An analysis of research methods in IJPR since inception

Andrew Manikas, Lynn Boyd, Qinghua Pang and Jian (Jeff) Guan

International Journal of Production Research, 2019, vol. 57, issue 15-16, 4667-4675

Abstract: Production research as an academic field has experienced tremendous growth in the last few decades. The progress in production research and operations management (OM) research is due in no small part to the increasing sophistication and availability of research methods in this field. This paper explores the role of research methods in OM publications through an analysis of the entire corpus of research as represented in a leading OM journal, the International Journal of Production Research (IJPR). This paper reports on a study of all 8653 academic article abstracts published in IJPR since inception to identify the research methods used to both generate and analyse data over the 55 years from the journal’s inception in 1961 through 2015. The study classifies articles using a 6 × 6 typology on the dimensions of data generation and data analysis and provides a summary of the use of research methods and the evolution of their use over time. For example, mathematical modelling has become the dominant method for data generation while experiments have become less popular. Though meta-heuristics and optimisation remain the most popular methods for data analysis, data mining methods have gained pained popularity, comparable to statistical methods.

Date: 2019
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DOI: 10.1080/00207543.2017.1362122

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