Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database
By Michael L. Anderson, Jeremy R. Magruder. 2012
466 citations recorded by the source corpus.
Subfields: Marketing, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/5329b5c460217bfce519eaad52a0837811c83c10
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Marketing
- Georgios Zervas
- Lu Cheng
- P. Richard Hahn
- Trang P. Tran
- Jundong Li
- David C. Yen
- Michael Luca
- Judith A. Chevalier
In Sociology and Political Science
- Georgios Zervas
- Lu Cheng
- P. Richard Hahn
- Trang P. Tran
- Jundong Li
- David C. Yen
- Kuanchin Chen
- Michael Luca
Citations in the corpus
- A Survey of Learning Causality with Data: Problems and Methods (2018, methodology): “For example, Gelman and Imbens [46] pointed out that high-order polynomials can be misleading in RDD.”
- Promotional Reviews: An Empirical Investigation of Online Review Manipulation (2014, background): “Later work examined the role of consumer reviews on product purchases online; for example, Chevalier and Mayzlin (2006), Anderson and Magruder (2012), Berger et al. (2010), and Chintagunta et al. (2010).”
- Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud (2016, background): “While Yelp purposely makes the filtering algorithm difficult to reverse engineer, we are able to test for differences in the observed attributes of published and filtered reviews.”
- Mobile Phone Coverage and Producer Markets: Evidence from West Africa (2014, unknown): “Access to such information has improved significantly in developed countries, especially with the introduction of online databases (Autor 2001; Anderson and Magruder 2012).”
- A Survey of Learning Causality with Data (2018, methodology): “The study of the causal effect of Yelp star rating 𝑟 on the customer flow 𝑦 is a perfect example for such a case [3].”
- A study of factors that contribute to online review helpfulness (2015, background): “As a result, both retailers and manufacturers are paying an increased level of attention to online reviews as they can either be a threat or an opportunity for businesses (Anderson & Magruder, 2012; Chen & Xie, 2008; Chevalier & Mayzlin, 2006; Hu, Zhang, & Pavlou, 2009; Li & Hitt, 2010).”