IDEAS home Printed from https://ideas.repec.org/a/spr/joamsc/v52y2024i6d10.1007_s11747-024-01027-8.html
   My bibliography  Save this article

The robo bias in conversational reviews: How the solicitation medium anthropomorphism affects product rating valence and review helpfulness

Author

Listed:
  • Dimitrios Tsekouras

    (Erasmus University Rotterdam)

  • Dominik Gutt

    (Erasmus University Rotterdam)

  • Irina Heimbach

    (WHU – Otto Beisheim School of Management)

Abstract

Companies are increasingly introducing conversational reviews—reviews solicited via chatbots—to gain customer feedback. However, little is known about how chatbot-mediated solicitation influences rating valence and review helpfulness compared to conventional online forms. Therefore, we conceptualized these review solicitation media on the continuum of anthropomorphism and investigated how various levels of anthropomorphism affect rating valence and review helpfulness, showing that more anthropomorphic media lead to more positive and less helpful reviews. We found that moderate levels of anthropomorphism lead to increased interaction enjoyment, and high levels increase social presence, thus inflating the rating valence and decreasing review helpfulness. Further, the effect of anthropomorphism remains robust across review solicitors’ salience (sellers vs. platforms) and expressed emotionality in conversations. Our study is among the first to investigate chatbots as a new form of technology to solicit online reviews, providing insights to inform various stakeholders of the advantages, drawbacks, and potential ethical concerns of anthropomorphic technology in customer feedback solicitation.

