20-PM1-07: ST 8.4 & 8.2 - Open Innovation
Over the last decades, innovators have increasingly relied on external collaboration for knowledge insourcing (Chesbrough, 2003). This shift in the innovation process from inbound knowledge to ongoing collaboration requires organizations to manage the knowledge flows across their boundaries (Chesbrough and Bogers, 2014) and to decide the right mix of knowledge sharing and protection against knowledge spillovers (Arora, Athreye, and Huang, 2016).
Some collaborations occur under the shadow of intellectual property rights enforcement. They involve exclusive dealings and the use of a range of governance mechanisms to prevent knowledge spillovers to external partners. While the locus of innovation shifts from inside to outside knowledge sources (Laursen and Salter, 2006), focal actors still maintain a tight control on proprietary knowledge.
Other collaborations adhere to norms of knowledge dissemination and disclosure of discoveries (David, 1998). “Openness” in such situations implies not only reliance on external collaborators but also purposeful knowledge sharing and non-enforcement of intellectual property rights (IPR). The collaborative models underlining knowledge-production activities span a wide range of practices, including firm participation in patent pools (Vakili, 2016), contribution to open source software (Nagle, 2018; Wen et al, 2016), and multisectoral collaboration for translational drug and therapeutic R&D (Bubela et al, 2012).
These contrasting trends raise numerous questions about the use of disclosure and exclusion strategies in Open Innovation and the effect of these choices on value creation and value capture by innovators. Accordingly, the proposed special track aims at stimulating a wider discussion on why and when firms use one strategy versus the other, and why and when they combine disclosure and exclusion and to what effect.
Our interests on these topics are far ranging, extend to different levels of analysis and broadly cover the research, development and commercialisation stages of innovation. The following is an indicative (but not exhaustive list) of topics to guide potential participants :
1. What are the various determinants of selective revealing of knowledge (Dahlander and Gann, 2010; Alexy et al., 2013; Henkel et al., 2014) and how does selective revealing affect innovation performance and value capture by firms?
2. How do firms organize internally to combine knowledge sharing and protection against valuable knowledge leakages? How do incumbent and startup firm differ in their ability to implement and benefit from knowledge sharing/protection strategies?
3. How do firms choose and incentivize partners to share or protect knowledge in innovation collaboration? How does the balance of cooperation and competition between partners change under various degrees of knowledge protection chosen by participating actors?
4. How does firms’ practices of knowledge sharing and exclusion vary across different institutional and legal contexts and technological regimes? What is the impact of these practices on the large business ecosystems?
5. What are some reputation, status, and competitive effects of protection and disclosure in open innovation?
Implementing open innovation through organizational routines. The case of a big established firm of the defence industry
Ecole Polytechnique, I3-CRG
This paper explores, at a micro-level, the process of scaling-up an Open Innovation practice inside a large company. The study takes place in Globaldef, a major player in the A&D (Aerospace and Defense) sector.
The benefits of Open Innovation have been highlighted in many industries (Chesbrough, 2003, 2006; Chesbrough, Van Haverbeke & West, 2014). Open Innovation occurs in various shapes and directions (Gassmann & Enkel, 2004). At the managerial level, Open Innovation processes are implemented through different structures and approaches (centralized/decentralized, bottom-up/top down) (Mortara & Minshall, 2011). The Open Innovation literature emphasized internal contingencies, which can affect the success of Open Innovation strategy of the focal firm (Alexy & Gann, 2014): cognitive bias (Katz, Allen, 1982), attention issues (Larsen, Salter, 2006), compatibility, are some of these contingencies. More specifically, the nature of the relationship between R&D and other department influences Open Innovation practices and their implementation(Eslami & Lakemond, 2015). The emergence of Open Innovation strategy in companies often entails new roles at the individual level, such as scouts (Monteiro & Birkinshaw, 2017) and Open Innovation Manager (Ollila & Yström, 2016)
There is still a need to understand better how these contingencies interact with each other and impact the overall implementation of Open Innovation strategies.
While many A&D companies are opening up their innovation process, the transition between fragmented OI processes to a more unified OI management process is a challenge. Therefore, we are addressing the following question: how firms manage the scale-up of fragmented Open Innovation initiatives ?
This paper is based on an 18-months longitudinal study currently taking place inside the Open Innovation team of Globaldef a European company based in four countries counting 10,000 employees.The data comprise interviews with players involved in the implementation of the open innovation strategy, meeting reports, project presentations. Our methodology is based on comprehensive research in an abductive approach (Dumez, 2015) and case study (Yin, 2002).
