Journal of Marketing Analytics

, Volume 5, Issue 2, pp 47–56 | Cite as

Uncovering the paths to helpful reviews using fuzzy-set qualitative comparative analysis

Original Article
First Online:
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Abstract

Researchers have found evidence that helpful product reviews written by other consumers have the potential to alter consumers’ purchase decision and influence overall sales. In the quest to find what makes a review helpful, prior studies have documented volume, valence, argument quality, and source certainty as determinants of helpful reviews. However, these studies used regression analysis and found significant effects of each of the determinants regardless of other variables. Taking a different perspective, the present study uncovers the “causal recipe” (combination of antecedent conditions) of helpfulness review by applying a fuzzy-set qualitative comparative analysis. Congruent with elaboration likelihood model, this study finds that high argument quality and high source certainty, together in a review, do not make a review helpful, and consumers use heuristics (peripheral cues) when reading a long review. Negative as well as long reviews are found to be helpful. Real consumer reviews collected from Amazon.com are used for this study. The study contributes to the literature by uncovering different paths (path signifies configuration of variables) that lead to helpful reviews by using a fs-QCA technique on real Amazon.com reviews and attends to the call of using sophisticated techniques in exploring new online data.

Keywords

Fuzzy-set qualitative comparative analysis Review Helpfulness 
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References

