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How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?

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  • Dokyun Lee

    (Carnegie Mellon University, Pittsburgh, Pennsylvania 15213;)

  • Kartik Hosanagar

    (he Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

We investigate the moderating effect of product attributes and review ratings on views, conversion|views (conversion conditional on views), and final conversion of a purchase-based collaborative filtering recommender system on an e-commerce site. We run a randomized field experiment on a top retailer with 184,375 users split into a recommender-treated group and a control group. We tag theory-driven attributes of 37,125 unique products via Amazon Mechanical Turk to augment the usual product data (e.g., review ratings, descriptions). By examining the recommender’s impact through different stages—awareness (views), salience ( conversion|views ), and final conversion—and across product types, we provide nuanced insights. The study confirms that the recommender increases views, conversion|views , and final conversion rates by 15.3%, 21.6%, and 7.5%, respectively, but this lift is moderated by product attributes and review ratings. We find that the lift on product views is greater for utilitarian products compared with hedonic products as well as for experience products compared with search products. In contrast, the lift on conversion|views rate is greater for hedonic products compared with utilitarian products. Furthermore, the lift on views rate is greater for products with higher average review ratings, which suggests that a recommender acts as a complement to review ratings, whereas the opposite is true for conversion|views , where recommender and review ratings are substitutes. Additionally, a recommender’s awareness lift is greater than its saliency impact. We discuss the potential mechanisms behind our results as well as their managerial implications.

Suggested Citation

  • Dokyun Lee & Kartik Hosanagar, 2021. "How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?," Management Science, INFORMS, vol. 67(1), pages 524-546, January.
  • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:1:p:524-546
    DOI: 10.1287/mnsc.2019.3546
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    as
    1. Avi Goldfarb & Catherine Tucker, 2011. "Online Display Advertising: Targeting and Obtrusiveness," Marketing Science, INFORMS, vol. 30(3), pages 389-404, 05-06.
    2. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
    3. Babin, Barry J & Darden, William R & Griffin, Mitch, 1994. "Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(4), pages 644-656, March.
    4. Oliver Hinz & Jochen Eckert, 2010. "The Impact of Search and Recommendation Systems on Sales in Electronic Commerce," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 67-77, April.
    5. Caudill, Steven B, 1988. "An Advantage of the Linear Probability Model over Probit or Logit," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 50(4), pages 425-427, November.
    6. Avi Goldfarb & Catherine Tucker, 2011. "Rejoinder--Implications of "Online Display Advertising: Targeting and Obtrusiveness"," Marketing Science, INFORMS, vol. 30(3), pages 413-415, 05-06.
    7. Nelson, Phillip, 1970. "Information and Consumer Behavior," Journal of Political Economy, University of Chicago Press, vol. 78(2), pages 311-329, March-Apr.
    8. Klein, Lisa R., 1998. "Evaluating the Potential of Interactive Media through a New Lens: Search versus Experience Goods," Journal of Business Research, Elsevier, vol. 41(3), pages 195-203, March.
    9. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
    10. Prabuddha De & Yu (Jeffrey) Hu & Mohammad S. Rahman, 2010. "Technology Usage and Online Sales: An Empirical Study," Management Science, INFORMS, vol. 56(11), pages 1930-1945, November.
    11. Dokyun Lee & Kartik Hosanagar, 2019. "How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment," Service Science, INFORMS, vol. 30(1), pages 239-259, March.
    12. Anuj Kumar & Yinliang (Ricky) Tan, 2015. "The Demand Effects of Joint Product Advertising in Online Videos," Management Science, INFORMS, vol. 61(8), pages 1921-1937, August.
    13. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    14. Nelson, Philip, 1974. "Advertising as Information," Journal of Political Economy, University of Chicago Press, vol. 82(4), pages 729-754, July/Aug..
    15. Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July.
    16. Strahilevitz, Michal & Myers, John G, 1998. "Donations to Charity as Purchase Incentives: How Well They Work May Depend on What You Are Trying to Sell," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(4), pages 434-446, March.
    17. Mark Armstrong & Robert Porter (ed.), 2007. "Handbook of Industrial Organization," Handbook of Industrial Organization, Elsevier, edition 1, volume 3, number 1.
    18. Il-Horn Hann & Christian Terwiesch, 2003. "Measuring the Frictional Costs of Online Transactions: The Case of a Name-Your-Own-Price Channel," Management Science, INFORMS, vol. 49(11), pages 1563-1579, November.
    19. Asher Wolinsky, 1983. "Prices as Signals of Product Quality," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 50(4), pages 647-658.
    20. Greevy, Robert & Silber, Jeffrey H. & Cnaan, Avital & Rosenbaum, Paul R., 2004. "Randomization Inference With Imperfect Compliance in the ACE-Inhibitor After Anthracycline Randomized Trial," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 7-15, January.
    21. Bin Gu & Jaehong Park & Prabhudev Konana, 2012. "Research Note ---The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products," Information Systems Research, INFORMS, vol. 23(1), pages 182-196, March.
    22. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-654, May.
    23. Girard, Tulay & Dion, Paul, 2010. "Validating the search, experience, and credence product classification framework," Journal of Business Research, Elsevier, vol. 63(9-10), pages 1079-1087, September.
    24. Maddala,G. S., 1986. "Limited-Dependent and Qualitative Variables in Econometrics," Cambridge Books, Cambridge University Press, number 9780521338257, October.
    25. Yili (Kevin) Hong & Paul A. Pavlou, 2014. "Product Fit Uncertainty in Online Markets: Nature, Effects, and Antecedents," Information Systems Research, INFORMS, vol. 25(2), pages 328-344, June.
    26. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    27. Anuj Kumar & Kartik Hosanagar, 2019. "Measuring the Value of Recommendation Links on Product Demand," Information Systems Research, INFORMS, vol. 30(3), pages 819-838, September.
    28. Animesh Animesh & Vandana Ramachandran & Siva Viswanathan, 2010. "Research Note ---Quality Uncertainty and the Performance of Online Sponsored Search Markets: An Empirical Investigation," Information Systems Research, INFORMS, vol. 21(1), pages 190-201, March.
    29. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    30. Prabuddha De & Yu (Jeffrey) Hu & Mohammad S. Rahman, 2013. "Product-Oriented Web Technologies and Product Returns: An Exploratory Study," Information Systems Research, INFORMS, vol. 24(4), pages 998-1010, December.
    31. Gerd Gigerenzer & Reinhard Selten (ed.), 2002. "Bounded Rationality: The Adaptive Toolbox," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262571641, April.
    32. Catherine Tucker & Juanjuan Zhang, 2011. "How Does Popularity Information Affect Choices? A Field Experiment," Management Science, INFORMS, vol. 57(5), pages 828-842, May.
    33. Gerard R. Butters, 1977. "Equilibrium Distributions of Sales and Advertising Prices," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 44(3), pages 465-491.
    34. Bagwell, Kyle, 2007. "The Economic Analysis of Advertising," Handbook of Industrial Organization, in: Mark Armstrong & Robert Porter (ed.), Handbook of Industrial Organization, edition 1, volume 3, chapter 28, pages 1701-1844, Elsevier.
    35. Gerald Häubl & Valerie Trifts, 2000. "Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Marketing Science, INFORMS, vol. 19(1), pages 4-21, May.
    Full references (including those not matched with items on IDEAS)

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    11. Ruiqi Rich Zhu & Cheng He & Yu Jeffrey Hu, 2023. "The Effect of Product Recommendations on Online Investor Behaviors," Papers 2303.14263, arXiv.org, revised Nov 2023.
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