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Avanci Patent Pool 1 Do Patent Pools Facilitate Innovation: A Case Study of the Avanci Patent Pool 1. Introduction Innovation is crucial to the success of any industry, including the automotive industry (Petti et al., 2021). Patents play a vital role in incentivizing innovation by providing exclusive rights to the inventors for a specific period, allowing them to reap the benefits of their research and development (Eisenberg, 1989). These can be particularly effective in industries where a large number of patents are required to develop a product, such as the automotive industry. Patent pools reduce transaction costs, increase efficiency, and lower the risk of patent infringement lawsuits (Nelson, 2020). The use of patent pools has been a topic of interest for scholars investigating its impact on innovation (Moser, 2013; Petti et al., 2021). While some studies indicate that patent pools encourage innovation by reducing transaction costs and licensing fees, others argue that it can declines incentives to innovate (Flamm, 2013’ Shapiro, 2000). One such patent pool is the Avanci Patent Pool, which was established in 2016. The Avanci Patent Pool comprises patents relating to standard essential patents (SEPs) for the Internet of Things (IoT) industry (Bhushan, 2017). The Avanci Patent Pool enables companies to license a bundle of SEPs for connected cars, smart homes, and other IoT devices at a reasonable cost, without the risk of patent infringement lawsuits (Henkel, 2021). National governments may mandate patent pools for national security reasons. An example of national governments mandating patent pools for national security reasons is the US government's creation of the National Semiconductor Technology Center (NSTC) in the 1980s (Jung, 2014). The NSTC was established to pool the intellectual property of semiconductor
Avanci Patent Pool 2 companies for national security purposes and to promote domestic semiconductor manufacturing in the face of competition from Japan (Bhushan, 2017). Proponents of patent pools argue that they act as intermediaries between inventors and manufacturers, facilitating the transfer of patent knowledge and promoting innovation (Joshi & Nerkar, 2011; Butchmann & Butchmann, 2015). Some studies have demonstrated the negative effects of patent pools, particularly those that include rival or substitute patents, leading to decreased competition and higher licensing fees (Shapiro, 2000; Joshi & Nerkar, 2011; Moser, 2013), there is limited research on how patent pools affect company innovation. This paper aims to contribute to this area of research by examining how the Avanci patent pool, a modern and up- to-date patent pool, affects innovation in the car manufacturing industry. 2. Literature Review A patent pool refers to a collective package of patents owned by various licensors who agree to license their patents as a group to outside firms (not each other). They collectively package their patents and agree to license them as a group to outside firms or entities (Wang, 2021). By operating as a single entity, a patent pool can streamline the licensing process for firms seeking access to multiple patented technologies (Heller & Eisenberg, 1998) Shapiro (2000) added that the concept of patent pools dates back to the early 20th century, where it was used in the telecommunications industry to combine patents owned by different companies to create a standardized technology. Lampe & Moser (2010) further added that it allows for the sharing of technology and the reduction of transaction costs, as companies no longer need to negotiate with each individual patent owner. Patent pools can also help to avoid patent disputes and litigation, which can be time-consuming and costly.
Avanci Patent Pool 3 The concept of "anti-commons" was first introduced by Michael Heller (1998). According to Heller, the "anti-commons" is a situation where multiple parties own property rights over a resource, and the fragmentation of these rights leads to underuse or non-use of the resource. In the automotive industry, the anti-commons problem has been a significant challenge in developing connected and autonomous vehicles (CAVs). Each company owns patents covering various aspects of CAV technologies, leading to the fragmentation of patent rights and an anti-commons problem (Ye & Yamamoto, 2018). This fragmentation has led to significant transaction costs, litigation, and delays in innovation. To address the anti-commons problem in the automotive industry, several initiatives have been proposed, including patent pools, cross-licensing agreements, and standard-setting organizations (SSOs). Patent pools, as discussed earlier, allow patent owners to license their patents collectively to other parties, reducing transaction costs and promoting innovation (Schaffer, 2012). Cross-licensing agreements are another solution where companies exchange licenses to use each other's patented technologies (Sperling et al., 2018). The Avanci patent pool, as mentioned earlier, is an example of a patent pool that has been established to address the anti- commons problem in the automotive industry (Pearah, 2017). By bringing together multiple patent holders and offering licenses to their patented technologies collectively, Avanci aims to reduce transaction costs and promote the development of CAVs. Empirical studies have shown that patent pools can increase innovation in the pharmaceutical and biotechnology industries (Dini & Piola, 2022; Henkel, 2021). Patent pools have also been effective in the telecommunication industry, where they have enabled companies to collaborate on the development of wireless technologies (Joshi & Nerkar, 2011). The concept of patent pools has been associated with both positive and negative impacts on innovation.
