Lead users play a vital role in next generation product development, as they help designers discover relevant product feature preferences months or even years before they are desired by the general customer base. Existing design methodologies proposed to extract lead user preferences are typically constrained by temporal, geographic, size, and heterogeneity limitations. To mitigate these challenges, the authors of this work propose a set of mathematical models that mine social media networks for lead users and the product features that they express relating to specific products. The authors hypothesize that: (i) lead users are discoverable from large scale social media networks and (ii) product feature preferences, mined from lead user social media data, represent product features that do not currently exist in product offerings but will be desired in future product launches. An automated approach to lead user product feature identification is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discovering innovative users from the ever increasing pool of social media users. The authors collect 2.1 × 109 social media messages in the United States during a period of 31 months (from March 2011 to September 2013) in order to determine whether lead user preferences are discoverable and relevant to next generation cell phone designs.

References

1.
Selden
,
L.
, and
MacMillan
,
I. C.
,
2006
, “
Manage Customer-Centric Innovation—Systematically
,”
Harv. Bus. Rev.
,
84
(
9
), pp.
149
150
.
2.
Shah
,
S.
,
2000
, “
Sources and Patterns of Innovation in a Consumer Products Field: Innovations in Sporting Equipment
,”
Sloan School of Management
,
Massachusetts Institute of Technology
,
Cambridge, MA
, WP-4105.
3.
Tietz
,
R.
,
Morrison
,
P. D.
,
Luthje
,
C.
, and
Herstatt
,
C.
,
2005
, “
The Process of User-Innovation: A Case Study in a Consumer Goods Setting
,”
Int. J. Prod. Dev.
,
2
(
4
), pp.
321
338
.10.1504/IJPD.2005.008005
4.
Luthje
,
C.
,
2004
, “
Characteristics of Innovating Users in a Consumer Goods Field: An Empirical Study of Sport-Related Product Consumers
,”
Technovation
,
24
(
9
), pp.
683
695
.10.1016/S0166-4972(02)00150-5
5.
Franke
,
N.
,
Von Hippel
,
E.
, and
Schreier
,
M.
,
2006
, “
Finding Commercially Attractive User Innovations: A Test of Lead-User Theory
,”
J. Prod. Innovation Manage.
,
23
(
4
), pp.
301
315
.10.1111/j.1540-5885.2006.00203.x
6.
Baldwin
,
C.
, and
Von Hippel
,
E.
,
2010
, “
Modeling a Paradigm Shift: From Producer Innovation to User and Open Collaborative Innovation
,” Harvard Business School Finance Working Paper, Paper No. 10-038, pp.
4764
4809
.
7.
Von Hippel
,
E.
,
1986
, “
Lead Users: A Source of Novel Product Concepts
,”
Manage. Sci.
,
32
(
7
), pp.
791
805
.10.1287/mnsc.32.7.791
8.
von Hippel
,
E.
,
Ogawa
,
S.
, and
de Jong Jeroen
,
P.
,
2011
, “
The Age of the Consumer–Innovator
,”
MIT Sloan Manage. Rev.
,
53
(
1
), pp.
27
35
.
9.
Herstatt
,
C.
, and
von Hippel
,
E.
,
1992
, “
From Experience: Developing New Product Concepts Via the Lead User Method: A Case Study in a “Low-Tech” Field
,”
J. Prod. Innovation Manage.
,
9
(
3
), pp.
213
221
.10.1016/0737-6782(92)90031-7
10.
von Hippel
,
E.
,
Thomke
,
S.
, and
Sonnack
,
M.
,
1999
, “
Creating Breakthroughs at 3M
,”
Harv. Bus. Rev.
,
77
(
5
), pp.
47
57
.
11.
Lilien
,
G. L.
,
Morrison
,
P. D.
,
Searls
,
K.
,
Sonnack
,
M.
, and
Hippel
,
E. V.
,
2002
, “
Performance Assessment of the Lead User Idea-Generation Process for New Product Development
,”
Manage. Sci.
,
48
(
8
), pp.
1042
1059
.10.1287/mnsc.48.8.1042.171
12.
Wu
,
X.
,
Zhu
,
X.
,
Wu
,
G.-Q.
, and
Ding
,
W.
,
2014
, “
Data Mining With Big Data
,”
IEEE Trans. Knowl. Data Eng.
,
26
(
1
), pp.
97
107
.10.1109/TKDE.2013.109
13.
Corporation
,
I.
,
2013
, “What is Big Data?—Bringing Big Data to the Enterprise.” Available at: http://www-01.ibm.com/software/ph/data/bigdata/
14.
Sakaki
,
T.
,
Okazaki
,
M.
, and
Matsuo
,
Y.
