Traditionally, consumer preference is modeled in terms of preference for the aesthetic and functional features of a product. This paper introduces a new means to model consumer preference that accounts for not only for how a product looks and functions but also how it feels to interact with it. Traditional conjoint-based approaches to preference modeling require a participant to judge preference for a product based upon a static 2D visual representation or a feature list. While the aesthetic forms and functional features of a product are certainly important, the decision to buy or not to buy a product often depends on more, namely, the experience or feel of use. To address the importance of the product experience, we introduce the concept of experiential conjoint analysis, a method to mathematically capture preference for a product through experience-based (experiential) preference judgments. Experiential preference judgments are made based upon the use, or simulated use, of a product. For many products, creating enough physical prototypes to generate a preference model is cost prohibitive. In this work, virtual reality (VR) technologies are used to allow the participant an interactive virtual product experience, provided at little investment. The results of this work show that providing additional interaction-based (interactional) information about a product through a product experience does not affect the predictive ability of the resulting preference models. This work additionally demonstrates that the preference judgments of virtual product representations are more similar to preference judgments of real products than preference judgments of 2D product representations are. When examining similarity of modeled preference, experiential conjoint is found to be superior to visual conjoint with respect to mean absolute error (MAE), but with respect to correlation no significant difference between visual and experiential is found.

Issue Section:
Design Automation
Topics:
Preferences, Design

References

1.
Creusen
,
M.
, and
Schoormans
,
J.
,
2004
, “
The Different Roles of Product Appearance in Consumer Choice
,”
J. Prod. Innov. Manag.
,
22
(
1
), pp.
63
–
81
.10.1111/j.0737-6782.2005.00103.x
2.
Orsborn
,
S.
,
Cagan
,
J.
, and
Boatwright
,
P.
,
2009
, “
Quantifying Aesthetic Form Preference in a Utility Function
,”
ASME J. Mech. Des.
,
131
(
6
), pp.
1
–
10
.10.1115/1.3116260
3.
Tseng
,
I.
,
Cagan
,
J.
, and
Kotovsky
,
K.
,
2011
, “
Learning Stylistic Desires and Generating Preferred Designs of Consumers Using Neural Networks and Genetic Algorithms
,”
ASME
Paper No. DETC2011-48642. 10.1115/DETC2011-48642
4.
Reid
,
T. N.
,
Gonzalez
,
R. D.
, and
Papalambros
,
P. Y.
,
2010
, “
Quantification of Perceived Environmental Friendliness for Vehicle Silhouette Design
,”
ASME J. Mech. Des.
,
132
(
10
), pp.
1
–
10
.10.1115/1.4002290
5.
Kelly
,
J.
, and
Papalambros
,
P.
,
2007
, “
Use of Shape Preference Information in Product Design
,”
International Conference on Engineering Design
, Paris, France, Vol. 7, pp.
1
–
11
.
6.
Desmet
,
P.
, and
Hekkert
,
P.
,
2007
, “
Framework of Product Experience
,”
Int. J. Des.
,
1
(
1
), pp.
1
–
7
.
7.
Arthur
,
E. J.
,
Hancock
,
P. A.
, and
Chrysler
,
S. T.
,
1997
, “
The Perception of Spatial Layout in Real and Virtual Worlds
,”
Ergonomics
,
40
(
1
), pp.
69
–
77
.10.1080/001401397188387
8.
Henry
,
D.
, and
Furness
,
T.
,
1993
, “
Spatial Perception in Virtual Environments: Evaluating an Architectural Application
,”
Virtual Reality Annual International Symposum
, Seattle, WA, pp.
1
–
107
.10.1109/vrais.1993.380801
9.
Green
,
P.
, and
Wind
,
Y.
,
1975
, “
New Way to Measure Consumers' Judgement
,” Harvard Business Review, July/August, pp.
107
–
117
.
10.
Green
,
P.
,
1974
, “
On the Design of Choice Experiments Involving Multifactor Alternatives
,”
J. Consum. Res.
,
1
(
2
), pp.
61
–
68
.10.1086/208592
11.
Luce
,
R.
, and
Tukey
,
J.
,
1964
, “
Simultaneous Conjoint Measurement: A New Type of Fundamental Measurement
,”
J. Math. Psychol.
,
1
(
1
), pp.
1
–
27
.10.1016/0022-2496(64)90015-X
12.
Sylcott
,
B.
,
Cagan
,
J.
, and
Tabibnia
,
G.
,
2013
, “
Understanding Consumer Tradeoffs Between Form and Function Through Metaconjoint and Cognitive Neuroscience Analyses
,”
ASME J. Mech. Des.
,
135
(
10
), p.
101002
.10.1115/1.4024975
13.
Reid
,
T.