- 12 interviews
- Meeting reports
- Project presentations
Based on the analysis of open innovation initiatives, we identify key contingencies that affect the scale-up of these initiatives at the film level. These include the role of institutional pressure and government incentive; the existing of informal and fragmented Open Innovation activities; the role of tools dedicated to the collaboration within open innovation projects (corporate social networks, platforms). Secondly, we propose a framework that can guide the scaling-up of the Open Innovation process in large companies.
Contribution to Scholarship
This study is a deep dive in the complexity of Open Innovation Management implementation and contribute to enrich the understanding of this topic at the micro-level.
Contribution to Practice
his paper will give insights to managers who are implementing Open Innovating practices and are trying to scale-up its activity, whether they come from a R&D or a Supply Chain department.
In the A&D sector, Open Innovation is promoted as a mean to tighten the link between research and industry and also to protect valuable technological resources as a common heritage for society. Improvement in Open Innovation Management theory could therefore contribute to bridging the gap between research, industry and society.
Bogers, M. (2011). The open innovation paradox: knowledge sharing and protection in R&D collaborations. European Journal of Innovation Management, 14(1), 93-117.
Eslami MH, Lakemond N. (2015). Internal integration in complex collaborative product development projects. International Journal of Innovation Management 20(01): 1650008.
Estrada, I., Faems, D., & de Faria, P. (2015). Coopetition and product innovation performance: The role of internal knowledge sharing mechanisms and formal knowledge protection mechanisms. Industrial Marketing Management.
Johnsen, T. E., Johnsen, R. E., & Lamming, R. C. (2008). Supply relationship evaluation: The relationship assessment process (RAP) and beyond. European Management Journal, 26(4), 274–287.
Le Dain, M. A., & Merminod, V. (2014). A knowledge-sharing framework for black, grey and white box supplier configurations in new product development. Technovation, 34(11), 688-701.
Schiele, H. (2010). Early supplier integration: the dual role of purchasing in new product development. R&d Management, 40(2), 138-153.
Servajean-Hilst, R., & Calvi, R. (2018). Shades Of The Innovation-Purchasing Function—The Missing Link Of Open Innovation. International Journal of Innovation Management, 22(01), 1850008.
Vanhaverbeke, W., Cheng, J., & Chesbrough, H. (2017). A Profile of Open Innovation Managers in Multinational Companies.
Wilhelm, M., & Dolfsma, W. (2018). Managing knowledge boundaries for open innovation–lessons from the automotive industry. International Journal of Operations & Production Management, 38(1), 230–248.
Baglieri, D., Carfì, D., & Dagnino, G. B. (2012). Asymmetric R&D Alliances and Coopetitive Games. In S. Greco, B. Bouchon-Meunier, G. Coletti, M. Fedrizzi, B. Matarazzo, & R. R. Yager (Eds.), Advances in Computational Intelligence (Vol. 300, pp. 607–621). Springer Berlin Heidelberg.
Donada, C., & Nogatchewsky, G. (2006). Vassal or lord buyers: How to exert management control in asymmetric interfirm transactional relationships? Management Accounting Research, 17(3), 259–287. https://doi.org/10.1016/j.mar.2006.06.002
Doz, Y. L. (1987). Technology Partnerships between Larger and Smaller Firms: Some Critical Issues. International Studies of Management & Organization, 17(4), 31–57.
Dussauge, P., Garrette, B., & Mitchell, W. (2004). Asymmetric performance: the market share impact of scale and link alliances in the global auto industry. Strategic Management Journal, 25(7), 701–711.
Estrada, I., Faems, D., & de Faria, P. (2015). Coopetition and product innovation performance: The role of internal knowledge sharing mechanisms and formal knowledge protection mechanisms. Industrial Marketing Management. https://doi.org/10.1016/j.indmarman.2015.11.013
Johnsen, T. E., Johnsen, R. E., & Lamming, R. C. (2008). Supply relationship evaluation:: The relationship assessment process (RAP) and beyond. European Management Journal, 26(4), 274–287. https://doi.org/10.1016/j.emj.2007.10.001
Le Dain, M., Calvi, R., & Cheriti, S. (2011). Measuring supplier performance in collaborative design: proposition of a framework. R&D Management, 41(1), 61–79. https://doi.org/10.1111/j.1467-9310.2010.00630.x
Mindruta, D., Moeen, M., & Agarwal, R. (2016). A two-sided matching approach for partner selection and assessing complementarities in partners’ attributes in inter-firm alliances. Strategic Management Journal, 37(1), 206–231. https://doi.org/10.1002/smj.2448
Najafi-Tavani, S., Najafi-Tavani, Z., Naudé, P., Oghazi, P., & Zeynaloo, E. (2018). How collaborative innovation networks affect new product performance: Product innovation capability, process innovation capability, and absorptive capacity. Industrial Marketing Management, 73, 193–205. https://doi.org/10.1016/j.indmarman.2018.02.009
Servajean-Hilst, R., & Calvi, R. (2018). Shades of the Innovation-Purchasing function - the missing link of Open Innovation. International Journal of Innovation Management, 22(1), 1850008. https://doi.org/10.1142/S1363919618500081
Vanhaverbeke, W., Cheng, J., & Chesbrough, H. (2017). A Profile of Open Innovation Managers in Multinational Companies. Retrieved from http://media.wix.com/ugd/d6c2f0_b3a48c87c13f48d090d9953855a94dfa.pdf
Wilhelm, M., & Dolfsma, W. (2018). Managing knowledge boundaries for open innovation–lessons from the automotive industry. International Journal of Operations & Production Management, 38(1), 230–248.
