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Uncovering Sustainability Insights from Amazon’s Eco-Friendly Product Reviews for Design Optimization

Author

Listed:
  • Muhammad Rifqi Maarif

    (Department of Industrial Engineering, Tidar University, Magelang 56116, Indonesia)

  • Muhammad Syafrudin

    (Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea)

  • Norma Latif Fitriyani

    (Department of Data Science, Sejong University, Seoul 05006, Republic of Korea)

Abstract

This research investigates consumer reviews of eco-friendly products on Amazon to uncover valuable sustainability insights that can inform design optimization. Using natural language processing (NLP) techniques, including sentiment analysis, key terms extraction, and topic modeling, this research reveals diverse perspectives related to sustainability aspects in eco-friendly products. Innovatively, we integrate the NLP approach with correspondence analysis (CA) to understand consumer sentiments and preferences related to sustainability aspects. Leveraging CA, we visualize the interplay between eco-friendly product features and consumer sentiments, revealing underlying relationships and patterns. The CA biplot showcases the alignment of specific sustainability attributes with consumer satisfaction, highlighting which sustainability aspects hold greater influence over overall product ratings. As sustainability becomes an increasingly crucial aspect of consumer choices, our paper emphasizes the significance of a multidimensional approach that embraces both qualitative and quantitative insights. By blending CA with consumer reviews, we equip designers and stakeholders with an innovative and comprehensive toolkit to enhance sustainable design practices, paving the way for more informed and effective product development strategies in the realm of eco-friendliness.

Suggested Citation

  • Muhammad Rifqi Maarif & Muhammad Syafrudin & Norma Latif Fitriyani, 2023. "Uncovering Sustainability Insights from Amazon’s Eco-Friendly Product Reviews for Design Optimization," Sustainability, MDPI, vol. 16(1), pages 1-23, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:172-:d:1306311
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    References listed on IDEAS

    as
    1. Zheng, Lili, 2021. "The classification of online consumer reviews: A systematic literature review and integrative framework," Journal of Business Research, Elsevier, vol. 135(C), pages 226-251.
    2. Khuong Ngoc Mai & Do Hanh Nhan & Phuong Thi Minh Nguyen, 2023. "Empirical Study of Green Practices Fostering Customers’ Willingness to Consume via Customer Behaviors: The Case of Green Restaurants in Ho Chi Minh City of Vietnam," Sustainability, MDPI, vol. 15(5), pages 1-27, February.
    3. Astrid Sailer & Harald Wilfing & Eva Straus, 2022. "Greenwashing and Bluewashing in Black Friday-Related Sustainable Fashion Marketing on Instagram," Sustainability, MDPI, vol. 14(3), pages 1-22, January.
    4. Michael Greenacre, 2003. "Singular value decomposition of matched matrices," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1101-1113.
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