 The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous researchMika Vanhala, Chien Lu, Jaakko Peltonen, Sanna Sundqvist, Jyrki Nummenmaa and Kalervo Järvelin Journal of Business Research, 2020, vol. 106, issue C, 46-59 Abstract: The paper reports the evolution of scientific research on usage of large datasets in online consumer behaviour between 2000 and 2018. Thus, it affords information regarding the evolution of the field in terms of identifying key publications and authors as well as how certain topics have evolved over time. In addition, by utilising topic modelling and text analytic techniques, it is identified certain research themes from the papers within the published articles included in the dataset. This offers a guide to those who want to contribute to the field. In addition, paper contributes to the methodology related to literature surveys and bibliometric analyses by conducted topic modelling to extract the latent topics from the collected literature by utilising Structural Topic Modelling in order to gain more elaborated results. Keywords: Bibliometric analysis; Consumer behaviour; Online; Large datasets; Text analysis (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:106:y:2020:i:c:p:46-59 DOI: 10.1016/j.jbusres.2019.09.009 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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