Attributes Of Organic Food Products: What Matters to The US Consumers in Their Purchasing Decision?

Green research in marketing is a rich field with multiple financial and brand implications to the firms and their stakeholders including consumers. This three-essay dissertation examines two important research streams in green marketing, organic food product attribute importance, and food safety transgressions. The first essay applies bibliometric methods to analyze the literature on green research in marketing. Based on the keyword analysis and literature review the essay identifies several gaps in green marketing research and offers potential research opportunities. Guided by the first essay’s insights, the second essay focuses on the relative importance of the organic food product attributes that influence consumer purchase decisions. The AHP analysis provides the relative importance of the attributes and finds that health is a major motivator for the purchase of organic foods. Moreover, clustering revealed three consumer segments based on the order of preference for the organic food attributes. The third essay investigates the effect of food safety transgressions on consumer reactions. Using the perspective of construal level theory, the essay specifically examines the effect of psychological distance of the transgression on consumer purchase intention and negative reactions like word of mouth, complaining, and boycotting. The essay focuses on three contextual factors: the chronic mindset of the consumers, perceived efforts of the firm, and gender. A one-factor experimental design studying the detrimental effects of food safety transgression demonstrates that the presence of low perceived efforts of the firm further strengthens the negative reactions. However, there were no effects of the high perceived efforts on the consumer’s negative reactions. Our findings advocate high efforts of the firm and strong prior relationships with the consumers offering a more nuanced understanding of the consumer relationships with the firms

Leveraging Machine Learning for Wellbeing Research in Marketing: Enhancing Federal Nutrition Programs and Food Decision-Making

Machine learning offers innovative tools to enhance research on federal nutrition programs and food decision-making, moving beyond traditional methods. Algorithms extract key features to pinpoint potential program issues, allowing for more refined predictions about participation and behavior using large-scale data, unlike prior studies that typically rely on controlled lab settings. We propose a case study using machine learning to predict participation in a major nutrition education program, with implications for diet quality and food security. Validation of machine learning insights will involve qualitative research and surveys. This approach demonstrates the potential for connecting wellbeing research and marketing by offering deeper insights into participant behavior and program effectiveness, which can inform marketing strategies for promoting healthier food choices and improving public health outcomes

Session 7 : \u3cem\u3eUsing Machine Learning to Predict Attrition in a Federal Nutrition Education Program\u3c/em\u3e

Attrition poses a significant challenge to the effectiveness of federal nutrition education programs, hindering their ability to achieve widespread impact. This study employs machine learning techniques to develop a predictive model for identifying participants at high risk of dropping out of the Expanded Food and Nutrition Education Program (EFNEP). Analysis is conducted using standardized EFNEP program data (pre-program 24-hour dietary recalls, food and physical activity questionnaires, and demographic information) on over 1.25 million adult participants from 2013 to 2022. Three machine learning algorithms (logistic regression, XGBoost, and random forest) were evaluated, with the XGBoost model achieving the highest predictive accuracy. Key predictors of attrition included Cooperative Extension region, funding tier, land-grant university type (1860 vs. 1890), enrollment year, household income, age, race, residence, number of children, number of foods consumed in 24-hour dietary recall, and physical activity level. These findings provide valuable insight to EFNEP administrators, enabling them to proactively identify at-risk program participants and implement targeted interventions to improve retention and program impact