Responsible innovators: open networks on the way to sustainability transitions

Technological Forecasting & Social Change 87 (2014) 28–40

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Technological Forecasting & Social Change

Responsible innovators: open networks on the way to sustainability transitions Marina van Geenhuizen ⁎, Qing Ye Delft University of Technology, Faculty of Technology Policy and Management, Jaffalaan 5, 2628 BX Delft, The Netherlands

a r t i c l e

i n f o

Article history: Received 30 March 2013 Received in revised form 17 March 2014 Accepted 1 June 2014 Available online xxxx Keywords: Responsible innovation Open knowledge networks University spin-off companies Socio-technical systems Sustainability transitions

a b s t r a c t This paper elucidates ways in which small high-technology companies through using open knowledge networks may contribute to sustainability transitions. The analysis focuses on young university spin-off companies as an important channel for bringing responsible innovations from university to market while it connects the micro-level with the meso-level of networks supporting socio-technical system changes. A conceptual reflection on responsible innovation, openness in knowledge networks and socio-technical systems’ transitions, is followed by an empirical research. Based on a hundred companies and four case studies, the results indicate that responsible innovation is one of the drivers of openness in knowledge networks. However, partner diversity in openness tends to have a negative effect on growth of the companies. Our preliminary evidence indicates that focus (product–market) and selectivity in the choice of partners connected to professional (venture) capital, market access, credibility and complementary assets are highly relevant when it comes to influencing change in socio-technical systems. A discussion of the implications of this study and suggestions for future research close the paper. © 2014 Elsevier Inc. All rights reserved.

1. Responsible innovation and networks There is an increasing awareness that innovative entrepreneurship can play a major role in enhancing changes towards higher levels of sustainability and respond to the world’s major challenges concerning food, water, the environment, energy and health. Innovative entrepreneurs recognize new opportunities using existing or new, disruptive, technologies and create new markets in interaction with important players in the socio-technical system [1–3]. The idea that sustainability can work as a driver of innovation and entrepreneurship is not new and embraces various types of entrepreneurship, including small ones and multinationals [4–7]. However, various studies have emphasized a higher level of complexity if innovation is motivated by sustainability goals, mainly due to a higher complexity in learning and innovation networks [8–11]. This ⁎ Corresponding author. Tel.: +31 152786729. E-mail address: [email protected] (M. van Geenhuizen).

http://dx.doi.org/10.1016/j.techfore.2014.06.001 0040-1625/© 2014 Elsevier Inc. All rights reserved.

higher complexity refers to a wider variety of subjects (knowledge domains), like regulation, customer needs, industry standards and to a wider variety of partners involved, like public policy-makers, pressure groups, testing institutes, and specialized financial investors. More recently, there has been a renewed interest in the role of entrepreneurial companies, indicated by new pathways highlighted by the World Business Council for Sustainable Development in 2010 [12]. In addition, the emphasis on gearing the innovation process towards societal needs and desirable outcomes is much stronger today than it has been in the past, as evidenced by many high-level policy and strategy EU documents, such as the EU Innovation 2020 strategy, designed to create smart growth, and the Horizon 2020 program, which defines Societal Challenges as one of the main priorities [13,14]. A concept that has increased in popularity in this context is responsible innovation, and connected with this is responsible entrepreneurship. Due to novelty of the use of this concept and the different perspectives

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involved, there is not yet a unified set of accepted definitions. In a preliminary way, responsible innovation can be described as a transparent, interactive process by which societal actors and innovators become mutually responsive with regard to the (ethical) acceptability, sustainability and societal desirability of an innovation and its marketable products, as forwarded by Von Schomberg in 2012 [15] and adopted in some European policy documents and processes [16,17]. A broader definition is provided by Owen et al. and Stilgoe et al. [18,19] including a prospective notion in terms of taking care of the future through collective stewardship of science and innovation in the present. This broader definition is translated into three sets of questions of public concern, namely, on the product, the process and the purpose, for example, how will the benefits and risks of the innovation be distributed, how should these be measured and who is in control, and is the motivation behind the innovation transparent and in the public interest [19]. While the above definition is clearly on the (public) governance level, responsible entrepreneurship has been defined on the micro-level of companies already some years ago, (early 2000s) with different emphases. For example, responsible entrepreneurship may refer to the relation with the own workforce and customers, but also to the relation with the community and the environment [20,21] and not necessarily to sustainability goals through bringing innovative products/ processes to market. The definition used in the current paper includes both sustainability aims through innovative products/ processes and the relation with the community as follows: involved in improving healthcare and making healthcare more affordable, in sustainable energy, sustainable transport, energy saving, efficient use of materials (including recycling), and improving safety. As a result of the broader context, R&D of the companies involved deals with a high degree of interaction and knowledge exchange with interest groups, customer groups, testing institutes and policy-makers. Policies supporting responsible innovation aim to avoid innovations that are contested, to focus on areas of societal needs that have been neglected so far, and to carry on developing successful innovations already recognized as responsible, like in sustainable energy [17]. The openness needed in responsible entrepreneurship can be perceived as a particular type of the model of open innovation, as has been forwarded in the general innovation literature in the past decade [22,23]. For example, understanding the needs of customers, as well as increasing legitimacy and credibility by connecting with prominent partners, tends to be vital ingredients in taking steps towards sustainability. However, research into the relation between openness, legitimacy, competitive power and progress in sustainability to date has been sparse, meaning that it is an area that deserves more attention [24–27]. Also, differentiation in open networks and underlying causes have rarely been revealed, with the exception of a few authors [28–31]. Gaining progress in sustainability or any other important societal change involving technology is usually perceived through the lens of socio-technical systems [32–35]. Sociotechnical systems are complex systems in which one or a set of technologies is dominant and changes, like following from responsible innovation, that are detrimental to the system are prevented from taking place by the impact from existing infrastructures, institutions and the vested interests, i.e., the interaction between society’s complex infrastructures and

