When finished with an electronic device, consumers choose between storing, recycling, giving away, trading-in, reselling, or throwing it away. This choice has environmental and data privacy implications, e.g., reuse of devices is generally environmentally preferable to recycling, which is better than throwing away in the trash. Through a survey of 4000 U.S. consumers and regression analysis, this study analyzes how stated attitude and knowledge connect to consumers’ previous and planned disposition choices. The binomial regression model (pseudo-$R^2=13\%$) models the decision to store or not store a device. Important factors leading to increased likelihood of storing are data security concerns when recycling (+14%) or reselling (+9%), lack of knowledge of recycling (+10%), and wanting a backup of data (+11%). Notably, data security concerns when recycling or reselling were significant for past behavior, but not for intended behavior. This suggests consumers take data security more seriously when faced with the actual disposition decision. Multinomial regression (pseudo-$R^2=15\%$) is used to model which non-storage option is chosen. Knowledge of (+47%) and perceived convenience (+9%) of recycling programs were important in consumers choosing to recycle, reselling of devices was strongly influenced by knowledge of reuse markets.

Consumer electronics are important for global energy use, material extraction, and toxic exposures. Within consumer behavior, End-of-Current-Use (EoCU) decisions (e.g., to resell, donate, recycle, or throw away) particularly are a planetary health concern, not just a waste management problem. The goal of this work is to inform interventions that nudge users toward more sustainable and data-secure choices. We surveyed 4000 U.S. users on their attitudes, knowledge, and planned EoCU choices, and constructed a Random Forest model to simulate planned behavior as a function of reported knowledge and attitudes. The key result is identification of behavioral “tipping points”, i.e., incremental shifts in knowledge or attitude that produce large changes in sustainable behavior. For example, moving the level of agreement relating to “reselling is worthwhile” from “Neutral” to “Agree” increases the probability of resale by 26%, with stronger agreement yielding minimal further gains, suggesting relatively small changes in perception may thus produce large behavioral changes. The model also identifies trade-offs, with increases in agreeing that resale is worthwhile leading to to decreases in giving away (−14%) and recycling (−10%). Economic considerations drive reselling decisions, while environmental concerns drive recycling. Identifying these critical thresholds enables efficient intervention design, as modest attitude changes at transition points may prove more effective than maximizing positive attitudes. These finding challenge assumptions of linearity of behavioral responses, informing circular economy and other policies. They also relate to managing planetary health issues raised by EoCU electronics, such as human and ecological well being, digital equity, and environmental justice. The framework is broadly applicable to identifying behavioral tipping points in other domains, e.g., healthcare, energy efficiency, and data security.