Firm strategic behavior and the measurement of knowledge flows with patent citations

Corsino, Marco; Mariani, Myriam; Torrisi, Salvatore

doi:10.1002/smj.3016

Cited by 57 publications

(32 citation statements)

References 55 publications

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“…Using citation linkages to indicate knowledge flow is an effective method in the existing literature [9]- [11]. Two kinds of patent citations are usually considered in the patent citation network: one kind is the linkages between patents and patents, the other is the linkages between patents and scientific literature.…”

Section: Introductionmentioning

confidence: 99%

Revealing Structural Patterns of Patent Citation by a Two-Boundary Network Model Based on USPTO Data

Chi

Song

2020

IEEE Access

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The patent is one of the carriers of scientific research and development, and an indicator of technical innovation. As a promising approach for modeling complex systems, complex networks could provide the sound theoretical framework for developing proper simulation models. Many researchers use the relations of patent citations and transfers to study knowledge propagate and output in the network. However, knowledge flow in patents should be fully considered by substantial and fruitful connections, both in the process of knowledge application and knowledge output. In this paper, we present a two-boundary network model with knowledge application boundary and knowledge output boundary to reveal the patent citation patterns in knowledge flow. The feasibility and effectiveness of the two-boundary network model are proved with theory and experiment. Utilizing 578,678 patents from the United States Patent and Trademark Office between 2015 and 2018 with the two-boundary network model, we put up a fixed effect ordinary least square equation to reveal the patent impacts of different structural patterns. Experimental results show that, in the perspective of structural patterns, the highly impacted patents without assigning are greatly influenced by their scientific literature references and provide knowledge for other assigned patents. However, considering all the fixed effect factors, patents that transfer knowledge from other patents to assigned patents are more likely to become highly impacted patents. Besides, we find the two-boundary network model fits the real patent knowledge flow well by comparing it with the other models. INDEX TERMS Two-boundary network model, knowledge flow, regression equation, fixed effect.

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Section: Introductionmentioning

confidence: 99%

Revealing Structural Patterns of Patent Citation by a Two-Boundary Network Model Based on USPTO Data

Chi

Song

2020

IEEE Access

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“…Scholars have also found that patenting propensity increases with firm size (Nagaoka et al, 2010). Apart from firm strategy and size, the technological characteristics of an innovation and the location of patent filing can impact measurement error for patent-based measures (Corsino, Mariani, & Torrisi, 2019). In addition, measurement error—caused by issues such as truncation in patent citations or differential patterns of patent citations across technology classes—substantially challenges our ability to interpret results based on patent data (Lerner & Seru, 2017).…”

Section: Assessing Measurement Issues In Patent-based Measuresmentioning

confidence: 99%

“…However, these citations may arise for reasons wholly unrelated to innovation, impact, or novelty. Many citations result from (a) attorneys, who tend to cite early patents and add self-citations; (b) patent examiners, who have been found to add a significant percentage of citations (between 20% and 60%) and who often add citations of patents that they had worked on previously (e.g., see Corsino et al, 2019); and (c) the firm, as part of managerial strategies to possibly increase the captured value for their entity. In our analysis, 44% of articles did not discuss or control for examiner-added citations and/or an entity’s own citations.…”

Section: Assessing Measurement Issues In Patent-based Measuresmentioning

confidence: 99%

“…As detailed by Somaya (2012), even the initial decision to utilize a patent to protect an innovation is a strategic decision involving many decision makers with diverse goals and objectives. Furthermore, these decisions are affected by the classification of the technology and industry life cycle stage (Corsino et al, 2019). Other publicly available and widely used data sets, such as Compustat, suffer considerably less systematic error due to the consistency demanded by regulatory, auditing, and training standards (i.e., Generally Accepted Accounting Principles, Auditing Standards Board).…”

Section: Assessing Measurement Issues In Patent-based Measuresmentioning

confidence: 99%

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Mapping Patent Usage in Management Research: The State of Prior Art

Savage

Turner

et al. 2020

Journal of Management

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Patents play an important, and increasingly influential, role in management scholarship. In this study, we conduct a broad and systematic review of patent-based empirical work in the management field, which involves mapping the ways in which scholars are using patent-based measures to represent concepts and assessing this usage based on measurement principles. With respect to mapping, our review identifies the different types of measures that researchers have constructed based on different types of patent data (e.g., patent counts, backward citations) as well as delineates the classes of theoretical concepts that are being represented by those measures. In terms of assessing, as a complement to prior surveys of patent-based research that have assessed patents as indicators based on features of patents, patenting, and patent offices, we develop a framework that is based on measurement principles. Using this framework, our assessment of patent-based research in management reveals important patterns surrounding foundational measurement issues, i.e., method bias, validation threats, model misspecification. Our review makes two core contributions: one centering on summarizing how patents have been used in management research and one focusing on guiding management scholars in terms of common measurement issues for patent-based indicators. These contributions have important implications for future scholarly work in management.

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“…Managing knowledge is increasingly popular in an industrial context, as organizations realize the importance of effective creation, management and application of knowledge (Lee et al, 2013). Therefore, there is a large volume of research focusing on studying knowledge flow in different contexts such as studying knowledge flow in a global economy (Ribeiro et al, 2014;Abramo et al, 2020), inside firms (Corsino et al, 2019;Brennecke and Rank, 2017) and technology domains (Park and Yoon, 2018;Kim and Magee, 2018). The latter places emphasis on studying technology evolution, stating that technology evolution is typically shaped by problem-solving activities integrating knowledge from technology areas and leveraging the cumulative character of knowledge (Kim and Magee, 2018).…”

Section: Introductionmentioning

confidence: 99%

Exploring knowledge flow within a technology domain by conducting a dynamic analysis of a patent co-citation network

Smojver

Štorga

Zovak

2020

JKM

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Purpose This paper aims to present a methodology by which future knowledge flow can be predicted by predicting co-citations of patents within a technology domain using a link prediction algorithm applied to a co-citation network. Design/methodology/approach Several methods and approaches are used: a dynamic analysis of a patent citation network to identify technology life cycle phases, patent co-citation network mapping from the patent citation network and the application of link prediction algorithms to the patent co-citation network. Findings The results of the presented study indicate that future knowledge flow within a technology domain can be predicted by predicting patent co-citations using the preferential attachment link prediction algorithm. Furthermore, they indicate that the patent – co-citations occurring between the end of the growth life cycle phase and the start of the maturation life cycle phase contribute the most to the precision of the knowledge flow prediction. Finally, it is demonstrated that most of the predicted knowledge flow occurs in a time period closely following the application of the link – prediction algorithm. Practical implications By having insight into future potential co-citations of patents, a firm can leverage its existing patent portfolio or asses the acquisition value of patents or the companies owning them. Originality/value It is demonstrated that the flow of knowledge in patent co-citation networks follows a rich get richer intuition. Moreover, it is show that the knowledge contained in younger patents has a greater chance of being cited again. Finally, it is demonstrated that these co-citations can be predicted in the short term when the preferential attachment algorithm is applied to a patent co-citation network.

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Firm strategic behavior and the measurement of knowledge flows with patent citations

Cited by 57 publications

References 55 publications

Revealing Structural Patterns of Patent Citation by a Two-Boundary Network Model Based on USPTO Data

Revealing Structural Patterns of Patent Citation by a Two-Boundary Network Model Based on USPTO Data

Mapping Patent Usage in Management Research: The State of Prior Art

Exploring knowledge flow within a technology domain by conducting a dynamic analysis of a patent co-citation network

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