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Machine Learning Methods Analysis of Preceding Factors Affecting Behavioral Intentions to Purchase Reduced Plastic Products
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Researchers applied machine learning to analyze factors preceding behavioral intentions related to environmental sustainability, using survey data and ML models to identify the most predictive variables. The ML approach outperformed conventional regression in capturing non-linear relationships between attitudes, norms, and behavioral intent toward pro-environmental actions.
The COVID-19 pandemic has led to an increase in the use of personal protective equipment and single-use plastics, which has exacerbated plastic littering on land and in marine environments. Consumer behaviors with regards to eco-friendly products, their acceptance, and intentions to purchase need to be explored to help businesses achieve their sustainability goals. This paper establishes the Sustainability Theory of Planned Behavior (STPB), an integration of the TPB and sustainability domains, in order to analyze the said objectives. The study employed a machine learning ensemble method and used MATLAB to analyze the data. The results showed that support and attitude from perceived authorities were the main variables influencing customers’ intentions for purchasing reduced plastic products. Customers with a high level of environmental awareness were more likely to embrace reduced plastic items as a way to lessen their ecological footprint and support environmental conservation, making perceived environmental concern another important factor. This shows that authorities play a big role in the community in influencing people to choose reduced plastic products, making it the duty of governments and companies to promote environmental awareness. This study emphasizes the significance of the latent variables considered when developing marketing plans and activities meant to promote products with less plastic.
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