Skip to main content
Log in

Negative online reviews of popular products: understanding the effects of review proportion and quality on consumers’ attitude and intention to buy

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

This study investigated the effects of negative online reviews on consumers’ attitude and purchase intention, more specifically in relation to popular products. The investigation took into account the proportion of negative online reviews (low and high) and their quality (low and high), as well as comparing their impact in relation to popular and unpopular products. As a control variable, a website was purposely developed to suit eight different experimental treatments and their manipulations. This study involved 382 participants, who were exposed to the specially created website and asked to perform a specific task. Their responses were captured via questionnaires. The results showed that consumers’ positive attitude to popular products decreased as the proportion of negative online reviews increased. The quality of reviews was found to have a less significant influence on consumer responses. Furthermore, this research revealed that unpopular products were more affected by negative online reviews than popular ones.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. 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. https://doi.org/10.1016/j.elerap.2007.05.004.

    Article  Google Scholar 

  2. Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. Journal of Marketing, 73(5), 90–102. https://doi.org/10.1509/jmkg.73.5.90.

    Article  Google Scholar 

  3. Park, D. H., & Kim, S. (2008). The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. Electronic Commerce Research and Applications, 7(4), 399–410. https://doi.org/10.1016/j.elerap.2007.12.001.

    Article  Google Scholar 

  4. Schlosser, A. E. (2011). Can including pros and cons increase the helpfulness and persuasiveness of online reviews? The interactive effects of ratings and arguments. Journal of Consumer Psychology, 21(3), 226–239. https://doi.org/10.1016/j.jcps.2011.04.002.

    Article  Google Scholar 

  5. Sen, S., & Lerman, D. (2007). Why are you telling me this? An examination into negative consumer reviews on the web. Journal of Interactive Marketing, 21(4), 76–94. https://doi.org/10.1002/dir.20090.

    Article  Google Scholar 

  6. 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. https://doi.org/10.1002/dir.10073.

    Article  Google Scholar 

  7. Chan, Y. Y. Y., & Ngai, E. W. T. (2011). Conceptualising electronic word of mouth activity: An input-process-output perspective. Marketing Intelligence & Planning, 29(5), 488–516. https://doi.org/10.1108/02634501211231946.

    Article  Google Scholar 

  8. Cheung, C. M. K., & Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision Support Systems, 54(1), 461–470. https://doi.org/10.1016/j.dss.2012.06.008.

    Article  Google Scholar 

  9. Dholakia, R. R., & Sternthal, B. (1977). Highly credible sources: Persuasive facilitators or persuasive liabilities? Journal of Consumer Research, 3(4), 223. https://doi.org/10.1086/208671.

    Article  Google Scholar 

  10. Herr, P. M., Kardes, F. R., & Kim, J. (1991). Effects of word-of-mouth and on product—An attribute persuasion: Perspective. Journal of Consumer Research, 17(March), 454–462.

    Article  Google Scholar 

  11. Sternthal, B., Phillips, L. W., & Dholakia, R. (1978). The persuasive effect of source credibility: A situational analysis. Public Opinion Quarterly, 42(3), 285–314. https://doi.org/10.1086/268454.

    Article  Google Scholar 

  12. Chen, Y., & Xie, J. (2008). Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Science, 54(3), 477–491. https://doi.org/10.1287/mnsc.1070.0810.

    Article  Google Scholar 

  13. Skowronski, J. J., & Carlston, D. E. (1987). Social judgment and social memory: The role of cue diagnosticity in negativity, positivity, and extremity biases. Journal of Personality and Social Psychology, 52(4), 689–699. https://doi.org/10.1037/0022-3514.52.4.689.

    Article  Google Scholar 

  14. Bailey, A. A. (2004). Thiscompanysucks.com: The use of the Internet in negative consumer-to-consumer articulations. Journal of Marketing Communications, 10(3), 169–182. https://doi.org/10.1080/1352726042000186634.

    Article  Google Scholar 

  15. Xia, L., & Bechwati, N. N. (2008). Word of Mouse: The role of cognitive personalization in online consumer reviews. Journal of Interactive Advertising, 9(1), 3–13. https://doi.org/10.1080/15252019.2008.10722143.

    Article  Google Scholar 

  16. See-To, E. W. K., & Ho, K. K. W. (2014). Value co-creation and purchase intention in social network sites: The role of electronic Word-of-Mouth and trust—A theoretical analysis. Computers in Human Behavior, 31(1), 182–189. https://doi.org/10.1016/j.chb.2013.10.013.

    Article  Google Scholar 

  17. Hsieh, Y. C., Chiu, H. C., & Chiang, M. Y. (2005). Maintaining a committed online customer: A study across search-experience-credence products. Journal of Retailing, 81(1), 75–82. https://doi.org/10.1016/j.jretai.2005.01.006.

    Article  Google Scholar 

  18. Jiménez, F. R., & Mendoza, N. A. (2013). Too popular to ignore: The influence of online reviews on purchase intentions of search and experience products. Journal of Interactive Marketing, 27(3), 226–235. https://doi.org/10.1016/j.intmar.2013.04.004.

