Subdivided or aggregated online review systems: Which is better for online takeaway vendors?

  • Hongpeng Wang
  • Rong Du
  • Jin LiEmail author
  • Weiguo Fan


This paper examines the role of a subdivided or aggregated online review system to help online takeaway vendors select the most appropriate information strategy. First, we develop two models to depict the interaction between online vendors’ information strategies and consumers’ responses. Second, we take the multidimensional product attributes with their corresponding weights into consideration and illustrate that the sensitivity to product misfits, instead of the relative importance of product attributes, dominates profit maximization. Third, we make a comparison to find the most appropriate scenario to adopt a full or partial information strategy. When a large number of consumers satisfy the delivery time performance, an aggregated review system will be a better choice. Otherwise, vendors are advised to host a subdivided review system. Finally, we universally identify a variance boundary in the rating-star review system, which not only prevents consumers from expressing their real feelings but also makes observing consumer feedback and strategic adjustments inconvenient for online vendors.


Online review systems Information strategies Multidimensional attributes Variance boundary 



Funding was provided by National Natural Science Foundation of China (Grant No. 71771184), Humanities and Social Science Talent Plan (Grant No. ER42015060002), Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2017JQ7012), Chinese Fundamental Research Funds for the Central Universities (Grant No. JB180602) and Innovation Fund of Xidian University.

