Quality Disclosures and Disappointment: Evidence from the Academy Nominations
References
- (2014) Reviews without a purchase: Low ratings, loyal customers, and deception. J. Marketing Res. 51(3):249–269.Crossref, Google Scholar
- (2024a) The informational role of online recommendations: Evidence from a field experiment. CESifo Working Paper No. 10129, CESifo, Munich, Germany.Google Scholar
- (2024b) The MovieLens beliefs data set: Collecting pre-choice data for online recommender systems. Di Noia T, Lops P, Joachims T, Verbert K, Castells P, Dong Z, London B, eds. RecSys ‘24: Proc. 18th ACM Conf. Recommender Systems (Association for Computing Machinery, New York), 1.Google Scholar
- (2019) Machine learning methods that economists should know about. Annual Rev. Econom. 11(1):685–725.Crossref, Google Scholar
- (2022) Expectation, disappointment, and exit: Evidence on reference point formation from an online marketplace. J. Eur. Econom. Assoc. 20(1):116–149.Crossref, Google Scholar
- (2013) Thirty years of prospect theory in economics: A review and assessment. J. Econom. Perspect. 27(1):173–196.Crossref, Google Scholar
- (2022) The death of awards shows: Do people no longer care? Boston Univ. News Service (April 28), https://bunewsservice.com/the-death-of-awards-shows-do-people-no-longer-care/.Google Scholar
- (1998) Learning collaborative information filters. Shavlik JW, ed. ICML’98: Proc. 15th Internat. Conf. Machine Learn. (Morgan Kaufmann Publishers Inc., San Francisco), 46–54.Google Scholar
- (2025) A model of social (mis)learning from consumer reviews. Marketing Sci. 44(6):1258–1277.Google Scholar
- (2024) The good, the bad and the picky: Consumer heterogeneity and the reversal of product ratings. Management Sci. 71(8):7200–7222.Link, Google Scholar
- (2015) Do pharmacists buy Bayer? Informed shoppers and the brand premium. Quart. J. Econom. 130(4):1669–1726.Crossref, Google Scholar
- (2023) When product markets become collective traps: The case of social media. Amer. Econom. Rev. 115(12):4105–4136.Google Scholar
- (2023) Reference dependence and attribution bias: Evidence from real-effort experiments. Amer. Econom. J. Microeconom. 15(2):271–308.Crossref, Google Scholar
- (2005) Third-party product review and firm marketing strategy. Marketing Sci. 24(2):218–240.Link, Google Scholar
- (2010) Social comparisons and contributions to online communities: A field experiment on MovieLens. Amer. Econom. Rev. 100(4):1358–1398.Crossref, Google Scholar
- (1997) Snobs, bandwagons, and the origin of social customs in consumer behavior. J. Econom. Behav. Organ. 32(3):333–347.Crossref, Google Scholar
- (2016) Navigating by the stars: Investigating the actual and perceived validity of online user ratings. J. Consumer Res. 42(6):817–833.Crossref, Google Scholar
- (2024) Deep learning for economists. NBER Working Paper No. 32768, National Bureau of Economic Research, Cambridge, MA.Google Scholar
- (2006) A statistical measure of a population’s propensity to engage in post-purchase online word-of-mouth. Statist. Sci. 21(2):277–285.Crossref, Google Scholar
- (2010) Quality disclosure and certification: Theory and practice. J. Econom. Literature 48(4):935–963.Crossref, Google Scholar
- (2011a) Collaborative filtering recommender systems. Foundations Trends Human–Comput. Interaction 4(2):81–173.Crossref, Google Scholar
- (2011b) Rethinking the recommender research ecosystem: Reproducibility, openness, and LensKit. RecSys’11: Proc. Fifth ACM Conf. Recommender Systems (Association for Computing Machinery, New York), 133–140.Google Scholar
- (2015) Market structure, reputation, and the value of quality certification. Amer. Econom. J. Microeconom. 7(4):83–108.Crossref, Google Scholar
- (2022) Consumer reviews and regulation: Evidence from NYC restaurants. NBER Working Paper No. 29715, National Bureau of Economic Research, Cambridge, MA.Google Scholar
- (2024) Consumer protection in an online world: An analysis of occupational licensing. Amer. Econom. J. Appl. Econom. 16(3):549–579.Crossref, Google Scholar
- (2023) How the Oscars lost cultural relevance. Movieweb (March 13), https://movieweb.com/the-oscars-changed-lost-viewership/.Google Scholar
- (2024) Debunking misinformation about consumer products: Effects on beliefs and purchase behavior. J. Marketing Res. 61(4):659–681.Crossref, Google Scholar
- (2021) Reciprocity and unveiling in two-sided reputation systems: Evidence from an experiment on Airbnb. Marketing Sci. 40(6):1013–1029.Link, Google Scholar
- (2012) Do expert reviews affect the demand for wine? Amer. Econom. J. Appl. Econom. 4(1):193–211.Crossref, Google Scholar
- (2022) Learning with misattribution of reference dependence. J. Econom. Theory 203:105473.Crossref, Google Scholar
- (2015) Expert opinion and product quality: Evidence from New York City restaurants. Econom. Inquiry 53(2):812–835.Crossref, Google Scholar
- (1992) Using collaborative filtering to weave an information tapestry. Comm. ACM 35(12):61–70.Crossref, Google Scholar
- (2021) Conflict of interest in third-party reviews: An experimental study. Management Sci. 67(12):7535–7559.Link, Google Scholar
