Sustainable Innovation

The discourse surrounding sustainable innovation is often driven by a mindset focusing on additive transformations, such as the introduction of new technologies, products and procedures. This, so-called additive bias, paradoxically often leads to solutions that exacerbate existing problems or generate new forms of unsustainable production. Consequently, the sustainability transition requires the implementation of subtractive solutions, entailing the elimination of certain procedures and the adoption of resource-efficient alternatives. While both additive and subtractive solutions offer distinct advantages for innovation practices, this project aims to examine the factors that mitigate the additive bias, thereby achieving a better balance between additive and subtractive changes. Drawing upon Construal Level Theory, we suggest that a concrete mindset favors additive solutions due to their accessibility and perceived feasibility. Conversely, an abstract mindset enhances cognitive flexibility, allowing individuals to consider a broader range of possibilities, while it also facilitates comprehensive and abstract problem evaluations. This may lead individuals to question the necessity of certain procedures and consider system simplification alternatives, especially when these alternatives are not very accessible. Our first study extends previous findings on the additive bias, by measuring participants’ preferred strategy of either adding or subtracting, using a grid-pattern puzzle. In order to influencing the accessibility of subtractive solutions, participants were assigned to two conditions, in which they were either cued to subtract or not. Their abstract or concrete mindset was measured using the Navon task. Our second study examined the causal relationship between mindset and subtractive ideas. Participants’ mindset was manipulated through priming, while they were again exposed to a subtraction cue or no-cue condition. Subsequently, they performed an idea generation task and their responses are coded for their additive or subtractive aspects. Results and implications of our findings are further discussed.