<fresh page><cn>4.<ct>In support of university spinoffs – what drives the organizational design of technology transfer ecosystems?
<au>Matthew Good and Mirjam Knockaert 1. <a> INTRODUCTION
Over the past 30 years, universities have increasingly recognized the role they play in generating new knowledge and in facilitating the commercialization of that knowledge often referred to as either technology transfer (TT) or academic entrepreneurship (Grimaldi et al., 2011). Commercialization occurs in multiple ways including through the formation of highly innovative university spin-offs and student-led startups (Mosey et al. 2017; Wright et al.
2017). To facilitate commercialization, an ecosystem of organizations dedicated to supporting the TT process has emerged around universities that includes incubators, science parks, university investment funds, TT offices and others (Siegel and Wright, 2015). We refer to this set of organizations as the “TT ecosystem” – a form of startup incubation ecosystem focused on the university context.
Recent research has begun to recognize the importance of the TT ecosystem in supporting research commercialization (e.g. Good et al., 2019; Hayter et al., 2018). With the recognition of its importance, there is a need to understand how these complex systems can be shaped to better support commercialization. In this chapter, we aim at understanding how the characteristics of TT ecosystems differ between universities and what drivers can explain such variation. Specifically, we employ a comparative case study design to investigate TT ecosystems in eight different Scandinavian universities and use an organizational design perspective to compare the different ecosystems. By doing so, we identify leverage points for influencing the design of ecosystems, which is critical for improving its effectiveness (Nadler and Tushman, 1997; Adner, 2017).
Following Djokovic and Souitaris’s (2006) approach to organizing the university spinout literature, we identify drivers of TT ecosystem design at the micro-, meso-, and macro-levels.
At the micro-level, we identify initiatives taken by different types of individuals (i.e.
entrepreneurs, managers, students, and academics) and collaborations between them that impact TT ecosystem design. At the meso-level, we identify major organizational change projects as drivers of design. At the macro-level, we identify governmental actions, public
organization initiatives, private organization engagement, availability of human capital, macro trends, and mimetic forces affecting TT ecosystem design.
Understanding the drivers of TT ecosystem design has important implications for policy and practice. Particularly, our study shows that policy makers have a significant influence on the design of TT ecosystems through the creation of policies and regulations and need to be conscious of how those policies affect TT ecosystems. Further, university and ecosystem managers need to be aware of the challenges and opportunities that emerge from changes at the meso-level so that they can take advantage of them to optimize ecosystem design. Finally, the study identifies characteristics at the micro-level that are important for TT ecosystem design and may help managers in selection, recruitment and evaluation of TT ecosystem builders.
From a research perspective, we contribute to the ecosystem literature by taking a unique organizational design perspective. By elaborating on the antecedents of the design of ecosystems in the context of TT and the incubation of university-based startups, we build upon structural approaches to understanding ecosystems (Adner, 2017). Additionally, we contribute to the TT and academic entrepreneurship literature by taking a broader approach to studying TT (Siegel and Wright, 2015) and applying a novel theoretical perspective to elaborate on the TT ecosystem (Good et al., 2019).
The next section discusses the theoretical background guiding this study followed by an explanation of our methodology. We then present our results and finish with a concluding section highlighting the implications of this study.
2. <a> THEORETICAL BACKGROUND 2.1. <b> TT Ecosystems
TT is a complex, non-linear process whereby university research is translated into innovative products and services (Bradley et al., 2013). Recent research has called for a more holistic and theory-based approach to understanding TT support mechanisms at universities (Siegel and Wright, 2015; Hayter et al., 2018) as a complement to the rather case-based and descriptive work on TT. To fill this gap, we use an ecosystem perspective combined with an organizational design framework to understand TT ecosystems at different universities. We define TT ecosystems as the set of organizations affiliated with a university that directly support TT and startup incubation in a university context (Good et al., 2019).
The term “ecosystems” is typically used in the context of biological systems where multiple interdependent organisms interact in complex ways within a specific environment.
