Source: http://gerpisa.org/node/4456
Timestamp: 2019-04-20 12:35:33+00:00

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This work aims to analyse a relational structure of patents in automotive sector, especially for electric vehicles. The automotive industry is equivalent to 22% of Brazil's industrial gross domestic product (GDP), 4% of Brazil's total GDP, and worldwide it should reach a mark of 100 million vehicles sold by 2020. In recent decades, innovation processes have become a matter of survival for companies. Resource Based View admits that companies are looking for strategic resources that are a source of competitive advantage. This work comprises the automotive market as an organizational field in which the actors struggle to reach more favourable positions from the available resources. The network approach was used together with patent analysis to highlight the existing relationships within this industry in the construction of innovation, through the analysis of patents of high commercial value in the automotive sector, collected through the Derwent database. Firstly, for data collection, a search term was defined that included patents with international patent code (IPC) B60 (referring to vehicles in general), but limiting those registered simultaneously in the American, European, Japanese and Chinese patent offices (called tetradic patents, with high commercial value). This search selected patents that had their main information (such as title, abstract, cited patents, authors and depositors and date of first deposit) extracted and analysed with the help of Vantage point software. The number of tetradic patents deposited annually for the period from 1995 to 2014 in the automotive sector was raised. There has been a steady increase in the number of patents filed annually. A special move takes place in 2009 and 2010, the only years in which the number of tetradic patents in the automotive sector has not grown. This movement also accompanies the general trend of patent growth, which has receded in recent years as a reflection of the economic crisis of 2008. For a better understanding of the sector, these patents were analysed by each patent group, that is, the IPCs were analysed up to their maximum level of division. By the analysis of the incidence of patents by specific IPCs, the predominance of groups related to the electric and hybrid vehicles theme was identified. It was also analysed how these IPCs related. This was done in order to better understand the groupings between these IPCs, in order to explain which groups of IPCs deal with the same issues. Each patent may use one or more IPCs in its registry. Thus, each time different IPCs were cited in the same patent, a link was registered between them. In addition to understanding the thematic areas of innovation in the sector, this study intends to understand how the technological domain of the sector is given. In this sense it is fundamental to carry out the survey of who are the depositors of these tetradic patents of the sector. The data make clear the dominance exercised by Toyota, both in the term of twenty years and in the last five years. The Bosch auto parts industry also has a large number of tetradic patents at IPC B60. It was also seen the position of Nissan, which has significant number of patents deposited in the last five years. Of the twenty first companies, nineteen are traditional in the automotive sector, being either vehicle assemblers, auto parts and accessories industry or tire factories. It is noteworthy the case of Panasonic, the home appliance, electronics and electrical components company that designs itself in the automotive sector. The lack of other companies outside the automotive sector can be partially explained by the patent search criteria. When looking for patents with high commercial value, in detriment of patents with high technical value, those that are already consolidated in the market are valued, not those that are in the technical development stage of innovation. This choice is based on the need for value generated as an indispensable element of innovation. These companies have different competitive strategies for innovation. It is necessary to understand which technologies are included in the strategies of each company. Again, Toyota's dominance over other companies in the sector is observed. Toyota is leading global vehicle sales as well as hybrid vehicles and was also the pioneer in the segment of cars powered by hydrogen fuel cell power. It is not surprising, therefore, that it also leads the technological development of ICPs related to electric and hybrid vehicles. Nissan shows balance between electric and hybrid vehicles, and has a good number of patents on anti-collision systems. Honda, Renault and Peugeot Citroen follow similar behaviour, but with smaller numbers. The Mitsubishi automaker has a higher inclination for fully electric vehicles. Volkswagen, Daimler Chrysler and BMW have their most concentrated patents on electrical accessories. Among the industries of auto parts and accessories, it is noted that the company Bosch has in addition to its expected development in electrical accessories and anti-collision systems, great relevance in the development of electric and hybrid cars. The same does not happen with the company Sumitomo, which focuses its patents on electrical accessories and tire composition. Panasonic already shows its almost exclusive focus on the market for fully electric cars. As a conclusion, the subject of electric and hybrid vehicles has become a dominant feature in the industry, with Toyota, Bosch and Nissan dominating the total number of patents, and organizing themselves in groups that gravitate around themselves by sharing patents only with companies that have strong commercial links, in particular cross-shareholding, joint ventures or formal strategic alliances.
