DOI QR코드

DOI QR Code

The Conformity Effect in Online Product Rating: The Pattern Recognition Approach

  • Kim, Hyung Jun (Graduate School of Culture Technology Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Kim, Songmi (Graduate School of Culture Technology Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Kim, Wonjoon (School of Business and Technology Management Korea Advanced Institute of Science and Technology (KAIST))
  • Received : 2017.08.10
  • Accepted : 2017.10.13
  • Published : 2017.12.28

Abstract

Since the advent of the Internet, and the development of smart devices, people have begun to spend more time in online platforms; this phenomenon has created a large number of online Words of Mouth (WOM) daily. Under these changes, one of the important aspects to consider is the conformity effect in online WOM; that is, whether an individual's own opinion would be influenced by the majority opinion of other people. This study, therefore, investigates whether there is the conformity effect in online product ratings for Amazon.com using the method called Markov Chain analysis. Markov Chain analysis considers the stochastic process that satisfies the Markov property, and we assume that the generation of online product ratings follows the process. Under the assumption that people are usually independent when they express their opinion in online platforms, we analyze the interdependency among rating sequences, and we find weak evidence that there exists the conformity effect in online product rating. This suggests that people who leave online product ratings consider others' opinions.

Keywords

References

  1. J. Habermas, Theory of Communicative Action, vol. 1, Boston: Beacon Press, 1984.
  2. I. Goyette, L. Ricard, J. Bergeron, and F. Marticotte, "e-WOM Scale: word-of-mouth measurement scale for e-services context," Canadian Journal of Administrative Sciences, vol. 27, no. 1, 2010, pp. 5-23. https://doi.org/10.1002/cjas.129
  3. S. Asch, Effects of Group Pressure upon the Modification and Distortion of Judgments, Documents of Gestalt Psychology, University of California Press, 1951.
  4. G. Marks, J. W. Graham, and W. B. Hansen, "Social Projection and Social Conformity in Adolescent Alcohol Use: A Longitudinal Analysis," Personality and Social Psychology Bulletin, vol. 18, no. 1, 1992, pp. 96-101. https://doi.org/10.1177/0146167292181014
  5. A. Mukherjee, B. Liu, and N. Glance, "Spotting Fake Reviewer Groups in Consumer Reviews," in Proceedings of International World Web Conference, 2012.
  6. A. A. Markov, Theory of Algorithms, Translated by Jacques J. Schorr-Kon and PST staff, Imprint Moscow, Academy of Sciences of the USSR, 1954.
  7. R. Serfozo, Basics of Applied Stochastic Processes, Springer Science & Business Media, 2009.
  8. Y. A. Rozanov, Markov Random Fields, Springer Science & Business Media, 2012.
  9. S. Huck and J. Oechssler, "Informational cascades in the laboratory: Do they occur for the right reasons?," Journal of Economic Psychology, vol. 21, no. 6, 2000, pp. 661-671. https://doi.org/10.1016/S0167-4870(00)00025-8
  10. Arndt, Word of Mouth Advertising: A Review of the Literature, New York: The Advertising Research Foundation Inc., 1967.
  11. M. L. Richins, "Negative Word-of-Mouth by dissatisfied consumers: A pilot study," Journal of Marketing, vol. 47, no. 1, 1983, pp. 68-78. https://doi.org/10.2307/3203428
  12. R. A. Higie, L. F. Feick, and L. L. Price, "Types and amount of Word-of-Mouth communications about retailers," Journal of Retailing, vol. 63, no. 3, 1987, pp. 260-279.
  13. R. A. Westbrook, "Product/consumption-based affective responses and post purchase processes," Journal of Marketing Research, vol. 24, no. 3, 1987, pp. 258-270. https://doi.org/10.2307/3151636
  14. G. Silverman, "The power of Word of Mouth," Direct Marketing, vol. 64, no. 5, 2001, pp. 47-52.
  15. D. Godes and D. Mayzlin, "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, vol. 23, no. 4, 2012, pp. 448-473.
  16. J. A. Chevalier and D. Mayzlin, "The effect of word of mouth on sales: Online book reviews," Journal of Marketing Research, vol. 43, no. 3, 2006, pp. 345-354. https://doi.org/10.1509/jmkr.43.3.345
  17. C. Dellarocas, X. M. Zhang, and N. F. Awad, "Exploring the value of online product reviews in forecasting sales: The case of motion pictures," Journal of Interactive Marketing, vol. 21, no. 4, 2007, pp. 23-45. https://doi.org/10.1002/dir.20087
  18. Y. Liu, "Word of mouth for movies: Its dynamics and impact on box office revenue," Journal of Marketing, vol. 70, no. 3, 2006, pp. 74-89. https://doi.org/10.1509/jmkg.70.3.74
  19. C. Forman, A. Ghose, and B. Wiesenfeld, "Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets," Information System Research, vol. 19, no. 3, 2008, pp. 291-313. https://doi.org/10.1287/isre.1080.0193
  20. W. Duan, B. Gu, and A. B. Whinston, "Informational cascades and software adoption on the Internet: An empirical investigation," MIS Quarterly, vol. 33, no. 1, 2009, pp. 23-48. https://doi.org/10.2307/20650277
  21. D. Godes and J. Silva, "Sequential and temporal dynamics of online opinion," Marketing Science, vol. 31, no. 3, 2012, pp. 448-473. https://doi.org/10.1287/mksc.1110.0653
  22. Ross, Introduction to Probability Models, Tenth Edition, Academic Press, 2009.
  23. G. P. H. Styan and H. Smith, "Markov chains applied to marketing," Journal of Marketing Research, vol. 1, no. 1, 1964, pp. 50-55.
  24. A. Knott, "Next-product-to-buy models for cross-selling applications," Journal of Interactive Marketing, vol. 16, no. 3, 2002, pp. 59-75. https://doi.org/10.1002/dir.10038
  25. S. W. Lee and R. Myung, "Using Markov Chains for Predicting Mobile Phone Menu Selection in Adaptive User Interface (AUI)," Journal of the Korean Institute of Industrial Engineers, vol. 33, no. 4, 2007, pp. 402-409.
  26. J. Devore, Probability and Statistics for Engineering and the Sciences, Cengage Learning, 2007.
  27. G. Dougherty, Pattern Recognition and Classification: An Introduction 2013 edition, Springer, 2012.
  28. T. H. Engler, P. Winter, and M. Schulz, "Understanding online product ratings: A customer satisfaction model," Journal of Retailing and Consumer Services, vol. 27, 2015, pp. 113-120. https://doi.org/10.1016/j.jretconser.2015.07.010
  29. N. Jindal, B. Liu, and E.P Lim, "Finding unusual review patterns using unexpected rules," Proceedings of the 19th ACM international conference on Information and knowledge management, 2010, pp. pp. pp. 1549-1552.