Exploring the antecedents of e-loyalty and eWOM in the context of food delivery application services in Finland - Mattayev, Mark (2022)
The novelty introduced by food delivery applications to meals provided by restaurants has created a new customer experience due to its unique properties. Nevertheless, harnessing the full potential of such a service requires a deep understanding of the factors that lead to desired behavioral consequences. In this study, various quality dimensions (Food quality, service quality, and electronic service quality) and perceived value were explored as potential antecedents of the highest predictive capability towards forming electronic word-of-mouth and electronic loyalty intentions, through the mediating role of customer satisfaction. In order to explore this, the author conducted a literature review and formed a new theoretical model based on previous research findings. Once a new theoretical model was formed and presented, a quantitative, self-medicated survey was formulated and translated to suit the Finnish context. The accompanying restaurant chain named Rolls provided its digital distribution channels to gather the data from 238 valid respondents which was later analyzed in SPSS for validity and reliability and a structural model was plotted in order to examine the hypothesized relationships. Contrary to the expectations set in the study, food quality was shown to not have any significant relationship with any of the proposed constructs, while service quality proved to have the highest impact on eWOM, and eQuality had the highest impact on eLoyalty intention through the indirect mediation of customer satisfaction. Furthermore, the role of perceived value as an antecedent of customer satisfaction was confirmed, as well as the mediating role of customer satisfaction towards eWOM and eLoyalty intention. The theoretical and managerial contributions of this study allow for additional improvement of service development for practitioners in the field to enhance intended customer behaviors while academics can test the proposed new model in different con-texts and add additional dimensions that would increase the predictive capabilities in the context of food delivery applications.