* Ph.D. Student in Post-Graduation Program in Production Engineering, Federal University of São Carlos (UFSCar), São Paulo, Brazil. Research Line: Organizations, Institutions and Work.
** MSc Student in Post-Graduation Program in Production Engineering, Federal University of São Carlos (UFSCar), São Paulo, Brazil. Research Line: Organizations, Institutions and Work.
*** Professor Doctor in Post-Graduation Program in Production Engineering, Federal University of São Carlos (UFSCar), São Paulo, Brazil. Research Line: Organizations, Institutions and Work.
This work aimed to analyze the relational structure of patents in the automotive sector, especially for electric vehicles. More specifically, the objective was: establish the profile of deposit of patents of the main depositors, referring to the emerging technologies; identify patent relational structure; identify the economic relations and patents of the great automakers and identify the technological relations and patents of the great automakers. In this research the network approach was used in conjunction with patent analysis to show the existing relationships within this industry in the construction of innovation, observing the patents deposited by these companies and the cooperation agreements made between them. Data collection was carried out through the analysis of patents of high commercial value in the automotive sector, collected through the Derwent database, collecting 111,094 patents of international patent code (IPC) B60 which refers to vehicles. The data were processed using Vatange Point software for the preparation of lists and data matrices related to specific fields of patents. The graphs representing the networks were obtained with the help of Gephi software. From the results, it is emphasized that the theme of electric and hybrid vehicles has become predominant in the sector, with Toyota, Bosch and Nissan companies dominate the total number of patents and are organized in groups that gravitate around them sharing patents only with companies with strong business links, especially cross-shareholding, formal strategic alliances or joint ventures. Companies like Volkswagen and Honda have a high number of patents, but no technological partnership between automakers. Others, such as Fiat, Ford and General Motors have no patents, but seek to position themselves within cooperative networks in order to remain competitive in the marketplace. There are still companies like Hyundai, which has no patents in the sample analyzed and at the same time does not establish technological alliances. Thus, it was sought to understand how part of the technological capital is distributed by the automotive sector, in order to collaborate for a better understanding of the sector.
Key words: Innovation. Network. Automotive Industry. Patents.
Innovation processes have become central elements in competitiveness among countries operating in a globalized scenario (Amorim-Borher et al., 2007) and also a matter of survival for companies in face of new market dynamics (Tidd & Bessant, 2015). Innovation is linked to the company's ability to increase its profit (Geroski, Machin & Vanreenen, 1993), market share and longevity (Banbury & Mitchell, 1995), reduce risk level (Zhang, 2015; Bosseti et al., 2011), and is strongly related to growth of the market value of firms (Hall, 2005).
Although the conceptual relationship between innovation and performance is well established, the literature draws attention to the difficulty of measuring innovation (Brito, Brito & Morganti, 2009). One of the most effective ways to observe the state of innovation in countries and companies is through patent analysis. Patent documents contain important search results that are valuable to industry and business. If they are carefully analysed, they can show details and technological relationships, reveal business trends, inspire innovative solutions and assist the formulation of investment policies (Tseng, Lin & Lin, 2007, Daim et al, 2006).
There are studies that use patent analysis to map the state of innovation in specific areas of knowledge (Corti et al., 2007; Wagner et al., 2013). Other studies address different patent analysis methodologies (Tseng, Lin & Lin, 2007; Park & ​​Yoon, 2004; Daim et al, 2006). There are also studies that verify application of these techniques in specific sectors of the economy (Huang et al., 2003, Ernest, 2003). In the field of electric vehicles, patent analyses have been carried out to identify the state of the art of fuel cells and battery responsible for powering electric motors (Mock & Schimid, 2009; Golembiewski et al., 2015).