,
Gonzalez
,
R.
, and
Papalambros
,
P.
,
2009
, “
A Methodology for Quantifying the Perceived Environmental Friendliness of Vehicle Silhouettes in Engineering Design
,” ASME Paper No. DETC2009-97095.10.1115/detc2009-87095
14.
Tovares
,
N.
,
Cagan
,
J.
, and
Boatwright
,
P.
,
2013
, “
Capturing Consumer Preference Through Experiential Conjoint Analysis
,”
ASME
Paper No.
DETC2013-12549. 10.1115/DETC2013-12549
15.
Gurnani
,
A.
, and
Lewis
,
K.
,
2005
, “
Robust Multiattribute Decision Making Under Risk and Uncertainty in Engineering Design
,”
Eng. Optim.
,
37
(
8
), pp.
813
–
830
.10.1080/03052150500340520
16.
Ferguson
,
S.
,
Kasprzak
,
E.
, and
Lewis
,
K.
,
2008
, “
Designing a Family of Reconfigurable Vehicles Using Multilevel Multidisciplinary Design Optimization
,”
Struct. Multidiscip. Optim.
,
39
(
2
), pp.
171
–
186
.10.1007/s00158-008-0319-3
17.
Li
,
H.
, and
Azarm
,
S.
,
2002
, “
An Approach for Product Line Design Selection Under Uncertainty and Competition
,”
ASME J. Mech. Des.
,
124
(
3
), pp.
385
–
392
.10.1115/1.1485740
18.
Maddulapalli
,
A. K.
, and
Azarm
,
S.
,
2006
, “
Product Design Selection With Preference and Attribute Variability for an Implicit Value Function
,”
ASME J. Mech. Des.
,
128
(
5
), pp.
1027
–
1037
.10.1115/1.2216728
19.
Ahlberg
,
G.
,
Heikkinen
,
T.
,
Iselius
,
L.
,
Leijonmarck
,
C.-E.
,
Rutqvist
,
J.
, and
Arvidsson
,
D.
,
2002
, “
Does Training in a Virtual Reality Simulator Improve Surgical Performance?
,”
Surg. Endosc.
,
16
(
1
), pp.
126
–
129
.10.1007/s00464-001-9025-6
20.
Aoki
,
H.
,
Oman
,
C. M.
,
Buckland
,
D. A.
, and
Natapoff
,
A.
,
2008
, “
Desktop-VR System for Preflight 3D Navigation Training
,”
Acta Astronaut.
,
63
(
7–10
), pp.
841
–
847
.10.1016/j.actaastro.2007.11.001
21.
Lapointe
,
J.
, and
Robert
,
J.
,
2000
, “
Using VR for Efficient Training of Forestry Machine Operators
,”
Educ. Inf. Technol.
,
5
(
4
), pp.
237
–
250
.10.1023/A:1012045305968
22.
Rizzo
,
A.
,
Morie
,
J.
,
Williams
,
J.
,
Pair
,
J.
, and
Buckwalter
,
J.
,
2005
, “
Human Emotional State and its Relevance for Military VR Training
,”
Proceedings of the 11th International Conference on Human Computer Interaction
, Las Vegas, NV, pp.
1
–
10
.
23.
Seymour
,
N. E.
,
2008
, “
VR to OR: A Review of the Evidence That Virtual Reality Simulation Improves Operating Room Performance
,”
World J. Surg.
,
32
(
2
), pp.
182
–
188
.10.1007/s00268-007-9307-9
24.
Ulicny
,
B.
, and
Thalmann
,
D.
,
2001
, “
Crowd Simulation for Interactive Virtual Environments and VR Training Systems
,”
Proceedings of the Eurographic Workshop on Computer Animation and Simulation
, pp.
163
–
170
.
25.
Xiaoling
,
W.
,
Peng
,
Z.
,
Zhifang
,
W.
, and
Yan
,
S.
,
2004
, “
Development an Interactive VR Training for CNC Machining
,”
Proceedings of the 2004 ACM SIGGRAPH International Conference on Virtual Reality Continuum and Its Applications in Industry
, New York, NY, pp.
131
–
133
.10.1145/1044588.1044612
26.
Bowman
,
D.
, and
McMahan
,
R.
,
2007
, “
Virtual Reality: How Much Immersion is Enough?
,”
Computer
(Long Beach, CA),
41
(
7
), pp.
36
–
43
.10.1109/MC.2007.257
27.
Liu
,
L.
, and
Van Liere
,
R.
,
2009
, “
Comparing Aimed Movements in the Real World and in Virtual Reality
,”
IEEE Virtual Reality
, Lafayette, LA, pp.
219
–
222
.10.1109/vr.2009.4811026
28.
Reuding
,
T.
, and
Meil
,
P.
