Forecasting technology success based on patent data Serkan Altuntas a, ⁎, Turkay Dereli b,1 , Andrew Kusiak c,2 a Yıldız Technical University, Department of Industrial Engineering, Istanbul, Turkey b Gaziantep University, Department of Industrial Engineering, Gaziantep, Turkey c The University of Iowa, Department of Mechanical and Industrial Engineering, Iowa City, IA, USA article info abstract Article history: Received 6 October 2014 Received in revised form 17 February 2015 Accepted 10 March 2015 Available online 29 March 2015 A novel method for forecasting technology success based on patent data is proposed. Four criteria, technology life cycle, diffusion speed, patent power, and expansion potential are considered for technology forecasting. Patent power and expansion potential are considered as technology scope indicators. A data fusion algorithm is applied to combine the results obtained from different criteria. The usefulness and potential of the proposed forecasting approach has been demon- strated using all U.S. patents related to three technologies, namely thin film transistor-liquid crystal display, flash memory system, and personal digital assistant. The results obtained from these patents demonstrate that the personal digital assistant technology is preferred over other technologies. Investments in thin film transistor liquid-crystal display and flash memory system technologies have equal priority. © 2015 Elsevier Inc. All rights reserved. Keywords: Technology forecasting Patent analysis Technology life cycle Technology diffusion Technology scope Condorcet method 1. Introduction Decisions related to investments in any technology are af- fected by different factors such as marketing, human resources, location, etc. Prediction of benefits from investment in a new technology is of great interest. Forecasting the success of future technology is key to the decision makers. Because, knowing or predicting the success of invested technology provides im- portant clues, such as the current technology life cycle of the technology under consideration, diffusion potential and tech- nology scope. In technology and business, it provides planners to choose the right strategies for the future (Kassicieh and Rahal, 2007). Therefore, the future technology success should be predicted prior to investment decision. Patent data may be used to predict the success of tech- nology when analyzed in the context of technology life cycle (TLC), diffusion potential, and technology scope (patent power and expansion potential). The future technology success of the investment alternatives has not been fore- casted based on patent data in the context of these four criteria in the literature so far. To fill this gap, the answer to the question of how future technology success for invest- ment alternatives can be forecasted is researched in this paper. Therefore, a novel method based on patent data is proposed to forecast technology success. There is a need to develop a technology forecasting (TF) method to predict future technology success. In this paper, TLC phases, initiation, growth, and saturation, are used with (i) the diffusion potential of the technology to determine possible acceptance, and with (ii) technology scope to determine the strength of the relationship of the technology with other tech- nologies. It should be noted that patent power and expansion potential are used as indicators of technology scope. The total number of International Patent Classification (IPC) codes included in retrieved patents is divided by the total number of Technological Forecasting & Social Change 96 (2015) 202–214 ⁎ Corresponding author at: Yıldız Technical University, Department of Industrial Engineering, 34349 Beşiktaş, Istanbul, Turkey. E-mail addresses: serkan@yildiz.edu.tr, saltuntas2@gmail.com (S. Altuntas), dereli@gantep.edu.tr (T. Dereli), andrew-kusiak@uiowa.edu (A. Kusiak). 1 Tel.: +90 342 360 10 23; fax: +90 342 360 19 22. 2 Tel.: +1 319 335 5934; fax: +1 319 335 5669. http://dx.doi.org/10.1016/j.techfore.2015.03.011 0040-1625/© 2015 Elsevier Inc. All rights reserved. Contents lists available at ScienceDirect Technological Forecasting & Social Change