Technological Forecasting & Social Change

Technological Forecasting & Social Change 148 (2019) 119731

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Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles T ⁎ Shuying Lia, Edwin Garcesb, Tugrul Daimb,c,d, a Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041, China b Department of Engineering and Technology Management Portland State University, USA c National Research University Higher School Of Economics, Russia d Chaoyang University of Technology, Taiwan

ARTICLE INFO ABSTRACT

Accepted by Scott Cunningham During the last years, new technologies have been developing at a rapid pace; however, new technologies carry Keywords: risks and uncertainties. Technology forecasting by analogy has been used in the case of emerging technologies; Technology forecasting nevertheless, the use of analogies is subject to several problems such as lack of inherent necessity, historical Analogy-based uniqueness, historically conditioned awareness, and casual analogies. Additionally, the natural process of se- Social network analysis lecting the analogy technology is based on subjective criteria for technological similarities or inductive in- SNA ference. Since many analogies are taken qualitatively and rely on subjective assessments, this paper presents a Complex networks quantitative comparison process based on the Social Network Analysis (SNA) and patent analysis for selecting Autonomous vehicles analogous technologies. In this context, the paper presents an analysis of complex patent network structures Network centrality metrics 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. Introduction planning. It is required to allocate resources more efficiently. However, due to the characteristics of new technology, a large amount of his- During the last century, science and technology have developed at a torical data can be used to reveal and describe the technology devel- rapid pace, especially in the past few years. However, although the opment pattern, to characterize it more precisely, and to maximize development of new technology is related to previous successful tech- gains or minimize loss from future conditions (Martino and Joseph, nology in the market, it is at the same time accompanied by failure. In 1993). The development, scale, and timing of forecasting technology order to reduce and avoid the uncertainties in the market, technology are not optimistic, especially in terms of reducing the uncertainty in- planning and forecasting have become more and more important, volved in technical systems under different conditions, which is more particularly, to support decisions of emerging technologies with less important and effective for technology forecasting. In this context, market information and to help organizations and governments to align technical forecasting provides a signal to adopt or prevent technological their decisions with high-level objectives. These aspects have been change by helping to apply and adapt the technology (Roper and Wiley mentioned by many studies, including the importance of patent analysis InterScience (Online service), 2011). Consequently, the prediction in technology forecasting (Sasikumar and Mohan, 2014). Moreover, the process of similar technologies in a historical context based on existing application of technology forecasting for market strategy and industries knowledge and information can help to reduce uncertainties and has been emphasized at all levels in the competitive global environ- eliminate subjective judgment in the future (Martino and Joseph, 1993; ment, including automotive, pharmaceutical, electronics, and so on Mcmanus and Senter, 2009). Often, in the absence of market informa- (Pelc et al., 1980), because technology forecasting can be used in or- tion and consumer surveys, cutting-edge emerging technologies can be ganizational and strategic procedures to implement technologies and used to infer trends in the development of new technologies by analogy. align firms with their business plans (Yoon et al., 2017). Technology forecasting by analogy involves a systematic compar- The future of technology development is critical in technology ison of the technology to be forecasted with the information of earlier