    Article  Google Scholar 

  19. Lu, L. C., Chang, W. P., & Chang, H. H. (2014). Consumer attitudes toward blogger’s sponsored recommendations and purchase intention: The effect of sponsorship type, product type, and brand awareness. Computers in Human Behavior, 34, 258–266. https://doi.org/10.1016/j.chb.2014.02.007.

    Article  Google Scholar 

  20. Mudambi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon.com. MIS Quarterly, 34(1), 185–200. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2175066.

  21. Weathers, D., Sharma, S., & Wood, S. L. (2007). Effects of online communication practices on consumer perceptions of performance uncertainty for search and experience goods. Journal of Retailing, 83(4), 393–401. https://doi.org/10.1016/j.jretai.2007.03.009.

    Article  Google Scholar 

  22. Cui, J., Pan, Y., & Wang, L. (2012). Impact of online review on sales: An empirical investigation of experience products with different popularities. In Proceedings2012 international conference on management of e-Commerce and e-Government, ICMeCG 2012 (pp. 48–53). https://doi.org/10.1109/icmecg.2012.31.

  23. Bolton, G. E., Katok, E., & Ockenfels, A. (2004). How effective are electronic reputation mechanisms? An experimental investigation. Management Science, 50(11), 1587–1602. https://doi.org/10.1287/mnsc.1030.0199.

    Article  Google Scholar 

  24. Pavlou, P. A., & Gefen, D. (2004). Building effective online marketplaces with institution-based trust. Information Systems Research. https://doi.org/10.1287/isre.1040.0015.

    Article  Google Scholar 

  25. Caminal, R., & Vives, X. (1996). Why market shares matter: An information-based theory. The Rand Journal of Economics, 27(2), 221–239. https://doi.org/10.2307/2555924.

    Article  Google Scholar 

  26. Chen, P.-Y., Wu, S., & Yoon, J. (2004). The impact of online recommendations and consumer feedback on sales. In Twenty-fifth international conference on information systems (pp. 711–723). Retrieved from http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1146&context=icis2004.

  27. Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74(March), 133–148. Retrieved from http://journals.ama.org/doi/abs/10.1509/jmkg.74.2.133.

  28. Kanouse, D. E., & Hanson Jr., R. L. (1972). Negativity in evaluations. In E. E. Jones, D. E. Kanouse, H. H. Kelley, R. E. Nisbett, S. Valins & B. Weiner (Eds.), Attribution: Perceiving the causes of behavior (pp. 47–62). Hillsdale: Lawrence Erlbaum Associates, Inc.

    Google Scholar 

  29. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292. https://doi.org/10.1111/j.1536-7150.2011.00774.x.

    Article  Google Scholar 

  30. Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of Business Research, 68(6), 1261–1270. https://doi.org/10.1016/j.jbusres.2014.11.006.

    Article  Google Scholar 

  31. Park, D.-H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125–148. https://doi.org/10.2753/JEC1086-4415110405.

    Article  Google Scholar 

  32. Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530–545. https://doi.org/10.1287/mnsc.29.5.530.

    Article  Google Scholar 

  33. Ives, B., Olson, M., & Baroudi, J. (1983). The measurement of user information satisfaction. Communications of the ACM, 26(10), 785–793. https://doi.org/10.1145/358413.358430.

    Article  Google Scholar 

  34. Wang, R. Y. W., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Source Journal of Management Information Systems, 12(4), 5–33. https://doi.org/10.2307/40398176.

    Article  Google Scholar 

  35. Cheung, C. M. K., Lee, M. K. O., & Rabjohn, N. (2008). The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities. Internet Research, 18(3), 229–247. https://doi.org/10.1108/10662240810883290.

    Article  Google Scholar 

  36. Petty, R. E., Cacioppo, J. T., & Schumann, D. (1983). Central and peripheral routes to advertising effectiveness: The moderating role of involvement. Journal of Consumer Research, 10(2), 135–146. https://doi.org/10.2307/2488919.

    Article  Google Scholar 

  37. Benedicktus, R. L., Brady, M. K., Darke, P. R., & Voorhees, C. M. (2010). Conveying trustworthiness to online consumers: Reactions to consensus, physical store presence, brand familiarity, and generalized suspicion. Journal of Retailing, 86(4), 310–323. https://doi.org/10.1016/j.jretai.2010.04.002.

    Article  Google Scholar 

  38. 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. https://doi.org/10.1287/isre.1080.0193.

    Article  Google Scholar 

  39. Pan, Y., & Zhang, J. Q. (2011). Born unequal: A study of the helpfulness of user-generated product reviews. Journal of Retailing, 87(4), 598–612. https://doi.org/10.1016/j.jretai.2011.05.002.

    Article  Google Scholar 

  40. Maryanchyk, I. (2008). Are ratings informative signals? The analysis of the netflix data. NET Institute working paper no. 08-22, (October), pp. 1–40. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1286307.

