Exploring factors influencing 50-plus generation’s repurchase intention of home meal replacement using elastic net regression and finite mixture modeling

There has been an increasing consumer demand for home meal replacement (HMR) products in South Korea over the last ten years. Consumers over age 50 have recently expressed their strong interest in healthy and high quality convenience foods. The main purpose of this paper is to use elastic net machine learning technique to elucidate the key factors in predicting repurchase intention of HMR products particularly for 50-plus generation. A finite mixture model is also applied to uncover consumer segments based on these major factors. A valid sample of 184 questionnaires was obtained. Our results show that the following factors are statistically significant: three selection attributes (quality, cost effectiveness, sanitation and cleanness), three food-related lifestyles (convenience-orientation, taste-orientation, fashion-orientation), a single psychological factor (trust). These results also reveal four segments. of HMR consumers aged over 50. This paper contributes to expanding our understanding of major factors associated with repurchase intention of HMR foods for 50-plus generation.