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Avanci Patent Pool 4 Licensees no longer need to negotiate individual licensing agreements with each patent holder (Lampe & Moser, 2010). This can lower the barriers to entry for small and medium-sized enterprises, promoting competition and innovation (Shapiro, 2001). Another potential benefit is the increase in expected returns on research and development (R&D) investment, as patent pools provide access to complementary intellectual property, eliminating the need to obtain patents separately (Lampe & Moser, 2016). While patent pools have the potential to enhance innovation, there is also evidence to suggest that they can hinder innovation in certain circumstances. The effects of patent pools on innovation are likely to be influenced by various factors, such as the specific patents included in the pool, the industry in which it operates, and the behavior of the pool members (Bhushan, 2017). Some studies have found that patent pools can discourage innovation by reducing the incentive for inventors to develop new technologies. One reason for this is that patent pools can create barriers to entry, making it more difficult for new firms and inventors to enter the market. This can lead to reduced competition and less pressure to innovate (Cho et al., 2017; Flamm, 2013). Additionally, Henkel (2021) said the patents included in the pool cover a broad range of technologies, which may be more difficult for inventors to work around them and develop new technologies that do not infringe on the pooled patents. Research conducted by Joshi and Nerkar (2011) found evidence to suggest that patent pools can decrease the quantity and quality of patents generated by pool members after the formation of the pool. This suggests that the effects of patent pools on innovation may depend on industry-specific factors and the specific patents included in the pool. The study suggests that patent pools can have negative effects on innovation when they create barriers to entry and limit competition in industries with rapidly evolving technologies. Some scholars like Juneja et al.,
Avanci Patent Pool 5 (2017) and Jung (2014) argue that patent pools can create a disincentive for inventors to develop new technologies, as they reduce the potential rewards for successful innovation. Patent pools can also lead to the formation of monopolies or oligopolies, which can limit competition and reduce the pressure to innovate. One of the key drivers of patent pools in the automotive industry has been the need to develop technologies related to electric vehicles (EVs). EVs require a number of complex technologies, including batteries, electric motors, power electronics, and charging infrastructure (Sun et al., 2021). Each of these areas involves numerous patents, which can make it difficult for companies to develop and commercialize new products without infringing on others' intellectual property (Nikolic, 2021). Hence, patent pools can help to address this issue by enabling companies to access the patents they need to develop new products without having to negotiate individual licensing agreements with multiple patent holders. Another trend in the automotive industry that has led to increased interest in patent pools is the development of autonomous driving technologies . As with EVs, autonomous driving requires the integration of multiple technologies, many of which are patented (Pearah, 2017). By pooling their patents, companies can reduce the costs and complexity of licensing these technologies and accelerate the development of autonomous driving systems. There have been several notable examples of patent pools in the automotive industry (Shapiro, 2000). In 2015, for example, nine automakers, including Ford, BMW, and Toyota, formed the " Consortium for Autonomous Road Transportation" (CART) to collaborate on the development of autonomous driving technologies. CART has since been renamed the "Automated Vehicle Safety Consortium" (AVSC) and has expanded to include a number of additional companies (Sperling et