Innovative characteristics of Small and Medium-Sized Enterprises (SMEs) that collaborate with Research and Technology Organizations (RTOs)
1UNISINOS; 2SEBRAE; 3UNISINOS; 4UNISINOS
We identify the growth of innovation-driven institutions in different countries, such as RTOs in which innovations are generated, technologies developed and research carried out in Research and Development (R&D) efforts in SMEs. We can expect the SMEs that deem RTOs as important source of knowledge have distinct innovation characteristics
Existing research shows that SMEs organize and manage Open Innovation (OI) differently from large enterprises in a specific way (Vanhaverbeke, 2017, Vanhaverbeke et al., 2018). The RTOs have been receiving attention in some international studies relating to the field of OI studies contributing to pushing the frontier of academic research on OI in SMEs foward (Albors-Garrigos et al., 2014; Readman et al., 2018; Garengo, 2018; Giannopoulou, 2019). We know that RTOs provide a wide range of services such as problem solving and technical assistance for SMEs lacking innovation capabilities (Albors-Garrigos et al., 2014; Garengo, 2018). However, few emphasis is laid on the innovative characteristics of SMEs and their need for internal R&D that leads to consider RTOs as important sources of knowledge. Thus, discussing the innovative characteristics of SMEs that collaborate with RTOs constitutes a relevant theoretical and empirical field of study with the potential to improve OI in SMEs.
Innovative characteristics of SMEs that collaborate with RTOs have not been thoroughly examined by researchers and raises several theoretical challenges (Albors-Garrigós et al., 2014; Rincón Díaz & Garrigós, 2017; Garengo, 2018; Readman et al., 2018, Intarakumnerd and Goto, 2018, Comin et al., 2018, Rincón Díaz & Garrigós, 2017; Giannopoulou, 2019).
Determined for practical and intellectual reasons about how managers of SMEs embrace OI and having the object of discussion the innovative characteristics of SMEs that collaborate with RTOs, in this paper we intend to answer the following research question: what the innovative characteristics of SMEs that collaborate with RTOs?
This study was based on a qualitative approach, characterized as a case study in Brazil's SMEs, located in the state of Rio Grande do Sul, which was highlighted in the first edition of Brazilian Micro and Small Business Support Service (SEBRAE ) Innovation Fund Call, having the largest number of projects approved at national level (27.5%). Different sources were used, including direct and indirect documentation provided by the RTOs, SMEs and SEBRAE. The analysis and discussion of the findings was carried out through categories of analysis from the theoretical basis of the dynamic capabilities (DC) for innovation success in SMEs.
We collected the data in Google Forms between november 2018 and february 2019, and the responses were recorded in an organized and automatic way. Information and graphs were consolidated in real time. The innovation projets submitted to the SEBRAE Innovation Fund Call and legal or complementary documents were accessed, as well as the Call itself and the reports presented by the SMEs after the development of the proposed innovations. We discuss the innovative characteristics of SMEs that collaborate with RTOs in the context of the first edition of the Call for Proposals in July 2016, involving the 52 SMEs selected from the 189 approved nationally in more than 650 projects. The State of Rio Grande do Sul totaled 27.5% of projects, followed by Minas Gerais (16.40%), Santa Catarina (11.64%), Paraná (9.52%) and Rio de Janeiro (8.46%) (InovAtiva Brasil, 2016). We also used data from the "Study and Mapping of the Innovation Ecosystem for SMEs in Rio Grande do Sul," developed by the SEBRAE to diagnose the main agents related to the movement of innovative entrepreneurship in the state of Rio Grande do Sul, obtained from 48 RTOs, totaling 88.89% of a total of 54 RTOs mapped in this state.