⁎ Corresponding author. E-mail address: [email protected] (T. Daim). https://doi.org/10.1016/j.techfore.2019.119731 Received 27 September 2018; Received in revised form 24 July 2019; Accepted 27 August 2019 0040-1625/ © 2019 Elsevier Inc. All rights reserved. S. Li, et al. Technological Forecasting & Social Change 148 (2019) 119731 technology that is believed to be similar in all or most important re- of change that happened in similar technology and applying that same spects (Martino and Joseph, 1993). However, the use of analogies is pattern and historical data to predict the possible futures of a new subject to several problems, such as lack of inherent necessity, historical technology. While some earlier technology is believed to have been uniqueness, historically conditioned awareness, and casual analogies. similar to the new technology, systematic comparisons between the One of the critical points about forecasting by analogy is the natural technologies are needed. The natural process of analogy is based on the process of analogy based on the intuition for the technological simila- intuition for the technological similarities or inductive inference rities or inductive inference (O'Connor, 1971). It is necessary to make a (Martino and Joseph, 1993), which is based on a qualitative method. systematic check of all the observable characteristics and to minimize Most analysts believe that analogy helps to understand the intricacies of the uncertainty before taking priory conclusions assuming the risk that what happened in the cases and that they will reduce the uncertainties the two are alike on yet unobservable characteristics. The inappropriate of the future (Schnaars, 1988). selection and inadequate analysis of analogies led decision makers to Analogous technologies need to be selected by following a sys- make poor forecasts of the decisions, and also noted that structured tematic methodology to reduce uncertainties. An analogy is typically judgmental processes are more effective than others (Armstrong, 1985; defined as a recognizable similarity or resemblance of form or function, Kahneman and Lovallo, 1993; Neustadt, 1986). Therefore, the key but no logical connection or equivalence – as distinguished from a success factor of forecasting by analogy is to choose the right technol- model (Daim et al., 2013). The structured analogy is recognized as the ogies with operational procedures for comparison that are truly ana- primary approach, describing a judgmental procedure for asking ex- logous to the one being forecast (Cho and Daim, 2013). perts to list, rate, and match similar analogies with the target (Green In the particular case related to automobile industry, although de- and Armstrong, 2007). A possible analogy should be compared in velopment models have been used in several automobile-related tech- technological, economic, managerial, political, social, cultural, in- nologies, there are very few studies for forecasting the development of tellectual, religious-ethical and ecological situations that the affected Autonomous Vehicles (AVs) in the U.S. In this case, AVs was taken as a technology changes (Martino and Joseph, 1993) so that potential ana- technology to be used in the case of an analogous method, and to be logous technologies should be sought and selected from a wide range of compared with the actual status of the development of the technology. candidates. The analogy system is also built on similar cases, theory, So far, the estimation of AVs development and forecast models were patent, and term of inter-technological comparison (Jun et al., 2017). based on the personal judgment of analogies and no technical strategy Moreover, historical analogy and life cycle analogy are often referred to developed. as the link between different candidate technologies. Based on histor- Although preceding studies have mentioned the promising tools, ical data, the phenomenon that co-evolution with other technologies in and enhanced the forecasting by analogy, their limitation was that the the patterns, processes, and timescales occurs in the diffusion of studies were based on the personal judgment of analogies and no emerging technologies (Grübler et al., 1999). For example, by analo- technical strategy was developed. To fill the gap, and to increase the gizing life cycles of products was demonstrated as the close relationship forecasting accuracy, this paper will focus on the companies in the co- between software and video cassette recorders and VCR tapes; micro- patenting network based on Social Network Analysis (SNA) and patent computers and floppy diskettes; and cameras and photographic film analysis, which are considered as the crucial nodes to connect patented (Bayus, 1987). However, there was no consistency in choosing the right technology and company's strategy. On the other hand, this study takes technology in prediction throughout most analogy methods (Jun et al., a fresh analysis in the technological development trend on the time 2017; Massiani and Gohs, 2015). series models and network analysis for the purpose of utilizing quan- Promising new tools in technology forecasting are combined with titative comparison in analogical forecasting. This allows us to compare various fields such as political science, computer science, sciento- the dynamic interrelations over time. metrics, innovation management, and complexity science (Coates et al., In this context, the objective of this paper is to develop a quanti- 2001). In this case, many different methods have been used and im- tative comparison process based on the Social Network Analysis (SNA) proved in analogical forecasting, such as Delphi and group interview and patent analysis for selecting analogous technologies. Therefore, the (Green and Armstrong, 2007), search traffic(Jun et al., 2017), biblio- paper answers the question of how to choose the right technology by metrics (Daim et al., 2006), technology or product life cycle (Bayus, analyzing all the observable characteristics and to minimize the un- 1987; Lee Kichun, 2012; Park et al., 2015), averaged analogy method certainty before concluding that the technologies are alike. The process based on parameters of multiple products (S, 2012), technology road- will lead to the selection of a better estimation for technology fore- mapping (D, 2013), and R&D investment (Taegu Kim and Lee, 2014). casting models. This will help to improve practices in comparison-based Since various analogy took place in data, life cycle, and other para- prediction, a model used in technology since forecasting autonomous meters, there is the need for developing of integrated and systematic technologies are used as a validation of the results. tools for the technology forecasting method to improve accuracy (Daim et al., 2006, 2013; Yu-Heng Chen and Chen, 2011), and further im- 2. Literature review provements are still necessary for network analysis and the forecasting method (You et al., 2017). 2.1. Forecasting by analogy 2.2. Patent analysis Extrapolation for technology forecasting is based on the assumes that the data in the past of a time series contains all the information Patent documents have long been recognized as a crucial data necessary for the future of that same series (Armstrong et al., 1984). source for the study of innovation, strategy decision, and market value Since there is available data for technologies that have completed their in a sequential time period. Patents contain significant technical in- technology cycle, the patterns of these technologies can be applied in formation for technological and economic development. Due to the other emerging technologies without enough data. According to ex- technical and market value of patent information, it is closer to the real perimental records or information, the typical kinds of similarity in network of technological innovation and development compared with trends, cycles, or even patterns may be obtained and used in other papers, news, twitter, and other information. Patent analysis has been application areas. For instance, the development of radio and TV was regarded as a key method or analytical tool to predict future scenarios used to forecast the future of the internet, and the sequences of changes of technology evolution. in the development of computer software were applied to the evolution Most patent analysis has been integrated into various researches and maturation of Internet of Things (IOT) (Daim and Suntharasaj, that focused on the possibilities or potentials of technology advance. 2009). Technology forecasting by analogy means identifying a pattern For example, patent analysis has been utilized for technology