Avanci Patent Pool 6 al., 2018). The AVSC has developed a set of safety principles for autonomous driving and is working to develop standards for autonomous vehicle technology. Another notable example is the " Vehicle Patent License" (VPL) program , which was launched by Toyota in 2015. The VPL program aims to promote the widespread use of hybrid and fuel cell vehicles by making Toyota's patents related to these technologies available for licensing to other companies (Fodor et al., 2016). By doing so, Toyota hopes to encourage the development and commercialization of these technologies by other companies, which could help to accelerate the adoption of low-emission vehicles. The Avanci patent pool, which was launched in 2016, is another example of a patent pool in the automotive industry. Avanci focuses on providing patent licenses for cellular-connected devices, including those used in connected cars (Joshi & Nerkar, 2011). By pooling the patents of multiple companies, Avanci aims to reduce transaction costs for companies developing connected car technologies (Fodor et al., 2016). While Avanci has been successful in attracting a number of prominent automotive companies to join its pool, it has also faced criticism from some quarters for its licensing practices. The Avanci Patent Pool is a consortium of major technology companies, including BMW, Ford, Qualcomm, Ericsson, and others, that came together in 2016 to offer access to their patents covering 2G, 3G, and 4G wireless technology to manufacturers of connected cars (Avanci, 2023). Several studies have explored the impact of the Avanci Patent Pool on innovation in the automotive industry. Fodor et al., (2016) and Shapiro (2000) argued that the Avanci Patent Pool provided automakers with a simplified licensing process and a one-stop-shop for accessing a large portfolio of wireless technology patents. This, in turn, allowed automakers to focus on developing new and innovative connected car technologies. In contrast, another study by Geradin
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Avanci Patent Pool 7 (2022) highlighted potential drawbacks of the Avanci Patent Pool. The authors argued that the Avanci Patent Pool could increase licensing costs for manufacturers of connected cars by allowing patent owners to charge higher licensing fees for a larger portfolio of patents. Furthermore, the author raised concerns about the possible anti-competitive effects of patent pools, as they could exclude smaller players from the market and reduce competition. The Avanci patent pools are necessary to implement a specific technical standard, and their use is required by companies that want to manufacture products that comply with that standard (Trappey et al., 2016). 2.1 Research Aim The main objective of this research is to thoroughly investigate and analyze the impact of the Avanci patent pool on innovation within the car manufacturing sector. To achieve this objective, the study used a rigorous methodology that involves collecting and analyzing current and contemporary annual data since the establishment of the patent pool in 2022. The research focused on key indicators of innovation, such as the number of new patented technologies, research and development activities, and the introduction of innovative features in car manufacturing. For this purpose, we have taken different licensed and non-licensed companies for better analysis and understanding of the impact of being patent pool licensee on innovation (in terms of ROI and technological advancement). The findings of this research will not only be valuable to academics and researchers studying intellectual property and innovation but also to industry managers who are considering launching new projects that require patented technology. By examining the impact of the Avanci patent pool, this research will offer insights and guidance to managers who may be interested in forming or joining a patent pool to facilitate innovation within their respective industries.
