Applied Data Science for Sustainability Marketing: Evidence from Structural Equation Modeling of Organic Product Consumers

Abstract

The global demand for organic products reflects increasing awareness of sustainability in consumer behavior, especially in emerging markets such as Indonesia. Despite this growing trend, limited studies have applied data science approaches to model behavioral relationships within sustainability marketing. This study aims to examine how Sustainable Marketing (SM) influences Prosumption Motivation (PM) and Consciousness for Sustainable Consumption (CSC) among Indonesian organic product consumers. Using a quantitative design, data were collected through purposive sampling, yielding 400 valid responses from participants across Java, Sumatra, Kalimantan, Sulawesi, and Bali. Structural Equation Modeling (SEM) with AMOS was employed as a data science tool to estimate latent constructs and test predictive relationships. The results show that SM has a significant positive effect on PM (β = 0.923, CR = 19.347, p < 0.001) and CSC (β = 0.991, CR = 21.764, p < 0.001), while PM also significantly influences CSC (β = 0.742, CR = 19.306, p < 0.001) and SM indirectly enhances CSC through PM (β = 0.652, CR = 19.306, p < 0.001). These findings confirm all hypotheses and reveal a reciprocal relationship between motivation and consciousness, emphasizing a behavioral feedback loop that strengthens sustainable consumption. The study contributes to sustainability marketing by integrating SM, PM, and CSC into a unified predictive framework. Methodologically, it demonstrates how SEM serves as an applied data science technique capable of transforming behavioral data into actionable insights. The novelty lies in bridging behavioral science and data science to provide decision-support evidence for marketers and policymakers promoting prosumption and responsible consumption in emerging economies.