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Open innovation in the bioenergy industry –
a case study analysis
_________________________________________________________________________________________________________________ Master thesis Strategic Innovation Management
University of Groningen, Faculty of Economics and Business
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Acknowledgment
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Abstract
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1. Introduction
Since the publication of Chesbrough (2003), Open Innovation (OI) is seen as an important innovation management topic by many scholars (Gassmann, 2006; Laursen & Salter, 2006). The traditional closed innovation view of organizations is creating innovations via internal R&D departments and commercialize innovation through internal market paths, to compete with external competitors. However, as innovations occur through recombination of knowledge flows and internal knowledge is often limited, OI focusses on gathering both internal and external knowledge to create new innovation and finding external paths to commercialize innovations, instead of using only in-house pathways (Chesbrough, 2003). With OI, firms are better able to deal with changes in the competitive environment such as globalization, technological turbulence, and increased competition (Bianchi, Cavaliere, Frattini & Chiesa, 2011).
Both managers and researchers have asked the question: When or in what circumstances does OI pay off (Alexy & Dahlander, 2013)? West, Vanhaverbeke and Chesbrough (2006) clarified the OI research field by identifying five levels of analysis: (1) national institutions and innovation systems, (2) industry or sector, (3) interorganizational value networks, (4) implications for firms and (5) individuals and groups. Within these literature streams, studies show that using OI successfully demands overcoming challenges. Using OI requires a different organizational mindset and opening up organizations (in the industry) (Gassmann, Enkel & Chesbrough, 2010) and firms should find appropriate partners to work with. When opening up, R&D departments require a change in the way of working to create new knowledge flows with external sources (Chesbrough, 2003). Also, individual and group challenges are identified such as the Not Invented Here (NIH) bias (Katz & Allen, 1982), collaboration with unknown external partners (Salter, Criscuolo & Ter Wal, 2014) and the paradox of disclosure (Arrow, 1971). It becomes clear that in order to let OI pay off, OI management is required.
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collaborations in that industry. Ibrahimov (2018) shows OI is used in the petroleum industry in such a way that there is no direct link between OI partnerships and new product development, as innovations do not often occur and therefore the strategic value of innovations is high. Sarkar and Costa (2008) examined OI in the food industry, addressing the industry characteristic of a large amount of parties involved in the innovation process that should be taken into account when using OI and arguing the essential role of the government in OI. Furthermore, Ili et al. (2010) analysed the automotive industry, which shows the industry experiences the challenge of having no acceptance for external ideas as it does not fit the firms’ brand. Altogether, previous research shows every industry experienced its own OI challenges. Therefore, a literature gap exists as coping with OI challenges seems crucial but OI is only researched to a limited extend in various industries, examining challenges and how to cope with them.
Consequently, this study adds to this literature gap by investigating the use of OI in the yet little researched bioenergy industry and identifies the challenges that firms experience. The bioenergy industry includes both the production and commercialization of bioenergy, such as biogas or biofuels (RVO, 2019). Given the high need for innovations in the bioenergy industry to reach national climate objectives (Rijksoverheid, 2019) and meet increasing demand due to market growth (IEA, 2018; IEA, 2019; IRENA, 2014), optimal innovation management is crucial. While Chesbrough (2003) already stated the bio-economy is opening up, nowadays several organizations in this industry request collaborative innovation to increase production and production efficiency of bioenergies (EERA, 2018; Elbersen & Van Ree, 2018; European Commission 2018; TNO, 2019). However, literature provides yet little insight in OI practices in the bioenergy industry and the challenges organizations experience when using OI. As identification of OI challenges in the industry are crucial for successful OI, this qualitative inductive research, based on five unstructured expert interviews and seven semi-structured case study interviews, answers the following research question(s): How is open innovation used in
the bioenergy industry and how do organizations cope with the OI challenges they experience?
This study focussed on the industrial, interorganizational and firm (implications for firms & individuals and groups) level of analysis, as defined by West et al. (2006).
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this industry created a cluster in which firms easily find each other when collaborative innovation projects are requested and external knowledge or capabilities are needed. Uniquely, cluster organizations cover the whole bioenergy production chain as a method to use OI. Furthermore, several OI challenges are identified that organizations cope with. First, the results address the presence of large multinational firms which acquire innovative technologies and keeping them internally, resulting in firms seeking innovation partnerships and making use of OI to compete with these multinationals. Also, in contrast with literature showing the use of OI requires a lot of contractual agreements and IP protection, this study shows IP protection is not that big of a deal when trust is present and clear innovation roles are defined in OI partnerships. And while literature states competition in OI projects result in long term conflict, this study shows a little competition may be beneficial for project outcomes. Furthermore, the challenge of unwanted knowledge spillover is experienced in this case but is overcome by maintaining innovation market leadership to outdate the knowledge that is spilled. Finally, the findings show the presence of the external knowledge challenge that is overcome by creating knowledge sharing facilities. Jointly, these findings add to the current literature about OI by examining the use of this concept in one industry and the challenges that are experienced. Moreover, the identified challenges and solutions provide relevant insights for managers that want to use OI in the industry of bioenergy in a successful way.
