IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v22y2022i3d10.1007_s10660-020-09432-1.html
   My bibliography  Save this article

The effect of product distance on the eWOM in recommendation network

Author

Listed:
  • Xue Pan

    (Nanjing University of Information Science and Technology
    University of Reading)

  • Lei Hou

    (Nanjing University of Information Science and Technology
    University of Reading)

  • Kecheng Liu

    (University of Reading)

Abstract

The online product recommendation networks (PRNs), connecting similar products with hyperlinks, have been widely implemented in user-generated content websites and ecommerce systems. With the PRNs as the virtual shelves, this paper explores the impact of the distance between products on the formation of product electronic Word-of-Mouth (eWOM). Employing an empirical book recommendation network of Amazon, the study one explores the effect of a focal product’s neighborhood (nearby others) on its eWOM, and study two explores the eWOM similarity between product pairs that are at one, two and three clicks away from each other. The results reveal the significant role played by the product distance on the association of their eWOM. On one hand, a focal product’s eWOM is largely influenced by that of its neighborhood. On the other hand, the good connectivity between two products, which is defined as the number of paths connecting them, is closely associated with the eWOM similarity between them. The findings suggest that the products should be considered as interactive collectives rather than separated individuals particularly in the eWOM studies.

Suggested Citation

  • Xue Pan & Lei Hou & Kecheng Liu, 2022. "The effect of product distance on the eWOM in recommendation network," Electronic Commerce Research, Springer, vol. 22(3), pages 901-924, September.
    • Handle: RePEc:spr:elcore:v:22:y:2022:i:3:d:10.1007_s10660-020-09432-1
    DOI: 10.1007/s10660-020-09432-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-020-09432-1
    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/s10660-020-09432-1?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. Gal Oestreicher-Singer & Arun Sundararajan, 2012. "The Visible Hand? Demand Effects of Recommendation Networks in Electronic Markets," Management Science, INFORMS, vol. 58(11), pages 1963-1981, November.
    2. Qihua Liu & Xiaoyu Zhang & Liyi Zhang & Yang Zhao, 2019. "The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation," Electronic Commerce Research, Springer, vol. 19(3), pages 521-547, September.
    3. Jonah Berger & Raghuram Iyengar, 2013. "Communication Channels and Word of Mouth: How the Medium Shapes the Message," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 40(3), pages 567-579.
    4. Chetna Kudeshia & Amresh Kumar, 2017. "Social eWOM: does it affect the brand attitude and purchase intention of brands?," Management Research Review, Emerald Group Publishing Limited, vol. 40(3), pages 310-330, March.
    5. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    6. repec:dau:papers:123456789/1757 is not listed on IDEAS
    7. Furnham, Adrian & Boo, Hua Chu, 2011. "A literature review of the anchoring effect," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 40(1), pages 35-42, February.
    8. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    9. Valenzuela, Ana & Raghubir, Priya & Mitakakis, Chrissy, 2013. "Shelf space schemas: Myth or reality?," Journal of Business Research, Elsevier, vol. 66(7), pages 881-888.
    10. Pierre Desmet & Valérie Renaudin, 1998. "Estimation of Product Category Sales Responsiveness to Allocated Shelf Space," Post-Print halshs-00143451, HAL.
    11. Els Breugelmans & Katia Campo & Els Gijsbrechts, 2007. "Shelf sequence and proximity effects on online grocery choices," Marketing Letters, Springer, vol. 18(1), pages 117-133, June.
    12. Muhammad Rifki Shihab & Audry Pragita Putri, 2019. "Negative online reviews of popular products: understanding the effects of review proportion and quality on consumers’ attitude and intention to buy," Electronic Commerce Research, Springer, vol. 19(1), pages 159-187, March.
    13. Katia Campo & Els Gijsbrechts, 2005. "Retail assortment, shelf and stockout management: issues, interplay and future challenges," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(4‐5), pages 383-392, July.
