Machine Learning Methods Analysis of Preceding Factors Affecting Behavioral Intentions to Purchase Reduced Plastic Products

David Jericho B. Villanueva, Ardvin Kester S. Ong () and Josephine D. German
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David Jericho B. Villanueva: School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
Ardvin Kester S. Ong: School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines
Josephine D. German: School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines

Sustainability, 2024, vol. 16, issue 7, 1-26

Abstract: 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.

Keywords: machine learning; neural network; plastic waste; random forest classifier; sustainable behavior (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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