Misinformation on sustainability has become a widespread phenomenon in many different contexts. However, relatively little is known about several important determinants of belief in misinformation, and even less is known about how to debias that belief. The present research proposes and investigates a new effect, the half-truth effect, to explain how message structure can influence belief in misinformation. Two survey-based experiments were conducted to show that people exhibit greater belief in a false claim when it is preceded by a true claim, even if the two claims are logically unrelated. Conversely, when a false claim is presented before the true claim, it reduces the belief in the entire statement. Experiment 1 shows the basic half-truth effect. Experiment 2 investigates an individual difference, propensity to believe meaningless statements are profound, which impacts the half-truth effect. Both experiments also investigate debiasing of the false information. The results of the experiments were analyzed using analysis of variance and regression-based mediation analysis. Results show that belief in misinformation is dependent on message structure, and show that the order in which true and false elements are presented has a strong influence on belief in sustainability misinformation. Finally, we present a discussion of how policy makers can use these findings to identify those people who are most likely to be swayed by the misinformation, and then design responses to debias sustainability misinformation.

For the behavioral marketing scholar, experimentation and the analysis of variance are among the most important and frequently relied upon tools of the trade, and many useful texts exist to guide researchers on these topics. This monograph is intended to be a supplemental resource and a helpful guide for conducting three essential analytical techniques that are also frequently useful to the behavioral researcher: (1) we discuss the practice of conducting a median split on a continuous variable to facilitate communication clarity. (2) We demonstrate the practice of centering variables about their means prior to creating product terms to reflect interaction effects in a moderated multiple regression model. (3) We discuss the practice of a mediation analysis to test for the relative impact of direct and indirect effects of predictors on dependent variables.