The objective of this work is to analyse the relational structure of patents for the automotive sector, especially electric vehicles. It is expected that by observing the data contained in the patents this will reveal the technological capital of automobile manufacturers and thus contribute to a better understanding of the industry and the innovation process itself. Specific objectives include: establish the profile of deposit of patents of the main depositors, referring to the emerging technologies; identify patent relational structure; identify the economic relations and patents of the great automakers and identify the technological relations and patents of the great automakers.
Patent analysis have been used to evaluate the technological performance of countries and also to identify the knowledge transfer between science and technology (Thelwall, 2008). Patenting has been shown to be valuable in technology development planning from a national perspective (Abraham & Morita, 2001), and to model specific technology (Sun et al, 2018).
Few patents end up developing into something of commercial value, but most are technically significant because they encourage and lead the development of technology (Sun et al, 2018). Thus, understanding and measuring growth in a technology area using patents can be an important source of knowledge for the enhancement of the technology itself (Daim et al., 2006).
Several index were introduced to measure the technological strength in terms of quantity and quality of patents. Some examples include patent citation index and regression models (Aristodemou and Tietze, 2018). As the total number of patents over time for a technology has a saturation point, the use of growth curves may also be useful. Other models are designed to map citation networks among patents (Daim et al., 2006).
Patent citing is a good measure of the technical quality of innovation, but not of its commercial power (Tidd & Bessant, 2015). A good indicator of the commercial value of a patent is related to the places where the patent was deposited. Patents that have been deposited simultaneously in the United States, Japan and the European office make up the so-called triadic patents. Triadics are strong indicators that the patent has high economic potential (OECD, 2009).
The use of patent data has several advantages. They reflect the corporation's ability to generate innovation; have detailed data for long periods of time and cover both small and large companies. The main disadvantage is that not all inventions are patented. Some aren`t technically patentable; there are sectors that don`t have the practice of patenting their inventions and also the patenting rate varies according to the country. Most patents are never exploited, they exist only to slow the development of competitors (Tidd & Bessant, 2015).
Many papers have used the Social Networking Analysis to explain how well-connected networks can improve knowledge transmission (Kildulff and Tsai, 2003). Other studies link social networks with the development of innovative clusters (Powel, Packalen and Whittington, 2012). The interest for networks is related to the dynamism of business that reveals an increase in the complexity of society (Ricciardi, Zardini and Rossignoli, 2016).
The network perspective considers the economic actors influenced by the social context in which they are inserted, and their actions are directly influenced by the positions that these actors occupy in the network (Gulati, 1998). The network can influence the actions of its members in two ways: by the flow and sharing of information within it; and by the difference of position of the actors in the network, a fact that promotes imbalances of power and control (Tidd & Bessant, 2015). Companies are involved in a co-occurrence scenario. They cooperate to raise the value generated in the productive chain, while at the same time competing in the appropriation of the generated results (Nalebuff & Brandenburger, 1996; Resende et al, 2018).
The boundaries of cooperation networks are flexible, repositioning of firms can occur driven by the resources required and by the relationship with other firms (Balestrin & Verschoore, 2016). Strong ties are important because they increase exchange ratios and enable joint action. However, it`s the weak ties that establish bridges that expand the flow of information and connect closed groups out of the network (Granovetter, 1973; Wang et al, 2017). Broad networks with stronger links tend to have better innovation results. However, in some cases, because of the commitment involved, networks can restrain innovation by rejecting ideas that come from outside the network in an attempt to protect its members (Christensen, 1997).
After almost 100 years of the fact that electric vehicles have been surpassed by combustion engines, they are mainly responsible for a movement that is expected to alter the competitive dynamics of the automotive sector in the coming decades (Castro & Ferreira, 2010). Driven by environmental concerns, the volatility of the oil market and the development of new technologies, electric vehicles are leading a transition process that seeks to break with the use of fossil fuels for a process based on renewable energies.
