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

2021 ◽  
pp. 135481662110346
Author(s):  
Simona Cicognani ◽  
Paolo Figini ◽  
Marco Magnani

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.

2021 ◽  
pp. 109634802110303
Author(s):  
Hengyun Li ◽  
Fang Meng ◽  
Simon Hudson

The research aims to examine how positive review disconfirmation (i.e., a positive deviance between a hotel consumer’s poststay evaluation and the average review rating by prior consumers) affects subsequent consumers’ willingness to post online reviews and their own review ratings. By employing an experimental research method, this study reveals that positive review disconfirmation increases hotel guests’ willingness to post online reviews, and increases their online review ratings through the mechanism of concern for others, demonstrating an act of altruism. In addition, comparatively the positive review disconfirmation effects are stronger when the variance of prior review ratings is smaller. This study enhances the online review social influence literature, and the consumer’s altruistic motivation of posting online reviews.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric Bogert ◽  
Aaron Schecter ◽  
Richard T. Watson

AbstractAlgorithms have begun to encroach on tasks traditionally reserved for human judgment and are increasingly capable of performing well in novel, difficult tasks. At the same time, social influence, through social media, online reviews, or personal networks, is one of the most potent forces affecting individual decision-making. In three preregistered online experiments, we found that people rely more on algorithmic advice relative to social influence as tasks become more difficult. All three experiments focused on an intellective task with a correct answer and found that subjects relied more on algorithmic advice as difficulty increased. This effect persisted even after controlling for the quality of the advice, the numeracy and accuracy of the subjects, and whether subjects were exposed to only one source of advice, or both sources. Subjects also tended to more strongly disregard inaccurate advice labeled as algorithmic compared to equally inaccurate advice labeled as coming from a crowd of peers.


2021 ◽  
pp. 106895
Author(s):  
Hong-Liang Sun ◽  
Kai-Ping Liang ◽  
Hao Liao ◽  
Duan-Bing Chen

2016 ◽  
Vol 2 (1) ◽  
pp. 99-118 ◽  
Author(s):  
Shu He ◽  
Gene Moo Lee ◽  
Sukjin Han ◽  
Andrew B. Whinston

2019 ◽  
Vol 10 (1) ◽  
pp. 2-14 ◽  
Author(s):  
Bruno Oliveira ◽  
Beatriz Casais

Purpose User-generated content and online reviews are highly relevant in purchase decision in the hospitality sector, including restaurants, but there is a lack of knowledge about the effect of sharing pictures in this context. This study aims to focus on the relevance of user-generated photos in online platforms for restaurants’ selection. Design/methodology/approach A research was conducted with a sample of 319 residents of Porto region, who had at least one meal in a restaurant over the 30 days before the answer of the survey and had searched online to select the restaurant. Findings The results show that while doing online research about restaurants, it is important for potential consumers to find pictures of food and physical evidences of restaurants generated by other users. Findings also show that consumers find user-generated photos especially at websites of reviews, although the importance of restaurant owned platforms, such as official social media pages and websites. Practical implications The research results appeal restaurant managers to understand the importance of user-generated photos in online platforms by promoting photo sharing in their restaurants with appropriate marketing activities for that purpose. Originality/value This paper expands the state-of-the-art about the importance of user-generated content, focusing on the importance of photos from restaurants shared by consumers in online platforms.


2016 ◽  
Vol 8 (2) ◽  
pp. 16-26 ◽  
Author(s):  
Zhihai Yang ◽  
Zhongmin Cai

Online rating data is ubiquitous on existing popular E-commerce websites such as Amazon, Yelp etc., which influences deeply the following customer choices about products used by E-businessman. Collaborative filtering recommender systems (CFRSs) play crucial role in rating systems. Since CFRSs are highly vulnerable to “shilling” attacks, it is common occurrence that attackers contaminate the rating systems with malicious rates to achieve their attack intentions. Despite detection methods based on such attacks have received much attention, the problem of detection accuracy remains largely unsolved. Moreover, few can scale up to handle large networks. This paper proposes a fast and effective detection method which combines two stages to find out abnormal users. Firstly, the manuscript employs a graph mining method to spot automatically suspicious nodes in a constructed graph with millions of nodes. And then, this manuscript continue to determine abnormal users by exploiting suspected target items based on the result of first stage. Experiments evaluate the effectiveness of the method.


Author(s):  
Mohammad Allahbakhsh ◽  
Aleksandar Ignjatovic ◽  
Boualem Benatallah ◽  
Seyed-Mehdi-Reza Beheshti ◽  
Elisa Bertino ◽  
...  

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