Several industries require operations and services to be provided at all hours of the day. Many organizations have implemented a shift work system for scheduling purposes to meet the demand of their industry. While some research has been conducted on the effects of shift work on employees, overall, there is still much to be learned, especially in the hotel industry. Most hotel front desks in the United States operate 24 hours a day and therefore, have to staff employees for the morning, afternoon, and overnight shifts. Previous literature states that each shift starts and ends at different times of the day as well as the tasks for each shift may be different. These differences may cause a hotel front desk agent to perceive their role differently based on the shift they work. The primary objective of this study is to investigate if there are moderating effects caused by working different shifts at a hotel front desk on three prevalent relationships often examined utilizing hotel frontline employee samples. Social support, employee engagement, job satisfaction, intention to quit, and quality service delivery willingness will all be examined in this research. A survey instrument was developed based on existing scales and distributed to hotels in the southeastern United States via paper and electronic methods. A total of 554 surveys were returned. Moderated multiple regression was conducted utilizing the Process tool in SPSS. Results indicated that the employees who worked the swing shift (both morning and afternoon shifts) were significantly different than front desk agents who worked the overnight shift. Most of the relationships tested were much weaker for the swing shift employees almost to the point that the relationship became non-existent. Implications, limitations, and future research will also be discussed.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-6260 |
| Date | 01 January 2016 |
| Creators | Ingram, William |
| 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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