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human behavior [32,33]. By adopting a multilevel approach to socio-technical systems, a distinction is made between three levels, namely, the niche level, where novelty is created, the regime level, as the structures of current practices and routines, and the landscape level, as the processes of long term change. Using such distinction, pathways to change (named transitions) can be conceptualized [34–36]. According to this approach, high-technology companies at the niche level cannot bring about changes in the system on their own, but they can contribute to change by acting in well-selected supportive and powerful networks (niches, platforms). Through a set of interrelated strategies in open networking and resource acquisition and through a strong market (customer) focus, entrepreneurial companies may contribute to system change, eventually by targeting early majority customers [7,26,37–39]. A related perspective is that of technological innovation systems (TIS). The key difference with the socio-technical system is that a TIS is conceived as being built around a particular new technology, like fuel cells. Accordingly, it comprises a set of actors and institutions whose (inter) actions contribute to the development and diffusion of a new technology, but does not necessarily adopt a multi-level perspective [40–43]. In the take-off phase of a TIS very few actors are involved and institutions may hinder progress of the new technology; however, after some time, the number of actors may grow, supporting networks get stronger and institutions get aligned in order to support the technology. In general, there are two main routes in bringing technology inventions from universities to the market, namely, licensing agreements with existing companies and other organizations, and the creation of spin-offs. Licensing agreements are usually seen as the least complicated and safest way for the university [44,45]. The current study has a focus on university spin-off companies, because these constitute the entrepreneurial route in bringing technology inventions to market. Spin-offs are young and flexible and may cause an entrepreneurial boost, eventually work as a ‘trigger’ and break with path-dependency in enhancing the emergence of a new technology innovation system. The study thus adopts a micro-level approach and connects the micro-level with the meso-level of networks that potentially support system changes [46]. Although spin-off companies are a channel of knowledge transfer and commercialization, generally speaking they lack resources, like investment capital, marketing skills and market knowledge, and management skills and time, etc. [47,48] and their growth in Europe is on average relatively modest [49] questioning the amount of support to them. A slow and small growth however, does not mean that the efforts of spin-off companies in bringing university knowledge to market may be in vain. Aside from the direct technology transfer or commercialization, there are also effects, particularly in the region, through the labor market and knowledge spillovers on developing regional innovative capacity and supporting economic growth [50]. Given the small growth in general and higher complexity faced by spin-offs specifically dealing with responsible innovation, we assume that such spin-offs need to be careful in the selection of network partners, a phenomenon already addressed for small companies in general [51,52]. To our experience, no study to date has investigated responsible innovation and openness in networking linked to system changes among university spin-off companies. The current paper contributes

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to the existing literature by connecting responsible innovation to open knowledge relationships at a micro-level, looking at potential impacts on the system level. We draw on a sample of 105 spin-off companies and four in-depth case studies from two technical universities, in Norway and the Netherlands, and address the following questions: (1) In which ways are spin-off companies involved in responsible innovation, and what is the relationship to open knowledge networks? What is the influence of responsible innovation and open knowledge networks on company growth? (2) Under which network conditions can responsible innovators create progress on the way to higher sustainability? The paper proceeds with theoretical background (Section 3), followed by the methodology (Section 4) and results of the empirical study (Section 5). In the empirical study, first, a descriptive analysis is provided on the involvement of spin-off companies in responsible innovation and the degree in which the involved ones employ open knowledge relationships. This is followed by a model estimation of open knowledge relationships to identify to what extent responsible innovation acts as a driving factor of openness. Next, attention focuses on growth of the spin-offs, including an estimation of the influence of responsible innovation and openness in knowledge networks. The subsequent case study analysis provides deeper insights into the role of openness in knowledge networks, alongside other factors important in gaining progress in the way to sustainability transition. The paper closes with implications of the results and suggestions for future study (Section 5). 2. Theoretical background 2.1. Open innovation and open knowledge relations Open innovation has changed the landscape of innovation since the early 2000s. Learning in innovation has become an interactive process involving a wide range of organizations, like suppliers, customers, competitors, and universities [22,23,53–55]. Open innovation can be defined as the use of purposive inflows and outflows of knowledge to accelerate internal innovation and expand markets for the external use of innovation, respectively. Three core processes are involved in open innovation [22,55]. First, there is an outside-in model, which means that innovation within a company benefits from external inputs (inbound), for instance advice from university technicians to spin-off companies in facility sharing at the university. Secondly, there is an inside-out process, which refers to the marketing of ideas, selling intellectual property (IP) and enriching technology by transferring them to the outside environment. Companies that have adopted this practice focus on externalizing their knowledge and innovation to bring ideas to market in a faster way than they could have done through internal learning and development. The third process is the ‘coupled’ process, which refers to collaborative research and development, in which ‘give and take’ are a basic condition for success in combining the outside-in process with the inside-out process. Specific cases are co-creation with a launching customer, which happens more often among small university spin-off

companies, and co-creation with civic groups (inhabitants) in design projects, such as in sustainable housing. Open innovation in itself is not a new phenomenon. Many companies were already engaged in open innovation before the early 2000s, for instance through outsourcing and research collaboration with lead customers, but the urgency to practice open innovation in a conscious and systematic way has increased over the past decade, due to the increased speed of technology development and an increasing global competition [56,57]. That the role of open innovation varies, like across sectors and technologies, is increasingly understood given the different degrees of within company learning and learning with other companies/organizations, and given the different emphasis on science-based learning and problem-based learning. Also, a different importance of protection of intellectual ownership between sectors may play a role in relevance of open learning [30,58,59]. In the current paper, we focus on openness in knowledge relationships which is a precondition in open innovation; other aspects of open innovation like the nature of the collaboration and joint activities fall beyond the scope of our quantitative analysis, but are included in the case study analysis. In recent empirical studies, a distinction is made between two dimensions of openness in knowledge relations, i.e., the size of the external knowledge pool, called capacity, and the diversity in this knowledge pool [31,53]. Capacity in openness can be seen as being composed of different types of knowledge (domains) and tie strength with the partners involved. Openness diversity deals with heterogeneity among partners in terms of the types of organizations involved, like universities, a large company as launching customer, a small company, public authorities, etc. and the regions of their location. These two dimensions are taken into account in the empirical analysis. A common theoretical approach to company growth is the resource-based view [60,61], in which a company’s ‘difficult to imitate resources’ are seen as determining its competitive advantage. In this vein, open knowledge relations are established when the partner(s) provide(s) knowledge complementarities, which in the case of spin-offs, often refer to support programs (in niches), low cost market entry, (technological) capability building, access to investment capital, etc. At the same time, the spin-offs can only establish such relations if they own sufficient resources by themselves to ‘afford’ open relationships, like the time, absorptive capacity and skills to search and identify the best partners and to manage the collaboration and benefit from it [62]. There is not much understanding of what exactly drives openness in knowledge relations at the micro-level of companies. The few existing studies point to strategy-related factors, like offensive strategies (‘first movers’), the nature of research and development and connected ways of learning [29,30,58] and external factors, like uncertainty due to quickly developing technology or delaying impacts from regulation [23], or a combination of factors internal to the company and external factors [30,31]. Openness may also increase with the increasing learning abilities as the company becomes older and bigger [24,30,60–62], but the increase may slow down due to decreasing returns caused by stiff routines and eventually locked-in situations grown at the same time, pointing to inverted U-shaped relations [53,62–65]. What may also happen after some years of existence is that knowledge previously