2 S. Li, et al. Technological Forecasting & Social Change 148 (2019) 119731 opportunity analysis to forecast emerging technologies by employing reveal the relative importance of technology areas (Ding Ma et al., text mining and bibliometric analysis (Liu and Wang, 2010). The tem- 2016), evaluate and compare the significance of communities in dif- poral bursts of R&D activity in patents have now been used to detect the ferent networks (Chen and Fang, 2009; Hu Yanqing et al., 2010), and to novelty of a new technology field (Hu Yanqing et al., 2010). Mean- visualize the overall structure of a network (Chen et al., 2012). Complex while, patents in emerging technologies have been considered as having network theory, as well as social network analysis, are used to in- a significantly higher impact on subsequent technological development vestigate the social network structure evolution of technology in term (Aliakbary et al., 2015). Patent indicators are widely analyzed to assess of global network properties, major actor's centrality and collaboration whether the technology is promising (Zager, 2003). Most researches relationships (Ding Ma et al., 2016). To be specific, the cooperative focused on the evolution of certain technology, which is based on the network of patentee can explore the cooperative relationship of R&D systematic analysis of patenting activity. However, it is seldom dis- innovation. The patent citation network can analyze the path of tech- cussed the evolutionary paths of different technologies over the same nology evolution, and the patent co-occurrence network can explore the stage of time. The comparisons in patent analysis can be applied into frontier fields of technology development. The visualization of a time- structuralizing, statistical analysis and visualization of patent in- line plot for each technology network is drawn as a function of size, formation (Jeong and Kim, 2014), to support patent-based technology average age, and time (Chen et al., 2012). Based on patent citation forecasting more efficiently. networks, the position of applicants within networks is identified to Based on the factors of technological changes, it is important to explain applicant behavior in the marketplace such as cooperation or compare the scenarios in the technology evolution, evaluate the risks, patent infringement trials (Park et al., 2018). Another technology net- and explore the opportunities of technology development. To compare work is based on the occurrence of core terms and provides a way to different technologies, patent network analysis tries to illuminate the understand technological evolution in details and to forecast future way to identify more functions, structures, or trends over time, such as opportunities (Huang et al., 2017). The previous studies implied that the number of patent applications, R&D collaborator, and technological the comparison between complex network analyses in sequential time relevance based on citation, et al. There are many technical bases or periods can capture the more detailed information of interactions in the features that can be compared based on patent analysis, including co- process of development. operative network (similar scale of R&D cooperation), co-occurrence The technology analogy prediction analysis assumes that in a spe- network (same technical topics or classification) and citation network cific innovation environment and with expected benefits, a series of (similar technical basis). Thus, we believe that patent documents ap- technologies can be improved, rapidly spread, and widely adopted over parently convey various kinds of observable information for antici- time, depending on the competition and cooperation of companies in pating and understanding the technological change in analogous fore- the market. These companies actively participate in patent applications casting. to protect and improve products and technologies in the market and to Patent application activities and cooperative behaviors largely promote the development of the industry in the process of technology promote the development and commercialization of technologies. development and maturity. Despite the importance of competition and Seemingly different technologies are comparable if they are in the same cooperation, little attention has been paid by analysts to the companies stage of technological development. The first link is between the as key players to be compared in the processes of technological accu- number of patent applications and patentees within a certain time mulation. Based on the theory of social network analysis, we applied period, which enables the quantitative study to identify the technology the collaboration network analysis to identify the extent to which life cycle. Patent life cycle has been used as an S-curve to predict the various candidate technologies act as a similar pattern or trend within development trend of a specific technology in terms of the cumulative the network. The visualization comparison of different technologies patent amount (Cioffi, 2005). The linear regression based on patent showed a direct way to identify technology evolution. The complex data was used in three S-curve models for measuring the inflection network is used for forecasting by analogy, mainly considering that the point, the growth, and the saturation time of the technology, which technology grew quickly and became more complex, thus the current foresees the development trend (Liu and Wang, 2010). The second is co- judgment of analogy based on certain characteristic cannot reveal the patenting, which is used as a knowledge spillover or competitive development track of the underlying innovation system comprehen- strategy for companies developing technology together. When tech- sively and objectively. In this paper, the cooperative network structure nologies grow, the networks of patent collaboration become denser. In is selected for analogy prediction to reveal the leading role of R&D the current open innovation ‘paradigm’, most firms open and collabo- innovators in the underlying innovation system, which is also the basis rate with external partners for accessing complementary resources, to promote the continuous development and maturity of industrial reducing cost and risk sharing, and faster product development (Jeong technology. and Kim, 2014). Based on the time series models and network analysis, The purpose of this paper is to find analogical technology that is the technological development trend was used to forecast greater de- similar to the target emerging technology and have been successfully velopment potential (You et al., 2017). commercialized. We believed that the cooperation network can be considered as identical topology to reveal the evolution of innovation 2.3. Social network analysis and market features. A large fraction of major actors represents and control the technology trajectories and market. This means that the Social network analysis (SNA) is considered as a quantitative need for market protection continues to grow, which will accelerate, in method to identify how actors interact with each other within a net- turn, the commercialization process. work. Many complex systems can be represented as networks, which could simplify functional analysis considerably (Hu Yanqing et al., 2.4. Autonomous vehicles in the US 2010). Many network analysis applications are based on topological comparison of complex networks, such as classification and clustering. We chose autonomous vehicles as our case study. Carmakers are Given two networks of different technologies, the structural similarity always trying to stay ahead of the automobile industry so they can of two networks is possible to compare in measurement such as density, develop cars that markets purchasers will buy in the future. There is a clustering coefficient, degree distribution and so on. Moreover, the wide agreement in the general car industry that autonomous vehicles graph isomorphism is considered as identical topology to reveal the are the future, as evidenced by the significant research and develop- correspondence between nodes of networks, which is hard to identify ment (R&D) investments from automotive players and tech giants alike. the overall structural similarities (Aliakbary et al., 2015; Zager, 2003). Much of the doubts are from common views and perceptions about In many studies, technology network analysis has been used to autonomous cars within the industry, and from misinformation from

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Table 1 Technology forecasting model in the car industry.