,
2004
, “
Predictive Value of Assessing Vehicle Interior Design Ergonomics in a Virtual Environment
,”
ASME J. Comput. Inf. Sci. Eng.
,
4
(
2
), pp.
109
–
113
.10.1115/1.1710867
29.
Ahmed
,
F.
, and
Cohen
,
J.
,
2010
, “
Influence of Tactile Feedback and Presence on Egocentric Distance Perception in Virtual Environments
,”
IEEE Virtual Reality
, Waltham, MA, pp.
195
–
202
.10.1109/vr.2010.5444791
30.
Söderman
,
M.
,
2005
, “
Virtual Reality in Product Evaluations With Potential Customers: An Exploratory Study Comparing Virtual Reality With Conventional Product Representations
,”
J. Eng. Des.
,
16
(
3
), pp.
311
–
328
.10.1080/09544820500128967
31.
Artacho-Ramírez
,
M. A.
,
Diego-Mas
,
J. A.
, and
Alcaide-Marzal
,
J.
,
2008
, “
Influence of the Mode of Graphical Representation on the Perception of Product Aesthetic and Emotional Features: An Exploratory Study
,”
Int. J. Ind. Ergon.
,
38
(
11–12
), pp.
942
–
952
.10.1016/j.ergon.2008.02.020
32.
Slater
,
M.
,
Khanna
,
P.
,
Mortensen
,
J.
, and
Yu
,
I.
,
2009
, “
Virtual Realism Enhances Realistic Responses in an Immersive Virtual Environment
,”
IEEE Comput. Graph. Appl.
,
29
(
3
), pp.
76
–
84
.10.1109/MCG.2009.55
33.
Bryson
,
S. T.
,
1993
, “
Effects of Lag and Frame Rate on Various Tracking Tasks
,”
Proc. SPIE
,
1915
, pp.
155
–
166
.10.1117/12.157034
34.
Ware
,
C.
, and
Balakrishnan
,
R.
,
1994
, “
Reaching for Objects in VR Displays: Lag and Frame Rate
,”
ACM Trans. Comput. Interact.
,
1
(
4
), pp.
331
–
356
.10.1145/198425.198426
35.
Berneburg
,
A.
,
2007
, “
Interactive 3-D Simulations in Measuring Consumer Preferences: Friend or Foe to Test Results?
,”
J. Interact. Advert.
,
8
(
1
), pp.
1
–
13
.10.1080/15252019.2007.10722132
36.
Dijkstra
,
J.
,
Leeuwen
,
J. Van
, and
Timmermans
,
H.
,
2003
, “
Evaluating Design Alternatives Using Conjoint Experiments in Virual Reality
,”
Environ. Plan. B, Plan. Des.
,
30
(
3
), pp.
357
–
367
.10.1068/b12932
37.
Dijkstra
,
J.
,
Roelen
,
W. A. H.
, and
Timmermans
,
H. J. P.
,
1996
, “
Conjoint Measurement in Virtual Environments: A Framework
,”
Proceedings of 3rd Design and Decision Support Systems in Architecture and Urban Planning Conference
, Vol. 1, pp.
1
–
13
.
38.
Orzechowski
,
M. A.
,
Arentze
,
T. A.
,
Borgers
,
A. W. J.
, and
Timmermans
,
H. J. P.
,
2005
, “
Alternate Methods of Conjoint Analysis for Estimating Housing Preference Functions: Effects of Presentation Style
,”
J. Hous. Built Environ.
,
20
(
4
), pp.
349
–
362
.10.1007/s10901-005-9019-0
39.
Kuhfeld
,
W.
,
Tobias
,
R.
, and
Garratt
,
M.
,
1994
, “
Efficient Experimental Design With Marketing Research Applications
,”
J. Mark. Res.
,
31
(
4
), pp.
545
–
557
.10.2307/3151882
40.
Swamy
,
S.
,
Orsborn
,
S.
,
Michalek
,
J.
, and
Cagan
,
J.
,
2007
, “
Measurement of Headlight Form Preference Using a Choice Based Conjoint Analysis
,”
ASME
Paper No.
DETC2007-35409.10.1115/detc2007-35409
41.
Reid
,
T.
,
MacDonald
,
E.
, and
Du
,
P.
,
2012
, “
Impact of Product Design Representation on Customer Judgment With Associated Eye Gaze Patterns
,”
ASME
Paper No.
DETC2012-70734.10.1115/detc2012-70734
42.
Kuhfeld
,
W. F.
,
2010
, “
Experimental Design: Efficiency, Coding, and Choice Designs
,” SAS Tech. Pap., MR-2010C, pp.
53
–
241
.
43.
Ariely
,
D.
