Internet of Things-Based Consumer Behavior Mapping for Identifying Drivers of Sustainable Consumption in Kamrup Metropolitan District, Assam - Barman, Sanskrita; Rroy, Aruna; Vasudevan, Asokan; Mohammad, Suleiman; Ozturk, Ilknur (International Review of Management and Marketing, 2026)
This is a research wherein the technology of IoT sensor network and machine learning analytics have been used to draw a map of consumer behaviour patterns and drivers of sustainable consumption of Kamrup Metropolitan District of Assam. Real time information gained from smart meters, retail applications combined with radio frequency identification (RFID) and mobile applications are used to capture buying behaviour, resource consumption and waste generation in 500 households. The classification variable of the random forest model is the variables which analyze the behavior such as income levels, awareness of the environment, preferences for products and social influences. Results show that price sensitivity (32%), eco-labeling visibility (28%) and peer influence (24%) are the significant driving force of sustainable choices. IoT framework is 89% accurate in prediction of the consumption pattern which can be used for targeted intervention. Findings suggest customized nudging approaches as well as adaptive pricing approaches can be useful for improving sustainable consumption adoption. This technology-driven approach can provide some actionable insights for policy makers and business for creating circular economy practices for fast urbanizing regions in northeast India.