,
2010
, “
Earthquake Shakes Twitter Users: Real-Time Event Detection by Social Sensors
,”
Proceedings of the 19th International Conference on World Wide Web
,
WWW’10
, ACM, New York, pp.
851
860
.10.1145/1772690.1772777
15.
Collier
,
N.
, and
Doan
,
S.
,
2012
, “
Syndromic Classification of Twitter Messages
,”
Electronic Healthcare
(Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering),
P.
Kostkova
,
M.
Szomszor
, and
D.
Fowler
, eds., Vol.
91
,
Springer
,
Berlin, Germany
, pp.
186
195
.
16.
Bollen
,
J.
,
Mao
,
H.
, and
Zeng
,
X.
,
2011
, “
Twitter Mood Predicts the Stock Market
,”
J. Comput. Sci.
,
2
(
1
), pp.
1
8
.10.1016/j.jocs.2010.12.007
17.
Zhang
,
X.
,
Fuehres
,
H.
, and
Gloor
,
P.
,
2012
, “
Predicting Asset Value Through Twitter Buzz
,”
Advances in Collective Intelligence 2011
,
Springer
, Berlin, Heidelberg, pp.
23
34
.10.1007/978-3-642-25321-8_3
18.
Tuarob
,
S.
, and
Tucker
,
C. S.
,
2013
, “
Fad or Here to Stay: Predicting Product Market Adoption and Longevity Using Large Scale, Social Media Data
,”
ASME
Paper No. DETC2013-12661.10.1115/DETC2013-12661
19.
Lin
,
J.
, and
Seepersad
,
C. C.
,
2007
, “
Empathic Lead Users: The Effects of Extraordinary User Experiences on Customer Needs Analysis and Product Redesign
,”
ASME
Paper No. DETC2007-35302.10.1115/DETC2007-35302
20.
Droge
,
C.
,
Stanko
,
M. A.
, and
Pollitte
,
W. A.
,
2010
, “
Lead Users and Early Adopters on the Web: The Role of New Technology Product Blogs
,”
J. Prod. Innovation Manage.
,
27
(
1
), pp.
66
82
.10.1111/j.1540-5885.2009.00700.x
21.
Bilgram
,
V.
,
Brem
,
A.
, and
Voigt
,
K.-I.
,
2008
, “
User-Centric Innovations in New Product Development-Systematic Identification of Lead Users Harnessing Interactive and Collaborative Online-Tools
,”
Int. J. Innovation Manage.
,
12
(
03
), pp.
419
458
.10.1142/S1363919608002096
22.
Ogawa
,
S.
, and
Piller
,
F. T.
,
2006
, “
Reducing the Risks of New Product Development
,”
MIT Sloan Manage. Rev.
,
47
(
2
), pp.
65
71
.
23.
Bodnar
,
T.
,
Tucker
,
C.
,
Hopkinson
,
K.
, and
Bilen
,
S.
,
2014
, “
Increasing the Veracity of Event Detection on Social Media Networks Through User Trust Modeling
,” 2014
IEEE
International Conference on Big Data, Institute of Electrical and Electronics Engineers, Washington, DC, Oct. 27–30, pp.
636
643
.10.1109/BigData.2014.7004286
24.
Von Hippel
,
E.
,
1978
, “
Successful Industrial Products From Customer Ideas
,”
J. Mark.
,
42
(
1
), pp.
39
49
.10.2307/1250327
25.
Hannukainen
,
P.
, and
Hölttä-Otto
,
K.
,
2006
, “
Identifying Customer Needs: Disabled Persons as Lead Users
,”
ASME
Paper No. DETC2006-99043.10.1115/DETC2006-99043
26.
Batallas
,
D.
, and
Yassine
,
A.
,
2006
, “
Information Leaders in Product Development Organizational Networks: Social Network Analysis of the Design Structure Matrix
,”
IEEE Trans. Eng. Manage.
,
53
(
4
), pp.
570
582
.10.1109/TEM.2006.883706
27.
Schreier
,
M.
,
Oberhauser
,
S.
, and
Prügl
,
R.
,
2007
, “
Lead Users and the Adoption and Diffusion of New Products: Insights From Two Extreme Sports Communities
,”
Mark. Lett.
,
18
(
1–2
), pp.
15
30
.10.1007/s11002-006-9009-3
28.
Vaughan
,
M. R.
,
Seepersad
,
C. C.
, and
Crawford
,
R. H.
,
2014
, “
Creation of Empathic Lead Users From Non-Users Via Simulated Lead User Experiences
,”
ASME
Paper No. DETC2014-3505256.10.1115/DETC2014-35052
29.
Zhao
,
K.
,
Qiu
,
B.
,
Caragea
,
C.
