Empirical analysis of the impact of recommender systems on sales

Journal of Management Information Systems

Volumn 27, Issue 2, 2010, Pages 159-188

  Pathak, Bhavik ^a   Garfinkel, Robert ^b   Gopal, Ram ^b   Venkatesan, Rajkumar ^c   Yin, Fang ^d  

^a INDIANA UNIVERSITY SOUTH BEND   (United States)

^b UNIVERSITY OF CONNECTICUT   (United States)

^c UNIVERSITY OF VIRGINIA   (United States)

^d UNIVERSITY OF OREGON   (United States)

Author keywords

collaborative filtering; e tail; electronic commerce; experience goods; recommender systems

Indexed keywords

COLLABORATIVE FILTERING; COMPARATIVE ANALYSIS; CROSS-SELLING; E-TAIL; EMPIRICAL ANALYSIS; EMPIRICAL MODEL; EMPIRICAL RESULTS; EXPERIENCE GOODS; FEEDBACK MECHANISMS; INDIRECT EFFECTS; ONLINE RECOMMENDER SYSTEMS; PANEL DATA; POSITIVE EFFECTS; RECOMMENDER SYSTEMS; SEARCH COSTS; VALUE ADDED SERVICE; WORD OF MOUTH;

COSTS; ELECTRONIC COMMERCE; ONLINE SYSTEMS; PURCHASING; SIGNAL FILTERING AND PREDICTION;

SALES;

EID: 78649357436     PISSN: 07421222     EISSN: None     Source Type: Journal    
DOI: 10.2753/MIS0742-1222270205     Document Type: Article

References (50)