In this transition companies that are not traditionally included in the automotive market also participate in the development of technology. Companies from the electric and electronic sectors appear as major patent depositor of related technologies, such as Panasonic and Samsung, due to their know how in batteries and electric motors (Barassa, 2015).
When analysing the patent deposits on automobiles, it is noted that they are concentrated in the technologies referring to the electric motors and batteries for storage of energy for this motor. The third most patented technology refers to electronic controllers, intended primarily for hybrid vehicles (Barassa, 2015).
From a commercial point of view, it is possible to notice that Hybrid cars dominate this market, with strong growth of electric cars in recent years. The cars powered by hydrogen fuel cells were not included in the figure because they did not yet have representative sales (Reuters, 2017). Figure 1 presents these data.
Figure 1: Total sales of electric and hybrid vehicles.
Despite the policies and actions adopted in favor of the electric vehicle, it faces the complex challenge of overcoming the condition of the technological lock-in that has formed around the internal combustion engine, and which prevents a greater expansion in the adhesion of electric vehicles (Barassa, 2015). This lock-in is linked to the fact that the components of electric vehicles have significant differences from those used in the combustion engine, the most significant being the inclusion of a battery for energy storage, which may represent more than 50% of the cost of the vehicle (Castro & Ferreira, 2010).
Initially a longitudinal study of the patents of the automotive sector was realized. To filter the data was chosen the period between 1995 and 2014, considerable time to carry out a history of the sector. The final year for analysis was 2014, since in subsequent years there may be documents that have not yet been published, or that may be in the confidentiality phase or not yet indexed in the database used (Milanez et al, 2013).
In addition, a filter was carried out on patents that had high commercial value, and therefore only tetrad patents were used. Tetradic patents are those deposited at the same time in the United States, Japan, European Patent Office and China. The addition of China is crucial for the industry, given that China is currently the largest automotive market in the world (OICA, 2016).
To collect automotive patents, different search expressions were tested in the Derwent database. By the qualitative analysis of the returned patents, it was decided to use patents with international patent code (IPC) B60, referring to vehicles in general. Data collection was done in April 2017. Figure 2 shows the final search expression used in the research and used for the construction of sector indicators.
Once the search expression was defined, the collection and storage of bibliographic records of patents must be done. At first the data were processed in the Earliest Priority Selector (EPS) software (Milanez et al, 2013). The analysis obtained by the EPS was used to provide data for the VantagePoint version 5.0 software, which was used for data processing allowing the development of lists and matrices of data related to specific fields of patents. Subsequently the data were treated with Microsoft Excel software and presented with the Gephi software to generate the graphs.
In a second moment, a longitudinal data collection of the automotive sector was carried out, in order to elaborate the economic connections within the sector, observing alliances of worldwide automotive organizations regarding the cross-share participation, joint ventures, manufacturing contracts and alliances of technology and parts. This data was collected from the Automotive News magazine's annual database through its annual Guide to Global Automotive Partnerships, as well as its automotive plant guides in North America and Europe (AUTOMOTIVE NEWS, 2013).
For this second sample of the research, only the data referring to the year of 2013, the last year in which this yearbook was made available, were used. This data was organized into an Excel software spreadsheet that contained three columns. The first two referred to the two companies involved in the connections, and the third described the structure of corporate governance between them (cross-share participation, joint ventures, manufacturing contracts and alliances of technology and parts). Thus, in this research, the links are all classified as dichotomous and non-directional.
An important point is that by the structure of these data the economic links (second sample of the research) were analysed only among automakers. The use of the 19 automakers presented as actors by Automotive News magazine was used as a criteria, and according to OICA (2016) represents 85% of the world automotive sales. All data were treated with Microsoft Excel software and presented with Gephi software.
In order to understand the sector's innovation themes, the main groups of CIPs of the sample were analysed, that is, the CIPs analysed in their maximum degree of division. These groups were divided according to the theme they represented. Figure 3 reports the patents used by each of the twenty largest depositors per related CIP group.
