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

Kanwal Gul, Syeda Fasih, Swapnil Morande () and Muhammad Ramish
Additional contact information
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

Sustainability, 2024, vol. 16, issue 16, 1-15

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.

Keywords: photovoice; artificial intelligence; food waste; sustainability; participatory research (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/16/6956/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/16/6956/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:16:p:6956-:d:1455825

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().