Technology forecasting using matrix map and patent clustering

Sunghae Jun, Sang Sung Park, Dong Sik Jang

Research output: Contribution to journalArticle

64 Citations (Scopus)

Abstract

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 (Ramp;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 Ramp;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.

Original languageEnglish
Pages (from-to)786-807
Number of pages22
JournalIndustrial Management and Data Systems
Volume112
Issue number5
DOIs
Publication statusPublished - 2012 May 28

Fingerprint

Patents
Technology forecasting
Clustering
Intellectual property
Management of technology
Marketing
Planning
Government
Industry
Experiments
Experiment
Intellectual property management
Technology marketing
China
Forecasting performance
Quantitative methods
Delphi
Design methodology

Keywords

  • China
  • Europe
  • K-medoids clustering
  • Matrix map
  • Patent clustering
  • Research and development
  • Statistical forecasting
  • Support vector clustering
  • United States of America
  • Vacant technology forecasting

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Industrial relations
  • Strategy and Management
  • Management Information Systems
  • Computer Science Applications

Cite this

Technology forecasting using matrix map and patent clustering. / Jun, Sunghae; Park, Sang Sung; Jang, Dong Sik.

In: Industrial Management and Data Systems, Vol. 112, No. 5, 28.05.2012, p. 786-807.

Research output: Contribution to journalArticle

Jun, Sunghae ; Park, Sang Sung ; Jang, Dong Sik. / Technology forecasting using matrix map and patent clustering. In: Industrial Management and Data Systems. 2012 ; Vol. 112, No. 5. pp. 786-807.
@article{ec07c4928ae24119ae895da565c0ecfa,
title = "Technology forecasting using matrix map and patent clustering",
abstract = "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 (Ramp;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 Ramp;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.",
keywords = "China, Europe, K-medoids clustering, Matrix map, Patent clustering, Research and development, Statistical forecasting, Support vector clustering, United States of America, Vacant technology forecasting",
author = "Sunghae Jun and Park, {Sang Sung} and Jang, {Dong Sik}",
year = "2012",
month = "5",
day = "28",
doi = "10.1108/02635571211232352",
language = "English",
volume = "112",
pages = "786--807",
journal = "Industrial Management and Data Systems",
issn = "0263-5577",
publisher = "Emerald Group Publishing Ltd.",
number = "5",

}

TY - JOUR

T1 - Technology forecasting using matrix map and patent clustering

AU - Jun, Sunghae

AU - Park, Sang Sung

AU - Jang, Dong Sik

PY - 2012/5/28

Y1 - 2012/5/28

N2 - 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 (Ramp;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 Ramp;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.

AB - 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 (Ramp;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 Ramp;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.

KW - China

KW - Europe

KW - K-medoids clustering

KW - Matrix map

KW - Patent clustering

KW - Research and development

KW - Statistical forecasting

KW - Support vector clustering

KW - United States of America

KW - Vacant technology forecasting

UR - http://www.scopus.com/inward/record.url?scp=84861411036&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84861411036&partnerID=8YFLogxK

U2 - 10.1108/02635571211232352

DO - 10.1108/02635571211232352

M3 - Article

AN - SCOPUS:84861411036

VL - 112

SP - 786

EP - 807

JO - Industrial Management and Data Systems

JF - Industrial Management and Data Systems

SN - 0263-5577

IS - 5

ER -