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“Sorry, too much information”—Designing online review systems that support information search and processing

Author

Listed:
  • Janina Seutter

    (Paderborn University)

  • Kristin Kutzner

    (University of Applied Sciences Zwickau)

  • Maren Stadtländer

    (University of Hildesheim)

  • Dennis Kundisch

    (Paderborn University)

  • Ralf Knackstedt

    (University of Hildesheim)

Abstract

When faced with a large number of reviews, customers can easily be overwhelmed by information overload. To address this problem, review systems have introduced design features aimed at improving the scanning, reading, and processing of online reviews. Though previous research has examined the effect of selected design features on information overload, a comprehensive and up-to-date overview of these features remains outstanding. We therefore develop and evaluate a taxonomy for information search and processing in online review systems. Based on a sample of 65 review systems, drawn from a variety of online platform environments, our taxonomy presents 50 distinct characteristics alongside the knowledge status quo of the features currently implemented. Our study enables both scholars and practitioners to better understand, compare and further analyze the (potential) effects that specific design features, and their combinations, have on information overload, and to use these features accordingly to improve online review systems for consumers.

Suggested Citation

  • Janina Seutter & Kristin Kutzner & Maren Stadtländer & Dennis Kundisch & Ralf Knackstedt, 2023. "“Sorry, too much information”—Designing online review systems that support information search and processing," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-19, December.
    • Handle: RePEc:spr:elmark:v:33:y:2023:i:1:d:10.1007_s12525-023-00664-1
    DOI: 10.1007/s12525-023-00664-1
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    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. King, Robert Allen & Racherla, Pradeep & Bush, Victoria D., 2014. "What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature," Journal of Interactive Marketing, Elsevier, vol. 28(3), pages 167-183.
    3. Yan Wan & Baojun Ma & Yu Pan, 2018. "Opinion evolution of online consumer reviews in the e-commerce environment," Electronic Commerce Research, Springer, vol. 18(2), pages 291-311, June.
    4. Eigenraam, Anniek W. & Eelen, Jiska & van Lin, Arjen & Verlegh, Peeter W.J., 2018. "A Consumer-based Taxonomy of Digital Customer Engagement Practices," Journal of Interactive Marketing, Elsevier, vol. 44(C), pages 102-121.
    5. Bettman, James R & Kakkar, Pradeep, 1977. "Effects of Information Presentation Format on Consumer Information Acquisition Strategies," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 3(4), pages 233-240, March.
    6. Schick, Allen G. & Gordon, Lawrence A. & Haka, Susan, 1990. "Information overload: A temporal approach," Accounting, Organizations and Society, Elsevier, vol. 15(3), pages 199-220.
    7. Kim, Jong Min & Jun, Mina & Kim, Chung K., 2018. "The Effects of Culture on Consumers' Consumption and Generation of Online Reviews," Journal of Interactive Marketing, Elsevier, vol. 43(C), pages 134-150.
    8. Elham Yazdani & Shyam Gopinath & Steve Carson, 2018. "Preaching to the Choir: The Chasm Between Top-Ranked Reviewers, Mainstream Customers, and Product Sales," Marketing Science, INFORMS, vol. 37(5), pages 838-851, September.
    9. Zhao, Haichuan & Jiang, Lan & Su, Chenting, 2020. "To Defend or Not to Defend? How Responses to Negative Customer Review Affect Prospective customers' Distrust and Purchase Intention," Journal of Interactive Marketing, Elsevier, vol. 50(C), pages 45-64.
    10. Iselin, Errol R., 1988. "The effects of information load and information diversity on decision quality in a structured decision task," Accounting, Organizations and Society, Elsevier, vol. 13(2), pages 147-164, March.
    11. Floyd, Kristopher & Freling, Ryan & Alhoqail, Saad & Cho, Hyun Young & Freling, Traci, 2014. "How Online Product Reviews Affect Retail Sales: A Meta-analysis," Journal of Retailing, Elsevier, vol. 90(2), pages 217-232.
    12. Yacheng Sun & Xiaojing Dong & Shelby McIntyre, 2017. "Motivation of User-Generated Content: Social Connectedness Moderates the Effects of Monetary Rewards," Marketing Science, INFORMS, vol. 36(3), pages 329-337, May.
    13. Chrysanthos Dellarocas, 2005. "Reputation Mechanism Design in Online Trading Environments with Pure Moral Hazard," Information Systems Research, INFORMS, vol. 16(2), pages 209-230, June.
    14. 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.