The ecosystem concept has been applied in many different contexts including digital ecosystems (Sussan and Acs, 2017), tourism (Brandt et al., 2017), knowledge ecosystems (Jarvi et al. 2018), entrepreneurial ecosystems (Stam, 2015) and innovation ecosystems (Adner and Kapoor, 2010). Despite the use in several domains, there does not appear to be a clear definition of ecosystems. However, the common features appear to be the concepts of interdependent actors, interaction between actors, a specific context and a common purpose or outcome.
Criticism of the ecosystem perspective, particularly in the related contexts of entrepreneurship and innovation, focuses on its similarity to other bodies of work such as the systems of innovation and clusters literature, the lack of a clear conceptual and analytical framework that also identifies possible cause-and-effect relationships, the lack of comparative work, the lack of research using a strong theoretical foundation, unclear boundaries and the need for a more systems-based approach (Alvedalen and Boschma, 2017;
Oh et al., 2016; Stam, 2015).
Using an organizational design framework, we address some of these criticisms by providing a theoretical base through which we analyze and compare different TT ecosystems and identify causal drivers of the organizational design of TT ecosystems.
2.2. <b> Organizational Design and TT Ecosystems
Organizations can be defined as a collection of actors that work together in achieving an overarching goal (Scott, 1998). In this respect, an organization can be conceptualized as either a collection of individuals or, as is the case with TT ecosystems, as a collection of organizations working toward a common goal (Fombrun, 1986; Gulati et al., 2012). The design of organizations can be studied by analyzing the four basic design elements of purpose, structure, activities and people (Scott, 1998). These elements are dependent on each other and maintaining alignment between these elements is necessary to successfully achieve an organization’s goals (Nadler and Tushman, 1997; Scott, 1998).
Purpose refers to the main reason the organization exists. Organizations, and the actors within those organizations, will pursue a set of goals or outcomes that they identify as being important for fulfilling their purpose. Organizations can have multiple purposes and may even present different purposes to different audiences (Warriner, 1965).
Structure relates to the configuration of relationships and processes internal to each component of the ecosystem and between components. The structure of TT ecosystems varies in terms of ownership, governance, internal structure, size, and physical location (Scott, 1998). Ownership relates to who owns components in the ecosystem, whether it is primarily just the university or a combination of public and private organizations with the university.
Governance is concerned with the degree to which components are integrated within the university organizational structure or set up as independent organizations. Internal structure relates to the degree of centralization, formalization and specialization within the ecosystem (Alter, 1990; Campbell et al., 1974). Size refers to the average size of components measured as number of employees. Location refers to the degree to which components are located on campus and co-located. Finally, structural relations refer to the degree to which the components of the ecosystem interact with one another, either formally or informally.
Activities are the tasks and routines that the organization performs in pursuit of its goals.
Activities are highly diverse and can include anything from daily activities to complex and creative routines performed over a longer period. Howells (2006) developed a framework for classifying activities related to TT which we use to structure our analysis. He identified 10 different categories of intermediation activities: foresight and diagnostics; scanning and information processing; knowledge processing, generation and combination; gatekeeping and brokering; testing, validation and training; accreditation and standards; regulation and arbitration; intellectual property; commercialization; and assessment and evaluation.
People refers to the capabilities and experience of the individuals employed by the different components and the culture in which those people reside. In the context of TT ecosystems, we look at the extent to which actors within the ecosystem have academic, industry, or entrepreneurial experience. Experience is an important element of human capital which is likely to affect performance in executing tasks (Dimov and Shepherd, 2005) and reaching entrepreneurial performance (Unger et al., 2011).
3. <a> METHODOLOGY 3.1. <b> Research Design
To conduct this study, we employed a comparative case study with the unit of analysis being the TT ecosystem at eight different universities in Scandinavia (Yin, 2014). Using a theoretical sampling approach, we selected cases based on both a replicative (i.e. similar cases) and divergent logic (i.e. major differences in cases) (Eisenhardt, 1989). To minimize
contextual factors and improve comparability, Scandinavia was chosen due to similarities in culture, innovation output, and many other factors (Eurostat, 2016). Furthermore, we focused on universities ranked in the top 200 of international rankings since these universities have higher research capacity and are more likely to be active in TT, which ensures the relevance of the selected cases.
Two universities were selected per country. The first case in each country was the highest ranked university (replication logic). The second case was chosen based on divergent characteristics such as a specialized research focus (divergent logic). The details of each case are presented in Table 4.1.
