Through application of multi-level structural equation modeling as the data analysis technique, the purpose of this paper is to analyze the group-level impacts on a couple’s food choices during travel at a coastal destination.
Researchers obtained 380 individual questionnaires from 190 mixed gender couples (who eat oysters) in Charleston and Beaufort County, South Carolina, USA. Data were collected from both members of the couple during their vacation. Due to missing data and normality issues 5 couples and 30 individuals were eliminated. The remaining data were analyzed with SPSS 21 and EQS 6.2 with advanced confirmatory factor analysis and multi-level (ML) regression techniques.
The study results indicated that while women have a more negative attitude than men toward oysters, their intention to eat oysters during vacation is not different from their partner. By detecting the interdependency of responses of individuals within a couple, this study revealed that a ML approach is a more powerful way to understand the decision-making process of couples. Additionally the difference in the results of single- and ML models showed that the latter approach lowers the chance of Type 2 error and provides more accurate results.
In tourism decision-making literature, the focus has been mostly on the individual despite the collectivistic nature of tourism activity. The current study is the first to analyze a couple’s decision-making process at the group level. Furthermore by collecting data from both members of the group during their vacation, this study has distinguished itself from previous studies.
