Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на этот ресурс: http://elar.urfu.ru/handle/10995/130579
Название: Evolution and recombination of topics in Technological Forecasting and Social Change
Авторы: Savin, I.
Дата публикации: 2023
Издатель: Elsevier Inc.
Библиографическое описание: Savin, I 2023, 'Evolution and recombination of topics in Technological Forecasting and Social Change', Technological Forecasting and Social Change, Том. 194, 122723. https://doi.org/10.1016/j.techfore.2023.122723
Savin, I. (2023). Evolution and recombination of topics in Technological Forecasting and Social Change. Technological Forecasting and Social Change, 194, [122723]. https://doi.org/10.1016/j.techfore.2023.122723
Аннотация: Technological Forecasting and Social Change (TFSC) is one of the main outlets in the literature on technological change. To assist its editors and future contributors in understanding the evolution of the journal, we review studies published between 1970 and 2022 identifying 25 main themes ranging from scenario foresight and forecasting methods that dominated the journal agenda in the first decades through innovation diffusion and patent analysis that gained popularity in 2006–2019 to social interaction and financial markets which experienced momentum in the last couple of years. We find that studies concentrated on more recent topics like firm performance, financial markets and environmental regulation have been cited more frequently and were contributed more often by scientists from China compared to the US. Inspired by the fact that studies recombining two or more topics are more impactful in terms of citations, we construct a graph of topics, both for the overall sample of 6240 studies reviewed and three periods of TFSC existence corresponding to different editors-in-chief. Our results illustrate knowledge complementarities explored in the journal so far and may indicate directions for further research. © 2023 The Author(s)
Ключевые слова: COMPUTATIONAL LINGUISTICS
KNOWLEDGE RECOMBINATION
LITERATURE REVIEW
MACHINE LEARNING
TOPIC MODELLING
COMMERCE
COMPUTATIONAL LINGUISTICS
ENVIRONMENTAL REGULATIONS
FINANCIAL MARKETS
MACHINE LEARNING
TECHNOLOGICAL FORECASTING
DIFFUSION ANALYSIS
FORECASTING METHODS
INNOVATIONS DIFFUSION
KNOWLEDGE RECOMBINATION
LITERATURE REVIEWS
MACHINE-LEARNING
PATENT ANALYSIS
SOCIAL CHANGES
TECHNOLOGICAL CHANGE
TOPIC MODELING
PATENTS AND INVENTIONS
KNOWLEDGE
LITERATURE REVIEW
MACHINE LEARNING
NUMERICAL MODEL
SOCIAL CHANGE
TECHNOLOGICAL CHANGE
CHINA
UNITED STATES
URI: http://elar.urfu.ru/handle/10995/130579
Условия доступа: info:eu-repo/semantics/openAccess
cc-by
Текст лицензии: https://creativecommons.org/licenses/by/4.0/
Идентификатор SCOPUS: 85163548388
Идентификатор WOS: 001035662900001
Идентификатор PURE: 41531536
ISSN: 0040-1625
DOI: 10.1016/j.techfore.2023.122723
Сведения о поддержке: European Research Council, ERC; Horizon 2020: 741087
Ivan Savin acknowledges support from the ERC Advanced Grant from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (grant agreement no. 741087 ).
Располагается в коллекциях:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

Файлы этого ресурса:
Файл Описание РазмерФормат 
2-s2.0-85163548388.pdf8,97 MBAdobe PDFПросмотреть/Открыть


Лицензия на ресурс: Лицензия Creative Commons Creative Commons