For reliability, a case study protocol was maintained and updated throughout the process.
A pilot study was conducted at Case A to test and revise the initial version of the case protocol. An anonymized case database has also been maintained.
Table 4.1 Case details
General university-level information TT-related information
C ase
Numb er of Interviews
Cou ntry
Loca tion
Scientific specialization
Size (5-year average)
TT Ecosystem Components
TT outcomes (5-year average)
Stud ents
Acade mic Staff
Gene ral Income
(mill.
€)
Spin outs
Lice nse
Dis- closures
A 5 Norw
ay City General 27,00
0 3,400 770 TTO, incubator, science
park, student org 9 42 241
B 3 Norw
ay City Specialize
d
39,00
0 3,700 750 TTO, proof of concept
fund, incubator, student hub 16 15 149
C 5 Den
mark City General 40,00
0 3,500 1,100 TTO, science park,
incubator, student hubs 2 20 72
D 7 Den
mark City General 37,00
0 4,400 850 TTO, science park,
incubator, student hubs 3 18 60
E 6 Swed
en City Specialize
d 6,000 2,400 650 Innovation office,
incubator, science park
Not available
Not
available 152
F 4 Swed
en
Subu
rban General 42,00
0 3,900 670
Innovation office, incubator, venture fund,
other
Not available
Not
available 97
G 3 Finla
nd City General 33,00
0 4,200 720 TTO, student hub 2 2 75
H 5 Finla
nd
Subu rban
Specialize d
19,00 0
2,800 390 TTO, incubator,
accelerator, student hub,
7 26 159
6
student orgs
7
3.2. <b> Data Collection and Analysis
We conducted 38 unstructured interviews with managers at each component of each TT ecosystem between April 2016 and June 2017. Thirty-one of the interviews were recorded and transcribed while the remaining seven were unrecorded with summaries written up after the interview. Interview questions centred on gathering information about each organizational design element in our theoretical framework combined with how the interviewee’s organization interacts with other organizations in the ecosystem. To enable triangulation and improve construct validity, we collected archival data which included websites, annual reports and strategic documents.
Analysis was completed in multiple stages using a case-oriented strategy concerned with identifying categories into which the various cases could be placed (Miles et al., 2014). The first author coded the transcriptions and summaries using a provisional coding approach which used codes derived from our theoretical framework. The results were then categorized and summarized in tables to allow for easier comparison. Diagrams were also created to visualize aspects of how each ecosystem is designed. The resulting tables and diagrams were used to conduct a cross-case analysis to compare the designs of each ecosystem. Interview transcripts were revisited a second time to identify statements that directly or indirectly elaborated on design choices. Throughout the process, the co-authors had repeated discussions using the previously mentioned materials as aids to further understand the data.
The findings from this process are discussed in the next section.
4. <a> WHAT DRIVES THE DESIGN OF TT ECOSYSTEMS?
In the interest of brevity and to keep the focus of this chapter on the drivers of ecosystem design, we begin this section with a summary of the organizational design characteristics of each case in Table 4.2. We then present and discuss the drivers of ecosystem design we identified.
Table 4.2 Organizational design of TT ecosystems by case
Case
A Case
B Case
C Case
D Case
E Case
F Case
G Case
H Purpose
Encourage and support academic scientists to participate in commercialization
X X X X X X X X
Encourage and support student
participation in entrepreneurship X X X X X
Facilitate the creation of valuable products and services based on
university research X X X X X X X X
Support and facilitate the licensing
of university research X X X X X X X X
Support establishment and growth of
startups and university spinoffs X X X X X X X X
Support establishment and growth of
established businesses X X X X X X X
Regional economic development X X X X X
Protect the rights of the university X X X X X X
Generate profit for owners X X X X
Activities (adapted from Howells, 2006) Articulation of needs and
requirements X X X X X X X X
Scanning and information
processing X X X X X X X X
Gatekeeping and brokering X X X X X X X X
Prototyping and pilot facilities X X X X X X
Intellectual property rights X X X X X X X X
Marketing, support and planning X X X X X X X X
Sales network and selling X X X X X X X X
Finding potential capital funding
and organizing funding or offerings X X X X X X X X
Venture capital X X X X X X
Initial public offering X
Assessment and evaluation X X X X X X X X
Structure Ownership
University only components? X X X X X X
Component-owned X X X
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components?
