Automated feature extraction from social media for systematic lead user identification

ABSTRACT

Manufacturers strive to rapidly develop novel products and offer solutions that meet the emerging customer needs. The Lead User Method, emerging from studies on sources of innovation by the scientific community, offers a validated approach to identify users with innovation ideas to support rapid and successful new product development process. The approach has been more recently applied on online communities, where collection and analysis of rich user data are performed by expert practitioners. In this paper, feature extraction techniques are outlined, that enable automated classification and identification of lead users that are present in online communities. The authors describe two case studies to construct a classification model that is then used to identify online lead users for confectionery products, and to evaluate the outlined feature extraction techniques. The presented research points to opportunities in automated identification within the lead user approach that further reduce the resource and time costs.

KEYWORDS:

• Lead user identification
• data mining
• social networks
• design management

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Sanjin Pajo is a Doctoral Researcher on systematic and automated identification of lead users in online communities at the Centre for Industrial Management at Katholieke Universiteit Leuven (KU Leuven). He holds a Professional Doctorate in Engineering in User-System Interaction from Eindhoven University of Technology and he obtained a Master of Science degree in Media Informatics from RWTH Aachen.

Dennis Vandevenne is a Postdoctoral Researcher on methods and algorithms for BID at the Centre for Industrial Management and a Researcher at KU Leuven. He obtained three information and communications technology- (ICT) related master’s degrees: electronics–ICT, artificial intelligence–engineering and computer science, and industrial management–ICT. Dennis previously performed research on identity management and biometrics in the Department of Computer Security and Industrial Cryptography.

Joost R. Duflou is a Professor in the Department of Mechanical Engineering at KU Leuven. He holds master degrees in architectural and electromechanical engineering and a PhD in engineering from KU Leuven. His principal research activities are situated in the field of design support methods and methodologies, with special attention for systematic innovation, ecodesign, and life cycle engineering. Dr Duflou is a member of CIRP and has published over 200 international publications.