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sourced outside the company becomes ‘internalized’ by improving expertise of the management team, causing a decrease in openness. Factors in the urban environment with regard to knowledge density and knowledge spill-overs, and other agglomeration advantages may also play a role in the need for adopting openness in knowledge relationships, though with different underlying assumptions [66–68]. 2.2. Socio-technical systems and transitions We adopt a socio-technical system including the technology innovation system perspective [33,40] in examining the specificities of the technology innovation and the networks, eventually niches, that enable taking steps towards higher levels of sustainability. Technology in itself does nothing, and there are many examples where the adoption of new technology has been prevented by social factors, for example, vested interests in the old technology and its infrastructure, like of gasoline producers and distributors in the case of electric cars and routines in reimbursement among insurance companies in healthcare. The entire socio-technical system – including institutions – should be aligned and coordinated to bring about a transition [32–36]. Transitions towards higher levels of sustainability inhibit three characteristics which prevent them to be achieved in a quick manner [69–71]. Firstly, sustainability transitions are goal-oriented, different from some other, emergent, transitions in history. Private actors have limited incentives to address sustainability transitions because the goal is related to a collective good (higher sustainability), implying behavior characterized among others by free riding. Instead, public authorities and civil society are seen as crucial to address collective goods, to change economic frame conditions, and to support ‘green’ niches [39]. Because sustainability is an ambiguous concept, there are also different opinions about the preferred direction(s) of sustainability transitions, the (dis)advantages of particular solutions, as well as the most appropriate policy instruments. Secondly, sustainability solutions often do not offer obvious user benefits to individuals or individual organizations; by contrast, they mostly score lower on price-performance compared to conventional solutions. This situation makes it unlikely that sustainability innovations will be quickly adopted, without changes in economic context conditions, like regulatory frameworks, taxes, subsidies, etc. Such changes will therefore not be brought about by policy measures without a fierce ‘struggle’ between vested interests and proponents of the new solution. As a third point, conventional markets in the domains involved in sustainability, like energy, transport, and healthcare are often dominated by large incumbent firms, like electric utilities, gasoline manufacturing, car industry, and pharmaceutical industry. These companies not only own large market shares, they also benefit from their complementary assets, like large scale test facilities, access to distribution channels, supporting service networks, and complementary technologies, in fact all supporting their vested interests. Aside from these circumstances, transitions are often rather unpredictable due to manifold complexity and uncertainty [72–75]. For instance, cause and effect relations are not always clear, unexpected dynamics may occur, and public policy measures may lead to absence of effects or adverse effects, etc. In these ‘tough arenas’, young high-tech companies proposing a new sustainability solution may only play a key role if these

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‘trigger’ existing path-dependencies, like in business models, offer a strongly improved price-performance of their solution and connect with the right incumbent firms and their network of complementary assets [4,7,25,26,37,76,77]. Though much is still unclear from empirical research, young high-tech companies also need to link themselves to niche-like networks supported by local policy and civic society, and aimed at providing a certain protection against competition from the conventional technology, including opportunities for experimentation and interactive learning, such that at one point in time they are sufficiently strong – including credibility – to survive in the conventional system or system that has slightly changed [36,39]. Overall, it seems that as a minimum the networks need to provide access to financial capital and to a large customer (both providing credibility) or to market (channels) and other complementary assets, and enable the spin-offs to perform under niche-like circumstances. Important milestones in such development could be a successful pilot project, the move from pilot production to series production, and some early mass production, if mass markets are involved. It seems that a selective building of networks, including the most beneficial partners, for example, providing access to complementary assets, is crucial given the small resources that young high-tech companies own. Despite similarity, the technological innovation systems involved may vary with regard to degree of rigidness, complexity, uncertainty, internal differences, etc. For example, the health sector tends to be more complex than the energy sector due to a high level of fragmentation derived from the diversity in therapeutic areas and different compositions of large companies active in these areas, and derived from the influence of additional stakeholders, like insurance companies and national approval institutes [26,78]. The traffic technology system with regard to electric vehicles seems also quite fragmented by including stakeholders like automobile manufacturers, battery manufacturers, electricity producing companies, charging equipment producers, car consumers, and public authorities dealing with infrastructure [79]. 3. Methodology The research design of this study is exploratory in nature, mainly using a quantitative approach in investigating the involvement of 105 university spin-off companies in responsible innovation and the importance of open knowledge networks in their growth and progress towards sustainability. In addition, four case-studies are investigated to explore through which open innovation networks they contribute to progress in socio-technical system changes. We draw on data from two technical universities in Europe, Delft University of Technology (Delft, the Netherlands) and the Norwegian University of Science and Technology (NTNU) (Trondheim, Norway). No differences are assumed in the national general innovation systems between the two countries, as they share a fairly risk-avoiding entrepreneurship culture, show similar scores on the main European Innovation Scoreboard indicators [80,81] and have relatively small domestic markets. However, in two particular technology innovation systems, there is a difference between the countries, namely, concerning wind energy, which has been promoted more strongly by national policy in Norway, and sustainable vehicle technology, which has been promoted more strongly in the Netherlands.

