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.

References

References
1.
Drucker
,
P.
,
1994
, “
The Theory of the Business
,”
Harvard Bus. Rev.
,
72
(
5
), pp.
95
104
.
2.
Ulrich
,
K. T.
,
Eppinger
,
S. D.
, and
Goyal
,
A.
,
2011
,
Product Design and Development
,
5th ed.
,
McGraw-Hill
,
New York
.
3.
Geroski
,
P.
,
2000
, “
Models of Technology Diffusion
,”
Res. Policy
,
29
(
4–5
), pp.
603
625
.10.1016/S0048-7333(99)00092-X
4.
Rogers
,
E.
,
1984
,
Diffusion of Innovations
,
The Free Press
,
New York
.
5.
Honda
,
T.
,
Chen
,
H.
,
Chan
,
K.
, and
Yang
,
M.
,
2011
, “
Propagating Uncertainty in Solar Panel Performance for Life Cycle Modeling in Early Stage Design
,”
2011 AAAI Spring Symposium Series
.
6.
Rucks
,
C. T.
, and
Whalen
,
J. M.
,
1983
, “
Solar-Energy Users in Arkansas: Their Identifying Characteristics
,”
Public Util. Fortn.
,
111
(
9
), pp.
36
38
.
7.
Wander
,
J.
,
2006
, “
Stimulating the Diffusion of Photovoltaic Systems: A Behavioural Perspective
,”
Energy Policy
,
34
(
14
), pp.
1935
1943
.10.1016/j.enpol.2004.12.022
8.
Faiers
,
A.
, and
Neame
,
C.
,
2006
, “
Consumer Attitudes Towards Domestic Solar Power Systems
,”
Energy Policy
,
34
(
14
), pp.
1797
1806
.10.1016/j.enpol.2005.01.001
9.
Jetter
,
A.
, and
Schweinfort
,
W.
,
2011
, “
Building Scenarios With Fuzzy Cognitive Maps: An Exploratory Study of Solar Energy
,”
Futures
,
43
(
1
), pp.
52
66
.10.1016/j.futures.2010.05.002
10.
Samuelson
,
P. A.
,
1938
, “
A Note on the Pure Theory of Consumer's Behaviour
,”
Economica
,
5
(
17
), pp.
61
71
.10.2307/2548836
11.
Little
,
I. M. D.
,
1949
, “
A Reformulation of the Theory of Consumer's Behaviour
,”
Oxford Econ. Pap.
,
1
(
1
), pp.
90
99
.
12.
Samuelson
,
P. A.
,
1948
, “
Consumption Theory in Terms of Revealed Preference
,”
Economica
,
15
(
60
), pp.
243
253
.10.2307/2549561
13.
Houthakker
,
H. S.
,
1950
, “
Revealed Preference and the Utility Function
,”
Economica
,
17
(
66
), pp.
159
174
.10.2307/2549382
14.
Szenberg
,
M.
,
Ramrattan
,
L.
, and
Gottesman
,
A. A.
,
2006
,
Samuelsonian Economics and the Twenty-First Century
,
Oxford University Press
, New York.
15.
Mark
,
E.
,
1980
, “
The Design, Analysis and Interpretation of Repertory Grids
,”
Int. J. Man-Mach. Stud.
,
13
(
1
), pp.
3
24
.10.1016/S0020-7373(80)80032-0
16.
Tan
,
F. B.
, and
Hunter
,
M. G.
,
2002
, “
The Repertory Grid Technique: A Method for the Study of Cognition in Information Systems
,”
MIS Q.
,
26
(
1
), pp.
39
57
.10.2307/4132340
17.
Netzer
,
O.
, and
Srinivasan
,
V.
,
2011
, “
Adaptive Self-Explication of Multiattribute Preferences
,”
J. Mark. Res.
,
48
(
1
), p.
140156
.10.1509/jmkr.48.1.140
18.
Marder
,
E.
,
1999
, “
The Assumptions of Choice Modelling: Conjoint Analysis and SUMM
,”
Can. J. Mark. Res.
,
18
, pp.
3
14
.
19.
Tseng
,
I.
,
Cagan
,
J.
, and
Kotovsky
,
K.
,
2011
, “
Learning Stylistic Desire and Generating Preferred Designs of Consumers Using Neural Networks and Genetic Algorithms
,”
ASME International Design Engineering Technical Conference
,
Washington, DC
.
20.
Cohen
,
S.
,
2003
, “
Maximum Difference Scaling: Improved Measures of Importance and Preference for Segmentation
,”
Sawtooth Software Conference Proceedings
,
Sawtooth Software, Inc.
, Vol.
530
, pp.
61
74
.
21.
Green
,
P. E.
,
Carroll
,
J. D.
, and
Goldberg
,
S. M.
,
1981
, “
A General Approach to Product Design Optimization via Conjoint Analysis
,”
J. Mark.
,
45
(
3
), pp.
17
37
.10.2307/1251539
22.
Green
,
P. E.
, and
Srinivasan
,
V.
