Abstract
Purpose The purpose of this paper is to examine the effect of the dining experience including food quality, service quality, convenience and ambiance on overall satisfaction and customers’ intention to revisit in quick service restaurants (QSRs). In addition, the mediating effects of overall satisfaction between dining experience dimensions and customer intention to revisit have been investigated in this study. Design/methodology/approach Data were collected online from 278 participants in the USA and analyzed using partial least square structure equation modeling (SmartPLS). Findings Food quality, service quality and convenience are strong predictors of both overall satisfaction and intention to revisit and recommend QSRs. However, ambiance did not directly influence the customer overall satisfaction in a QSR setting, Also, overall satisfaction did not mediate the relationship between the dining experience attributes and future intention to revisit and recommend QSRs. Research limitations/implications This study makes a significant contribution to the QSRs literature by examining the effects of dining experience on satisfaction and intention to revisit, as well as the meditation role of satisfaction. Moreover, this study has several practical implications for QSRs practitioners and foodservice marketers. Self-selection to take the online questionnaire is considered one of this study’s limitations. Practical implications Restaurant managers, especially in the QSR segment, could benefit from the outcome of this study by utilizing their limited resources on improving their customers’ satisfaction and restaurants profitability. Social implications By understanding which attributes of the dining experience value most during their visit to QSRs, this study aims to provide some insight on how to improve QSR customers overall satisfaction and future intention. Originality/value This study is unique as it applies attributes from fine dining and casual dining attributed to QSRs in the USA. In addition, this study is the first on QSRs to use SmartPLS as statistical tool for analyzing the collected data and simultaneously accounting the relationships between the constructs introduced in this study.
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