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The population of companies satisfied important conditions: they were involved in knowledge created at the university, had an age between 1.5 and 10 years in 2006/7, and enjoyed some support from the incubation organization/university (150 in total). The overall response rate in 2006/7 was 70% and data were collected using a semi-structured questionnaire in faceto-face interviews with principal managers, focusing on the following themes: firm characteristics such as firm size, founding team size and pre-start experience; strategic choice including type of innovation activity (sustainability aim, dominant learning mode) and product/market (focus); and profile and openness of the knowledge networks. In 2012, data were collected from the same companies mainly regarding growth in employment and turnover using an e-mail questionnaire and website study. In addition, the four case studies (2012) included a more detailed analysis of strategic choice, main investors (prominence), other network partners and niche-like conditions, and stage in the process of market introduction (e.g. pilot, small series), all gained to assess the potential contribution of the company and its network to progress in system change. The selection of the four case studies was based on the following criteria: different growth, diverse openness in the networks and different types of technological innovation systems, the last in terms of national support and fragmentation (Netherlands and Norway; energy and healthcare).

blades and turbines, as well as energy saving in cooling systems. The explanation of the above composition of responsible innovators may be as follows. The composition reflects the typical specializations at the two technical universities involved, which are in energy, maritime activities, transport and the medical sector, and it reflects the larger opportunities for small companies to be innovative in the medical sector and sustainable energy, compared to for example, industrial process technology and waste treatment/recycling where inventions are more often brought to market by large civil engineering companies. Also the first sectors encompass many different segments increasing the chance for spin-off entrepreneurship, thus, the medical area encompasses diagnostics, medicines, instruments, and health care, thereby covering many different therapeutic areas. Sustainable energy also encompasses many different segments, such as energy production including solar, wind, hydrogen, biomass and hydro, and energy storage (batteries) and distribution. In a next step of identifying responsible innovators, we select only those ones involved in responsible products, processes etc. at a relatively high level of innovativeness (Appendix A). Accordingly, depending on whether using a narrow definition or a broader definition, we can qualify 27% and 33% of all sampled spin-off companies as ‘highly innovative responsible innovators’. 4.2. Openness in knowledge networks

4. Results 4.1. Involvement in responsible innovation A distinction is made between a full involvement and a partial involvement in responsible innovation. The former means that all activities of the company are focused on producing a responsible innovation product or service, while the latter means that only part of the activity can be labeled as such. It is important to note that, in this study, the processes that have led to responsible innovation are not examined, except for the underlying openness in knowledge networks. This means that we cannot identify the values and nature of the processes in interaction with society and economic actors, but we can identify and characterize the nature of the networks, the type of partners involved and concerns related to impacts of the innovation, the last in the case studies only. A small majority of the sampled companies (56%) is engaged in responsible innovation, either fully or partially (Table 1). Conversely, some 40% of the companies are not dealing with responsible innovation in terms of bringing a sustainability product/process to market. Full involvement mostly refers to the medical sector and sustainable energy, both at 19% of all involved companies, with the last sector also partially involvement (at a level of 10%). Sustainable mobility (including vehicle technology) follows with 12% full involvement and 12% partial involvement. Overall, new technology in medical care and sustainable energy and mobility tends to be the most important, at a level of 40% of the full sample of spin-off companies. The medical sector includes new medicines, instruments for minimal invasive surgery, ergonomic furniture and practical help in daily care of the elderly using sensor technology. Sustainable energy refers to new types of solar cells, improved batteries, improved windmills in terms of

We explore to what extent responsible innovators have adopted open knowledge networks using two dimensions, i.e. openness capacity and openness diversity [31,53] (see Appendix 1). The first dimension, openness capacity, is measured using breadth and depth. Breadth is the number of different types of knowledge (domains) and depth is tie strength between the company and partners involved, and these constitute the knowledge pool accessed by the spin-off. The mathematical modelling used is unique in the sense that it assigns weights to three strength variables, namely, frequency of contact, duration of the relation and emotional intensity, using entropy-weight method, which measures the effective amount of information of the data and better reflects reality than other measures [31]. The second dimension, openness diversity, describes the heterogeneity in the social background of partners, including spatial orientation (local versus regional). A distinction is made between partners from large companies and from small ones, government representatives, university professors, (lead) customers, family and friends, financial investors, etc. We may assume that responsible innovation requires relatively high levels of openness in knowledge relationships. Comparing the openness capacity and openness diversity of responsible innovators with other spin-off companies shows that the first are indeed more engaged in open relations, but only as far as capacity is concerned (Table 2). The results are robust because using both the narrow and broad definition of responsible innovators yields statistically significant results. Responsible innovators have an average score of 5.5, compared to 4.4 among the other spin-offs. This pattern indicates that the knowledge accessed differs in number of domains and intensity of partner contact per knowledge domain. The similarity in the openness diversity scores, however, indicates that there is no need or possibility to be engaged with knowledge partners

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Table 1 Application area of responsible innovation products/services. Application area

Full involvement

Medical care and cure Sustainable energy b) Sustainable mobility, vehicle technology Efficient industrial processes Efficient waste treatment (recycling) Sustainable buildings and safety Totals spin-offs a) b)

Partial involvement a)

Abs.

%

11 11 7 1 6 4 40

18.6 18.6 11.9 1.7 10.2 6.8

All spin-offs a)

Abs.

%

0 6 7 5 0 1 19

– 10.2 11.9 8.5 – 1.7

Abs.