,
1990
, “
Conjoint Analysis in Marketing: New Developments With Implications for Research and Practice
,”
J. Mark.
,
54
(
4
), pp.
3
19
.10.2307/1251756
23.
MacDonald
,
E. F.
,
Gonzalez
,
R.
, and
Papalambros
,
P. Y.
,
2009
, “
Preference Inconsistency in Multidisciplinary Design Decision Making
,”
J. Mech. Des.
,
131
(
3
), p.
031009
.10.1115/1.3066526
24.
Horsky
,
D.
,
Nelson
,
P.
, and
Posavac
,
S.
,
2004
, “
Stating Preference for the Ethereal But Choosing the Concrete: How the Tangibility of Attributes Affects Attribute Weighting in Value Elicitation and Choice
,”
J. Consum. Psychol.
,
14
(
1 & 2
), p.
132140
.10.1207/s15327663jcp1401&2_15
25.
Cummings
,
R.
,
Brookshire
,
D.
,
Schulze
,
W.
,
Bishop
,
R.
, and
Arrow
,
K.
,
1986
,
Valuing Environmental Goods: An Assessment of the Contingent Valuation Method
,
Rowman & Allanheld Totowa
,
NJ
.
26.
Kahneman
,
D.
, and
Knetsch
,
J. L.
,
1992
, “
Valuing Public Goods: The Purchase of Moral Satisfaction
,”
J. Environ. Econ. Manage.
,
22
(
1
), pp.
57
70
.10.1016/0095-0696(92)90019-S
27.
Beshears
,
J.
,
Choi
,
J. J.
,
Laibson
,
D.
, and
Madrian
,
B. C.
,
2008
, “
How are Preferences Revealed?
,”
J. Public Econ.
,
92
(
89
), pp.
1787
1794
.10.1016/j.jpubeco.2008.04.010
28.
Adamowicz
,
W.
,
Louviere
,
J.
, and
Williams
,
M.
,
1994
, “
Combining Revealed and Stated Preference Methods for Valuing Environmental Amenities
,”
J. Environ. Econ. Manage.
,
26
(
3
), pp.
271
292
.10.1006/jeem.1994.1017
29.
Tuv
,
E.
,
Borisov
,
A.
,
Runger
,
G.
, and
Torkkola
,
K.
,
2009
, “
Feature Selection With Ensembles, Artificial Variables, and Redundancy Elimination
,”
J. Mach. Learn. Res.
,
10
(
7
), pp.
1341
1366
.
30.
Agard
,
B.
, and
Kusiak
,
A.
,
2004
, “
Data-Mining-Based Methodology for the Design of Product Families
,”
Int. J. Prod. Res.
,
42
(
15
), pp.
2955
2969
.10.1080/00207540410001691929
31.
Ferguson
,
C. J.
,
Lees
,
B.
,
MacArthur
,
E.
, and
Irgens
,
C.
,
1998
, “
An Application of Data Mining for Product Design
,”
IEE Colloquium on Knowledge Discovery and Data Mining (1998/434), IET
, pp.
5/1
5/5
.
32.
Kusiak
,
A.
, and
Salustri
,
F.
,
2007
, “
Computational Intelligence in Product Design Engineering: Review and Trends
,”
IEEE Trans. Syst., Man, Cybern., Part C: Appl. Rev.
,
37
(
5
), pp.
766
778
.10.1109/TSMCC.2007.900669
33.
Griffin
,
A.
, and
Page
,
A. L.
,
1993
, “
An Interim Report on Measuring Product Development Success and Failure
,”
J. Prod. Innovation Manage.
,
10
(
4
), pp.
291
308
.10.1016/0737-6782(93)90072-X
34.
Griffin
,
A.
, and
Page
,
A. L.
,
1996
, “
PDMA Success Measurement Project: Recommended Measures for Product Development Success and Failure
,”
J. Prod. Innovation Manage.
,
13
(
6
), pp.
478
496
.10.1016/S0737-6782(96)00052-5
35.
CSI
,
2011
,
California Solar Initative: Current CSI Data
, http://www.californiasolarstatistics.org/current_data_files/
36.
SEIA
,
2011
, “
U.S. Solar Market Insight
,”
Executive Summary, SEIA/GTM Research
.
37.
Cortes
,
C.
, and
Vapnik
,
V.
,
1995
, “
Support-Vector Networks
,”
Mach. Learn.
,
20
(
3
), pp.
273
297
.
38.
Stine
,
R. A.
,
1995
, “
Graphical Interpretation of Variance Inflation Factors
,”
Am. Stat.
,
49
(
1
), pp.
53
56
.
39.
Wold
,
S.
,
R. A. W. H.
, and
Dunn
,
W. J. I.
,
1984
, “
The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses
,”
Chemom. Intell. Lab. Syst.
,
5
(
3
), pp.
735
743
.
40.
Efron
,
B.
,
H. T. J. I.
, and
Tibshirani
,
R.
,
2004
, “
Least Angle Regression
,”
Ann. Stat.
