A Framework for Enhancing Functional Food Development through Consumer Preference Modeling

Thesis Abstract

The growth of the functional food sector is increasingly driven by consumer demand for healthier dietary options, yet the development of such products often encounters challenges in aligning product attributes with consumer preferences, resulting in products that fail to achieve market acceptance. This study aims to develop a comprehensive framework that integrates consumer preference modeling to enhance functional food product development, thereby bridging the gap between product innovation and consumer expectations. The specific objectives include identifying key determinants of consumer preferences for functional foods, analyzing the influence of demographic and psychographic factors on preferences, and constructing a predictive preference model that can be employed by product developers to tailor functional foods to target markets. The research adopts a mixed-methods approach, beginning with a qualitative phase involving focus group discussions with 12 consumer groups in the urban region of a major metropolitan area, to explore perceptions, attitudes, and preferences related to functional foods. This is followed by a quantitative survey of 500 consumers recruited via stratified random sampling across different age, gender, income, and health status groups. Data collection instruments include structured questionnaires validated through Content Validity Index (CVI) and test-retest reliability measures, as well as semi-structured interview guides. Quantitative data are analyzed using Exploratory and Confirmatory Factor Analyses (EFA and CFA) to identify underlying preference factors, while multiple regression analysis and structural equation modeling (SEM) are employed to establish predictive relationships between consumer characteristics and preferences. The anticipated findings suggest that consumer preferences for functional foods are significantly influenced by health consciousness, sensory attributes, perceived efficacy, and socio-cultural factors, with demographic variables such as age and income moderating these relationships. The study expects to develop a preference prediction model based on multinomial logistic regression and SEM approaches that accurately forecasts consumer choices of functional food products under various market scenarios. These findings will illustrate how integrating consumer preference insights into product development can lead to more targeted, acceptable, and commercially viable functional foods. This research contributes to the theoretical understanding by extending the application of consumer behavior theories, notably the Theory of Planned Behavior (TPB) and the Innovation-Decision Process, within the context of functional food development. It also advances methodological approaches by proposing a hybrid modeling framework that combines qualitative insights with quantitative predictive techniques. Practically, the framework offers food technologists, marketers, and product developers a systematic tool to incorporate consumer preferences at early development stages, reducing market risks and increasing product success rates. In conclusion, the study emphasizes that consumer preference modeling is pivotal to formulating functional foods aligned with market demands. It recommends the adoption of the proposed framework in food innovation processes and advocates for continuous consumer engagement through both qualitative and quantitative methods during product development. Further research is suggested to validate the framework across diverse cultural and regional contexts, and to explore emerging digital tools such as conjoint analysis and machine learning algorithms for preference prediction, thereby ensuring adaptive and consumer-centric functional food innovation strategies.