Our results suggest that SMEs that collaborate with RTOs have a higher probability of developing innovation with greater assertiveness and agility than those that do not collaborate with RTOs, invest less in internal R&D accessing funds intermediated by RTOs and aiding internal R&D efforts from RTOs which act as a type of innovation laboratory for SMEs, contributing from the most elementary stages to the creation of innovations, and developing service innovation. The SMEs use the RTOs to shape connections between other actors in a network of innovation especially large enterprises or through their insertion into innovation ecosystems. The SMEs combine inbound (outside-in) open innovation strategies and outbound (inside-out) open innovation strategies creating a coupled model of open innovation with the RTOs and broadly with the innovation network, that involves bidirectional and interactive flows, generating moments aimed at value cocreation in which innovation is created collaboratively by those involved. Additionally, we identified that only one RTO was responsible for the approval 32.69% of projects in the state of Rio Grande do Sul, and it was the RTO that most approved project at national level. This RTO acts regularly in the intermediation of aspects of OI creating convenience for SMEs.
Contribution to Scholarship
The main theoretical implication of this study involves the generation of new insights about innovative characteristics of SMEs that collaborate RTOs, taking as reference the elements of the theoretical basis of the dynamic capabilities (DC) in the delineation of a dynamic capability to shape connections between other actors in a network of innovation especially large enterprises or through their insertion into innovation ecosystems originating from the narrowing of their relations with an RTO. The findings suggest the adoption by SMEs, RTOs and other actors, especially large enterprises, of coupled open innovation strategies that involves bidirectional and interactive flows. We explain the creation of innovations by the adoption of interactive open innovation that leads to the creation of value through the key process of creative collaboration that happens between SMEs, RTOs and other actors, especially large enterprises, interested in the creation of innovations through cocreation of value.
Contribution to Practice
The main managerial implication involves understanding the innovative characteristics of SMEs that collaborate with RTOs for managing open innovation in SMEs. Managing SMEs should direct their efforts towards the development of capability to ensure creative collaboration with RTOs and other actors, especially large enterprises, interested in the creation of innovations through cocreation of value, as well as to explore the capability of RTOs to orchestrate innovation networks as a source of competitive advantage for SMEs as they connect with actors who would not have access to them individually, rreinforcing the common view of IO studies on the role of innomediaries.
The relevance of this research to the key topics of the conference in general and to the theme of this particular edition is in the discussion about aiding SMEs internal R & D efforts bridging research with RTOs and other actors in innovation networks that enable SMEs to innovate openly.
Albors-Garrigós, J., Rincon-Diaz, C. A., & Igartua-Lopez, J. I. (2014). Research technology organisations as leaders of R&D collaboration with SMEs: role, barriers and facilitators. Technology Analysis & Strategic Management, 26(1), 37-53.
Barge-Gil, A., Santamaría, L., & Modrego, A. (2011). Complementarities between universities and technology institutes: New empirical lessons and perspectives. European Planning Studies, 19(2), 195-215.
Barlatier, P. J., Giannopoulou, E., & Pénin, J. (2017). Exploring the Role of Open Innovation Intermediaries: The Case of Public Research Valorization. In Global Intermediation and Logistics Service Providers (pp. 87-103). IGI Global.
Comin, D., Licht, G., Pellens, M., & Schubert, T. (2018). Do companies benefit from public research organizations? The impact of the Fraunhofer Society in Germany. Papers in Innovation Studies, (2018/7).
Garengo, P. (2018). How bridging organisations manage technology transfer in SMEs: an empirical investigation. Technology Analysis & Strategic Management, 1-15.
Giannopoulou, E., Barlatier, P. J., & Pénin, J. (2019). Same but different? Research and technology organizations, universities and the innovation activities of firms. Research Policy, 48(1), 223-233.
Hossain, M. and Kauranen, I. (2016). Open innovation in SMEs: a systematic literature review. Journal of Strategy and Management 9(1), pp. 58–73.
InovAtiva Brasil (2016). Retrieved from: https://www.inovativabrasil.com.br/edital-sebrae-de-inovacao-divulga-projetos-aprovados [Accessed 22 february 2019]
Intarakumnerd, P., & Goto, A. (2018). Role of public research institutes in national innovation systems in industrialized countries: The cases of Fraunhofer, NIST, CSIRO, AIST, and ITRI. Research Policy, 47(7), 1309-1320.
Readman, J., Bessant, J., Neely, A., & Twigg, D. (2018). Positioning UK research and technology organizations as outward‐facing technology‐bases. R&D Management, 48(1), 109-120.
