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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 | Размер | Формат | |
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2-s2.0-85163548388.pdf | 8,97 MB | Adobe PDF | Просмотреть/Открыть |
Лицензия на ресурс: Лицензия Creative Commons