Haiyi Zhu
Bernardo Huberman
ABSTRACT
We designed and ran an experiment to measure social influence in online recommender systems, specifically how often people's choices are changed by others' recommendations when facing different levels of confirmation and conformity pressures. In our experiment participants were first asked to provide their preferences between pairs of items. They were then asked to make second choices about the same pairs with knowledge of others' preferences. Our results show that others people's opinions significantly sway people's own choices. The influence is stronger when people are required to make their second decision sometime later (22.4%) than immediately (14.1%). Moreover, people seem to be most likely to reverse their choices when facing a moderate, as opposed to large, number of opposing opinions. Finally, the time people spend making the first decision significantly predicts whether they will reverse their decisions later on, while demographics such as age and gender do not. These results have implications for consumer behavior research as well as online marketing strategies.
- Asch, S.E. (1956) Studies of independence and conformity: I. A minority of one against a unanimous majority. Psychological Monographs, Vol 70(9), 1956, 70.Google Scholar
Cross Ref
- Bacon, F., (1939), Novum organum. In Burtt, E.A. (Ed), The English philosophers from Bacon to Mill (pp.24--23). New York: Random House. (Original work published in 1620).Google Scholar
- Bendor. J., Huberman, B.A., Wu,F., (2009) Management fads, pedagogies, and other soft technologies, Journal of Economic Behavior & Organization, Volume 72, Issue 1, October 2009, Pages 290--304Google ScholarCross Ref
- Bond, R, Smith, P. B., Culture and conformity: A metaanalysis of studies using Asch's (1952b, 1956) line judgment task. Psychological Bulletin, Vol 119(1), Jan 1996, 111--137.Google Scholar
- Breese JS, Heckerman D, Kadie C. (1998) Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann: San Francisco, CA. Google ScholarDigital Library
- Brehm, J.W. (1966) A Theory of Psychological Reactance. New York: Academic Press.Google Scholar
- Burnkrant, R.E., and Cousineau, A., Informational and Normative Social Influence in Buyer Behavior, Journal of Consumer Research, Vol. 2, No. 3 (Dec., 1975), pp. 206--215.Google Scholar
- Cialdini, R.B., and Goldstein, N.J., (2004) Social Influence: Compliance and Conformity. Annual Review of Psychology. Vol. 55: 591--621.Google ScholarCross Ref
- Cohen, Joel B.; Golden, Ellen (1972) Informational social influence and product evaluation. Journal of Applied Psychology, Vol 56(1), Feb 1972, 54--59.Google ScholarCross Ref
- Cosley. D., Lam, S.K, Albert., I, Konstan., J.A., and Riedl. J. (2003). Is seeing believing?: how recommender system interfaces affect users' opinions. In Proceedings of the SIGCHI conference on Human factors in computing systems (CHI '03). Google ScholarDigital Library
- Festinger, L. (1957). A theory of cognitive dissonance. Stanford University Press.Google Scholar
- Festinger, L., (1954). A theory of social comparison processes. Human Relations 7, 117--140.Google ScholarCross Ref
- Gerard, H.B., Wilhelmy, R.A., & Conolley, E.S.(1968) Conformity and group size. Journal of Personality and Social Psychology, 1968, 8, 79--82.Google ScholarCross Ref
- Hennig-Thurau, T., Gwinner, K. P., Walsh, G., Gremler, D. D., (2004) Electronic word-of-mouth via consumeropinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing.Google ScholarCross Ref
- Kittur, A., Chi, E.H., and Suh, B. (2008) Crowdsourcing user studies with Mechanical Turk. In Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems (CHI '08). ACM, New York, NY, USA, 453--456. Google ScholarDigital Library
- Latané, B.,(1981) The psychology of social impact. American Psychologist, Vol 36(4), Apr 1981, 343--356.Google Scholar
- Linden, G.; Smith, B.; York, J.; , Amazon.com recommendations: item-to-item collaborative filtering, Internet Computing, IEEE , vol.7, no.1, pp. 76--80, Jan/Feb 2003 Google ScholarDigital Library
- Lorenz. J., Rauhut, H., Schweitzer, F., and Helbing, D. How social influence can undermine the wisdom of crowd effect. PNAS 2011 108 (22) 9020--9025.Google Scholar
- Luon, Y., Aperjis, C., and Huberman, B.A., Rankr: A Mobile System for Crowdsourcing Opinions. http://www.hpl.hp.com/research/scl/papers/rankr/rankr.p dfGoogle Scholar
- McNee, S.M., Riedl, J., and Konstan, J.A. (2006). Being accurate is not enough: How accuracy metrics have hurt recommender systems. CHI Extended Abstracts, 1097--1101. Google ScholarDigital Library
- Nickerson, R. S.(1998) Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, Vol 2(2), Jun 1998, 175--220.Google ScholarCross Ref
- Paolacci, G, Chandler, J, and Ipeirotis, P.G. (2010) "Running experiments on Amazon Mechanical Turk." Judgment and Decision Making, vol 5, no 5.Google Scholar
- Pincus, S.; Waters, L. (1977) K. Informational social influence and product quality judgments. Journal of Applied Psychology, Vol 62(5), Oct 1977, 615--619.Google ScholarCross Ref
- Pu, P., Chen, L., and Hu, R. (2011). A user-centric evaluation framework for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems (RecSys '11). ACM, New York, NY, USA, 157--164. Google ScholarDigital Library
- Ross, L., Lepper, M. R., & Hubbard, M. (1975). Perserverance in self perception and social perception: Biased attributional processes in the debriefing paradigm. Journal of Personality and Social Psychology, 32, 880--892.Google ScholarCross Ref
- Salganik, M.J., Dodds, P.S., and Watts. D.J., Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market. Science 10 February 2006: Vol. 311 no. 5762 pp. 854--856.Google Scholar
- Senecal, S., Nantel, J., The influence of online product recommendations on consumers' online choices, Journal of Retailing, Volume 80, Issue 2, 2004, Pages 159--169.Google ScholarCross Ref
- Schwind, C., Buder, J., and Hesse, F.W. 2011. I will do it, but i don't like it: user reactions to preferenceinconsistent recommendations. In Proceedings of the 2011 annual conference on Human factors in computing systems (CHI '11). Google ScholarDigital Library
- Stauss, B. (1997). Global Word of Mouth. Service Bashing on the Internet is a Thorny Issue. Marketing Management, 6(3), 28--30.Google Scholar
- Wason, P. C. (1960) On the failure to eliminate hypotheses in a conceptual task.The Quarterly Journal of Experimental Psychology, Vol 12, 1960, 129--140.Google Scholar
- Wu, F., and Huberman, B.A., (2010) Opinion formation under costly expression. ACM Trans. Intell. Syst. Technol. 1, 1, Article 5 (October 2010), 13 pages. Google ScholarDigital Library
- Xiao, B. and Benbasat, I. (2007). Ecommerce Product Recommendation Agents: Use, Characteristics, and Impact. MIS Quarterly 31(1), 137--209. Google ScholarDigital Library
Index Terms
- To switch or not to switch: understanding social influence in online choices
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