The Relationship between Weather and Meal choices: A Case Study of Restaurants and Cafés on Korean University Campus
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The food service industry is a major driver of global sustainable food consumption. By understanding food consumption behavior, restaurant managers can forecast demands and reduce pre-consumer food waste. This study investigates the relationship between influencing factors and the number of customers at restaurants and cafés. These factors are weather-related factors, including rain and temperature, and school-related factors, including exams and the day of the week. Based on these four factors, 24 possible combinations were created. Three representtive days were chosen for each weekday combination. Besides, one representative day was chosen for each weekend combination. In total, 48 days were sampled throughout the year. Customer data were collected from six restaurants and cafes on a Korean university campus. Conjoint analysis was used to determine the relative importance of each variable to customer numbers. Following that, utility scores were standardized and mapped to determine the best condition when the number of customers was at its peak. In addition, each store’s sales were compared using Pearson’s Correlation Coefficient. The findings support that temperature and rain influences are correlated with the number of customers. Furthermore, we discovered that temperature was far more significant than rain in determining the number of customers. The paper discusses the implications of weather to forecast food and beverage demand and predict meal choices.
Abstract
The food service industry is a major driver of global sustainable food consumption. By understanding food consumption behavior, restaurant managers can forecast demands and reduce pre-consumer food waste. This study investigates the relationship between influencing factors and the number of customers at restaurants and cafés. These factors are weather-related factors, including rain and temperature, and school-related factors, including exams and the day of the week. Based on these four factors, 24 possible combinations were created. Three representtive days were chosen for each weekday combination. Besides, one representative day was chosen for each weekend combination. In total, 48 days were sampled throughout the year. Customer data were collected from six restaurants and cafes on a Korean university campus. Conjoint analysis was used to determine the relative importance of each variable to customer numbers. Following that, utility scores were standardized and mapped to determine the best condition when the number of customers was at its peak. In addition, each store’s sales were compared using Pearson’s Correlation Coefficient. The findings support that temperature and rain influences are correlated with the number of customers. Furthermore, we discovered that temperature was far more significant than rain in determining the number of customers. The paper discusses the implications of weather to forecast food and beverage demand and predict meal choices.
참고문헌 (22)
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