Richard Gonzalez
Center Director, Research Center for Group Dynamics, Institute for Social Research
Director, BioSocial Methods Collaborative, RCGD
Amos N Tversky Collegiate Professor, Psychology and Statistics, LSA
Professor of Marketing, Stephen M Ross School of Business
Professor of Integrative Systems and Design, College of Engineering
| E-mail: | Email Richard Gonzalez |
| Address: | Research Center for Group Dynamics Institute for Social Research University of Michigan 426 Thompson Street Ann Arbor, Michigan 48106 |
| Phone: | 734-647-6785 |
How do we measure perceived craftsmanship and how do we engineer it into products?
This paper merges traditional techniques from psychology with some methods from engineering to tackle the difficult question of what influences an individual’s rating of craftsmanship. We find a high degree of correspondence between the clusters and dimenions people use when rating the interior of a vehicle and clusters/dimensions engineers would consider. This correspondence suggests new directions for optimizing perceived craftsmanship in vehicle design.
Ersal (Hossoy), I., Papalambros, P., Gonzalez, R., & Aitken, T. (2011). Modeling customer perceptions of craftsmanship in vehicle interior design. Journal of Engineering Design, 22, 129-144. doi:10.1080/09544820903095219 (PDF)
Abstract
In the automobile industry, one approach for assessing craftsmanship is to have experienced designers evaluate the craftsmanship of a vehicle interior on a set of vehicle craftsmanship characteristics. This article extends one industry approach by evaluating vehicle interior craftsmanship in a quantitative manner.
Study 1 presents data suggesting that an existing craftsmanship scale does not lead to acceptable levels of consensus across evaluators nor to interpretable clusters or dimensional spaces after data reduction.
A new list of interior characteristics and perceived attributes of craftsmanship is developed and analysed using a functional dependence table (FDT). Study 2 uses the new list of perceived attributes and shows there is an improved degree of consensus across evaluators, meaningful clusters and spatial arrangements emerge using cluster analysis and multidimensional scaling, and the clusters from the evaluators’ data are consistent with the subproblems that emerged from the FDT of product attributes and characteristics. The
paper demonstrates that engineering designers can use this approach to guide their work about perceived
craftsmanship. One benefit of the proposed method is that engineering designers can work at the level
of perceived product attributes (the same attributes potentially observed by the consumer) and map those
attributes to engineering characteristics.