Suggested Citation

  • Dimitrios Tsekouras & Dominik Gutt & Irina Heimbach, 2024. "The robo bias in conversational reviews: How the solicitation medium anthropomorphism affects product rating valence and review helpfulness," Journal of the Academy of Marketing Science, Springer, vol. 52(6), pages 1651-1672, November.
    • Handle: RePEc:spr:joamsc:v:52:y:2024:i:6:d:10.1007_s11747-024-01027-8
    DOI: 10.1007/s11747-024-01027-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11747-024-01027-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11747-024-01027-8?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. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    2. Sam Ransbotham & Nicholas H. Lurie & Hongju Liu, 2019. "Creation and Consumption of Mobile Word of Mouth: How Are Mobile Reviews Different?," Marketing Science, INFORMS, vol. 38(5), pages 773-792, September.
    3. Noble, Stephanie M. & Mende, Martin & Grewal, Dhruv & Parasuraman, A., 2022. "The Fifth Industrial Revolution: How Harmonious Human–Machine Collaboration is Triggering a Retail and Service [R]evolution," Journal of Retailing, Elsevier, vol. 98(2), pages 199-208.
    4. Christian Hildebrand & Anouk Bergner, 2021. "Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 659-676, July.
    5. Howard, Daniel J & Gengler, Charles, 2001. "Emotional Contagion Effects on Product Attitudes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(2), pages 189-201, September.
    6. Tom M. Palmer & Jonathan A. C. Sterne, 2015. "Fitting fixed- and random-effects meta-analysis models using structural equation modeling with the sem and gsem commands," Stata Journal, StataCorp LP, vol. 15(3), pages 645-671, September.
    7. Thomas P. Novak & Donna L. Hoffman, 2019. "Relationship journeys in the internet of things: a new framework for understanding interactions between consumers and smart objects," Journal of the Academy of Marketing Science, Springer, vol. 47(2), pages 216-237, March.
    8. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    9. Christoph Schneider & Markus Weinmann & Peter N.C. Mohr & Jan vom Brocke, 2021. "When the Stars Shine Too Bright: The Influence of Multidimensional Ratings on Online Consumer Ratings," Management Science, INFORMS, vol. 67(6), pages 3871-3898, June.
    10. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    11. Uttara Ananthakrishnan & Davide Proserpio & Siddhartha Sharma, 2023. "I Hear You: Does Quality Improve with Customer Voice?," Marketing Science, INFORMS, vol. 42(6), pages 1143-1161, November.
    12. Sherry He & Brett Hollenbeck & Davide Proserpio, 2022. "The Market for Fake Reviews," Marketing Science, INFORMS, vol. 41(5), pages 896-921, September.
    13. Stefano Puntoni & Bart de Langhe & Stijn M. J. van Osselaer, 2009. "Bilingualism and the Emotional Intensity of Advertising Language," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 35(6), pages 1012-1025, April.
    14. Ben Mimoun, Mohammed Slim & Poncin, Ingrid, 2015. "A valued agent: How ECAs affect website customers' satisfaction and behaviors," Journal of Retailing and Consumer Services, Elsevier, vol. 26(C), pages 70-82.
    15. Beam, Emily A., 2023. "Social media as a recruitment and data collection tool: Experimental evidence on the relative effectiveness of web surveys and chatbots," Journal of Development Economics, Elsevier, vol. 162(C).
    16. Felipe Thomaz & Carolina Salge & Elena Karahanna & John Hulland, 2020. "Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 43-63, January.
    17. Gordon Burtch & Yili Hong & Ravi Bapna & Vladas Griskevicius, 2018. "Stimulating Online Reviews by Combining Financial Incentives and Social Norms," Management Science, INFORMS, vol. 64(5), pages 2065-2082, May.
    18. Xueming Luo & Siliang Tong & Zheng Fang & Zhe Qu, 2019. "Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases," Marketing Science, INFORMS, vol. 38(6), pages 937-947, November.
    19. Sara Kim & Rocky Peng Chen & Ke Zhang, 2016. "Anthropomorphized Helpers Undermine Autonomy and Enjoyment in Computer Games," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 43(2), pages 282-302.
    20. Theodoros Lappas & Gaurav Sabnis & Georgios Valkanas, 2016. "The Impact of Fake Reviews on Online Visibility: A Vulnerability Assessment of the Hotel Industry," Information Systems Research, INFORMS, vol. 27(4), pages 940-961, December.
    21. Pei-Yu Chen & Yili Hong & Ying Liu, 2018. "The Value of Multidimensional Rating Systems: Evidence from a Natural Experiment and Randomized Experiments," Management Science, INFORMS, vol. 64(10), pages 4629-4647, October.
    22. Warut Khern-am-nuai & Karthik Kannan & Hossein Ghasemkhani, 2018. "Extrinsic versus Intrinsic Rewards for Contributing Reviews in an Online Platform," Information Systems Research, INFORMS, vol. 29(4), pages 871-892, December.
    23. Goldberg, Marvin E & Gorn, Gerald J, 1987. "Happy and Sad TV Programs: How They Affect Reactions to Commercials," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(3), pages 387-403, December.
    24. Palan, Stefan & Schitter, Christian, 2018. "Prolific.ac—A subject pool for online experiments," Journal of Behavioral and Experimental Finance, Elsevier, vol. 17(C), pages 22-27.
    25. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
    Full references (including those not matched with items on IDEAS)

