Purpose – The social TV phenomenon has raised the interest of some researchers in studying the production of online reviews. However, little is known about the characteristics of reviewers that, without having had indeed a real experience of consumption, still dare to assess the service. The purpose of this research is to understand these reviewers better, using an experiment conducted in Brazil. Design/methodology/approach – Through a cluster analysis with 2547 reviewers of 7 restaurants that participated in a reality show in Brazil, we were able to create 4 fours. Using Spearman Correlation and Kruskal-Wallis Test, differences among groups were analysed in the search of behavioural changes among different types of reviewers. Findings – We conclude that social TV influence fake online reviews of restaurants that were involved in a tv show. Furthermore, we were able to verify that some reviewers indeed assess the service without indeed having tried the service, which strongly bias the influence they are going to cause in potential consumers. Four types of reviewers were identified: the real expert, the amateur reviewer, the speculator and the pseudo expert. The 2 latter types are analyzed through the anthropologic lens of the popular Brazilian culture and the TV influence in that country. Research limitations/implications – we were able to understand how TV can influence the construction of fake online reviews for restaurants. Practical implications – It is important for the restaurant and hospitality industry in general, to be able to be attentive to the phenomenon of fake reviews that can totally biased the advantages of this assessment system that was created to produce trust among consumers, but that can act exactly the other way around. Originality/value – This study highlights the relevance of taking into account cultural background of the country where the restaurant is located, as well as emphasizing the relevance of conducting a previous analysis of the decision of embarking on a reality show that it has high chances to biasedly influence consumers’ decisions.
Creating and detecting fake reviews of online products