- (2012) The heterogeneous geographic and socioeconomic incidence of cigarette taxes: Evidence from Nielsen Homescan data. Amer. Econom. J. Econom. Policy 4(4):169–198.Crossref, Google Scholar
- (2015) The MovieLens data sets: History and context. ACM Trans. Interactive Intelligent Systems 5(4):1–19.Crossref, Google Scholar
- (2022) The market for fake reviews. Marketing Sci. 41(5):896–921.Link, Google Scholar
- (2011) Expert opinion and the demand for experience goods: An experimental approach in the retail wine market. Rev. Econom. Statist. 93(4):1289–1296.Crossref, Google Scholar
- (2009) Why do online product reviews have a J-shaped distribution? Overcoming biases in online word-of-mouth communication. Comm. ACM 52(10):144–147.Crossref, Google Scholar
- (2017) On self-selection biases in online product reviews. MIS Quart. 41(2):449–475.Crossref, Google Scholar
- (2016) Reputation and regulations: Evidence from eBay. Management Sci. 62(12):3604–3616.Link, Google Scholar
- (2012) Between the mass and the class: Antecedents of the “bandwagon” luxury consumption behavior. J. Bus. Res. 65(10):1399–1407.Crossref, Google Scholar
- (2014) ‘Explaining variation in conspicuous luxury consumption: An individual differences’ perspective. J. Bus. Res. 67(10):2147–2154.Crossref, Google Scholar
- (2009) Matrix factorization techniques for recommender systems. Comput. 42(8):30–37.Crossref, Google Scholar
- (2006) A model of reference-dependent preferences. Quart. J. Econom. 121(4):1133–1165.Crossref, Google Scholar
- (2022) Rating consistency is consistently underrated: An exploratory analysis of movie-tag rating inconsistency. SAC ‘22: Proc. 37th ACM/SIGAPP Sympos. Appl. Comput. (Association for Computing Machinery, New York), 1355–1364.Google Scholar
- (2014) The paradox of publicity: How awards can negatively affect the evaluation of quality. Admin. Sci. Quart. 59(1):1–33.Crossref, Google Scholar
- (2023) A matter of taste: The negative welfare effect of expert judgments. CESifo Working Paper No. 11298, CESifo, Munich, Germany.Google Scholar
- (2015) Do I follow my friends or the crowd? Information cascades in online movie ratings. Management Sci. 61(9):2241–2258.Link, Google Scholar
- (1950) Bandwagon, snob, and Veblen effects in the theory of consumers’ demand. Quart. J. Econom. 64(2):183–207.Crossref, Google Scholar
- (2008) Self-selection and information role of online product reviews. Inform. Systems Res. 19(4):456–474.Link, Google Scholar
- (2025) Can lower(ed) expert opinions lead to better consumer ratings?: The case of Michelin stars. Management Sci., ePub ahead of print July 28, https://doi.org/10.1287/mnsc.2023.04302.Google Scholar
- (2016) Fake it till you make it: Reputation, competition, and Yelp review fraud. Management Sci. 62(12):3412–3427.Link, Google Scholar
- (2012) Online product opinions: Incidence, evaluation, and evolution. Marketing Sci. 31(3):372–386.Link, Google Scholar
- (1997) Consumer information search revisited: Theory and empirical analysis. J. Consumer Res. 23(4):263–277.Crossref, Google Scholar
- (2015) The limits of reputation in platform markets: An empirical analysis and field experiment. NBER Working Paper No. 20830, National Bureau of Economic Research, Cambridge, MA.Google Scholar
- (2007) Improving regularized singular value decomposition for collaborative filtering. Proc. KDD Cup Workshop (Association for Computing Machinery, New York), 39–42.Google Scholar
- (2018) You get what you give: Theory and evidence of reciprocity in the sharing economy. Quant. Marketing Econom. 16:371–407.Crossref, Google Scholar
- (2025) The impact of sustainability programs on consumer purchase behavior: Evidence from Amazon. Internat. J. Res. Marketing, ePub ahead of print May 8, https://doi.org/10.1016/j.ijresmar.2025.04.009.Crossref, Google Scholar
- (2024) Expectations, satisfaction and utility from experience goods. BSE Working Paper No. 944, Barcelona School of Economics, Barcelona, Spain.Google Scholar
- (2000) Analysis of recommendation algorithms for e-commerce. EC’00: Proc. 2nd ACM Conf. Electr. Commerce (Association for Computing Machinery, New York), 158–167Google Scholar
- (2020) The polarity of online reviews: Prevalence, drivers and implications. J. Marketing Res. 57(5):853–877.Crossref, Google Scholar
- (2020) Targeting prospective customers: Robustness of machine-learning methods to typical data challenges. Management Sci. 66(6):2495–2522.Link, Google Scholar
- (2019) What drives herding behavior in online ratings? The role of rater experience, product portfolio, and diverging opinions. J. Marketing 83(6):93–112.Crossref, Google Scholar
- (2016) Movie review analysis: Emotion analysis of IMDb movie reviews. ASONAM ‘16: Proc. 2016 IEEE/ACM Internat. Conf. Adv. Soc. Networks Anal. Mining (IEEE Press, Piscataway, NJ), 1170–1176.Google Scholar
- (2012) The tag genome: Encoding community knowledge to support novel interaction. ACM Trans. Interactive Intelligent Systems 2(3):1–44.Crossref, Google Scholar
- (2018) Socially nudged: A quasi-experimental study of friends’ social influence in online product ratings. Inform. Systems Res. 29(3):641–655.Link, Google Scholar

