Project

Psychology, Big Data, and Consumer Understanding

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Project log

Rosa Lavelle-Hill
added a research item
Despite the success of plastic bag charges in the UK, there are still around a billion single-use plastic bags bought each year in England alone, and the government have made plans to increase the levy from 5 to 10 pence. Previous research has identified motivations for bringing personal bags to a supermarket, but little is known about the individuals who are continuing to frequently purchase single-use plastic bags after the levy. In this study, over a million loyalty card transaction records from a high-street health and beauty retailer were harnessed to study 12,968 individuals’ bag buying behaviour (analysed using descriptive statistics). Statistical regional differences in plastic bag buying throughout the UK were found. From the transaction data 2,326 frequent single-use plastic bag buyers were then identified and matched randomly to infrequent buyers, creating a balanced sub-sample which was used for predictive modelling (N = 4,652). For each individual in the modelling sample, their transaction data was matched to questionnaire responses measuring demographics, shopping motivations, and individual differences. Using this data, an exploratory machine learning approach was utilised to investigate the demographic and psychological predictors of frequent plastic bag consumption. It was found that frequent bag buyers spent more money in store, were younger, more likely to be male, less frugal, open to new experiences, and more displeased with their appearance. Interestingly, environmental concerns did not predict plastic bag consumption, highlighting the disconnect between predicting pro-environmental attitudes and real world environmental behaviour.
Rosa Lavelle-Hill
added a research item
Despite the success of plastic bag charges in the UK, there are still around a billion single-use plastic bags bought each year in England alone, and the government have made plans to increase the levy from 5 to 10 pence. Previous research has identified motivations for bringing personal bags to the supermarket, but little is known about the individuals who are continuing to frequently purchase single-use plastic bags after the levy. In this study, we harnessed over a million loyalty card transaction records from a high-street health and beauty retailer linked to 12,968 questionnaire responses measuring demographics, shopping motivations, and individual differences. We utilised an exploratory machine learning approach to expose the demographic and psychological predictors of frequent plastic bag consumption. In the transaction data we identified 2,326 frequent single-use plastic bag buyers, which we matched randomly to infrequent buyers to create the balanced sub-sample we used for modelling (N=4,652). Frequent bag buyers spent more money in store, were younger, more likely to be male, less frugal, open to new experiences, and displeased with their appearance compared with infrequent bag buyers. Statistical regional differences also occurred. Interestingly, environmental concerns did not predict plastic bag consumption, highlighting the disconnect between predicting pro-environmental attitudes and a specific ecological behaviour measured objectively in the real world.
Rosa Lavelle-Hill
added a research item
Despite the success of plastic bag charges in the UK, there are still around a billion single-use plastic bags bought each year in England alone (DEFRA, 2018), and politicians are currently debating whether to double the 5 pence charge to 10 pence (Rawlinson, 2018). Previous research has identified motivations for bringing personal bags to the supermarket (Jakovcevic et al., 2014; Poortinga et al., 2016) but little is known about whom is continuing to regularly purchase plastic bags after the levy. In this study, we harness the power of big data to investigate who the regular plastic bag buyers are that future campaigns ought to target. We utilise a dataset of 12,968 questionnaire responses measuring demographics and individual differences matched to respondents' loyalty card transaction data from a UK health and beauty retailer. We used the transaction data to identify regular single-use plastic bag buyers and non-bag buyers in the real world. We optimised different prediction models and evaluated them using out-of-sample testing. A logistic regression model performed the best, predicting regular bag buyers with 76% accuracy. The results showed that England had significantly less bag sales than Wales, Northern Ireland, and Scotland per 1000 items in the 9 months after the English levy. Across the UK, more bags per 1000 items were bought during the Christmas festive period. Shopping frequency, amount spent, age (negatively), frugality (negatively), income, impulsivity, a present time focus, and Openness significantly predicted regular bag buying. Notably, climate change concerns did not. In summary, this research has identified a young, high-income demographic of regular bag buyers to target. The additional insights that this sub-population are impulsive and unconcerned with climate change also helps to inform the content type and communication style for future plastic bag reduction campaigns.
Rosa Lavelle-Hill
added a research item
Examining two different case studies in which machine learning methods have enhanced psychological investigation, compared with an explanation based approach. Case 1: Using random forests to extract which personality variables are most predictive of plastic bag purchases. Case 2: Using decision trees to understand the sub-groups and non-linear relationships between the variables predicting well-being.