Abstract

In order for a sustainable product to be successful in the market, designers must create products that are not only sustainable in reality but are also sustainable as perceived by the customer—and reality versus perception of sustainability can be quite different. This paper details a design method to identify perceptions of sustainable features (PerSFs) by collecting online reviews, manually annotating them using crowdsourced work, and processing the annotated review fragments with a natural language machine learning algorithm. We analyze all three pillars of sustainability—social, environmental, and economic—for positive and negative perceptions of product features of a French press coffee carafe. For social aspects, the results show that positive PerSFs are associated with intangible features, such as giving the product as a gift, while negative PerSFs are associated with tangible features perceived as unsafe, like sharp corners. For environmental aspects, positive PerSFs are associated with reliable materials like metal while negative PerSFs are associated with the use of plastic. For economic aspects, PerSFs mainly serve as a price constraint for designers to satisfy other customer perceptions. We also show that some crucial sustainability concerns related to environmental aspects, like energy and water consumption, did not have a significant impact on customer sentiment, thus demonstrating the anticipated gap in sustainability perceptions and the realities of sustainable design, as noted in previous literature. From these results, online reviews can enable designers to extract PerSFs for further design study and to create products that resonate with customers' sustainable values.

References

1.
McCaskill
,
A.
,
2015
, “
Consumer-Goods’ Brands That Demonstrate Commitment To Sustainability Outperform Those That Don’t
,”
Nielsen
. https://www.nielsen.com/us/en/press-room/2015/consumer-goods-brands-that-demonstrate-commitment-to-sustainability-outperform.html.
2.
She
,
J.
, and
MacDonald
,
E. F.
,
2017
, “
Exploring the Effects of a Product’s Sustainability Triggers on Pro-Environmental Decision-Making
,”
ASME J. Mech. Des.
,
140
(
1
), p.
011102
. 10.1115/1.4038252
3.
Kim
,
E.-H.
, and
Lyon
,
T. P.
,
2015
, “
Greenwash vs. Brownwash: Exaggeration and Undue Modesty in Corporate Sustainability Disclosure
,”
Organ. Sci.
,
26
(
3
), pp.
705
723
. 10.1287/orsc.2014.0949
4.
2018
, “
Quarterly Share of E-Commerce Sales of Total U.S. Retail Sales From 1st Quarter 2010 to 3rd Quarter 2018
,”
Statista
. https://www.statista.com/statistics/187439/share-of-e-commerce-sales-in-total-us-retail-sales-in-2010/.
5.
Roghanizad
,
M. M.
, and
Neufeld
,
D. J.
,
2015
, “
Intuition, Risk, and the Formation of Online Trust
,”
Comput. Hum. Behav.
,
50
(
Sept.
), pp.
489
498
. 10.1016/j.chb.2015.04.025
6.
MacDonald
,
E. F.
,
Gonzalez
,
R.
, and
Papalambros
,
P. Y.
,
2009
, “
Preference Inconsistency in Multidisciplinary Design Decision Making
,”
ASME J. Mech. Des.
,
131
(
3
), p.
031009
. 10.1115/1.3066526
7.
Ren
,
Y.
,
Burnap
,
A.
, and
Papalambros
,
P.
,
2013
, “
Quantification of Perceptual Design Attributes Using a Crowd
,”
International Conference on Engineering Design
,
Seoul, Korea
,
Aug. 19–22
.
8.
Engström
,
P.
, and
Forsell
,
E.
,
2018
, “
Demand Effects of Consumers’ Stated and Revealed Preferences
,”
J. Econ. Behav. Organ.
,
150
(
June
), pp.
43
61
. 10.1016/j.jebo.2018.04.009
9.
Netzer
,
O.
,
Toubia
,
O.
,
Bradlow
,
E. T.
,
Dahan
,
E.
,
Evgeniou
,
T.
,
Feinberg
,
F. M.
,
Feit
,
E. M.
,
Hui
,
S. K.
,
Johnson
,
J.
,
Liechty
,
J. C.
,
Orlin
,
J. B.
, and
Rao
,
V. R.
,
2008
, “
Beyond Conjoint Analysis: Advances in Preference Measurement
,”
Mark. Lett.
,
19
(
3/4
), pp.
337
354
. 10.1007/s11002-008-9046-1
10.
Decker
,
R.
, and
Trusov
,
M.
,
2010
, “
Estimating Aggregate Consumer Preferences From Online Product Reviews
,”
Int. J. Res. Mark.
,
27
(
4
), pp.
293
307
. 10.1016/j.ijresmar.2010.09.001
11.
Qiao
,
Z.
,
Wang
,
G. A.
,
Zhou
,
M.
, and
Fan
,
W.
,
2017
, “The Impact of Customer Reviews on Product Innovation: Empirical Evidence in Mobile Apps,”
Analytics and Data Science
,
Springer
,
Cham
, pp.
95
110
.
12.
Liu
,
Y.
,
Jin
,
J.
,
Ji
,
P.
,
Harding
,
J. A.
, and
Fung
,
R. Y. K.
,
2013
, “
Identifying Helpful Online Reviews: A Product Designer’s Perspective
,”
Comput. Aided Des.
,
45
(
2
), pp.
180
194
. 10.1016/j.cad.2012.07.008