%

11 17 14 6 6 5 105

10.5 16.2 13.4 5.8 5.8 4.8 100

Percentage share of all companies involved in responsible innovation (N = 59). Including energy efficiency.

from many different socio-economic circles among responsible innovators compared to other spin-offs, a situation that may be attributed to limited (managerial) capacity and need for a careful selection of a few beneficial partners. In a next step, we explore whether being involved with responsible innovation is a driver of open knowledge relations, in terms of openness capacity and openness diversity. We use multiple regression analysis in which we start with a model including all relevant factors, divided into three blocks: firstly, enabling factors like size (company and starting team) and experience, secondly, strategic choice factors, including among others responsible innovation, and thirdly, one control variable for the region emphasizing a different presence of knowledge sources and knowledge spillovers. Table 3 shows the end-results of the stepwise reduction procedure, namely, the best model structure in terms of largest R2 and smallest number of factors. With R2 at 0.52, the openness diversity model is a quite strong model. The results indicate that involvement in responsible innovation plays a clear role among various other factors. Interestingly, while many factors are not significant for both dimensions of openness (capacity and diversity) or – if significant – show different directions of influence, involvement in responsible innovation is significant to both dimensions with a positive influence. Responsible innovation tends to drive openness in both dimensions of capacity and diversity, aside from other strategic choices, namely, concerning the nature of innovation activity (science-based innovation for the dimension of openness diversity) and the type of market (strong competition for the dimension of openness capacity). The result that responsible innovation is the only consistent and positive influence on openness capacity and openness diversity may Table 2 Scores on openness capacity and diversity.

Openness capacity

Openness diversity

a)

average s.d. min–max t-test average s.d. min-max t-test

Responsible Innovators (N = 28)

Other companies (N = 77)

5.5 2.7 1.5–11.7 −1.99** 0.8 0.2 0–0.9 −1.09

4.4 2.4 1.1–12.4 0.8 0.1 0–0.9

*p b 0.1; **p b 0.05 a) Using the somewhat broader definition of responsible innovators (N = 35) produces similar results: average scores of 5.2 versus 4.4 and a T test result for openness capacity (−1.52) that is significant (p b 0.1).

suggest that responsible innovation is a solid and multidimensional driver of openness in knowledge gaining and exchange. Note that company size also tends to positively influence openness diversity (not openness capacity), pointing to relevance of the previously indicated management capacity concerning partners from different socio-economic circles which tends to increase with size [30,52]. 4.3. Growth trends Next, we address the question to what extent beinginvolved in responsible innovation influences the growth of spin-off companies. The literature does not provide clear indications on whether responsible innovators show different growth trends compared to other companies. Based on the idea that more radical innovations are involved in solving sustainability problems, one may expect a slower growth due to resistance from traditional solutions or existing technologies and institutions. On the other hand, one may also assume a stronger growth if the solutions are developed in interaction with (future) users and other stakeholders, and are protected and supported in a kind of niche environment [36,39]. With regard to job growth in the past five years (2006–2011) responsible innovators tend to perform better in the category strong growth, as witnessed by a share of 36% among responsible innovators (narrow definition) compared to 18% among other spin-offs (Appendix C). The last trend also holds for turnover growth, as witnessed by a share of 32% among responsible innovators versus 11% among other spin-offs. Overall, the trends are somewhat ambiguous, witness the class of negative/no growth for job growth, which indicates a relatively large share among responsible innovators, namely, 43% versus 33%, reason why in our next step of the analysis we explore a growth model (Table 4). This simplified model includes both responsible innovation and openness, and also moderating effects between them, as well as two control variables, namely, age of the spin-off and growth strategy set at early age. The narrow definition of responsible innovation is used as it leads to the best outcomes. The results can be summarized as follows. Firstly, the model outcomes show no significant influences of openness on growth, except for a weak trend of negative influence of openness diversity on turnover growth. This trend may conform to our previous finding of absence of a difference in degree of openness diversity between the two subsamples, responsible innovators versus other spin-offs. Secondly, the interesting results are the moderating effects between responsible innovation and openness, indicating an opposite trend for openness capacity

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Table 3 Stepwise regression analysis of openness: optimal model structure

a)

.

Openness capacity (knowledge pool)

Openness diversity (knowledge partners)

(s.e.)

(s.e.)

Enabling factors Company size Size of founding team Pre-start experience (breadth) Pre-start experience (depth)

−0.28 (0.11)** 0.81 (0.25)*** – −0.05 (0.02)**

0.91 (0.10)*** −0.30 (0.21) 0.19 (0.08)** –

Strategic choice Innovation activity (science-based = 1) Market (pressure from competition = 1) Responsible innovation (involved = 1)

– 0.57 (0.18)*** 0.52 (0.21)**

0.42 (0.18)** – 0.39 (0.18)**

Control factor Urban/regional environment (peripheral = 1) N b) F R2 Root MSE

– 102 6.44*** 0.25 0.84

0.43 (0.16)*** 102 16.96*** 0.52 0.72

* p b 0.1; ** p b 0.05; *** p b 0.01 a) Common diagnostics have been performed: linear regression diagnostics, addressing e.g. data causing bias, test for normality of residuals and for heteroscedasticity of residuals, as well as multicollinearity and endogeneity tests, all indicating absence of serious impacts on the results. b) Three outliers have been removed (different cases for the two models).

(positive for turnover) compared to openness diversity (negative for jobs). Accordingly, open knowledge relations tend to play a role in the influence of responsible innovation on growth, with a large capacity (knowledge pool) stimulating growth and a large diversity (partners) hampering growth. Apparently, diversity in partners follows an inverted U-shaped pattern in which increase in partner diversity after an early point tends to be difficult to handle due to limited managerial capacity and vulnerability in the uncertain start of a new technological innovation system. This situation suggests the need for a careful selection of the most appropriate or beneficial partners and not to exceed a certain number of them, in other words, a 'selective openness' in

Table 4 A simplified ordinal regression analysis of growth in 2006–2011.

