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.
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July 2015
Research-Article
Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks
Suppawong Tuarob,
Suppawong Tuarob
Computer Science and Engineering,
Industrial and Manufacturing Engineering,
e-mail: suppawong@psu.edu
Industrial and Manufacturing Engineering,
The Pennsylvania State University
,University Park, PA 16802
e-mail: suppawong@psu.edu
Search for other works by this author on:
Conrad S. Tucker
Conrad S. Tucker
Engineering Design and Industrial
and Manufacturing Engineering,
Computer Science and Engineering,
e-mail: ctucker4@psu.edu
and Manufacturing Engineering,
Computer Science and Engineering,
The Pennsylvania State University
,University Park, PA 16802
e-mail: ctucker4@psu.edu
Search for other works by this author on:
Suppawong Tuarob
Computer Science and Engineering,
Industrial and Manufacturing Engineering,
e-mail: suppawong@psu.edu
Industrial and Manufacturing Engineering,
The Pennsylvania State University
,University Park, PA 16802
e-mail: suppawong@psu.edu
Conrad S. Tucker
Engineering Design and Industrial
and Manufacturing Engineering,
Computer Science and Engineering,
e-mail: ctucker4@psu.edu
and Manufacturing Engineering,
Computer Science and Engineering,
The Pennsylvania State University
,University Park, PA 16802
e-mail: ctucker4@psu.edu
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 15, 2014; final manuscript received February 9, 2015; published online May 19, 2015. Assoc. Editor: Wei Chen.
J. Mech. Des. Jul 2015, 137(7): 071402 (11 pages)
Published Online: July 1, 2015
Article history
Received:
September 15, 2014
Revision Received:
February 9, 2015
Online:
May 19, 2015
Citation
Tuarob, S., and Tucker, C. S. (July 1, 2015). "Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks." ASME. J. Mech. Des. July 2015; 137(7): 071402. https://doi.org/10.1115/1.4030049
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