    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. Hsing Kenneth Cheng & D. Daniel Sokol & Xinyu Zang, 2024. "The rise of empirical online platform research in the new millennium," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 416-451, March.
    2. Cheng Zhao & Chong Alex Wang, 2023. "A cross-site comparison of online review manipulation using Benford’s law," Electronic Commerce Research, Springer, vol. 23(1), pages 365-406, March.
    3. Young Kwark & Gene Moo Lee & Paul A. Pavlou & Liangfei Qiu, 2021. "On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data," Information Systems Research, INFORMS, vol. 32(3), pages 895-913, September.
    4. Harrison-Walker, L. Jean & Jiang, Ying, 2023. "Suspicion of online product reviews as fake: Cues and consequences," Journal of Business Research, Elsevier, vol. 160(C).
    5. Yipu Deng & Jinyang Zheng & Warut Khern-am-nuai & Karthik Kannan, 2022. "More than the Quantity: The Value of Editorial Reviews for a User-Generated Content Platform," Management Science, INFORMS, vol. 68(9), pages 6865-6888, September.
    6. Hui Zhao & Xiaoyuan Wang & Debing Ni & Kevin W. Li, 2023. "The Quality-Signaling Role of Manipulated Consumer Reviews," Group Decision and Negotiation, Springer, vol. 32(3), pages 503-536, June.
    7. Marios Kokkodis & Theodoros Lappas & Gerald C. Kane, 2022. "Optional purchase verification in e‐commerce platforms: More representative product ratings and higher quality reviews," Production and Operations Management, Production and Operations Management Society, vol. 31(7), pages 2943-2961, July.
    8. Pengkun Wu & Eric W. T. Ngai & Yuanyuan Wu, 2023. "Impact of praise cashback strategy: Implications for consumers and e‐businesses," Production and Operations Management, Production and Operations Management Society, vol. 32(9), pages 2825-2845, September.
    9. Balázs Kovács, 2024. "The Turing test of online reviews: Can we tell the difference between human-written and GPT-4-written online reviews?," Marketing Letters, Springer, vol. 35(4), pages 651-666, December.
    10. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    11. Young Joon Park & Jaewoo Joo & Charin Polpanumas & Yeujun Yoon, 2021. "“Worse Than What I Read?” The External Effect of Review Ratings on the Online Review Generation Process: An Empirical Analysis of Multiple Product Categories Using Amazon.com Review Data," Sustainability, MDPI, vol. 13(19), pages 1-22, September.
    12. Uttara M. Ananthakrishnan & Beibei Li & Michael D. Smith, 2020. "A Tangled Web: Should Online Review Portals Display Fraudulent Reviews?," Information Systems Research, INFORMS, vol. 31(3), pages 950-971, September.
    13. Warut Khern-am-nuai & Karthik Kannan & Hossein Ghasemkhani, 2018. "Extrinsic versus Intrinsic Rewards for Contributing Reviews in an Online Platform," Information Systems Research, INFORMS, vol. 29(4), pages 871-892, December.
    14. Peiyu Chen & Lorin M. Hitt & Yili Hong & Shinyi Wu, 2021. "Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application," Information Systems Research, INFORMS, vol. 32(4), pages 1470-1489, December.
    15. Ni Huang & Tianshu Sun & Peiyu Chen & Joseph M. Golden, 2019. "Word-of-Mouth System Implementation and Customer Conversion: A Randomized Field Experiment," Information Systems Research, INFORMS, vol. 30(3), pages 805-818, September.
    16. Tao Lu & May Yuan & Chong (Alex) Wang & Xiaoquan (Michael) Zhang, 2022. "Histogram Distortion Bias in Consumer Choices," Management Science, INFORMS, vol. 68(12), pages 8963-8978, December.
    17. Wang, Qiang & Zhang, Wen & Li, Jian & Ma, Zhenzhong, 2023. "Complements or confounders? A study of effects of target and non-target features on online fraudulent reviewer detection," Journal of Business Research, Elsevier, vol. 167(C).
    18. Heeseung Andrew Lee & Angela Aerry Choi & Tianshu Sun & Wonseok Oh, 2021. "Reviewing Before Reading? An Empirical Investigation of Book-Consumption Patterns and Their Effects on Reviews and Sales," Information Systems Research, INFORMS, vol. 32(4), pages 1368-1389, December.
    19. Ko, Eunhee Emily & Bowman, Douglas, 2023. "Suspicious online product reviews: An empirical analysis of brand and product characteristics using Amazon data," International Journal of Research in Marketing, Elsevier, vol. 40(4), pages 898-911.
    20. Wei Chen & Bin Gu & Qiang Ye & Kevin Xiaoguo Zhu, 2019. "Measuring and Managing the Externality of Managerial Responses to Online Customer Reviews," Service Science, INFORMS, vol. 30(1), pages 81-96, March.

    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:spr:joamsc:v:52:y:2024:i:6:d:10.1007_s11747-024-01027-8. 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.