Exploring Suitable Technology for Small and Medium-Sized Enterprises (SMEs) Based on a Hidden Markov Model Using Patent Information and Value Chain Analysis

Abstract

R&D cooperative efforts between large firms and small and medium-sized enterprises (SMEs) have been accelerated to develop innovative projects and deploy profitable businesses. In general, win-win alliances between large firms and SMEs for sustainable growth require the pre-evaluation of their capabilities to explore high potential partners for successful collaborations. Thus, this research proposes a systematic method that identifies SME-suitable technology where SMEs have a competitive edge in R&D collaborations. First, such technology fields are identified by various factors that influence successful R&D activities by applying the Hidden Markov Model (HMM) and using information on value chains of an industry. To identify these fields, innovation factors such as the current impact index and technology cycle time are composed using the bibliographic information of patents. Second, patent information is analyzed to obtain observation probability in terms of technical competitiveness, and value chain data is used to calculate transition probability in HMMs. Finally, the Viterbi algorithm is employed to formulate the aforementioned two types of probability as a tool for selecting appropriate fields for SMEs. This paper applies the proposed approach to the solar photovoltaic industry to explore SME-suitable technologies. This research can contribute to help develop successful R&D partnership between large firms and SMEs.