1. Adams, W.J., and Yellen, J.L. Commodity bundling and the burden of monopoly. Quarterly Journal of Economics, 90, 3 (1976), 475-498.
2. Adomavicius, G., and Tuzhilin, A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17, 6 (2005), 734-749.
3. Alba, J.; Lynch, J.; Weitz, B.; Jeniszewski, C.; Lutz, R.; Sawyer, A.; and Wood, S. Interactive home shopping: Consumer, retailer, and manufacturer incentives to participate in electronic marketplaces. Journal of Marketing, 61, 3 (1997), 38-53.
4. Anderson, C. The Long Tail: Why the Future of Business Is Selling Less of More. Boston: Harvard Business School Press, 2006.
5. Ansari, A.; Essegaier, S.; and Kohli, R. Internet recommendation systems. Journal of Marketing Research, 37, 3 (2000), 363-375.
6. Benbasat, I., and Zmud, R.W. The identity crisis within the IS discipline: Defining and communicating the disciplines core properties. MIS Quarterly, 27, 2 (2003), 183-194.
7. Bergemann, D., and Ozmen, D. Optimal pricing with recommender systems. Cowles Foundation Discussion Paper no. 1563, Yale University, New Haven, 2006.
8. Brynjolfsson, E.; Hu, Y.; and Smith, M.D. Consumer surplus in the digital economy: Estimating the value of increased product variety at online booksellers. Management Science, 49, 11 (2003), 1580-1596.
9. Caves, R.E., and Greene, D.P. Brands quality levels, prices, and advertising outlays: Empirical evidence on signals and information costs. International Journal of Industrial Organization, 14, 1 (1996), 29-52.
10. Chen, P.; Wu, S.; and Yoon, J. The impact of online recommendations and consumer feedback on sales. In R. Agarwal, L. Kirsch, and J.I. DeGross (eds.), Proceedings of the 25th International Conference on Information Systems. Atlanta: Association for Information Systems, 2004, pp. 710-724.
11. Chevalier, J.A., and Goolsbee, A. Measuring prices and price competition online: Amazon.com and BarnesandNoble.com. Quantitative Marketing and Economics, 1, 2 (2003), 203-222.
12. Chevalier, J.A., and Mayzlin, D. The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43, 3 (2006), 345-354.
13. Dellarocas, C. The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49, 10 (2003), 1407-1424.
14. Eliashberg, J., and Shugan, S.M. Film critics: Influencers or predictors? Journal of Marketing, 61, 2 (1997), 68-78.
15. Flynn, L.J. Like this? Youll hate that-Not all Web recommendations are welcome. New York Times (January 23, 2006) (available at www.nytimes.com/2006/ 01/23/technology/23recommend.html ).
16. Ghose, A.; Smith, M.D.; and Telang, R. Internet exchanges for used books: An empirical analysis of product cannibalization and welfare impact. Information Systems Research, 17, 1 (2006), 3-19.
17. Grenci, R.T., and Todd, P.A. Solutions-driven marketing. Communications of the ACM, 45, 3 (2002), 64-71.
18. Gretzel, U., and Fesenmaier, D.R. Persuasion in recommender systems. International Journal of Electronic Commerce, 11, 2 (Winter 2006-7), 81-100.
19. Guernsey, L. Making intelligence a bit less artificial. New York Times (May 1, 2003) (available at www.nytimes.com/2003/05/01/technology/circuits/ 01reco.html ).
20. Haubl, G., and Trifts, V. Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Science, 19, 1 (2000), 4-21.
21. Herlocker, J.L.; Konstan, J.A.; Terveen, K.; and Riedl, J.T. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22, 1 (2004), 5-53.
22. Hostler, R.E.; Yoon, V.Y.; and Guimaraes., T. Assessing the impact of Internet agent on end users performance. Decision Support Systems, 41, 1 (2005), 313-323.
23. Konstan, J.A.; Miller, B.N.; Maltz, D.; Herlocker, J.L.; Gordon, L.R.; and Reidl, J. GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM, 40, 3 (1997), 77-87.
24. Kumar, N., and Benbasat, I. The influence of recommendations and consumer reviews on evaluations of Websites. Information Systems Research, 17, 4 (2006), 425-439.
25. Latcovich, S., and Smith, H. Pricing, sunk costs, and market structure online: Evidence from book retailing. Oxford Review of Economic Policy, 17, 2 (2001), 217-234.
26. Liang, T.-P., and Tanniru, M.R. Customer-centric information systems. Journal of Management Information Systems, 23, 3 (Winter 2006-7), 9-15.
27. Liang, T.-P.; Lai, H.-J.; and Ku, Y.-C. Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings. Journal of Management Information Systems, 23, 3 (Winter 2006-7), 45-70.
28. Linden, G.; Smith, B.; and York, J. Amazon.com recommendation: Item-to-item collaborative filtering. IEEE Internet Computing, 7, 1 (2003), 76-80.
29. Mild, A., and Reutterer, T. An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data. Journal of Retailing and Consumer Services, 10, 3 (2003), 123-133.
30. Mobasher, B.; Dai, H.; Luo, T.; and Nakagawa, M. Effective personalization based on association rule discovery from Web usage data. In R. Chiang and E.-P. Lim (eds.), Third ACM Workshop on Web Information and Data Management. New York: ACM Press, 2001, pp. 9-15.
31. Mui, Y.Q. Wal-Mart blames Web site incident on employees error. Washington Post (January 7, 2006) (available at www.washingtonpost.com/wp-dyn/ content/article/2006/01/06/AR2006010601875.html ).
32. Nelson, P. Information and consumer behavior. Journal of Political Economy, 78, 2 (1970), 311-329.
33. Nelson, P. Advertising as information. Journal of Political Economy, 82, 4 (1974), 729-754.
34. Nikolaeva, R., and Sriram, S. The moderating role of consumer and product characteristics on the value of customized on-line recommendations. International Journal of Electronic Commerce, 11, 2 (Winter 2006-7), 101-123.
35. Null, C. How Netflix is fixing Hollywood by finding a market for niche titles-And keeping discs in constant circulation-The online DVD rental pioneer is shaking up the movie biz. CNNMoney.com (July 1, 2003) (available at ).
36. Oestreicher-Singer, G., and Sundararajan, A. Network structure and the long tail of electronic commerce. In D. Straub, S. Klein, W. Haseman, and C. Washburn (eds.), Proceedings of the 27th International Conference on Information Systems. Atlanta: Association for Information Systems, 2006, pp. 382-392.
37. Reinstein, D.A., and Snyder, C.M. The influence of expert reviews on consumer demand for experience goods: A case study of movie critics. Journal of Industrial Economics, 53, 1 (2005), 27-51.
38. Resnick, P., and Varian, H.R. Recommender systems. Communications of the ACM, 40, 3 (1997), 56-58.
39. Schafer, J.B.; Konstan, J.; and Riedl, J. Recommender systems in e-commerce. In S. Feldman and M. Wellman (eds.), Proceedings of 1st ACM Conference on Electronic Commerce. New York: ACM Press, 1999, pp. 158-166.
40. Schafer, J.B.; Konstan, J.A.; and Riedl, J. E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5, 1-2 (2001), 115-153.
41. Senecal, S., and Nantel, J. The influence of online product recommendations on consumers online choices. Journal of Retailing, 80, 2 (2004), 159-169.
42. Shardanand, U., and Maes, P. Social information filtering: Algorithms for automating word of mouth. In I.R. Katz, R. Mack, L. Marks, M. Rosson, and J. Nielson (eds.), Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM Press, 1995, pp. 210-217.
43. Smith, M.D., and Brynjolfsson, E. Consumer decision making at an Internet shopbot: Brand still matters. Journal of Industrial Economics, 49, 4 (2001), 541-558.
44. Stiglitz, J.E. Imperfect information in the product market. In R. Schmalensee and R. Willig (eds.), Handbook of Industrial Organization. New York: Elsevier Science, 1989, pp. 769-847.
45. Tam, K.Y., and Ho, S.Y. Web personalization as a persuasion strategy: An elaboration likelihood model perspective. Information Systems Research, 16, 3 (2005), 271-291.
46. Vakratsas, D., and Ambler, T. How advertising works: What do we really know? Journal of Marketing, 63, 1 (1999), 26-43.
47. Wang, W., and Benbasat, I. Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs. Journal of Management Information Systems, 23, 4 (Spring 2007), 217-246.
48. Westbrook, R.A. Product/consumption-based affective responses and postpurchase processes. Journal of Marketing Research, 24, 3 (1987), 258-270.
49. Xiao, B., and Benbasat, I. E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31, 1 (2007), 137-209.
50. Yang, Y., and Padmanabhan, B. Evaluation of online personalization systems: A survey of evaluation schemes and a knowledge-based approach. Journal of Electronic Commerce Research, 6, 2 (2005), 112-122.