19-PM2-05: ST10.3 - Monitoring Technology Dynamics and Convergence
Technologies play a major role for the creation of product as well as process innovations and are able to establish a competitive advantage. For this reason, R&D managers should monitor their dynamics, in particular if driven by competitors or external research institutes. Furthermore, technologies might move towards each other; technology convergence might occur and change the business models of companies. On the other side, technologies might conquer new niches and form technology speciation.
All three tasks, monitoring technology dynamics, convergence as well as speciation, can be supported by advanced analyses. Time has come to consider all the possible methods and techniques to assess technology dynamics, convergence and speciation. Participants may consider – but not be limited to – the following:
Indicators for assessing technology dynamics, convergence and speciation
o patent- or publication-based
o blog or social network-based
o applying bibliometric or semantic approaches
o using network or indicator-based methods,
o applying advanced machine learning methods
Insights on processes leading to technology dynamics, convergence and speciation
o the importance of the time dimension
o life-cycle perspective
Insights on the determinants of technology dynamics, convergence and speciation
o specialization and diversification strategies
o search practices
o collaboration practices
o knowledge recombination
o network practices
Insights on the outcomes of technology dynamics, convergence and speciation
o emergence of general purpose technologies
o emergence of niche technologies
o emergence of radical or incremental innovations
o generation of new markets and industries
o market disruptions
How to Enhance the Property Rights in the International Standardization? - The Analysis of Patent Citations Between Firms in the Telecommunication Industry–
1National Cheng Kung U.,Taiwan; 2Yokohama National University,Japan
Firm’s technologies are required to disclose in standards-development organizations(SDOs). Although technologies are protected as standard essential patents(SEPs), its disclosed technologies still could be the source of reinventions for other firms. This study suggests firm should assimilate its technologies of SEPs for proprietary patents as exclusive property rights in SDOs.
Rysman and Simcoe (2008) comprehend that SDOs can be regarded as a shared technology platform, where any firm can pursue its innovation. In such an environment, firm will disclose its technologies but also need to maintain its control of technology development (Shapiro and Varian, 1998; Gawer and Henderson, 2007). The SEPs declared by firm in SDOs are regarded as firm’s intellectual property rights over technology developments (Bekkers, Catalini , Martinelli, Righi and Simcoe, 2017). However, the technical information stated in SEPs may be fundamentally and inevitably used to develop new technologies by other firms. That is to say, focal firm’s SEPs are more cited by other firms compared to other forms of patents (e.g., proprietary patents) (Rysman and Simcoe, 2008), which will decrease focal firm’s competitive advantages in SDOs (Kang and Motohashi, 2015).
We think that the high citation rate of focal firm’s SEPs by other firms is meant to a focal firm hardly possess its knowledge assets in developing technologies in SDOs. However, we found that existing researches haven’t discussed how to maintain firm’s property rights on technology developments in SDOs.
When standards are set up by industrial consensus with an opened manner, then firm may be located in a weak regime of appropriability. This study argues that how firm should enhance its property rights on technology developments by conducting proper patent portfolio in SDOs.
Since many antecedents have took the SDOs of telecommunication industry as their analysis objective, we think the SDOs of telecommunication industry are also creditable for our research concerns. In this study, we choose only U.S. and E.U. patents for the analysis because American and European telecommunication market grew more rapidly than other countries since the mid 1990s. This study use quantitative research methods to reveal how many citations were made from representative firms’ SEPs by their proprietary patents.
This study chose the ETSI (European Telecommunications Standards Institute) and 3GPP (3rd Generation Partnership Project) as the SDOs, in which firms’ participation in telecommunication industry. From the ETSI website, where firms releases the SEPs, we downloaded 106 firms’ SEPs which are published in the US Patent Office (USPTO) and the European Patent Office (EPO), and their declaration date is from April 4, 1990 to December 30, 2016. Next, we select representative 24 firms and download their proprietary patents from EPO on May 10th, 2017. These firms are Nokia, Ericsson, Motorola, Samsung, LG, Apple, Huawei, Panasonic, RIM, NEC, Sharp, HTC, Sony, ZTE, Nortel network, Fujitsu, NTT DoCoMo, Intel, Qualcomm, TI, Interdigital, Freescale, Infineon, Mediatek. Finally, we classified there are 124,056 backward citation relationships (i.e., citing SEPs by 24 firms’ proprietary patents) of 13,056 SEPs of 24 firms. We also controlled the timing of backward citation of proprietary patents, and finally acquired 34,505 proprietary patents with 76,254 backward citations from 10,254 SEPs as the final panel data.