  1. Ahmad, S.N., and M. Laroche. 2015. How do expressed emotions affect the helpfulness of a product review? Evidence from reviews using latent semantic analysis. International Journal of Electronic Commerce 20(1): 76–111.CrossRefGoogle Scholar
  2. Baek, H., J. Ahn, and Y. Choi. 2012. Helpfulness of consumer reviews: Readers objective and review cues. International Journal of Electronic Commerce 17(2): 99–126.CrossRefGoogle Scholar
  3. Bell, G., I. Filatotchev, and R. Aguilera. 2014. Corporate governance and investors’ perceptions of foreign IPO value: An institutional Perspective. Academy of Management Journal 57(1): 301–320.CrossRefGoogle Scholar
  4. Blitzer, J., M. Dredze, and F. Pereira. 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, 440–447. Association for Computational Linguistics: Prague.Google Scholar
  5. Casaló, L.V., C. Flavián, M. Guinalíu, and Y. Ekinci. 2015. Do online hotel rating schemes influence booking behaviors? International Journal of Hospitality Management 49: 28–36.CrossRefGoogle Scholar
  6. Chaiken, S., and Y. Trope. 1999. Dual process theories in social psychology. New York: The Guilford Press.Google Scholar
  7. Cheng, Y., and H. Ho. 2015. Social influence’s impact on reader perceptions of online reviews. Journal of Business Research 68(4): 883–887.CrossRefGoogle Scholar
  8. Chevalier, J., and D. Mayzlin. 2006. The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research 43(3): 345–354.CrossRefGoogle Scholar
  9. Cooper, B., and J. Glaesser. 2012. Qualitative work and the testing and development of theory: Lessons from a study combining cross-case and within-case analysis via Ragin’s QCA. Forum: Qualitative Social Research 13(2), Art. 4.Google Scholar
  10. Crilly, D. 2013. London Business School Presentation for QCA PDW, Orlando. [Power Points Slides]. Retrieved from http://www-bcf.usc.edu/~fiss/Crilly_Fuzzy-Set%20Analysis_August_2013.pdf.
  11. Dunbar, N., et al. 2014. Fear appeals, message processing cues, and credibility in the websites of violent, ideological, and non-ideological groups. Journal of Computer-Mediated Communication 19(1): 871–889.CrossRefGoogle Scholar
  12. Fiss, P.C. 2012. Crisp and fuzzy set qualitative comparative analysis(QCA) [PowerPoint slides].Retrieved from http://www.indiana.edu/~wim/docs/11_9_2012_Peer%20Fiss_slides.pdf.
  13. Fiss, P.C. 2011. Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal 54(2): 393–420.CrossRefGoogle Scholar
  14. Ghose, A., and P. Ipeirotis. 2006. Designing ranking systems for consumer reviews: The impact of review subjectivity on product sales and review quality. In Proceedings of the 16th annual workshop on information technology and systems, 2006. (available at http://pages.stern.nyu.edu/~panos/publications/wits2006.pdf).
  15. Hennessey, J., and S.C. Anderson. 1990. The interaction of peripheral cues and message arguments on cognitive responses to an advertisement. In NA—advances in consumer research volume 17, eds. Marvin E. Goldberg, Gerald Gorn, and Richard W. Pollay, 237–243. Provo: Association for Consumer Research.Google Scholar
  16. Herr, P.M., F. Kardes, and J. Kim. 1991. Effects of word of mouth and product attribute information on persuasion: An accessibility-diagnosticity perspective source. Journal of Consumer Research 17(4): 454–462.CrossRefGoogle Scholar
  17. Karmarkar, U., and Z. Tormala. 2009. Believe me, I have no idea what I’m talking about: The effects of source certainty on consumer involvement and persuasion. Journal of Consumer Research 36(April): 1033–1049.Google Scholar
  18. Legewie, N. 2013. An introduction to applied data analysis with qualitative comparative analysis (QCA) [88 paragraphs]. Forum: Qualitative Social Research 14(3), Art. 15.Google Scholar
  19. Li, J., and L. Zhan. 2011. Online persuasion: How the written word drives WOM: Evidence from consumer-generated product reviews. Journal of Advertising Research 51(1): 239–257.CrossRefGoogle Scholar
  20. Li, M., L. Huang, C.-H. Tan, and K.-K. Wei. 2013. Helpfulness of online product reviews as seen by consumers: Source and content features. International Journal of Electronic Commerce 17(4): 101–136.CrossRefGoogle Scholar
  21. Liu, Y. 2006. Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing 70(3): 74–89.CrossRefGoogle Scholar
  22. Meyer, A.D., A.S. Tsui, and C.R. Hinnings. 1993. Configurational approaches to organizational analysis. Academy of Management Journal 36(6): 1175–1195.CrossRefGoogle Scholar
  23. Mohammadreza, M., M. Koohikamali, and M. Salehan. 2015. The effect of central and peripheral cues on online review helpfulness: A comparison between functional and expressive products. In Proceedings of Thirty Sixth International Conference on Information Systems, 13–16 December, Fort Worth, TX.Google Scholar
  24. Mudambi, S., and D. Schuff. 2010. What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly 34(1): 185–200.Google Scholar
