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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
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. repec:dau:papers:123456789/1757 is not listed on IDEAS
    9. 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.
    10. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    11. Valenzuela, Ana & Raghubir, Priya & Mitakakis, Chrissy, 2013. "Shelf space schemas: Myth or reality?," Journal of Business Research, Elsevier, vol. 66(7), pages 881-888.
    12. Pierre Desmet & Valérie Renaudin, 1998. "Estimation of Product Category Sales Responsiveness to Allocated Shelf Space," Post-Print halshs-00143451, HAL.
    13. 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.
    14. 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.
    15. 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.
    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)

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