IDEAS home Printed from https://ideas.repec.org/a/kap/mktlet/v33y2022i4d10.1007_s11002-022-09630-x.html
   My bibliography  Save this article

Language matters: humanizing service robots through the use of language during the COVID-19 pandemic

Author

Listed:
  • Smriti Kumar

    (University of Massachusetts Amherst)

  • Elizabeth G. Miller

    (University of Massachusetts Amherst)

  • Martin Mende

    (Florida State University)

  • Maura L. Scott

    (Florida State University)

Abstract

Service robots are emerging quickly in the marketplace (e.g., in hotels, restaurants, and healthcare), especially as COVID-19-related health concerns and social distancing guidelines have affected people’s desire and ability to interact with other humans. However, while robots can increase efficiency and enable service offerings with reduced human contact, prior research shows a systematic consumer aversion toward service robots relative to human service providers. This potential dilemma raises the managerial question of how firms can overcome consumer aversion and better employ service robots. Drawing on prior research that supports the use of language for building interpersonal relationships, this research examines whether the type of language (social-oriented vs. task-oriented language) a service robot uses can improve consumer responses to and evaluations of the focal service robot, particularly in light of consumers’ COVID-19-related stress. The results show that consumers respond more favorably to a service robot that uses a social-oriented (vs. task-oriented) language style, particularly when these consumers experience relatively higher levels of COVID-19-related stress. These findings contribute to initial empirical evidence in marketing for the efficacy of leveraging robots’ language style to improve customer evaluations of service robots, especially under stressful circumstances. Overall, the results from two experimental studies not only point to actionable managerial implications but also to a new avenue of research on service robots that examines customer-robot interactions through the lens of language and in contexts that can be stressful for consumers (e.g., healthcare or some financial service settings).