On average, the backward citations of each SEP are 2.83 times by those representative firms’ proprietary patents. We found that the greater the number of SEPs firms declared, the more backward citations by other firms’ proprietary patents would occur. Qualcomm’s 4,306 SEPs have been cited by 22 firms’ proprietary patents for 13,664 times. Nokia has 2,664 SEPs more than Motorola’s 823 SEPs. Nokia’s SEPs have been cited by other firms’ proprietary patents for 6,733 times compared to Motorola’s 3,952 times. In other words, when firm disclosed its technologies and declared as SEPs, those SEPs would be assimilated and reinvented into new technologies published as proprietary patents by other firms. We also found that firm had different ways to enhance its property rights in terms of proprietary patents. We further illustrated the relations between citing-others, self-citation, SEPs and proprietary patents. On average, the self-citation times of each SEP are 0.29 times. More specifically, we found that LG intended to cite other firms’ SEPs and Qualcomm was the one who intended to cite their own SEPs for their proprietary patents, respectively.
Contribution to Scholarship
In this study, we extend the existing researches of property rights in SDOs and suggest that firm can use self-cations and citing-others to assimilate knowledge of SEPs to proprietary patents in SDOs. Usually, self-citations and citing-others can be regarded as the exploitation and exploration (Kim, Song and Nerkar, 2012; Rosenkopf and Nerkar, 2001; Song et al., 2003; Sorensen and Stuart, 2000; Joglekar, 2005) for firm to conduct technology developments. Since under standardization, technologies are not developed radically but more incrementally with consensus based agreement among stakeholders. Therefore, proposing and improving firms’ “existing” technologies under standardization while also increasing their property rights on their similar scheme of technologies will consolidate firms’ capabilities of technology developments continuously. We emphasize that rather than exploration, firm exploits existing technologies of its SEPs by using self-citations, can help firm to compensate knowledge spillovers and enhance property rights (i.e., proprietary patents) for continuing technology development.
Contribution to Practice
In SDOs, the greater the focal firm declares SEPs as open strategy, the more difficult that focal firm can possess its property rights on technology developments. That is to say, the disclosed technologies of focal firm’s SEPs may give opportunities to other firms to develop new technologies protected by proprietary patents of other firms. Our findings provide some implication to this issue. The case of Qualcomm shows that rather than citing others, self-citation may be more necessary to assimilate the existing knowledge of SEPs into proprietary patents in SDOs.
Patent helps firm to secure the appropriability of innovation. However, we still don’t know what’s the relationship between the standard essential patents and the proprietary patents in standards-development organization. Our paper can provide a new perspective framework for firm to understand how to assimilate standard essential patents for property rights.
Rysman, M., & Simcoe, T. (2008). Patents and the performance of voluntary standard-setting organizations. Management science, 54(11), 1920-1934.
Shapiro, C., Carl, S., & Varian, H. R. (1998). Information rules: a strategic guide to the network economy. Harvard Business Press.
Gawer, A., & Henderson, R. (2007). Platform owner entry and innovation in complementary markets: Evidence from Intel. Journal of Economics & Management Strategy, 16(1), 1-34.
Bekkers, R., Catalini, C., Martinelli, A., Righi, C., & Simcoe, T. (2017). Disclosure rules and declared SEPs (No. w23627). National Bureau of Economic Research.
Kang, B., & Motohashi, K. (2015). Essential intellectual property rights and inventors’ involvement in standardization. Research Policy, 44(2), 483-492.
Kim, C., Song, J., & Nerkar, A. (2012). Learning and innovation: Exploitation and exploration trade-offs. Journal of Business Research, 65(8), 1189-1194.
