@article{e0aaab9f1eb04d3ebc1588d310408517,
title = "Robots and the origin of their labour-saving impact",
abstract = "This paper investigates the presence of explicit labour-saving heuristics within robotic patents. It analyses innovative actors engaged in robotic technology and their economic environment (identity, location, industry), and identifies the technological fields more exposed to labour-saving innovations. It exploits advanced natural language processing and probabilistic topic modelling techniques applied to the universe of USPTO patent applications between 2009 and 2018, matched with the ORBIS (Bureau van Dijk) firm-level dataset. The results show that labour-saving patent holders comprise not only robot producers, but mainly adopters. Consequently, labour-saving robotic patents appear along the entire supply chain. Additionally, labour-saving innovations are directed towards manual activities in services (e.g. in the logistics sector), activities entailing social intelligence (e.g. in the healthcare sector) and cognitive skills (e.g. learning and predicting).",
keywords = "Robotic patents, Labour-saving technology, Search heuristics, Probabilistic topic models, EMBODIED TECHNOLOGICAL-CHANGE, RESEARCH-AND-DEVELOPMENT, EMPLOYMENT, JOBS, PATTERNS, INDUSTRY, FUTURE, GROWTH, SKILLS, FIRMS",
author = "F. Montobbio and J. Staccioli and M.E. Virgillito and M. Vivarelli",
note = "Funding Information: The authors wish to thank two anonymous reviewers for their valuable comments, as well as participants at the 2020 GEOINNO Conference (Stavanger, Norway), the 2019 CONCORDi Conference (Seville, Spain), the 2019 EAEPE Conference (Warsaw, Poland), the 2019 EMAEE Conference (Brighton, UK), the 2019 RENIR Workshop on the impact of automation and artificial intelligence on regional economies (Turin, Italy), and the 2019 Workshop on Innovation, firm dynamics, employment and growth (Greenwich, UK) for helpful comments and insightful suggestions at various stages of this work. Fabio Montobbio and Marco Vivarelli acknowledge support by the Italian Ministero dell{\textquoteright}Istruzione, dell{\textquoteright}Universit{\`a} e della Ricerca, PRIN-2017 project 201799ZJSN : “Technological change, industry evolution and employment dynamics” (principal investigator: Marco Vivarelli). Maria Enrica Virgillito acknowledges support from European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement no. 822781 “GROWINPRO – Growth Welfare Innovation Productivity”. Funding Information: The authors wish to thank two anonymous reviewers for their valuable comments, as well as participants at the 2020 GEOINNO Conference (Stavanger, Norway), the 2019 CONCORDi Conference (Seville, Spain), the 2019 EAEPE Conference (Warsaw, Poland), the 2019 EMAEE Conference (Brighton, UK), the 2019 RENIR Workshop on the impact of automation and artificial intelligence on regional economies (Turin, Italy), and the 2019 Workshop on Innovation, firm dynamics, employment and growth (Greenwich, UK) for helpful comments and insightful suggestions at various stages of this work. Fabio Montobbio and Marco Vivarelli acknowledge support by the Italian Ministero dell'Istruzione, dell'Universit? e della Ricerca, PRIN-2017 project 201799ZJSN: ?Technological change, industry evolution and employment dynamics? (principal investigator: Marco Vivarelli). Maria Enrica Virgillito acknowledges support from European Union's Horizon 2020 research and innovation programme under grant agreement no. 822781 ?GROWINPRO ? Growth Welfare Innovation Productivity?. Publisher Copyright: {\textcopyright} 2021 Elsevier Inc.",
year = "2022",
month = jan,
day = "1",
doi = "10.1016/j.techfore.2021.121122",
language = "English",
volume = "174",
journal = "Technological Forecasting and Social Change",
issn = "0040-1625",
publisher = "Elsevier Science",
}