Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels
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2013-chenEtal.pdf
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708.63 KB
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d2e14a754f623683825cdaafc8551038
Author(s)
Chen, Heidi Qianyi
Honda, Tomonori
Yang, Maria
Date Issued
May 2013
Journal
Journal of Mechanical Design
Publisher
ASME International
Citation
Chen, Heidi Q., Tomonori Honda, and Maria C. Yang. “Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels.” Journal of Mechanical Design 135, no. 6 (May 9, 2013): 061007.
Version
Author's final manuscript
Abstract
This paper investigates ways to obtain consumer preferences for technology products to help designers identify the key attributes that contribute to a product's market success. A case study of residential photovoltaic panels is performed in the context of the California, USA, market within the 2007–2011 time span. First, interviews are conducted with solar panel installers to gain a better understanding of the solar industry. Second, a revealed preference method is implemented using actual market data and technical specifications to extract preferences. The approach is explored with three machine learning methods: Artificial neural networks (ANN), Random Forest decision trees, and Gradient Boosted regression. Finally, a stated preference self-explicated survey is conducted, and the results using the two methods compared. Three common critical attributes are identified from a pool of 34 technical attributes: power warranty, panel efficiency, and time on market. From the survey, additional nontechnical attributes are identified: panel manufacturer's reputation, name recognition, and aesthetics. The work shows that a combination of revealed and stated preference methods may be valuable for identifying both technical and nontechnical attributes to guide design priorities.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Engineering Systems Division
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1115/1.4024232