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2. Theoretical background
In order to examine OI, important concepts need elaboration. In this section, the concept of OI is explained, as well as why organizations could benefit from it, what current research on OI shows to be important parts of the concept, and what identified organizational challenges are. 2.1 The open innovation paradigm
In 1934, Schumpeter defined ‘innovation’ as a process of combining existing knowledge elements in a novel way (Subramanian & Soh, 2016). More recent studies have broadened this definition, by adding different components related to innovation practices. A widely accepted definition of innovation is that of Crossan and Apaydin (2010). They define innovation as “production or adoption, assimilation, and exploitation of a value-added novelty in economic
and social spheres; renewal and enlargement of products, services, and markets; development of new methods of production; and establishment of new management systems.” (p.1155).
Traditionally, the innovation process was mainly including organizational knowledge creation taking place in the internal organization via R&D centers, using new knowledge to create new ideas, which may lead to internal inventions. Here, internal R&D was seen as an important source of technical know-how (Mowery, 1983). Commercialization via internal paths to markets lead to sales of innovations. At that time, this internal focus was thought to be the best way to compete with competition (Chesbrough, 2003). Later, other studies address different options of innovation management. Cohen and Levinthal (1990) introduced the definition of ‘absorptive capacity’, stating that firms’ ability to source and make use of external information is crucial for its innovativeness. This concept has been revised by Zahra and George (2002), adding the concepts of potential- and realized absorptive capacity. Laursen and Salter (2006) also add to the increasing research on external knowledge sourcing, showing a positive (curvilinear) relationship between external sourcing and innovativeness of a firm. Other research shows that firms have increasingly used external knowledge sourcing as it has shown a positive relationship with firms’ performance (Arora & Gambardella, 2010; Fritsch & Lukas, 2001; Katila & Ahuja 2002; Levinthal & March 1993).
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The OI concept of Chesbrough (2003) consists of a twofold. First, it insists that organizations should not only use the internal organization to create new knowledge, but they may also use external sources of knowledge to create new innovations. The reasoning behind this statement is that due to increasing knowledge of workers and the ease to share knowledge due to the internet and globalisation, internal knowledge is not always sufficient enough. As a lot of knowledge is created every day, firms should understand that it is not always their internal R&D department that is creating the best novel and innovative ideas. External sources are therefore critical in innovation management, to secure new innovations. This reasoning is later described as inbound- or outside-in OI (process) (Chesbrough & Crowther, 2006; Enkel, Gassmann & Chesbrough, 2009), or technology exploration (Van de Vrande, De Jong, Vanhaverbeke & Rochemont, 2009). Examples of external sources that could be used are customers, competitors, suppliers, regulators, other industries, universities or start-ups (Ili, Alberts & Miller, 2010). With inbound OI, organizations may buy knowledge (acquiring) or source for external knowledge outside the organization (sourcing) (Dahlander & Gann, 2010). Organizational modes of outside-in OI are licencing-in, joint venture, joint development or external networking (Abulrub & Lee, 2012).
Second, OI argues that instead of using internal paths towards market commercialization, also already existing pathways of external sources may be used to commercialize innovations. This is known as outbound- or inside-out OI (process) (Chesbrough and Crowther, 2006; Enkel et al., 2009), or technology exploitation (Van de Vrande et al., 2009). The reasoning behind outbound OI is that other people may already have better paths to the market, or more knowledge to commercialize the innovation. Therefore, organizations should carefully consider if they are the right organization to commercialize an innovation. An example of outbound OI is the use of licence-partnerships, alliance- or joint venture creation to get to external market parts (Ili et al. 2010). Organizations may licence out innovations or bring it outside their own firm boundaries as the innovation is not the core business of the focal organization. In this way, organizations may acquire money for bringing innovation outside firms’ boundaries (selling) or share it for free (revealing) (Dahlander & Gann, 2010). Modes of inside-out OI are licencing-out, selling or open sourcing (Abulrub & Lee, 2012).
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West et al. (2006) clarified the OI research field by identifying five levels of analysis of OI and the concepts related to each level. These levels are (from wide to narrow) (1) national institutions and innovation systems, (2) industry or sector, (3) interorganizational value networks, (4) implications for firms and (5) individuals and groups. Literature can be grouped in one or several groups, creating a clearer overview of the present research and the challenges that are identified in the use of OI.
2.2 Challenges in using OI
As West et al. (2006) already argued, different challenges are shown when making use of OI. As (4) industry or sector is the environment a firm is operating in and (3) interorganizational value networks, (2) firm implications and (1) individual and group challenges are all levels of analysis directly affecting the firm, challenges per level of analysis are identified that have an effect on organizations working with OI. Table 1 provides an overview of the different challenges per level of analysis that are frequently mentioned in literature.