    15. Reza Mousavi & Bidyut Hazarika & Kuanchin Chen & Muhammad Razi, 2021. "The Effect of Online Q&As and Product Reviews on Product Performance Metrics: Amazon.com as a Case Study," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 20(01), pages 1-16, March.
    16. Camilleri, Adrian R., 2017. "The Presentation Format of Review Score Information Influences Consumer Preferences Through the Attribution of Outlier Reviews," Journal of Interactive Marketing, Elsevier, vol. 39(C), pages 1-14.
    17. Jin Zhang & Cong Wang & Guoqing Chen, 2021. "A Review Selection Method for Finding an Informative Subset from Online Reviews," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 280-299, January.
    18. Christopher P. Furner & Robert A. Zinko, 2017. "The influence of information overload on the development of trust and purchase intention based on online product reviews in a mobile vs. web environment: an empirical investigation," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 211-224, August.
    19. Prasad Vana & Anja Lambrecht, 2021. "The Effect of Individual Online Reviews on Purchase Likelihood," Marketing Science, INFORMS, vol. 40(4), pages 708-730, July.
    20. 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.
    21. Robert C Nickerson & Upkar Varshney & Jan Muntermann, 2013. "A method for taxonomy development and its application in information systems," European Journal of Information Systems, Taylor & Francis Journals, vol. 22(3), pages 336-359, May.
    22. Benjamin Scheibehenne & Rainer Greifeneder & Peter M. Todd, 2010. "Can There Ever Be Too Many Options? A Meta-Analytic Review of Choice Overload," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 37(3), pages 409-425, October.
    23. Srivastava, Vartika & Kalro, Arti D., 2019. "Enhancing the Helpfulness of Online Consumer Reviews: The Role of Latent (Content) Factors," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 33-50.
    24. Daniel Szopinski & Thorsten Schoormann & Thomas John & Ralf Knackstedt & Dennis Kundisch, 2020. "Software tools for business model innovation: current state and future challenges," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(3), pages 469-494, September.
    25. Stephen X. He & Samuel D. Bond, 2015. "Why Is the Crowd Divided? Attribution for Dispersion in Online Word of Mouth," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 41(6), pages 1509-1527.
    26. 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.
    27. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
    28. Dokyun Lee & Kartik Hosanagar, 2021. "How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?," Management Science, INFORMS, vol. 67(1), pages 524-546, January.
    29. Malhotra, Naresh K, 1982. "Information Load and Consumer Decision Making," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 8(4), pages 419-430, March.
    30. Hu, Han-fen & Krishen, Anjala S., 2019. "When is enough, enough? Investigating product reviews and information overload from a consumer empowerment perspective," Journal of Business Research, Elsevier, vol. 100(C), pages 27-37.
    31. Robert Zinko & Paul Stolk & Zhan Furner & Brad Almond, 2020. "A picture is worth a thousand words: how images influence information quality and information load in online reviews," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(4), pages 775-789, December.
    32. Reimer, Thomas & Benkenstein, Martin, 2018. "Not just for the recommender: How eWOM incentives influence the recommendation audience," Journal of Business Research, Elsevier, vol. 86(C), pages 11-21.
    33. Jin, Liyin & Hu, Bingyan & He, Yanqun, 2014. "The Recent versus The Out-Dated: An Experimental Examination of the Time-Variant Effects of Online Consumer Reviews," Journal of Retailing, Elsevier, vol. 90(4), pages 552-566.
    34. Hongpeng Wang & Rong Du & Jin Li & Weiguo Fan, 2020. "Subdivided or aggregated online review systems: Which is better for online takeaway vendors?," Electronic Commerce Research, Springer, vol. 20(4), pages 915-944, December.
    35. 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.
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    2. Han Yan & Rui Wang & Jin Qin, 2025. "Design factors of the tag-based review summarization system: Perspective of the consumers’ intention to use," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-17, December.

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    More about this item

    Keywords

    Online review system; Design feature; Information overload; Taxonomy;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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