Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles

Abstract During the last years, new technologies have been developing at a rapid pace; however, new technologies carry risks and uncertainties. Technology forecasting by analogy has been used in the case of emerging technologies; nevertheless, the use of analogies is subject to several problems such as lack of inherent necessity, historical uniqueness, historically conditioned awareness, and casual analogies. Additionally, the natural process of selecting the analogy technology is based on subjective criteria for technological similarities or inductive inference. Since many analogies are taken qualitatively and rely on subjective assessments, this paper presents a quantitative comparison process based on the Social Network Analysis (SNA) and patent analysis for selecting analogous technologies. In this context, the paper presents an analysis of complex patent network structures using centrality and density metrics in order to reduce the lack of information or the presence of uncertainties. The case of Autonomous Vehicles (AVs) is explored in this paper, comparing three candidate technologies which have been chosen based on the similarities with the target technologies. The best candidate technology is selected based on the analysis of two main centrality metrics (average degree and density).

[1]  Kichun Lee,et al.  Forecasting demand for a newly introduced product using reservation price data and Bayesian updating , 2012 .

[2]  Yiman Du,et al.  Market Penetration Model for Autonomous Vehicles on the Basis of Earlier Technology Adoption Experience , 2016 .

[3]  Thomas J. O'Connor,et al.  A METHODOLOGY FOR ANALOGIES. , 1971 .

[4]  Jérôme Massiani,et al.  The Choice of Bass Model Coefficients to Forecast Diffusion for Innovative Products: An Empirical Investigation for New Automotive Technologies , 2015 .

[5]  Alan L. Porter,et al.  On the Future of Technological Forecasting , 2001 .

[6]  JinHyo Joseph Yun,et al.  The relationship between technology, business model, and market in autonomous car and intelligent robot industries , 2016 .

[7]  Young Jun Kim,et al.  A moderated mediation model of technology roadmapping and innovation: The roles of corporate foresight and organizational support , 2019, Journal of Engineering and Technology Management.

[8]  Türkay Dereli,et al.  Patent analysis of wind energy technology using the patent alert system , 2012 .

[9]  J. Scott Armstrong,et al.  Long-Range Forecasting. , 1979 .

[10]  Chen-Yuan Liu,et al.  Forecasting the development of the biped robot walking technique in Japan through S-curve model analysis , 2009, Scientometrics.

[11]  Shu Fang,et al.  Mapping the evolving patterns of patent assignees’ collaboration networks and identifying the collaboration potential , 2014, Scientometrics.

[12]  Laura Zager,et al.  Graph similarity and matching , 2005 .

[13]  Mu-Hsuan Huang,et al.  Identifying and visualizing technology evolution: A case study of smart grid technology , 2012 .

[14]  Chia-Yon Chen,et al.  Technology forecasting and patent strategy of hydrogen energy and fuel cell technologies , 2011 .

[15]  R. Tomlinson,et al.  The Management of Research and Development (Selected papers from a conference in Wroclaw, Poland, September 1978) , 1980 .

[16]  Kwangsoo Kim,et al.  Creating patents on the new technology using analogy-based patent mining , 2014, Expert Syst. Appl..

[17]  Yongsheng Yu,et al.  Dynamics of waste-to-energy incineration R&D collaboration networks: a social network analysis based on patent data , 2017 .

[18]  Bingfeng Ge,et al.  Development trend forecasting for coherent light generator technology based on patent citation network analysis , 2017, Scientometrics.

[19]  Denis F. Cioffi.  A tool for managing projects: an analytic parameterization of the S-curve , 2005 .

[20]  Nidhi Kalra,et al.  Autonomous Vehicle Technology: A Guide for Policymakers , 2014 .

[21]  Tugrul U. Daim,et al.  Technology diffusion: forecasting with bibliometric analysis and Bass model , 2009 .

[22]  Barry L. Bayus,et al.  Forecasting Sales of New Contingent Products: An Application to the Compact Disc Market , 1987 .

[23]  Cheng-Hsin Chiang,et al.  Network structure of innovation: can brokerage or closure predict patent quality? , 2009, 2009 42nd Hawaii International Conference on System Sciences.

[24]  Jae-Jun Kim,et al.  The structure and knowledge flow of building information modeling based on patent citation network analysis , 2018 .

[25]  Jafar Habibi,et al.  Distance metric learning for complex networks: towards size-independent comparison of network structures. , 2015, Chaos.