,
2000
, “
Controlling the Information Flow: Effects on Consumers' Decision Making and Preferences
,”
J. Consum. Res.
,
27
(
2
), pp.
233
–
248
.10.1086/314322
44.
Malhotra
,
N.
,
1982
, “
Information Load and Consumer Decision Making
,”
J. Consum. Res.
,
8
(
4
), pp.
419
–
430
.10.1086/208882
45.
Milord
,
J.
, and
Perry
,
R.
,
1977
, “
A Methodological Study of Overload
,”
J. Gen. Psychol.
,
97
(
1
), pp.
131
–
137
.10.1080/00221309.1977.9918509
46.
Kuhfeld
,
W. F.
,
2010
, “
Conjoint Analysis
,” SAS Tech. Pap., MR-2010H, pp.
681
–
801
.
47.
Louviere
,
B. J. J.
,
1988
, “
Conjoint Analysis Modelling of Stated Preferences: A Review of Theory, Methods, Recent Developments and External Validity
,”
J. Transp. Econ. Policy
,
22
(
1
), pp.
93
–
119
.
48.
Allenby
,
G.
,
Arora
,
N.
, and
Ginter
,
J.
,
1995
, “
Incorporating Prior Knowledge Into the Analysis of Conjoint Studies
,”
J. Mark. Res.
,
32
(
2
), pp.
152
–
162
.10.2307/3152044
49.
Kalish
,
S.
, and
Nelson
,
P.
,
1991
, “
A Comparison of Ranking, Rating and Reservation Price Measurement in Conjoint Analysis
,”
Mark. Lett.
,
2
(
4
), pp.
327
–
335
.10.1007/BF00664219
50.
Klein
,
A.
,
Nihalani
,
K.
, and
Krishnan
,
K.
,
2010
, “
A Comparison of the Validity of Interviewer-Based and Online-Conjoint Analyses
,”
J. Manag. Mark. Res.
,
4
, pp.
1
–
15
.
51.
Rodgers
,
J.
, and
Nicewander
,
W.
,
1988
, “
Thirteen Ways to Look at the Correlation Coefficient
,”
Am. Stat.
,
42
(
1
), pp.
59
–
66
.10.2307/2685263
52.
Kass
,
S.
,
Cole
,
K.
, and
Stanny
,
C.
,
2007
, “
Effects of Distraction and Experience on Situation Awareness and Simulated Driving
,”
Transp. Res. Part F: Traffic Psychol. Behav.
,
10
(
4
), pp.
321
–
329
.10.1016/j.trf.2006.12.002
53.
Hanowski
,
R.
,
Perez
,
M.
, and
Dingus
,
T.
,
2005
, “
Driver Distraction in Long-Haul Truck Drivers
,”
Transp. Res. Part F: Traffic Psychol. Behav.
,
8
(
6
), pp.
441
–
458
.10.1016/j.trf.2005.08.001
54.
Casaló
,
L.
,
Flavián
,
C.
, and
Guinalíu
,
M.
,
2008
, “
The Role of Perceived Usability, Reputation, Satisfaction and Consumer Familiarity on the Website Loyalty Formation Process
,”
Comput. Human Behav.
,
24
(
2
), pp.
325
–
345
.10.1016/j.chb.2007.01.017
55.
Normark
,
C.
,
Kappfjell
,
M.
,
Tretten
,
P.
,
Lundberg
,
J.
, and
Garling
,
A.
,
2007
, “
Evaluation of Car Instrumentation Clusters by Using Eye-Tracking
,” Proceedings 11th European Automotive Congress (EAEC), Budapest, Hungary, pp.
1
–
11
.
56.
Parkinson
,
M.
, and
Reed
,
M.
,
2005
, “
Robust Truck Cabin Layout Optimization Using Advanced Driver Variance Models
,”
ASME
Paper No. DETC2005-84179.10.1115/detc2005-84179
57.
Garneau
,
C. J.
, and
Parkinson
,
M. B.
,
2009
, “
Including Preference in Anthropometry-Driven Models for Design
,”
ASME J. Mech. Des.
,
131
(
10
), pp.
1
–
6
.10.1115/1.3211092
58.
Parkinson
,
M. B.
,
Reed
,
M. P.
,
Kokkolaras
,
M.
, and
Papalambros
,
P. Y.
,
2007
, “
Optimizing Truck Cab Layout for Driver Accommodation
,”
ASME J. Mech. Des.
,
129
(
11
), pp.
1
–
8
.10.1115/1.2771181
59.
Chaffin
,
D. B.
,
2008
, “
Digital Human Modeling for Workspace Design
,”
Rev. Hum. Factors Ergon.
,
4
(
1
), pp.
41
–
74
.10.1518/155723408X342844
Copyright © 2014 by ASME
You do not currently have access to this content.