Industry or sector challenges
The industry or sector level of analysis as described by West et al. (2006) has to do with industrial characteristics and its effect on the use of OI. Concepts that play a role here are the appropriability of IP, how are organizations in the industry structured, how valuable are internal and external knowledge sources in a certain industry (West et al., 2006), in what industry life cycle the industry is in and how the industry copes with rapidly changing technologies (Zucker and Darby, 1997) and if there are entry barriers affecting the ability of entering the market (Porter, 2008). West et al. (2006) for example state: “during the past two decades patents and
university research have played a greater role in innovation for biotechnology and pharmaceutical industries than for consumer electronics.” (p.297). Laursen and Salter (2014)
conclude openness of firms comes with a moderate level of appropriability. By examining this level of analysis, it becomes clear what the strategic value is of the use of OI in an industry. Interorganizational challenges
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for example speed (‘how fast should be go together?’), priorities (of resource commitments or priority changes over time) and common understanding (organization do not (always) have the same ‘language’). The contract-challenge insists that in contrast to ‘open’ innovation, organizations tend to create lengthy agreements and contracts defining the collaboration. Elements in this challenge are agreements (including relational governance, ownership rights, exclusivity and (long term) resource allocation), intellectual property (IP) ownership (defining organizations’ IP and who has which IP owned) and risk management (compliance from employees committed to the OI, or about primary-secondary partners). Finally, the competition-challenge consists of long-term competitive implications. Examples of elements to take into consideration are direct competition (working together with direct competitors may be risky in OI over time) and selection of OI partners (and how this partnership may change the industrial structure and value chains). Furthermore, Chesbrough & Brunswicker (2013) contribute to this level of analysis by identifying seven most frequently mentioned OI goals at large organizations. Examples are ‘establishing new partnerships’, ‘exploring new technological trends’, ‘identifying new business opportunities’, ‘accelerating time to complete’, ‘R&D mitigating risks of innovation projects’, ‘identifying new business opportunities’ and ‘reducing R&D costs per project’. Differences in goals may have a negative effect on the collaboration (Brunswicker, 2013).
Firms implications
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should take into consideration their core competences when using OI, making sure their innovation activities are in line with their core competences and other firms’ core competences are used for the other activities (Christensen, 2006).
Individual and group challenges
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3. Methodology
This research paper aims to achieve a better understanding of the use of OI in the bioenergy industry. This section elaborated on what type of study is done and how the data is collected and analyzed. Furthermore, a case description is provided.
3.1 Research design
The main goal of this study is to examine how organizations in the bioenergy industry work with OI. To examine this topic, a case study of the Bio-Energiecluster Oost Nederland (BEON) was conducted, in which different collaboration partners are interviewed about working with OI in the cluster. In table 2, a basic overview is given of the cluster. Section 3.2 provides a case description including goals, dynamics and innovation processes of the cluster.
Table 2. Basic overview of BEON
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upcoming themes. It is important to perform this type of research, because it provides insights in understanding how organizations and industries work, resulting in insights about the phenomenon of OI in the industry and showing surprising results which contributes to better understanding of the topic (Edmondson & McManus, 2007). As different organizations of the cluster are interviewed, extraneous variation within the cluster is minimalized (Eisenhardt, 1989).
3.2 Case description
The BEON case is examined in this research. This case provides an appropriate research setting as the cluster consists of several partners (see figure 1) that act in different parts of the production chain of bioenergy and work together to accomplish the goal of collaborative innovation creation. As this cluster covers the whole bioenergy production chain, it provides a good overview of the industry (BEON, 2019). Furthermore, with the focus on collaboration in the cluster, resulting in collaborative OI, this cluster forms a unique case that covers both aspects of OI: sourcing for external knowledge to create new innovations and find external paths to commercialize them (Chesbrough, 2003).
Figure 1. An overview of BEON including industrial groups and the amount of organizations participating.
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combination of several companies and institutes from different industries, working together in the cluster, provides a unique collaboration that covers the whole production chain of bioenergy, from biomass feedstocks till bioenergy use by the customer. Through good interaction, this cluster is able to create and implement new innovation quickly and effectively. Nowadays, 25 organizations are participating in the cluster (BEON, 2019).
BEON (2019) has actively participated in several successful bioenergy OI projects. Collaborative lobbying towards government agencies is done to create a more beneficial legal and financial environment for all parties involved in the cluster. Furthermore, companies meet during organized small- or large-scale events, to communicate their industrial updates, project developments and challenges they experience in their environment. Often, this communication leads to project creation based on a request from the field. As a result of the short link that the cluster facilitates between participating partners, different organizations find each other easily. Projects rise in which multilateral or bilateral partnerships set project goals and divide needed activities. Sometimes, these projects are public-private collaborations (PPCs) in which both industrial parties, universities and government money is involved. Results of these projects vary from action plans created for local government to cope with an environmental bioenergy challenge, till the creation of innovative technologies to optimize the production or the creation of new bioenergy and commercialization of these. Besides that, organizations individually benefit from the cluster through the facilitating role of creating short communication links with different important organizations in the environment. Therefore, the cluster serves as a stratification of activities whereby parties benefit from each other at the industrial level (towards government), the interorganizational level and the individual firm level (see figure 2).
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The data collection of this study consists of a three phase data collection and analysis method, to get from a surface level to a deeper level of data collection and analysis (Estrada, Faems, Martin Cruz & Perez Santana, 2016; Faems, Janssens, Madhok & Looy, 2008). Table 3 provides an overview of the three conducted phases.
Table 3. Conducted phases and its main actions and outcomes
In the first phase, literature research was conducted to provide an overview of the theoretical concept of OI. Furthermore, five unstructured interviews were conducted with industry experts, to gather information about the current industry and innovation climate in the bioenergy industry. This resulted in overarching themes that play an important role in the bioenergy industry when it comes to creating innovations. This information was taken as a starting point to create an image of the industrial level of analysis (West et al. 2006) and served as input for the case study interview schemes. Furthermore, the BEON case was identified and information about the cluster was examined to get a clear image of the clusters’ goals, mission, setting and participants. An exploratory interview with the cluster coordinator was conducted and additional published documents were read. Information of this interview and present information on the internet resulted in a case description (section 3.3).