Target technologies Authors Candidate technologies

Electric vehicles (EVs) Massiani and Gohs (2015) EV, LPG, CNG Cordill (2012) EV Prius, EV Hybrid Civic, EV Ford Escape Hybrid Hybrid electric vehicles (HEVs) Cao (2004) E85, CNG, Hybrids Lamberson (2008) HEV, EV Mcmanus and Senter (2009) HEV, EV Sang Yong Park and Kim (2011) HEV, HFCV Plug-in hybrid electric vehicles (PHEV) Mcmanus and Senter (2009) PHEV, HEVS Autonomous vehicles Lavasani et al. (2016) Auto, HEV, internet, cell phone the media. (besides the business model) is more productive in the early stage than Previous studies have been conducted to predict the AV adoption on in the mature stage (Yun et al., 2019). the basis of earlier vehicle-related technologies, such as HFCVs, EVs, The autonomous vehicle industry is considered as a converted in- and HEVs (Table 1). Similar factors that affected the innovators' pre- dustry as it is replacing the already existing types of vehicles. The de- ference are considered as comparable, for instance, the influence of velopment of technology is related to any of the complex related sys- market size, user behavior, and historical data from other products tems that implies the following components: perception, control, (Lavasani et al., 2016). Although technology forecasting has been stu- planning, system management, and localization features (GPS), laser died in automobile-related technologies, there are very few studies for scanner, camera, radar, automated steering, steering, automated forecasting the development of Autonomous Vehicles (AVs). In this braking, automated throttle, central processing unit, motion sensor, and case, AVs is taken as a technology to be used in the case of an analogous speed sensor. The levels of automation of these vehicles are “Level 1, method, and to be compared with the actual status of the development function-specific automation; Level 2, combined function automation; of the technology. So far, the estimation of AVs development and Level 3, limited self-driving automation; Level 4, self-driving under forecast models were based on the personal judgment of analogies, and specified conditions; and Level 5, full self-driving automation”. The not from the technical strategy for an analogy. Therefore, in this study, companies competing for developing autonomous vehicles are ICT the empirical analysis was focused in the similar technologies of AVs firms (Google, Apple, and Baidu), companies that produce the entire from the inception to maturity, and visualized the evolution of their vehicle (Audi, Ford, Mercedes –Benz, Volkswagen, Volvo, Hyundai, and collaborative networks by measuring the co-ownership of patent ap- Kia Motors, BMW, and GM), and electric car companies (Tesla). plications over time. Additionally, IT companies related to this type of vehicles are Apple, Amazon, Alibaba, Google, Uber, and Baidu. Considering the dynamics of the relationship between technology, business model, and market, 2.5. Autonomous vehicles: new emerging industry and the relationship the number of patents related to autonomous vehicles increases based between technology patent, business model, and market on the development of the knowledge-based economy. At the same time, business model patents for autonomous vehicles grows less than Demand-pull theory, technology push theory, and integrated tech- the increasing number of cited patents and the business model respec- nology innovation theory can describe the relationship between tech- tively (Yun et al., 2019). nology and market. However, a new theory that combines technology and market includes a business model element describing the dynamic relationship among these three elements. The inclusion of business 2.6. The use of patent data and SNA to forecast and simulate technologies model describing the technology development and market is evidenced by the existing frame of regulations and standards in the autonomous The analysis of patents by Social Network Analysis has been used in vehicles industry (Yun et al., 2016). different aspects and the complexity of its use evolved through time. The autonomous vehicle industry has developed key technologies to First, patents and SNA is used to analyze the structure of technologies introduce and expand the market in the automotive industry. However, by bipartite network SNA graphs to see the technology clusters and then there exist factors, such as the regulation and standards, that these used them to forecast the technology (Jun, 2012a). Posterior analysis companies have to face. The regulations and standards impact on de- has been done in the side of isomorphism and analysis of trajectories of fining the business models, as well as the scope and scale of the market. technologies (Weng and Lai, 2010), quality of patents and its prediction Therefore, the dynamics of these three elements are as follow: by using patent citation (Wang et al., 2010), and the use of statistics and Technology innovation affects the business model development which statistics models of patent networks to forecast technologies (Jun, 2016; is highly related to market development and expansion, levels of sales, Jun and Sung Park, 2013). and profits. Consequently, these profits can be used for investing again Posteriorly, the combination of using patents and SNA to forecast in new technology (Yun et al., 2016). technologies has been applied in a variety of cases and industries such Activities related to autonomous vehicles are increasing strongly per as autonomous vehicles. For example, the analysis of knowledge (by patent citation analysis. Also, the dynamic cycle using the patent cita- patents) and the importance of building information technologies (Park tion reveals that the technology innovation in the period n-1 results in et al., 2018), analysis of the technology lifecycle for forecasting future technology innovation in the next period n and then to the period trends of semiconductor industry (Chiu and Su, 2014), and forecasting n + 1. Each of these periods effects is related to patents' causal loops central technologies (key technologies) (Jun, 2012b). (dynamic loop of technology innovation, business model, and market) During the last years, the use of patents and SNA to forecast and (Yun et al., 2016). simulate technologies has also focused on the particular case of electric, The role of business models in technologies and market can be of autonomous vehicles, and elements of the system of this type of ve- two types, an existing market-based converted industry and a newly hicles. For example, patents of Battery electric vehicles (BEVs) are market-based emerging industry. In a converted industry, the business analyzed by SNA to identify core companies to forecast the BEV's future model strategy is valuable in the early stage while focusing on tech- (Yun et al., 2016). Additionally, the technological knowledge of eco- nology is important in the mature stage of the life cycle. In the other systems of battery electric vehicles (BEVs), hybrid electric vehicles case, a new emerging industry, the strategy focused on technology (HEVs) and fuel cell electric vehicles (FCEVs) can be seen by analyzing

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Fig. 2. Collaboration network comparison in SNA.