13.
Rai
,
R.
,
2012
, “
Identifying Key Product Attributes and Their Importance Levels From Online Customer Reviews
,”
ASME 2012 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference
,
Chicago, IL
,
Aug. 12–15
.
14.
Stone
,
T.
, and
Choi
,
S.-K.
,
2013
, “
Extracting Customer Preference From User-Generated Content Sources Using Classification
,”
ASME 2013 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference
,
Portland, OR
,
Aug. 4–7
.
15.
Singh
,
A. S.
, and
Tucker
,
C. S.
,
2015
, “
Investigating the Heterogeneity of Product Feature Preferences Mined Using Online Product Data Streams
,”
ASME 2015 International Design Engineering Technical Conferences & Computers and Information in Engineering Conferences
,
Boston, MA
,
Aug. 2–5
.
16.
Singh
,
A.
, and
Tucker
,
C. S.
,
2017
, “
A Machine Learning Approach to Product Review Disambiguation Based on Function, Form and Behavior Classification
,”
Decis. Support Syst.
,
97
(
2016
), pp.
81
91
. 10.1016/j.dss.2017.03.007
17.
Kataria
,
S.
,
Mitra
,
P.
, and
Bhatia
,
S.
,
2010
, “
Utilizing Context in Generative Bayesian Models for Linked Corpus
,”
24th AAAI Conference on Artificial Intelligence
,
Atlanta, GA
,
July 11–15
.
18.
Krestel
,
R.
,
Fankhauser
,
P.
, and
Nejdl
,
W.
,
2009
, “
Latent Dirichlet Allocation for Tag Recommendation
,”
Third ACM Conference on Recommender Systems
,
New York, NY
,
Oct. 23–25
.
19.
Tuarob
,
S.
,
Pouchard
,
L. C.
, and
Giles
,
C. L.
,
2013
, “
Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling
,”
13th ACM/IEEE-CS Joint Conference on Digital Libraries
,
Indianapolis, IN
,
July 22–26
.
20.
Tuarob
,
S.
,
Pouchard
,
L. C.
,
Noy
,
N.
,
Horsburgh
,
J. S.
, and
Palanisamy
,
G.
,
2012
, “
ONEMercury: Towards Automatic Annotation of Environmental Science Metadata
,”
Second International Workshop on Linked Science
,
Boston, MA
,
Nov. 12
.
21.
Zhang
,
X.
, and
Mitra
,
P.
,
2010
, “
Learning Topical Transition Probabilities in Click Through Data With Regression Models
,”
13th International Workshop on the Web and Databases
,
Indianapolis, IN
,
June 6
.
22.
Tuarob
,
S.
, and
Tucker
,
C. S.
,
2015
, “
Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks
,”
ASME J. Mech. Des.
,
137
(
7
), p.
071402
. 10.1115/1.4030049
23.
Tuarob
,
S.
, and
Tucker
,
C. S.
,
2015
, “
A Product Feature Inference Model for Mining Implicit Customer Preferences Within Large Scale Social Media Networks
,”
ASME 2015 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference
,
Boston, MA
,
Aug. 2–5
.
24.
Thelwall
,
M.
,
Buckley
,
K.
,
Paltoglou
,
G.
, and
Cai
,
D.
,
2010
, “
Sentiment Strength Detection in Short Informal Text
,”
Am. Soc. Inf. Sci. Technol.
,
61
(
12
), pp.
2544
2558
. 10.1002/asi.21416
25.
Wang
,
W. M.
,
Li
,
Z.
,
Tian
,
Z. G.
,
Wang
,
J. W.
, and
Cheng
,
M. N.
,
2018
, “
Extracting and Summarizing Affective Features and Responses From Online Product Descriptions and Reviews: A Kansei Text Mining Approach
,”
Eng. Appl. Artif. Intell.
,
73
(
Aug.
), pp.
149
162
. 10.1016/j.engappai.2018.05.005
26.
Nagamachi
,
M.
, and
Imada
,
A. S.
,
1995
, “
Kansei Engineering: An Ergonomic Technology for Product Development
,”
Int. J. Ind. Ergon.
,
15
(
1
), pp.
3
11
. 10.1016/0169-8141(94)00052-5
27.
Paolacci
,
G.
, and
Chandler
,
J.
,
2014
, “
Inside the Turk: Understanding Mechanical Turk as a Participant Pool
,”
Curr. Dir. Psychol. Sci.
,
23
(
3
), pp.
184
188
. 10.1177/0963721414531598
28.
Goodman
,
J. K.
, and
Paolacci
,
G.
,
2017
, “
Crowdsourcing Consumer Research
,”
J. Consum. Res.
,
44
(
1
), pp.
196
210
. 10.1093/jcr/ucx047
29.
Jurafsky
,
D.
,
2018
, “N-Gram Language Models,”
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
.
30.
James
,
G.
,
Witten
,
D.
,
Haste
,
T.
, and
Tibshirani
,
R.
,
2006
,
An Introduction to Statistical Learning With Applications in R
.
31.
MacDonald
,
E. F.
, and
She
,
J.
,
2015
, “
Seven Cognitive Concepts for Successful Eco-Design
,”
J. Cleaner Prod.
,
92
(
Apr.
), pp.
23
36
. 10.1016/j.jclepro.2014.12.096
32.
2019
, “
Sustainable Minds
,”
Sustainable Minds, LLC
. https://www.sustainableminds.com.
33.
Slimak
,
M. W.
, and
Dietz
,
T.
,
2006
, “
Personal Values, Beliefs, and Ecological Risk Perception
,”
Risk Anal.
,
26
(
6
), p.
1689e1705
. 10.1111/j.1539-6924.2006.00832.x
34.
2018
, “
reviewsampler
,”
Github
. https://github.com/wrossmorrow/reviewsampler.
You do not currently have access to this content.