Compliance with ethical standards

Conflict of interest

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


  1. 1.
    Anderson, M., & Anderson, M. (2014). 88% of consumers trust online reviews as much as personal recommendations. Retrieved November 18, 2015.Google Scholar
  2. 2.
    Anderson, S. P., De Palma, A., & Thisse, J. F. (1992). Discrete choice theory of product differentiation. Cambridge: MIT Press.Google Scholar
  3. 3.
    Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 90(2), 511–515.CrossRefGoogle Scholar
  4. 4.
    Celik, L. (2014). Information unraveling revisited: Disclosure of horizontal attributes. The Journal of Industrial Economics, 62(1), 113–136.CrossRefGoogle Scholar
  5. 5.
    Chen, P. Y., Hong, Y., & Liu, Y. (2017). The value of multidimensional rating systems: Evidence from a natural experiment and randomized experiments. Management Science. Scholar
  6. 6.
    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.CrossRefGoogle Scholar
  7. 7.
    Chen, Y., Yang, S., & Wang, Z. (2016). Service cooperation and marketing strategies of infomediary and online retailer with eWOM effect. Information Technology and Management, 17(2), 109–118.CrossRefGoogle Scholar
  8. 8.
    Dimoka, A., Hong, Y., & Pavlou, P. A. (2012). On product uncertainty in online markets: Theory and evidence. MIS Quarterly, 36(2), 395–426.Google Scholar
  9. 9.
    Dolan, R., Conduit, J., Fahy, J., & Goodman, S. (2016). Social media engagement behavior: A uses and gratifications perspective. Journal of Strategic Marketing, 24(3–4), 261–277.CrossRefGoogle Scholar
  10. 10.
    Floh, A., Koller, M., & Zauner, A. (2013). Taking a deeper look at online reviews: The asymmetric effect of valence intensity on shopping behaviour. Journal of Marketing Management, 29(5–6), 646–670.CrossRefGoogle Scholar
  11. 11.
    Floyd, K., Freling, R., Alhoqail, S., Cho, H. Y., & Freling, T. (2014). How online product reviews affect retail sales: A meta-analysis. Journal of Retailing, 90(2), 217–232.CrossRefGoogle Scholar
  12. 12.
    Ge, Y., & Li, J. (2015). Measure and mitigate the dimensional bias in online reviews and ratings. In Proceedings of the 36th international conference on information systems, Fort Worth, TX.Google Scholar
  13. 13.
    Goldhaber, M. H. (1997). The attention economy and the net. First Monday, 2(4).
  14. 14.
    Gu, Z., & Xie, Y. (2013). Facilitating fit revelation in the competitive market. Management Science, 59(5), 1196–1212.CrossRefGoogle Scholar
  15. 15.
    Hao, L., Li, X., Tan, Y., & Xu, J. (2011). The economic value of ratings in app market. Available at SSRN 1892584.Google Scholar
  16. 16.
    Herrmann, P., Kundisch, D., Zimmermann, S., & Nault, B. (2015). How do different sources of the variance of consumer ratings matter? Economics of information systems. Fort Worth, TX: ICIS 2015.Google Scholar
  17. 17.
    Ho, Y. C., Wu, J., & Tan, Y. (2017). Disconfirmation effect on online rating behavior: A structural model. Information Systems Research, 28(3), 626–642.CrossRefGoogle Scholar
  18. 18.
    Hu, N., Pavlou, P. A., & Zhang, J. (2007). Why do online product reviews have a J-shaped distribution? Overcoming biases in online word-of-mouth communication. Communications of the ACM, 52(10), 144–147.CrossRefGoogle Scholar
  19. 19.
    Hu, N., Pavlou, P. A., & Zhang, J. (2017). On self-selection biases in online product reviews. MIS Quarterly, 41(2), 449–471.CrossRefGoogle Scholar
  20. 20.
    Hu, N., Zhang, J., & Pavlou, P. A. (2009). Overcoming the J-shaped distribution of product reviews. Communications of the ACM, 52(10), 144–147.CrossRefGoogle Scholar
  21. 21.
    Jiang, Y., & Guo, H. (2015). Design of consumer review systems and product pricing. Information Systems Research, 26(4), 714–730.CrossRefGoogle Scholar
  22. 22.
    Kahneman, D., & Tversky, A. (1984). Choices, values, and frames. American Psychologist, 39(4), 341.CrossRefGoogle Scholar
  23. 23.
    Kuan, K. K., Hui, K. L., Prasarnphanich, P., & Lai, H. Y. (2015). What makes a review voted? An empirical investigation of review voting in online review systems. Journal of the Association for Information Systems, 16(1), 48.CrossRefGoogle Scholar
  24. 24.
    Kuksov, D., & Xie, Y. (2010). Pricing, frills, and customer ratings. Marketing Science, 29(5), 925–943.CrossRefGoogle Scholar
  25. 25.
    Kwark, Y., Chen, J., & Raghunathan, S. (2014). Online product reviews: Implications for retailers and competing manufacturers. Information systems research, 25(1), 93–110.CrossRefGoogle Scholar
  26. 26.
    Kwon, B. C., Kim, S. H., Duket, T., Catalán, A., & Yi, J. S. (2015). Do people really experience information overload while reading online reviews? International Journal of Human-Computer Interaction, 31(12), 959–973.CrossRefGoogle Scholar
  27. 27.
    Lee, J. (2013). What makes people read an online review? The relative effects of posting time and helpfulness on review readership. Cyberpsychology, Behavior, and Social Networking, 16(7), 529–535.CrossRefGoogle Scholar
  28. 28.
    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.CrossRefGoogle Scholar
