Research Output
Let us talk about something: The evolution of e-WOM from the past to the future
  Because e-WOM is one of the useful digital marketing elements for any organization, a better understanding of its process will help individuals take more advantage of this concept. e-WOM enables individuals to form relationships with firms, brands, and other customers, which leads to benefits for both consumers and companies. It plays a significant role in a firm's performance. The present study implements a different approach to reviewing by combining two bibliometric methods, multidimensional scaling analysis (MDS) and hierarchical cluster analysis (HCA), via Bibexcel software to have a deeper investigation of the process. Considering the 468 journal papers on e-WOM allowed us to study the intellectual streams and significant perceptions underpinning e-WOM. By dividing the study timeframe into three periods, we realized that there have always been three main concepts in this field: consumer behavior, sales, and the tourism and hotel industry. Further, by proposing a framework, we have expanded these concepts accompanied by the role of artificial intelligence and robots in the process of e-WOM. Consequently, new concepts "r-WOM", "automated user engagement", and "smart selling" are introduced and demonstrated as a consequence of using technology-based tools in the process of e-WOM. Finally, the future scope of this field has been designed. We contribute to the literature by offering theoretical and managerial implications.

  • Date:

    31 October 2022

  • Publication Status:

    Published

  • Publisher

    Elsevier BV

  • DOI:

    10.1016/j.jbusres.2022.05.061

  • ISSN:

    0148-2963

  • Funders:

    Advance HE

Citation

Akbari, M., Foroudi, P., Zaman Fashami, R., Mahavarpour, N., & Khodayari, M. (2022). Let us talk about something: The evolution of e-WOM from the past to the future. Journal of Business Research, 149, 663-689. https://doi.org/10.1016/j.jbusres.2022.05.061

Authors

Keywords

e-WOM, Multidimensional scaling analysis, Hierarchical cluster analysis, Bibliometric, r-WOM

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