Rosenkopf, L., & Nerkar, A. (2001). Beyond local search: boundary‐spanning, exploration, and impact in the optical disk industry. Strategic Management Journal, 22(4), 287-306.
Song, J., Almeida, P., & Wu, G. (2003). Learning–by–Hiring: When is mobility more likely to facilitate interfirm knowledge transfer?. Management science, 49(4), 351-365.
Sorensen, J. B., & Stuart, T. E. (2000). Aging, obsolescence, and organizational innovation. Administrative science quarterly, 45(1), 81-112.
Joglekar, N. (2005, July). A Behavioral View of Core-Periphery Dynamics in Social Networks. In Conference Proceedings, The 23 rd International Conference of the System Dynamics Society.
Convergence in the additive manufacturing industry: A tale of two firms
1Institute for New Economic Thinking at Oxford Martin School; School of Geography and the Environment, University of Oxford; Harvard Law School, Harvard University; 2Saïd Business School, University of Oxford; 3Institute for Manufacturing, Department of Engineering, University of Cambridge
Thirty years of additive manufacturing have been dominated by two firms – 3D Systems and Stratasys. This article explores whether conventional differentiation strategies have played out or whether the two firms have converged over the years with regards to intellectual property management, acquisition strategy, business model design, and financial performance.
Conventional view argues that competing firms need to differentiate themselves for competitive advantage. However, an emerging industry presents a different context in which no dominant design exists.
The positioning school argues that competitive advantage emerges from cost advantage or differentiation strategies (Porter 1980; Dess and Davis 1984). The resource-based view (RBV) offers an alternative perspective to analyse firms in a volatile environment whereby competitive advantage arises from differentiation in resources and capabilities (Wernerfelt 1984). In emerging industries where a dominant design is not yet established, firms might help create the market for the new technology through similar strategies and business models (Suárez and Utterback 1995). Additive manufacturing is one such emerging industry where the dominant design for the product needs to be established (Srinivasan, Lilien, and Rangaswamy 2006). Therefore, the interplay between differentiation to create competitive advantage and convergence to help create the market is important to explain competitive dynamics.
In tech-centric firms, the appropriateness of differentiation strategy or convergence strategy is unclear for the early stages of an emerging technology industry.
How do tech-firms competitive strategies play out in the era of rapidly changing technology? In a technology-intensive emerging industry, would the firms still adhere to differentiation strategies? In particular, will new tech-firms strategically choose to diverge or converge?
This study analyses two vanguards of additive manufacturing – Stratasys and 3D Systems – over the first three decades of the industry. They were founded in the initial stages of this industry and have survived to this day.
The competitive strategies between these two firms is analysed in four arenas – intellectual property management, acquisition strategy, business model design, and financial performance. Outcomes of the competitive strategies in these four arenas will illustrate whether the two firms are differentiating or converging within this industry. Data were gathered about both firms from USPTO, Annual Reports, Factiva and Capital IQ.
Each of the four arenas of competitive strategies required different empirical data to understand the divergence or convergence between these two firms.
1. Intellectual property management: We find that four patent classifications represent more than 70% of all patents held by both 3D Systems and Stratasys individually. We employ the Herfindahl index to measure the diversity of technological capabilities. We also test their technology similarity and technology complementarity.
2. Acquisition strategy: The additive manufacturing industry went through a phase of expansion and acquisitions. Details of acquisitions by both firms were collated from their 10-K/20-F annual reports. We analysed their acquisition strategies, including the type, the number and the scale of companies that they acquired. The patents held by their acquired firms were also analysed for divergence or convergence.
3. Business model design: The Factiva database was employed to identify key events in the history of these two firms separately. These events were then labelled for changes in the firm’s business model, supported by descriptions in annual reports about major strategic decisions.
4. Financial performance: The financial performance of each firm was analysed using their annual financial statements from Capital IQ and other databases.
The results from the four arenas of competitive strategies are:
1. Intellectual property management: The Herfindahl index measurement shows their technological capabilities were converging. We find high technological similarity, and the technological complementarity of these two firms were converging.