Avanci Patent Pool 8 3. Data Collection 3.1 Sampling To address our research question, we carefully selected a subset of Avanci licensees along with a representative comparison group. Avanci's platform is presently being utilized by a total of 46 automobile licensees, making it essential to ensure that our sample is comprehensive and representative of the broader population. We decided to select a sample of 14 firms, consisting of 7 Avanci licensees and 7 non-Avanci licensees (table 1), to provide the necessary insights and information for our study. The sample aims to provide a balanced representation of firms in the car manufacturing sector. By selecting an equal number of Avanci and non-Avanci licensees, the study seeks to ensure that both groups are adequately represented in the analysis. This approach helps to avoid any bias that may arise from an imbalanced sample. In this case, selecting 7 firms from each group provides a reasonable sample size to conduct a detailed analysis and draw meaningful conclusions (as it is a fair and adequate sample size). The treatment group is composed of 7 Avanci licensees, while the control group is made up of 7 firms that are not members of the pool. To ensure the validity and reliability of our results, each firm in the control group was carefully matched to a member of the treatment group, with the matching criteria based on operating in the same car manufacturing industry and similar price range. The sample selection of companies is based on the price range of their products. The companies were selected to represent a range of different prices in the car manufacturing industry. For the high-price range , two licensees were selected – Lamborghini and Porsche along with two non-licensees Ferrari and Tesla. Lamborghini and Ferrari are well-known luxury car
Avanci Patent Pool 9 brands that are seen as direct competitors. Porsche and Tesla are both known for their high- performance luxury vehicles and are considered for the analysis in terms of innovation. For the mid-range price point , two licensees were selected – Honda and KIA along with two non-licensees – Tata and Suzuki. Honda and KIA are both well-established car manufacturers with a wide range of models and a reputation for reliability. Tata is an Indian automaker that has been expanding its global reach in recent years which is compared to Suzuki. For the affordable price point , three licensees were selected - Hyundai, Toyota and Volkswagen - along with three non-licensees – Stellantis, Renault and Geely. The reason for choosing a diverse range of companies is to ensure that the study captures the impact of pool membership on different types of firms in the industry. The price range was used as a criterion for categorizing the companies into high, mid-range, and affordable, but it was not the only reason. For example, Lamborghini, Ferrari, and Porsche are high-end luxury car manufacturers, while Tesla is also high-end but specialize in electric and supercars. The selection of these specific firms was based on a variety of factors, including their market share, brand reputation, innovation capacity, and financial performance. These factors were considered to ensure that the study captures the impact of pool membership on firms with different levels of resources, innovation, and competitiveness. Table 1: Avanci and Non-Avanci Group for Comparison Avanci (Licensees) Non-Licensees Lamborghini Ferrari Porsche Tesla Honda Tata Kia Suzuki Hyundai Stellantis Toyota Renault
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Avanci Patent Pool 10 Volkswagen Geely 4. Research Methodology 4.1 Dependent Variables The purpose of this study is to examine whether joining a patent pool has an effect on the level of innovation within the automotive sector. To accomplish this aim, two dependent variables will be utilized to assess innovation specifically in the realm of connectivity technologies and connected vehicles. The first dependent variable is the Connected Car Innovation Index (CCI) , measures the performance and innovation of 28 global car manufacturers. The information is taken for year 2022 (Connected Car Innovation, 2022). The second dependent variable is the number of patents filed by each car manufacturing firm, which measures the quantity of intellectual property created by the firm. Patents are a common measure of intellectual property and are often used as a proxy for innovation (Fodor et al., 2016). 4.2 Independent Variables The independent variables presented in the table 2 were obtained from both the treatment and control groups for each car manufacturing company over a 5-year period (e.g., 2017-2018, 2018-2019, 2019-2020, 2020-2021, and 2021-2022). These variables were utilized in various explanatory models. The data were collected from official annual and other reports issued by the car manufacturing firms for the corresponding years. Sales growth, net Profit, R&D Expenditures, and Firm Size (market capital) are commonly used financial variables that are used to explain the performance of firms. In addition, Pool Membership Status was chosen as it is the
Avanci Patent Pool 11 focus of the research question, which seeks to determine the impact of patent pool membership on the financial performance of firms in the automotive industry. To further validate the appropriateness of these variables, I conducted a literature review to confirm their relevance to the research question. The literature review also helped to identify other potential variables that could be included in the analysis. Table 2: Dependent Variables and their Definitions Variables Definition Sales Growth (%) Sales growth is a measure of the rate at which a company's sales revenue is increasing over time. We took sales growth (not the sales), because different companies might have different size, hence, sales growth can indicate better performance. Besides, the currencies used were also different, hence, we took percentage as an indicator (WSJ, 2023). Net Profit (%) It refers to the amount of money that a company earns after deducting all its expenses from its total revenue. All amounts are in percentage, because, the data was available in different currencies (WSJ, 2023). R&D Expenditures Amount that a company spends on research and development activities to develop new products, processes, and technologies. The information is taken from annual reports of companies and presented in $. Firm Size ($) Market capitalization of the company. All amount are in billion dollar (Companies Market Cap, 2023). Pool Membership Status It refers to whether a company is a member of Avanci patent pool or not. It is a binary variable where 1=member of the pool and 0= not member.