4. Results and analysis

4.1. Data source

This paper combined SNA and patent analysis methods to compare the trajectory and characteristics of related technologies and tried to identify the most similar ones. The data sources used in this paper were the paper publication from Web of Science (WOS), patent applications from Derwent Innovation Index (DII), engineering papers from Compendex, grants from Nation Science Foundation (NSF), and news from EBSCO related to autonomous vehicles. We collected patent data in the DII based on Derwent Manual Codes (DMC), which is a hier- archical indexing system used for patent retrieval and analysis. Under the advice of the experts, we retrieved data on April 19, 2018, with all Fig. 1. Overall process of the proposed methodology. the patents filed in related candidate technology fields of US Autonomous Vehicles (AVs) in the automobile industry, including Electricity Vehicle (EVs), Hybrid Electricity Vehicles (HEVs), and Fuel the technological knowledge areas (Aaldering et al., 2019). Cell Vehicles (FCVs). The value of various indicators was obtained via Ucinet to measure the collaboration graph. The data sources, which were selected according to the technology 3. Research methodology life cycle, are indicated in Table 3. The journal papers were used to describe the fundamental and applied research, while the patent data In this section, we introduced the methodology used in this study was used to the technology trajectory based on SNA. The number of and explain the steps and sequential strategy to the data. Fig. 1 in- grants was an indicator that helped to describe the effects of incentives dicates the five steps for the process of the analogy. First, this study for R&D in the development of the technology. explores the technology development from basic research to social media based on bibliometric analysis, and the development trend of target technologies was analyzed to identify the trend and patterns of 4.2. Bibliometrics analysis of autonomous vehicles in the US change. Second, to facilitate the comparison of technologies generated in different periods, time and trends were normalized based on the Based on bibliometric analysis of the development trend of fi maximum number of patents for the S-curve in the technology life American autonomous vehicle technology in the U.S., over the past ve cycle, which was helpful to identify the technology life periods within years, self-driving vehicle technology has gained important attention the same process of development. Thus, the periods for each subnet- with the rapid growth in U.S (Fig. 3). social media and R&D investment, work were divided into six-time spans with five years each, which en- while the growth rate of applied engineering and basic research has fl abled us to capture the characteristics of each technology development been slow and uctuating. The number of company patent applicants period. Third，the collaboration networks of patent applicants were has also been increasing. By 2017, there were around 400 related en- built. Companies that filed patents were abstracted as nodes, and co- terprises and institutions in the U.S. The patent race of autonomous parenting behavior was depicted as edges. Subnetworks per each period vehicles has become more and more competitive among both auto- of technology candidates were built, and two main centrality metrics makers and tech companies. The R&D budget rose 20% for tech players were considered to compare the subnetworks: Average degree and and 5% for automakers. According to research conducted by Oliver density. Wyman and WIPO, between 2012 and 2016, there were slightly > 5000 fi In this study, we proposed a dynamic analogy process in the colla- mobility patents led by 12 leading automakers and global tech com- boration network for the same period of growth based on the SNA panies (Chen and Fang, 2009)(Fig. 4). measures and visualizations. According to previous research, as a field According to the patent application trend, it is obvious that AVs develops, it undergoes a topological transition in its collaboration technology in the U.S. can be divided into three phases. Before 2007, structure between a small disconnected graph to a much larger net- the study of AVs in the U.S. was in the period of accumulation and “ ” work, where a giant connected component of collaboration appears exploration around the Grand Challenges that remarkably accelerated (Bettencourt et al., 2009). These kinds of studies show that the evolu- advancement (Anderson et al., 2016). From 2008 to 2012, Google's tion of technology could be observed by the quantitative measure of the driverless car initiative was in place and moved to complex city streets. collaboration network. Therefore, we built and compared the tech- In 2013, more companies, such as Audi, Toyota, Intel, etc., began to nology collaboration network (Fig. 2) in order to apply SNA to identify enter the driverless car industry and unveiled R&D plans, visions, and similar technology within various parameters (Table 2). roadmaps. This is consistent with the U.S. self-driving technology development and the expectation of experts in the U.S. market. We believe

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Table 2 SNA indicators for comparison.

Network Dimension Indicators Deﬁnition Meaning for high values

Whole network Size of network Average degree Number of average links of nodes High amount of connections in the network connectivity (AD > 0) H-Index Highest number x so that there are x vertices of Higher concentration of nodes with a high degree (HI > 0) degree at least equal to x Degree of centralization The overall network degree centralization High quality of nodes (central) on the network with (DC) significant direct influence on their contacts Density of network Density Number of links divided by the maximum High density in network efficiency (0 < DE < 1) possible number of links Connectedness Number of not reachable vertices Not fragmented (0 < CN < 1) Avg distance Average geodetic distance between reachable Significant distance between the nodes (APL > 0) pairs Diameter Length of the longest geodetic distance Significant distance between the nodes (Di > 0) Compactness The adjacent distance of all nodes Greater cohesiveness (0 < CM < 1) Efficiency Extent to which underlying networks have Lower density (0 < EF < 1) redundant links Densification and Scaling exponent The densification effect in a way that is Grow and densify growth independent of scale (number of nodes). Subnetwork Connectivity Average degree Average of the degrees of all the nodes in the High amount of connections in the subnetwork subnetwork Density Density The number of links divided by the maximum High density in the subnetwork possible number of links in the subnetwork

