1. Can machine learning approaches predict green purchase intention? -A study from Indian consumer perspective.
- Author
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Choudhury, Nanda, Mukherjee, Rohan, Yadav, Rambalak, Liu, Yang, and Wang, Wei
- Subjects
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MACHINE learning , *CONSUMERS , *CONSUMER behavior , *SUSTAINABLE consumption , *SOCIAL media , *SUPPORT vector machines , *GREEN technology - Abstract
This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system. • Machine learning techniques were employed to predict green purchase intention (GPI). • Green Self-identification & Environmental Consciousness were the crucial factors in influencing GPI. • Social and health consciousness had less of an impact on influencing GPI. • Perceived personal inconvenience was reported to have no significant impact on GPI. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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