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Participatory Visual Methods and Artificial Intelligence-Driven Analysis for Sustainable Consumption Insights

Author

Listed:
  • Kanwal Gul

    (Department of Economics and Management, University of Naples, 80126 Napoli, Italy)

  • Syeda Fasih

    (Department of Management and HR, Institute of Business Management, Karachi 75190, Pakistan)

  • Swapnil Morande

    (Department of Economics and Management, University of Naples, 80126 Napoli, Italy)

  • Muhammad Ramish

    (Department of Marketing & Entrepreneurship, Institute of Business & Health Management, Karachi 74200, Pakistan)

Abstract

Sustainable consumption is crucial for mitigating global sustainability challenges. Understanding consumer behaviors and motivations, particularly in developing regions, is essential for designing effective interventions. This study pioneers an innovative methodology integrating participatory visual methods (photovoice) and artificial intelligence analysis to investigate food waste perceptions in an emerging economy context. Twenty-six university students participated in the study, documenting their lived experiences and perspectives on household food waste through photographs and narratives. The key results included 32% of participants expressing shock at the extent of food waste in their daily lives, while 28% showed relative indifference. AI-powered (Artificial Intelligence) computer vision and natural language processing were used to efficiently analyze the large visual and textual dataset. The mixed methods approach generated nuanced, situated insights into consumer attitudes, behaviors, and socio-cultural drivers of wastage. The key themes included low waste consciousness, aesthetic and convenience motivations, social norms, and infrastructural limitations. The participatory process proved effective for raising critical consciousness and uncovering consumption practice dynamics. AI analysis enabled rapid knowledge discovery from the qualitative data while mitigating researcher bias. This innovative integration of participatory methodologies and computational analytics advances sustainable consumption research by empowering marginalized voices and generating contextual insights from unstructured data. With further development, such human-centered AI approaches can transform the study and governance of sustainable consumption.

Suggested Citation

  • Kanwal Gul & Syeda Fasih & Swapnil Morande & Muhammad Ramish, 2024. "Participatory Visual Methods and Artificial Intelligence-Driven Analysis for Sustainable Consumption Insights," Sustainability, MDPI, vol. 16(16), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6956-:d:1455825
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    References listed on IDEAS

    as
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    2. Tammara Soma & Belinda Li & Virginia Maclaren, 2020. "Food Waste Reduction: A Test of Three Consumer Awareness Interventions," Sustainability, MDPI, vol. 12(3), pages 1-19, January.
    3. Yannick Oswald & Anne Owen & Julia K. Steinberger, 2020. "Large inequality in international and intranational energy footprints between income groups and across consumption categories," Nature Energy, Nature, vol. 5(3), pages 231-239, March.
    4. Benjamin Saunders & Julius Sim & Tom Kingstone & Shula Baker & Jackie Waterfield & Bernadette Bartlam & Heather Burroughs & Clare Jinks, 2018. "Saturation in qualitative research: exploring its conceptualization and operationalization," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1893-1907, July.
    5. Hermanussen, Henrike & Loy, Jens-Peter & Egamberdiev, Bekhzod, 2022. "Determinants of food waste from household food consumption: A case study from field survey in Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(21), pages 1-22.
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