IDEAS home Printed from https://ideas.repec.org/a/spr/elmark/v33y2023i1d10.1007_s12525-023-00664-1.html
   My bibliography  Save this article

“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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12525-023-00664-1
    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/s12525-023-00664-1?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. 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. 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.
    16. 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.
    17. 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.
    18. Prasad Vana & Anja Lambrecht, 2021. "The Effect of Individual Online Reviews on Purchase Likelihood," Marketing Science, INFORMS, vol. 40(4), pages 708-730, July.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    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. 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.
    2. 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.
    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. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    5. Li, Yiming & Li, Gang & Tayi, Giri Kumar & Cheng, T.C.E., 2019. "Omni-channel retailing: Do offline retailers benefit from online reviews?," International Journal of Production Economics, Elsevier, vol. 218(C), pages 43-61.
    6. Ana Babić Rosario & Kristine Valck & Francesca Sotgiu, 2020. "Conceptualizing the electronic word-of-mouth process: What we know and need to know about eWOM creation, exposure, and evaluation," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 422-448, May.
    7. Fink, Lior & Rosenfeld, Liron & Ravid, Gilad, 2018. "Longer online reviews are not necessarily better," International Journal of Information Management, Elsevier, vol. 39(C), pages 30-37.
    8. Dirk van Straaten & Vitalik Melnikov & Eyke Hüllermeier & Behnud Mir Djawadi & René Fahr, 2021. "Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation Processes," Working Papers Dissertations 72, Paderborn University, Faculty of Business Administration and Economics.
    9. Zhuolan Bao & Wenwen Li & Pengzhen Yin & Michael Chau, 2021. "Examining the impact of review tag function on product evaluation and information perception of popular products," Information Systems and e-Business Management, Springer, vol. 19(2), pages 517-539, June.
    10. 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).
    11. Mariani, Marcello M. & Borghi, Matteo & Laker, Benjamin, 2023. "Do submission devices influence online review ratings differently across different types of platforms? A big data analysis," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    12. Zhen Li & Fangzhou Li & Jing Xiao & Zhi Yang, 2020. "Topic Features in Negative Customer Reviews: Evidence Based on Text Data Mining," The Review of Socionetwork Strategies, Springer, vol. 14(1), pages 19-40, April.
    13. 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.
    14. Gottschalk, Sabrina A. & Mafael, Alexander, 2017. "Cutting Through the Online Review Jungle — Investigating Selective eWOM Processing," Journal of Interactive Marketing, Elsevier, vol. 37(C), pages 89-104.
    15. Yi Feng & Yunqiang Yin & Dujuan Wang & Lalitha Dhamotharan & Joshua Ignatius & Ajay Kumar, 2023. "Diabetic patient review helpfulness: unpacking online drug treatment reviews by text analytics and design science approach," Annals of Operations Research, Springer, vol. 328(1), pages 387-418, September.
    16. 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.
    17. Wanshu Niu & Liqiang Huang & Xixi Li & Jie Zhang & Mingliang Chen, 2023. "Beyond the review information: an investigation of individual- and group-based presentation forms of review information," Information Technology and Management, Springer, vol. 24(2), pages 159-176, June.
    18. Zhu, Yongmin & Liu, Miaomiao & Zeng, Xiaohua & Huang, Pei, 2020. "The effects of prior reviews on perceived review helpfulness: A configuration perspective," Journal of Business Research, Elsevier, vol. 110(C), pages 484-494.
    19. Jake Hoskins & Shyam Gopinath & J. Cameron Verhaal & Elham Yazdani, 2021. "The influence of the online community, professional critics, and location similarity on review ratings for niche and mainstream brands," Journal of the Academy of Marketing Science, Springer, vol. 49(6), pages 1065-1087, November.
    20. Srikanth Parameswaran & Pubali Mukherjee & Rohit Valecha, 2023. "I Like My Anonymity: An Empirical Investigation of the Effect of Multidimensional Review Text and Role Anonymity on Helpfulness of Employer Reviews," Information Systems Frontiers, Springer, vol. 25(2), pages 853-870, April.

    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

    Statistics

    Access and download statistics

    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:elmark:v:33:y:2023:i:1:d:10.1007_s12525-023-00664-1. 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.