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

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اقرأ أكثر حفظ في قائمتي
  • معلومة اضافية
    • Contributors:
      Lappeenrannan-Lahden teknillinen yliopisto LUT; Lappeenranta-Lahti University of Technology LUT; fi=School of Business and Management|en=School of Business and Management
    • بيانات النشر:
      Elsevier
    • الموضوع:
      2019
    • Collection:
      LUTPub (LUT University / LUT yliopisto)
    • نبذة مختصرة :
      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. ; Post-print / Final draft
    • File Description:
      fulltext; 46-59
    • ISSN:
      0148-2963
      1873-7978
    • Relation:
      106; Journal of Business Research; https://doi.org/10.1016/j.jbusres.2019.09.009; Vanhala, M., Lu, C., Peltonen, J., Sundqvist, S., Nummenmaa, J., Järvelin, K. (2020). The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research. Journal of Business Research, vol. 106. pp. 46-59. DOI:10.1016/j.jbusres.2019.09.009; https://lutpub.lut.fi/handle/10024/159969; URN:NBN:fi-fe2019091027628
    • الدخول الالكتروني :
      https://lutpub.lut.fi/handle/10024/159969
    • Rights:
      CC BY-NC-ND 4.0 ; openAccess ; © 2019 Elsevier
    • الرقم المعرف:
      edsbas.AF6DEFCD