2026-09-19T07:39:04Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/744592022-01-13T07:54:36Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Maria C. Yang.
Chen, Heidi Qianyi
Massachusetts Institute of Technology. Dept. of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2012-10-26T18:10:33Z
2012-10-26T18:10:33Z
2012
2012
http://hdl.handle.net/1721.1/74459
813424084
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 91-94).
This thesis 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 solar PV panels is conducted 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, 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 non-technical 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 non-technical attributes to guide design priorities.
by Heidi Qianyi Chen.
S.M.
94 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
Approaches for identifying consumer preferences for the design of technology products : a case study of residential solar panels
Thesis
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Publication
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<Abstract>This thesis 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 solar PV panels is conducted 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, 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 non-technical 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 non-technical attributes to guide design priorities.</Abstract>
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