IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04779123.html
   My bibliography  Save this paper

Customer Emotions in Service Robot Encounters: A Hybrid Machine-Human Intelligence Approach

Author

Listed:
  • R. Filieri

    (Audencia Business School)

  • Z. Lin
  • Y. Li
  • X. Lu
  • X. Yang

Abstract

Understanding consumer emotions arising from robot-customers encounters and shared through online reviews is critical for forecasting consumers' intention to adopt service robots. Qualitative analysis has the advantage of generating rich insights from data, but it requires intensive manual work. Scholars have emphasized the benefits of using algorithms for recognizing and differentiating among emotions. This study critically addresses the advantages and disadvantages of qualitative analysis and machine learning methods by adopting a hybrid machine-human intelligence approach. We extracted a sample of 9707 customers reviews from two major social media platforms (Ctrip and TripAdvisor), encompassing 412 hotels in 8 countries. The results show that the customer experience with service robots is overwhelmingly positive, revealing that interacting with robots triggers emotions of joy, love, surprise, interest, and excitement. Discontent is mainly expressed when customers cannot use service robots due to malfunctioning. Service robots trigger more emotions when they move. The findings further reveal the potential moderation effect of culture on customer emotional reactions to service robots. The study highlights that the hybrid approach can take advantage of the scalability and efficiency of machine learning algorithms while overcoming its shortcomings, such as poor interpretative capacity and limited emotion categories.

Suggested Citation

  • R. Filieri & Z. Lin & Y. Li & X. Lu & X. Yang, 2022. "Customer Emotions in Service Robot Encounters: A Hybrid Machine-Human Intelligence Approach," Post-Print hal-04779123, HAL.
  • Handle: RePEc:hal:journl:hal-04779123
    DOI: https://doi.org/10.1177/10946705221103937
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vecchietti, Giuseppe & Liyanaarachchi, Gajendra & Viglia, Giampaolo, 2025. "Managing deepfakes with artificial intelligence: Introducing the business privacy calculus," Journal of Business Research, Elsevier, vol. 186(C).
    2. Gül Yazıcı & Tuğçe Ozansoy Çadırcı, 2024. "Creating meaningful insights from customer reviews: a methodological comparison of topic modeling algorithms and their use in marketing research," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(4), pages 865-887, December.
    3. Gupta, Rohit & Rathore, Bhawana, 2024. "Exploring the generative AI adoption in service industry: A mixed-method analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-04779123. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.