Rincón Díaz, C. A., & Albors Garrigós, J. (2017). Research and technology organizations’ mobilizers of the regional environment: Competitive strategies. European Journal of Management and Business Economics, 26(2), 180-198.
Towards Improved Measures in Innovation Management: An Application of Item Response Theory to Open Innovation
Sant'Anna School of Advanced Studies, Italy
OI is undoubtedly an important area of innovation management research. A key debate concerns the measurement of “openness”, a multi-dimensional and context-related construct. While the use of different perspectives in prior empirical research contributed to enrich this concept, it poses specific challenges calling for the development of new measurement methods.
The OI concept encompasses various definitions and practices. Most empirical studies define and operationalise “openness” as the extent to which firms draw on a variety of external sources (Laursen and Salter, 2006), collaboration modes (Parida, et al., 2012), types of partners (Vahter et al., 2015). Such approaches combine a set of observable items into a composite variable (additive index/scale); variables are then associated with a common latent dimension (“openness”). In such cases, the internal consistency of the construct depends on the items used for the analysis (i.e. the specific definition), while the proportion of firms with a certain “degree of openness” is contingent to the sample characteristics. Yet, the same “scores” might apply to firms with very different OI profiles. This is evident in the context of SMEs, in which individual heterogeneity is high and some OI practices appear more challenging than others (van De Vrande et al., 2009).
OI embraces different activities, each linked to specific strategic approaches and types of change (Ahn, et al., 2015). The lack of a unitary perspective and the use of specific samples in empirical research has limited the development of a de-facto standard measure of “openness”, challenging the generalizability of the results.
This study shows the potential of Item Response Theory (IRT) models in improving measures in innovation management research. We apply IRT to the field of OI research with the following question: how does the use of IRT models improve upon the actual measures of “openness”?
We specify a theoretical model of how SMEs make decisions on the adoption of OI practices given a latent trait representing their attitude to “openness”. Two assumptions of the IRT models are relevant to this purpose. First, the level of the latent trait (i.e. “openness”) affects the adoption of certain OI practices with respect to others. Thus, along with the estimation of “openness” for each firm as a continuous variable, a difficulty parameter for each OI practice is estimated. Second, the estimates are independent of the sample used, being pairwise responses statistically independent given the underlying latent trait.
We use survey-based data on 383 SMEs which introduced a new product/service to the market in the last three years. We collected detailed information on 8 items related to the adoption of inbound/outbound and pecuniary/non-pecuniary OI practices (van De Vrande et al., 2009; Dahlander and Gann, 2010). We focused on a one parameter (1pl) IRT model for binary data, allowing to estimate the latent trait at the firm level (θi) and 8 item-level coefficients (aj), measuring the conditional probability that a firm adopts a given jth OI practice as a function of the degree of “openness” (difficulty parameters). The general model is specified through a logistic link function (van der Linden and Hambleton, 2013):
P(yij=1|i,aj)=e(θ -aj)/1+e(θ -aj)
We estimated the robust standard errors to demonstrate how well “openness” reflects the extent through which different OI practices occur together, rather than separating it into different dimensions. We generated the Item Characteristic Curves using the estimated difficulty parameter for each of the 8 binary items, showing how well each OI innovation practice distinguishes between firms with different “openness” levels.
We estimated “openness” as the empirical Bayes mean for θi and we compared it with additive measures used in OI research, showing a high degree of consistency (correlation=0.9***).
In addition, the analysis of the difficulty parameters allowed us to evaluate the different importance that OI practices assume in characterising the “openness” construct.
R&D_Collaborations (inbound/non-pecuniary): aj=2.349***
Clients/Users_Involvement (inbound/non-pecuniary): aj=2.038***
Licensing-in/acquiring (inbound-pecuniary): aj=4.801***
Customer co-development (outbound/non-pecuniary): aj=1.263***
Strategic alliances (outbound/non-pecuniary): aj=3.306***
Licensing_out/selling (outbound/pecuniary): aj=3.372***
Our preliminary results show that inbound and outbound OI practices characterised by traditional supply chain linkages report low difficulty levels: this means that firms with lower levels of “openness” involve clients/users in external knowledge sourcing and customers in product co-development.
Higher levels of “openness” are required to pursue R&D collaborations in the inbound stage and strategic alliances in the outbound stage. Firms with higher levels of openness aim at higher partner diversity when sourcing external ideas, as their sourcing strategies cover different knowledge domains. Pecuniary OI modes are the most difficult: very high levels of the latent trait (“openness”) are required to perform IP in licensing/external technology acquisition and IP out-licensing/selling unused technologies.
