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The power of emotions: Leveraging user generated content for customer experience management

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  • Sykora, Martin
  • Elayan, Suzanne
  • Hodgkinson, Ian R.
  • Jackson, Thomas W.
  • West, Andrew

Abstract

Customer experience management (CEM) in the social media age finds itself needing to adapt to a rapidly changing digital environment and hence there is a need for innovative digital data analytical solutions. Drawing on an action case study of a large global automotive manufacturer, this study presents a digital innovation for enhanced emotion analytics on user generated content (UGC) and behaviour (UGB), to improve consumer insights for CEM. The digital innovation captures customer experience in real time, enabling measurement of a wide range of discrete emotions on the studied social media platform, which goes beyond traditional tools that capture positive or negative sentiment only. During the digital intervention, a substantial number of inauthentic and bot like behaviours was revealed, unbeknown to the case organisation. These accounts were found to be posting and amplifying highly emotional and potentially damaging content surrounding the case brand and its products. The study illustrates how emotion in the context of customer experience should go beyond typical categorisations, given the complexity of human emotion, while a distinction between bot and authentic users is imperative for CEM.

Suggested Citation

  • Sykora, Martin & Elayan, Suzanne & Hodgkinson, Ian R. & Jackson, Thomas W. & West, Andrew, 2022. "The power of emotions: Leveraging user generated content for customer experience management," Journal of Business Research, Elsevier, vol. 144(C), pages 997-1006.
    • Handle: RePEc:eee:jbrese:v:144:y:2022:i:c:p:997-1006
    DOI: 10.1016/j.jbusres.2022.02.048
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    References listed on IDEAS

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    Cited by:

    1. Bigné, Enrique & Ruiz-Mafé, Carla & Badenes-Rocha, Alberto, 2023. "The influence of negative emotions on brand trust and intention to share cause-related posts: A neuroscientific study," Journal of Business Research, Elsevier, vol. 157(C).
    2. Wang, Qiping & Yiu Keung Lau, Raymond, 2024. "Social mood and M&A performance: An empirical investigation enhanced by multimodal analytics," Journal of Business Research, Elsevier, vol. 176(C).
    3. Blasco-Arcas, Lorena & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2023. "Leveraging user behavior and data science technologies for management: An overview," Journal of Business Research, Elsevier, vol. 154(C).
    4. Stockheim, Inbal & Perez, Dikla & Podkamien, Yael, 2024. "Friend and Foe: The impact of complimentary competitor content (CCC) on consumer response towards the endorsing competitor," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    5. Just, Julian, 2024. "Natural language processing for innovation search – Reviewing an emerging non-human innovation intermediary," Technovation, Elsevier, vol. 129(C).

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