Skip to main content
Log in

The effect of product distance on the eWOM in recommendation network

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Desmet, P., & Renaudin, V. (1998). Estimation of product category sales responsiveness to allocated shelf space. International Journal of Research in Marketing, 15(5), 443–457.

    Article  Google Scholar 

  2. Campo, K., & Gijsbrechts, E. (2005). Retail assortment, shelf and stockout management: Issues, interplay and future challenges. Applied Stochastic Models in Business and Industry, 21(4–5), 383–392.

    Article  Google Scholar 

  3. Liang, T.-P., & Lai, H.-J. (2002). Effect of store design on consumer purchases: An empirical study of on-line bookstores. Information & Management, 39(6), 431–444.

    Article  Google Scholar 

  4. Breugelmans, E., & Campo, K. (2011). Effectiveness of in-store displays in a virtual store environment. Journal of Retailing, 87(1), 75–89.

    Article  Google Scholar 

  5. Hou, L., Liu, K., & Liu, J. (2017). Navigated random walks on amazon book recommendation network. In: International workshop on complex networks and their applications, Lyon, France (pp. 935–945).

  6. Senecal, S., & Nantel, J. (2004). The influence of online product recommendations on consumers’ online choices. Journal of Retailing, 80(2), 159–169.

    Article  Google Scholar 

  7. Breugelmans, E., Campo, K., & Gijsbrechts, E. (2007). Shelf sequence and proximity effects on online grocery choices. Marketing Letter, 18(1–2), 117–133.

    Article  Google Scholar 

  8. Oestreicher-Singer, G., & Sundararajan, A. (2012). Recommendation networks and the long tail of electronic commerce. MIS Quarterly, 36(1), 65–83.

    Article  Google Scholar 

  9. See-To, E. W., & Ho, K. K. (2014). Value co-creation and purchase intention in social networksites: The role of electronic Word-of-Mouth and trust–—A theoretical analysis. Computers in Human Behavior, 31, 182–189.

    Article  Google Scholar 

  10. Kudeshia, C., & Kumar, A. (2017). Social eWOM: Does it affect the brand attitude and purchase intention of brands? Management Research Review, 40(3), 310–330.

    Article  Google Scholar 

  11. Pan, X., Hou, L., Liu, K., & Niu, H. (2018). Do REVIEWS FROM FRIENDS AND THE CROWD AFFECT ONLINE CONSUMER POSTING BEHAVIOUR DIFFERENtly? Electronic Commerce Research and Applications, 29(3), 102–112.

    Article  Google Scholar 

  12. Lin, Z., & Wang, Q. (2018). E-commerce product networks, word-of-mouth convergence, and product sales. Journal of the Association for Information Systems, 19(1), 23–39.

    Article  Google Scholar 

  13. Furnham, A., & Boo, H. C. (2011). A literature review of the anchoring effect. The Journal of Socio-Economics, 40(1), 35–42.

    Article  Google Scholar 

  14. Yang, Z., Zhang, Z. K., & Zhou, T. (2013). Anchoring bias in online voting. Europhysics Letters, 100(6), 68002.

    Article  Google Scholar 

  15. Hou, L., Pan, X., Guo, Q., & Liu, J. G. (2014). Memory effect of the online user preference. Scientific Reports, 4, 6560.

    Article  Google Scholar 

  16. Allsop, D. T., Bassett, B. R., & Hoskins, J. A. (2007). Word-of-mouth research: Principles and applications. Journal of Advertising Research, 47(4), 398–411.

    Article  Google Scholar 

  17. Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter? An empirical investigation of panel data. Decision Support Systems, 45(4), 1007–1016.

    Article  Google Scholar 

  18. Shihab, M. R., & Putri, A. P. (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, 19(1), 159–187.

    Article  Google Scholar 

  19. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354.

    Article  Google Scholar 

  20. Chintagunta, P. K., Gopinath, S., & Venkataraman, S. (2010). The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5), 944–957.

    Article  Google Scholar 

  21. Li, X., & Hitt, L. M. (2008). Self-selection and information role of online product reviews. Information Systems Research, 19(4), 456–474.

    Article  Google Scholar 

  22. Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.

    Article  Google Scholar 

  23. Dellarocas, C., Awad, N., & Zhang, M. (2005). Using online ratings as a proxy of word-of-mouth in motion picture revenue forecasting. Working paper, Smith School of Business. University of Maryland.

  24. Clemons, E. K., Gao, G. G., & Hitt, L. M. (2006). When online reviews meet hyperdifferentiation: A study of the craft beer industry. Journal of Management Information Systems, 23(2), 149–171.

