Who’s gonna use this? Acceptance prediction of emerging technologies with Cognitive-Affective Mapping and transdisciplinary considerations in the Anthropocene

Abstract

In the Anthropocene, mankind is facing enormous challenges. Science and technology obviously have an essential role to play in addressing these challenges but have to be supplemented by the collaboration of different actors from the scientific and non-scientific community. Possibly beneficial technologies can only unfold their full potential if they are socially accepted. Participation and transdisciplinarity are key concepts in this regard. The need for methods fostering collaborative knowledge production and reverse communication from the public to the scientific community is accordingly high. In this article, we propose to apply Cognitive-Affective Mapping (CAM) to predict psychological acceptance of novel research fields and potentially resulting technologies. As an example, we use life-like materials systems. CAM enables acceptance prediction at an early stage—already for basic research when prototypes are not yet available. The method bridges the gap between qualitative and quantitative research traditions. Its product—Cognitive-Affective Maps (CAMs)—is a vivid visual tool. In perspective, CAM-based technology acceptance assessment can be conceived as a participatory, transdisciplinary practice.

Identifying key-psychological factors influencing the acceptance of yet emerging technologies–A multi-method-approach to inform climate policy

“…The procedure for summarizing CAM data is following a five-step procedure, which was theoretically motivated by existing CAM literature [cf. 107,108,112,113] and qualitative handbooks [114][115][116]: (1) In a first deductive step, super-and subordinate categories are derived from existing literature. Thereby, semantically identical terms can be positive or negative depending on the context.…”

“…As an exploratory method, CAMs offer added value compared to a survey, as the latter can only consider pre-identified potential factors influencing SAI acceptability [cf. 107,108]. This method enabled us to enrich the theoretical proposed integrative model by ethical and climate change related concerns (see Fig 1).…”