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Analysis of the healthy lifestyle consumer

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Abstract

Purpose – The purpose of this study is to gain a better understanding of the healthy lifestyle consumer by examining demographic, personal value and psychographic antecedents. Design/methodology/approach – A cluster analysis was used to divide consumers into healthy and unhealthy lifestyle segments based on their diet and exercise behavior. A logistic regression analysis was then run on these segments to test the impact of 17 hypothesized antecedents. Findings – Results indicate that people who maintain a healthy lifestyle tend to be female, older, more educated, place less importance on the value of “excitement”, have a greater tendency to plan ahead and tend to experience less role overload. Research limitations/implications – One limitation is that the response rate of the mail survey used to collect data was only 28.8 percent. Another limitation was that the specific types of diet and exercise behavior used to classify respondents into clusters did not encompass the full range of diet and exercise options available to all consumers. Practical implications – The financial impact of the healthy lifestyle consumer on a number of industries is documented. A demographic profile of the healthy lifestyle consumer was obtained which should assist companies seeking to target this segment. The significance of two time-related psychographic predictors suggests that companies looking to expand the market for healthy products may want to focus on ways of making their products/services more convenient to time-pressured consumers. Originality/value – This paper represents one of the first attempts in the marketing literature to study the healthy lifestyle segment.
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Dietary patterns are associated with various impacts, both social and environmental, and constitute sources of greenhouse gas (GHG) emissions. Hence, reinforcing sustainable diets that are based on environmentally friendly foods, changing food consumption habits, and increasing demand for organic food can reduce the food-related carbon footprint, mitigate the negative impacts of climate change, improve the quality of human life, and promote changes in the retail, distribution, and marketing functions of business. In the framework of this study, scientific publications were selected which were evaluated by the use of a descriptive analysis through a systematic literature review. The reviewed articles were examined to answer the following two questions: (1) Which are the factors affecting consumer attitudes and habits toward healthy and environmentally friendly products? (2) Are consumers willing to change their food choices in a more environmentally friendly direction? In addition, this chapter tries to demonstrate the existing policy recommendations that reinforce public awareness and encourage environmentally friendly food consumption patterns. Findings suggest that the established attitudes toward climate change, preferences for healthier food products, and the social awareness toward environmentally friendly products seem to be the key factors to change purchase and consumption behavior.
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A short questionnaire was completed by 276 South Australian consumers, which examined postulated relationships between personal values, food lifestyle, demographics and their usual consumption of 24 vegetables. Principal components analyses showed that consumers' vegetable consumption could be divided into several categories, most notably salad and boiled vegetables. In multiple regression analyses different sets of values and lifestyle factors predicted intakes of overall vegetable (Rsq=27 per cent), salad (16 per cent) and boiled (27 per cent) vegetables. Path analysis revealed a complex set of pathways leading from values and personal demographics through motives, perceived food attributes and cooking skills to consumption. These partly confirmed the food lifestyle model proposed by Grunert et al. The findings show that vegetable consumption has a number of contextual and cognitive antecedents but strongly suggest that other likely predictive variables require investigation.