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Tracing the evolution of service robotics: Insights from a topic modeling approach

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  • Ott, Ingrid
  • Savin, Ivan
  • Konop, Chris

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, where some topics appear isolated while others are highly interconnected. Furthermore, we propose a novel approach to match the constructed topics to the IFR classification of service robots based on frequency and exclusivity of words overlapping between them. We identify around 20 topics belonging to service robotics. Our results corroborate earlier findings, but also provide novel insights on the content and stage of development of application areas in service robotics. With this study we contribute to a better understanding of the highly dynamic field of robotics and contribute to new practices of utilizing the topic modeling approach.

Suggested Citation

  • Ott, Ingrid & Savin, Ivan & Konop, Chris, 2021. "Tracing the evolution of service robotics: Insights from a topic modeling approach," Kiel Working Papers 2180, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwkwp:2180
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    Cited by:

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    2. Bongini, Paola & Osborne, Francesco & Pedrazzoli, Alessia & Rossolini, Monica, 2022. "A topic modelling analysis of white papers in security token offerings: Which topic matters for funding?," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    3. Albina Latifi & David Lenz & Peter Winker, 2024. "Identification of Innovation Drivers Based on Technology-Related News Articles," MAGKS Papers on Economics 202401, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    4. Deng, Shichang & Zhang, Jingjing & Lin, Zhengnan & Li, Xiangqian, 2024. "Service staff makes me nervous: Exploring the impact of insecure attachment on AI service preference," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    5. Ghaffari, Mohsen & Aliahmadi, Alireza & Khalkhali, Abolfazl & Zakery, Amir & Daim, Tugrul U. & Yalcin, Haydar, 2023. "Topic-based technology mapping using patent data analysis: A case study of vehicle tires," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    6. Frank, Darius-Aurel & Otterbring, Tobias, 2023. "Being seen… by human or machine? Acknowledgment effects on customer responses differ between human and robotic service workers," Technological Forecasting and Social Change, Elsevier, vol. 189(C).

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    More about this item

    Keywords

    knowledge diffusion; latent Dirichlet allocation; networks; patents; topic matching;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

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