Consumers’ perceptions about authenticity are subjectively driven and rely on social constructions making the concept hard to be defined. The current study is proposing an innovative big data approach by extracting insights from user-generated data in order to capture consumers’ perceptions and an in-depth analysis of the beliefs concerning authenticity. The current study expands also consumer culture theory to a digital setting showing the influence of consumers upon each other’s consumption perceptions. For these purposes, we propose a new, exploratory approach which relies on the analysis of 50 in-depth interviews and an innovative 3-step characterization of a big data repository concerning reviews for 51.710 restaurants extracted from TripAdvisor during an eight-year period. The applied characterization exploits both sentiment analysis and graph data models. Our methodological approach has high usability as it is automated, scalable and does not require a priori assumptions. Our findings propose a depiction of consumers’ authenticity perceptions via e-word of mouth. The big data analysis on both ratings and comments showed the impact of authenticity and the country of origin on consumer reviews. As such, consumers, after visiting the country of origin, were more critical and they were focusing more on authentic atmosphere and service, showing the evolution of their perceptions.
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