Lecture description:
Emerging technologies often promise to solve major current problems, e.g. regarding functionality, sustainability, societal fairness, yet they might also bear potential risks that are hardly foreseeable. Classical technology acceptance often relied on an expert approach. Yet, inclusive, participatory approaches increase acceptance and potential use of novel technologies. Here, I will give an overview of state-of-the art methods to assess technology acceptance based on the technology acceptance model (TAM) of Venkatesh and colleagues and own research in the field of driver assistance systems. Then, I will explain the problems when aiming to predict acceptance for not-yet existing technologies, like future livMatS technologies. I will explain data acquisition based on interviews, questionnaires and cognitive affective maps (CAM) as a novel tool for mixed-method research. Finally, I will show an example how CAMs enriched our research to predict acceptance for future climate engineering technologies.
After the lecture, students should be able to:
• Understand importance for integrating public perspectives and concerns when developing novel technologies
• Differentiate between various study designs and data acquisition techniques
• Recognize the complexity of prediction of technology acceptance