Phase two consisted of seven semi-structured interviews with organizational informants
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18 Table 4. Interviewees and interview details.
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4. Results
First (in section 4.1), five conducted expert interviews provide information of the current innovation state of the bioenergy industry in which the case operates in. Industry characteristics are identified such as the presence of large multinationals, the high complexity of knowledge and the importance of subsidies for innovation projects. Hereafter (in section 4.2), the case study results show OI is used in a cluster where every participating organization is contributing to several particular innovation process phases in created OI cluster projects. This section identifies challenges the organizations experience and cope with. In appendix C, results will be displayed in a theoretical framework, grouped by the different levels of analysis.
4.1 Expert interview results: Shaping the innovation landscape
The exploratory character of the unstructured interviews provides an interesting oversight of the innovation landscape of the bioenergy industry. Figure 3 provides a schematic overview of the (open) innovation process in the bioenergy industry.
Innovation in the industry. There are a lot of innovation activities in the industry, resulting in innovations such as BioLNG and optimized bio-digesters. As mentioned before, large investments are needed to create innovations. Experts address several ways in which innovation in the bioenergy industry are created, namely through 1) internal R&D, 2) small start-ups, financially stimulated or acquired by large multinationals, 3) PPCs, including both private and public organizations and often financially supported by the government and 4) contract research (e.g. temporarily outsourcing of research activities). Furthermore, within the biofuel industry, a few large multinational companies are present, having large financial resources. Interestingly, experts address an industry innovation paradox: the complexity of knowledge requests both large financial resources and the need for openness and collaboration. However, while the large industrial firms have the financial resources, they often do not have a culture of openness that SMEs have and vice versa.
Innovation process. Within the bioenergy industry, Technology Readiness Level (TRL) theory is used to systematically innovate. TRLs, originally used at NASA, are “a
systematic metric/measurement system that supports assessments of the maturity of a particular technology and the consistent comparison of maturity between different types of technology”
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generation (TRL 1/2), 2) laboratory research (TRL 3/4), 3) piloting (TRL 5/6), 4) demo (TRL 7/8), and 5) finally commercialization (TRL 9). Industry experts mention that in this industry, universities are often involved in fundamental research (TRL 1-2), HBO schools*1are often involved in more direct applicable research (TRLs 3-5), research departments are involved in TRLs 4-8 and marketing and engineering and marketing departments in TRL 9. Experts address the high costs of TRLs (e.g. piloting phase costs several million euros).
Present OI in the industry. Within the bioenergy industry, collaborations for innovation occur varying from completely closed to completely open. Some firms use parts of OI (e.g. knowledge sharing or collaborative commercialization) while others use several parts of OI (e.g. collaborative research). Industry experts see a requirement for successful OI, namely the importance of sharing knowledge by the whole value chain. Experts acknowledge that the use of OI in this industry is harder compared to for example the electronics industry, as opening up the innovation process is not yet accepted by the majority of the industry. One expert mentions: “You must ensure that you share more information throughout the whole value chain,
because sharing information with a direct competitor is something that many companies do not feel comfortable with.” (E1). Interesting is that experts see a link between the openness of the
innovation system and the different TRL levels. Two experts argue that transparency and collaboration play an important role in TRLs one (idea generation) to four (laboratory research), but also experience that in higher TRLs, firms tend to a more closed innovation system. The main reason is that from the piloting level, a business case is shaped which shows the financial benefit of an innovation and drives firms to capture competitive advantage. The risk of disclosure of the innovation, the investment costs for the pilot and demo phase and the strategic benefits of getting the innovation operational are all factors that drives firms to close the innovation process.
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Figure 3. Overview of expert interview results: The bioenergy OI process, its involved parties and its external knowledge
sourcing/sharing during different TRLs.
Intellectual property. In the bioenergy industry, experts see IP protection as a crucial factor for successful innovations. For example, in the biofuel industry critical innovation components such as a more effective micro-organism could easily be stolen by competitors without any form of protection. Firms use both formal protection mechanisms (e.g. patents) and informal protection mechanisms (e.g. non-disclosure agreements (DNAs) or confidentiality agreements). In PPCs, industrial companies often try to patent as late as possible due to limited protection time, while universities want to publish information as quick as possible. This sometimes leads to complicit, as one expert explains: “There are differences between what
universities want, openness of knowledge, and firms that use an IP strategy to protects IP.”
[E3]
Subsidies. For the creation and commercialization of new innovation, subsidies in this industry are crucial, because innovation processes are costly. Dutch and EU subsidies are available for innovation projects, however one expert explains: “However, if you look at
stimulation funds, what the government does to stimulate biofuels, then that is actually very limited, […]it seems that innovation for biofuels is often excluded”” [E2]. They claim the main
reason for this seems to be that the benefits of biofuels are underestimated, compared to the published, often untrue disbenefits. Surprisingly, in PPCs government often do support through subsidising innovation costs. The analysis shows that for projects at lower TRLs, subsidies are easier to obtain compared to higher TRL-projects. An expert explains: “[…] The closer a
project gets to the market, the bigger the chance of unfair competition” [E5]. Another expert
adds to this: “If it is developed with public money, then the money should not go to one
organization” [E3]. Subsidies are meant to stimulate social welfare and are therefore more often
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4.2 The BEON case: using open innovation in the field
As a result of interviews held with seven organizations participating in the cluster, the OI phenomenon was further investigated. Frequently mentioned concepts have occurred. Below, the most interesting results of the interview analysis are shown. Here, industrial, interorganizational and firm level findings are grouped and elaborated on.
