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Eliciting food waste perceptions using an AI-driven approach

Kanwal Gul & Swapnil Morandé

International Journal of Technology Intelligence and Planning2024https://doi.org/10.1504/ijtip.2024.140625article
AJG 1
Weight
0.40

What the paper says

Food waste is a pressing global issue threatening sustainability. This research uses a participatory photovoice methodology to explore students' perceptions of food waste in a developing nation. Students capture images of food waste in their surroundings, and an artificial intelligence engine conducts unbiased analysis of the visual and textual data. The study reveals insights into participants' awareness, attitudes, and emotions surrounding waste, with key themes emerging around waste consciousness, guilt, helplessness, and the influence of affluence and social norms. The research makes two significant contributions. First, the photovoice technique effectively elicits youth perspectives on socio-economic issues in an inclusive, bottom-up manner. Second, AI-powered analytics enables rigorous, objective interpretation of complex subjective data. The study offers a novel approach to understand multi-faceted food waste perceptions, facilitating the design of context-specific interventions. By mobilising youth and leveraging AI, this research aims to spark innovative solutions for reducing waste and building sustainable food systems.

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https://doi.org/https://doi.org/10.1504/ijtip.2024.140625

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@article{kanwal2024,
  title        = {{Eliciting food waste perceptions using an AI-driven approach}},
  author       = {Kanwal Gul & Swapnil Morandé},
  journal      = {International Journal of Technology Intelligence and Planning},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1504/ijtip.2024.140625},
}

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Eliciting food waste perceptions using an AI-driven approach

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Evidence weight

0.40

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.23 × 0.4 = 0.09
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.