A structured MPA approach to explore technological core competence, knowledge flow, and technology development through social network patentometrics

Abstract

Purpose This study aims to propose a methodology by integrating three approaches, namely, internal core technology, external knowledge flow and industrial technology development to help companies improve their decision-making quality for technology planning and enhance their research and development (R&D) portfolio efficiency. Design/methodology/approach The primary focus of this study is thin-film solar technology and patent data is retrieved from the United States Patent and Trademark Office (USPTO) database. This study presents a methodology based on the proposed integrated analysis method, constructed with patent indicators, centrality analysis of social networks and main path analysis. Findings The results of this study can be itemized as – the core technological competency: companies involved in two specific technology fields have lower strength in R&D portfolio than leading companies with single-core technology. Knowledge flow: most companies in a network are knowledge producers/absorbers and technological development: diverse source and sink nodes were identified in the global main path during 1997-2003, 2004-2010 and 2011-2017. Research limitations/implications Latecomer companies can emulate leaders’ innovation and enhance their technological competence to seek niche technology. Using the global main path, companies monitor outdated technologies that can be replaced by new technologies and aid to plan R&D strategy and implement appropriate strategic decisions avoiding path dependency. Originality/value The knowledge accumulation process helps in identifying the change of position and the role of companies; understanding the trend of industrial technology knowledge helps companies to develop new technology and direct strategic decisions. The novelty of this research lies in the integrated approach of three methods aiding industries to find their internal core technical competencies and identify the external position in the competitive market.

Cross-country learning from patents: an analysis of citations flows in innovation trajectories

“…In fact, according to Giglio, (2021), prior innovations always provide some quotas of relevant knowledge, which (directly or indirectly) contribute to either the "learningby-acquisition" process or the creative one. This is coherent with Lai et al, (2021) findings on knowledge flows and patent connectivity, that may form a technology development path over time (see also Hummon and Dereian, (1989). In detail, when prior acquired knowledge is not utilized -i.e.…”

Identifying Firm Significance and Positions in the Patent Innovation Based on Centrality Measures’ Clustering Approach

“…2, there is only one path between any pair of nodes. For calculation we need to consider all pairs of nodes from {2,3,4,5} , which is 6 {(2,3), (2,4), (2,5), (3,4), (3,5), (4, 5)}. Furthermore, only one path exists between each pair that passes through node 1.…”

“…Patent data can be analyzed to identify multiple dimensions of an innovation landscape. Using patent analysis, previous studies have identified trends and trajectories of evolving and established technologies [3][4], technology convergence and divergence, and firm innovation capabilities. Patent citation analysis is employed to assess the knowledge flow [5][6], as well as knowledge providers and absorbers [7] across the technology trajectory.…”

The moderating effect of network power on relational risks and knowledge flow in R&D network

“…First, this paper contributes to extending prior research investigating the consequences of knowledge flow by exploring the relationship between relational risks and knowledge flow. Previous studies indicate that the preconditions required for knowledge flow are mainly frslom network resource (Huggins and Johnston, 2010), inter-firms patterns (Melissa, 1996) and technological core competence (Lai et al , 2020). Moreover, combined with the knowledge-based view and network theory, relational risks regard, as an internal factor, knowledge flow within the process of R&D projects (Delerue and Simon, 2009; Tang, 2006).…”