  25. O’Keefe, D.J. 2008. Elaboration likelihood model. In International encyclopedia of communication, ed. W. Donsbach, vol. 4, 1475–1480. Oxford and Malden: Wiley-Blackwell.Google Scholar
  26. O’Keefe, D.J. 2002. Persuasion: Pheory and research. 2nd ed. Thousand Oaks: Sage. p. 167.Google Scholar
  27. Pan, Y., and J. Zhang. 2011. Born unequal: A study of the helpfulness of user-generated product reviews. Journal of Retailing 87(4): 598–612.CrossRefGoogle Scholar
  28. Park, D.-H., J. Lee, and H. Ingoo. 2007. The effect of on-line consumer reviews on consumer purchasing intentions: The moderating role of involvement. International Journal of Electronic Commerce 11(4): 57–80.CrossRefGoogle Scholar
  29. Pennebaker, J.W., R. Booth, and M. Francis. 2007. Linguistic inquiry and word count: LIWC [Computer software]. Austin: LIWC.net.Google Scholar
  30. Petty, R., and J. Cacioppo. 1981. Attitudes and persuasion: Classic and contemporary approaches. Dubuque: William C. Brown.Google Scholar
  31. Petty, R., and J. Cacioppo. 1984. The effects of involvement on response to argument quantity and quality: Central and peripheral routes to persuasion. Journal of Personality and Social Psychology 46(1): 69–81.CrossRefGoogle Scholar
  32. Price, P.C., and E.R. Stone. 2004. Intuitive evaluation of likelihood judgment procedure: Evidence from a confidence heuristics. Journal of Behavioral Decision Making 17(December): 39–57.CrossRefGoogle Scholar
  33. Ragin, C. 2000. Fuzzy-Set Social Science. Chicago: University of Chicago Press.Google Scholar
  34. Ragin, C. 2006. Set relations in social research: Evaluating their consistency and coverage. Political Analysis 14(3): 291–310.CrossRefGoogle Scholar
  35. Ragin, C. 2007. Qualitative comparative analysis using fuzzy sets (fsQCA). In Configurational comparative analysis, ed. B. Rihoux, and C.C. Ragin. Thousand Oaks: Sage.Google Scholar
  36. Ragin, C. 2008. Redesigning social enquiry: Fuzzy sets and beyond. Chicago: Chicago University Press.CrossRefGoogle Scholar
  37. Ragin, C.C., and B. Rihoux. 2004. Qualitative comparative analysis (QCA): State of art and prospects. Qualitative Methods 2(2): 3–13.Google Scholar
  38. Rubin, V.L., E. Liddy, and N. Kando. 2005. Certainty identification in texts: Categorization model and manual tagging results. In Computing attitude and affect in text: Theory and applications (The information retrieval series), eds. J.G. Shanahan, Y. Qu, and J. Wiebe. New York: Springer.Google Scholar
  39. Rucker, D., and R.E. Petty. 2006. Increasing the effectiveness of communications to consumers: Recommendations based on elaboration likelihood and attitude certainty perspectives. Journal of Public Policy and Marketing 25(1): 39–52.CrossRefGoogle Scholar
  40. Schneider, M., and A. Eggert. 2014. Embracing complex causality with the QCA method: An invitation. Journal of Business Marketing Management 7(1): 312–328.Google Scholar
  41. Senecal, S., and J. Nantel. 2004. The influence of online product recommendations on consumers’ online choices. Journal of Retailing 80(2): 159–169.CrossRefGoogle Scholar
  42. Sniejek, J., and L.V. Swol. 2001. Trust, confidence and expertise in a judge advisor system. Organizational Behavior and Human Decision Processes 84(March): 288–307.CrossRefGoogle Scholar
  43. Sussman, S.W., and W. Seigal. 2003. Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research 14(1): 47–65.CrossRefGoogle Scholar
  44. Tausczik, Y.R., and J. Pennebaker. 2010. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology 29(1): 24–54.CrossRefGoogle Scholar
  45. Thiem, Alrik 2010. Set-relational fit and the formulation of transformational rules in fsQCA. COMPASSS Working Paper Series, 61, http://www.compasss.org/wpseries/Thiem2010.pdf [Accessed: Nov 19, 2016].
  46. Wageman, C., and C. Schneider. 2010. Standards of good practice in qualitative comparative analysis (QCA) and fuzzy sets. Qualitative Comparative Sociology 9(3): 397–418.CrossRefGoogle Scholar
  47. Woodside, A., and R. McDonald. 2012. Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research 66(4): 463–472.CrossRefGoogle Scholar
  48. Woodside, A., and M. Zhang. 2012. Identifying X consumers using Causal recipes: “Whales” and “Jumbo Shrimps” casino gamblers. Journal of Gambling Studies 28: 13–26.CrossRefGoogle Scholar
  49. Wu, P.F., H. Van der Heijden, and N. Korfiatis. 2011. The influences of negativity and review quality on the helpfulness of online reviews. International Conference on Information Systems. Available at SSRN: http://ssrn.com/abstract=1937664.
  50. Zhu, L., G. Yin, and W. He. 2014. Is this opinion leaders review useful? Peripheral cues for online review helpfulness. Journal of Electronic Commerce Research 15(4): 267–280.Google Scholar

Copyright information

© Macmillan Publishers Ltd 2017

Authors and Affiliations

  1. 1.Earl G Graves School of BusinessMorgan State UniversityBaltimoreUSA

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