EcoCycle as A Comprehensive UX Design Approach for AI-Driven Recycling and Sustainability Platforms in US Communities

References

[1] Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. (2018). Trends and trajectories for explainable, accountable and intelligible systems: An HCI research agenda. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-18). ACM.

[2] Blevis, E. (2007). Sustainable interaction design: Invention & disposal, renewal & reuse. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 503-512). ACM.

[3] Brynjarsdottir, H., Håkansson, M., Pierce, J., Baumer, E., DiSalvo, C., & Sengers, P. (2012). Sustainably unpersuaded: How persuasion narrows our vision of sustainability. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 947-956). ACM.

[4] Campbell, H. E., Oh, S., & Noh, Y. (2021). Determinants of recycling behavioral intention among urban residents: An application of the theory of planned behavior. Sustainability, 13(11), 6056.

[5] DiSalvo, C. (2022). Design as democratic inquiry: Putting experimental civics into practice. MIT Press.

[6] Esmaeilian, B., Wang, B., Lewis, K., & Duarte, F. (2020). The future of waste management in smart and sustainable cities: A review and concept paper. Waste Management, 103, 417-431.

[7] Forlano, L., & Mathew, A. (2014). From design fiction to design friction: Speculative and participatory design of values-embedded urban technology. Journal of Urban Technology, 21(4), 7-24.

[8] Geissdoerfer, M., Savaget, P., Bocken, N. M., & Hultink, E. J. (2017). The circular economy–A new sustainability paradigm? Journal of Cleaner Production, 143, 757-768.

[9] International Organization for Standardization (ISO). (2019). ISO 9241-210:2019 Ergonomics of human-system interaction—Part 210: Human-centered design for interactive systems. ISO.

[10] Kaza, S., Yao, L., Bhada-Tata, P., & Van Woerden, F. (2018). What a waste 2.0: A global snapshot of solid waste management to 2050. World Bank Publications.

[11] Knowles, B., Bates, O., & Hakansson, M. (2018). This changes sustainable HCI. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-12). ACM.

[12] Miafodzyeva, S., & Brandt, N. (2013). Recycling behavior among householders: Synthesizing determinants via a meta-analysis. Waste and Biomass Valorization, 4(2), 221-235.

[13] Nahiduzzaman, M., Hassan, M. S., Islam, M. R., Hossain, M. I., Khandakar, A., & Azad, A. (2023). Artificial intelligence in sustainable solid waste management: A review and a case study of smart bins. Sustainability, 15(3), 1952.

[14] Nash, N., Whitmarsh, L., Capstick, S., Thøgersen, J., Gouveia, V., de Carvalho Rodrigues Araújo, R., & Westlake, S. (2021). How personal norms encourage sustainable lifestyles: The role of social norms and group identity. Global Environmental Change, 71, 102448.

[15] Norman, D. A. (2013). The design of everyday things: Revised and expanded edition. Basic books.

[16] Owens, S., & Driffill, L. (2008). How to change attitudes and behaviors in the context of energy. Energy Policy, 36(12), 4412-4418.

[17] Pettersen, I. N., & Boks, C. (2008). The ethics in balancing control and freedom when engineering solutions for sustainable behavior. International Journal of Sustainable Engineering, 1(4), 287-297.

[18] Piras, G., Alhassan, I. M., & Masullo, M. (2023). Do smart waste management systems contribute to sustainable development? A critical review and research agenda. Journal of Cleaner Production, 414, 137635.

[19] Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137-141.

[20] Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68.

[21] Sengers, P., Boehner, K., David, S., & Kaye, J. J. (2005). Reflective design. In Proceedings of the 4th decennial conference on Critical computing: Between sense and sensibility (pp. 49-58). ACM.

[22] Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551.

[23] Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.

[24] Tiefenbeck, V., Goette, L., Degen, K., Tasic, V., Fleisch, E., Lalive, R., & Staake, T. (2019). Overcoming salience bias: How real-time feedback fosters resource conservation. Management Science, 65(1), 396-413.

[25] Tomitsch, M., Borthwick, M., Bendi, M., & Ahmadpour, N. (2022). Designing for the circular economy. Routledge.

[26] Wölker, A., & Powell, T. E. (2021). Algorithms in the newsroom? News readers’ perceived credibility and selection of automated journalism. Journalism, 22(1), 86-103.

[27] World Wide Web Consortium (W3C). (2018). Web Content Accessibility Guidelines (WCAG) 2.1. https://www.w3.org/TR/WCAG21/

[28] Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.

@article{1715196,
      author = {Emmanuel Adegbe},
      title = {EcoCycle as A Comprehensive UX Design Approach for AI-Driven Recycling and Sustainability Platforms in US Communities},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {7},
      pages = {818-824},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715196.pdf},
      abstract = {Recycling and sustainability models powered by AI are becoming popular among communities in the United States in response to growing challenges in waste management. Many of these systems have low adoption, limited user engagement, and a lack of trust among the masses, often due to poor user experience design. The proposed research is EcoCycle, a conceptual framework for UX design utilizing artificial intelligence, behavioral psychology, and community-based sustainability to develop useful, inclusive recycling systems. With reference to human-centered design, explainable AI, and environmental behavior theories, the conceptualization of sustainability platforms presented by EcoCycle views them as cyclical systems in which user engagement, artificial intelligence, and environmental feedback sustain one another. The framework focuses on transparency, empowerment, inclusiveness, and ethical AI implementation, and makes UX design a core tool for cultivating trust and long-lasting engagement. The study uses a conceptual use case to explain how EcoCycle can achieve better recycling accuracy, increased user retention, and greater community engagement. The paper introduces a new UX-focused approach to the design of AI-powered sustainability technologies and provides designers, policymakers, and practitioners with practical advice. Through the harmonization of AI potential, human experience, and environmental objectives, EcoCycle is a step in the right direction toward holistically designing sustainable technology.},
      keywords = {User Experience (UX) Design; Artificial Intelligence; Sustainable Technology; Recycling Systems; Human-Centered Design; Behavioral Change; Community Sustainability; Environmental Engagement.},
      month = {January},
      doi = {https://doi.org/10.64388/IREV7I7-1715196}
  }