Organic Food Consumption in South India: Factors Influencing Customer Satisfaction in Tiruchirappalli District

Abstract

Increasing awareness of health, nutrition, and environmental factors has spearheaded the rapid growth of the global organic food market. However, in the Indian context, customer satisfaction determinants with respect to organic food products are not well known, especially at the sub-regional level. This study explores the significant determinants of customer satisfaction for consumers of organic foods in the Tiruchirappalli District, Tamil Nadu, which is a glaring gap in empirical data of the district in the context of the Indian organic food business. A convenience sample of 300 consumers of organic food in the retail outlets and weekly markets of Tiruchirappalli was used for the quantitative, cross-sectional survey design. Questionnaires using five-point Likert scales were used to measure perceptions of quality related to the product, price perception, trust in organic certification, availability of the products, and health consciousness. Descriptive statistics, Pearson correlation analysis, and multiple linear regression were used to analyze the data. The regression model explained 68.1% of the variance in customer satisfaction (R2=0.681,F=125.26, p <.001). The strongest predictors were product quality (β = 0.470, p < .001), trust in certification (β = 0.285, p < .001), health consciousness (β = 0.182, p < .001), and price perception (β = 0.106, p = .005) in that order. The availability of products was not significant. The results highlight the key role of quality assurance and reliable certification frameworks in boosting customer satisfaction in Tier-2 organic food markets. These findings have significant implications for retailers and policymakers who wish to develop sustainable consumption in emerging economies. The cross-sectional design and convenience sampling of existing organic buyers limit causal inferences and generalizability. Future research should employ longitudinal designs and probability sampling in multiple Tier-2 cities.