Table 3 4.3. Technology life cycle Data source. Source: Based on information from Chiu and Su (2014). The growth curve method is about ﬁtting the growth curve into a set Source Type of source Technology life cycle of data on technical performance and then extrapolating the growth indicator curve beyond the data range to get an estimate of future performance (Yu-Heng Chen and Chen, 2011). The adjustment of the growth curve Web of Science (WOS) Journal papers Fundamental research works better when there are enough existing data, and the assumptions Compendex Engineering paper Applied research Grants from National Science Number of grants Social impact about limits, parameters, and trends are close to realistic and existing Foundation (NSF) conditions. However, when the information is limited, the trends of the News from EBSCO Newspapers Social impact curve can have many alternatives depending on the decisions deﬁned Derwent Innovation Index (DII) Patent application Development by the assumed trend. A solution for this situation, as indicated above, is to use the pattern and characteristics of the technologies that have completed their cycle. The use of the pattern of these technologies as that compared with data sources such as news, papers, and fund pro- analogies requires systematic steps to evaluate the analogous tech- jects, patent documents can better reveal the performance of technol- nology, assumptions, and adjustments to be taken. Step 3 in the pre- ogies in the market. sented method can be applied to the following conditions and steps:

Fig. 3. Bibliometric analysis of AVs in the U.S. (2000–2017).

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Fig. 4. Technology life cycle of AVs based on patent application.

- The target technology is in the stage of growth (the case of AVs). - The S-curve is used as the pattern of the cycle. Therefore, it is required to obtain the curve, including calculation of parameters, estimation, and drawing the S-curve for candidate technologies. - Normalization of the time periods. - Division of the cycle in periods that cover each stage of the cycle (six time periods for the analysis of AVs).

Due to the fact that the life cycle of all candidate technologies could correspond to different periods and have different characteristics, the curves need to be normalized for the candidate technologies. The critical points to be normalized are based on the time (year) and the alignment of the technology life cycle stages. The time normalization corresponds to the time identification of the absolute maximum point of the curves and the corresponding lags of time that each technology needs to have to coincide with the maximum points. At the same time, the elongation of each part of the curves needs to Fig. 5. Candidate technologies' life cycles: EVS, HEVS, FCVS. be considered. This is an important point since the development of each technology could take a shorter or longer period of time. However, one applications. Fig. 6 shows the three candidate technologies. EVs and of the important points to consider is that the candidate technologies HEVs technologies' life cycles were very similar in terms of the number are chosen based on their similarities to the “objective technology” of patent applications and their time differences in each stage; however, (AVs). Since the candidate technologies have similar characteristics, the time differences needed to be considered. The differences between adjusting the life cycle curves to one common point will show that the FCs is more evident, especially in terms of the number of patents. In technology life cycle curves may have similar trends and shapes. order to see these differences more clearly, Fig. 7 shows the differences Another important point is that the analysis needs to be divided into in time of each stage in each technology (the circumferences in green periods that cover each stage of the technology life cycle curves. In our denote the year-subnetworks). case, the time period was 44 years, which was divided into six four-year Table 4 shows the time period of each technology and for each time periods. This is was for two reasons, one was that the number of stage, which comprises of six four-year periods. It can be observed that theoretical stages, the networks, and their respective connections, were the “emerging stage” is the longest period, following by the growth built in “periods”. Therefore, this process allowed for the synchroni- stage, and then the maturity stage. zation of the theoretical stages, the network characteristics by period, and the “elongation” of each stage. The curves' elongation character- izing the time that each technology takes to reach the maturity level is 4.4. Technology collaboration networks an important element to be analyzed. This characteristic principally implies the different time duration in each stage of the curves. Fig. 5 The patent co-ownership network can be used to construct un-di- characterizes the technology life cycle and the stages that need to be rected technology networks by identifying the whole and sub-network identified and characterized. of collaboration relationships. When the technology grows, the number The three technology life cycle curves had close characteristics. The of companies who file patent applications will increase accordingly and main differences were shown in the amplitude or number of patent their networks of cooperation also become denser. This means that the

