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Mrs GEORGIANA NICA-AVRAM's Outputs (14)

Machine learning on national shopping data reliably estimates childhood obesity prevalence and socio-economic deprivation (2025)
Journal Article
Long, G., Nica-Avram, G., Harvey, J., Lukinova, E., Mansilla, R., Welham, S., Engelmann, G., Dolan, E., Makokoro, K., Thomas, M., Powell, E., & Goulding, J. (2025). Machine learning on national shopping data reliably estimates childhood obesity prevalence and socio-economic deprivation. Food Policy, 131, Article 102826. https://doi.org/10.1016/j.foodpol.2025.102826

Deprivation pushes people to choose cheap, calorie-dense foods instead of nutritious but expensive alternatives. Diseases, such as obesity, cardiovascu-lar disease, and diabetes, resulting from these poor dietary choices place a significant burden on... Read More about Machine learning on national shopping data reliably estimates childhood obesity prevalence and socio-economic deprivation.

Utilising User Data from a Food-Sharing App to Evidence the "Heat-or-Eat" Dilemma (2024)
Presentation / Conference Contribution
Semple, T., Harvey, J., Rodrigues, L., Gillott, M., Figueredo, G., & Nica-Avram, G. (2024, May). Utilising User Data from a Food-Sharing App to Evidence the "Heat-or-Eat" Dilemma. Presented at 2nd Digital Footprints Conference: Linking Digital Data for Social Impact, Bristol, UK

Introduction & Background
Previous literature has found that financially vulnerable households often make involuntary spending trade-offs between necessities, particularly energy and food. This effect is especially pronounced during winter, when hom... Read More about Utilising User Data from a Food-Sharing App to Evidence the "Heat-or-Eat" Dilemma.

Foodinsecurity.london: Developing a food-insecurity prevalence map for London - a machine learning from food-sharing footprints (2024)
Presentation / Conference Contribution
Milligan, G., Nica-Avram, G., Harvey, J., & Goulding, J. (2024, May). Foodinsecurity.london: Developing a food-insecurity prevalence map for London - a machine learning from food-sharing footprints. Presented at 2nd Digital Footprints Conference: Linking Digital Data for Social Impact, Bristol, UK

Introduction & Background
The ability of policymakers to positively transform food environments requires robust empirical evidence that can inform decisions. At present, there is limited data on food-insecurity in the UK that can be used to inform i... Read More about Foodinsecurity.london: Developing a food-insecurity prevalence map for London - a machine learning from food-sharing footprints.

An empirical critique of the low income low energy efficiency approach to measuring fuel poverty (2024)
Journal Article
Semple, T., Rodrigues, L., Harvey, J., Figueredo, G., Nica-Avram, G., Gillott, M., Milligan, G., & Goulding, J. (2024). An empirical critique of the low income low energy efficiency approach to measuring fuel poverty. Energy Policy, 186, Article 114014. https://doi.org/10.1016/j.enpol.2024.114014

Fuel poverty is a complex socioenvironmental issue of increasing global significance. In England, fuel poverty is assessed via the Low Income Low Energy Efficiency (LILEE) indicator, yet concerns exist regarding the efficacy of this metric given its... Read More about An empirical critique of the low income low energy efficiency approach to measuring fuel poverty.

Detecting iodine deficiency risks from dietary transitions using shopping data (2024)
Journal Article
Mansilla, R., Long, G., Welham, S., Harvey, J., Lukinova, E., Nica-Avram, G., Smith, G., Salt, D., Smith, A., & Goulding, J. (2024). Detecting iodine deficiency risks from dietary transitions using shopping data. Scientific Reports, 14(1), Article 1017. https://doi.org/10.1038/s41598-023-50180-7

Plant-based product replacements are gaining popularity. However, the long-term health implications remain poorly understood, and available methods, though accurate, are expensive and burdensome, impeding the study of sufficiently large cohorts. To i... Read More about Detecting iodine deficiency risks from dietary transitions using shopping data.

Consumer Decision-Making, Analytics & AI (2023)
Book
Smith, A., Lukinova, E., Harvey, J., Smith, G., Mansilla, R., Goulding, J., & Nica-Avram, G. (in press). Consumer Decision-Making, Analytics & AI. Routledge

Many decisions in our lives are decisions we make as consumers and buyers. Analytics driven marketing underpinned by machine learning and AI has had a seismic impact on the execution of these decisions and on behavioural outcomes. This book dissects... Read More about Consumer Decision-Making, Analytics & AI.

Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data (2023)
Presentation / Conference Contribution
Milligan, G., Harvey, J., Dowthwaite, L., Vallejos, E. P., Nica-Avram, G., & Goulding, J. (2023, December). Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data. Presented at 2023 IEEE International Conference on Big Data, Sorrento, Italy

Life satisfaction significantly contributes to wellbe-ing and is linked to positive outcomes for individual people and society more broadly. However, previous research demonstrates that many factors contribute to the life satisfaction of an individua... Read More about Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data.

Mapping the landscape of Consumer Food Waste (2021)
Journal Article
Harvey, J., Nica-Avram, G., Smith, M., Hibbert, S., & Muthuri, J. (2022). Mapping the landscape of Consumer Food Waste. Appetite, 168, Article 105702. https://doi.org/10.1016/j.appet.2021.105702

Since 2015 there has been a surge of academic publications and citations focused on consumer food waste. To introduce a special issue of Appetite focused on the drivers of consumer food waste we perform a transdisciplinary and historical review of th... Read More about Mapping the landscape of Consumer Food Waste.

Identifying food insecurity in food sharing networks via machine learning (2020)
Journal Article
Nica-Avram, G., Harvey, J., Smith, G., Smith, A., & Goulding, J. (2021). Identifying food insecurity in food sharing networks via machine learning. Journal of Business Research, 131, 469-484. https://doi.org/10.1016/j.jbusres.2020.09.028

© 2020 Elsevier Inc. Food insecurity in the UK has captured public attention. However, estimates of its prevalence are deeply contentious. The lack of precision on the volume of emergency food assistance currently provided to those in need is made ev... Read More about Identifying food insecurity in food sharing networks via machine learning.

The Smart Home: How Consumers Craft New Service Networks by Combining Heterogeneous Smart Domestic Products (2020)
Journal Article
Harvey, J., Poorrezaei, M., Woodall, T., Nica-Avram, G., Smith, G., Ajiboye, T., Kholodova, K., & Zhu, K. (2020). The Smart Home: How Consumers Craft New Service Networks by Combining Heterogeneous Smart Domestic Products. Journal of Service Research, 23(4), 504-526. https://doi.org/10.1177/1094670520929095

Service research suggests homes are becoming increasingly connected as consumers automate and personalize new forms of service provision. Yet large-scale empirical evidence on how and why consumers automate smart domestic products is lacking. To addr... Read More about The Smart Home: How Consumers Craft New Service Networks by Combining Heterogeneous Smart Domestic Products.

FIMS: Identifying, Predicting and Visualising Food Insecurity (2020)
Presentation / Conference Contribution
Lucas, B., Smith, A., Smith, G., Perrat, B., Nica-Avram, G., Harvey, J., & Goulding, J. (2020, April). FIMS: Identifying, Predicting and Visualising Food Insecurity. Presented at The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020, Taipei Taiwan

Food insecurity is a persistent and pernicious problem in the UK. Due to logistical challenges, national food insecurity statistics are unmeasured by government bodies - and this lack of data leads to any local estimates that do exist being routinely... Read More about FIMS: Identifying, Predicting and Visualising Food Insecurity.