Openness Capacity Openness Diversity Responsible innovation (narrowly defined =1) Moderating effects Responsible Innovation × Openness Capacity Responsible Innovation × Openness Diversity Control variables Firm age a) Growth strategy N LR chi2 Log likelihood Pseudo R2

Employment

Turnover

(s.e.) −0.27 (0.18) 0.27 (0.19) 0.33 (0.46)

(s.e.) 0.20 (0.19) −0.38 (0.20)* 0.29 (0.48)

0.25 (0.41)

0.78 (0.45)*

−0.95 (0.46)**

−0.55 (0.46)

−0.26 (0.25) 0.52 (0.20)** 105 17.38** −130.30 0.06

−0.56 (0.25)** 0.32 (0.20) 104 17.70** −127.56 0.06

* p b 0.1; ** p b 0.05 a) The negative sign indicates that mainly older spin-offs with already considerable turnover were affected by the economic crisis that began in 2008.

terms of diversity [52]. On the other hand, we must also mention that the period of growth taken into account in the study, 2006 to 2011, covers the first years of the economic crises since 2008, in which markets were shrinking and investors became increasingly reluctant, reinforcing uncertainty [82]. 4.4. A microscopic view on networks and system changes The four case studies qualify as responsible innovators, have adopted open knowledge networks and have shown a different employment growth in the past five years (Table 5). All four case studies are relatively young, established between 2003 and 2005, and deal with technology that is protected by patents. Our next analysis focuses on the strategic choices in product-markets (focus or diversified), the types of partners in early and later knowledge networks as well as the main aim of the later networks, niche-like conditions, progress made with regard to testing and pilot studies, and a guess on the potential impact on a higher sustainability. But first we reflect on the responsible innovation. Case study A is involved in wind energy by elaborating a generic technology named boundary layer suction and applying it to the optimal shaping of rotor blades in order to increase energy productivity. This company – for the time it is still active in wind energy – is recently also involved in re-shaping the rotor blades aimed at a reduction of noise annoyance of turning blades. Accordingly, one of the objections against wind energy, particularly on land among inhabitants of living areas, may lose ground on the basis of their solution. Case study B is also involved in wind energy, by focusing on increasing energy productivity, that is by applying a gearless drivetrain in larger turbines on sea and solving problems of stability of the turbine body. Case study C has developed a charger (hardware and software) for electric vehicles that shortens the charging time significantly without damaging the battery, and accordingly has increased the user value of electric vehicles. The main parties

M. van Geenhuizen, Q. Ye / Technological Forecasting & Social Change 87 (2014) 28–40

that already benefit from the improvement are transport nodes (airports, seaports). In addition, some municipalities in the Netherlands intend to improve air quality in their area through emission reduction and perceive electric vehicles as a solution. Furthermore, electric vehicles produce less noise compared to conventional vehicles and electricity may be cheaper during night, reason why some companies/municipalities consider to make distribution trips (like of beer to pubs and express delivery services of mail/parcels) and collect waste in the city during the night, thereby relieving the traffic burden during the day. And finally, Case study D – active in a small segment of healthcare, namely, eye care – contributes to getting eye care more accurate and cheaper through laser-based retinal imaging, which is also more comfortable for patients. Through the mobile character of the equipment and ease of connecting to laptops, the innovation is a good solution for developing countries in mobile clinics. The company particularly challenges a better diagnosis of retinal damage from diabetes, an illness which is worldwide increasing, and preventing blindness. Case study A started to reduce its activity in wind energy a few years ago, due to the decrease in national support, and diversified with a broader application of its technology in other products and sectors, like construction of buildings and food

35

processing industry, while remaining active in optimal shaping of vehicles. This situation of a relatively weaker focus tends to exclude a main contribution of the spin-off to change in the wind energy technology system. Aside from reduction of national government support to wind energy, the company is also faced with small domestic players in the Netherlands’ wind turbine market. The large diversity in the network originates from the different sectors of application involved, namely, companies as (launching) customer. Case study B illustrates a consistent focus on wind energy while the company benefits from various national support in Norway and actively connects with main players in Denmark (through a subsidiary there). In elaborating a gearless drivetrain and a solution for greater stability of the main turbine body, the company in the past years attracted national government investment and venture capital from consortia, including support by two national test customers, among others Statoil Hydro, and accordingly benefited from niche-like conditions. However, this case study also illustrates a path in which a 2nd generation of the new technology (gearless drivetrain) has to be developed, causing some delay. Job growth of the company is clearly larger compared to the previous case study.

Table 5 Case studies of responsible innovators. Spin-off

a)

A. Delft (2005) 1.3 fte

New technology/product/service

Key features of the spin-off (main partners/investors)

Guess on impact on system

Flow dynamics (boundary layer suction) and various improved applications

-Early network: wind turbine (rotor blade) manufacturer, car manufacturers, university, small public venture fund, and consultant -Added other sectors, like process industry, construction and consultancy, due to decrease of national support for wind energy Increased involvement in different sectors and networks

-Introduced in various sectors

Gearless drivetrain for wind turbines (highly efficient hydraulic transmission) and stabilization solution

-Early network involved university, large company and governmental body ENOVA as customer -Later investment in venture capital consortia, e.g. including national actors (e.g. Statoil Hydro) also serving as test customer. -2nd site to access main players in cluster in Denmark Maintained focus and gained credibility through collaboration/investment by prominent partners

-Main pilot (5 MW) in 2010 -since 2012: developing 2nd generation technology

-Early network (since 2008) involved university, small company, car manufacturer as customer, energy company and battery manufacturer -2nd site to access main players in North-Brabant (NL) -Later network also involved public authorities (pilots) and various national support in research -International venture capital from the sector (2008 and 2010) -Joined MNC (2012) Developed focus in application and network, and gained great credibility by international collaboration and venture capital

-Pilots within company area (transport nodes); main pilot of fast charging public stations in 2010 followed by series production

-Early network involved university, large firm, family and friends (overlap with professional network) -Later on: international and national venture capital (partly from the sector) and a market-oriented company Maintained focus in application and network, and gained credibility through (inter)national venture capital and complementary assets through a market-focused partner

-Approval by US Food and Drugs Administration (2011) -First market introduction (2011) followed by global ‘roll-out’ Medical system (eye care): (very) large

Networks and impact B. Trondheim (2005) 3.8 fte

Networks and impact

C. Delft (2005) 9.0 fte

Fast chargers of electrical vehicles (without battery damage), hardware and software

Networks and impact

D. Delft (2003) 7.5 fte

Networks and impact

Mobile and smart retinal imaging instrument (laser ophthalmoscope technology)

Source: in-depth interviews 2006, mail interview 2011, website analysis 2012, newspaper coverage 2012/13 a) Year of establishment and average annual growth in 2006–2011.