Multiple owner components? X X X X X X X X
Governance
Primarily internal to the
university X X X
Primarily external
organizations X X X
Mix of internal and external X X
Internal Structure
Centralization Med. Med. High High Low Low High High
Formalization Med. Med. High High Low Low High Low
Specialization Med. Low Low Low High Med. Low Low
Size
Primarily small components X X X X X X
Primarily large components X X
Location
On campus X X X X X X X X
Off campus X X X X
Mostly co-located? Yes No Yes Yes Yes Yes No No
Structural Relations
Primarily formal, contract
based X X X X X X
Primarily informal X X X X X
People
Entrepreneurial experience X X X X X X X X
Industry experience X X X X X X X X
Research experience X X X X X X X X
Business experience X X X X X X X X
Legal experience X X X X X X
Finance experience X X X X X X X
Clinical trials experience X X X X X
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We identified drivers of design at three different levels: micro, meso and macro (Djokovic and Souitaris, 2006). The micro-level focuses on drivers emerging from the action of individuals within the ecosystem. The meso-level includes drivers caused by the actions of the university and TT ecosystem members. The macro-level includes drivers related to governmental, economic, or institutional mechanisms acting on the ecosystem. Table 4.3 summarizes the drivers we identified during our study and clearly illustrates how they impact ecosystem design. This table is useful for identifying overlaps between design impacts and shows that not all drivers impact every aspect of design.
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Table 4.3 Overview of drivers of organizational design of TT ecosystems
Event(s) Impact of events initiated by diverse drivers on TT ecosystem organizational design
Purpose Activities Structure People
Micro-level drivers Individual initiatives
Entrepreneurs Successful entrepreneurs renewed a component (science park in Case A; TTO in Case H) or create new components (incubator in Case H).
Purposes were changed or expanded to include the support for startups and university spin-offs
Activities were expanded to include greater support for startups
The creation of a new component in Case A had the effect of reducing centralization
Formalization reduced in both cases
Human capital of the ecosystem was developed through the inclusion of these entrepreneurs and through individuals they recruited from their network
Academics Academic scientists were instrumental in developing components such as an entrepreneurial internship program (Case A) or initiated the formation of the ecosystem (Case F).
Introduction of new purposes including inspiring students to be entrepreneurs (Case A) or to support the commercialization of research generally (Case F)
Activities expanded to include student engagement (Case A)
Creation of centrally run, student focused
component (Case A) increases centralization and formalization
Indirectly encourages students to participate in the ecosystem (Case A) Recruitment of leaders to start building the
ecosystem (Case F)
Students In Case H, an inspired group of students established a startup accelerator, student internship program and two annual events to support startups
Expansion of purpose to include inspiring students to be entrepreneurs and supporting startups
Introduced startup acceleration, network building, student engagement
Formation of student-led components reduces centralization and formalization
Engaging fellow students to participate in
ecosystem (part- or full- time)
Collaborations between individuals Collaboration
between individuals in the ecosystem
Collaboration between
component managers led to the development of a shared vision for the ecosystem (Case F)
Creation of a coherent unifying purpose to which all components support (Case F)
Reduction of overlap or duplication of activities (Case F)
Shared vision in Case F encouraged
decentralization and moderate specialization combined with co- location of components and significant formal and informal structural
-
12
relations Collaboration
between students, academic scientists and administrators
Collaboration between students, academics and administrators led to the creation of new component focused on supporting student startups (Case D)
Expanded purpose to include support of student startups (Case D)
Addition of startup support and acceleration, provision of physical space for student startups (Case D)
Reduced centralization due to distance from other components,
formalization reduced due to lack of formal
management and specialization increased due to attachment to specific faculty or department
-
Meso-level drivers Large
organizational change projects
The merger of universities (Case H) and an externally funded entrepreneurship project (Case D) enabled the creation of new components (i.e. incubator, accelerator and others) and reorganization of the ecosystem
Broadening overall purpose of ecosystem to include startup support and increasing university–industry collaboration
Activities expanded to include startup support, acceleration, industry engagement, student engagement
Every aspect of an ecosystem’s structure can be affected by such projects and the impact is unique to each project
Required the recruitment of individuals to lead and develop the new
components (Case D)
Macro-level drivers Governmental Government –
regulations Government regulations affect or incentivize specific purposes (e.g. spin-off creation) and activities (e.g. patenting activities) and affect who owns the TT ecosystem (e.g. the university) and how it is governed (e.g. central to the university administration)
Prescribe or incentivize specific purposes such as supporting startups
Impacts which activities are conducted or prioritized such as startup creation or licensing
Regulations can determine ownership (such as mixed ownership) and governance structures (such as all external organizations)
-
Government – funding
Reduction of government funding due to changes in government priorities or economic decline
Led to the narrowing of purpose to those that can be realistically be achieved or those considered “core”
Reduction of activities based on resources (e.g.
