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The convergence of industries exposes the involved firms to various challenges. In such a setting, a firm's response time becomes key to its future success. Hence, different approaches to anticipating convergence have been developed in the recent past. So far, especially IPC co-classification patent analyses have been successfully applied in different industry settings to anticipate convergence on a broader industry/technology level. Here, the aim is to develop a concept to anticipate convergence even in small samples, simultaneously providing more detailed information on its origin and direction.

The authors assigned 326 US-patents on phytosterols to four different technological fields and measured the semantic similarity of the patents from the different technological fields. Finally, they compared these results to those of an IPC co-classification analysis of the same patent sample.

An increasing semantic similarity of food and pharmaceutical patents and personal care and pharmaceutical patents over time could be regarded as an indicator of convergence. The IPC co-classification analyses proved to be unsuitable for finding evidence for convergence here.

Semantic analyses provide the opportunity to analyze convergence processes in greater detail, even if only limited data are available. However, IPC co-classification analyses are still relevant in analyzing large amounts of data. The appropriateness of the semantic similarity approach requires verification, e.g. by applying it to other convergence settings.

The study assesses the most significant architectural core technological system that converges into a Battery Electric Vehicle (BEV).

Conceptually grounded in the convergence phenomenon and utilizing the graph theory-based network construction approach, based on the Betweenness Centrality (BC) metric, core International Patent Classifications (IPCs) have been empirically identified. Based on these IPCs, the ownership structure of the patents was established through assignee analysis.

Analyzing the networks obtained at different IPC levels, we found that multiple technologies have converged in a BEV, from battery chemistry to electrical engineering and thermal management of electrical machines.

The outcome of this work has led to the identification of BEV technologies, which can be further developed to assess the trends of technologies and associated gaps and aid technology management for the selection, acquisition, and exploitation of technology.

The outcome of this work will aid technology management practitioners in better planning the selection, acquisition, and exploitation of technologies associated with BEV.

The paper adds an evidence-based approach to the body of knowledge to identify the built-in technologies that produce a BEV.

The purpose of this paper is to analyze the network structure of technology in and between different fields, as well as the evolution of their relations.

Using the patent data in Derwent Innovation Index (DII) from 1991 to 2010, this paper analyzes the co-classification of Derwent Manual Code (DMC) of patents in all technology fields. Large-scaled co-classification matrices are employed to generate the DMC co-classification networks. In addition, analyses are pursued at different levels of aggregation in four five-year windows: 1991-1995, 1996-2000, 2001-2005 and 2006-2010. Using Girvan-Newman algorithm in the clustering process, the structure transformations over time are detected.

The paper identifies the key technological knowledge in certain fields and finds out how different technological fields are connected and integrated. What is more, the dynamic evolution between networks in different time periods reveals the trend of generic technology development in the macroscopic level.

The paper investigates a large quantity of data – all the patent data in DII from 1991 to 2010 in this paper. The paper applies Girvan-Newman algorithm in the co-classification analysis and uses co-classification networks to reveal technology network structures. Evolution coincident with the realistic technological shifts can be observed.

The purpose of this paper is to provide a framework for understanding core technological competencies and identifying the trends on the technological convergence of a business ecosystem using the patent information of leading firms in the system.

The proposed framework is composed of two steps: time-sequential text clustering analysis for comprehending changes in general technological fields and association rule analysis for identifying the trends of convergences in each field. The authors applied the proposed framework to the patents applied to United States Patent Trademark Office by Samsung Electronics, a market leader of the electronics industry, during the period from 2000 to 2011.

In the sequential text clustering analysis, trends of 14 technological fields such as data storage medium and data processing, mobile, lights and heats and memory are identified. Moreover, changes of technological convergence in each field are identified using association rule analysis. For instance, in the case of technologies related to lights and heats, convergences occurred between radio transmission systems and modulated-carrier systems during the period from 2000 to 2001. However, recent convergences appeared between technologies regarding controlling lights and liquid crystal materials since 2008.

Utilization of the framework will suggest new business opportunities to SMEs in a business ecosystem by identifying the trends of technological convergences.

