Trends and topics in IJPR from 1961 to 2017: a statistical history

R. Romero Silva, Erika Marsillac

    Research output: Contribution to JournalArticleAcademicpeer-review

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    Abstract

    This paper studies the history of the International Journal of Production Research (IJPR) by analysing the topics that have received the most attention in each of the journal’s publication years. Text mining exposed for scrutiny the most frequently mentioned and cited terms contained in the titles, abstracts and keywords of IJPR papers. Analyses suggest that the triad of scheduling/optimisation/simulation and supply-chain-related topics have been IJPR’s mainstays, but valuable opportunities remain for relevant topics that have not yet been concurrently and frequently studied. Results also show that terms related to sustainability and risk management topics have gained recent relevance. In addition, IJPR appears to complement its modelling technique focus with empirical methodological approaches to provide a well-balanced perspective, since the ‘case study’ term is common. Finally, a linear relationship is found between the number of papers that have covered certain topics and the number of citations those topics have received, highlighting which topics had fewer or more citations than expected, given the number of papers that covered those topics. IJPR stands as one of the most prestigious and established journals in its field and the results from this study indicate the evolving interests of the field for over half a century.
    Original languageEnglish
    Pages (from-to)4692-4718
    Number of pages27
    JournalInternational Journal of Production Research
    Volume57
    Issue number15-16
    Early online date13 Dec 2018
    DOIs
    Publication statusPublished - 2019

    Bibliographical note

    Special Issue: Selected Surveys on Cutting-edge Problems in Production Research

    Keywords

    • Scopus
    • bibliometric analysis
    • industrial engineering
    • operations management
    • operations research
    • text mining

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