Segmenting Consumer Knowledge and Attitudes: Behavioral Insights for End-of-First-Use Electronics

The goal of this study is to find patterns in how consumers disposition electronic devices at End-of-First-Use, i.e. store, recycle, resell, trade in, donate/give or and throw in the trash. K-means clustering was used on survey data from 3,747 U.S. respondents across 10 device categories to divide the population into three clusters of consumers based on stated attitudes and knowledge of data privacy, environmental benefits, convenience and other aspects of End-of-First-Use options. We then measure the reported intended disposition of devices for each cluster and compare with the general population. Cluster 1 has higher data security concerns when recycling, reselling or donating, and less knowledge and trust in End-of-First-Use options overall. The intended behavior of cluster 1 shows higher than average uncertainty in what to do at End-of-First-Use and more intent to store (lower values for other options - recycling, reselling and donating). Cluster 2 shows higher knowledge and trust in recycling, reselling, and donation, and slightly higher than average concern about data security of these options. The intended behavior of cluster 2 shows higher intent to resell, trade-in or donate, and lower levels of being uncertain of what to do and of storing. Cluster 3 expresses much less concern about data security, and lower utility of a stored device. Their intended behavior shows less storage and higher levels of other End-of-First-Use options. While cluster analysis does not yield causal connections, the groups show consistent trends in stated knowledge and attitudes towards different End-of-First-Use options and corresponding planned behaviors. These results indicate there are subgroups of the general population with similar reported attitudes, knowledge and behaviors. The three subgroups do not have distinct demographic characteristics, i.e. knowledge and attitudes regarding disposition of electronics does not depend strongly on age, education level, income and similar factors. Understanding segmentation is useful to investigate more effective interventions to influence behavior for better sustainability outcomes.