Wind energy system: modest

Wind energy system: large

Traffic system: (very) large

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Case study C seems most successful and combines a quick growth, namely, 9 full time equivalents per year in 2006–2011, with an increased technology focus and focused network. The networks after 2008 covered various pilots of fast charging stations, dealing with five different knowledge partners: car manufacturer, electricity company, battery manufacturer, public authority (for pilot testing), and a multinational energy/ electrical installation company. At the same time, the company benefited from national subsidies in research and (more indirectly) favorable taxation on electric vehicles, and from municipalities eager to reduce emission levels in their area. The company also benefited from two rounds of venture capital funding from Canada, by a ‘respected’ partner in the sector, thus increasing its international credibility. In 2012, the company – with a workforce of 80 people – joined its former multinational collaborator, for reasons of accelerating the process of scalingup the production and achieving complementary assets, mainly market access. The multinational perceives itself as world leader in charging equipment due to this acquisition [83]. The fourth case study illustrates a consistent focus on a relatively small part of the medical sector, i.e. eye-care and within that segment on a specific diagnostic field. However, what caused some delay is that market introduction usually has to wait for approval by the US Food and Drugs Administration (FDA). In addition, large-scale market introduction in the near future in developed countries has to wait until the conventional equipment can be replaced in eye-hospitals and eye-doctor practices according to existing replacement regimes. The company was successful in a first market introduction in 2011, right after the FDA approval, and its subsequent success can be attributed to its focus and later networks including national and international venture capital (Switzerland) from the medical sector improving credibility, and collaboration with a market-focused company, providing access to complementary assets concerning market access. The most recent investment allows the company to accelerate the global roll-out of its innovations.

means of opening a second site and/or a subsidiary. This illustrates gaining assets in other regions, like subsidy, regional venture capital and access to localized knowledge networks in automotive (Province of North Brabant, the Netherlands) and a variety of assets in Central Denmark for high-tech wind turbines, like new technology and access to various segments of the value chain. Although the companies in all four case studies are active in responsible innovation, only two of them (C and D) can be considered as bringing about changes in the system context in a relatively short time, i.e. seven/eight years after establishment of the company. Considering the previous findings, we may conclude that market introduction and progress on pathways to sustainability are influenced by specific factors in openness in networks, related to the issue of capacity and diversity, including:

4.5. Practical insights from comparison

The first condition is beyond the direct power of spin-off companies, but the second to the fourth, along with the need for selectivity in the choice of partners are within the scope of spin-offs’ decisions and may serve as broad guidelines.

Overall, we may note differences between the early knowledge networks and the later networks, particularly the university is involved in the first, while professional venture capital is involved in the last networks (except for case study A). With regard to the number of different partners in the later networks, we may think of five at maximum for already grown companies, as illustrated by case study C, and three at maximum for smaller ones, including a (launching) customer or market-focused partner providing complementary assets, a venture capitalist or other investor from the sector, and a public authority which co-finances research and pilot testing or creates any other niche-like condition. However, the minimum number of network partners needed depends on the complexity in the sector, and in addition on whether a particular partner can serve as a bridge (or platform) towards other partners, allowing the spinoff to be connected without managing the relationships, or can take two essential roles simultaneously, namely, a main investor and testing/launching customer, as illustrated by case study B. Remarkably, two companies (A and B) accessed other regional technology clusters than the one of their location, by

- Public support on a national or municipal level: This includes a range of measures such as subsidies for research programs, favorable taxation for consumers, financial investment, and acting as a pilot initiator or launching customer. In the absence of such niche-like conditions, spin-offs face a strong uncertainty and tend to diversify. - Professional (venture) capital: This is necessary to finance a refining of the invention or a market introduction; it also provides credibility, particularly if the investment is international and from the sector. - Access to the market: This may work through linking with a launching customer or a market-oriented company; credibility of the spin-off increases if multinational or respected national players are involved. - Complementary assets through the network: These assets include key services, complementary technology and specific market channels, and can be gained through an alliance with a large company, a market-focused company, or an additional site of the spin-off in a leading technology cluster where large parts of the value chain are present.

5. Discussion and conclusion This paper has connected responsible innovation with open knowledge networks and has extended the issue by including potential progress in socio-technical change among a specific type of high-technology companies, university spin-offs. As one of the first, this paper has adopted a micro-level approach linked to the meso-level in the context of socio-technical systems. The main distinctive characteristic of spin-off companies is their lack of resources, including financial capital, time and management experience, but they are also able to break with path-dependency that constitutes a limit among large incumbent firms. A sample of 105 companies was used, in addition to four in-depth case studies. The study showed that almost 60% of the sampled companies engage in responsible innovation, with medical care, sustainable energy and sustainable mobility as the largest sectors, and that some 40% of the companies are not involved in responsible innovation.

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The emphasis in this study has been on open knowledge networking as an important process characteristic of responsible innovation. The results of the quantitative analysis indicated that responsible innovation drives openness with regard to capacity (knowledge domains) and diversity (knowledge partners), among other factors. However, results on the growth of the spin-offs pointed to a negative influence of openness diversity, calling for a 'selective openness' regarding types of network partners. This finding was supported by case study evidence from which it would appear that for larger spin-offs a set of five different partners and for smaller ones a set of three different partners is the maximum they can manage. However, when one of the partners acts as an intermediary or platform, or can adopt two different roles at the same time, the situation is different and larger benefits can be gained. Connected with this, networks need to provide access to professional (venture) capital and market channels, gains in credibility and access to complementary assets. At the same time, it was also found that the potential of spin-offs to contribute to system changes depends on the systems themselves, including fragmentation and complexity (e.g. regulation in testing), but also the amount of national or other policy support in creating protective nichelike circumstances. Despite the interesting results, this study does have some limitations, the first of which has to do with the relatively limited definition of responsible innovation as a qualification based on the sustainability goal of the product/services being brought to market. A large part of the role of values in the interaction with society was left outside the analysis, and as a result, for example, those responsible innovators that have withdrawn from involvement in a contested technology and the role of civic groups might have been overlooked. The current paper thus gives various unique but partial insights into responsible innovation among university spin-off companies. The second limitation refers to the countries from where the sample was drawn, i.e. the Netherlands and Norway. The results can be generalized on the basis of similarity with a limited number of regional economies encompassing strong maritime and energy clusters and medical clusters, like in Sweden, Denmark and parts of the northern UK (e.g. Scotland). These countries cover a rather small section of the European Union, and together with the assumption that the interpretation of responsible innovation may be culturally defined and context-dependent, this calls for extending the research on other parts of Europe. Future research could first of all focus on a more comprehensive construction of the responsible innovation variable connected with values being negotiated with societal actors, including more attention for value-sensitive design and customer participation [16,17,84]. Secondly, ways need to be found to increase the involvement of university spin-offs in responsible innovation, even though their lack of resources places limits on the possible number of different partners and complexity in open networks. Our preliminary evidence indicated that focus (product-market) and selectivity in partner diversity connected with credibility and complementary assets are highly relevant. These findings need to be tested rigorously, which leads to a third area of future research, moving away from the company to the network and niche, and moving away from a static to a dynamic perspective [85–87]. Accordingly, in longitudinal research, networks need to be characterized in terms of benefits and, at the end, an increased competitiveness and outlook on

