Introduction & BackgroundThe 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 interventions by local authorities, due to the prohibitive costs and logistical challenges of administering longitudinal surveys. This study builds on existing research and a key pilot study developed in partnership between Olio - a food-sharing app with 7 million registered users as of 2023, the University of Nottingham and Havering Council in 2020, which resulted in the world’s first map prototype of food-insecurity. Objectives & ApproachOur approach leverages Machine Learning methods applied to unprecedented food-acquisition behavioural data and open area-level deprivation statistics to model and predict individuals' experience of food-insecurity across London. We used Olio’s extensive network of users to distribute 2,849 surveys, asking respondents across London about their experiences of food-insecurity. The survey was distributed online, adapting the US Department of Agriculture Food Security module. Respondents were asked about their experiences, including (1) eating smaller meals or skipping meals, (2) being hungry but being unable to eat, and (3) not eating for a whole day, because they could not afford food or because they could not get access to food. Using the household, rather than the individual-level version of the food insecurity module helped shed light on the experience of vulnerable groups - such as children. Relevance to Digital FootprintsThe survey responses provided a ground truth about users' experiences of destitution. Deprivation metrics and digital footprint data in the form of food-acquisition behavioural data were then used in a Random Forests Machine Learning model to predict whether households were experiencing food-insecurity, achieving high accuracy. Food-sharing data from almost 50,000 London-based users active on Olio’s platform were then used to identify relevant food-seeking behaviours and aggregate recognised instances of food-insecurity at neighbourhood (MSOA) level. Conclusions & ImplicationsTo identify and rank relevant socio-demographics and food-seeking behaviours most informative for describing food-insecurity an extensive variable selection analysis was performed. The resulting SHAP (SHapley Additive exPlanations) values showed that a combination of food solicitation and the general deprivation of an area were important predictors of food-insecurity.
Big data from food retail stores is increasingly being used for population dietary surveillance, epidemiological studies of diet-related diseases, and evaluations of public health interventions. However, for retail data to be useful it is necessary to understand the spatio-temporal variation of when and where food is purchased and consumed. While some customers willingly share home location data with retailers as part of loyalty programs such data is typically too fine-grained/sensitive to be applied for research purposes. The aim of this study was to analyse differences between privacy-preserving models and actual retail catchments, and investigate if machine learning techniques could improve the accuracy of such catchment models. Based on a UK-wide sample of 4 million grocery store loyalty card holders, covering 485 million transactions over 29 months (2019-2021) and distributed across 33,000 neighbourhoods (Lower Super Output Areas, or LSOA), the study demonstrates how models trained on geolocated data perform at predicting, per store, catchment areas which contain 50, 80, and 95% of its customers' primary location. Through comparative assessment of machine learning approaches, we find better performance from tree-based models (RF, XGB) with the best performance from an XGB model achieving an R 2 of 0.72 and MAE of 1.06. To conclude, we review variable importance measures using SHAP values and discuss the relative merits of including specific features when modeling catchment areas.
The redistribution of surplus food is a challenging problem, yet a crucial one to address given the urgent nature of climate change. However, designing computer-mediated food sharing systems is made even harder due to failed interactions between users and ensuing complaints, which can dissuade others from participating when shared within a public forum. To examine the phenomenon of complaints within such data, we analyze the public forum of a food sharing platform, OLIO. We characterize complaining behaviour and augment it through qualitative labeling and a machine learning approach to model complaints using affective indicators of dissatisfaction across a corpus of 3,195 forum posts. Results emphasize that linguistic features yield high prediction accuracies, with negative, nonconstructive sentiment being of greatest relevance. We discuss how machine learning can further enrich qualitative understandings and validation of complaints in the sharing economy.
Some people are used to change, and to them, the constancy associated with routine might appear to be in contradiction with the postmodern world. Others may find a sense of security in loyalty towards brands or behavioural habitualness. In this context, the aim of this thesis is a better understanding of what constitutes habitualness in shopping by summarising the range and variety of behaviours emerging in two retail settings, and what is driving them. Chapter 2 has indicated opportunities for understanding the transactional and social nature of habitualness by drawing on consumer research, sociology and cognitive psychology. With data-mining approaches currently under-explored in consumer research, an opportunity also arose for investigating and measuring these behaviours via novel computational approaches. Health & beauty and grocery shopping were chosen as the focuses for the research, on the basis that in these settings, customers’ patronage and product-related behaviours are long-term, periodic and multi-modal. Using a mixed-methods sequential design, the initial scoping study presented in Chapter 5 provides an exploratory, descriptive analysis of shopping behaviours and modalities in these two retail settings. It also identifies two axes along which shopping habits develop. These denote when customers visit and what product categories they buy, and informed the studies in Chapters 6 and 7.The results of the three case studies provided evidence for the existence of foot-fall and shopping mission trends, as well as individual expressions of these habits among the customers of the two retailers in focus. Moreover, the results indicated a complex dynamic between these identified habits and contextual, impulsive and reflective factors. This analysis could not categorically identify instances of retailer, brand or product-variant loyalty, as cognitive, affective or conative valences remain unexplored. In turn, the explored exemplars and clusters opened a discussion around aspects of loyalty within the confines of the two retail settings, and how they relate to broader managerial goals and conceptualisations of loyalty in general. The value of researching shopping habits, then, is two-fold. Firstly, it helps find generalisable ‘rules’, or ‘patterns’ of behaviour across contexts and populations. If we know the who, what and where of everyday shopping habits, then we can better understand the drivers of market-wide behaviours. These, in turn, can be used as a starting point to further consumer research. A series of practical implications emerged as a result of these findings, in terms of methodological innovations, as well as managing customer relationships and their well-being.
