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Electronic Commerce Research

, Volume 18, Issue 2, pp 291–311 | Cite as

Opinion evolution of online consumer reviews in the e-commerce environment

  • Yan Wan
  • Baojun Ma
  • Yu Pan
Article

Abstract

Online consumer reviews play an important role in shaping potential customers’ purchase decisions in e-commerce. Previous studies have analyzed the influence of online consumer reviews on sales, mainly considering factors such as reviewers’ and viewers’ profiles, information provided, and product features. However, there are relatively few studies that discuss how online consumer reviews interact with each other and how consumers’ opinions evolve over time. This paper proposes an opinion evolution dynamics model that is applicable to online consumer reviews in the e-commerce environment by taking into account influencing factors such as viewer reading limits, review sorting and releasing strategies, convergence parameters, review posting possibilities, and confidence thresholds. Using multi-agent simulation based on the proposed opinion evolution dynamics model, the paper discusses how these factors affect viewers’ opinions, and the opinion evolution process itself. Finally, conclusions and managerial implications of the simulation results are discussed.

Keywords

Opinion evolution Online consumer review Opinion dynamics Multi-agent simulation 

Notes

Acknowledgements

This work was supported by the Humanities and Social Science Project of the Ministry of Education of China (13YJA630084), the National Natural Science Foundation of China (71471019, 71402007, 71201011, 71473143), the Doctoral Scientific Fund Project of the Ministry of Education of China (No. 20120005120001), and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (No. TP2015031).