2. Acquisition strategy: 3D Systems were more prolific in acquisitions, targeting small companies. Stratasys made fewer acquisitions but for larger amounts. When accounting for acquired firms that held patents, we find the two firms’ portfolio are increasingly converging.
3. Business model design: They were experiencing external events in a similar manner– the collapse of desktop printing business, the entry of other firms into the industry, and the specific opportunities opened by automotive, aerospace and medical sectors. The two firms developed different underlying technologies – Fused Deposition Modelling by Stratasys, and Stereolithography by 3D Systems but are converging towards a dominant business model design to serve customers, as corroborated by similarity of patent classifications.
4. Financial performance: Both firms appear to be converging in R&D spending as a percentage of sales and their gross margin. The asset turnover ratio, which reflects the business models adopted by firms, appears to converge over time indicating the similarities in the business models of both firms.
Contribution to Scholarship
This study suggests that tech-firms in an emerging industry may choose to converge in order to help create the market and survive. The two firms appear to be converging in their intellectual property management despite differences in their acquisition strategies. They also appear to be converging in their business model design which is reflected in the similarity of their financial performance.
Our findings have implications for setting boundary conditions for the conventional view of strategy that suggests differentiation as a means to create competitive advantage. In particular, market creation based on establishing dominant design drives the competitive strategies of firms in emerging technology-based industries which calls for convergence rather than divergence or differentiation as the core of their strategic stance. We contribute to the debate between divergence for competitive advantage and convergence for building an industry around an emerging technology such as additive manufacturing.
Contribution to Practice
The finding of our study may help managers of tech-firms understand that differentiation strategies may not be the panacea in tech-competition, especially in emerging industries. Decisions that help firms converge in intellectual property management, acquisition strategy, business model design, and financial performance should also be emphasised. Entrepreneurs in emerging industries need to keep a close eye on their competitors in order to encourage dominant design and market creation via convergence in their strategic stance.
This paper on technology convergence in an emerging industry aligns closely with the special track 10.3 - Monitoring Technology Dynamics and Convergence, under the R&D theme (Theme 10).
Dess, Gregory G., and Peter S. Davis. 1984. “Porter’s (1980) Generic Strategies as Determinants of Strategic Group Membership and Organizational Performance.” Academy of Management Journal 27 (3): 467–88.
Porter, M. E. 1980. Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York, NY: Free Press.
Srinivasan, Raji, Gary L. Lilien, and Arvind Rangaswamy. 2006. “The Emergence of Dominant Designs.” Journal of Marketing 70 (2): 1–17.
Suárez, Fernando F., and James M. Utterback. 1995. “Dominant Designs and the Survival of Firms.” Strategic Management Journal 16 (6): 415–30.
Wernerfelt, Birger. 1984. “A Resource-Based View of the Firm.” Strategic Management Journal 5 (2): 171–80.
Measuring the Performance of University-Industry Collaborative Research: Technological Recombination and Coevolution
National United University, Taiwan
This study provides a comprehensive performance measuring on U-I collaborative inventions through the knowledge interactions in the phases of collaboration from both sides of the university and industrial inventors. Furthermore, this paper also examines the knowledge exchange, technological recombination, and coevolution in U-I Collaboration.
Promoting university-industry collaboration is one of the critical modes in the innovation system approach to enforcing the knowledge interactions between the actors in the system (Maietta, 2015). In several modes of U-I collaboration, collaborative research is regarded as a valuable mechanism to create mutual benefit for innovation activities in both. The partners could exchange and recombine their knowledge in the process (Mindruta, 2013). As well, it provides communication between academia and industry to facilitate the convergence and coevolution in future technological innovation (Bjerregaard, 2010). Measuring dyadic knowledge interactions in the phases (input, in-process, output, and impact) of collaborative research does not only examine the performance of the mechanism but also provide the strategic guidelines to stimulate U-I collaboration (Perkmann, Neely, & Walsh, 2011).
Although many studies have measured the knowledge interactions in U-I collaboration, there are fewer studies on dyadic performance measuring as well as knowledge interactions in whole phases. Therefore, it is difficult to confirm the mutual effect of collaborative research which could facilitate technological recombination and coevolution in academia and industry.
