مدل دو عاملی رضایت مصرف کننده
Abstract
1- Introduction
2- Literature review
3- Field study
4- Results
5- Discussion
Appendix. Verbal scripts used in the study to provide additional information
Appendix A. Supplementary data
References
Abstract
We applied theories of behavioral economics and conducted a field research on 881 tourists from China visiting Seoul through guided tour programs. We randomly assigned participants to study conditions based on theories of expectation, reciprocity, and peak-end rule. At the end of the tour, participants evaluated various aspects related to tour satisfaction and general impression of the city. A confirmatory factor analysis supported that these variables can be explained by two correlated factors, identified as the Current Satisfaction Factor (CSF) and the Future Behavior Factor (FBF). The multiple indicator multiple causes (MIMIC) model showed that CSF was impacted by expectation and tour season, and FBF by expectation, tour season, and first visit. Our results suggest that providing additional information before each activity can improve tourism satisfaction and non-manipulated variables such as tour season and first visit can be incorporated to further enhance tourism satisfaction.
Introduction
Research has shown that consumers try to retaliate for failed services, regardless of who is directly responsible for the service in question (Ariely, 2007; de Quervain, Fischbacher, Treyer, & Schellhammer, 2004). For example, an unsatisfied restaurant customer may attempt to penalize the wait staff by leaving a smaller tip, even if the wait staff is not responsible for the unsatisfactory food. At other times, a customer may try to punish a higher level of authority, such as a restaurant owner or an entire city. The same idea can be applied to tourism. When people travel using tour packages, they are under the impression that they are visiting Paris or London, not a package route of a travel company. Tourists can blame the whole city for an unsatisfactory experience. Consequently, it may be useful to implement policy-level controls on tourism management instead of relying on individual companies’ service control. In recent years, policymakers have begun to embrace behavioral economics to make interventions for human behavior and decisionmaking (Bhargava & Loewenstein, 2015). This approach was popularized as a “nudge” by a best-selling book with the same title (Thaler & Sunstein, 2008). Nudges can alter people's behavior in predictable ways without removing options or significantly changing economic incentives (Thaler & Sunstein, 2008). For example, human behavior can be modified by strategically placing fruit in the school lunch line.