Information Effects for Restaurant Reviews
“The effect of… prior online reviews on consumers’ restaurant evaluation”. Li, et al. 2019.
The paper above considers the effect of prior online reviews and restaurant ratings on influencing consumers to try out restaurants and write their own reviews, which links to the topics of information-based benefits and cascades discussed in class. One phrase that is defined is the “electronic word of mouth” which details the spread of information- be it truthful or not- through online sources and media. This word of mouth plays a key role in how people write reviews for products or services online, where reviews can be largely influenced by prior reviews and ratings rather than being entirely truthful. Many people utilize reviews as a key source of information before actually purchasing something and may form biased expectations as a result. The analysis in the paper formulated two hypotheses: the prior average rating and previous number of reviews socially influences subsequent reviews for restaurants.
The hypotheses were tested by collecting data from online restaurant review platforms and compiling statistics for the average ratings and number of reviews while controlling for different types of restaurants. One parameter that I found interesting was the “temporal distance”- time between restaurant experience and writing of the review. The results showed that a larger temporal distance correlated with greater influence from previous reviews, suggesting that more bias may be affecting the ratings due to memory issues with recalling one’s experience. Overall, the analysis confirmed the researcher’s hypotheses about the influence of prior postings; the next time you go to read Yelp or TripAdvisor, make sure to read the reviews with a grain of salt. Furthermore, reviews are pervasive throughout online culture, not just for restaurants, and I think it is an interesting idea to question the validity of reviews on sites like Amazon, though measures have been taken in recent years to try to make reviews more accurate (i.e. “verified buyer”).