IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v32y2021i3p801-819.html
   My bibliography  Save this article

Focus Within or On Others: The Impact of Reviewers’ Attentional Focus on Review Helpfulness

Author

Listed:
  • Zhanfei Lei

    (Isenberg School of Management, University of Massachusetts Amherst, Amherst, Massachusetts 01003)

  • Dezhi Yin

    (Muma College of Business, University of South Florida, Tampa, Florida 33620)

  • Han Zhang

    (Scheller College of Business, Georgia Institute of Technology, Atlanta, Georgia 30308)

Abstract

When reviewers write online reviews, they differ in the focus of their attention: some focus on their own experiences, whereas some direct their attention to others—prospective consumers who may read the reviews in the future. This paper explores how, why, and when reviewers’ attentional focus can influence the helpfulness evaluation of reviews beyond the impact of substantive review content. Drawing on the attentional focus and persuasion literatures, we develop a theoretical model proposing that reviewers’ attentional focus may influence consumers’ perception of review helpfulness through opposing processes, and that its overall effect is contingent on the review’s two-sidedness. Results of one archival analysis and five controlled experiments provide consistent support for our hypotheses. This work challenges the predominant view of the positive impact of other-focus (vs. self-focus), explores the interpersonal impact of a reviewer’s attentional focus on prospective consumers who are total strangers, and reveals an important, context-specific boundary condition.

Suggested Citation

  • Zhanfei Lei & Dezhi Yin & Han Zhang, 2021. "Focus Within or On Others: The Impact of Reviewers’ Attentional Focus on Review Helpfulness," Information Systems Research, INFORMS, vol. 32(3), pages 801-819, September.
  • Handle: RePEc:inm:orisre:v:32:y:2021:i:3:p:801-819
    DOI: 10.1287/isre.2021.1007
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.2021.1007
    Download Restriction: no

    File URL: https://libkey.io/10.1287/isre.2021.1007?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
    ---><---

    References listed on IDEAS

    as
    1. Clee, Mona A & Wicklund, Robert A, 1980. "Consumer Behavior and Psychological Reactance," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 6(4), pages 389-405, March.
    2. Wang, Fang & Karimi, Sahar, 2019. "This product works well (for me): The impact of first-person singular pronouns on online review helpfulness," Journal of Business Research, Elsevier, vol. 104(C), pages 283-294.
    3. Charness, Gary & Gneezy, Uri & Kuhn, Michael A., 2012. "Experimental methods: Between-subject and within-subject design," Journal of Economic Behavior & Organization, Elsevier, vol. 81(1), pages 1-8.
    4. Quentin Jones & Gilad Ravid & Sheizaf Rafaeli, 2004. "Information Overload and the Message Dynamics of Online Interaction Spaces: A Theoretical Model and Empirical Exploration," Information Systems Research, INFORMS, vol. 15(2), pages 194-210, June.
    5. Friestad, Marian & Wright, Peter, 1994. "The Persuasion Knowledge Model: How People Cope with Persuasion Attempts," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(1), pages 1-31, June.
    6. Richard A. Kronmal, 1993. "Spurious Correlation and the Fallacy of the Ratio Standard Revisited," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 156(3), pages 379-392, May.
    7. Crowley, Ayn E & Hoyer, Wayne D, 1994. "An Integrative Framework for Understanding Two-Sided Persuasion," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(4), pages 561-574, March.
    8. 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.
    9. Christopher F Baum, 2008. "Stata tip 63: Modeling proportions," Stata Journal, StataCorp LP, vol. 8(2), pages 299-303, June.
    10. Dezhi Yin & Sabyasachi Mitra & Han Zhang, 2016. "Research Note—When Do Consumers Value Positive vs. Negative Reviews? An Empirical Investigation of Confirmation Bias in Online Word of Mouth," Information Systems Research, INFORMS, vol. 27(1), pages 131-144, March.
    11. Nanda Kumar & Izak Benbasat, 2006. "Research Note: The Influence of Recommendations and Consumer Reviews on Evaluations of Websites," Information Systems Research, INFORMS, vol. 17(4), pages 425-439, December.
    12. Jean L Johnson & John B Cullen & Tomoaki Sakano & Hideyuki Takenouchi, 1996. "Setting the Stage for Trust and Strategic Integration in Japanese-U.S. Cooperative Alliances," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 27(5), pages 981-1004, December.
    13. Jean L Johnson & John B Cullen & Tomoaki Sakano & Hideyuki Takenouchi, 1996. "Setting the Stage for Trust and Strategic Integration in Japanese-U.S. Cooperative Alliances," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 27(4), pages 981-1004, December.
    14. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    15. Juliano Laran & Amy N. Dalton & Eduardo B. Andrade, 2011. "The Curious Case of Behavioral Backlash: Why Brands Produce Priming Effects and Slogans Produce Reverse Priming Effects," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 37(6), pages 999-1014.
    16. Escalas, Jennifer Edson & Stern, Barbara B, 2003. "Sympathy and Empathy: Emotional Responses to Advertising Dramas," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 29(4), pages 566-578, March.
    17. Sparks, Beverley A. & Perkins, Helen E. & Buckley, Ralf, 2013. "Online travel reviews as persuasive communication: The effects of content type, source, and certification logos on consumer behavior," Tourism Management, Elsevier, vol. 39(C), pages 1-9.
    18. Mingfeng Lin & Henry C. Lucas & Galit Shmueli, 2013. "Research Commentary ---Too Big to Fail: Large Samples and the p -Value Problem," Information Systems Research, INFORMS, vol. 24(4), pages 906-917, December.
    19. 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)