4.2.1 Industry level: Industry incentives and barriers
Besides the expert interviews, also organizational informants have mentioned important industry characteristics that have an influence on OI projects.
Innovation development characteristics. Three characteristics of innovation development are mentioned in interviews. First, innovations are often created bilateral as protection of knowledge is seen as important. Seconds, innovation projects often have large acquisition costs, leading to entry barriers. Third, besides high acquisition costs, innovation projects are often lengthy and therefore costly. Technological innovation projects may take up to 40 years. Innovation project are influenced by these industry characteristics and firms should be taken into consideration when firms open up their innovation processes and collaborate for innovation.
Government and multinational interference. Besides the strong regulating role of government in the bioenergy industry, government also provides subsidies for several innovation projects. As previously mentioned, technology development in this industry is financially intensive and time consuming. As only large multinational firms have the financial resources to create technology development processes themselves, subsidy support is crucial for small and medium enterprises (SMEs). Innovation project financing is seen as a challenge, as an informant explains: “We would like to develop a biogas network, but the business case
stands and falls with the subsidy amount that we receive, [...] because sustainable gas has an unprofitable peak, the price difference between fossil and renewable energy production” [O4].
In the cluster, firms collaborate to meet the subsidy requirements and claim subsidies (e.g. SDE, TKI, EFRO or European subsidies) for collaborative innovation projects.
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always is the problem with technology development, is that capital-rich parties say: we have the money, without our money it will never be a success” [O2].
4.2.2 Interorganizational level: Creating an innovation chain
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All parties participated in sharing knowledge, leading to new ideas and the start of several OI projects. This overall collaboration of parties involved in the cluster results in a unique combination of diverse knowledge which makes a valuable atmosphere for OI. Figure 4 provides an overview of the different roles of parties in the cluster, and cluster activities towards environmental actors. This includes only the interviewed parties. It is likely that other cluster participants are also contributing to the different innovation phases.
Figure 4. The role of different cluster participants in the OI process.
4.2.3 Interorganizational level: Intellectual property protection
The interview analysis shows several organizations have mentioned the importance of IP protection for their organization, as created IP is of high value and therefore needs to be gained by the organization. The analysis shows ways in which organizations protect their IP. Identified subjects are IP creation in collaborations, IP agreements and patents.
Ownership in collaborative IP creation. As OI requires collaboration for IP creation, firms in the cluster make clear agreements about collaboratively created IP. A university informant clarifies: “All IP you develop together, you also own together. Everything that you
acquire in the context of new knowledge in the project remains among the partners” [O6]. This
also holds for PPCs in which universities are involved.
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which technology licensing agreements are used. In this ‘factory’ example, one company bought a factory with a license for the use of the technology in the factory. The technology developer has rights to improve the technique and show the technique to external parties, while the factory purchaser is owner of the output of the technology. An interesting finding is that although these informal protection mechanisms are used to protect IP, for some cluster participants IP protection is not so important. One organizational informant explains: “Of
course IP rights are mentioned in collaboration agreements, for sure. However, the manner in which I respond says how I feel. […] I always think: if you want to protect knowledge, you should not share it.” [O1]. It is mentioned that organizations know using OI comes with sharing
knowledge and not all knowledge can be protected. Firms take this risk for granted.
Patents. Within complex technology innovation projects, patents play an important role to formally protect IP. Some technology developers in this cluster own several patents and use these to share developed technologies while capturing value. The analysis shows that within university-industry collaborations, the industrial firms often buy out the university from collaboratively created patents on IP. A surprising finding is that even though patents seems important for IP protection, in this cluster some organizational informants state within collaborations with universities, patent management is not that big of a deal because trust is available and roles are clearly separated. Here the industrial parties want patents for created IP, while universities do see publishing as their main benefit of the collaboration. A university informant clarifies: “That usually means that industrial companies say: you are the basis of the
idea, we see so much potential in it and we want to buy the rights. And then that is paid in one go to the university” [O6]. Figure 5 provides an overview of the factors that have an effect on
the protection of IP in cluster collaborations.
Figure 5. Factors having an effect on IP protection in the cluster.
4.2.4 Interorganizational level: Open innovation climate
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Knowledge giving and taking. A frequently mentioned advantage of participation in the cluster is the ability of firms to receive valuable information. However, within the cluster organizations are also expected to share information. Firms in the cluster have the mentality of both giving and taking knowledge. In fact, cluster participants see knowledge sharing as a requirement for knowledge seeking. Knowledge sharing is seen as an important factor for making progress together and creating innovations. Cluster meetings and information days are used to stimulate information sharing and creating new innovative ideas or projects.
Open culture. In addition to the firms’ open culture that is needed for good OI, the analysis shows an open culture of all partners involved is needed in order to use OI successfully. Without having companies with open cultures in collaborations, OI processes are not able to take place as companies do not feel comfortable with sharing knowledge openly. This shows knowledge giving and taking is more likely to occur within an open culture.