References

  1. 1.
    Li, X., & Hitt, L. M. (2010). Price effects in online product reviews: An analytical model and empirical analysis. MIS Quarterly, 34(4), 809–832.CrossRefGoogle Scholar
  2. 2.
    Pee, L. G. (2016). Customer co-creation in B2C e-commerce: Does it lead to better new products? Electronic Commerce Research, 4(6), 1–27.Google Scholar
  3. 3.
    Dou, X., Walden, J. A., Lee, S., & Lee, J. Y. (2012). Does source matter? Examining source effects in online product reviews. Computers in Human Behavior, 28(5), 1555–1563. doi: 10.1016/j.chb.2012.03.015.CrossRefGoogle Scholar
  4. 4.
    Bao, D., Dong, D., & Meng, X. (2011). Parasocial interaction between browser and poster in virtual communities: An empirical study on dianping.com. Chinese Journal of Management, 8(7), 1010–1020.Google Scholar
  5. 5.
    Hu, N., Liu, L., & Zhang, J. (2008). Do online reviews affect product sales? The role of reviewer characteristics and temporal effects. Information Technology and Management, 9(3), 201–214. doi: 10.1007/s10799-008-0041-2.CrossRefGoogle Scholar
  6. 6.
    Lee, J., Park, D. H., & Han, I. (2008). The effect of negative online consumer reviews on product attitude: An information processing view. Electronic Commerce Research and Applications, 7(3), 341–352. doi: 10.1016/j.elerap.2007.05.004.CrossRefGoogle Scholar
  7. 7.
    Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133–148. doi: 10.1509/jmkg.74.2.133.CrossRefGoogle Scholar
  8. 8.
    Fan, Y. W., & Miao, Y. F. (2012). Effect of electronic word-of-mouth on consumer purchase intention: The perspective of gender differences. International Journal of Electronic Business Management, 10(3), 175–181.Google Scholar
  9. 9.
    Cui, G., Lui, H. K., & Guo, X. (2012). The effect of online consumer reviews on new product sales. International Journal of Electronic Commerce, 17(1), 39–58.CrossRefGoogle Scholar
  10. 10.
    Shao, K. (2012). The effects of controversial reviews on product sales performance: The mediating role of the volume of word of mouth. International Journal of Marketing Studies, 4(4), 32–38. doi: 10.5539/ijms.v4n4p32.CrossRefGoogle Scholar
  11. 11.
    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. doi: 10.1016/j.dss.2008.04.001.CrossRefGoogle Scholar
  12. 12.
    Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291–313.CrossRefGoogle Scholar
  13. 13.
    Ghose, A., & Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498–1512. doi: 10.1109/tkde.2010.188.CrossRefGoogle Scholar
  14. 14.
    Purnawirawan, N., Dens, N., & De Pelsmacker, P. (2012). Balance and sequence in online reviews: The wrap effect. International Journal of Electronic Commerce, 17(2), 71–98.CrossRefGoogle Scholar
  15. 15.
    Pumawirawan, N., De Pelsmacker, P., & Dens, N. (2012). Balance and sequence in online reviews: How perceived usefulness affects attitudes and intentions. Journal of Interactive Marketing (Mergent, Inc.), 26(4), 244–255. doi: 10.1016/j.intmar.2012.04.002.CrossRefGoogle Scholar
  16. 16.
    Li, H. Z., Lin, L. Z., Sun, H., et al. (2008). The Sznajd model with team work. International Journal of Modern Physics C: Computational Physics and Physical Computation, 19(4), 549–555.CrossRefGoogle Scholar
  17. 17.
    Vannucchi, F. S., & Prado, C. P. C. (2009). Sznajd model and proportional elections: The role of the topology of the network. International Journal of Modern Physics C: Computational Physics and Physical Computation, 20(6), 979–990.CrossRefGoogle Scholar
  18. 18.
    Ru, W., & Xu, C. A. I. (2008). The strong consensus opinion dynamics on adaptive networks. International Journal of Modern Physics C: Computational Physics and Physical Computation, 19(12), 1939–1947.CrossRefGoogle Scholar
  19. 19.
    Deffuant, G., Neau, D., Amblard, F., & Weisbuch, G. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 3(4), 87–98.CrossRefGoogle Scholar
  20. 20.
    Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence: models, analysis and simulation. Journal of Artificial Societies and Social Simulation, 5(3), 1–33.Google Scholar
  21. 21.
    Fortunato, S., et al. (2004). Universality of the threshold for complete consensus for the opinion dynamics of Deffuant. International Journal of Modern Physics C: Computational Physics and Physical Computation, 15(9), 1301–1307.CrossRefGoogle Scholar
  22. 22.
    Fortunato, S. (2005). On the consensus threshold for the opinion dynamics of Krause–Hegselmann. International Journal of Modern Physics C: Computational Physics and Physical Computation, 16(2), 259–270.CrossRefGoogle Scholar
  23. 23.
    Etesami, S. R., & Basar, T. (2015). Game-theoretic analysis of the Hegselmann–Krause model for opinion dynamics in finite dimensions. IEEE Transactions on Automatic Control, 60(7), 1886–1897.CrossRefGoogle Scholar
  24. 24.
    Zhou, X., Chen, B., Liu, L., Ma, L., & Qiu, X. G. (2015). An opinion interactive model based on individual persuasiveness. Computational Intelligence and Neuroscience, 2015(4), 1–10.Google Scholar
  25. 25.
    Liggett, T. M. (1985). Interacting particle systems (Vol. 276). New York: Springer.Google Scholar
  26. 26.
    Galam, S. (2002). Minority opinion spreading in random geometry. The European Physical Journal B-Condensed Matter and Complex Systems, 25(4), 403–406.Google Scholar
  27. 27.
    He, M., Li, B. E. I., & Luo, L. (2004). Sznajd model with “social temperature” and defender on small-world networks. International Journal of Modern Physics C: Computational Physics and Physical Computation, 15(7), 997–1003.CrossRefGoogle Scholar
  28. 28.
    Sousa, A. (2005). Consensus formation on a triad scale-free network. Physica A: Statistical Mechanics and its Applications, 348, 701–710.CrossRefGoogle Scholar
  29. 29.
    Elgazzar, A. (2003). Applications of small-world networks to some socio-economic systems. Physica A: Statistical Mechanics and its Applications, 324(1), 402–407.CrossRefGoogle Scholar
  30. 30.
    Quattrociocchi, W., Caldarelli, G., & Scala, A. (2014). Opinion dynamics on interacting networks: media competition and social influence. Scientific Reports, 4(4938), 1–7.Google Scholar
  31. 31.
    Bernardes, A. T., Stauffer, D., & Kertész, J. (2002). Election results and the Sznajd model on Barabasi network. The European Physical Journal B-Condensed Matter and Complex Systems, 25(1), 123–127.Google Scholar
  32. 32.
    Sznajd-Weron, K. (2005). Sznajd model and its applications. Acta Physica Polonica B, 36(8), 2537–2547.Google Scholar
  33. 33.
    Galland, S., Knapen, L., Gaud, N., Janssens, D., Lamotte, O., Koukam, A., et al. (2014). Multi-agent simulation of individual mobility behavior in carpooling. Transportation Research Part C: Emerging Technologies, 45, 83–98.CrossRefGoogle Scholar
  34. 34.
    Moe, W. W., & Schweidel, D. A. (2011). Online product opinions: Incidence, evaluation, and evolution. Marketing Science, 31(3), 372–386.CrossRefGoogle Scholar
  35. 35.
    Godes, D., & Silva, J. C. (2012). Sequential and temporal dynamics of online opinion. Marketing Science, 31(3), 448–473.CrossRefGoogle Scholar
  36. 36.
    Mochon, D., & Schwartz, J. (2014). The individual dynamics of online reviews. Advances in Consumer Research, 42, 613–614.Google Scholar
  37. 37.
    Chen, Y., Fay, S., & Wang, Q. (2011). The role of marketing in social media—How online consumer reviews evolve. Journal of Interactive Marketing, 25(2), 85–94.CrossRefGoogle Scholar
  38. 38.
    Wan, Y. (2015). The Matthew effect in social commerce: The case of online review helpfulness. Electronic Markets, 25(4), 313–324.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Economics and ManagementBeijing University of Posts and TelecommunicationsBeijingPeople’s Republic of China
  2. 2.School of Business and ManagementShanghai International Studies UniversityShanghaiPeople’s Republic of China

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