Learning in New Space: Knowledge Sourcing for Innovation in Northern Swedish New Space Companies

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Department of Business Administration

Master's Program in Business Development and Internationalisation Master's Thesis in Business Administration III, 30 Credits, Spring 2020

Supervisor: Medhanie Gaim

LEARNING IN NEW SPACE:

Knowledge Sourcing for Innovation in Northern Swedish New Space Companies

Authors:

Filip Nikitas Metallinos Log

Sandra Lipic Persson

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Acknowledgements

Before exploring our thesis, we would like to divert attention towards the circumstances in which it was written. In March 2020, the COVID-19 pandemic led to closed universities, business bankruptcies, and many more adverse effects. However, none of them can even compare to the massive loss of human life.

In relation to this, we extend our deepest gratitude to Umeå University and our supervisor, Professor Medhanie Gaim, for providing us with supervision through remote and unorthodox methods, regardless of the challenges that presented. We also extend our gratitude to the many companies and Kvarken Space Center project personnel that gave us their time to conduct our research. Producing a master’s thesis is a time of learning, and we learned so much, both separately and together.

Thanks to Professor Sujith Nair for giving us the opportunity to conduct independent thesis research under Professor Tomas Blomquist’s research group at the Projects, Innovations and Networks profile at the Umeå School of Business, Economics and Statistics.

Thanks to Ignite Sweden for allowing us to partake in Ignite Space Luleå 2020, and Johanna Bergström Roos with RIT 2021 for allowing us to partake in Space Innovation Forum in Kiruna.

Thanks to Markus Metallinos Log for thorough proof-reading and invaluable guiding.

But most of all, we want to put our thoughts towards those who were with us yesterday but will not be with us tomorrow.

Sandra Lipic Persson Filip Nikitas Metallinos Log

______________________________________ ______________________________

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Abstract

The New Space industry is a novel branch of the space industry focusing on innovation and commercialization. It experiences very swift growth, although only a fraction of this growth has taken place in Sweden. In order to change this, policymakers are investing funds and efforts into developing the Swedish New Space industry, including the Kvarken Space Center project, aimed at developing the Northern Swedish New Space industry. Here, we see public support in developing a high-tech innovation ecosystem in a peripheral area. This is a topic offering multiple research streams on the most efficient development methods, two of which juxtapose the knowledge ecosystem and intercompany collaborations respectively. With that in mind, we formulated the following research question:

How are collaborations and the knowledge ecosystem used to source knowledge for the innovation process?

To approach to the subject, we gathered literature on innovation systems and ecosystems in order to analyze the importance of the knowledge ecosystem and the various shapes the industry can assume. This information is linked to theory on knowledge types and sourcing methods considering tacit and codified knowledge, which through different constellations form different needs of knowledge sources.

Our empirical approach investigated how the companies used different knowledge sources, namely collaborations, the knowledge ecosystem, and other sources, including networks, monitoring, and mobility. Thereafter, we considered the effects of outstanding factors, including funding and the peripheral region, on knowledge input in innovation.

We identified that companies in the upstream industry node, i.e. those related to launch activities and vehicles, and companies in the downstream node, i.e. those extracting data from space, both use engineering knowledge. Engineering knowledge requires both tacit and some codified knowledge, suggesting similar knowledge inputs for both nodes. However, different node traits lead to different inputs. Upstream companies see low degrees of knowledge transfer, especially from the knowledge ecosystem and from collaborations due to NDAs and intellectual property regards, and tacit knowledge input from external sources is particularly lacking.

Downstream actors see few constraints to using the investigated knowledge sources, although collaborations saw difficulties due to complexities in structuring them. However, many unilateral complementarities are seen from the knowledge ecosystem, leading to higher knowledge input particularly from networks, while also boosting collaborations to some extent.

This was also partly observed in upstream companies. Thus, the knowledge ecosystem sees significant use, although much is indirect, while collaborations see less use.

Our main findings are that policymakers and the knowledge ecosystem should focus more on sources of tacit knowledge, such as students, while investing in network-boosting activities as industry events. Companies, especially upstream ones, should utilize collaborations more.

Upstream companies should also utilize the local knowledge ecosystem more, as the rights to intellectual property produced by private actors in universities belong to the producer.

