Mental Models of New Technology
The goal of this project is to integrate social science expertise and domain knowledge into technology development, and to understand how people conceptualize and use everyday technologies as a foundation for informing new technologies.
New technologies may not be adopted by those who need them. This is especially true if people don’t appreciate the benefits of such technologies. Even if people do appreciate the benefits, uptake will be inhibited if the technologies don’t fit people’s self-identity or their own perceived needs. In-depth data can produce insights into promising avenues for innovation. Later study can rely on large-scale power to determine whether strategies were effective. The result of incorporating these insights into early product development will be greater uptake and satisfaction.
This project’s preliminary work will lay the foundation for better understanding of people’s needs for new quality of life technology (QoLT) tools. The findings from this line of research will ultimately lead to development of tools that will serve those needs by responding to the context in which (QoLT) is to be deployed. This work will lead to adoption of new technologies at a greater rate, more effectively, and more quickly. The mental models study provides both methodological and informational input. Methodologically, this study will illustrate a technique that can be applied directly to each of the core technologies as they are being developed, using in-depth mental models interviews. Additionally, the findings from this project broadly inform the link from current to potential technologies from the perspective of the end user.
The fundamental limitation of this line of research is the ability of end users to conceptualize problems broadly enough to add input beyond their experience. We address this limitation by formulating our interviews to begin with known technologies, describe them in explicit detail, and then push very systematically away from the current technology by branching out using drawbacks of the existing products. From that point forward, in systematic but small steps, we push end users to think more and more broadly about how a product could be easier to use, provide additional functionality, and work in different ways. By moving slowly and systematically, we hope to overcome the limitation of availability bias and identify the needs and possibilities of future technologies.