Information Systems Research · 2020 · 76 citations · 37 references
BusinessInformation SystemsManagement Information SystemsConsumer ProtectionCybercrimeWeb AnalyticsDisinformationFraud DetectionFraud Risk ManagementMisinformation DetectionOnline FraudPlatform TrustE-commerce Fraud DetectionFake Review DetectionOnline Review MiningRandomized ExperimentsTechnology Platforms
Consumers rely on review platforms when deciding where to stay, where to eat, what movies to watch, or even which doctor to use. This is great for consumers, but it has makes online review platforms a target for fraud. Review platforms have responded by developing tools and algorithms to identify potentially fraudulent reviews. But the question remains: What should platforms do with fraudulent reviews after detecting them? Our research answers this question using randomized experiments and large-scale data analysis from Yelp’s review platform. Our results show that after detecting fraudulent reviews, platforms should keep them on their platforms, but should display them with a flag that identifies them as potentially fraudulent. Doing so will increase consumers' trust in the platform by demonstrating that the platform takes fraud serious and will also penalize dishonest businesses. Together, these results provide strong managerial and policy guidance to developing truthful, transparent, and accountable online ecosystems. Our research topic is particularly timely given the presence of misinformation on technology platforms, the incentives of actors to exploit anonymity to manipulate consumer beliefs, and the influence these actions can have on consumer trust.
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The spread of true and false news online
Soroush Vosoughi, Deb Roy, Sinan Aral · Science · 2018 · 8.4K citations
Trust and TAM in Online Shopping: An Integrated Model1
Elena Karahanna, Straub · MIS Quarterly · 2003 · 8.2K citations