Understanding the determinants of reviewer credibility: an interpretive structural modeling and artificial neural network approach

With the digitization of the tourism and hospitality sector, all hotel and travel bookings can be done online. Since the quality of experience is highly valued, feedback given by the customers hold great importance. This feedback in the form of electronic word of mouth can also serve as a deciding factor for potential customers. For this purpose, it is very crucial to have trust in the reviewer. Unlike the actual encounter, where trust is developed with the tone, body language, and facial expressions of the person, users must rely on the reviewer's profile in the online scenario. Therefore, this study aims to understand the factors that impact reviewer credibility. Parameters for the same are identified through literature review and expert opinions which are then converted into a hierarchical model using Interpretive Structural Modelling (ISM) technique to analyse the relationship among them. Further understanding of the interrelationships among the reviewer characteristics is attained using MICMAC analysis. Artificial neural network (ANN) has been used to investigate the efficiency of the proposed approach. The findings reveal a set of 10 variables corresponding to the reviewer credibility which have been shortlisted for the further analysis. The hierarchical graph created through ISM indicates that reviewer helpfulness is the most influenced variable whereas reviewer identity disclosure is the most influential variable. All other variables are at the intermediary levels. ANN shows that the proposed approach has a good level of accuracy. This paper holds the potential to make significant contributions to this research area from both theoretical and practical perspectives.