Abstract
Many companies take the sustainability of their technologies very seriously, because companies with sustainable technologies are better able to survive in the market. Thus, sustainable technology analysis is important issue in management of technology (MOT). In this paper, we study the management of sustainable technology (MOST). This focuses on the sustainable technology in various MOT fields. In the MOST, sustainable technology analysis is dependent on time periods. We propose a method of sustainable technology analysis using a Bayesian structural time series (BSTS) model based on time series data. In addition, we use the Bayesian regression to find the relational structure between technologies. To show the performance of our method and how the method can be applied to practical works, we carry out a case study using the patent data related to artificial intelligence technologies.
Highlights
Technology is the most important factor for changing the world [1]
This paper showed how the sustainable technology analysis can be applied to defense technology of Korea
In the most previous studies, the technology keywords or international patent classification (IPC) codes extracted from patent documents were analyzed by statistics and machine learning algorithms without time considerations
Summary
Technology is the most important factor for changing the world [1]. Many technologies have changed the world to date, with artificial intelligence (AI) technology dominating. Kim et al (2015) proposed a method of sustainable technology forecasting in the defense technology field. We use the Bayesian structural time series (BSTS) to perform time-related sustainable technology analysis and the Bayesian regression to find the technological association [9,10,11]. The Bayesian statistical approach can be used effectively in sustainable technical analysis This is because the Bayesian approach builds analytical models based on prior experience (prior distribution) and given data (likelihood function). The prior distribution deals with the expert’s knowledge of a given technology domain, and the likelihood function covers the patent data related to a target technological field. We propose a technology analysis model for sustainable technology management by using these characteristics of Bayesian statistics. We conclude our research and describe our future works related to MOST
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