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Fig. 6. Candidate technologies' life cycles: EVS, HEVS, FCVS. need for market protection continues to grow, which will in turn ac- nodes) (Table 6). celerate the commercialization process. Based on the analysis of the Fig. 8 shows that the EVs and AVs show a significant increase in the candidate technical cooperation network, the indicators of the candi- number of edges per node as the field grows (α > 1). In the light of date technical cooperation network were calculated to compare the densification and growth by time, there are two possible conclusions. overall situation of the candidate technical cooperation. Table 5 shows First, when exponent αwas around 1, it showed a constant average that AVs and candidate technologies are similar in the network con- degree over time, so for EVs and AVs, it was not extremely dense and nectivity and density with tiny differences. HEVs are probably the most they were still in the early stages. Second, although the application year notable in the performance of connections and efficiency with the for EVs was later than AVs, the collaboration network of EVs increased highest connectedness, distance, diameter, and compactness in the in density, which means probably more innovators participated in this whole network. This can also indicate that hybrid cars may be far away area and EVs were adopted faster than AVs. In addition, when com- from AVs when compared to others. We found some evidence in the paring technologies based on the trend of the overall technical co- U.S. market that HEVs are now perceived as a core segment of the operation network, it was difficult to make a comparison in view of the automotive market. American sales of hybrid electric vehicles represent different development cycles of technologies. Therefore, it was neces- about 36% of the > 11 million hybrids sold worldwide (Cobb, 2016), sary to analyze different time intervals of the candidate technology, and which is different from the AVs in the early development process. then calculate the cooperative network indicators to compare the de- However, the whole network cannot reveal and compare the char- velopment process of different technologies. acteristics and network structure of technological development and Fig. 9 reveals a collaboration subnetwork structure at different evolution. If the social network indicators of the overall technical co- times. In terms of self-driving technology, which was invented earlier operation network are similar, this can indicate that in these technical than other candidate technologies, there have been great technological fields there are similar cumulative numbers of companies who partici- advances, and the cooperation network has become increasingly dense pate in technology creation and improvement and that actively pursue in the past decade. Companies are still in a period of full bloom. the market protection. However, technology cooperation networks in EVs, HEVs, FCVs are In practice, the collaboration networks based on paper co-author- becoming more centralized, especially EVs and HEVs. Their cooperative ship expressed that the average number of edges per node tends to relationship between companies is closer than ever. increase over time (Bettencourt et al., 2009). When technologies grow, The density and average degree of the subnetworks during the first their networks of collaboration either in the publication or in patent stage is clearly less dense as compared with the stages closer to the documents also become denser. So we referred to the densification laws maturity stage. The average density is the average of the degrees of all of the scaling exponent proposed by Leskovec et al. (2005) to compare nodes in the network (sub-networks) and measures the connectivity the growth patterns in candidate technologies. level of the network. Additionally, the density of the network (subnetwork) measures the efficiency of the network by dividing the α edges= A(nodes ) number of links by the maximum number of links. These metrics were measured in each subnetwork and for each technology. where A and α are constants. The scaling exponent, α, expresses the The average degree and the density metrics were calculated in each densification effect in a way that is independent of scale (number of

Fig. 7. Candidate technologies life cycle stages: EVS, HEVS, FCVS.

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Table 4 Standardization of time in each stage.

Stage Emerging stage Emerging stage Emerging stage Growth stage Growth stage Maturity stage

Period P1 P2 P3 P4 P5 P6 EVS 1978 to 1992 1993 to 1997 1998 to 2002 2003 to 2007 2008 to 2012 2013 to 2017 HEVS – 1993 to 1997 1998 to 2002 2003 to 2007 2008 to 2012 2013 to 2017 FCVS – 1989 to 1993 1994 to 1998 1999 to 2003 2004 to 2008 2009 to 2013 AVS 1987 to 1991 1992 to 1996 1997 to 2002 2003 to 2007 2004 to 2008 2013 to 2017

Table 5 one, the variance was calculated with respect to the average points in Whole networks cohesion measures of AVs and candidate technologies. each period. The formula to calculate the variance, where x represents the values of the centrality metrics, is presented below. The curve with Measurement The range of value AVs EVs HEVs FCVs the lowest variance was chosen, in this case, the FCVS, where both Avg degree (AD > 0) 1.707 1.745 1.891 1.746 connections and density variance were the lowest. H-Index (HI > 0) 7 8 9 7 Deg centralization – 0.051 0.062 0.063 0.034 Density (0 < DE < 1) 0.005 0.005 0.005 0.005 4.5. Compare real values with found analogous technology Connectedness (0 < CN < 1) 0.08 0.162 0.209 0.166 Avg. distance (APL > 0) 4.288 4.899 6.536 6.362 As it is seen in the last two figures, the selected technology had a Diameter (Di > 0) 9 13 19 15 Compactness (0 < CM < 1) 0.025 0.042 0.047 0.035 better approximation to the target technology trajectory (Fig. 11). The Efficiency (0 < EF < 1) 0.992 0.996 0.996 0.996 cumulative number of patents of FVCs was similar to the AVs trajectory in two aspects: one was the number of patents in each year, and the second was that the elongation of the curve gave a better approxima- Table 6 tion to trends over the long run. Densification exponents for EVs and AVs.

Densiﬁcation equation α 4.6. Technology forecasting based on selected analogous technology

EVs y = 0.6175x1.2075 1.2075 AVs y = 0.9601x1.1088 1.1088 Based on the selected technology to be used as analogous (FCVs), it is possible to forecast the performance and know the trajectory of the technology. Since this paper utilizes the number of patents as the da- period for each technology (see Fig. 10). As it was expected, the curves tabase, it is used the parameters and characteristics of the growth curve for the density followed similar patterns. In terms of density, these close of the selected technology (FCVs) to estimate or fit the growth curve of values confirmed that these technologies follow similar patterns. the target technology (AVs). However, a clear difference was shown in the earlier stage, especially The applications of the technology forecasting are extensively ex- for the values of density. This can describe the differences among each plained by the existing literature which is mainly based on growth technology due to differences in starting time (starting at the emerging curves such as the Bass Model, Lotka-Volterra Method, Pearl Curve, and period), and the knowledge of certain technology. In terms of average Gompertz Curve (Chiu and Su, 2014). Based on the characteristics of degree, the number of connections among patents increased according the technology, the symmetry of the curve, the potential substitution of to the periods closer to the maturity stage. AVs in the market, existing past progress or unexploited early im- In order to compare the curves, and choose the most representative provements offset the fact that stimulate growth, the Pear growth curve

Fig. 8. Densiﬁcation of collaboration graphs in EVs and AVs.

