How Online Crowds Influence the Way Individual Consumers Answer Health Questions

Summary

Objective: To investigate whether strength of social feedback, i.e. other people who concur (or do not concur) with one’s own answer to a question, influences the way one answers health questions.

Methods: Online prospective study. Two hundred and twenty-seven undergraduate students were recruited to use an online search engine to answer six health questions. Subjects recorded their pre- and post-search answers to each question and their level of confidence in these answers. After answering each question post-search, subjects were presented with a summary of post-search answers provided by previous subjects and were asked to answer the question again.

Results: There was a statistically significant relationship between the absolute number of others with a different answer (the crowd’s opinion volume) and the likelihood of an individual changing an answer (P<0.001). For most questions, no subjects changed their answer until the first 10–35 subjects completed the study. Subjects’ likelihood of changing answer increased as the percentage of others with a different answer (the crowd’s opinion density) increased (P=0.047). Overall, 98.3% of subjects did not change their answer when it concurred with the majority (i.e. >50%) of subjects, and that 25.7% of subjects changed their answer to the majority response when it did not concur with the majority. When subjects had a post-search answer that did not concur with the majority, they were 24% more likely to change answer than those with answers that concurred (P<0.001).

Conclusion: This study provides empirical evidence that crowd influence, in the form of online social feedback, affects the way consumers answer health questions.

 

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Keywords

Consumer decision making - social feedback - online information searching - crowd influence - majority influence

 

 

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Conflict of Interest

The University of New South Wales and some of the researchers could benefit from the commercial exploitation of the Quick Clinical search engine or its technologies.