    14. Dixit, Saumya & Jyoti Badgaiyan, Anant & Khare, Arpita, 2019. "An integrated model for predicting consumer's intention to write online reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 46(C), pages 112-120.
    15. Breugelmans, Els & Campo, Katia, 2011. "Effectiveness of In-Store Displays in a Virtual Store Environment," Journal of Retailing, Elsevier, vol. 87(1), pages 75-89.
    16. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
    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. Hou, Lei & Huang, Yichen, 2024. "Optimizing the connectedness of recommendation networks for retrieval accuracy and visiting diversity of random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(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. Yuchi Zhang & David Godes, 2018. "Learning from Online Social Ties," Marketing Science, INFORMS, vol. 37(3), pages 425-444, May.
    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. Marchand, André & Hennig-Thurau, Thorsten & Wiertz, Caroline, 2017. "Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 336-354.
    4. Zheng, Lili, 2021. "The classification of online consumer reviews: A systematic literature review and integrative framework," Journal of Business Research, Elsevier, vol. 135(C), pages 226-251.
    5. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    6. Yufei Zhang & Clay M. Voorhees & G. Tomas M. Hult, 2024. "Dynamic interplays between online reviews and marketing promotions," Journal of the Academy of Marketing Science, Springer, vol. 52(6), pages 1820-1841, November.
    7. Antioco, Michael & Coussement, Kristof, 2018. "Misreading of consumer dissatisfaction in online product reviews: Writing style as a cause for bias," International Journal of Information Management, Elsevier, vol. 38(1), pages 301-310.
    8. Supriyo Mandal & Abyayananda Maiti, 2022. "Network promoter score (NePS): An indicator of product sales in E-commerce retailing sector," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1327-1349, September.
    9. Liye Ma & Baohong Sun & Sunder Kekre, 2015. "The Squeaky Wheel Gets the Grease—An Empirical Analysis of Customer Voice and Firm Intervention on Twitter," Marketing Science, INFORMS, vol. 34(5), pages 627-645, September.
    10. 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.
    11. Juan Feng & Xin Li & Xiaoquan (Michael) Zhang, 2019. "Online Product Reviews-Triggered Dynamic Pricing: Theory and Evidence," Information Systems Research, INFORMS, vol. 30(4), pages 1107-1123, December.
    12. 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.
    13. Luo, Anita & Baker, Andrew & Donthu, Naveen, 2019. "Capturing dynamics in the value for brand recommendations from word-of-mouth conversations," Journal of Business Research, Elsevier, vol. 104(C), pages 247-260.
    14. Delre, Sebastiano A. & Luffarelli, Jonathan, 2023. "Consumer reviews and product life cycle: On the temporal dynamics of electronic word of mouth on movie box office," Journal of Business Research, Elsevier, vol. 156(C).
    15. Daniel Kaimann & Joe Cox, 2014. "The Interaction of Signals: A Fuzzy set Analysis of the Video Game Industry," Working Papers Dissertations 13, Paderborn University, Faculty of Business Administration and Economics.
    16. Linyi Li & Shyam Gopinath & Stephen J. Carson, 2022. "History Matters: The Impact of Online Customer Reviews Across Product Generations," Management Science, INFORMS, vol. 68(5), pages 3878-3903, May.
    17. 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.
    18. Nima Jalali & Sangkil Moon & Moon-Yong Kim, 2023. "Profiling diverse reviewer segments using online reviews of service industries," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 130-148, June.
    19. (Kay) Byun, Kyung-ah & Ma, Minghui & Kim, Kevin & Kang, Taeghyun, 2021. "Buying a New Product with Inconsistent Product Reviews from Multiple Sources: The Role of Information Diagnosticity and Advertising," Journal of Interactive Marketing, Elsevier, vol. 55(C), pages 81-103.
    20. Li Jie & Xue Wenyi & Yang Fang & Li Yakun, 2017. "An Integrated Research Framework for Effect of EWOM," Journal of Systems Science and Information, De Gruyter, vol. 5(4), pages 343-355, August.

    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:elcore:v:22:y:2022:i:3:d:10.1007_s10660-020-09432-1. 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.