Regarding future research, we warrant studies on knowledge sourcing in New Space companies and other knowledge sources, such as networks as a compensatory knowledge source.

Key words: New Space industry, downstream, upstream, innovation, knowledge sourcing, knowledge ecosystem, collaboration, monitoring, networks, mobility, industry events, periphery

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List of Figures

Figure 1: Access to Technological Competencies in Sweden with Kvarken Outlined...2

Figure 2: Certain Vital Inputs in Reaching Optimal Innovation Performance...3

Figure 3: Upstream Versus Downstream Activities in the Space Industry...7

Figure 4: The 6 Parts that Comprise this Research Project...8

Figure 5: Network Versus Ecosystem...9

Figure 6: Order of Priorities in Various Ecosystem Approaches...12

Figure 7: Perceived Distribution of Public Funds for Kvarken Space Center and Expected Effects on Innovation...25

Figure 8: The Triangulation of Observations and Interviews Applied for the Qual-Qual Approach...32

Figure 9: Jurisdictions in Norway, Sweden and Finland Partaking in the Interreg Botnia- Atlantica Project...35

Figure 10: Regions of Sweden with Actors Undertaking Work Packages in Kvarken Space Center...38

Figure 11: Effects of Studied External Sources of Knowledge Input in the Innovation Process of Northern Swedish New Space Industry...64

Figure 12: Effect of Studied External Source of Knowledge Input in the Upstream Innovation Process...72

Figure 13: Effect of Studied External Sources of Knowledge Input in the Downstream Innovation Process...73

List of Tables

Table 1: The Four Elements for How Value Is Created through an Ecosystem-as-Structure or Ecosystem-as-Affiliation ...11

Table 2: Summary of Ecosystem Classification ...14

Table 3: Effect of Various Types of Government Involvement in Triple Helix Innovation System...16

Table 4: Indicators to Identify Core versus Peripheral Regions ...18

Table 5: Interplay between Knowledge Types, Formalization Degree, Bases, and Sources ...24

Table 6: Extended “Research Onion” of Methodological Decisions with Accompanying Explanations ...27

Table 7: Jurisdictions Partaking in the Botnia-Atlantica Interreg Project...36

Table 8: Swedish Institutions Partaking in Kvarken Space Center Project, Locations and their Project Leader(s) ...38

Table 9: Observation Events Attended by Researchers...40

Table 10: Collected Semi-Structured Interviews...42

Table 11: Open Coding of the Empirical Data, Example....44

Table 12: Summary of Quality Criteria and Approaches...47

Table 13: Respondents and Accompanying Space Industry Classification...48

Table 14: Summary of Key Empirical Findings...63

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List of Abbreviations

ERDF – European Regional Development Fund

ESA BIC – European Space Agency Business Innovation Center KIBS – Knowledge- and Innovation-Based Services

NDA – Non-Disclosure Agreement RIS – Regional Innovation System SME – Small-to-Medium Enterprise SSC – Swedish Space Corporation

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1. Introduction

This chapter presents why ecosystems and industry innovation are topics of interest, before relating this to the empirical setting and scope. The setting and scope are used to identify relevant literature streams, from which the research gap and question are derived. Thereafter, the industry is introduced, for clarification of factors surrounding the novel New Space industry.

1.1 Choice of Subject

“It is not the strongest of the species that survives, nor the most intelligent, but the one most responsive to change.”

-Charles Darwin Due to rapid change in the competitive business environment (Tassey, 2008, p. 561), innovation has become a vital factor for success and survival (Freytag & Young, 2014, p. 361). Therefore, companies are moving towards collective creation of innovation to sustain competitive advantage (Chesbrough, 2003, pp. 68, 185) and further contribute to economic growth (Fürlinger et al., 2015, p. 9). In order to collectively create innovation, Moore (1993, p. 76) stresses the need for business ecosystems, where competitors and collaborators alike co-create value and innovation. The ecosystems concept has its roots in ecology, where they are

“system[s] involving … interactions between a community of living organisms in a particular [environment]”. All factors in an ecosystem are mutually dependent on the other factors (National Geographic, 2011). The concept has later become applicable to other fields. In the business context, they describe “collectives of heterogeneous, yet complementary organizations who jointly create some kind of system-level output” (Thomas & Autio, 2020, p. 2). Thomas

& Autio (2020) refer to the innovation-focused business ecosystems as innovation ecosystems, which is one of many kinds of ecosystems.