elimination of proof of concept funding)
Elimination of
components (e.g. closure of science park in Case E)
Inability to hire new individuals or potentially reducing the number of individuals in the ecosystem
Public Public organizations provide Incentivizes or requires Enables a broader range Supports the creation of Supports the recruitment 13
organizations’
initiatives funding for ecosystem components (e.g. proof of concept funds) or invest directly in new components (i.e. as owners of science parks or incubators)
certain purposes (e.g.
student support, creation of startups)
of activities (e.g. proof of
concept testing) new components (e.g.
science parks or incubators)
of talent (e.g. to lead the new components)
Economic Private organizations’
engagement
Private organizations’
engagement in the ecosystem through collaboration or direct investment/ownership.
Influence the purpose of the ecosystem through collaboration
requirements or ownership control (e.g.
more profit oriented)
Increases types of activities conducted by the ecosystem (e.g.
startup support, specialized commercialization support, investment)
The structure of the ecosystem will have a greater degree of mixed ownership and external organizations combined with a greater degree of specialization. Supports formation of new components, can act as owners, can increase specialization
Private organizations can also provide access to human capital within their organization or through their network
Availability of
human capital The failure or decline of a large local company such as in Case F provided ecosystems with the ability to recruit highly talented individuals to participate in the ecosystem.
- - - Provides a significant
pool of human capital to recruit into the ecosystem
Institutional Trends and mimetic
forces The pressure to follow current trends and mimic successful ecosystems (i.e. all cases) has a significant impact on ecosystem design.
Choice of purposes influenced by need for legitimacy, copying successful ecosystems, or specific trend characteristics (i.e.
creation of startups, economic development)
Range of activities depends on purpose, activities reflect best practices or learnings from other jurisdictions.
(i.e. copying the
“Stanford model” or attending community of practice conferences)
Structures are then adapted to reflect these other jurisdictions but are adapted for the
differences in context (e.g. only external organizations or co- location).
The people recruited into the ecosystem reflect these choices (e.g.
recruiting patent lawyers or entrepreneurs)
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4.1. <b> Micro-level
At the micro-level, we observed the importance of individuals possessing an entrepreneurial mindset, having access to resources and having been inspired to act. These influential individuals can affect every aspect of organizational design from shifting or expanding purposes (e.g. Case H), increasing the range of activities conducted (e.g. Case A), changing structure through the creation or renewal of components (e.g. Case F), or recruiting skilled individuals to the ecosystem (e.g. Case A).
Such individuals can include previously successful entrepreneurs (from inside and outside the university), academic scientists, or students. For example, the renewal of the science park in Case A is the result of hiring an ex-entrepreneur and investor who then engaged a second group of successful entrepreneurs to create the connected incubator. In this case, the motivation of these entrepreneurs appears to be a desire to give back to their community, the challenge of renewing and building these components and an interest in creating attractive investment opportunities. Case H has a thriving set of components created and driven by an inspired group of students with the support of other actors behind the scenes. In this case, the students were driven by the negative attitude of a business professor toward entrepreneurship in combination with their experiences visiting highly successful entrepreneurial environments in the U.S. Academic scientists have also been inspired by their experience at innovative universities or working with industry to develop supporting components such as an entrepreneurial internship program (Case A).