  41. DeSarbo, W. S., Kim, J., Choi, S. C., & Spaulding, M. (2002). A gravity-based multidimensional scaling model for deriving spatial structures underlying consumer preference/choice judgments. Journal of Consumer Research, 29(1), 91–100. https://doi.org/10.1086/339923.

    Article  Google Scholar 

  42. Huang, P., Lurie, N. H., & Mitra, S. (2009). Searching for experience on the Web: An empirical examination of consumer behavior for search and experience goods. Journal of Marketing, 73(2), 55–69. https://doi.org/10.1509/jmkg.73.2.55.

    Article  Google Scholar 

  43. MacKenzie, S. B., Lutz, R. J., & Belch, G. E. (1986). The role of attitude toward the ad as a mediator of advertising effectiveness: A test of competing explanations. Journal of Marketing Research, 23(2), 130–143. https://doi.org/10.2307/3151660.

    Article  Google Scholar 

  44. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research (Vol. 480). Reading, MA: Addison Wesley. https://doi.org/10.2307/2065853.

    Book  Google Scholar 

  45. De Magistris, T., & Gracia, A. (2008). The decision to buy organic food products in Southern Italy. British Food Journal, 110(9), 929–947. https://doi.org/10.1108/00070700810900620.

    Article  Google Scholar 

  46. Spears, N., & Singh, S. N. (2004). Measuring attitude toward the brand and purchase intentions. Journal of Current Issues & Research in Advertising, 26(2), 53–66. https://doi.org/10.1080/10641734.2004.10505164.

    Article  Google Scholar 

  47. Wu, P. C. S., Yeh, G. Y. Y., & Hsiao, C. R. (2011). The effect of store image and service quality on brand image and purchase intention for private label brands. Australasian Marketing Journal, 19(1), 30–39. https://doi.org/10.1016/j.ausmj.2010.11.001.

    Article  Google Scholar 

  48. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.

    Article  Google Scholar 

  49. Kim, D. J., Ferrin, D. L., & Raghav Rao, H. (2009). Trust and satisfaction, two stepping stones for successful e-commerce relationships: A longitudinal exploration. Information Systems Research, 20(2), 237–257. https://doi.org/10.1287/isre.1080.0188.

    Article  Google Scholar 

  50. Sia, C. L., Lim, K. H., Leung, K., Lee, M. K. O., Huang, W. W., & Benbasat, I. (2009). Web strategies to promote internet shopping: Is cultural-customization needed? MIS Quarterly, 33(3), 491–512.

    Article  Google Scholar 

  51. Freedman, L. (2008). Merchant and customer perspectives on customer reviews and user-generated content. http://www.powerreviews.com/socialshopping/solutions/whitepaper/2008\_WhitePaper\_0204\_4.pdf.

  52. Goh, Y. S. (2010). The influence of product-brand fit and product-category fit on product attitude and purchase intention: The role of brand strength and processing fluency. ProQuest dissertations and theses. Retrieved from http://search.proquest.com/docview/768026318?accountid=51189.

  53. Lee, M., & Youn, S. (2009). Electronic word of mouth (eWOM). International Journal of Advertising, 28(3), 473–499. https://doi.org/10.2501/S0265048709200709.

    Article  Google Scholar 

  54. Nunnally, J. (1978). Psychometric theory. New York: McGraw-Hill. https://doi.org/10.1037/018882.

    Book  Google Scholar 

  55. Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64. https://doi.org/10.2307/3150876.

    Article  Google Scholar 

  56. Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98–104. https://doi.org/10.1037/0021-9010.78.1.98.

    Article  Google Scholar 

  57. Gefen, D., & Straub, D. W. (2005). A practical guide to factorial validity using PLS-GRAPH:tutorial and annotated example. Communications of the Association for Information Systems, 16(5), 20. Retrieved from file:///F:/Mendeley/2005/Gefen/Gefen-2005-PLS-GRAPHTUTORIALANDANNOTATEDEXAMPLE.pdf.

  58. Hair, J. F. J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Long Range Planning. https://doi.org/10.1016/j.lrp.2013.01.002.

    Article  Google Scholar 

  59. Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8.

    Article  Google Scholar 

  60. Zhu, L., Zhang, W., & Zhu., Y. (2012). Research on the influence of online reviews on internet consumer purchasing decision. In 2012 international conference on management of e-Commerce and e-Government (ICMeCG) (pp. 38–41). IEEE.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Muhammad Rifki Shihab.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Appendices

Appendix 1

See Table 4.

Table 4 Correlation of constructs

Appendix 2

See Table 5.

Table 5 Cross loadings of indicators

Appendix 3

See Table 6.

Table 6 Fornell–Larcker criterion

Appendix 4

See Table 7.

Table 7 Heterotrait–Monotrait (HTMT) criterion

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shihab, M.R., Putri, A.P. Negative online reviews of popular products: understanding the effects of review proportion and quality on consumers’ attitude and intention to buy. Electron Commer Res 19, 159–187 (2019). https://doi.org/10.1007/s10660-018-9294-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-018-9294-y

Keywords

Navigation