Avanci Patent Pool 12 5. Data Analysis The analysis is based on the following hypothesis: H1: Avanci patent pool membership has a association on innovation in automobile companies. The motivation behind H1 is to examine whether there is a positive association between Avanci patent pool membership and innovation in automobile companies. By specifically examining the association between Avanci patent pool membership and innovation in automobile companies, the research investigates whether being a member of the Avanci patent pool is linked to increased innovation activities or outcomes in the car manufacturing sector. 5.1 Descriptive Statistics of Independent Variables For the data analysis, different tests are conducted. For example, first the descriptive analysis was performed for independent variables: Table 3: Descriptive Analysis (n=14) Variable Mean Std. Deviation Pool Membership Status 0.50 0.519 Sales Growth Average (%) 39.8361 122.12899 NP Growth Average (%) -91.4243 334.72315 R&D Expenditures Average ($) 4466.2071 4630.90070 Current Market Capital (Billion $) 553.3357 1182.60440 The descriptive statistics table 3 provides information about the central tendency and variability of six variables for a sample of 14 companies. The mean value for Pool Membership Status is 0.50, indicating that half of the companies are pool members. For Sales Growth Average, the mean is 39.8361% (122.13), indicating that the average sales growth is positive, but there is considerable variation in the sales growth rates among the companies in the sample.
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Avanci Patent Pool 13 The NP Growth Average variable has a negative mean of -91.4243% (334.72), indicating that, on average, the companies in the sample experienced a decline in net profit. The R&D Expenditures Average variable has a mean of 4466.2071 (4630), suggesting that the companies in the sample vary widely in their R&D expenditures. For Current Market Capital (Billion $), the mean is 553.3357 (1182.60), indicating that the companies vary widely for market capitalization. Table 4: Difference in Mean Analysis of Membership Status Pool Membership Status Sales Growth % (2018-2022) Net Profit % (2018-2022) R&D Expenditures (2018-2022) Market Capital Non- member Mean 11.4780 -132.3514 2360.8571 SD 16.76805 442.04869 1320.48297 Member Mean 68.1943 -50.4971 6571.5571 SD 173.66433 208.41542 5863.31966 P (Sig). .407 .666 .089 0.159 F .740 .196 3.44 2.253 Table 4 presents the descriptive statistics for sales growth, net profit growth, and R&D expenditures for two groups of companies: pool members and non-members. The results show that pool membership is associated with higher sales growth and R&D expenditures over the observed period. Specifically, the mean sales growth for members is much higher (68.19%) than for non-members (11.48%), while the mean R&D expenditures for members is better (-50.49%) than for non-members (-132.35), but both in negative. In contrast, both groups experience a decline in net profit growth, but the decline is less steep for members than for non-members. The discussion summarizes that pool members have high sales growth and R&D expenditures. Their aggregate net profit is also relatively better than non-members. For sales growth and net profit growth, the p-values are less than 0.1 (significant value), indicating no significant difference between pool members and non-members. On the other hand,
Avanci Patent Pool 14 for R&D expenditures, the p-value is marginally higher than the conventional threshold of 0.05, and the F-value is 3.436, indicating a possible difference between pool members and non- members in R&D expenditures. There is no significant difference in current market capitalization as the F-values and p-values for both variables are not significant. The discussion summarizes that p ool members have high sales growth, NP Growth and R&D expenditures. However, these results are not significant for sales growth, NP growth and market capital, but only for R&D expenditures (as p-value is below 0.1). 5.4 Difference in Mean Analysis for Independent vs. Dependent Variables The section represents the results of an independent sample test comparing membership status (members vs. non-members) with two variables: Connected Car Innovation Index (CCI) and the number of innovation patents filed. The table 5 discusses the descriptive statistics and table 6 includes information on Levene's test for equality of variances and the t-test for equality of means. By taking pool membership status as an independent, following results are generated. The results of the Levene's test provide information about the homogeneity of variances between the groups. Table 5: Descriptive Statistics of CCI and Number of Patents Mean SD CCI 36.6 25.78 Number of Patents 67786.43 113049.5 Table 6: Membership Status with CCI and Number of Patents Innovation Independent Samples Test Levene's Test t-test for Equality of Means F Sig. T Sig. (2- tailed) 95% Confidence Lower Upper Connected Car Innovation Members 0.755 0.418 0.96 0.374 -27.2645 62.464 Non- 0.96 0.379 -28.9234 64.1234