,
32
(
2
), pp.
407
499
.10.1214/009053604000000067
41.
Tibshirani
,
R.
,
1996
, “
Regression Shrinkage and Selection via the Lasso
,”
J. R. Stat. Soc. B
,
58
(
1
), pp.
267
268
.
42.
Hoerl
,
A. E.
, and
Kennard
,
R. W.
,
1970
, “
Ridge Regression: Biased Estimation for Nonorthogonal Problems
,”
Technometrics
,
12
(
1
), pp.
55
67
.10.1080/00401706.1970.10488634
43.
Tuv
,
E.
,
Borisov
,
A.
,
Runger
,
G.
, and
Torkkola
,
K.
,
2005
, “
Performance of Some Variable Selection Methods When Multicollinearity is Present
,”
Chemom. Intell. Lab. Syst.
,
78
(
1–2
), pp.
103
112
.10.1016/j.chemolab.2004.12.011
44.
O'brien
,
R. M.
,
2007
, “
A Caution Regarding Rules of Thumb for Variance Inflation Factors
,”
Qual. Quant.
,
41
(
5
), pp.
673
690
.10.1007/s11135-006-9018-6
45.
Brierley
,
P.
,
Vogel
,
D.
, and
Axelrod
,
R.
,
2011
, “
How We Did It Team Market Makers
,” Technical Report,
Heritage Provider Network Health Prize Round 1 Milestone Winner's Report
.
46.
Rojas
,
R.
,
1996
,
Neural Networks: A Systematic Introduction
,
Springer
,
New York
.
47.
Breiman
,
L.
,
2001
, “
Random Forests
,”
Mach. Learn.
,
45
(
1
), pp.
5
32
.10.1023/A:1010933404324
48.
Friedman
,
J.
,
2001
, “
Greedy Function Approximation: A Gradient Boosting Machine
,”
Ann. Stat.
, pp.
1189
1232
.10.1214/aos/1013203451
49.
Friedman
,
J.
,
2002
, “
Stochastic Gradient Boosting
,”
Comput. Stat. Data Anal.
,
38
(
4
), pp.
367
378
.10.1016/S0167-9473(01)00065-2
50.
Beale
,
M.
, and
Demuth
,
H.
,
1998
, “
Neural Network Toolbox
,” For Use With matlab, User's Guide,
The MathWorks
,
Natick
.
51.
Liaw
,
A.
, and
Wiener
,
M.
,
2002
, “
Classification and Regression by randomForest
,”
R News
,
2
(
3
), pp.
18
22
.
52.
Ridgeway
,
G.
,
2007
, “
Generalized Boosted Models: A Guide to the GBM Package
,”
Update
,
1
, p.
1
.
53.
Laroche
,
M.
,
Bergeron
,
J.
, and
Barbaro-Forleo
,
G.
,
2001
, “
Targeting Consumers Who are Willing to Pay More for Environmentally Friendly Products
,”
J. Consum. Mark.
,
18
(
6
), pp.
503
520
.10.1108/EUM0000000006155
54.
Gaskell
,
G. D.
,
O'muircheartaigh
,
C. A.
, and
Wright
,
D. B.
,
1994
, “
Survey Questions About the Frequency of Vaguely Defined Events: The Effects of Response Alternatives
,”
Public Opin. Q.
,
58
(
2
), pp.
241
254
.10.1086/269420
55.
Bennett
,
J.
, and
Blamey
,
R.
,
2001
,
The Choice Modelling Approach to Environmental Valuation
,
Edward Elgar Publishing
, Cheltenham, UK.
56.
Courage
,
C.
, and
Baxter
,
K.
,
2005
,
Understanding Your Users: A Practical Guide to User Requirements Methods, Tools, and Techniques
,
Gulf Professional Publishing
, San Francisco, CA.
57.
Price
,
L. L.
,
Feick
,
L. F.
, and
Higie
,
R. A.
,
1989
, “
Preference Heterogeneity and Coorientation as Determinants of Perceived Informational Influence
,”
J. Bus. Res.
,
19
(
3
), pp.
227
242
.10.1016/0148-2963(89)90021-0
58.
Dietrich
,
E.
,
2002
, “
Combining Revealed and Stated Data to Examine Housing Decisions Using Discrete Choice Analysis
,”
J. Urban Econ.
,
51
(
1
), pp.
143
169
.10.1006/juec.2001.2241
59.
Brownstone
,
D.
,
Bunch
,
D.
, and
Train
,
K.
,
2000
, “
Joint Mixed Logit Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles
,”
Transp. Res. Part B: Methodol.
,
34
(
5
), pp.
315
338
.10.1016/S0191-2615(99)00031-4
60.
Hensher
,
D.
, and
Bradley
,
M.
,
1993
, “
Using Stated Response Choice Data to Enrich Revealed Preference Discrete Choice Models
,”
Mark. Lett.
,
4
(
2
), pp.
139
151
.10.1007/BF00994072
You do not currently have access to this content.