Introduction & BackgroundPrevious 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 homes require greater energy expenditure to maintain an adequate temperature. Despite frequent colloquial and journalistic references to the "heat-or-eat dilemma”, there remains limited recent empirical evidence of this phenomenon in the UK. This is a considerable knowledge gap, given recent economic hardship and rising energy costs. Objectives & ApproachThis study uses survey data (n=2877), collected during winter 2022 in London, UK, to analyse the sociodemographic and behavioural characteristics of respondents affecting self-reported heat-or-eat trade-offs. The survey was deployed via users of the food-sharing app, OLIO, and quota restraints were enforced to ensure the socioeconomic representativeness of the sample (based on Index of Multiple Deprivation). The survey question of interest (i.e., the dependent variable) was ""in the past year, how frequently did your household reduce or forego expenses for basic household necessities, such as medicine or food, in order to pay an energy bill?"" and responses were recorded using a discrete, ordinal scale: never; 1-2 months; some months but not every month; almost every month. Given the nature of the dependent variable, the Random Parameters Ordered Probit (RPOP) model, a statistical modelling framework used in the case of discrete, ordered outcomes, was considered suitable. The RPOP approach allows the effect of various independent variables to be explored, which in this case, are sociodemographic and behavioural characteristics of respondents. Relevance to Digital FootprintsThe relevance to the digital footprints theme is embedded in the study’s aim: to draw insights into social issues through the analysis of sociodemographic and behavioural data retrieved from the users of a mobile app. ResultsInitial results show that a considerable proportion (~37%) of the sample made heat-or-eat trade-offs at least one month of the year. Interestingly, this is several times higher than the official rate of fuel poverty in London (11.9%), suggesting that the government’s fuel poverty metric fails to capture many homes that display signs of energy unaffordability. The RPOP model estimation results show that a broad range of sociodemographic variables (including features of household composition and disability), as well as several behavioural features derived from the respondents’ use of the OLIO app, including the frequency of app usage and food requests, significantly affected the likelihood of heat-or-eat trade-offs. Conclusions & ImplicationsOur results can be used to guide remedial food and fuel poverty policies. It may be particularly useful to focus on the sociodemographic variables that lead to heat-or-eat trade-offs, given that the English fuel poverty metric places arguably unjust focus on a home’s energy efficiency, rather than occupant characteristics.
Introduction & BackgroundThe shift towards plant-based diets remains on the rise. Several observational studies have suggested that adopting these diets can result in some fundamental nutrient deficiencies, such as iodine deficiency. This can be especially harmful to a developing fetus, leading to growth impairment and, in extreme cases, cretinism. Nonetheless, understanding of long-term health consequences of this shift remains a challenge, particularly regarding nutritional impact at broader population scales.
Objectives & ApproachOur study focuses on the effects of transitioning to plant-based diets on the purchasing and assumed intake of essential nutrients like iodine, calcium, and vitamin B12. We analysed anonymized shopping records of 10,626 loyal customers who switched from regular milk to alternative milk products. By matching the transaction data with nutritional information, we estimated the weekly nutrient purchases before and after the transition. Our data was collected from a national food retailer across the UK.
Relevance to Digital FootprintsLoyalty-card transactional logs held by retailers reflect a valuable lens into nutritional intake data. This data can provide insight into the potential impact of purchasing behaviours, such as the potential health effects of dietary changes at scale. Our approach leverages AI modelling accompanied by rigorous variable importance methods to uncover potentially hidden insights on the impact of nutritional shifts to plant-based goods.
ResultsResults indicate that 83% of individuals deemed regular customers, who switched to plant-based milk, experienced a decrease in their purchases of iodine (44%), calcium (30%), and vitamin B12 (39%) from their normal purchase patterns at the retailer. Additionally, 57% of these individuals decreased their iodine purchases by more than 50%. The reduction in these nutrients is even more significant for those who switch to plant-based dairy and meat products.
Conclusions & ImplicationsOur research indicates that dietary changes, such as switching from purchasing regular milk to alternative milks, may lead to insufficient intake of essential dietary nutrients such as iodine. This represents a significant potential health concern for the public if not remediated, especially in countries that do not require salt to be fortified with iodine.
Life satisfaction significantly contributes to wellbeing 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 individual person, including: demography, socioeconomic status, health, deprivation, family life, friendships, social networks, living environment, and the broad range of behaviours enacted by the person, such as helping or volunteering. Consequently, it is challenging to disentangle the factors that contribute most significantly to life satisfaction, and thus more importantly, inform public policies designed to help foster positive wellbeing. We analyse primary survey data $(\mathrm{n}=2849)$ on self-reported life satisfaction in relation to a range of self-reported and observed variables associated with wellbeing. Specifically, we draw on a massive paired dataset related to use of a food sharing application in London, to augment the analysis using additional socioeconomic, environmental, and behavioural variables. Through a random forest machine learning approach and variable importance measures, we evaluate how a range of factors, that are often only evaluated individually, provide relative contributions towards life satisfaction. Result reveal that factors such as employment and social reliance contribute most significantly towards the experience of life satisfaction.
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 questioned by policymakers. We demonstrate a data-driven approach to address this issue, deriving national estimates of food insecurity via combination of supervised machine learning with network analysis of user behaviour, extracted from the world's most popular peer-to-peer food sharing application (OLIO). Despite long-standing theoretical links between social graph topologies and physical neighbourhoods, prior research has not considered dimensions of geography, network interactions and behaviours in the digital/analogue space simultaneously. In addressing this oversight, we produce a browser-based, interactive and rapidly updateable visualisation, which can be used to analyse the spatial distribution of food insecurity across the UK, and provide new perspective for policy research.
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