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Social influence bias in ratings: A field experiment in the hospitality sector

We investigate the empirical phenomenon of rating bubbles, that is, the presence of a disproportionate number of extremely positive ratings in user-generated content websites. We test whether customers are influenced by prior ratings when evaluating their stay at a hotel through a field experiment that exogenously manipulates information disclosure. Results show the presence of (asymmetric) social influence bias (SIB): access to information on prior ratings that are above the average positively influences the consumers’ rating of the hotel. In contrast, information on ratings that are below the average does not affect reviewers. Furthermore, customers who have never been to the hotel before the intervention are more susceptible to prior ratings than customers who have repeatedly been to the hotel before. Finally, customers who are not used to writing online reviews are more prone to SIB than customers who frequently write online reviews. Our findings suggest that online rating systems should be adjusted to mitigate this bias, especially as these platforms become more relevant and widespread in the hospitality sector.

Audio-Tokens: a toolbox for rating, sorting and comparing audio samples in the browser.

Here we describe a Javascript toolbox to perform online rating studies with auditory material. The main feature of the toolbox is that audio samples are associated with visual tokens on the screen that control audio playback and can be manipulated depending on the type of rating. This allows the collection of single- and multi-dimensional feature ratings, as well as categorical and similarity ratings. The toolbox (github.com/pwdonh/audio_tokens) can be used via a plugin for the widely-used jsPsych, as well as using plain Javascript for custom applications. We expect the toolbox to be useful in psychological research on speech and music perception, as well as for the curation and annotation of datasets in machine learning.

Dissatisfaction Factors That Influence Customers To Give Low Online Rating To Hospitals

The study attempts to identify factors of dissatisfaction that significantly influence customers to give low rating to the hospital on online platforms, based on the context of India. The study conducts a qualitative analysis of a sample of 669 reviews given to private for-profit hospitals on online platform. Through textual analysis of the reviews, five distinct factors of dissatisfaction were identified. Each factors were statistically tested to identify those that were significantly present in reviews that gave low rating to the hospital. Three out of five factors, inferior medical care, inappropriate behaviour of staff and profiteering attitude, were found to be significant. Within these three factors no significant difference was found in their strength of association with the low online rating.