Firms' knowledge profiles: Mapping patent data with unsupervised learning

Suominen, Arho; Toivanen, Hannes; Seppänen, Marko

doi:10.1016/j.techfore.2016.09.028

Cited by 94 publications

(34 citation statements)

References 42 publications

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“…The central thread relates to the use of topic modeling to map cultural dynamics around social structures. A macro approach involves mapping the meaning structures that comprise business environments (Pröllochs & Feuerriegel, 2018), knowledge profiles of firms (Suominen et al, 2017), emerging fields (Hannigan & Casasnovas, 2019), and political issues (Kim et al, 2018). Researchers have modeled the topics and rhetorical attributes of scientific articles, in turn finding links between the hidden topic structure of scientific communities as "thought collectives" and impacts on knowledge consumption patterns (Antons et al, 2018).…”

Section: Trends In Rendering Theoretical Artifactsmentioning

confidence: 99%

“…The author considered differences in legal language in government budgeting legislation as a basis for distinguishing between legal traditions. Both studies offer an approach to overcome biases associated with interpreting cultural events.In similar articles, scholars have used topic modeling to study topic-based classifications in patent dataSuominen, Toivanen, & Seppänen, 2017;Venugopalan & Rai, 2015). The practice of mapping knowledge structures in science is in its infancy, and the use of topic modeling has the potential to change how scientific fields are classified (seeSong, Heo, & Lee, 2015;Song & Kim, 2013;Yau, Porter, Newman, & Suominen, 2014) since topic modeling analyses do not perfectly correspond to formal systems of classification(Cho et al, 2017;.…”

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confidence: 99%

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Topic Modeling in Management Research: Rendering New Theory from Textual Data

Hannigan

Haans

Vakili

et al. 2019

ANNALS

374

251

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____ We would like to thank the editors of the Academy of Management Annals for their support and helpful comments. We also thank the participants in our various topic modeling presentations and reviewers and division organizers (specifically Peer Fiss and Renate Meyer) at the Academy of Management meetings. In addition, we would like to recognize Marc-David Seidel and Christopher Steele from the Interpretive Data Science (IDeaS) group for their role in germinating these ideas, Mike Pfarrer for his comments on a later draft of the paper, and Kara Gehman for her fine-grained edits on next-to-final drafts. Finally, we wish to express our appreciation to our life partners for not only putting up with, but actively discussing this paper as it evolved.

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Section: Trends In Rendering Theoretical Artifactsmentioning

confidence: 99%

mentioning

confidence: 99%

Topic Modeling in Management Research: Rendering New Theory from Textual Data

Hannigan

Haans

Vakili

et al. 2019

ANNALS

374

251

View full text Add to dashboard Cite

show abstract

“…It tried to identify and understand technological hot topics in logistics using International Patent Classification at the subclass level, but the pre-determined patent classification scheme has limitations in providing specific information about topics of patented technologies [79,80]. In contrast, the textual data in patent documents can provide richer information [81]. In addition, although analyzing the patents held by some 3PL providers can aid in assessing those firms' technological strength and performance, it is deficient for investigating logistics-related technologies thoroughly.…”

Section: Patent Analysis For Technology Explorationmentioning

confidence: 99%

Exploring Technological Trends in Logistics: Topic Modeling-Based Patent Analysis

Choi

Song

2018

Sustainability

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Abstract:With the strategic importance of discerning opportunities and threats from technological development to achieve sustainable competitiveness, exploring technological trends becomes critical for a successful technology strategy in logistics. Given the rapid pace of development and varying technological options, logistics also increasingly requires methodological support and appropriate data to reduce the complexity and burden of exploring technology trends. While previous research has largely relied on experts' insights, the value of patent-based approaches for exploring technological trends has been underestimated in logistics. To address this gap, this study proposes a topic modeling-based approach using logistics-related patents registered at the United States Patents and Trademark Office (USPTO). The core of the suggested approach is latent Dirichlet allocation (LDA), allowing the identification of logistics-related technological topics behind patents. The topics identified by LDA are further investigated regarding both filed-level and firm-level trends. The suggested approach is expected to offer implications of the use of patents for the purpose of exploring the trends of technology development outside the organization in logistics. In addition, we believe that the information on the technological topics and their trends generated by the suggested approach can offer an enhanced understanding of the technological landscape in logistics.

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“…Due to such immense importance of patents, it is necessary to invest in intelligent decision making. Patents are the most feasible approach for analyzing the breadth and depth of research and development within a company as the patent data provides insight to its competences [26]. The depth corresponds to the diversity of patents in an organization while the depth corresponds to the number of patents in a particular domain.…”

Section: Introductionmentioning

confidence: 99%