Work

A predictive modelling approach to decoding consumer intention for adopting energy-efficient technologies in food supply chains

Year: 2025

Type: article

Abstract: The transition towards energy-efficient practices in the food supply chain (FSC) is essential for addressing the dual imperatives of sustainability and cost-effectiveness. As consumers become increasingly aware of the environmental impact of their food choices, their willingness to support energy-efficient technologies (EET) has become a critical factor in shaping the future of sustainable FSC. This study empirically investigates consumer intention and desire to pay for food products characterized by a reduced energy footprint, utilizing machine learning (ML) algorithms to predict consumer preferences within the FSC. Association rule mining (ARM) was employed to uncover key patterns in consumer intentions, while multiple ML algorithms were compared to identify the most effective algorithm for predicting willingness to pay. The results reveal that the Random Forest algorithm achieved the highest accuracy at 82%, significantly outperforming other models. These findings underscore the potential of ML to refine marketing strategies and operational decisions, facilitating the broader adoption of EET within the FSC (EET-FSC). The study offers valuable implications for industry professionals seeking to enhance sustainability efforts through data-driven decision-making. The research contributes to optimizing FSC through improved decision-making, resource allocation, and sustainability initiatives. Future research directions include expanding the dataset scope, exploring advanced ML techniques, and examining the economic impacts of EET-FSC. • Examine consumer intention regarding blockchain technology in energy-efficient food supply chain. • Utilize a synthesis of association rule mining and predictive machine learning methodologies. • Disclose a neutral perspective of consumer intention on blockchain’s function in energy-efficient food supply chains. • Illustrate the dominance of Random Forest in forecasting willingness to incur additional costs. • Emphasize the necessity for focused communication to remedy consumer neutrality.

Source: Decision Analytics Journal

DOI: 10.1016/j.dajour.2025.100561

Authors: Brintha Rajendran, Manivannan Babu, Veeramani Anandhabalaji

Institution: Bharathidasan University

Language: English

Topic: Sustainable Supply Chain Management

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDGs: Responsible consumption and production, Industry, innovation and infrastructure, Zero hunger, Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $1,790