The application of laser and optical technologies in the industry is wide and extensive; the development and application of laser and optical technologies have become a promising research domain. However, most existing studies have focused on the technical aspects or the application aspects; these studies have not highlighted the technology distribution and application development of laser and optical technologies from the big picture. Additionally, the manner in which the research and development (R&D) results of universities correspond to the needs of enterprises and industry has become a topic of concern for the public. Therefore, this study aims to adopt the academic patents as the basis for analysis and to construct a laser and optical technology network.

Therefore, in the current study, the researchers have analyzed relevant academic patent technology networks, using academic patents of laser and optical technologies as a basis of analysis.

The study results indicated that the key technologies mainly lie in nanostructures, metal-working, material analysis and semiconductor devices. Additionally, these technologies are mainly applied in industries, such as optics, medical technology, pharmaceuticals, biotechnology and organic fine chemistry; this indicated that a large proportion of academia’s R&D outcomes are applied in these industries.

In this study, the researchers have constructed a technology network model to explore the technical development direction of laser and optical technologies; the results of the current study could serve as a reference for universities and industry for allocation of R&D resources.

This paper aims to better understand how codified knowledge that originates in organizations contributes to the generation of idiosyncratic knowledge embedded at a more expansive level, such as that of an ecosystem. In doing so, the authors introduce the concept of patent ecosystems – conceived as configurations of codified knowledge advancements protected via patents.

Using a patent co-classification method and introducing a novel validated software, the authors map and visualize the patent ecosystem of Singapore and examine 173,597 patents published from 1995 to 2020.

Results reveal the prominent growth of Singapore’s patenting activities, capturing a patent ecosystem shift, from a more diverse knowledge configuration to a more specialized one. The codified knowledge mainly generated deals with pharmaceuticals and high-tech knowledge domains; further, newly emerging technologies such as blockchain are also noted.

The research investigates Singapore’s context, a country in which research directions and focus areas are influenced by government interventions and leadership. Thus, future studies might examine other patent ecosystems to draw comparisons with more laissez-faire policies or ecosystems with more pronounced organic development.

The novelty of this research is the introduction of the concept of a patent ecosystem for advancing a more fine-grained understanding of the aggregated knowledge generated at the ecosystem level and its specific features, composition and development. The authors consider patents as “carriers” of different codified pieces of knowledge and patent ecosystems represent the configuration that emerges from connections of these elements. The novel approach can aid both researchers, practitioners and policymakers with future examinations in the field.

Technological tools for knowledge management (KM) actively support and enhance knowledge acquisition and sharing in organizations. However, technology for KM has been understudied, especially in terms of disruptive technologies (DTs). There is a need to identify how DTs, which are becoming increasingly important in industry and society, are applied to KM and their impact. This paper aims to examine the current state of technology and DT adoption in KM.

The analysis involves four steps. First, we examine the current status of DT in academia through a keyword co-occurrence network of literature. Second, we analyze the technological convergence (TC) of KM technology through the cooperative patent classification code co-classification analysis of patents. Third, we explore the main topics of KM technologies using BERTopic, and finally, we explore the introduction of DT into KM technologies and suggest potential TC combinations for the future.

KM technologies can be categorized into four main topics (knowledge acquisition, sharing, searching, and transfer), and DT is most often applied to knowledge transfer and acquisition. The DTs that are attracting attention from academia and industry are artificial intelligence, augmented and virtual reality, and blockchain, which have applications in healthcare, supply chain management, and human resource management.

The findings provide useful insights for organizations to build a technology roadmap for KM. They can also improve the rigid mindset of organization employees toward DT adoption and innovation. By adopting a KM system that leverages DT, organizations will be able to manage and operate efficiently and systematically.

In the context of collaborative research and development (R&D), multi-actor participation and multi-resource integration of technological knowledge has become the mainstream paradigm for the R&D and spillover of industry generic technology (GT). As GT's core characteristics, “fundamentality” and “externality,” make differential requests on knowledge bases regarding the R&D and spillover of GT (SGT). Knowledge breadth can enhance the generality of technology. The purpose of this paper is to integrate “generic technology R&D” and “generic technology spillover” into a single study, and try to solve the theoretical problem of “whether broader mean more general?”

