Co-design is seen as crucial for designing solutions for resource-constrained people living in developing countries. To best understand their needs, user engagement and co-design strategies need to first be developed. In this Design Practice Brief, a process of co-design was created and used to understand ways telecommunication engineers could engage with rural communities in Uganda. It reports and reflects on (i) the experience of co-designing with nondesigners and (ii) creating a co-design structure and developing co-design methods of engaging with community members living in developing countries. In doing so, it offers a format and case study for future practitioners facilitating and conducting co-design with nondesigners and contributes to a knowledge gap in the reporting and reflection of co-design practice. This case study is unique as the co-design practice was achieved remotely (online), crossed disciplines (designers and telecommunication engineers) and cultural boundaries (European and African). It finds that in co-designing with nondesigners, preparation and structure are key, with acknowledgement and management of cultural and discipline differences.

Perceived quality refers to customers’ cognitive and emotional responses to a particular design, often also associated with craftsmanship and customer satisfaction. Previous research defined a taxonomy of perceived quality and provided understanding about how engineering design decisions impact customer satisfaction. Furthermore, development of new products is frequently based on carrying over attributes of existing products, either from the same producer or from competitors. Previous research offered a new product development methodology combining variations of subsystems to carry over from existing products. This brief presents how these two lines of research combined to design the central console of the Porsche Panamera automobile and discusses the opportunities and challenges posed in the practical implementation of this research.

A critical task in product design is mapping information from the consumer space to the design space. This process is largely dependent on the designer to identify and relate psychological and consumer level factors to engineered product attributes. In this way, current methodologies lack provision to test a designer’s cognitive reasoning and may introduce bias through the mapping process. Prior work on Cyber-Empathic Design (CED) supports this mapping by relating user–product interaction data from embedded sensors to psychological constructs. To understand consumer perceptions, a network of psychological constructs is developed using Structural Equation Modeling for parameter estimation and hypothesis testing, making the framework falsifiable in nature. The focus of this technical brief is toward automating CED through unsupervised deep learning to extract features from raw data. Additionally, Partial Least Square Structural Equation Modeling is used with extracted sensor features as inputs. To demonstrate the effectiveness of the approach a case study involving sensor-integrated shoes compares three models – a survey-only model (no sensor data), the existing CED approach with manually extracted sensor features, and the proposed deep learning based CED approach. The deep learning based approach results in improved model fit.