Avanci Patent Pool 15 Index Members number of innovation filed Members 10.472 0.007 2.266 0.043 4564.329 233938.5 Non- Members 2.266 0.064 -9330.69 247833.5 The table 6 shows the results of two independent samples t-tests that examine the differences in Connected Car Innovation Index (CCI) and the number of innovation filed between pool members and non-members. The Levene's test for equality of variances indicates that the variances between the two groups are not significantly different for CCI, but they are significantly different for the number of innovation filed. The t-test for equality of means shows that there is no significant difference in CCI between pool members and non-members, as the p- value is greater than 0.05. However, there is a significant difference in the number of innovation filed between pool members and non-members, as the p-value is less than 0.05. The confidence interval of the difference suggests that the mean number of innovation filed for pool members is between 4,564.329 and 233,938.5 higher than non-members. The innovation filed for pool member is higher than non-members and near to significant (p=0.007). However, no difference can be seen in terms of Connected Car Innovation Index. 6. Discussion The hypothesis tested in this study is whether membership in the Avanci patent pool has a positive correlation toward innovation in automobile companies. The null hypothesis (H0) states that there is no effect of pool membership on innovation, while the alternative hypothesis (H1) suggests that membership has a positive effect on innovation. Descriptive statistics were first performed on the independent variables, including pool membership status, sales growth, net profit growth, R&D expenditures and current market capitalization. The results showed that half of the companies in the sample were pool members,
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Avanci Patent Pool 16 with a mean sales growth of 39.8361%, a negative mean net profit growth of -91.4243%, a mean R&D expenditure of 4466.2071% and a mean market capitalization of 553.3357 billion USD. The descriptive statistics also revealed that there was considerable variation in the variables among the companies in the sample. Next, the descriptive statistics for sales growth, net profit growth, and R&D expenditures were compared between pool members and non-members. The results indicated that pool membership was associated with higher sales growth and R&D expenditures, although both groups experienced a decline in net profit growth. Pool members tend to have higher sales growth and R&D expenditures compared to non-members. Additionally, their aggregate net profit is relatively better. This provides some support for the alternative hypothesis (H1) as it indicates a positive effect of pool membership on financial performance indicators. Finally, ANOVA tests were conducted to compare the differences in sales growth, net profit growth, R&D expenditures, and current market capitalization between pool members and non-members. The ANOVA results show that pool members have higher sales growth, net profit growth, and R&D expenditures. However, the significance level is not reached for sales growth and net profit growth, suggesting that the observed differences could be due to chance. The result being close to significance for R&D expenditures implies a potential positive effect of pool membership on R&D spending. The analysis indicates that pool membership status does not have a significant association with differences in current market capitalization. This implies that pool membership does not appear to have a direct impact on these financial indicators. Pool members tend to have higher levels of innovation in terms of patents filed compared to non-members, and this result is approaching significance. However, no difference is observed between pool members and non-
Avanci Patent Pool 17 members in terms of the Connected Car Innovation Index. This suggests that pool membership may have a specific impact on patent filings but does not necessarily affect the broader measure of connected car innovation. The significant difference found in the number of innovation filed between pool members and non-members supports previous studies that have highlighted the positive association between patent pool membership and innovation outcomes. These findings suggest that being a member of a patent pool, such as the one examined in our analysis, can provide access to a broader range of patented technologies, which may facilitate and encourage innovation activities. However, our analysis did not find a significant difference in the Connected Car Innovation Index (CCI) between pool members and non-members. This finding is consistent with some previous research that has reported mixed or inconclusive results regarding the impact of patent pools on innovation performance. It is important to acknowledge that innovation is a multifaceted concept, and different measures or indices may capture various aspects of innovation, leading to varying results across studies. 7. Conclusion In summary, the findings of this study suggest that pool membership in the Avanci patent pool has a positive associated with certain aspects of innovation, such as patent filings, sales growth, and R&D expenditures. However, the impact on financial performance indicators, such as net profit growth and market capitalization, is not significant. Pool membership in the Avanci patent pool does not have a direct correlation on financial indicators such as current market capitalization . Our analysis did not uncover a significant difference in the Connected Car Innovation Index (CCI) between pool members and non-members.