This concept has shown great outcomes through businesses such as Alibaba. They have received great attention due to the Alibaba Group’s success in ecosystem development (Tsai &

Christian, 2016, p. 54) leading to unique innovations (Zeng, 2018, p. 88). Specifically, the company started by linking buyers and sellers of goods online. As technology advanced, the company required innovation in order to move more of its business functions online. To spur innovation, the business had to expand its ecosystem, interacting with additional actors to achieve the desired outcomes. Simultaneously, the ecosystem expansion created new online businesses, which further advanced the ecosystem (Zeng, 2018, p. 88). Due to their importance, it is important for business research to understand innovation ecosystems and the ways they work.

1.2Problem Background

1.2.1 Scope

The studied aspect of innovation ecosystems was knowledge sourcing. We chose a peripheral setting, as literature disagrees on knowledge sourcing within them. Particular focus is placed on intercompany collaborations and the knowledge ecosystem as knowledge sources, which

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317). Specifically, our setting was the Kvarken region of Northern Sweden, to which we had access through Umeå University, a participant in the multiorganizational industry development organ Kvarken Space Center (Kvarken Space Center, 2020b). The Kvarken region, which in this context refers to the counties of Västernorrland, Västerbotten, and Norrbotten, and the municipality of Nordanstig. The types of periphery concerned were geographical and knowledge peripheries (cf. Lagendijk & Lorentzen, 2007, p. 463). Figure 1 shows a graphical representation of both the geographical and knowledge peripheries in Sweden, as access to technological competencies is centered in Sweden’s southern metropolitan regions. Technological competencies are generally considered as professionals in research and development, or holders of 2+ years of higher education in a technological field as science and engineering. Northern Sweden is left with few such competencies and no metropolitan regions, and is thus both a knowledge and a geographical periphery.

1.2.2 Conflicting Ways of Studying Innovation

Initial business innovation theories on complementary innovation effects mainly focused on companies located in larger agglomerations, cf. the companies studied by Moore (1993). Accordingly, a significant portion of research on innovation ecosystems has taken place in metropolitan areas, with emphasis on highly urbanized and dense settings (cf. Graf, 2016, p. 4; Schaeffer et al., 2018, pp. 57-58; Engel & del-Palacio, 2011, pp. 44-45, Rissola et al., 2019, p. 8). Here, knowledge ecosystems, meaning collectives of actors creating pre-commercial knowledge, are very beneficial for business innovation.

This research also indicates that peripheral settings

reduce, or even remove the positive effects of such activities. Suburbs and regions outside of the “core” are significantly outperformed by the innovation ecosystem core, and require different models to be assessed (cf. Schaeffer et al., 2018, p. 58; Clarysse et al., 2014; Lerner, 2009).

To recognize the differences present in peripheral and metropolitan regions, two main literature streams have arguably originated, considering knowledge ecosystems (cf. Etzkowitz &

Leydesdorff, 2000, p. 2; Budden & Murray, 2019, p. 3) and intercompany cooperation (cf.

Tödtling et al., 2012, pp. 19-20; Grillitsch & Nilsson, 2015, p. 317) as vital sources for knowledge required for optimal innovation performance. This is based on the notion that, in order to innovate as successfully as possible, innovative efforts must be accompanied by significant knowledge input, cf. Figure 2. The constellation of knowledge inputs can be expected to differ if the setting changes.

Figure 1: Access to Technologi- cal Competencies in Sweden with Kvarken Outlined. Adapted from Grillitsch & Nilsson (2015, p. 309).

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Figure 2: Certain Vital Inputs in Reaching Optimal Innovation Performance. Adapted from Tödtling et al. (2012), Grillitsch & Nilsson (2015), and Etzkowitz & Leydesdorff (2000).

1.3 Research Problematization

Typically, policymakers and knowledge ecosystem actors focus on stimulating knowledge ecosystems to promote innovation and innovation ecosystems (Smith & Leyesdorff, 2012, p.

