The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research

Mika Vanhala, Chien Lu, Jaakko Peltonen, Sanna Sundqvist, Jyrki Nummenmaa, Kalervo Järvelin

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)46-59
Number of pages14
JournalJournal of Business Research
Volume106
Issue number1
Early online date2019
DOIs
Publication statusPublished - Jan 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Bibliometric analysis
  • Consumer behaviour
  • Online
  • Large datasets
  • Text analysis

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