Who's Driving this Conversation? Systematic Biases in the Content of Online Consumer Discussions

When consumers post questions online, who influences the content of the discussion more: the consumer posting the question or those who respond to the post? Analyses of data from real online discussion forums and four experiments show that early responses to a post tend to drive the content of the discussion as much as or more than the content of the initial query. Although advice seekers posting to online discussion forums often explicitly tell respondents which attributes are most important to them, the authors demonstrate that one common online posting goal, affiliation, makes respondents more likely to repeat attributes mentioned by previous respondents, even if those attributes are less important to the advice seeker or support a suboptimal choice given the advice seeker's decision criteria. Firms “listening in” on social media should account for this systematic bias when making decisions on the basis of the discussion content.

[1]  Noah J. Goldstein,et al.  A Room with a Viewpoint: Using Social Norms to Motivate Environmental Conservation in Hotels , 2008 .

[2]  Chris Arney,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Easley, D. and Kleinberg, J.; 2010) [Book Review] , 2013, IEEE Technology and Society Magazine.

[3]  R. Nisbett,et al.  The dilution effect: Nondiagnostic information weakens the implications of diagnostic information , 1981, Cognitive Psychology.

[4]  S. O'Donohoe,et al.  Groundswell: Winning in a World Transformed by Social Technologies , 2008 .

[5]  Ann E. Schlosser.  Posting versus Lurking: Communicating in a Multiple Audience Context , 2005 .

[6]  Louise F. Pendry,et al.  Individual and social benefits of online discussion forums , 2015, Comput. Hum. Behav..

[7]  A. Banerjee,et al.  A Simple Model of Herd Behavior , 1992 .

[8]  Jocelyn M. DeGroot,et al.  Self-Governance Through Group Discussion in Wikipedia , 2011 .

[9]  A. Hollingshead,et al.  From cooperative to motivated information sharing in groups: moving beyond the hidden profile paradigm , 2004 .

[10]  David A. Schweidel,et al.  Online Product Opinions: Incidence, Evaluation, and Evolution , 2012, Mark. Sci..

[11]  Grant Packard,et al.  Compensatory Knowledge Signaling in Consumer Word-of-Mouth , 2013 .

[12]  S. Schulz-Hardt,et al.  Knowing others' preferences degrades the quality of group decisions. , 2010, Journal of personality and social psychology.

[13]  David Godes,et al.  Sequential and Temporal Dynamics of Online Opinion , 2012, Mark. Sci..

[14]  J. R. Larson,et al.  Discussion of shared and unshared information in decision-making groups , 1994 .

[15]  Norbert Schwarz,et al.  Judgment in a Social Context: Biases, Shortcomings, and the Logic of Conversation , 1994 .

[16]  Martin Wetzels,et al.  More than Words: The Influence of Affective Content and Linguistic Style Matches in Online Reviews on Conversion Rates , 2013 .

[17]  Noah J. Goldstein,et al.  Social influence: compliance and conformity. , 2004, Annual review of psychology.

[18]  S. Bonaccio,et al.  Advice taking and decision-making: An integrative literature review, and implications for the organizational sciences , 2006 .

[19]  Dwayne D. Gremler,et al.  Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? , 2004 .

[20]  Chrysanthos Dellarocas,et al.  A Statistical Measure of a Population’s Propensity to Engage in Post-Purchase Online Word-of-Mouth , 2006 .

[21]  D. Ariely,et al.  Sequential Choice in Group Settings: Taking the Road Less Traveled and Less Enjoyed , 2000 .

[22]  Nicholas H. Lurie,et al.  Temporal Contiguity and Negativity Bias in the Impact of Online Word of Mouth , 2013 .

[23]  Mark F. Zimbelman,et al.  A cognitive footprint in archival data: Generalizing the dilution effect from laboratory to field settings , 2003 .

[24]  M. Deutsch,et al.  A study of normative and informational social influences upon individual judgement. , 1955, Journal of abnormal psychology.

[25]  P. Nail.  Toward an integration of some models and theories of social response , 1986 .

[26]  G. Stasser,et al.  Pooling of Unshared Information in Group Decision Making: Biased Information Sampling During Discussion , 1985 .

[27]  Ann E. Schlosser.  The effect of computer-mediated communication on conformity vs. nonconformity: An impression management perspective , 2009 .

[28]  Jonah Berger,et al.  Broadcasting and Narrowcasting: How Audience Size Affects What People Share , 2013 .

[29]  Donald R. Lehmann,et al.  Is Anyone Listening? Modeling the Impact of Word-of-Mouth at the Individual Level , 2009 .

[30]  R. Rust,et al.  Reliability Measures for Qualitative Data: Theory and Implications , 1994 .

[31]  David Godes,et al.  Using Online Conversations to Study Word-of-Mouth Communication , 2004 .

[32]  Ann E. Schlosser.  Can including pros and cons increase the helpfulness and persuasiveness of online reviews? The interactive effects of ratings and arguments ☆ , 2011 .

[33]  Muzafer Sherif,et al.  Status in Experimentally Produced Groups , 1955, American Journal of Sociology.

[34]  Siobhan Chapman.  Logic and Conversation , 2005 .