Competition or collaboration? – Analysis of technological knowledge ecosystem within the field of alternative powertrain systems: A patent-based approach

Abstract In light of the transition towards a low-carbon economy and growing social demands for sustainable energy consumption, alternative powertrain systems, such as battery electric vehicles (BEVs), hybrid electric vehicles (HEVs) and fuel cell electric vehicles (FCEVs), have gained significance as a viable solution to revolutionize the dominant design logic of internal combustion engines (ICE) in automotive applications. By assuming that technological innovations emerge from the ability to recombine existing technologies, tracking and forecasting the technological knowledge interaction trajectory could assist experts and decision-makers in guiding their future R&D activities. To this end, a patent-based analysis framework is introduced to study the technological knowledge ecosystem of BEVs, HEVs and FCEVs in terms of technological knowledge flow (TKF). In this study, the principles of social network analysis are firstly used to quantify and visualize the connections among interacting technological knowledge areas. Secondly, link prediction technique is applied to predict the newly emerging and decaying links from a forward-looking perspective as well as the changing interrelationships among different technological knowledge areas. The findings show that the alternative powertrain systems form a cohesive technological knowledge ecosystem, pushing the underlying technological knowledge ecosystem to adopt a coopetition-based growth strategy. This study can provide valuable information for stakeholders interested in evaluating the evolving characteristics of alternative powertrain system technologies and offer an enhanced understanding on the technological competition in the automotive industry with regard to developing cleaner and more efficient mobility solutions.

[1]  Zhao Yang,et al.  A Comparative Analysis of Community Detection Algorithms on Artificial Networks , 2016, Scientific Reports.

[2]  Tahsin Koroglu,et al.  A comprehensive review on estimation strategies used in hybrid and battery electric vehicles , 2015 .

[3]  Thomas Budde Christensen,et al.  Modularised eco-innovation in the auto industry , 2011 .

[4]  John Scott,et al.  Social network analysis: developments, advances, and prospects , 2010, Social Network Analysis and Mining.

[5]  Chie Hoon Song,et al.  Mapping the Patent Landscape in the Field of Personalized Medicine , 2017, Journal of Pharmaceutical Innovation.

[6]  Fredrik Tell,et al.  Where and how to search? Search paths in open innovation , 2016 .

[7]  Liuqing Yang,et al.  Social influence analysis in the big data era: a review , 2016, Big Data over Networks.

[8]  Geng Wu,et al.  Total cost of ownership of electric vehicles compared to conventional vehicles: A probabilistic analysis and projection across market segments , 2015 .

[9]  S. McGaughey,et al.  Network cohesion in global expansion: An evolutionary view , 2013 .

[10]  Xiaoli Zhao,et al.  Similarity-based link prediction in social networks: A path and node combined approach , 2017, J. Inf. Sci..

[11]  Curba Morris Lampert,et al.  Entrepreneurship in the large corporation: a longitudinal study of how established firms create breakthrough inventions , 2001 .

[12]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Jun Li,et al.  A link prediction approach for item recommendation with complex number , 2015, Knowl. Based Syst..

[14]  Tatsuya Akutsu,et al.  Structure of n-clique networks embedded in a complex network , 2007, physics/0702160.

[15]  K. Pavitt,et al.  Patent statistics as indicators of innovative activities: Possibilities and problems , 2005, Scientometrics.

[16]  B. Nykvist,et al.  Rapidly falling costs of battery packs for electric vehicles , 2015 .

[17]  Nigel P. Brandon,et al.  Assessing and comparing German and UK transition policies for electric mobility , 2015 .

[18]  Geert Asche,et al.  “80% of technical information found only in patents” – Is there proof of this [1]? , 2017 .

[19]  Namuk Ko,et al.  Analyzing interdisciplinarity of technology fusion using knowledge flows of patents , 2014, Expert Syst. Appl..

[20]  Lee Fleming,et al.  Special Issue on Design and Development: Recombinant Uncertainty in Technological Search , 2001, Manag. Sci..

[21]  Thomas Y. Choi,et al.  Structural investigation of supply networks: A social network analysis approach , 2011 .

[22]  David Dallinger,et al.  New business models for electric cars: A holistic approach , 2011 .

[23]  Zhi Wei,et al.  A link prediction approach to cancer drug sensitivity prediction , 2017, BMC Systems Biology.

[24]  Wenpin Tsai.  Knowledge Transfer in Intraorganizational Networks: Effects of Network Position and Absorptive Capacity on Business Unit Innovation and Performance , 2001 .

