2022
DOI: 10.1016/j.techfore.2021.121280
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Tracing the evolution of service robotics: Insights from a topic modeling approach

Abstract: Taking robotic patents between 1977 and 2017 and building upon the topic modeling technique, we extract their latent topics, analyze how important these topics are over time, and how they are related to each other looking at how often they are recombined in the same patents. This allows us to differentiate between more and less important technological trends in robotics based on their stage of diffusion and position in the space of knowledge represented by a topic graph, where some topics appear isolated while… Show more

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Cited by 32 publications
(10 citation statements)
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“…This can cause lower competition at the stage the startups are founded in the former classes, stimulate investors to provide venture capital, and require larger investment to proceed with scaling up the production. Another reason is the unprecedented penetration of new technologies (AI, cloud computing, gene editing tools) in agricultural and health sectors (Savin et al, 2022 ), which attract new investors. An additional reason for increasing attractiveness of startups in pharmaceutics is the shift of biopharmaceutical drug development from in-house production of large pharma companies to small and mid-sized companies to diversify risky internal R&D programs (Melchner von Dydiowa et al, 2021 ; Murphey, 2019 ).…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…This can cause lower competition at the stage the startups are founded in the former classes, stimulate investors to provide venture capital, and require larger investment to proceed with scaling up the production. Another reason is the unprecedented penetration of new technologies (AI, cloud computing, gene editing tools) in agricultural and health sectors (Savin et al, 2022 ), which attract new investors. An additional reason for increasing attractiveness of startups in pharmaceutics is the shift of biopharmaceutical drug development from in-house production of large pharma companies to small and mid-sized companies to diversify risky internal R&D programs (Melchner von Dydiowa et al, 2021 ; Murphey, 2019 ).…”
Section: Resultsmentioning
confidence: 99%
“…For example, Larsen et al ( 2019 ) analyze news from the Norwegian business newspaper, and link obtained topics and their prevalence to the economic fluctuations on the asset markets. TM has recently been applied also to patent data in order to (i) (re)classify those into product and technology sub-classes and later explore technological convergence for the photovoltaic technology in the USA (Venugopalan & Rai, 2015 ); (ii) identify emerging topics in the USA, EU, and Japan (Lee et al, 2015 ); (iii) detect pioneering patent introducing new topics (Kaplan & Vakili, 2015 ); and (iv) predict trends in patent topics (Chen et al, 2017 ; Choi & Song, 2018 ; Savin et al, 2022 ; Suominen et al, 2017 ). TM has been further applied to survey open-ended questions to examine public perceptions of economic growth (Savin et al, 2021 ) and climate change (Tvinnereim & Fløttum, 2015 ; Tvinnereim, Liu, et al, 2017 ), to collect ideas on what people think about climate change mitigation measures in general (Tvinnereim, Fløttum, et al, 2017 ) and carbon pricing as a policy instrument in particular (Savin et al, 2020 ), or about other individuals’ beliefs about climate change (Mildenberger & Tingley, 2017 ).…”
Section: Literature Reviewmentioning
confidence: 99%
“…This process reduces researcher bias because foreknowledge of document content does not affect the topic classifications (Zhang et al, 2021). The LDA topic model is widely used in patent content analysis (Wang et al, 2015;Zhang et al, 2021) and technology topics evaluation (Li et al, 2021;Wang et al, 2020;Savin et al, 2022aSavin et al, , 2022b In order to apply LDA to the STO white papers, we first pre-processed the corpus by 1) converting words to lowercase, 2) removing standard English stop words and punctuation, and 3) lemmatizing all the words by means of the Natural Language Toolkit 6 lemmatiser. We then analysed the distribution of terms with domain experts and filtered out generic terms that appeared in more than 60% of the white papers (Zhang et al, 2021).…”
Section: Identifying Topics With Ldamentioning
confidence: 99%
“…Applications of topic modelling have been even broader including, for example, tracing development of agricultural and water technologies over long time [ 21 ] and understanding public attitudes towards municipal solid waste sorting policy in China [ 22 ]. For a recent literature review on applications of topic modelling see [ 23 ].…”
Section: Introductionmentioning
confidence: 99%