37

system changes. Regarding longitudinal research, there are sufficient indications that knowledge networks change over time, given the different development stages of spin-offs [48,88]. More solid indicators need to be designed and tested such that they reflect critical events and changes in these networks in relation to transition, while at the same time accounting for differences between the technological innovation systems concerned. Appendix A. Responsible innovation and level of innovativeness.

Involvement and level of innovativeness (category 1–7)

Abs.

%

1. Full involvement and highly innovative 2. Partial involvement and highly innovative 3. Full involvement and medium innovative 4. Partial involvement and medium innovative 5. Full/partial involved and low innovative 6. Not involved and highly innovative 7. Not involved and medium/low innovative Totals (full sample)

28 7 8 5 11 14 32 105

26.7 6.7 7.6 4.8 10.5 13.3 30.5

We define highly innovative ‘responsible innovators’ as category 1 (28 companies) or as category 1 plus category 2 (35 companies). Appendix B The value of openness capacity was calculated as: Cap ¼

Xn i¼1

ðBi  Di Þ

ðA:1Þ

where n is the number of knowledge domains, like market, technology, etc. The breadth Bi is the counted number of partners within a type of knowledge. There are Bi partners within the knowledge domain i; each has a “depth” as dj(j = 1 … N), which is a composite variable derived from frequency of interaction (r), duration of relationship (u), and closeness of the relationship (c, M-rank categorical variable) calculated as: 8 rj ¼ r  l > < ¼ 1Þ u j Inðu þ c > : cj ¼ M

ðA:2Þ

where rj, uj and cj are the frequency of interaction, duration of relationship and closeness of the relationship for the partner j. r × l can be seen as “frequency–distance product”. Next, a weighting method is used derived from thermodynamic theories. Entropy is a measure of the degree of disorder, uncertainty, or randomness of a probabilistic system, while information entropy can also measure the effective amount of information of the data. If there are m criterions and n objects which need to be evaluated, the entropy of the ith criterion is defined as Hi:   Xn H i ¼ −k f In f ij j¼1 ij

ði ¼ 1; 2; …; mÞ

ðA:3Þ

38

M. van Geenhuizen, Q. Ye / Technological Forecasting & Social Change 87 (2014) 28–40 r

Where f ij ¼

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  Xm wi ¼ ð1−Hi Þ= m− i¼1 H i

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ðA:4Þ

The entropy weights for the three indicators of tie strength can now be calculated, as wu = 0.30, wr = 0.38, wc = 0.32 And the formula for the tie strength is as follows: 





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Appendix C. Job growth and turnover growth (2006–2011). Job growth

Responsible innovators a)

Other spin-offs

Turnover growth

Responsible innovators b)

Other spin-offs

Ceased to exist Negative/no growth Low/medium growth Strong growth Total Chi2 = 7.24, p = 0.07

2 (7.1%) 12 (42.9%) 4 (14.3%) 10 (35.7%) 28 (100)

11 (14.3%) 25 (32.5%) 27 (35.1%) 14 (18.2%) 77 (100)

Ceased to exist/negative growth No growth Low/medium growth Strong growth

6 (21.4%) 9 (32.1%) 4 (14.3%) 9 (32.1%) 28 (100)

16 (21.1%) 35 (46.1%) 17 (22.4%) 8 (10.5%) 76 (100) c)

Chi2 = 7.45, p = 0.06

a) Using the somewhat broader definition of responsible innovators (N = 35) does not produce significant results. b) Using the somewhat broader definition of responsible innovators (N = 35) does produce significant results. c) One missing value.

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[86] T. van der Valk, M.M.H. Chappin, G. Gijsbers, Evaluating innovation networks in emerging technologies, Technol. Forecast. Soc. Chang. 78 (2011) 25–39. [87] A. Lopolito, P. Morone, R. Sisto, Innovation niches and socio-technical transitions: a case-study of bio-refinery production, Futures 43 (2011) 27–38. [88] A. Vohora, M. Wright, A. Lockett, Critical junctures in the development of university high-tech spinout companies, Res. Policy 33 (2004) 147–175. Marina van Geenhuizen (1951) is Professor of Innovation and Innovation Policy at Delft University of Technology, Delft, The Netherlands. Her main research interests are in theory and practice of the knowledge economy, particularly knowledge transfer (commercialization) and entrepreneurship at universities. With regard to technology and responsible innovation, her interests are in life sciences and health care and in sustainable energy and sustainable transport solutions. She has published over 90 reviewed articles in English journals and is principal editor of seven edited volumes in English on regional innovation, creative cities, sustainable transport and environmental protection. She acts as advisor of the city of Delft.

Qing Ye (1986) was educated as an electronic designer with special interest in video gaming. He holds a Bachelor in Engineering on Microelectronics from the University of Electronic Science and Technology of China. His interests, however, changed towards more social and commercial thinking, which led to a MSc in Management of Technology at Delft University of Technology (cum laude) Delft, The Netherlands. He is now an extraneous PhD student of TU Delft, with major research interests in academic spin-off firms, entrepreneurship, knowledge valorization, responsible innovation and sustainability issues.