9 S. Li, et al. Technological Forecasting & Social Change 148 (2019) 119731

Fig. 9. Collaboration subnetwork structure based diﬀerent times. is used following the curve (Martino and Joseph, 1993): 5.1. Methodological contributions

L y = The paper makes multiple methodological contributions as listed 1 + αe−bt below:

L = Upper limit to the growth of variable y • Research presented in this paper introduced an integrated metho- e = Base of the natural logarithms dology for analogy based technology forecasting as shown in Fig. 1. t = Time The integration of multiple methods as described throughout the a, b = Coeﬃcients of the curve paper provides a repeatable process for researchers as well as practitioner professionals. Prior research on technology forecasting In order to estimate the parameters of the curve, the formula can be using analogy has not provided such a platform approach before expressed as (Martino and Joseph, 1993): (Jun et al., 2017; Kwasnicka et al., 1983; O'Connor, 1971). The L paper builds upon the earlier work (Gonçalves Pereira et al., in y = press; Lin et al., in press; Cho and Daim, 2016; Daim et al., 2012; 110+ α ABt− Gibson et al., 2017) which provided earlier integrated platforms for technology forecasting. y Y = LOG⎡ ⎤ =−ABt + • The paper also makes point contributions. Integrated use of multiple ⎢ Ly− ⎥ ⎣ ⎦ data sources ranging from publications to grants demonstrates another repeatable platform building upon earlier work using some of As a and b, A indicate the location and B the shape of the curve, but these data sources (Gonçalves Pereira et al., in press; Lin et al., in with diﬀerent magnitudes (Martino and Joseph, 1993). press; Cho and Daim, 2016; Daim et al., 2012; Gibson et al., 2017; First, the parameters A and B are estimated based on the cumulative Madani et al., 2017; Madani et al., 2018) number of patents for the FCVs (Fig. 12). These A and B values are Use of SNA in technology forecasting is not new (Behkami and taking for shaping the forecasting curve of AVs and the limit value of L • Daim, 2012; Garces et al., 2017; Marzi et al., 2017). However, using is adjusted to the new conditions of the patent and maturity level of the SNA to identify collaborations and using collaboration density for technology. The estimated parameters and the limit value are: analogy purposes is another contribution presented in this paper.

L (AVs) 40,000 The paper also makes contributions through the analyses of the A 314.9616 autonomous vehicle case. The following is what we learn through the B 0.156563 analyses.

• Self-driving vehicle technology has gained important attention with 5. Discussion the rapid growth in U.S. social media and R&D investment, while the growth rate of applied engineering and basic research has been “ ” This section will address the so what question for the research slow and ﬂuctuating. The invention race in autonomous vehicles has presented in this paper. The paper makes both methodological and become more competitive among both automakers and tech com- application focused contributions. We will explain these contributions panies. below:

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Fig. 10. Average degree and density metrics per each technology and period.

• Technology life cycle stages as well as collaboration networks and patterns and fill the gap of lack of information and reduce the un- their dynamics for emerging automotive technologies were identi- certainties. fied. • The paper takes AVs technologies as a technology to be analyzed • Final analysis based on the prior points reveal that major invention and tests the data and results. Three candidate technologies were activity in the autonomous vehicle technology is maturing and we considered that were chosen based on the similarities to the target should start seeing this industry pick up soon. Of course technology technologies. To fill the lack of a method that provided systematic alone is not just the enabler. Technical innovations will always de- steps to select the best candidate technology, this paper proposed pend on other types of innovations including social, political or steps and described an application case. To select the best candidate ethical. technology, the paper describes, based on the technology life cycle, the main characteristics of the candidates based on journal papers, 6. Conclusions grants, and the news media. Based on the selected technologies, SNA was performed based on patent data. The best candidate technology There are several key conclusions in this paper. The first set is was selected based on the analysis of two main centrality metrics methodological: (Average Degree and Density), and finally, we selected the technology based on the lower variance of these metrics. • This paper describes the most important aspects about selecting candidate technologies to be considered as an analogous technology The second set are in respect to the automotive technologies. in order to forecast the development of technologies, given the lack of data to fully describe the trajectory of target technologies. In this • The paper clearly identified the invention activity and the stages of context, the information of patents is used with using SNA that al- technology life cycle for the emerging technologies. These can be lows the analysis of the complex structure of patent networks. The critical in assessing and new emerging technologies analysis of complex networks is important since it allows finding • The paper also identified collaboration networks and their density

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Fig. 11. Trajectory of selected technology and target technology.

over time. This also can be essential in assessing new emerging 7. Limitations technologies in this area. • Finally the forecast shows that future growth of autonomous ve- The paper presents an alternative to ﬁll out the gap of using hicles will not so much depend on new technological inventions but quantitative methods for forecasting technologies when there is a lack more likely on complementary innovations in the product, regula- of enough data and taking as a reference the technology trajectory of tion or social systems. similar technologies that have completed already their lifecycle. The use of patents and their structural analysis by SNA is presented as the

Fig. 12. Forecasting the cumulative number of patents for AVs.

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