This paper collects and collates the patent data from the two patent databases of Derwent and SooPAT, and then makes an empirical analysis of the patent data collected by the authors with the data analysis software Stata.

Taking 352 strategic emerging firms in China as the sample, this paper examined the effects of general knowledge breadth (GKB) and specific knowledge breadth (SKB) on the R&D and SGT. The authors concluded that both general and SKB have a positive effect on the R&D of GT (RGT), and the latter has a greater effect. There is a significant inverted U-shaped relationship between SKB and SGT.

The theoretical contributions of this paper are as follows. GT can effectively link different technologies and knowledge fields (Gambardella and Giarratana, 2013; Appio et al., 2017a, b). Therefore, existing studies regard the role of knowledge breadth on the R&D and SGT as an existing hypothesis. This paper challenges such hypothesis in two ways. First, this paper divides knowledge breadth into “general knowledge breadth” and “specific knowledge breadth” in response to the insufficient division of knowledge breadth in previous research, although some existing studies have examined the antecedents of the R&D and SGT from the perspective of R&D and SGT. Thus, the authors define GKB as the scope of context-free knowledge and SKB as the scope of context-specific knowledge, both of which shows differential nature, source and application. Second, this paper decomposes the effect of knowledge breadth on RGT, as well as on SGT, basing on distinguishing the SKB from GKB. Existing research reaches a consensus of the positive role of knowledge breadth, no matter on RGT or SGT (e.g. Schmidt et al., 2016; Appio et al., 2017a, b). Yet, such hypothesis ignores the refinement and decomposition of “knowledge breadth” in the research field of R&D and SGT, which is essential in promoting the development of GT theory. In this paper, the authors find that these two types of knowledge breadths play different roles in the RGT, and especially SKB plays a double-edged sword effect on the SGT.

The purpose of this paper is to propose a quantitative method for identifying multiple and hierarchical knowledge trajectories within a specific technological domain (TD).

The proposed method as a patent-based data-driven approach is basically based on patent classification systems and patent citation information. Specifically, the method first analyzes hierarchical structure under a specific TD based on patent co-classification and hierarchical relationships between patent classifications. Then, main paths for each sub-TD and overall-TD are generated by knowledge persistence-based main path approach. The all generated main paths at different level are integrated into the hierarchical main paths.

This paper conducted an empirical analysis by using Genome sequencing technology. The results show that the proposed method automatically identifies three sub-TDs, which are major functionalities in the TD, and generates the hierarchical main paths. The generated main paths show knowledge flows across different sub-TDs and the changing trends in dominant sub-TD over time.

To the best of the authors’ knowledge, the proposed method is the first attempt to automatically generate multiple hierarchical main paths using patent data. The generated main paths objectively show not only knowledge trajectories for each sub-TD but also interactive knowledge flows among sub-TDs. Therefore, the method is definitely helpful to reduce manual work for TD decomposition and useful to understand major trajectories for TD.

Using the large database of patent, the purpose of this paper is to structure a technology convergence network using various patent network analysis for integrating different results according to network characteristics.

The patent co-class analysis and the patent citation analysis are applied to discover core safety fields and technology, respectively. In specific, three types of network analysis, which are centrality analysis, association rule mining analysis and brokerage network analysis, are applied to measure the individual, synergy and group intensity.

The core safety fields derived from three types of network analysis used by different nature of data algorithms are compared with each other to understand distinctive meaning of cores of patent class such as medical safety, working safety and vehicle safety, differentiating network structure. Also, to be specific, the authors find the detailed technology contained in the core patent class using patent citation network analysis.

The results provide meaningful implications to various stakeholders in organization: safety management, safety engineering and safety policy. The multiple patent network enables safety manager to identify core safety convergence fields and safety engineers to develop new safety technology. Also, in the view of technology convergence, the strategy of safety policy can be expanded to collaboration and open innovation.

This is the initial study on applying various network analysis algorithms based on patent data (class and citation) for safety management. Through comparison among network analysis techniques, the different results are identified and the collective decision making on finding core of safety technology convergence is supported. The decision maker can obtain the various perspectives of tracing technology convergence.