Contribution to Scholarship
We contribute to scholarship on innovation management by demonstrating the potential of IRT models in improving current methods used in quantitative studies. We apply the IRT method to the field of research in OI, showing how it improves upon the actual measures of “openness”, mostly based on the use of additive indexes and scales. More specifically, the use of IRT models in OI research can: a) help researchers to generate measures based upon a richer theory-driven understanding of how the latent construct is reflected in its proxies (i.e. how “openness” is reflected in firms’ observable behaviour), b) provide more statistically grounded measures of latent constructs (such as “openness”), allowing to overcome generalizability issues and to reduce inconsistencies across the results of empirical research.
Contribution to Practice
The use of IRT models has also the potential to improve the contribution of OI research to practice. Since IRT estimates allow to perform in-depth analyses of how “openness” is manifested in firms’ observable behaviour, the use of such research methods would significantly improve the communication between researchers and practitioners. As a result, research based on IRT models would better inform managerial choices for further development of OI strategies by reducing the gap between companies’ self-perception of their “degree of openness” and the actual stage of OI adoption.
As emphasised in a recent call for papers in the R&D Management Journal, the use of new/emerging methods allowing to add rigour and, at the same time, to foster academics and practitioners collaboration in innovation management research would be relevant to the topic of bridging research industry and society.
Ahn, J. M., Minshall, T. and Mortara, L. (2015) ‘Open innovation : a new classification and its impact on firm performance in innovative SMEs’, 2, pp. 33–54.
Dahlander, L. and Gann, D. M. (2010) ‘How open is innovation?’, Research Policy. Elsevier B.V., 39(6), pp. 699–709. doi: 10.1016/j.respol.2010.01.013.
Laursen, K. and Salter, A. (2006) ‘Open for innovation: the role of openness in explaining innovation performance among UK manufacturing firms’, Strategic management journal, 27(2), pp. 131–150. Available at: http://onlinelibrary.wiley.com/doi/10.1002/smj.507/abstract (Accessed: 2 May 2014).
Parida, V., Westerberg, M. and Frishammar, J. (2012) ‘Inbound Open Innovation Activities in High-Tech SMEs: The Impact on Innovation Performance’, Journal of Small Business Management, 50(2), pp. 283–309. doi: 10.1111/j.1540-627X.2012.00354.x.
Vahter, P., Love, J. H. and Roper, S. (2015) ‘Openness and Innovation Performance: Are Small Firms Different?’, Industry & Innovation, 2716(March 2015), pp. 1–21. doi: 10.1080/13662716.2015.1012825.
van De Vrande, V. Van De et al. (2009) ‘Open innovation in SMEs: Trends, motives and management challenges’, Technovation, 29, pp. 423–437. doi: 10.1016/j.technovation.2008.10.001.
van der Linden, W. J., & Hambleton, R. K. (Eds.). (2013). Handbook of modern item response theory. Springer Science & Business Media.
A Tale of Two Jurisdictions: Intellectual Property Rights and the Commercialisation of Innovations at the International Level
HEC Paris, France
The migration of commercialisation activities in function of intellectual property rights (IPR) protection regimes holds clear business strategy and policy implications, but has received little attention in the literature. In this project, we rely on an exogenous shock that completely reversed the appropriability regime in the US genomics industry.
The impact of intellectual property rights (IPR) on innovation has become a central topic in the literature (Huang & Murray, 2009; Sampat & Williams, 2019). An other stream of research has shown how firms can align their strategies with the market’s appropriability regime – strong or weak IPR – to profit from their innovations (Gans et al, 2002; Gans & Stern, 2003). The literature on the impact of IPR at the international level has mainly focused on the consequences of variances in the strength of national IPR regimes for the organisation of R&D activities within multinational enterprises (Branstetter et al, 2006; Zhao, 2006). Nevertheless, this latter stream of literature has not addressed the impact of IPR on firms’ commercialisation activities at the international level.
We propose to study the impact of the invalidation of IPR in a specific jurisdiction on legal forum shopping by firms, which we define in this context as the relocation of firms’ existing activities or the launch of firms’ new activities in the jurisdiction with the most convenient IPR regime.
We predict that the invalidation of IPR should lead to a decrease in the commercialisation of new technologies in the affected jurisdiction relatively to unaffected jurisdictions. Moreover, we expect the weakening of the appropriability environment to hold different implications for incumbents, diversifying entrants and startups.
In this project, we rely on an exogenous shock that completely reversed the appropriability regime in the United States (US) genomics industry. In a 2013 decision, the US Supreme Court invalidated gene patents (USSC, 2013). The European Union (EU) specified and harmonised IPR law protection of biotechnological inventions in 1998, allowing for gene patenting in all member countries (EU, 1998). This distinction in the rules of the IPR regimes covering the patenting of genetic sequences in the US and in the EU offers the opportunity to test our hypotheses using a difference-in-differences research design.