    Article  Google Scholar 

  25. Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of- mouth communication. Marketing Science, 23(4), 545–560.

    Article  Google Scholar 

  26. Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38–52.

    Article  Google Scholar 

  27. Berger, J., & Iyengar, R. (2013). Communication channels and word-of-mouth: How the medium shapes the message. Journal of Consumer Reseasch, 40(3), 567–579.

    Article  Google Scholar 

  28. Fu, J. R., Ju, P. H., & Hsu, C. W. (2015). Understanding why consumers engage in electronic word-of-mouth communication: Perspectives from theory of planned behavior and justice theory. Electronic Commerce Research and Applications, 14(6), 616–630.

    Article  Google Scholar 

  29. Dixit, S., Badgaiyan, A. J., & Khare, A. (2019). An integrated model for predicting consumer’s intention to write online reviews. Journal of Retailing and Consumer Services, 46, 112–120.

    Article  Google Scholar 

  30. Liu, Q., Zhang, X., Zhang, L., & Zhao, Y. (2019). The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: An empirical investigation. Electronic Commerce Research, 19(3), 521–547.

    Article  Google Scholar 

  31. Oestreicher-Singer, G., Libai, B., Sivan, L., Carmi, E., & Yassin, O. (2013). The network value of products. Journal of Marketing, 77(3), 1–14.

    Article  Google Scholar 

  32. Goldenberg, J., Oestreicher-Singer, G., & Reichman, S. (2012). The quest for content: How user-generated links can facilitate online exploration. Journal of Marketing Research, 49(4), 452–468.

    Article  Google Scholar 

  33. Oestreicher-Singer, G., & Sundararajan, A. (2012). The visible hand? Demand effects of recommendation networks in electronic markets. Management Science, 58(11), 1963–1981.

    Article  Google Scholar 

  34. Leem, B., & Chun, H. (2014). An impact of online recommendation network on demand. Expert Systems with Applications, 41(4), 1723–1729.

    Article  Google Scholar 

  35. Lin, Z., Goh, K. Y., & Heng, C. S. (2017). The demand effects of product recommendation networks: An empirical analysis of network diversity and stability. MIS Quarterly, 41(2), 397–426.

    Article  Google Scholar 

  36. Huang, H. J., Yang, J., & Zheng, B. (2019). Demand effects of product similarity network in e-commerce platform. Electronic Commerce Research, 40, 1–31.

    Google Scholar 

  37. Carmi, E., Oestreicher-Singer, G., Stettner, U., & Sundararajan, A. (2017). Is Oprah contagious? The depth of diffusion of demand shocks in a product network. MIS Quarterly, 41(1), 207–221.

    Article  Google Scholar 

  38. Chen, Y. L., Chen, J. M., & Tung, C. W. (2006). A data mining approach for retail knowledge discovery with consideration of the effect of shelf-space adjacency on sales. Decision Support Systems, 42(3), 1503–1520.

    Article  Google Scholar 

  39. Chen, M. C., & Lin, C. P. (2007). A data mining approach to product assortment and shelf space allocation. Expert Systems with Applications, 32(4), 976–986.

    Article  Google Scholar 

  40. Valenzuela, A., & Raghubir, P. (2009). Position-based beliefs: The center-stage effect. Journal of Consumer Psychology, 19(2), 185–196.

    Article  Google Scholar 

  41. Valenzuela, A., Raghubir, P., & Mitakakis, C. (2013). Shelf space schemas: Myth or reality? Journal of Business Research, 66(7), 881–888.

    Article  Google Scholar 

  42. Ert, E., & Fleischer, A. (2016). Mere position effect in booking hotels online. Journal of Travel Research, 55(3), 311–321.

    Article  Google Scholar 

  43. Won Jeong, S., Fiore, A. M., Niehm, L. S., & Lorenz, F. O. (2009). The role of experiential value in online shopping: The impacts of product presentation on consumer responses towards an apparel web site. Internet Research, 19(1), 105–124.

    Article  Google Scholar 

  44. Smith, B., & Linden, G. (2017). Two decades of recommender systems at amazon.com. IEEE Internet Computing, 21(3), 12–18.

    Article  Google Scholar 

  45. Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly, 34, 185–200.

    Article  Google Scholar 

Download references

Acknowledgements

This work is partially supported by the startup foundation of Nanjing University of Information Science and Technology (1441182001001, 1441182001008).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lei Hou.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, X., Hou, L. & Liu, K. The effect of product distance on the eWOM in recommendation network. Electron Commer Res 22, 901–924 (2022). https://doi.org/10.1007/s10660-020-09432-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-020-09432-1

Keywords

Navigation