Title: A new model based on patent data for technology early warning research

Authors: Ying Guo; Ganlu Sun; Lili Zhang; Fan Yang; Junfang Guo; Lin Ma

Addresses: School of Management and Economics, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, China ' School of Management and Economics, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, China ' School of Management and Economics, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, China ' Wide-Code Information Technology (Beijing) Co., Ltd., Lize Zhong Er Road, Chaoyang District, Beijing, China ' School of Information and Electronics, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, China ' National Institute of Clean and Low Carbon Energy, Shenhua NICE, Future Science & Technology City, Changping District, Beijing, China

Abstract: As technology competition among enterprises become more intense, technical crisis occurs in enterprises, such as technological substitution and technology divulgence. Thus, it is necessary to warn enterprises of those technical crises that can be called technology early warning. As patent data contains much technology information, it becomes an efficient source to analyse technology. This paper proposes a technology early warning model based on patent data to help enterprises execute technology early warning from the perspective of its technology status. To do so, we set ten indicators from four aspects to evaluate the enterprise's technology status at first, calculate the index of enterprise's technical crisis with AHP, and then propose five early warning levels. China Petroleum & Chemical Corporation (Sinopec Group) and the China National Petroleum Corporation (CNPC) are taken as comparative case studies.

Keywords: technology early warning; patent data; forecast; technical crisis.

DOI: 10.1504/IJTM.2018.092969

International Journal of Technology Management, 2018 Vol.77 No.4, pp.210 - 234

Accepted: 15 Dec 2016
Published online: 04 Jul 2018 *

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