,
Wu
,
D.
,
Mitra
,
P.
,
Yen
,
J.
,
Greer
,
G. E.
, and
Portier
,
K.
,
2011
, “
Identifying Leaders in an Online Cancer Survivor Community
,”
21st Annual Workshop on Information Technologies and Systems (WITS'11)
, pp.
115
120
.
30.
Song
,
X.
,
Chi
,
Y.
,
Hino
,
K.
, and
Tseng
,
B.
,
2007
, “
Identifying Opinion Leaders in the Blogosphere
,”
Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management
,
CIKM’07
, ACM, New York, pp.
971
974
.10.1145/1321440.1321588
31.
Tang
,
X.
, and
Yang
,
C.
,
2010
, “
Identifying Influential Users in an Online Healthcare Social Network
,”
2010 IEEE International Conference on Intelligence and Security Informatics (ISI)
, pp.
43
48
.
32.
Li
,
Y.-M.
,
Lin
,
C.-H.
, and
Lai
,
C.-Y.
,
2010
, “
Identifying Influential Reviewers for Word-of-Mouth Marketing
,”
Electron. Commer. Res. Appl.
,
9
(
4
), pp.
294
304
.10.1016/j.elerap.2010.02.004
33.
Trusov
,
M.
,
Bodapati
,
A. V.
, and
Bucklin
,
R. E.
,
2010
, “
Determining Influential Users in Internet Social Networks
,”
J. Mark. Res.
,
47
(
4
), pp.
643
658
.10.1509/jmkr.47.4.643
34.
Aral
,
S.
, and
Walker
,
D.
,
2012
, “
Identifying Influential and Susceptible Members of Social Networks
,”
Science
,
337
(
6092
), pp.
337
341
.10.1126/science.1215842
35.
Tucker
,
C.
, and
Kim
,
H.
,
2011
, “
Trend Mining for Predictive Product Design
,”
ASME J. Mech. Des.
,
133
(
11
), p.
111008
.10.1115/1.4004987
36.
Tucker
,
C. S.
, and
Kim
,
H. M.
,
2009
, “
Data-Driven Decision Tree Classification for Product Portfolio Design Optimization
,”
ASME J. Comput. Inf. Sci. Eng.
,
9
(
4
), p.
041004
.10.1115/1.3243634
37.
Tucker
,
C.
, and
Kim
,
H.
,
2011
, “
Predicting Emerging Product Design Trend by Mining Publicly Available Customer Review Data
,”
18th International Conference on Engineering Design (ICED11)
, Vol.
6
, pp.
43
52
.
38.
Popescu
,
A.-M.
, and
Etzioni
,
O.
,
2005
, “
Extracting Product Features and Opinions From Reviews
,”
Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing
,
HLT’05
, Association for Computational Linguistics, ACM, New York, pp.
339
346
.10.3115/1220575.1220618
39.
Rai
,
R.
,
2012
, “
Identifying Key Product Attributes and Their Importance Levels From Online Customer Reviews
,”
ASME
Paper No. DETC2012-70493.10.1115/DETC2012-70493
40.
Ren
,
Y.
, and
Papalambros
,
P. Y.
,
2012
, “
On Design Preference Elicitation With Crowd Implicit Feedback
,”
ASME
Paper No. DETC2012-70605.10.1115/DETC2012-70605
41.
Stone
,
T.
, and
Choi
,
S.-K.
,
2013
, “
Extracting Consumer Preference From User-Generated Content Sources Using Classification
,”
ASME
Paper No. DETC2013-13228.10.1115/DETC2013-13228
42.
Ahlqvist
,
T.
, and
Teknillinen Tutkimuskeskus
,
V.
,
2008
,
Social Media Roadmaps: Exploring the Futures Triggered by Social Media
(VTT Tiedotteita—Research Notes), No. 2454,
VTT
, Finland.
43.
Thelwall
,
M.
,
Buckley
,
K.
,
Paltoglou
,
G.
,
Cai
,
D.
, and
Kappas
,
A.
,
2010
, “
Sentiment in Short Strength Detection Informal Text
,”
J. Am. Soc. Inf. Sci. Technol.
,
61
(
12
), pp.
2544
2558
.10.1002/asi.21416
44.
Fox
,
E.
,
2008
,
Emotion Science: Cognitive and Neuroscientific Approaches to Understanding Human Emotions
,
Palgrave Macmillan
.
45.
Thelwall
,
M.
,
2013
, “
Heart and Soul: Sentiment Strength Detection in the Social Web With Sentistrength
,”
Proceedings of the CyberEmotions
, pp.
1
14
.
46.
Tuarob
,
S.