Avanci Patent Pool 18 These findings highlight the need for further research to gain a deeper understanding of the mechanisms underlying the impact of patent pools on innovation within the connected car industry. Future studies should explore additional factors and dimensions of innovation to provide a more comprehensive understanding of how patent pool membership influences innovation outcomes. Overall, our analysis contributes to the existing body of knowledge by shedding light on the relationship between patent pool membership and innovation within the context of the connected car industry. It confirms the positive association between patent pool membership and the number of innovation filed while also recognizing the complexity and context-dependency of measuring innovation.
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Avanci Patent Pool 19 References Alfred, J. (2019). Licensing Standards Essential Patents (Seps)—Round Two. les Nouvelles- Journal of the Licensing Executives Society , 54 (4). Avanci (2023). About the Company. Accessed from https://www.avanci.com/ Azzam, J. E., & Berkowitz, H. (2018). Patterns of coopetition in meta-organizations. In The Routledge Companion to Coopetition Strategies (pp. 258-269). Routledge. Bercovitz, J., & Feldman, M. (2006). Entpreprenerial universities and technology transfer: A conceptual framework for understanding knowledge-based economic development. The journal of technology transfer , 31 , 175-188. Bhushan, B. (2017). Introduction to nanotechnology. Springer handbook of nanotechnology , 1- 19. Buchmann, T., & Buchmann, T. (2015). An Automotive Innovation Network (pp. 109-169). Springer Fachmedien Wiesbaden. Cho, R. L. T., Liu, J. S., & Ho, M. H. C. (2021). Exploring a Patent's Essentiality to the HEVC Standard: A Retrospective View. IEEE Transactions on Engineering Management . Connected Car Innovation (2022). The Statistics of Connected car Innovation. Accessed from https://connected-car-innovation.de/ Dini, R., & Piola, V. (2022). How to Reduce Complexity in the Licensing Landscape of Standardised Technology. Journal of Law, Market & Innovation , 1 (3), 56-80. Eisenberg, R. S. (1989). Patents and the progress of science: Exclusive rights and experimental use. The University of Chicago Law Review , 56 (3), 1017-1086.
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Avanci Patent Pool 21 Jung, H. J. (2014). The impacts of science and technology policy interventions on university research: Evidence from the US National Nanotechnology Initiative. Research policy , 43 (1), 74-91. Lampe, R., & Moser, P. (2010). Do patent pools encourage innovation? Evidence from the nineteenth-century sewing machine industry. The Journal of Economic History , 70 (4), 898-920. Mariniello, M. (2011). Fair, Reasonable and Non-Discriminatory (FRAND) terms: a challenge for competition authorities. Journal of Competition Law and Economics , 7 (3), 523-541. Moser, P. (2013). Patents and innovation: evidence from economic history. Journal of economic perspectives , 27 (1), 23-44. Nelson, R. R. (2019). On technological capabilities and their acquisition. In Science and Technology (pp. 71-80). Routledge. Nikolic, I., & Galli, N. (2022). Patent pools in 5G: The principles for facilitating pool licensing. Telecommunications Policy , 46 (4), 102287. Nikolic, I. (2021). Licensing negotiations groups for SEPs: collusive technology buyers arrangements?: their pitfalls and reasonable alternatives. LES nouvelles, 2021, pp. 350- 357 - https://hdl.handle.net/1814/73846 Pearah, P. J. (2017). Opening the door to self-driving cars: how will this change the rules of the road. J. High Tech. L. , 18 , 38. Petti, C., Spigarelli, F., Lv, P., & Biggeri, M. (2021). Globalization and innovation with Chinese characteristics: the case of the automotive industry. International journal of emerging markets , 16 (2), 303-322.