Figure 3: Number of patents used by each of the twenty largest depositors per related CIP.
There is clear dominance of the Toyota automaker in this sector, being the main depositor of patents in 8 of the 10 IPCs analysed.. It leads the worldwide sales of vehicles in general as well as hybrid vehicles and was also the pioneer in the segment of cars powered by the energy generated by hydrogen fuel cells. Therefore, it`s not surprising that it also leads the technological development of CIPs related to electric and hybrid vehicles.
Nissan shows balance between electric and hybrid vehicles and has a good number of patents on anti-collision systems. Honda, Renault and Peugeot Citroen shows similar behavior, but with smaller numbers. The Mitsubishi automaker has a higher inclination for fully electric vehicles. Volkswagen, Daimler and BMW in the analysed sample have their most concentrated patents on electrical accessories.
Among the industries of auto parts and accessories, it is noted that the Bosch company has in addition to its expected development in electrical accessories and anti-collision systems, great relevance in the development of electric and hybrid cars. The same does not happen with Sumitomo company, which focuses its patents on electrical accessories and tire composition.
To understand better the sector, it`s necessary to understand the way in which they cooperate to generate value. Thus, figure 4 shows the cooperation network of these companies. The links represent patents created jointly by the two companies (nodes). Companies that have multiple patents shared with each other have stronger link, visually thicker bonds. The patents analysed are those classified with IPC B60, tetradic and using one of the ten groups mentioned in figure 2.
The analysis of the network reveals four distinct groups of cooperation, and another twenty-three companies with no connection to other nodes. These companies have strategies to protect innovation in a more isolated way than those included in the groups.
The groups reveal a structure of competition centered on three main actors. The first group, in blue, is structured around Toyota. The company has strong relationships with Aisin and Denso. This is justified by these companies participating in the Toyota Group, which holds 30% and 25% of the share capital of the companies, respectively, according to information from Toyota itself. The relationship with Yazaki and Sumitomo originates in the commercial field, being these suppliers of Toyota. The relationship between Toyota and Sumitomo also includes the formation of an investment fund with the objective of encouraging technology companies to create products that enable the use of hydrogen as a commercial fuel (Toyota, 2015).
The second group, in green, is led by the German auto parts company Bosch, which has the largest centralities of degree and intermediation of the sample. Surprisingly it`s an auto parts company, not an automaker, as a central element of the group.
SB LiMotive was a joint venture formed by Bosch and Samsung for the development of lithium-ion batteries and other electrical components (Bosch, 2012). The relationship between Bosch and Continental is in a scenario of coopetition, since the companies have products competing in several segments of this sector. Meanwhile, the relationship between Continental and Siemens arises from the purchase of the Siemens automotive division of Continental in a transaction of 11.4 billion euros (Continental, 2007).
At the other end of the group, the relationship between Bosch and Michelin intensified as they established a strategic alliance, given the leading position in innovation in their markets (Autonews, 2001). Finally, the relationship between Michelin and Peugeot Citroen is longstanding, involving, for a certain time, a cross-shareholding between companies (Marklew, 1995).
The third group, in orange, is led by Nissan. The company's main link is with Jatco, which owns 75% of its capital owned by Nissan. The relationship between Nissan and Volvo also involves shareholding, especially in the heavy-goods segment (Exame, 2010). It is interesting to note the lack of connection between Renault and Nissan, which indicates that although they are strategic partners, they do not file patents together.
The last group, in red, comprises only the relationship between Exxon Mobil and Yokohama, which have a cooperation agreement focused on new tire technologies (Yokohama, 2004).
Another important point is to understand how the formation of economic groups can interfere in the companies' innovation strategy. The question to be considered is whether the economic links are related to the company's capacity for innovation.
