Innovation in technology has been growing rapidly in recent years. Many restaurants have been utilizing different types of self-service technologies (SSTs) to enhance their operations and customer satisfaction. Despite, the rapid spread of SSTs in the restaurant industry, very limited empirical research has been conducted to evaluate the influence of SSTs type on customer dining experience. Therefore, the purpose of this dissertation was to examine the SSTs values that influence restaurant customers' satisfaction and their decision to continue to reuse SSTs. More specifically, this study utilized the Theory of Consumption Values (TCV) to examine consumers' perception of the SST values across different types of restaurant proprietary SSTs (kiosk, tabletop, restaurant mobile app, and web-based SSTs). In order to examine the hypothesized relationships, a quantitative research approach was utilized with the survey research method. An online self-administered questionnaire was developed in Qualtrics for each type of SSTs. The questionnaires were distributed utilizing Amazon mechanical Turk (MTurk). Data was collected in May 2019 from restaurant customers who previously used/experienced one of four SSTs. A total of 619 questionnaires were usable and retained for the data analysis procedures. PLS-SEM and PLS-MGA were utilized to evaluate the conceptual model. The results revealed that emotional values were the most significant SST values that influence customer satisfaction with the restaurant SST experience and continuance intention. SSTs customization features were positively related to customer satisfaction across all the SSTs included in this study. The theoretical and practical implications of the results were discussed as well as the limitations of the study and future research directions.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-7596 |
| Date | 01 January 2019 |
| Creators | Zaitouni, Motaz |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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