39c
Yuan, Zhenghui
f8e5fb25-fa4b-4078-9e50-81c7ee331a66
Shi, Yangyan
4574f553-2b1f-4aa6-ba51-161b67fe24f7
Qi, Xinli
900aec4c-d5dd-49fb-9cc9-edde0378fe50
Zhao, Changping
b96dd9b8-f264-4724-ae75-79925700b4b6
Gong, Yu
86c8d37a-744d-46ab-8b43-18447ccaf39c
Yuan, Zhenghui
f8e5fb25-fa4b-4078-9e50-81c7ee331a66
Shi, Yangyan
4574f553-2b1f-4aa6-ba51-161b67fe24f7

Qi, Xinli, Zhao, Changping, Gong, Yu, Yuan, Zhenghui and Shi, Yangyan (2023) Predicting future new technology trade relations among regional comprehensive economic partnership member countries - based on a link prediction method. International Journal of Technology Management, 93 (1-2), 133-164. (doi:10.1504/IJTM.2023.132603).

Record type: Article

Abstract

Based on 2009-2018 technical trade data, this article uses link prediction methods to explore RCEP's potential technological innovation cooperation and trade relationship rules from the perspective of physical topology networks and national node attributes, and to predict the possible RCEP technical trade links that may be established in the future. The research results show that: 1) the number of national trading partners in a node country has a positive impact on the potential links of its technology trade; 2) countries with technology trade cooperation will be more likely to conduct technology transactions in the future; 3) there will be more technology trade between RCEP and EU countries in the future; 4) ASEAN countries and enterprises have weak capacity to establish new technological trade relations. Priority should be given to expanding technology trade with other RCEP enterprises to enhance ASEAN's international technology trading capacity.

This record has no associated files available for download.

More information

e-pub ahead of print date: 1 January 2023
Published date: 1 January 2023
Additional Information: Publisher Copyright: © 2023 Inderscience Enterprises Ltd.
Keywords: RCEP, international technology trade, link prediction, social network, technological innovation cooperation

Identifiers

Local EPrints ID: 481867
URI: http://eprints.soton.ac.uk/id/eprint/481867
ISSN: 0267-5730
PURE UUID: 607f8caa-94c9-4f3c-8b47-552be2f268c4
ORCID for Yu Gong: ORCID iD orcid.org/0000-0002-5411-376X

Catalogue record

Date deposited: 12 Sep 2023 16:33
Last modified: 06 Jun 2024 01:58

Export record

Altmetrics

Contributors

Author: Xinli Qi
Author: Changping Zhao
Author: Yu Gong ORCID iD
Author: Zhenghui Yuan
Author: Yangyan Shi

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×