Trust. In order to increase efficiency in collaborative innovation projects, trust in a partner is needed. Several organizational informants mention trust as a requirement for successful OI collaborations. An informant explains: “What is very important is that there is
mutual trust, that you know each other so that you know the strengths and weaknesses of the other members” [O1]. More specific, when IP sensitive information or a patent is involved in
OI projects, trust becomes even more important as firms tend to share important information quicker when there is a high level of trust between collaboration partners. Due to trust in the cluster, an open and knowledge sharing culture occurs. “That is precisely the advantage if you
know each other well, because then you also need little consultation to understand each other. I think that works very well” [O5]. Figure 6 provides an overview of the factors that have an
effect on the creation of an OI climate.
Figure 6. Factors having an effect on OI climate creation.
4.2.5 Interorganizational level: Partnership creation and management
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important. Figure 7 provides an overview of the factors that have an effect on partnership creation. Below, the most important factors are elaborated.
Competition. Organizational informants mention the challenge of competition in OI projects. Firms experience a decrease in knowledge sharing and an increase of tension when competitors work together. Therefore, direct competition is tried to be minimalized in the cluster. When an innovation project and its partner composition is created, no direct competitors are put together. An informant explains: “when it comes down to it, and when it comes to new
innovations, they are reluctant because they see each other as a real competitor” [O6].
However, little competition is present in the cluster. A few parties are delivering the same product types but are serving different market segments. Interestingly, this little competition is seen as beneficial for the cluster. An informant explains: “A bit of competition in a cluster is
actually good, because then you keep each other sharp and focused” [O1].
Complementarity. Complementarity is seen as an important success factor for OI collaboration projects. The willingness to open up innovation processes increase when firms are complementary, as firms do not interfere but rather strengthen each other. The benefit of complementarity becomes clear when looking at the earlier mentioned ‘factory’ example in which the factory of one technology developer was bought by a waste processor, while the technology of this factory was licenced out to the waste processor. A participating informant explains: “It is in the interest of both parties that the technology continues to run well. We
benefit from their technology improvements and the technology developer has an interest in our provided feedback from the field” [O3]. In this situation firms rather complemented each other
instead of competed for the same technology.
Figure 7. Factors having an effect on OI partnership creation.
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positive project outcome. As a result of this, several organizational informants mention a collaborative focus in innovation projects is required for a good project outcome, which becomes even more important when cluster parties want to acquire large innovation projects. By creating a collaborative focus in the cluster, the business selfishness challenge is overcome. Figure 8 provides an overview of the factors that have an influence on OI partnership management
Figure 8. Factors having an effect on OI partnership management. 4.2.6 Firm level: Firm level management
Analysis of the interviews with organisational informants show how organizations in the cluster experience working with OI and what management is needed to capture benefit and overcome challenges. Informants mentioned the importance of employee management and recruitment, knowledge flow, organizational culture and the overall way of innovation management in the firm.
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Knowledge flow. Organizational informants address the importance of knowledge flow in OI. Here, knowledge flow includes both internal and external knowledge flow. The risk of unwanted knowledge spillovers is experienced by some organization informants, addressing that when OI is used, unwanted knowledge spillovers always occur. Although they see the importance of sharing knowledge, they also experience information gets ‘stolen’. However, firms in the cluster try to overcome this risk by maintaining high innovativeness and making sure the organization is always one step ahead of the parties they compete with. In this way, unwanted shared knowledge is being outdated quickly, resulting in a decrease of risk. Organizational informants mention that even though this is a seemingly effective method to overcome the unwanted knowledge spillovers, it is also risky as innovations are not guaranteed and therefore staying ahead is not easy.
Organizations in the cluster also experiences a challenge of external knowledge usage. They experience that knowledge is not automatically shared between innovation partners’ business units. An informant explains: “That was my biggest concern and that is still my most
important point of attention in innovation: how do I transfer the knowledge that is in the minds of my R&D people in a pleasant and workable way, to a new spin-off company, where you have other people” [O1]. This challenge is two-sided, because it is important to overcome as business
units need each other’s knowledge to get an innovation to a next phase, but also to keep initial inventors motivated. An organizational informant argues that goodwill needs to be created between the different parties involved, to overcome this challenge. As an example of how to create this goodwill, they mentioned giving the employees the possibility to talk with parties with which the innovation is shared as it is important that initial technology developers know how other parties have made progress in their initially created technique to keep motivation for creating new innovative ideas. If these employees have the possibility to regularly have a quick chat, knowledge is easier shared between entities. A culture should be created in which employees have goodwill and therefore share knowledge easily.
Firm culture. An open firm culture is needed for employees sharing knowledge. Furthermore, opening up the innovation process requires a certain amount of employees’ willingness to share information with external parties. Interestingly, organizational informants argue their employees to not experience external knowledge sharing as negative. When asking whether the openness of the innovation has a positive or negative effect on the employees, an organizational information stated: “We do not notice anything negative about it, […] employees
take over the culture of openness” [O1]. An OI culture results in employees that are used to
30
based on a cultural fit, resulting in recruited employees that see innovative information sharing is beneficial instead of demotivating.
Employee recruitment. OI firms attract people that have an information sharing character. While some organizational informants argue they recruit employees to capture knowledge needed for new innovations, others mention the availability and use of external knowledge sourcing. In this case, IP sensitivity plays an important role in the choice between recruiting employees and external knowledge sourcing via information agreements. When IP sensitive information is involved, firms tend to recruit employees and let them work internally. When less IP sensitive information is involved, firms also seek for knowledge through external knowledge sourcing agreements. An informant explains: “if it is IP sensitive information and
it is something new, then of course I want to protect that knowledge. […] Then I often hire people” [O2].