7), and Sweden is no exception (Tödtling & Grillitsch, 2012, p. 352). Technology hotspots see the greatest extent of this (cf. partners involved in Kvarken Space Center, 2020b; Clarysse et al., 2014, p. 1164). However, Clarysse et al. (2014, p. 1175) conclude that public policy funding may fail to bridge knowledge and innovation ecosystems. Clarysse et al.’s (2014) research presents doubt as to whether public investment practices allocate means well by focusing on knowledge-producing institutions, when collaborations may be the strongest source of knowledge in innovation ecosystems. This is relevant for the Kvarken region, as it sees high input from the European Regional Development Fund (ERDF), for example (Interreg Botnia- Atlantica, n.d.). The problem of also presents itself for industry practitioners, who face a number of choices regarding knowledge sourcing without knowing which constellation of sources is most effective. To better understand the needs of the multiple stakeholders, including policymakers, knowledge ecosystem actors, and industry practitioners, we must know more about the knowledge ecosystem and collaborations as knowledge sources.

1.3.1 Knowledge Ecosystems as Driver of Innovation

One of the widest literature streams regarding knowledge input for innovation, studies knowledge ecosystems and actors therein, including research institutions, universities and other academic actors (cf. Etzkowitz & Leydesdorff, 1995; Etzkowitz & Leydesdorff, 2000; Thomas

& Auto, 2020, p. 2; Zhao et al., 2019, p. 25313). Two of the most vital directions within this literature stream include innovation helix theory, often treated as universal models of innovation that can be applied regardless of setting (Etzkowitz & Leydesdorff, 1995; Etzkowitz

& Leydesdorff, 2000; Budden & Murray, 2019, p. 3), and entire ecosystems, emphasizing that complementary effects are present when combining knowledge and innovation ecosystems (Schaeffer et al., 2018, pp. 58; Heaton et al., 2019, pp. 936-937; Zhao et al., 2019, p. 25313).

A significant degree of interplay and several complementary effects are seen through the existence of multiple ecosystems, such as in the example of the Triple Helix of Innovation model. This model provides a framework where innovation is contingent on alignment of academia, industry, and state to reach optimal performance. Knowledge ecosystems, seen as whole ecosystems or through proxies like academia and universities, aid the creation of the most successful innovation ecosystems (Heaton et al., 2019, pp. 927, 936-937; Schaeffer et al.,

Optimal Innovation Performance

Knowledge Input

Intercompany Collaborations Knowledge

Ecosystem

Innovation Efforts

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2018, pp. 57-58). However, this is contingent on state support, industry existence and the industry’s level of involvement (Etzkowitz & Leydesdorff, 2000, p. 2). By changing the geographical setting from one place with a strong knowledge ecosystem to another with a strong knowledge ecosystem, one effectively alters the state, academic and industry actors present.

However, knowledge ecosystems may still act complementarily to innovation ecosystems when viewed in different settings, or without even considering the setting (Schaeffer et al. (2018, pp.

57-58; Zhao et al., 2019, p. 35322). Different constellations of external environmental factors create various degrees complementary effects, with the greatest effects seen in metropolitan and densely populated areas (Schaeffer et al., 2018, p. 58; Shearmur & Doloreux, 2009, p. 98).

Europe’s regions often specialize their innovation development approaches to capture the benefits of these variances (European Week of Regions and Cities, 2019). For instance, the Kvarken region has numerous active research institutions and universities, with multiple academic actors involved in its Kvarken Space Center project (Kvarken Space Center, 2020b).

Liberal politics and active state support of academia, as evidenced by e.g. high public spending on universities per full-time equivalent student compared to other countries (National Center for Education Statistics, 2019), suggest that academia can drive New Space industry innovation in the region. In other words, knowledge ecosystems can be beneficial to innovation even in the peripheral Kvarken region.