Figure 5 shows the representation of these relations in network format. In this figure, each link represents an economic relationship between the two actors and may refer to four different types of corporate governance structures: shareholding, joint ventures, innovation and parts agreements, and manufacturing contracts. The size of the nodes is proportional to the number of patents filtered for each automaker in question. This filter comprises the tetrad patents, registered in IPC B60 and that are in the 10 classes of patents most used in the sample.
Figure 5 shows the composition of four distinct groups formed by economic links between companies. In the upper part of the graph there is a group composed only of Japanese and Chinese companies. This group has as main player Toyota, the only one of the companies with significant amount of patents collected in the sample.
Despite being in the group represented by the green color, Toyota maintains connections with BMW and PSA / Peugeot-Citroen, companies that have a reasonable number of patents. Honda, the third-largest carmaker in the sample, has different behavior from its rival Toyota. While this is well connected, Honda seems isolated in the economic scenario, having only a relationship with General Motors.
Another important group is formed by Nissan, Renault, Mitsubishi, Daimler and Ford. Despite Nissan's patent domain, the other automakers also have a fair number of relevant patents. This fact may indicate a group of companies that can trade beyond commercial contracts, as well as the knowledge encoded in these patents.
The latter group is made up of Volkswagen, Fiat and Chrysler. It is evident that there is a strong link between the latter two, which is explained by the process already underway to merge the two companies, consolidated only after the sampling time of this research.
Besides these groups, it draws the attention of Hyundai, who did not have relations with the other automakers in the sample analysed at the time. The data show an important role of General Motors, which has high centrality of degree and intermediation. By connecting Honda to the rest of the automakers, it consolidates itself as an intermediary and plays a key role in the network. Meanwhile, Honda places itself in a peripheral position on the network, despite its commercial and technological importance.
It is evident in the analysis of this network that the most central companies are not those with the highest number of patents, is, greater technological capital. Although there are relationships that convey the acquisition and transmission of knowledge, actors of great relevance in the scope of patents do not appear in central positions in the network.
In order to observe more in detail how the relations in the scope of the innovation between the companies happen, it was also observed how they behaved when they were selected only the connections of technological alliances between the companies. Figure 5 shows all the economic connections existing between the companies, classified in four different types of governance structures: shareholding, joint ventures, commercial agreement and alliances of technology and parties. In figure 6, it uses only the connections referring to the technology alliances and parties, evidencing the agreements of technological cooperation among the companies.
The analysis of figure 6 shows the existence of four distinct groups. The first, in light blue is commanded by Toyota, both for its high number of patents, and for having a role of intermediation in the network, by connecting different actors.
The second, represented by the green color, has the largest number of connections and representatives. The existence of this high number of links, coupled with the high number of patents that its actors possess, may actually characterize a group for technological cooperation. Nissan, in this context, appears as a central element of the group, both for having more patents in the sample, and for being the great intermediary of this group.
BMW and PSA / Peugeot-Citroen form a group very connected with the other groups, while the group formed by General Motors, Honda, Fiat and Chrysler almost has no connection with the other groups. In this group, the company that plays the role of intermediary is General Motors, through links with Honda, Fiat and PSA / Peugeot-Citroen.
There are also four automakers with no technological links to the others, including Hyundai, Volkswagen, Saic Motors, and China Faw Industries. This fact may indicate that automakers are free to share knowledge among themselves, creating barriers to innovation in the industry.
Toyota's role changes over the business link network. In this network, formed exclusively by technological alliances, the company starts to play a more central role in the network, with high degree centralization and intermediation. The PSA / Peugeot Citroen also appears in a privileged position on the network as it connects the entire group 1 (in purple) to the rest of the network.
The data also show that the role of connection in the network, except for exceptions like Toyota and Fiat, is not done by the companies with the highest number of patents in the sample, nor by those with the lowest numbers, but by an intermediate block. They are companies that have entered the technological race of the green car in a delayed way and try, from their position in the network, to improve this aspect.