Employee management. Informants acknowledge employee management is important to keep employees’ satisfaction and motivation on a good level. Organizational informants argue they experience a NIH bias due to different ways of working between different business units of different parties involved in the OI process. An organizational informant explains: “When the spin-off was established, the new forces came in and started their own point of
direction. This was a different direction and that sometimes led to friction between the R&D people who actually partially transfer the technology and knowledge to those new people” [O2].
To overcome these NIH biases, the earlier mentioned goodwill again plays an important role. In this case, the NOH bias was overcome after a culture of information sharing was created in which people are willing to share their own created innovative innovation. Here, employees were given the possibility to create this goodwill by talking in the corridors or through update meetings. Figure 9 provides an overview of the factors that have an effect on the management of OI in the firm.
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5. Discussion
This study focussed on the application of OI in the bioenergy industry and the OI challenges this industry cope with. While most studies examine one level of analysis to add to the overall understanding of the use of OI, this study provides an overview on several levels on analysis, resulting in an overarching image of the use of OI in the case, operating in bioenergy industry. In this section, findings of the case study are elaborated and compared to present theory to create new insights and propositions for future research.
5.1 Use of open innovation in the cluster
This case study illustrates a cluster collaboration in which 25 companies collaborate to create a fruitful OI climate (through lobbying and informing) and through which OI projects occur. Interesting is the creation of an innovation chain in which several parties contribute to different innovation phases. In the OI projects created, firms collaborate in bilateral and multilateral forms and seek each other’s expertise when needed. Potential inventions are shared to create market commercialization possibilities. As Chesbrough (2003) described both inbound- and outbound OI, this cluster therefore organized itself to have the possibility to use both concepts for collaborative innovation creation.
5.2 Open innovation as a spectrum
Literature describes OI as an extreme in which firm fully open up their innovation process, through the use of inbound and outbound OI (Chesbrough, 2003; Chesbrough & Crowther, 2006; Enkel et al., 2009). However, practice shows firms open up their innovation process to some extent, using some parts of OI or create partially OI processes. Therefore, we argue OI should be seen as a spectrum on which firms position their selves on a spectrum from fully closed innovation to fully OI use. This way of thinking has two main benefits. First, firms can define their own level of openness on a scale, which can be beneficial for organizations that do not want to open up fully but want to position themselves in the OI paradigm. In addition, the more precise level of openness may be used by the government (e.g. policy makers, subsidy granters) to determine which projects have the right to get subsidy and which not. Second, this way of thinking may be beneficial for organizations that want to use OI and seek for suitable partnerships. As having a collaborative focus is important in partnerships, firms may seek partners that have the same level of openness, which increases the chance of OI success.
32 Figure 10. Open innovation as a spectrum.
5.3 Industry level insights
As West et al. (2006) address the importance of defining the nature, value and organization of innovations, the insights of this study show that innovations in this industry are created through internal R&D departments (multinationals), PPCs, contractual research or via the creation of new start-ups. Entry barriers exist due to high acquisition costs and the capital intensity of innovation projects (Porter, 2008). Furthermore, subsidies play an important role in the creation of OI projects. In projects, TRL theory is used to create innovation systemically and openness of R&D processes varies in this industry from completely closed to completely open. Expert interviews show that till the piloting phase organizations are willing to share knowledge but in later TRLs firms share less knowledge. However, an interesting industry specific finding of this study is the effect of a few large multinational companies in the industry, that have a closed patenting culture which opposes opening up the industry’s innovation process. As a result, SMEs in this case argue they collaborate to create synergy effects and a collaborative voice towards government, policy makers and these multinational companies, as is shown in the case. Therefore, the following proposition is made:
Proposition 2: The presence of large multinationals in an industry stimulates the use of OI as SMEs seek innovation partnerships to compete with large parties.
5.4 Interorganizational level insights
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a healthy home market, the need for a collaborative voice or the need for regionally innovation testing. Surprisingly, whereas Munsch (2009) insisted OI often comes with a large amount of contractual agreements to separate collaboratively created IP, this case study shows that due to a clear separation of roles in the innovation process and the presence of trust in project partners, contractual agreements and patents are seen as less important in the OI collaboration. Firms do protect IP and discuss collaborative expectations and duties, but also argue the importance of agreements is limited. Therefore, the following proposition is made:
Proposition 3: The importance of contractual agreement and IP protection for OI partners decreases when partnerships occur through a cluster in which trust is present and roles in the innovation process are clearly divided.
Furthermore, this case study confirms the important role of partner complementarity in OI collaborations as addressed by Christensen (2006), because complementarity leads to a higher willingness to share (IP sensitive) information as no direct competition is involved. However, the data also addresses the challenge of competition in OI collaborations. Munsch (2009) stated collaborating with competitors in OI projects may lead to long term implications. The findings show firms collaborating in the cluster acknowledge the risk of working together with competitors. Clusters’ experience has shown friction between competitors working together and they learned from this by keeping competitors separate when composing innovation projects. Interestingly, the interview results however also show that little competition in the cluster is beneficial as it keeps companies sharp and focussed. It can be concluded that little competition is beneficial, however too much competition leads to a negative effect such as distrust and negative project outcomes.
Proposition 4: Competition has an inverted U shape effect on the success of OI projects, as little competition is beneficial due to a sharpened focus while too much competition leads to distrust.