1.3.2 Intercompany Collaborations as Driver of Innovation

There is opposing research suggesting that in areas where knowledge is not widely sourced from knowledge ecosystems, i.e. there is a lack of knowledge transfer from them, innovative efforts suffer (Tödtling et al., 2012, pp. 19-20; Grillitsch & Nilsson, 2015, p. 317). For example, business innovation research often finds that technical competence is mainly centered in metropolitan regions with a high presence of academia. This is also the case for local knowledge spillover, i.e. knowledge transfer taking place between actors closely located, be they institutions or companies (e.g. Audretsch & Feldman, 1996, p. 639; Jaffe et al., 1993). This makes the case that metropolitan regions increase the ability of companies to form intercompany collaborations, compared to peripheral settings. However, while most articles study how knowledge ecosystems, local knowledge spillover and intercompany collaborations may complement each other (e.g. Bathelt et al., 2004; Camagni, 1995; Cooke, 2002a), there may be a compensatory relationship between collaborations and spillover from knowledge ecosystems (Tödtling et al., 2012, pp. 19-20; Grillitsch & Nilsson, 2015, p. 317).

As Lagendijk & Lorentzen (2007, p. 465) explain, “organizational proximity plays a pivotal role in addressing peripherality”. Furthermore, they state that “from the perspective of places and actors, geographical proximity underpins their connectivity and positionality …”

(Lagendijk & Lorentzen, 2007, p. 460). The lack of geographical proximity in peripheral companies makes them depend more on direct interaction with other companies than companies in areas with more local knowledge spillover and other complementary effects. This is compensated for through collaborations on all levels, including globally, nationally, regionally, and locally. Grillitsch & Nilsson (2015, p. 316) explain this compensatory phenomenon through stating that “firms in peripheral regions can be innovative only to the extent to which they are able to compensate for lacking opportunities of local knowledge spillovers”. Geographical proximity is substituted by organizational proximity through collaborations, effectively replacing the importance of local knowledge-providing institutions such as academia.

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1.4 Research Gap

The two literature streams, whether knowledge ecosystems or intercompany collaborations lead innovation in peripheral areas, introduce a clear split in the research community. On one hand, both geographical and knowledge peripheries can be studied using methods imported from metropolitan regions. This may find knowledge ecosystems as a highly beneficial in the development of innovation. On the other hand, since peripheral regions see lower population densities and more organizational thinness than metropolitan regions, companies see a lack of both partners and potential personnel. Knowledge spillover is sparser due to fewer actors in knowledge ecosystems. To compensate, firms shifts focus to decreasing organizational proximity to other industry actors through collaborations, which also becomes the main way through which these firms acquire knowledge. In this thesis, we aspire to fill this important gap by clearly identifying how collaborations and knowledge ecosystems are used as knowledge sources. Although the two dominant streams of literature explore two distinct mechanisms of knowledge sourcing, the intersection which realistically portrays knowledge sourcing in the peripheral areas, remains unexplored. This is important because actors in those areas, compared to metropolitan actors, see fewer available knowledge sources and less local knowledge spillover. In this thesis, we aspire to fill this important gap by clearly identifying how collaborations and knowledge ecosystems are used as knowledge sources.

1.5 Research Question

The following research question is a manifestation on the vital factors studied. It considers what sources of knowledge input are used in the innovation process for highly innovative and technology-intensive industries. The specific setting is the peripherally located Northern Swedish New Space industry. So, by focusing on the Northern Swedish New Space companies, we formulate the following research question:

How are collaborations and the knowledge ecosystem used to source knowledge for the innovation process?

1.6 Research Purpose

The purpose of this thesis is to increase the understanding of how high-tech companies in a peripheral region utilize the knowledge ecosystem and intercompany collaborations in order to source knowledge input for the innovation process. This is of interest due to the disagreements in previous research regarding best knowledge sourcing practices in peripheral regions. Several literature streams, including innovation helix and ecosystems theory, outline beneficial effects of governments using academia to develop industry innovation (Etzkowitz & Leydesdorff, 2000, p. 7; Schaeffer et al., 2018, p. 58), while other literature streams disagree by mainly being in favor of industry development (cf. Grillitsch & Nilsson, 2015, p. 314; Tödtling et al., 2012, pp. 19-20; Tödtling & Trippl, 2005, p. 1213). In doing so, we also aim to demystify the New Space industry, because it faces much confusion and uncertainty (Kennedy, 2020). The project does that through studying the industry in a tangible, empirical setting and treating it with business development analyses applicable to other industries, to show that it is an approachable field.