[25]  Kuei-Kuei Lai,et al.  Exploring technology diffusion and classification of business methods: Using the patent citation network , 2009 .

[26]  Jung-Hsien Chiang,et al.  Literature-based discovery of new candidates for drug repurposing , 2016, Briefings Bioinform..

[27]  Nigel P. Brandon,et al.  Comparative analysis of battery electric, hydrogen fuel cell and hybrid vehicles in a future sustainable road transport system , 2010 .

[28]  Kees Maat,et al.  Technological diversity of emerging eco-innovations: a case study of the automobile industry , 2012 .

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

[30]  So Young Sohn,et al.  Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents , 2015 .

[31]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

[32]  Hans-Otto Günther,et al.  The role of electric vehicles for supply chain sustainability in the automotive industry , 2015 .

[33]  Antti Uusitalo,et al.  Technology competition in the internal combustion engine waste heat recovery: a patent landscape analysis , 2016 .

[34]  T. Turrentine,et al.  Barriers to the adoption of fuel cell vehicles: A qualitative investigation into early adopters attitudes , 2017 .

[35]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[36]  Gaël Gueguen,et al.  Coopetition and business ecosystems in the information technology sector: the example of Intelligent Mobile Terminals , 2009 .

[37]  Rahul C. Basole.  Visualization of interfirm relations in a converging mobile ecosystem , 2008, 2008 7th International Conference on Mobile Business.

[38]  Robert van den Hoed,et al.  Sources of radical technological innovation: the emergence of fuel cell technology in the automotive industry , 2007 .

[39]  C. Haythornthwaite.  Social network analysis: An approach and technique for the study of information exchange☆ , 1996 .

[40]  Yong Lu,et al.  Social Network Analysis of a Criminal Hacker Community , 2010, J. Comput. Inf. Syst..

[41]  Jens Leker,et al.  Current research trends and prospects among the various materials and designs used in lithium-based batteries , 2013, Journal of Applied Electrochemistry.

[42]  Wei-Ru Chen,et al.  Changes in firm knowledge couplings and firm innovation performance: : The moderating role of technological complexity , 2015 .

[43]  Chao Lu,et al.  Business ecosystem and stakeholders' role transformation: Evidence from Chinese emerging electric vehicle industry , 2014, Expert Syst. Appl..

[44]  Kim B. Clark,et al.  Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of , 1990 .

[45]  Sai Yayavaram,et al.  Decomposability in Knowledge Structures and Its Impact on the Usefulness of Inventions and Knowledge-base Malleability , 2008 .

[46]  Gerhard Schewe,et al.  Paving the Road to Electric Vehicles – A Patent Analysis of the Automotive Supply Industry , 2017 .

[47]  Florin Mariasiu,et al.  Electric vehicle battery technologies: From present state to future systems , 2015 .

[48]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[49]  M. Tushman,et al.  Technological Discontinuities and Dominant Designs: A Cyclical Model of Technological Change , 1990 .

[50]  Roger Guimerà,et al.  Missing and spurious interactions and the reconstruction of complex networks , 2009, Proceedings of the National Academy of Sciences.

[51]  Janghyeok Yoon,et al.  Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering , 2017 .

[52]  Peng Wang,et al.  Link prediction in social networks: the state-of-the-art , 2014, Science China Information Sciences.

[53]  Jeremy Neubauer,et al.  The impact of range anxiety and home, workplace, and public charging infrastructure on simulated battery electric vehicle lifetime utility , 2014 .

[54]  B. Wee,et al.  The influence of financial incentives and other socio-economic factors on electric vehicle adoption , 2014 .

[55]  Ke Rong,et al.  Organizing business ecosystems in emerging electric vehicle industry: Structure, mechanism, and integrated configuration , 2017 .

[56]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[57]  Vanessa Oltra,et al.  Variety of technological trajectories in low emission vehicles (LEVs) : a patent data analysis. , 2006 .

[58]  Ronald Rousseau,et al.  Social network analysis: a powerful strategy, also for the information sciences , 2002, J. Inf. Sci..

[59]  M. Hekkert,et al.  How competitive forces sustain electric vehicle development , 2014 .

[60]  Jens Leker,et al.  Anticipation of converging technology areas — A refined approach for the identification of attractive fields of innovation , 2017 .

[61]  Karoline Augenstein,et al.  Analysing the potential for sustainable e-mobility – The case of Germany , 2015 .

[62]  Jens Leker,et al.  Analyzing the impact of industry sectors on the composition of business ecosystem: A combined approach using ARM and DEMATEL , 2018, Expert Syst. Appl..