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Finally, Wallin and Von Koch (2010) question how conflict in the collaboration is managed and Munsch (2009) adds to this by addressing the importance of overcoming the culture-challenge in OI. This study insights show firms do overcome this culture-culture-challenge by creating a collaborative OI culture of ‘knowledge taking and sharing’ in the cluster. Interestingly, a solution for overcoming the culture challenge is providing space and having limited expectations from each other and creating a non-committal environment. As experience shows, some firms may leave innovation projects of the cluster, however in the end most firms have a healthy collaboration climate in which complementarity gets the upper hand.
5.5 Firm level insights
While West et al. (2006) address the potential challenge of need for organizational structure changes, agreement on investments and changing R&D expectation, surprisingly, in the examined case these challenges are not an issue. Firms do not experience the need to change the organizational structure for the use of OI, neither do they experience big challenges on investment agreements or changing R&D expectations. What employees working in organizations using OI do experience is the pleasance of an OI culture. However, West et al. (2006) also address the challenge of spillover management, as in OI collaborations unwanted knowledge spillovers may occur. The results do show firms acknowledge these spillovers happen, however the challenge is overcome by keeping innovation speed and maintaining markets’ innovation lead. In this way, over-spilled knowledge gets outdated quickly, reducing the risk of competitors taking advantage of the knowledge spillovers.
Proposition 5: The unwanted knowledge spillover challenge of OI use can be overcome by capturing market leadership when it comes to innovation creation and increase innovation speed, as this results in depreciation of knowledge value.
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Finally, firms do experience the challenge of external knowledge usage. This study shows this challenge is two-sided. First, when innovations are handed over from one organization or department to another, information should be transferred too to get to the next innovation phase. Results show this is a challenging exercise. Second, when innovations are transferred from one organization or business unit to another, employees that worked on the initial invention request updates of their transferred innovation as this motivates employees to create new inventions. To tackle both sides of the coin, firms should create possibilities to share this knowledge about created innovations frequently. Organizations address the importance of having these sharing possibilities, as innovations cannot be taken to the next innovation phase and as employees are otherwise getting demotivated in OI projects. Therefore, the following proposition is made:
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6. Conclusion
This study addresses the following research question: How is open innovation used in the
bioenergy industry and how do organizations cope with the OI challenges they experience? As
current OI literature provides a scattered overview of the use of OI in organizations and challenges that they experience, this study contributes to literature by addressing the use of OI in a specific industry, namely the bioenergy industry. Specific challenges experienced in this industry are addressed, adding to current OI literature. Findings can be beneficial for both future research and managers that want to use OI.
6.1 Theoretical implications
The study results show organizations in the bioenergy industry are collaborating for innovation, by creating a cluster in which OI projects are created and in which both inbound and outbound OI is used. Uniquely, every organization is contributing to specific innovation process phases, covering the whole bioenergy production chain. As a result, this study concludes that the concept of OI application can be seen as a spectrum in which firms can argue opening up to some extent, rather than the current extreme literature assumption that firms can only use OI by fully opening up their innovation process. Furthermore, this study identifies some OI challenges on different levels of analysis that organizations working in the bioenergy industry experience. Here, presence of large multinationals, importance of contractual agreements and IP protection, competition in OI partnerships, unwanted knowledge spillovers and innovation updates are addressed as challenges that need to be taken into account in order to benefit from OI practices. 6.2 Managerial implications
37 6.3 Limitations and future research
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Appendix
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A. Semi-structured interview scheme Describing the case
- Could you describe in as much detail as possible how the cluster is created (including how it started, how it elaborated, and how it is now)?
- Where there typical periods of type that could be defined?
Starting the cluster Motives and selection
- How did you decide to participate in OI? / What was the main reason to participate? - What was your main goal of participating in the cluster?
- How did you decide with how to collaborate? / what were selection criteria? - Did the cultural fit play an important role in the selection process?
- Where there any competitors in the cluster? If so, what were your thoughts about this?
Interests and agreements
- How was the distribution of IP arranged?
- How did the process of arranging distribution of profit and loss look like? - How was the decision making process in the collaboration arranged?
- What did the cluster arrange on forehand about possible conflict that may occur in the collaboration?
- Where there other important contracts that were signed before the cluster started? - Who was the cluster management arranged?
Working in the cluster (organizational challenges) Individual and group level of analysis
- Wat was het effect van de samenwerking op de medewerkers van het personeel?
- How did employees reacted to opening up the innovation system of the organization to some extend?
- How was the motivation of employees (intrinsic and extrinsic) affected by opening up the innovation system?
- How did your organization make sure that the Not Invented Here (NIH) bias was prevented / overcome?
- And how was this the case for the Not Sold Here (NSH) bias?
Firm level of analysis
- How does the cluster make sure every collaboration partner is committed to the cluster? - How does your organization make sure unwanted knowledge spillovers are prevented? - How does your organization manage their Intellectual Property in the organization? - How did your organizational structure changes when participating in the cluster?
- What are the core competences with which your organization contributes to the collaboration?
Ending (current state)
- How does participation in the cluster affected your organization?
- What are your thoughts about the current benefits of losses of participation?
- What was the most complicated barrier that your organization had to overcome during the participation in the cluster?
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Appendix B: Illustration of the coding process
1. Coding transcripts in Atlas.ti (including arching)
43