The data collection strategy is inspired by prior work by Sampat and Williams (2019). We first identified all US patents mentioning genetic sequences in their claims section using the United States Patent and Trademark Office’s (USPTO) PatFT database and retrieved the claimed genetic sequences as well as bibliographic data. We then identified the equivalent EU patents using the European Patent Office’s Open Patent Services database and retrieved bibliographic data. Using the same database, we also identified all EU patents mentioning genetic sequences in their claims section and retrieved the claimed genetic sequences as well as bibliographic data. Then, using the American National Center for Biotechnology Information’s (NCBI) BLAST software to compare the patented genetic sequences previously retrieved to the human genome, we subsequently identified the specific genes covered by the retrieved US and EU patents. We then moved on to collecting data on clinical trials for drugs specifically targeting patented genes using the DrugBank database, a database with comprehensive information on genetic drug targets, as well the Clinicaltrials.gov database. Finally, we are in the process of collecting complementary firm-level data from the Thomson One and Orbis databases.
We present preliminary results of the difference-in-differences analysis of the impact of the invalidation of IPR in a specific jurisdiction on the commercialisation of innovations. The results support Hypothesis 1, showing that the invalidation of gene patents in the US led to a statistically significant decrease in the launch of clinical trials for drugs targeting these genes. In order to be able to test hypotheses 2, 3 and 4, we are in the process of cleaning the firm-level complementary data which we obtained from Thomson One and Orbis. This work is in an early stage, but we have all necessary data at hand, and if the proposal is accepted in the conference we will present the full results.
Contribution to Scholarship
The paper contributes to the burgeoning literature aiming to understand the implications of openness on firms’ innovation and commercialisation activities. We attempt to shed new light on a central question in competitive strategy: what is the impact of the appropriability regime on firms’ technology commercialisation strategies? Previous work has assumed that firms are constrained by the boundaries of the local appropriability regime. We however underlined that IPR regulations are edicted and implemented at the national level, with significant variations across jurisdictions. By relaxing this assumption, this paper is the first (to the best of our knowledge) to assess the implications of the appropriability regime for the commercialisation of innovations at the international level. Our preliminary results are interesting because they demonstrate that the weakening of the appropriability regime in a specific jurisdiction influences the location of following innovation commercialisation activities.
Contribution to Practice
In subsequent analyses, we aim to understand the extent to which firms adapt to changes in the appropriability regime by moving their technology commercialisation activities to jurisdictions with a more favorable appropriability regime. We also aim to delve deeper into the incentives of incumbents, diversifying entrants and startups to take advantage of international differences across IP regimes. The results of these analyses are likely to inform practioners' decisions concerning the redeployment of research and development and commercialisation resources at the international level in function of local IPR regimes to maximise profit.
Our research fits well with the open innovation theme and the general focus of the R&D Management conference. More specifically, we are applying to "Track 8.5 - Disclosure and Exclusion: The challenges of collaboration in Open Innovation", which is clearly aligned with topics addressed in the study.
Branstetter, Lee G., Raymond Fisman & C. Fritz Foley. 2006. “Do stronger intellectual property rights increase international technology transfer? Empirical evidence from U.S. firm-level panel data”. Quarterly Journal of Economics 121(1): 321-349.
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Partner selection in research-intensive coopetitive innovation projects: Evidence from mature industries
1University of Agder, Norway; 2RMIT University, Australia
The importance of partner selection for the success of coopetitive OI projects (Kraus et al., 2018) is widely recognised. While most research has focused on the choice of partners in high-tech alliances, we explore how firms in mature industries evaluate partners and decide about joining coopetitive research intensive OI projects.
Coopetitive partnerships in OI bear challenges related to the risk of technology imitation, knowledge and know-how leakage, weakening of market position or risk that competitors will enter into a targeted market (Gnyawali and Park, 2011). Thus, the literature recognises partner selection as a strategic decision which might have long term consequences (Kraus et al., 2018). Extant research has explored the criteria, processes and models for partner selection in high-tech alliances (e.g., Alves and Meneses, 2015). The findings reveal that factors such as trust, commitment, mutual benefits (Akdoğan and Bektas, 2015), competitors’ market position (Kraus et al., 2018), personal relationships (Alves and Meneses, 2015) and competitor size and scope (Cygler et al., 2018) are important when firms select partners.
Most OI literature discusses partner selection from a firm or alliance perspective.A recent literature review on OI (Bogers et al., 2017) call for more research on the project level. Furthermore, most extant research discusses selection criteria and processes for competitors and noncompetitors in separation (Le Roy, Robert and Lasch, 2016).