The collaborations between these individuals can also impact TT ecosystem design. The clearest example of this is the interaction between managers in the ecosystem. For example, managers can develop a shared vision for the ecosystem which acts as a guiding design proposition for the ecosystem. This is true in Case F, where both university and component managers developed a shared vision that prioritized collaboration and minimization of duplication. This has a clear effect on the overall structure of the ecosystem in Case F, where components are co-located in the same office space and in which structural relations are characterized by significant knowledge sharing and communication.
In Case D, collaboration between an engaged student, a faculty researcher and faculty management led to the conversion of an empty space into an innovation and incubation hub.
This initiative has since developed its own identity and branding and has grown to support multiple student-led companies but lacks defined routines and business development 15
expertise. Similar instances are now being developed in other faculties but in a more formal, top-down process led initially by faculty leadership. From an organizational design perspective, this dynamic collaboration between students, researchers and faculty management is an example of a driver that increases the range of activities conducted by the ecosystem and affects the structure of the ecosystem by adding components, which increases specialization and reduces centralization.
4.2. <b> Meso-level
At the meso-level, we found two cases (Case D and H) where a critical event, specifically a large, externally funded organizational change project at the university, had a significant impact on the types of components present and how those components were integrated into the ecosystem. In Case H, the education authorities in Finland decided to merge multiple universities into a single, multidisciplinary university, as part of renewing the higher education sector in Finland and with a goal of creating a university that fosters innovation. The merger enabled both academic scientists and student-related actors from the merging institutions to claim spaces that became available due to these changes. These spaces became the starting point of hubs for university–industry collaborations, entrepreneurship- related events and startup acceleration. In Case D, the university secured funding for a project attempting to enhance its entrepreneurial activity. Specifically, this project enabled the creation of an incubator and spin-off related program, which were subsequently merged and centralized into the internal TTO. As a result, the ecosystem’s purpose in each case was broadened to include the support of startup companies (both student and external) and increasing university–industry collaboration. Furthermore, the activities of the ecosystem were expanded to include business acceleration, entrepreneurial internships, entrepreneurship related events, hackathons and industry sponsored design workshops. Every aspect of ecosystem structure was affected by these projects but in different ways. For example, while both became more centralized, in Case H, self-governed student-led components were created whereas Case D does not have student-led components. Finally, the addition of these elements increased the numbers of people involved in the ecosystem.
4.3. <b> Macro-level
Influential actors at the macro-level reside outside the TT ecosystem and include government, public organizations, private organizations, macro-trends and mimetic forces.
Government has arguably the greatest impact on ecosystem design through the creation of 16
policies and programs directed at regulating academic TT. In many cases, government policies define the boundaries of the TT process and the freedom universities and other actors have in organizing for academic entrepreneurship. For example, Swedish universities are not allowed to directly earn profit but have been allowed to create holding companies that can profit from TT activities. In addition, the Swedish government provides funding for innovation offices that support TT internally at a limited number of universities (Cases E and F). Government policies and programs can, therefore, affect the overall purpose, activities and structure of TT ecosystems. Specifically, governments can prescribe or heavily incentivize particular purposes such as setting a mandate for publicly owned components or tying financial support to specific performance measures, which influences the activities prioritized by the ecosystem. Finally, government policies can require specific structures such as the holding company structure in Sweden.
Another government-related driver was the reduction of university budgets due to economic decline (Case C, E, H and G). This had a major impact on resources available to some components. The lack of financial resources narrowed the purpose of the ecosystem to what could reasonably be accomplished, reduced the range of activities conducted and limited the ability to hire additional people.
Public organizations that support TT also impact the design of TT ecosystems. Public organizations are government-owned organizations that operate as independent companies and act to fulfil a mandate set by government. In a TT context, these organizations typically provide some form of funding that can be accessed for the purposes of research or the development of proofs of concept. In some countries such as Norway, public organizations also provide support and direct investment for the creation of incubators and science parks which can affect the prevalence and internal configuration of these organizations. Overall, public organizations can influence an ecosystem’s activities, structure and people by providing dedicated funding for specific outcomes, creation of new components, and recruitment of skilled individuals.