Could collaborative research facilitate the dyadic exchange, recombination, and coevolution in technological innovation between university faculty and firm R&D employees?
We measure the knowledge interactions by technological relatedness in dyadic interactions between university and firm inventors and the phases of in U-I collaborative inventions (Petruzzelli, 2011). Through OLS regression, we examine the knowledge exchange by the effect on the late inventions from the involvement of collaborator in the collaboration. Moreover, we examine technological recombination by the effect on the proximity in late inventions from the collaborator in the collaboration. Finally, we examine technological coevolution by the effect on the degrees of technological concentration and relatedness in late inventions from the dyadic interaction of the impact of collaboration on late inventions.
For measuring the knowledge interactions in a U-I collaborative research, the paper collected 728 utility patents which are co-assigned by universities and industrial firms in Taiwan patent office from 1988 to 2017. Then, we collect patent profiles of all inventors which has the same assignee in 5 years before the collaboration and 3 years after the collaboration. After removing the inventors without full patent profiles, there are 171 collaborative inventions and 196 inventors, and all inventors have 4793 patent filing. In the final, we organized three dyadic data sets by different focal actors in the collaboration, 323 university inventors, 319 industrial inventors, and 624 for all inventors.
On measuring the knowledge exchange in collaborative research, the results indicated there is a positive effect on the late inventions of university inventors from the involvement of prior technologies of firm inventors in the collaboration, but there is no significant effect on the reverse examination for university inventors. On measuring the technological recombination in collaborative research, there is a positive effect on the proximity of late inventions between the university and firm inventors from the involvement of prior technologies of university inventors in the collaboration, but there is also no significant effect on the reverse examination for industrial inventors. On measuring the technological coevolution in collaborative research, there is a positive effect which the dyadic interactions of the impact of collaborative inventions on the late inventions have significantly promoted not only technological concentration but also the proximity in the late inventions for university and industrial inventors.
Contribution to Scholarship
In this study, it provides a comprehensive performance measurement for U-I collaborative research by knowledge interactions in both sides and phases of collaboration. According to the results, U-I collaborative research could facilitate knowledge exchange, technological recombination, and coevolution. At the same time, this study also finds that the different performance in university and industrial investors. For example, there is no significant relationship between the interaction of prior knowledge of university inventors and collaborative inventions and the interaction of prior knowledge of university inventors and the later inventions of industrial inventors. Furthermore, there is also no significant relationship between the interaction of prior knowledge of university inventors and collaborative inventions and the interaction of later inventions of the university and industrial inventors. In other words, it still exists heterogonous performance in U-I collaborative research.
Contribution to Practice
In U-I collaborative research, university and industrial inventors may have different motivations to engage in the U-I collaboration. From the results, industrial inventors look like a solution seeker which serious concerns the university inventors have a significant contribution on the collaboration, but university inventors look like having a strive for learning the new knowledge from industrial inventors.
The paper attempt to measuring the knowledge interactions from the university and industrial inventors in phases of U-I collaborative research. and it would provide the dyadic dynamics on knowledge exchange, technological recombination, and coevolution.
Bjerregaard, T. (2010). Industry and academia in convergence: Micro-institutional dimensions of R&D collaboration. Technovation, 30(2), 100-108.
Maietta, O. W. (2015). Determinants of university–firm R&D collaboration and its impact on innovation: A perspective from a low-tech industry. Research Policy, 44(7), 1341-1359.
Mindruta, D. (2013). Value creation in university-firm research collaborations: A matching approach. Strategic Management Journal, 34(6), 644-665. doi:10.1002/smj.2036
Perkmann, M., Neely, A., & Walsh, K. (2011). How should firms evaluate success in university–industry alliances? A performance measurement system. 41(2), 202-216. doi:doi:10.1111/j.1467-9310.2011.00637.x
Petruzzelli, A. M. (2011). The impact of technological relatedness, prior ties, and geographical distance on university–industry collaborations: A joint-patent analysis. Technovation, 31(7), 309-319. doi:http://dx.doi.org/10.1016/j.technovation.2011.01.008