Filtering Reviews by Random Individual Error

It’s all about the text: An experimental investigation of inconsistent reviews on restaurant booking platforms

“…Similar to prior studies (e.g., Geierhos et al, 2015;Mudambi et al, 2014), a sentiment analysis helped classify the qualitative review components regarding their polarity. In particular, we used restaurant reviews of the publicly available Yelp data set (Yelp, 2020).…”

“…While 5% of the reviews with positive quantitative components were inconsistent, 12% of negative ones were inconsistent. Geierhos et al (2015) developed a method to identify inconsistencies for reviews with multiple quantitative criteria. Focusing on the two German physician rating platforms, jameda and DocInsider, they found varying inconsistencies within the categories, from 3% for "time" (time taken for the treatment) to 12% for "responsiveness" (accessibility and waiting time) (Geierhos et al, 2015).…”

“…Geierhos et al (2015) developed a method to identify inconsistencies for reviews with multiple quantitative criteria. Focusing on the two German physician rating platforms, jameda and DocInsider, they found varying inconsistencies within the categories, from 3% for "time" (time taken for the treatment) to 12% for "responsiveness" (accessibility and waiting time) (Geierhos et al, 2015). Shan et al (2018) focused on the occurrence of individual inconsistencies within authentic and fake reviews.…”

“…For instance, 30% of Amazon's product reviews (Mudambi et al, 2014) were inconsistent. Also, multiple seller reviews on TripAdvisor (6%) (Fazzolari et al, 2017) and the German physician rating platforms jameda and DocInsider (more than 3%) (Geierhos et al, 2015) were inconsistent.…”

“…Inconsistent reviews send conflicting information and signal different levels of quality to consumers which could have several consequences, such as increased cognitive processing costs, suboptimal purchase decisions, and lower overall utility of the platform (Mudambi et al, 2014). As these reviews can be confusing for consumers (Geierhos et al, 2015), inconsistency in reviews could negatively affect the review helpfulness (Aghakhani et al, 2020). Hence, the conflicting information could affect the effort involved in the information processing and the evaluation of a subset of restaurants within the decision-making process.…”