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1.7 Industry Introduction 1.7.1 The Space Industry

Traditionally, the space industry has been characterized by expensive, large-scale projects issued and controlled by government initiatives (Paikywsky, 2017, pp. 85-86). National interests such as national security, military consideration and economic growth are at the forefront of such activities, such as the Space Race and the moon landing undertaken by NASA (Durko, 2019, p. 1). Furthermore, projects issued by the US government to NASA under the guise of national interests use cost-plus pricing (Dinardi, 2020). This is a strategy often used on government contracts to choose the most qualified contractor, not necessarily the cheapest one, through removing economic pressure and accompanying failure rates (Dinardi, 2020; Kenton, 2019). These cost-inefficient approaches revolving around national interests have come to hinder innovation, largely proving their inability to keep up with new inventions and technologies in the industry, being dubbed “Old Space” accordingly (Dinardi, 2020).

To characterize the ways through which value is created in the space industry, activities are further divided into two sub-sectors; upstream or downstream (Space Safety Magazine, 2014;

UK Parliament, 2007). Hereby, these sub-sectors will be referred to as nodes. Upstream refers to work focused on sending objects into space and space exploration, whilst downstream work is aimed at extracting data from upstream work and missions. An arguable third node exists as well, namely infrastructure, aimed at facilitating upstream work (Dinardi, 2020). This relationship often makes infrastructure and upstream grouped together. Traditional space- related activities have predominantly focused on upstream and infrastructure development, laying the foundations for and enabling space technology.

The turnover involved in upstream and downstream work differs significantly, with a majority of it being derived from downstream activities. In the UK space industry for example, 80-85%

of the industry’s £13.7 billion turnover is consistently generated from downstream work (UK Parliament, 2007; UK Space Agency, 2016, pp. 1, 8). See Figure 3 for example activities in a two-node model of the space industry focusing on upstream and downstream actors.

1.7.2 The Emergence of New Space

The “New Space” concept originates from the “Old Space” terminology and is a relatively new phenomenon due to recent innovative and technological changes in the Space industry (Paikowsky, 2017, p. 84). In recent years, the space industry has changed due to a decline in costs and greater access to space capabilities which, in turn, have allowed greater involvement by smaller firms in the private sector and small and developing countries, for example. From these changes, the New Space industry evolved, allowing potential actors to focus on innovative technologies and new models for performing e.g. R&D, whilst also engaging in entrepreneurial activities and commercialization (Paikowsky, 2017, p. 84). In other words, the New Space industry is much more innovative compared to the “Old Space” industry (Paikowksy, 2017, p.

86). Since the New Space industry offers e.g. services such as manufacturing Smallsat systems and components, satellite communication and geo-information, navigation, satellite servicing and mining of asteroids (Frischauf et al., 2018, p. 135), this industry focuses more downstream involvement, with only some upstream involvement. The Old Space industry, however, mainly focuses on upstream services (Subari & Hassan, 2016, p. 19). Today, the space industry consists of both Old and New Space ecosystems. However, New Space ecosystems are relatively new and unexplored, and therefore require further attention. This is where our thesis is positioned.

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Figure3:UpstreamVersusDownstreamActivitiesintheSpaceIndustry(UK Parliament,2007).

1.7.3 Relation to Northern Sweden

According to Durko (2019), New Space innovation is, in general, inspired throughout an ecosystem that further leads to the creation of entirely new markets. Furthermore, Paikowsky (2017, p. 84) states that New Space should be referred to as a new ecosystem for global and local space activities. The New Space ecosystem detected in the United States is based on novel relationships between academia, NASA, and the aerospace industry (Hubbard, 2013, p. 1).

These actors align well with the factors of the Triple Helix of Innovation model put forth by Etzkowitz & Leydesdorff (1995) when studying innovation systems; academia, state involvement (mainly through NASA), and industry involvement (through e.g. Boeing) are present. It is evident from these findings that the space industry at large is both an innovation system and an innovation ecosystem. This is also the case for the Kvarken region; academia is heavily involved through the several universities in the region, state involvement is high through regional development programs such as the ERDF, local incubator programs and ESA BIC funding, while several industry actors within the New Space industry are present. As such, analyses and theories regarding ecosystems and knowledge sourcing are highly applicable to the empirical setting of Northern Sweden’s New Space industry.