Thus, after the analysis of the economic and technological links, it is noticed that, although the latter are rarer, there are real cooperation groups for the evolution of technologies. In the comparison between economic and technological links some actors in particular need to be observed. Volkswagen, China Faw Industries, Mazda, Suzuki and Fiat have many commercial links and few or no alliances for innovation and parts. This may demonstrate that these companies have strong social skills in creating business relationships but do not use the same ability to promote relationships that encourage innovation.
Firstly, we sought to understand the most relevant technological topics within the sector. This was done by means of the lifting of the patent filing codes (IPC). This analysis indicated a predominance of themes related to electric and hybrid cars. These data corroborate authors who also point out the emergence of these themes (Castro & Ferreira, 2010). The second step was to establish the patent deposit profile of the main depositors, referring to emerging technologies. The data showed a dominance exercised by Toyota. The Bosch auto parts industry also has a large number of patents.
The analysis of the relational structure of the automotive patents revealed that the companies in the automotive sector do not have the habitual behavior of patenting jointly with other companies, contrary to the expectations that, based on the theories of innovation (Chesbrough, 2003), indicate that the sharing of ideas tends to favor innovation. When there was a co-proprietary patent deposit, the companies had strong bonds, such as a cross-shareholding. In the absence of that, there were strategic alliances, joint ventures, and strong and traditional economic relations.
The groups revealed subnets of innovation within the sector, being centralized in the most relevant actors. Thus, in the network of patent co-ownership there are no strong links between the three main players: Toyota, Bosch and Nissan. More than that, these companies structure relationships in their surroundings, reinforcing their role of central and dominant actor within their respective groups.
Subsequently, we analyzed the economic links among the main automakers in the sector. The analysis of the network generated by these links indicated that the companies with the largest number of patents didn’t have a central role in it. The groups approximate those presented Sacomano Neto et al. (2016).
Afterwards, we observed only the alliances formed by the companies with the objective of developing technologies. In this network, which is less dense than the previous one, the companies that owned the technology remained without having a central role, except for Toyota. The major intermediation role was with companies like PSA / Peugeot Citroen, Ford and General Motors. This becomes important as well-established cooperation networks can foster innovation (Tidd & Bessant, 2015).
Throughout this work, data were collected to identify which are, and how they relate, the companies of the automotive sector that hold the technological domain of the sector. For this, this work was based mainly on the Social Network Analysis.
When analysing the data, we could notice the clear predominance of some automakers in the field of technological capital of the sector. Toyota and Nissan are driving this revolution in the industry. They started their research earlier, have considerably more patents than their competitors, and have a good deal of innovation partnerships, either with other automakers or with auto parts suppliers.
Another group of automakers, which includes Volkswagen and Honda, have large patents, which indicates their efforts to develop new technologies, but until the year of the sample (2013) they did not have major partnerships for innovation. They are companies that have resources for innovation, but place themselves in peripheral positions by not connecting with the others. This can cause them, in the long run, to lose the advantage gained so far. It is interesting to note that in commercial terms Volkswagen places itself in a good relational position, it just does not replicate the same behaviour regarding alliances for technological cooperation.
There is also a group of automakers which doesn’t hold many valuable patents but has been investing in technology alliances to reduce its technological capital gap. This group, which includes General Motors, Ford, Fiat and Suzuki, seems to share a coherent vision for the field and try to reposition themselves within the technology cooperation network. They are companies that have a high number of vehicle sales, and therefore take advantage of the current status quo within the sector, but which observe the imminence of major changes in the competitive logic of the automotive market.
There is also one last case, such as Hyundai's, which does not have many patents in the sample and does not have links, in the sample analyzed, with other companies, whether economic or technology alliances and parts. The company seems isolated and left behind in this competitive race, despite all of its recent commercial success.
Finally, it is understood that, although the automotive sector does not have its innovation traditionally codified in patents (Marsili, 2001), it is possible to verify a certain type of structure in the relational analysis of these patents. Thus, this work presented an analytical framework for the study of patents in the automotive sector, which can be replicated to different sectors, especially those with a high degree of patentability.
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