• References

• 1 Lieberman MA, Golant M, Giese-Davis J, Winzlenberg A, Benjamin H, Humphreys K. et al. Electronic support groups for breast carcinoma: A clinical trial of effectiveness. Cancer 2003; 97: 920-925.
• 2 Lorig KR, Laurent DD, Deyo RA, Marnell ME, Minor MA, Ritter PL. Can a back pain e-mail discussion group improve health status and lower health care costs?: A randomized study. Arch Intern Med 2002; 167 (Suppl. 07) 792-796.
• 3 Dawes M, Sampson U. Knowledge management in clinical practice: A systematic review of information seeking behavior in physicians. Int J Med Inform 2003; 71: 9-15.
• 4 Coumou HCH, Meijman FJ. How do primary care physicians seek answers to clinical questions? A literature review. J Med Libr Assoc 2006; 94: 55-56.
• 5 Shaw BR, McTavish F, Hawkins R, Gustafson DH, Pingree S. Experiences of women with breast cancer: Exchanging social support over the chess computer network. J Health Commun 2000; 5: 135-159.
• 6 McGettigan P, Golden J, Fryer J, Chan R, Feely J. The sources of information used by doctors for prescribing suggest that the medium is more important than the message. Br J Clin Pharmacol 2001; 51: 184-189.
• 7 Berkman LF, Glass T. Social integration, social networks, social support and health. In: Berkman L, Kawachi I editors. Social epidemiology. New York: Oxford University Press; 2000
• 8 Lau AYS, Kwok TMY. Social features in online communities for healthcare consumers –a review. In: Ozok AA, Zaphiris P editors. Online Communities, LNCS 5621. Berlin Heidelberg: Springer-Verlag; 2009 p. 682-689.
• 9 Latané B. The psychology of social impact. Am Psychol 1981; 36: 343-356.
• 10 Clark AE, Lohéac Y. It wasn‘t me, it was them!“ Social influence in risky behavior by adolescents. J Health Econ 2007; 26: 763-784.
• 11 Romer D, Black M, Ricardo I, Feigelman S, Kaljee L, Galbraith J. et al. Social influences on the sexual behavior of youth at risk for HIV exposure. Am J Public Health 1994; 84: 977-985.
• 12 Frost JH, Massagli MP, Wicks P, Heywood J. How the Social Web Supports patient experimentation with a new therapy: The demand for patient-controlled and patient-centered informatics. AMIA Annu Symp Proc 2008: 217-221.
• 13 Amichai-Hamburger Y, McKenna KYA. The contact hypothesis reconsidered: Interacting via the internet. J Comput Mediat Commun 2006; 11: 825-843.
• 14 Berten H. Peer influences on risk behavior: a network study of social influence among adolescents in Flemish secondary schools. Annual meeting of the American Sociological Association Annual Meeting Sheraton Boston and the Boston Marriott Copley Place; Boston: MA 2008
• 15 Pirolli P. An elementary social information foraging model. Computer human interaction conference (CHI 2009); Boston: MA2009.
• 16 Vogel DL, Wade NG, Wester SR, Larson L, Hackler AH. Seeking help from a mental health professional: the influence of one’s social network. J Clin Psychol 2007; 63: 233-245.
• 17 Fowler JH, Christakis NA. Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study. BMJ 2008; 337: a2338.
• 18 Ginsberg J, Mohebbi MH, Patel R, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature 2009; 457: 1012-1014. http://dx.doi.org/10.1038/nature07634.
• 19 Eysenbach G. Infodemiology and Infoveillance: Framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the internet. J Med Internet Res 2009; 11: e11.
• 20 Eysenbach G. Medicine 2.0: Social networking, collaboration, participation, apomediation, and openness. J Med Internet Res 2008; 10: e22.
• 21 Lau AYS, Coiera EW. Do people experience cognitive biases while searching for information?. J Am Med Inform Assoc 2007; 14: 599-608.
• 22 Lau AYS, Coiera EW. Can cognitive biases during consumer health information searches be reduced to improve decision making?. J Am Med Inform Assoc 2009; 16: 54-65.
• 23 Lau AYS, Coiera EW. A Bayesian model that predicts the impact of web searching on decision making. J Am Soc Inf Sci Technol 2006; 57: 873-880.
• 24 Lau AYS, Coiera EW. Impact of web searching and social feedback on consumer decision making: A prospective online experiment. J Med Internet Res 2008; 10: e2.
• 25 Coiera E, Walther M, Nguyen K, Lovell N. Architecture for knowledge-based and federated search of online clinical evidence. J Med Internet Res 2005; 7: e52.
• 26 PubMed. [2009 July 18]; Available from: http://www.pubmed.gov.
• 27 Eagly AH, Chaiken S. The psychology of attitudes. Orlando, FL, US: Harcourt Brace Jovanovich College Publishers; 1993
• 28 Littlejohn SW, Foss KA. Theories of human communication. 9th ed: Wadsworth Publishing; 2008
• 29 Encyclopædia Britannica.. Britannica attacks. Nature 2006; 440 7084 582-30.
• 00 Giles J. Internet encyclopaedias go head to head. Nature 2005; 438: 900-901.
• 31 Chesney T. An empirical examination of Wikipedia’s credibility. First Monday 2006: 11. URL: http://first//monday.org/issues/issue11_11/chesney/index.html.
• 32 Moscovici S. Toward a theory of conversion behavior. In: Berkowitz L. editor. Advances in experimental social psychology. New York: Academic Press; 1980. p. 209-239.
• 33 Moscovici S. Social influence and conformity. In: Lindzey G, Aronson E. editors. The handbook of social psychology. New York: Random House; 1985. p. 347-412.
• 34 Mackie DM. Systematic and nonsystematic processing of majority and minority persuasive communications. J Pers Soc Psychol 1987; 53: 41-52.
• 35 Ross L, Green D, House P. The false consensus effect“: An egocentric bias in social perception and attribution processes. J Exp Soc Psychol 1977; 13: 279-301.
• 36 Baker SM, Petty RE. Majority and minority influence: source-position imbalance as a determinant of message scrutiny. J Pers Soc Psychol 1994; 67: 5-19.
• 37 Knobloch S, Sharma N, Hansen D, Alter SM. Impact of popularity indications on readers’ selective exposure to online news. Journal of Broadcasting Electronic Media 2009; 49 (Suppl. 03) 296-313.
• 38 Westbrook JI, Coiera EW, Gosling AS. Do online information retrieval systems help experienced clinicians answer clinical questions?. J Am Med Inform Assoc 2005; 12: 315-321.
• 39 Westbrook JI, Gosling AS, Coiera EW. The impact of an online evidence system on confidence in decision making in a controlled setting. Med Decis Making 2005; 25: 178-185.
• 40 Coiera E, Magrabi F, Westbrook JI, Kidd MR, Day RO. Protocol for the quick clinical study: a randomised controlled trial to assess the impact of an online evidence retrieval system on decision-making in general practice. BMC Med Inform Decis Mak 2006; 6: 33.
• 41 Reips UD. Standards for Internet-based experimenting. Exp Psychol 2002; 49: 243-256.
• 42 Coiera E. Information economics and the internet. J Am Med Inform Assoc 2000; 7: 215-221.
• 43 Gruber T. Collective knowledge systems: Where the social web meets the semantic web. Web Semant 2007; 6: 4-13.