A retrospective analysis of Journal of Forecasting: From 1982 to 2019

This research offers a thorough examination of Journal of Forecasting (JoF) from two angles. On the one hand, this research examines a total of 1403 publications indexed by Web of Science (WoS) from 1982 to 2019. Not only some fundamental statistics like the number of publications and citations each...

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Published inJournal of forecasting Vol. 42; no. 4; pp. 1008 - 1035
Main Authors Yu, Dejian, Sheng, Libo, Shi, Shunshun
Format Journal Article
LanguageEnglish
Published Chichester Wiley Periodicals Inc 01.07.2023
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ISSN0277-6693
1099-131X
DOI10.1002/for.2921

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Summary:This research offers a thorough examination of Journal of Forecasting (JoF) from two angles. On the one hand, this research examines a total of 1403 publications indexed by Web of Science (WoS) from 1982 to 2019. Not only some fundamental statistics like the number of publications and citations each year, the most prolific countries/regions/authors, the highly cited countries/regions/authors, and papers are counted, but content analysis is also done to look at things like document co‐citation clusters and hot and emerging subjects. On the other hand, to illustrate the knowledge flow of JoF that underlies citation connections, this research, which is based on the citing papers of JoF publications, exposes the direction of JoF knowledge dissemination from Economics and Management (i.e., the categories where JoF belongs) to other categories. Moreover, a deeper investigation on semantic clustering of five representative categories is conducted to figure out what topics are the citing papers of JoF are most interested in and whether there is any relationship between the knowledge of these categories and the knowledge produced by JoF. The framework for journal evaluation presented in this research is novel and comprehensive. With this comprehensive perspective, we are able to look back on past accomplishments, identify historical patterns of knowledge spread, illustrate the current course of development, and partially predict future dynamics.
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ISSN:0277-6693
1099-131X
DOI:10.1002/for.2921