Private organizations influence primarily the activities in the ecosystem. For instance, they can affect the types of technologies that are prioritized for commercialization by the ecosystem. In some cases, private organizations will even partly own different components allowing for influence over their operations. This can even lead to the ecosystem specializing or creating specialized components focused on that particular industry. An example of this is Case B where local companies invested in the university incubator and provide funding for 17
one of the student entrepreneurship organizations. Another example is Case A where an industry cluster organization supported the creation of an incubator focused on medical technologies. Overall, private organizations can have an impact on the purpose, activities and structure of the ecosystem.
Another driver related to private organizations (Case F, G and H) was the failure or decline of large industrial actors in the region. Individuals who previously worked in these large companies stayed in the region and with their expertise either joined the ecosystem, started their own companies, or became investors. This is consistent with the concept of recycling within entrepreneurial ecosystems where individuals from failed or closed ventures re-enter the ecosystem in other capacities bringing with them their resources and experience (Spigel and Harrison, 2018). These recycled resources enabled the creation or expansion of components and enabled collaborations with industry. Such events have the greatest impact on the people aspect of organizational design since these individuals take up roles within the ecosystem, adding their human capital to the system. For example, in Case H, one of these individuals was an influential leader and became a micro-level driver of change within the ecosystem.
Macro-trends and mimetic forces impacted the design of ecosystems in all of the cases.
The increasing focus on TT from universities combined with the digitalization of society are examples of trends that have put pressure on ecosystems to adapt their designs. For example, the incubator in Case A is primarily focused on supporting startups developing machine learning or artificial intelligence-based products. Mimetic forces have led ecosystem components to adopt “best practices” from other ecosystems to their context in a bid to increase legitimacy and solve operational challenges. For example, the TTOs or similar components at each of the universities implemented very similar approaches to their commercialization processes often referred to as the “Stanford model” or “Oxford model.” It was also quite common for leaders from different universities to take “field trips” and visit other universities to better understand what other universities were doing related to TT. Both trends and mimetic forces have a pervasive effect on every aspect of organizational design with purposes being adapted to the requirements of the trend and copied from other jurisdictions with the hope of building legitimacy and potentially recreating success.
Similarly, activities may be adapted to reflect what is considered “best practice” in the relevant communities of practice or to show that the ecosystem is adapting to emerging trends. Structure will then need to be changed to support these changes and this can take the 18
form of changing ownership, creation or elimination of components, modifications in governance, reorganization of internal structures, or changing of structural relations.
5. <a> IMPLICATIONS AND CONCLUSIONS
In our study of the TT ecosystems of eight different universities in Scandinavia, we have identified a range of micro-, meso- and macro-level drivers that affect the overall organizational design of TT ecosystems (see Figure 4.1). At the micro-level, we identify influential entrepreneurs, academics and students whose actions and interactions affect TT ecosystem design. At the meso-level, we identify one major driver of design which stems from university or ecosystem level organizational development projects. Finally, at the macro-level, we identify government policy and programs, public organizations, private organizations, macro-trends, and mimetic forces as drivers of ecosystem design.
--- Insert Figure 4.1 about here
Figure 4.1 Drivers of the organizational design of TT ecosystems
From a research perspective, we build on the structural perspective of ecosystems (Adner, 2017) by identifying dynamic mechanisms that impact ecosystem design. Future research can study these mechanisms in more detail to understand how these drivers impact alignment, how the different drivers interact, and how they influence TT outcomes.
Furthermore, we contribute to the TT and academic entrepreneurship literatures by responding to calls for studying the broader context in which TT occurs (Siegel and Wright, 2015) by elaborating on the TT ecosystem concept (Good et al. 2019). We also contribute by applying a well-understood theoretical perspective in a novel way that adds to what has been a largely atheoretical body of work (e.g. Lamine et al., 2018; Spigel and Harrison, 2018).
From a practitioner’s perspective, each of these drivers are potential leverage points that practitioners can use to influence the design of the ecosystem in order to achieve alignment between the different components and thereby improving effectiveness.
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Our study provides a deeper understanding of how TT ecosystems are designed to help research scientists and students to turn their ideas and technologies into new businesses. By doing so, we contribute to the ecosystem, TT and academic entrepreneurship literatures.
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