Research Papers

Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels

[+] Author and Article Information
Heidi Q. Chen

Mechanical Engineering,
Massachusetts Institute of Technology,
Cambridge, MA 02139
e-mail: heidiqc@mit.edu

Tomonori Honda

Research Scientist
Mechanical Engineering,
Massachusetts Institute of Technology,
Cambridge, MA 02139
e-mail: tomonori@mit.edu

Maria C. Yang

Assistant Professor
Mechanical Engineering and Engineering Systems,
Massachusetts Institute of Technology,
Cambridge, MA 02139
e-mail: mcyang@mit.edu

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received August 19, 2012; final manuscript received April 6, 2013; published online May 9, 2013. Assoc. Editor: Jonathan Cagan.

J. Mech. Des 135(6), 061007 (May 09, 2013) (12 pages) Paper No: MD-12-1419; doi: 10.1115/1.4024232 History: Received August 19, 2012; Revised April 06, 2013

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.

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Fig. 1

Cumulative market share of panels

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Fig. 2

Flowchart of methodology

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Fig. 3

ANN bootstrapping error validation

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Fig. 4

Importance ranking of technical attributes

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Fig. 5

Relative importance of technical and nontechnical attributes: Open ended question

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Fig. 6

Relative importance of technical and nontechnical attributes: Rating question

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Fig. 7

Aesthetic preferences for PV panel categories

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Fig. 8

Name recognition of panel manufacturers

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Fig. 9

Reputation of panel manufacturers

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Fig. 10

Service level of panel manufacturers




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