    Citations

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


    Cited by:

    1. Wen Zhang & Qiang Wang & Jian Li & Zhenzhong Ma & Gokul Bhandari & Rui Peng, 2023. "What makes deceptive online reviews? A linguistic analysis perspective," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    2. Dezhi Yin & Triparna de Vreede & Logan M. Steele & Gert-Jan de Vreede, 2023. "Decide Now or Later: Making Sense of Incoherence Across Online Reviews," Information Systems Research, INFORMS, vol. 34(3), pages 1211-1227, September.

    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. 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).
    2. Lorenz Graf-Vlachy & Tarun Goyal & Yannick Ouardi & Andreas König, 2021. "Reviews Left and Right: The Link Between Reviewers’ Political Ideology and Online Review Language," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 403-417, August.
    3. Moradi, Masoud & Dass, Mayukh & Kumar, Piyush, 2023. "Differential effects of analytical versus emotional rhetorical style on review helpfulness," Journal of Business Research, Elsevier, vol. 154(C).
    4. 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.
    5. 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.
    6. 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.
    7. Ismagilova, Elvira & Dwivedi, Yogesh K. & Slade, Emma, 2020. "Perceived helpfulness of eWOM: Emotions, fairness and rationality," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    8. Chih-Hung Peng & Dezhi Yin & Han Zhang, 2020. "More than Words in Medical Question-and-Answer Sites: A Content-Context Congruence Perspective," Information Systems Research, INFORMS, vol. 31(3), pages 913-928, September.
    9. Kyungmin Park & Stephanie Lee & Shahryar Doosti & Yong Tan, 2023. "Provision of helpful review videos: Effects of video characteristics on perceived helpfulness," Production and Operations Management, Production and Operations Management Society, vol. 32(7), pages 2031-2048, July.
    10. Lien Thi Kim Nguyen & Hao-Hsuan Chung & Kristine Velasquez Tuliao & Tom M. Y. Lin, 2020. "Using XGBoost and Skip-Gram Model to Predict Online Review Popularity," SAGE Open, , vol. 10(4), pages 21582440209, December.
    11. 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.
    12. 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.
    13. 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.
    14. Lutz, Bernhard & Pröllochs, Nicolas & Neumann, Dirk, 2022. "Are longer reviews always more helpful? Disentangling the interplay between review length and line of argumentation," Journal of Business Research, Elsevier, vol. 144(C), pages 888-901.
    15. Arend, Richard J. & Wisner, Joel D., 2005. "Small business and supply chain management: is there a fit?," Journal of Business Venturing, Elsevier, vol. 20(3), pages 403-436, May.
    16. Zhao Du & Fang Wang & Shan Wang, 2021. "Reviewer Experience vs. Expertise: Which Matters More for Good Course Reviews in Online Learning?," Sustainability, MDPI, vol. 13(21), pages 1-17, November.
    17. Mariani, Marcello M. & Fosso Wamba, Samuel, 2020. "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, Elsevier, vol. 121(C), pages 338-352.
    18. M. Max Evans & Ilja Frissen & Anthony K. P. Wensley, 2018. "Organisational Information and Knowledge Sharing: Uncovering Mediating Effects of Perceived Trustworthiness Using the PROCESS Approach," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-29, March.
    19. Irina Heimbach & Oliver Hinz, 2018. "The Impact of Sharing Mechanism Design on Content Sharing in Online Social Networks," Information Systems Research, INFORMS, vol. 29(3), pages 592-611, September.
    20. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2023. "sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics," Information Systems Research, INFORMS, vol. 34(1), pages 137-156, 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:inm:orisre:v:32:y:2021:i:3:p:801-819. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.