Text mining to gain technical intelligence for acquired target selection: A case study for China's computer numerical control machine tools industry

Ma, Tingting; Zhang, Yi; Huang, Lu; Shang, Lining; Wang, Kangrui; Yu, Huizhu; Zhu, Ding

doi:10.1016/j.techfore.2016.10.061

Cited by 20 publications

(6 citation statements)

References 66 publications

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“…At this juncture, it is also worth considering the data to be searched. We chose patents as our basic knowledge resource because our aim is to detect potential technological recombinations, and patents play an important role in identifying both innovations and their commercialization (Ma et al 2017).…”

Section: Stage 1: Identifying Existing Domains and Research Patternsmentioning

confidence: 99%

A hybrid approach to detecting technological recombination based on text mining and patent network analysis

et al. 2019

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Detecting promising technology groups for recombination holds the promise of great value for R&D managers and technology policymakers, especially if the technologies in question can be detected before they have been combined. However, predicting the future is always easier said than done. In this regard, Arthur's theory (The nature of technology: what it is and how it evolves, Free Press, New York, 2009) on the nature of technologies and how science evolves, coupled with Kuhn's theory of scientific revolutions (Kuhn in The structure of scientific revolutions, 1st edn, University of Chicago Press, Chicago, p 3, 1962), may serve as the basis of a shrewd methodological framework for forecasting recombinative innovation. These theories help us to set out quantifiable criteria and decomposable steps to identify research patterns at each stage of a scientific revolution. The first step in the framework is to construct a conceptual model of the target technology domain, which helps to refine a reasonable search strategy. With the model built, the landscape of a fieldits communities, its technologies, and their interactions-is fleshed out through community detection and network analysis based on a set of quantifiable criteria. The aim is to map normal patterns of research in the domain under study so as to highlight which technologies might contribute to a structural deepening of technological recombinations. Probability analysis helps to detect and group candidate technologies for possible recombination and further manual analysis by experts. To demonstrate how the framework works in practice, we conducted an empirical study on AI research in China. We explored the development potential of recombinative technologies by zooming in on the top patent assignees in the field and their innovations. In conjunction with expert analysis, the results reveal the cooperative and competitive relationships among these technology holders and opportunities for future innovation through technological recombinations.

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Section: Stage 1: Identifying Existing Domains and Research Patternsmentioning

confidence: 99%

A hybrid approach to detecting technological recombination based on text mining and patent network analysis

et al. 2019

Self Cite

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“…Thirdly, TF can track the evolution of technology in a certain period [ 39 , 40 ]; show the internal relationship among the market, product, and technology in the form of the technology roadmap [ 41 , 42 ]; and study the diffusion mechanisms of technology [ 43 , 44 ]. Finally, the use of TF in technical cooperation and competition [ 45 ], knowledge management [ 34 ], and decision support is gradually increasing [ 46 ].…”

Section: Resultsmentioning

confidence: 99%

“…This context involves two types of data sources: publications and patents. Important technical information can be retrieved from IPC and text information based on patent data to determine the objectives of technology acquisition [ 46 ]. Patents can also be used as a source of information to analyze the breadth and depth of knowledge within companies, as data provide insight into their capabilities [ 56 ].…”

Section: Resultsmentioning

confidence: 99%

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A Review of Technological Forecasting from the Perspective of Complex Systems

Feng

Wang

et al. 2022

Entropy

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Technology forecasting (TF) is an important way to address technological innovation in fast-changing market environments and enhance the competitiveness of organizations in dynamic and complex environments. However, few studies have investigated the complex process problem of how to select the most appropriate forecasts for organizational characteristics. This paper attempts to fill this research gap by reviewing the TF literature based on a complex systems perspective. We first identify four contexts (technology opportunity identification, technology assessment, technology trend and evolutionary analysis, and others) involved in the systems of TF to indicate the research boundary of the system. Secondly, the four types of agents (field of analysis, object of analysis, data source, and approach) are explored to reveal the basic elements of the systems. Finally, the visualization of the interaction between multiple agents in full context and specific contexts is realized in the form of a network. The interaction relationship network illustrates how the subjects coordinate and cooperate to realize the TF context. Accordingly, we illustrate suggest five trends for future research: (1) refinement of the context; (2) optimization and expansion of the analysis field; (3) extension of the analysis object; (4) convergence and diversification of the data source; and (5) combination and optimization of the approach.

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“…Advances in data science, however, have enabled researchers to apply text mining techniques that extract important textual features from technical documents (e.g. publications and patents) for the TOA (Ma et al, 2013), emerging topic detection (Porter et al, 2019;Rotolo et al, 2015;Small et al, 2014), technology trend (Chen et al, 2017) and technology assessment and impact analyses (Choi et al, 2007), technology roadmapping (Bildosola et al, 2017) and technology and innovation policy studies (Ma et al, 2017). Please refer to Abbas et al (2014), Bonino et al (2010) and Madani and Weber (2016) for a detailed literature review.…”

Section: Literature Review Of the Topic-based Patent Analytics Approachmentioning

confidence: 99%

A topic-based patent analytics approach for exploring technological trends in smart manufacturing

Wang

Hsu

2020

JMTM

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PurposeSmart manufacturing can lead to disruptive changes in production technologies and business models in the manufacturing industry. This paper aims to identify technological topics in smart manufacturing by using patent data, investigating technological trends and exploring potential opportunities.Design/methodology/approachThe latent Dirichlet allocation (LDA) topic modeling technique was used to extract latent technological topics, and the generalized linear mixed model (GLMM) was used to analyze the relative emergence levels of the topics. Topic value and topic competitive analyses were developed to evaluate each topic's potential value and identify technological positions of competing firms, respectively.FindingsA total of 14 topics were extracted from the collected patent data and several fast growth and high-value topics were identified, such as smart connection, cyber-physical systems (CPSs), manufacturing data analytics and powder bed fusion additive manufacturing. Several leading firms apply broad R&D emphasis across a variety of technological topics, while others focus on a few technological topics.Practical implicationsThe developed methodology can help firms identify important technological topics in smart manufacturing for making their R&D investment decisions. Firms can select appropriate technology strategies depending on the topic's emergence position in the topic strategy matrix.Originality/valuePrevious research studies have not analyzed the maturity levels of technological topics. The topic-based patent analytics approach can complement previous studies. In addition, this study provides a multi-valuation framework for exploring technological opportunities, thus providing valuable information that supports a more robust understanding of the technology landscape of smart manufacturing.

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Text mining to gain technical intelligence for acquired target selection: A case study for China's computer numerical control machine tools industry

Cited by 20 publications

References 66 publications

A hybrid approach to detecting technological recombination based on text mining and patent network analysis

A hybrid approach to detecting technological recombination based on text mining and patent network analysis

A Review of Technological Forecasting from the Perspective of Complex Systems

A topic-based patent analytics approach for exploring technological trends in smart manufacturing

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