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DOI: 10.4338/ACI-2011-01-RA-0006
How Online Crowds Influence the Way Individual Consumers Answer Health Questions
An Online Prospective StudyCorrespondence to:
Publication History
Received:
20 January 2011
Accepted:
16 April 2011
Publication Date:
16 December 2017 (online)
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.
Keywords
Consumer decision making - social feedback - online information searching - crowd influence - majority influence
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.
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References
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- 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.
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- 42 Coiera E. Information economics and the internet. J Am Med Inform Assoc 2000; 7: 215-221.
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Correspondence to:
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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.