Scholars have proposed that the Protection Motivation Theory provides a valuable framework to explain pro-environmental choices, by employing a wide set of predictors, such as the costs and benefits of current (maladaptive) behavior as well as prospective adaptive behavior. However, no comprehensive empirical tests of the Protection Motivation Theory in the slow onset environmental risk domain have been published yet to our knowledge. This paper aims at closing this gap. We first conceptualized the Protection Motivation Theory for the use in this environmental domain. Next, we present results of a questionnaire study among a large representative sample of Dutch drivers that showed that the Protection Motivation Theory is a relevant theory for modeling different indicators of full electric vehicle adoption. Notably, all theoretical antecedents proved to be significant predictors of different adoption indicators. Respondents were particularly more likely to adopt an electric vehicle when they perceived the negative consequences caused by conventional vehicles as more severe, and when they expected electric vehicles to decrease these consequences. The most important barriers for electric vehicle adoption were perceived high monetary and non-monetary costs of electric vehicles, and benefits associated with the use of a conventional vehicle. Interestingly, we found that environmental risks are more prominent in predicting close adoption indicators; while energy security risks are more prominent in predicting distant adoption indicators. As expected, our findings suggest that both collective concerns and individual concerns predict different indicators of adoption. Individual concerns (in particular perceived costs of driving an electric vehicle) played a more prominent role when predicting close measures of adoption, while collective concerns (e.g., perceived severity of environmental and energy security risks) played a somewhat more prominent role when predicting distant measures of adoption. Implications for research and practice are provided. (C) 2014 Elsevier Ltd. All rights reserved.
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