Technology forecasting using matrix map and patent clustering

• Published in: Industrial management + data systems Vol. 112; no. 5; pp. 786 - 807
• Main Authors: Jun, Sunghae, Sung Park, Sang, Sik Jang, Dong
• Format: Journal Article
• Language: English
• Published: Wembley Emerald Group Publishing Limited 01.01.2012
• Subjects: Bibliometrics; Clustering; Document delivery; Estoppel; Forecasting; Governments; Information technology; Intellectual property; Knowledge; Machine learning; Management; Mapping; Mathematical models; Planning; Policies; Product development; R&D; Research & development; Research and development; Studies; Subject specialists; Technological planning; Trends; Vacant technology forecasting; Matrix map; United States of America; Europe; China; Patent clustering; Support vector clustering; K-medoids clustering; Statistical forecasting; Research and development
• ISSN: 0263-5577; 1758-5783
• DOI: 10.1108/02635571211232352

Purpose - The purpose of this paper is to propose an objective method for technology forecasting (TF). For the construction of the proposed model, the paper aims to consider new approaches to patent mapping and clustering. In addition, the paper aims to introduce a matrix map and K-medoids clustering based on support vector clustering (KM-SVC) for vacant TF.Design methodology approach - TF is an important research and development (R&D) policy issue for both companies and government. Vacant TF is one of the key technological planning methods for improving the competitive power of firms and governments. In general, a forecasting process is facilitated subjectively based on the researcher's knowledge, resulting in unstable TF performance. In this paper, the authors forecast the vacant technology areas in a given technology field by analyzing patent documents and employing the proposed matrix map and KM-SVC to forecast vacant technology areas in the management of technology (MOT).Findings - The paper examines the vacant technology areas for MOT patent documents from the USA, Europe, and China by comparing these countries in terms of technology trends in MOT and identifying the vacant technology areas by country. The matrix map provides broad vacant technology areas, whereas KM-SVC provides more specific vacant technology areas. Thus, the paper identifies the vacant technology areas of a given technology field by using the results for both the matrix map and KM-SVC.Practical implications - The authors use patent documents as objective data to develop a model for vacant TF. The paper attempts to objectively forecast the vacant technology areas in a given technology field. To verify the performance of the matrix map and KM-SVC, the authors conduct an experiment using patent documents related to MOT (the given technology field in this paper). The results suggest that the proposed forecasting model can be applied to diverse technology fields, including R&D management, technology marketing, and intellectual property management.Originality value - Most TF models are based on qualitative and subjective methods such as Delphi. That is, there are few objective models. In this regard, this paper proposes a quantitative and objective TF model that employs patent documents as objective data and a matrix map and KM-SVC as quantitative methods.