, and
Tucker
,
C. S.
,
2014
, “
Discovering Next Generation Product Innovations by Identifying Lead User Preferences Expressed Through Large Scale Social Media Data
,”
ASME
Paper No. DETC2014-34767.10.1115/DETC2014-34767
47.
Huang
,
J.
,
Etzioni
,
O.
,
Zettlemoyer
,
L.
,
Clark
,
K.
, and
Lee
,
C.
,
2012
, “
RevMiner: An Extractive Interface for Navigating Reviews on a Smartphone
,”
Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology
,
UIST’12
, ACM, New York, pp.
3
12
.10.1145/2380116.2380120
48.
Hu
,
M.
, and
Liu
,
B.
,
2004
, “
Mining Opinion Features in Customer Reviews
,”
Proceedings of the 19th National Conference on Artificial Intelligence
, AAAI’04, AAAI Press, pp.
755
760
.
49.
Yin
,
P.
,
Ram
,
N.
,
Lee
,
W.-C.
,
Tucker
,
C.
,
Khandelwal
,
S.
, and
Salathé
,
M.
,
2014
, “
Two Sides of a Coin: Separating Personal Communication and Public Dissemination Accounts in Twitter
,”
Advances in Knowledge Discovery and Data Mining
,
Springer
, pp.
163
175
.10.1007/978-3-319-06608-0_14
50.
Tuarob
,
S.
,
Tucker
,
C. S.
,
Salathe
,
M.
, and
Ram
,
N.
,
2014
, “
An Ensemble Heterogeneous Classification Methodology for Discovering Health-Related Knowledge in Social Media Messages
,”
J. Biomed. Inf.
,
49
, pp.
255
268
.10.1016/j.jbi.2014.03.005
51.
Manning
,
C. D.
,
Raghavan
,
P.
, and
Schütze
,
H.
,
2008
,
Introduction to Information Retrieval
,
Cambridge University Press
,
New York
.10.1017/CBO9780511809071
52.
Tuarob
,
S.
,
Pouchard
,
L. C.
, and
Giles
,
C. L.
,
2013
, “
Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling
,” Proceedings of the 13th
ACM/IEEE-CS
Joint Conference on Digital Libraries, JCDL’13, ACM, New York, pp.
239
248
.10.1145/2467696.2467706
53.
Tuarob
,
S.
,
Bhatia
,
S.
,
Mitra
,
P.
, and
Giles
,
C. L.
,
2013
, “
Automatic Detection of Pseudocodes in Scholarly Documents Using Machine Learning
,”
2013 12th International Conference on Document Analysis and Recognition
(
ICDAR
), Washington, DC, Aug. 25–28, pp.
738
742
.10.1109/ICDAR.2013.151
54.
Sehgal
,
A.
, and
Iowa Computer Science, T. U.
,
2007
,
Profiling Topics on the Web for Knowledge Discovery
,
University of Iowa
,
Iowa City, IA
.
55.
Berthon
,
P. R.
,
Pitt
,
L. F.
,
McCarthy
,
I.
, and
Kates
,
S. M.
,
2007
, “
When Customers Get Clever: Managerial Approaches to Dealing With Creative Consumers
,”
Bus. Horiz.
,
50
(
1
), pp.
39
47
.10.1016/j.bushor.2006.05.005
56.
Tuarob
,
S.
, and
Tucker
,
C. S.
, “
Quantifying Product Favorability and Extracting Notable Product Features Using Large Scale Social Media Data
,”
ASME J. Comput. Inf. Sci. Eng.
(in press).10.1115/1.4029562
57.
Tuarob
,
S.
,
Tucker
,
C. S.
,
Salathe
,
M.
, and
Ram
,
N.
,
2013
, “
Discovering Health-Related Knowledge in Social Media Using Ensembles of Heterogeneous Features
,”
Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management
,
CIKM’13
, ACM, New York, pp.
1685
1690
.10.1145/2505515.2505629
58.
Bhatia
,
S.
,
Tuarob
,
S.
,
Mitra
,
P.
, and
Giles
,
C. L.
,
2011
, “
An Algorithm Search Engine for Software Developers
,”
Proceedings of the 3rd International Workshop on Search-Driven Development: Users, Infrastructure, Tools, and Evaluation
,
SUITE’11
, ACM, New York, pp.
13
16
.10.1145/1985429.1985433
59.
Tuarob
,
S.
,
Mitra
,
P.
, and
Giles
,
C. L.
,
2012
, “
Taxonomy-Based Query-Dependent Schemes for Profile Similarity Measurement
,”
Proceedings of the 1st Joint International Workshop on Entity-Oriented and Semantic Search
,
JIWES'12
, ACM, New York, pp.
8:1
8:6
.10.1145/2379307.2379315
You do not currently have access to this content.