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Avanci Patent Pool 22 Schaffer, C. (2012). Prohibiting discriminatory cross-licensing. Information & Communications Technology Law , 21 (1), 91-106. Shapiro, C. (2000). Navigating the patent thicket: Cross licenses, patent pools, and standard setting. Innovation policy and the economy , 1 , 119-150. Sperling, D., van der Meer, E., & Pike, S. (2018). Vehicle automation: our best shot at a transportation do-over?. Three revolutions: Steering automated, shared, and electric vehicles to a better future , 77-108. Statista (2020). The Information about Patents of Automobile Companies. Accessed from https://www.statista.com/statistics/1178549/number-of-patents-owned-by-the-top- automobile-manufacturers/ Sun, W., Yuan, M., & Zhang, Z. (2021). The Evolutionary Game Analysis and Optimization Algorithm of Electric Vehicle Cell Innovation Diffusion Based on a Patent Pool Strategy. World Electric Vehicle Journal , 12 (4), 251. Trappey, A. J., Trappey, C. V., Govindarajan, U. H., Sun, J. J., & Chuang, A. C. (2016). A review of technology standards and patent portfolios for enabling cyber-physical systems in advanced manufacturing. Ieee Access , 4 , 7356-7382. Wang, L. X. (2021). Global Drug Diffusion and Innovation with the Medicines Patent Pool. Available at SSRN 3426554 . WSJ (2023). Net Profit Growth, Sales Growth of Automobiles. Accessed from https://www.wsj.com/?mod=nav_top_section Ye, L., & Yamamoto, T. (2018). Modeling connected and autonomous vehicles in heterogeneous traffic flow. Physica A: Statistical Mechanics and its Applications , 490 , 269-277.
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Avanci Patent Pool 23 Appendix 1: Avanci Licensees (n=46) No. Country Companies 1 Czech Republic Skoda 2 Germany Audi, BMW Group, Daimler Truck AG, MAN, Mercedes Benz, Porsche, Volkswagen 3 Italy Lamborghini, Scuderia 4 Japan Honda, Infiniti, Izuzu, Komatsu, Lexus, Mazda, Mitsubishi Motors, Nissan, Subaru, Suzuki, Toyota 5 Netherlands Stellantis 6 South Korea Genesis, Hyundai, Kia 7 Spain Cupra, Seat 8 Sweden Polestar, Volvo, Volvo Workhorse 9 United Kingdom Arrival, Aston Martin, Bentley, BMW, Mini Cooper, Rolls Royce, Jaguar, Land Rover 10 United States Fisker, Ford, General Motors, Karma, Navistar, Lucid, Volta Appendix 2: Avanci Licensors (n=56) No . Country Licensors 1 Canada Blackberry 2 China Convesant, Miics, OPPO, TCL, Unisoc, ZTE 3 Finland NEC 4 France Orange 5 Germany Docomo, Fraunhofer, Siemens 6 Italy TIM 7 Japan Fujitsu, IP Bridge, Jvckenwood, KDDI, Kyocera, Mistubishi Electric, NEC, Nokia, NTT, Panasonic, Sharp, Sony, SSVEL
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Avanci Patent Pool 24 8 Netherlands KPN, Philips, TNO 9 South Korea Hargang IP, KPN, LG Electronics, Pantech, SK Telecom, Wilus 10 Sweden Ericsson 11 Taiwan Acer, ASUS, Mediatek 12 United Kingdom BT Group, Vodafone 13 United States Ariscale, Celerity, China Mobile, DT Mobile, Equo IP, HP Enterprise, Intellectual Discovery, InterDigital, Longhorn IP, Panasonic, Panoptis, Qualcomm, Samsung, Sun Patent Trust, T Mobile, Unwired Planet, Sol IP
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