  29. 29.
    Lee, E. J., & Shin, S. Y. (2014). When do consumers buy online product reviews? Effects of review quality, product type, and reviewer’s photo. Computers in Human Behavior, 31(1), 356–366.CrossRefGoogle Scholar
  30. 30.
    Lelis, S., & Howes, A. (2011). Informing decisions: How people use online rating information to make choices. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 2285–2294). ACM.Google Scholar
  31. 31.
    Li, X., & Hitt, L. M. (2008). Self-selection and information role of online product reviews. Information Systems Research, 19(4), 456–474.CrossRefGoogle Scholar
  32. 32.
    Li, X., & Hitt, L. M. (2010). Price effects in online product reviews: An analytical model and empirical analysis. MIS Quarterly, 34(4), 809–831.CrossRefGoogle Scholar
  33. 33.
    Liu, Q., Huang, S., & Zhang, L. (2016). The influence of information cascades on online purchase behaviors of search and experience products. Electronic Commerce Research, 16(4), 553–580.CrossRefGoogle Scholar
  34. 34.
    Liu, Q. B., & Karahanna, E. (2017). The dark side of reviews: The swaying effects of online product reviews on attribute preference construction. MIS Quarterly, 41(2), 427–448.CrossRefGoogle Scholar
  35. 35.
    Mudambi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on MIS Quarterly, 34(1), 185–200.CrossRefGoogle Scholar
  36. 36.
    Park, D. H., Lee, J., & Han, I. (2006). Information overload and its consequences in the context of online consumer reviews. In PACIS 2006 proceedings (p. 28).Google Scholar
  37. 37.
    Park, D. H., & Lee, J. (2009). eWOM overload and its effect on consumer behavioral intention depending on consumer involvement. Electronic Commerce Research and Applications, 7(4), 386–398.CrossRefGoogle Scholar
  38. 38.
    Punj, G. (2012). Consumer decision making on the web: A theoretical analysis and research guidelines. Psychology & Marketing, 29(10), 791–803.CrossRefGoogle Scholar
  39. 39.
    Purnawirawan, N., Eisend, M., De Pelsmacker, P., & Dens, N. (2015). A meta-analytic investigation of the role of valence in online reviews. Journal of Interactive Marketing, 31, 17–27.CrossRefGoogle Scholar
  40. 40.
    Research and Markets. (2016). Global delivery and takeaway food market 2016–2020 with Delivery Hero, Just Eat, Foodpanda, Takeaway & Grubhub Dominating. Accessed 21 Sept 2016.
  41. 41.
    Sicilia, M., & Ruiz, S. (2010). The effects of the amount of information on cognitive responses in online purchasing tasks. Electronic Commerce Research and Applications, 9(2), 183–191.CrossRefGoogle Scholar
  42. 42.
    Sun, M. (2011). Disclosing multiple product attributes. Journal of Economics & Management Strategy, 20(1), 195–224.CrossRefGoogle Scholar
  43. 43.
    Sun, M. (2012). How does the variance of product ratings matter? Management Science, 58(4), 696–707.CrossRefGoogle Scholar
  44. 44.
    Sun, H., & Xu, L. (2016). Online reviews and collaborative service provision: A signal-jamming model. Production and Operations Management. Scholar
  45. 45.
    Sutton, J. (1986). Vertical product differentiation: Some basic themes. The American Economic Review, 76(2), 393–398.Google Scholar
  46. 46.
    Technavio Research. (2016). Top 5 vendors in the delivery and takeaway food market from 2016 to 2020. Accessed 5 Sept 2016.
  47. 47.
    The New York Times. (2012). The best book reviews money can buy. Accessed 25 Aug 2012.
  48. 48.
    Yin, D., Bond, S. D., & Zhang, H. (2014). Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quarterly, 38(2), 539–560.CrossRefGoogle Scholar
  49. 49.
    Yin, D., Mitra, S., & Zhang, H. (2016). When do consumers value positive vs. negative reviews? An empirical investigation of confirmation bias in online word of mouth. Information Systems Research, 27(1), 131–144.CrossRefGoogle Scholar
  50. 50.
    Yu, J. (2015) Delivery service assortment and product pricing in online retailing: The impact of pricing flexibility and customer rating (Doctoral dissertation, University of British Columbia).Google Scholar
  51. 51.
    Zhang, K. Z., Cheung, C. M., & Lee, M. K. (2014). Examining the moderating effect of inconsistent reviews and its gender differences on consumers’ online shopping decision. International Journal of Information Management, 34(2), 89–98.CrossRefGoogle Scholar
  52. 52.
    Ziegele, M., & Weber, M. (2015). Example, please! Comparing the effects of single customer reviews and aggregate review scores on online shoppers’ product evaluations. Journal of Consumer Behaviour, 14(2), 103–114.CrossRefGoogle Scholar
  53. 53.
    Zou, P., Yu, B., & Hao, Y. (2011). Does the valence of online consumer reviews matter for consumer decision making? The moderating role of consumer expertise. Journal of Computers, 6(3), 484–488.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Economics and ManagementXidian UniversityXi’anChina
  2. 2.Department of Management Sciences, Tippie College of BusinessUniversity of IowaIowa CityUSA
  3. 3.Institutes of Science and DevelopmentChinese Academy of SciencesBeijingChina

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