To address the multiple partner selection in complex OI projects which involve both competitors and noncompetitors we raise the following research question: How do firms in mature industries evaluate partners when deciding whether to join coopetitive research-intensive OI projects?
We chose multiple case study approach since this method is appropriate when the aim is to understand a complex phenomenon and build theory (Eisenhardt and Graebner, 2007).Therefore, analysis of data within each, and across different cases, enables a more convincing theory building.
A combination of strategic sampling and snowballing was applied to sample relevant projects and informants. The resulting sample consisted of seven complex coopetitive OI projects, and we conducted 27 in-depth interviews with key employees (top and middle managers, project managers) from different actors involved in these projects. Each interview lasted approximately one hour. The interviews were audio recorded and transcribed, and the data was coded and analysed in an abductive manner in NVivo.
Our findings exposed that the most important factors when firms evaluate competitive partners were related to the motivation, goal, organisational culture and structure of the competitor. Time perspective when analysing potential partners together with the previous experience in similar projects appeared to have a considerable impact on the decision to participate in this type of projects. Our findings also suggested that competing companies often had a very positive attitude towards joining the projects if customers were also involved. We also found that invitation from external research institutions was evaluated based on mutual trust and understanding, while business clusters were often involved as moderators in the early phases of coopetitive OI projects.
Contribution to Scholarship
This study enriches our knowledge about partner selection in coopetitive OI projects in the context of mature industries and constitutes a basis for building theory about this phenomenon. Furthermore, it extends dynamic capabilities theory with in-depth insights about sensing capabilities in the context of coopetition. Four propositions are offered: (1) Choice of competitors should be based on similar motivation and goals for joining the project and similarity of organisational structures and cultures. (2) Building trust and mutual understanding with the research partners in the early phases of the project is very important for the decision to participate. (3) Time perspective and previous experience in similar projects influence the evaluation of the partners. (4) The involvement of customers motivates companies to engage in coopetitive OI projects.
Contribution to Practice
The propositions may serve as tentative recommendations for managers of projects, companies, business clusters and research institutions, and may provide them with more reliable criteria for evaluation of potential partners for coopetitive OI projects. Furthermore, reflection on our findings will help managers to direct their behaviour and increase the success of the projects.
The findings of this paper contribute to Track 8.5: Disclosure and Exclusion: The challenges of collaboration in open innovation, and particularly to the topic three related to the influence that choice of the partners has on knowledge sharing or protection.
Akdoğan, A. A. and Bektas, N. H. (2015) ‘Coopetition as a Business Strategy: Determining the Effective Partner Selection Criteria Using Fuzzy AHP’, International Review of Management and Business Research, 4, pp. 137-151.
Available at: http://www.irmbrjournal.com/papers/1425722959.pdf (Accessed: 8 February 2019).
Alves, J. and Meneses, R. (2015) ‘Partner selection in co-opetition: a three step model’, Journal of Research in Marketing and Entrepreneurship, 17(1), pp. 23–35. doi: 10.1108/JRME-10-2014-0026
Bogers, M. et al. (2017) ‘The open innovation research landscape: established perspectives and emerging themes across different levels of analysis’, Industry and Innovation, 24(1), pp. 8–40. doi: 10.1080/13662716.2016.1240068
Cygler, J. et al. (2018) ‘Benefits and Drawbacks of Coopetition: The Roles of Scope and Durability in Coopetitive Relationships’. Sustainability, 10(8), p.2688. Available at: http://dx.doi.org/10.3390/su10082688.
Eisenhardt, K. M. and Graebner, M. E. (2007) ‘Theory building from cases: Opportunities and challenges’, The Academy of Management Journal, 50(1), pp. 25–32. doi: 10.5465/AMJ.2007.24160888
Gnyawali, D. R. and Park, B.-J. R. (2011) ‘Co-opetition between giants: Collaboration with competitors for technological innovation’, Research Policy, 40(5), pp. 650–663. doi: 10.1016/j.respol.2011.01.009
Kraus, S. et al. (2018) ‘In search for the ideal coopetition partner: an experimental study’, Review of Managerial Science. Springer Berlin Heidelberg, 12(4), pp. 1025–1053. doi: 10.1007/s11846-017-0237-0
Le Roy, F., Robert, M. and Lasch, F. (2016) ‘Choosing the Best Partner for Product Innovation: Talking to the Enemy or to a Friend?‘, International Studies of Management & Organization, 46(2–3), pp. 136–158. doi:10.1080/00208825.2016.1112148