#446 | ResearchBox

ResearchBox #446 - 'Consensus'


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  Study 1 - AsPredicted 59835.pdf



  Study 1 - Qualtrics survey.(file id 148).pdf



  Study 1.(file id 149).qsf


  


  Study 1.(file id 104).csv


  Study 1.(file id 129).do



  Study 2 - AsPredicted 145985.pdf


 pdf - Qualtrics survey 1st Stage


  Study 2 - Qualtrics survey 1st Stage.pdf



  Study 2 - Qualtrics survey 2nd Stage.pdf



  Study 2 - qsf 1st Stage.qsf



  Study 2 - qsf 2nd Stage.qsf


 csv 1st Stage

  


  Study 2 - csv 1st Stage.csv


  


  Study 2 - csv 2nd Stage.csv


  Study 2 - R Code 1st Stage.r



  Study 2 - R Code 2nd Stage.r



  Study 3 - AsPredicted 102913.pdf



  Study 3 - Qualtrics survey.pdf



  Study 3.qsf


  


  Study 3.csv


  Study 3.r


 AsPredicted #107,511


  Study 4 - AsPredicted 107511.pdf



  Study 4 - Qualtrics survey.pdf



  Study 4.qsf


 csv

  


  Study 4.csv


  Study 4.r



  Study 5 - AsPredicted 37337.pdf



  Study 5 - Qualtrics survey.pdf



  Study 5.qsf


  


  Study 5.(file id 101).csv


  Study 5.(file id 99).do



  Study 6 - AsPredicted 44559.pdf



  Study 6 - Qualtrics survey.pdf



  Study 6.qsf


  


  Study 6.csv


  Study 6.do



  Study 7 - AsPredicted 154007.pdf



  Study 7 - Qualtrics survey.pdf


 qsf


  Study 7.qsf


 csv

  


  Study 7.csv


  Study 7.r


  Supplement Beers.r


  Supplement Jokes.r


  Supplement Modal Analysis.r


  Supplement Movies.r



  Supplement Movies - txt- read me file.txt


  Supplement Sensitivity Analysis.r


  Supplement Simulations.r



  Supplement Study S1 - AsPredicted #65379.pdf



  Supplement Study S2 - Qualtrics survey.pdf


 qsf


  Supplement Study S2.qsf


  


  Supplement Study S2.csv


  Supplement Study S2.do



  Supplement Study S2 - AsPredicted #147507.pdf



  Supplement Study S3 - Qualtrics survey.pdf


 qsf


  Supplement Study S3.qsf


  


  Supplement Study S3.csv


  Supplement Study S3.r


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Dear Reader,


Thanks for visiting our box. We do not provide the data for the jokes here because the creator of the data requests to be notified of its use. If you'd like this data please visit the website (https://goldberg.berkeley.edu/jester-data/) directly for download and inform the creator of your usage. Similarly, the movie data requires that users do not redistribute it, so we do not post it here but you can find it here: https://grouplens.org/datasets/movielens/25m/. Finally, for the beer data we also do not post it here because of similar concerns about redistribution and usage. You can find it here: https://www.kaggle.com/rdoume/beerreviews. 

This version: October 14, 2021
(may be edited at any time)

BOX INFORMATION

SUPPLEMENTARY FILES FOR
Overton G, Evangelidis I, Vosgerau J. (2024) 'People Believe If 90% Prefer A over B, A Must Be Much Better than B Are They Wrong?'. Journal of Consumer Research. .
doi: https://doi.org/10.1093/jcr/ucae055

CITING THIS RESEARCHBOX
Overton, G., Vosgerau, J., & Evangelidis, I. (2025). ResearchBox 446, 'Consensus', https://ResearchBox.org/446. Zenodo. https://doi.org/10.5281/zenodo.15032978


LICENSE FOR USE
All content posted to ResearchBox is under a CC By 4.0 License (all use is allowed as long as authorship of the content is attributed). When using content from ResearchBox please cite the original work, and provide a link to the URL for this box (https://researchbox.org/446).

BOX PUBLIC SINCE
August 29, 2024   

BOX CREATORS
Graham Overton (goverton@nus.edu.sg)
Joachim Vosgerau (joachim.vosgerau@unibocconi.it)
Ioannis Evangelidis (ioannis.evangelidis@esade.edu)

ABSTRACT
We show that consumers confuse consensus information in polls—such as 90% prefer product A over product B—with differences in liking—the extent to which poll respondents like A better than B. Consequently, they interpret a 90% consensus in favor of A as the average liking of A being considerably higher than the average liking of B. We demonstrate empirically and with simulations that—while this can be true—it is more probable that the average liking of A is only slightly higher than that of B. This regularity is robust to the sign and size of the correlation between ratings for A and B, and across most distributions for A and B’s liking. Consumers are not aware of this regularity, and believe that 90% consensus implies A being much better than B. Communicators (marketers, managers, public policy makers, etc.) can capitalize on these erroneous inferences and strategically display preference information as consensus or as liking ratings leading to dramatic shifts in choices. Consumers’ erroneous inferences can be corrected by educating them about the shape of the distribution of liking differences. We discuss theoretical and managerial implications for the understanding and usage of polls.