Multivariate multiple regression modelling for technology analysis

ABSTRACT

Technology analysis is important for technology management areas such as research and development strategy and new product development. So many studies on technology analysis have been used across a diverse array of fields. Most of these were based on patent analysis, which analyses patent documents using text mining and statistics. The studies on conventional patent analyses constructed models consisting of various independent variables (technologies) and one dependent variable. But in reality, we have to consider a model that includes several dependent variables at the same time, because most technologies influence each other. In this paper, we propose a methodology for patent analysis that reflects the various response technologies simultaneously. We perform multivariate multiple regression modelling in order to efficiently conduct our technology analysis. To show how our modelling can be applied to realistic context, we carry out a case study using the patent documents related to three-dimensional printing technology.

KEYWORDS:

• Multivariate multiple regression
• technology analysis
• three-dimensional printing
• management of technology

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Sunghae Jun is a Professor in the Department of Statistics, Cheongju University, Chungbuk, Korea. He received BS, MS, and PhD degrees from the Department of Statistics, Inha University, Incheon, Korea in 1993, 1996, and 2001, respectively. He also received a PhD degree from the Department of Computer Science, Sogang University, Seoul, Korea in 2007, and a PhD from Information Management Engineering from Korea University, Seoul, Korea in 2013. He was a visiting scholar in the Department of Statistics, Oklahoma State University, Stillwater, OK, USA from 2009 to 2010. His current research interests include big data learning and technology forecasting.

Jacob Wood is a professor in the Department of International Trade at Chungnam National University, Daejeon, Korea. He has a PhD degree in International Trade from Sogang University, Seoul, Korea in 2015. He teaches undergraduate and graduate school courses in international trade, economics, international management, and development. His research interests include scientometric-based studies using social network analysis, IT management, technology management, and international trade.

Sangsung Park is a professor in the Graduate School of Management of Technology, Korea University, Seoul Korea. He received a PhD degree in Industrial Engineering from Korea University in 2006. He teaches the strategic management of electronic business, computer systems, and the strategic application of patent information. His research interests are management of technology, patent analysis, data mining, and technology forecasting.

Notes

1 Statistics obtained from a 2014 Canalys 3D printing study published in Price Waterhouse Coopers research.

2 Disruptive technologies as viewed as those that ‘disrupt’ the current level of capability that has been established within a given marketplace.

Additional information

Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01059742).