Suggested Citation

  • Smriti Kumar & Elizabeth G. Miller & Martin Mende & Maura L. Scott, 2022. "Language matters: humanizing service robots through the use of language during the COVID-19 pandemic," Marketing Letters, Springer, vol. 33(4), pages 607-623, December.
    • Handle: RePEc:kap:mktlet:v:33:y:2022:i:4:d:10.1007_s11002-022-09630-x
    DOI: 10.1007/s11002-022-09630-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11002-022-09630-x
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11002-022-09630-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sander Greenland, 2019. "Valid P-Values Behave Exactly as They Should: Some Misleading Criticisms of P-Values and Their Resolution With S-Values," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 106-114, March.
    2. Chiara Longoni & Andrea Bonezzi & Carey K Morewedge, 2019. "Resistance to Medical Artificial Intelligence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 629-650.
    3. Blakeley B. McShane & David Gal & Andrew Gelman & Christian Robert & Jennifer L. Tackett, 2019. "Abandon Statistical Significance," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 235-245, March.
    4. repec:oup:jconrs:v:47:y:2021:i:5:p:787-806. is not listed on IDEAS
    5. Markus Blut & Cheng Wang & Nancy V. Wünderlich & Christian Brock, 2021. "Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 632-658, July.
    6. Gremler, Dwayne D. & Gwinner, Kevin P., 2008. "Rapport-Building Behaviors Used by Retail Employees," Journal of Retailing, Elsevier, vol. 84(3), pages 308-324.
    7. Valentin Amrhein & Sander Greenland & Blake McShane, 2019. "Scientists rise up against statistical significance," Nature, Nature, vol. 567(7748), pages 305-307, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Yao, Ruiqi & Qi, Guijie & Wu, Zhiqiang & Sun, Hua & Sheng, Dongfang, 2024. "Digital human calls you dear: How do customers respond to virtual streamers’ social-oriented language in e-commerce livestreaming? A stereotyping perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    2. Hai Lan & Xiaofei Tang & Yong Ye & Huiqin Zhang, 2024. "Abstract or concrete? The effects of language style and service context on continuous usage intention for AI voice assistants," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    3. Zhang, Huixian & Song, Mengmeng, 2024. "Optimizing service encounters through mascot-like robot with a politeness strategy," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Poushneh, Atieh & Vasquez-Parraga, Arturo & Gearhart, Richard S., 2024. "The effect of empathetic response and consumers’ narcissism in voice-based artificial intelligence," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    2. Yang, Yikai & Zheng, Jiehui & Yu, Yining & Qiu, Yiling & Wang, Lei, 2024. "The role of recommendation sources and attribute framing in online product recommendations," Journal of Business Research, Elsevier, vol. 174(C).
    3. Zhang, Yaqiong & Wang, Shifu, 2023. "The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    4. Markku Maula & Wouter Stam, 2020. "Enhancing Rigor in Quantitative Entrepreneurship Research," Entrepreneurship Theory and Practice, , vol. 44(6), pages 1059-1090, November.
    5. Helmut Wasserbacher & Martin Spindler, 2024. "Credit Ratings: Heterogeneous Effect on Capital Structure," Papers 2406.18936, arXiv.org.
    6. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    7. Li, Xueni (Shirley) & Kim, Sara & Chan, Kimmy Wa & McGill, Ann L., 2023. "Detrimental effects of anthropomorphism on the perceived physical safety of artificial agents in dangerous situations," International Journal of Research in Marketing, Elsevier, vol. 40(4), pages 841-864.
    8. Keith R Lohse & Kristin L Sainani & J Andrew Taylor & Michael L Butson & Emma J Knight & Andrew J Vickers, 2020. "Systematic review of the use of “magnitude-based inference” in sports science and medicine," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-22, June.
    9. Wien, Anders Hauge & Peluso, Alessandro M., 2021. "Influence of human versus AI recommenders: The roles of product type and cognitive processes," Journal of Business Research, Elsevier, vol. 137(C), pages 13-27.
    10. Nika Meyer (née Mozafari) & Melanie Schwede & Maik Hammerschmidt & Welf Hermann Weiger, 2022. "Users taking the blame? How service failure, recovery, and robot design affect user attributions and retention," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2491-2505, December.
    11. Heckelei, Thomas & Huettel, Silke & Odening, Martin & Rommel, Jens, 2021. "The replicability crisis and the p-value debate – what are the consequences for the agricultural and food economics community?," Discussion Papers 316369, University of Bonn, Institute for Food and Resource Economics.
    12. Alabed, Amani & Javornik, Ana & Gregory-Smith, Diana, 2022. "AI anthropomorphism and its effect on users' self-congruence and self–AI integration: A theoretical framework and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    13. Liu, Yun & Wang, Xingyuan & Wang, Shuyang, 2022. "Research on service robot adoption under different service scenarios," Technology in Society, Elsevier, vol. 68(C).
    14. Stephanie M. Noble & Martin Mende, 2023. "The future of artificial intelligence and robotics in the retail and service sector: Sketching the field of consumer-robot-experiences," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 747-756, July.
    15. Johnstone, David, 2022. "Accounting research and the significance test crisis," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 89(C).
    16. Jae H. Kim, 2022. "Moving to a world beyond p-value," Review of Managerial Science, Springer, vol. 16(8), pages 2467-2493, November.
    17. Darima Fotheringham & Michael A. Wiles, 2023. "The effect of implementing chatbot customer service on stock returns: an event study analysis," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 802-822, July.
    18. Chen, Changdong, 2024. "How consumers respond to service failures caused by algorithmic mistakes: The role of algorithmic interpretability," Journal of Business Research, Elsevier, vol. 176(C).
    19. Mia Papasideris & Scott T Leatherdale & Kate Battista & Peter A Hall, 2021. "An examination of the prospective association between physical activity and academic achievement in youth at the population level," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-15, June.
    20. Tinglong Dai & Sridhar Tayur, 2022. "Designing AI‐augmented healthcare delivery systems for physician buy‐in and patient acceptance," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4443-4451, December.

    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:kap:mktlet:v:33:y:2022:i:4:d:10.1007_s11002-022-09630-x. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.