[26]  Steven P. Schnaars,et al.  Megamistakes: Forecasting and the Myth of Rapid Technological Change, Steven P. Schnaars. 1988. Free Press. 224 pages. ISBN: 0-02-927952-6. $19.95 , 1989 .

[27]  Luís M. A. Bettencourt,et al.  Scientific discovery and topological transitions in collaboration networks , 2009, J. Informetrics.

[28]  Tugrul U. Daim,et al.  Technology Forecasting Methods , 2013 .

[29]  Tae-Eung Sung,et al.  Forecasting by analogy using the web search traffic , 2017 .

[30]  Sunghae Jun,et al.  A Network Analysis Model for Selecting Sustainable Technology , 2015 .

[31]  Jie Cheng,et al.  Measuring the significance of community structure in complex networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  D. Kahneman,et al.  Timid choices and bold forecasts: a cognitive perspective on risk taking , 1993 .

[33]  Sunghae Jun.  Central Technology Forecasting Using Social Network Analysis , 2012 .

[34]  Sunghae Jun.  Technology Network Model Using Bipartite Social Network Analysis , 2012 .

[35]  Walter McManus,et al.  Market models for predicting PHEV adoption and diffusion , 2009 .

[36]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.

[37]  Tugrul U. Daim,et al.  Keyword-based patent citation prediction via information theory , 2018, Int. J. Gen. Syst..

[38]  Yi Zhang,et al.  Patent Network Analysis for Identifying Technological Evolution: A Case Study of China's Artificial Intelligence Technologies , 2017, 2017 Portland International Conference on Management of Engineering and Technology (PICMET).

[39]  Taegu Kim,et al.  Determining the scale of R&D investment for renewable energy in Korea using a comparative analogy approach , 2014 .

[40]  Nebojsa Nakicenovic,et al.  Dynamics of energy technologies and global change , 1999 .

[41]  Sunghae Jun,et al.  Examining technological innovation of Apple using patent analysis , 2013, Ind. Manag. Data Syst..

[42]  JinHyo Joseph Yun,et al.  The role of a business model in market growth: The difference between the converted industry and the emerging industry , 2019, Technological Forecasting and Social Change.

[43]  Karima Dyussekeneva,et al.  The use of analogies in forecasting the annual sales of new electronics products , 2013 .

[44]  Tugrul U. Daim,et al.  Forecasting emerging technologies: Use of bibliometrics and patent analysis , 2006 .

[45]  J. Scott Armstrong,et al.  Structured Analogies for Forecasting , 2007 .

[46]  Tugrul U. Daim,et al.  ‘Smart building’ technology network analysis: applying core–periphery structure analysis , 2017 .

[47]  H. Su,et al.  Analysis of patent portfolio and knowledge flow of the global semiconductor industry , 2014, Proceedings of PICMET '14 Conference: Portland International Center for Management of Engineering and Technology; Infrastructure and Service Integration.

[48]  Tugrul U. Daim,et al.  Research Forecasting for Health Information Technology (HIT), using technology intelligence , 2012 .

[49]  Halina Kwasnicka,et al.  Technological Substitution Forecasting with a Model Based on Biological Analogy : Technological Forecasting and Social Change , 1983 .

[50]  Tugrul U. Daim,et al.  OLED TV technology forecasting using technology mining and the Fisher-Pry diffusion model , 2016 .

[51]  Ernest R. May,et al.  Thinking in Time: The Uses of History for Decision-Makers , 1986 .

[52]  Chie Hoon Song,et al.  Competition or collaboration? – Analysis of technological knowledge ecosystem within the field of alternative powertrain systems: A patent-based approach , 2019, Journal of Cleaner Production.

[53]  Kuei-Kuei Lai,et al.  The isomorphic development of insurance - the perspective of social network analysis , 2010, Int. J. Serv. Technol. Manag..

[54]  Alan L. Porter,et al.  Forecasting and Management of Technology , 1991 .

[55]  Tugrul U. Daim,et al.  Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology , 2019, Technological Forecasting and Social Change.

[56]  Sang Yong Park,et al.  Development of a market penetration forecasting model for Hydrogen Fuel Cell Vehicles considering infrastructure and cost reduction effects , 2011 .

[57]  David Probert,et al.  Exploring industry dynamics and interactions , 2013 .