How Endogenous Crowd Formation Undermines the Wisdom of the Crowd in Online Ratings
Bibliographic Data
| ID | 7535845 |
|---|---|
| Authors | Gaël Le Mens (0000-0003-4800-0598, Universitat Pompeu Fabra, corresponding author), Balázs Kovács (0000-0001-6916-6357, Yale School of Management, Yale University), Judith Avrahami (0000-0001-5810-6279, The Federmann Center for the Study of Rationality, The Hebrew University of Jerusalem), Yaakov Kareev (0000-0001-9646-4720, The Federmann Center for the Study of Rationality, The Hebrew University of Jerusalem) |
| Year | 2018 |
| Volume | 29 |
| Issue | 9 |
| Pages | 1475-1490 |
| Publication date | 2018-07-25 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Psychological Science (JOURNAL) |
| Journal identifiers | ISSN: 0956-7976 • E-ISSN: 1467-9280 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/0956797618775080 |
| PMID | 30044721 |
| OpenAlex | W2884786845 |
| Language | EN |
| Citations received | 5 |
| References cited | 29 |
People frequently consult average ratings on online recommendation platforms before making consumption decisions. Research on the wisdom-of-the-crowd phenomenon suggests that average ratings provide unbiased quality estimates. Yet we argue that the process by which average ratings are updated creates a systematic bias. In analyses of more than 80 million online ratings, we found that items with high average ratings tend to attract more additional ratings than items with low average ratings. We call this asymmetry in how average ratings are updated endogenous crowd formation. Using computer simulations, we showed that it implies the emergence of a negative bias in average ratings. This bias affects items with few ratings particularly strongly, which leads to ranking mistakes. The average-rating rankings of items with few ratings are worse than their quality rankings. We found evidence for the predicted pattern of biases in an experiment and in analyses of large online-rating data sets
Crowdsourcing · Quality (philosophy · Ranking (information retrieval · Response bias · Complex Network Analysis Techniques · Computer Science · Digital Marketing and Social Media · Opinion Dynamics and Social Influence · Psychology · Social Psychology · Artificial Intelligence
Social Influence Bias
Self-Selection and Information Role of Online Product Reviews
How social influence can undermine the wisdom of crowd effect
Decisions from Experience and the Effect of Rare Events in Risky Choice
Matrix Factorization Techniques for Recommender Systems
Exploring the value of online product reviews in forecasting sales
Vox Populi
Promotional Reviews
Why Most People Disapprove of Me
Nudge
A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades
Navigating by the Stars
The wisdom of select crowds
Posting versus Lurking
| Unique citing works | 5 |
|---|---|
| Citations per year | 0,71 |
| Citation span | 2019 - 2025 (7) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |