IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2023i1p172-d1306311.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/1/172/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/1/172/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shobhit Kakaria & Aline Simonetti & Enrique Bigne, 2024. "Interaction between extrinsic and intrinsic online review cues: perspectives from cue utilization theory," Electronic Commerce Research, Springer, vol. 24(4), pages 2469-2497, December.
    2. Hea Young Lim & Ki Han Kwon, 2023. "Sustainable Assessment of the Environmental Activities of Major Cosmetics and Personal Care Companies," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    3. Román, Sergio & Riquelme, Isabel P. & Iacobucci, Dawn, 2023. "Fake or credible? Antecedents and consequences of perceived credibility in exaggerated online reviews," Journal of Business Research, Elsevier, vol. 156(C).
    4. Magdalena Stefańska & Iwona Olejnik, 2024. "Retailers’ social impact perspective–discrepancies between theory and practice," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 51(3), pages 355-368, September.
    5. María José Aramburu & Rafael Berlanga & Indira Lanza-Cruz, 2024. "A Data Quality Multidimensional Model for Social Media Analysis," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(6), pages 667-689, December.
    6. Evangelia Ktisti & Leonidas Hatzithomas & Christina Boutsouki, 2022. "Green Advertising on Social Media: A Systematic Literature Review," Sustainability, MDPI, vol. 14(21), pages 1-25, November.
    7. Eugene Kim & Choong C. Lee & Jaeyoung An, 2024. "Examining how online store managers’ responses to negative reviews affect potential shoppers," Electronic Commerce Research, Springer, vol. 24(2), pages 863-900, June.
    8. Akhtar, Naeem & Siddiqi, Umar Iqbal & Gugnani, Ritika & Islam, Tahir & Attri, Rekha, 2024. "The potency of audiovisual attractiveness and influencer marketing: The road to customer behavioral engagement," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    9. Torres, Anna & Bijmolt, Tammo H.A., 2009. "Assessing brand image through communalities and asymmetries in brand-to-attribute and attribute-to-brand associations," European Journal of Operational Research, Elsevier, vol. 195(2), pages 628-640, June.
    10. Gower, John C., 2006. "An application of the modified Leverrier-Faddeev algorithm to the spectral decomposition of symmetric block-circulant matrices," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 89-106, January.
    11. Zárraga, A. & Goitisolo, B., 2009. "Simultaneous analysis and multiple factor analysis for contingency tables: Two methods for the joint study of contingency tables," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3171-3182, June.
    12. Sandra Rousseau & Machteld Joly & Eline Poelmans, 2024. "Search characteristics, online consumer ratings, and beer prices," Agribusiness, John Wiley & Sons, Ltd., vol. 40(4), pages 804-824, October.
    13. Ahmed Hassan Abdou & Thowayeb H. Hassan & Amany E. Salem, 2023. "Promoting Sustainable Food Practices in Food Service Industry: An Empirical Investigation on Saudi Arabian Restaurants," Sustainability, MDPI, vol. 15(16), pages 1-22, August.
    14. Ulrike Bechtold & Natalie Stauder & Martin Fieder & Harald Wilfing, 2022. "Stuck in the Present: A Human Lack of Ability to Visualise (Different) Needs in the Future May Hamper Timely Implementation of AAL and Supportive Technology," IJERPH, MDPI, vol. 19(11), pages 1-14, June.
    15. Zhai, Mengfan & Wang, Xinyue & Zhao, Xijie, 2024. "The importance of online customer reviews characteristics on remanufactured product sales: Evidence from the mobile phone market on Amazon.com," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    16. Zhikun Ding & Tao Huang & Qifan Yang & Lian Duan, 2024. "The Effect of Awe on Willingness to Pay for Construction Waste Recycled Products: A Functional Near-Infrared Spectroscopy Study," Sustainability, MDPI, vol. 16(24), pages 1-17, December.
    17. Michael Greenacre, 2021. "Compositional data analysis — linear algebra, visualization and interpretation," Economics Working Papers 1805, Department of Economics and Business, Universitat Pompeu Fabra.
    18. Zhang, Ruonan & Yu, Zhaoyang & Yao, Weiran, 2024. "Navigating the complexities of online opinion formation: An insight into consumer cognitive heuristics," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    19. Xin Zhao & Zhiyan Xu & Fei Ding & Zichang Li, 2024. "The Influencers’ Attributes and Customer Purchase Intention: The Mediating Role of Customer Attitude Toward Brand," SAGE Open, , vol. 14(2), pages 21582440241, April.
    20. Escudero, Stephanie B. & Jestratijevic, Iva & Short, Jeremy C. & Wolfe, Marcus T., 2023. "B Corp certification in the age of fast fashion: Using hierarchical clustering and correspondence factor analysis to highlight social entrepreneurial advancement in the fashion industry," Journal of Business Venturing Insights, Elsevier, vol. 20(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:172-:d:1306311. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.