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1.8 Disposition

In approaching our topic, we used a 6-chapter split, as portrayed in Figure 4.

Figure 4: The 6 Parts that Comprise this Research Project.

Chapter 1 presents ecosystems and innovation as topics of interest, before introducing the scope and empirical setting. The scope and setting are used to further identify relevant literature streams, from which the research gap and question are derived. Thereafter, the industry studied is introduced, for clarification of factors surrounding the novel New Space industry.

Chapter 2 presents the theoretical framework for our thesis and includes previous research on business ecosystems, ecosystem classification and identification through different perspectives, innovation systems, peripheries in business research and organizational knowledge sourcing.

Lastly, a research model is provided, based on the presented literature and the current situation.

Chapter 3 treats methodological considerations. It starts by reflecting upon the literature of chapter 2, followed by an overview of the methodology. Thereafter, the philosophical points of departure, research approach, strategy and case study design are explained, followed by the empirical case setting, data collection and data analysis methods. Lastly, the relevant quality criteria and ethical considerations are emphasized.

Chapter 4 is arranged in six sections covering the empirical findings. Observational and interview-based findings are presented together. The three first sections cover knowledge sourcing processes from the knowledge ecosystem, collaborations, and residual sources, including networks, monitoring and mobility. The fourth section covers the knowledge input gaps identified by the industry. The fifth section regards residual results. Lastly, the key empirical findings are summarized in the sixth section.

Chapter 5 analyzes the findings and discusses them in light of the presented literature and the research question. Knowledge sourcing in the downstream, upstream, and support industry nodes are assessed separately. Lastly, outstanding industry-level factors and complementarities are presented, in order to assess the knowledge sourcing composition in Northern Sweden’s New Space industry. Emerging models and results are discussed as they appear.

Chapter 6 summarizes the main findings, contributions, and limitations in the present research project, before our concluding remarks are presented.

1.

Introduction

2.

Literature Review

3.

Methodology

4.

Findings

5.

Analysis and Discussion

6.

Conclusion and Implications

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2. Literature Review

In this chapter, we present the theoretical framework of the thesis. It includes the origins of ecosystems research, classification of ecosystems through different perspectives, formation of innovation systems, peripheries, and theories on organizational knowledge types and sourcing methods.

2.1 The Origins of Ecosystem Research – Networks Theory

The discovery of business ecosystems theory revolved around identifying the synergistic effects of networks (Thomas & Autio, 2020, p. 4), and thus, it is imperative to understand networks research. Networks research emerged from Granovetter (1985), who explored social behavior in economic activities. The importance of managing dependencies through social interactions was a core focus of his research. This can be done through informal assistance through contacts, formal alliances, and other means of cooperation, for example. However, business actors are, through networks, also affected by other actors that they are not directly involved with. This is called non-generic complementarities by Shipilov & Gawer (2020, p. 96), see Figure 5. This existence of an effect not directly explicable through networks theory were one of the factors that drove the research of business ecosystems as a separate concept. This effect is called complementarities, i.e. interdependence of network participants that are not directly linked, linked in a manner that affects their performance (Thomas & Autio, 2020, p. 4).

As networks research has matured, ecosystems theory has been increasingly separated from networks theory. As put forth by Instead & Gawer (2020, p. 92) when discussing how dependences separate ecosystems from networks, “[they] are two distinct manifestations of how organizations can manage this dependence.” However, it has proven difficult to move from ecosystems as a concept to defining what ecosystems and their effects are, which is the source of much confusion today (Thomas & Autio, 2020, p. 2).

Figure 5: Network Versus Ecosystem.

Legend: Thick Lines Represent Interorganizational Network Relationships; Dashed Lines Are Non-Generic Complementarities (Shipilov & Gawer, 2019, p. 96).

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2.2 Classifications of Ecosystems

The ecosystem concept has gained significant attention in both practice and theory. Despite increased devotion, the concept struggles to provide a clear definition (Tsujimoto et al., 2018, p. 2). Moore (1996) initiated the use of the term in the business literature and defines it as:

An economic community supported by a foundation of interacting organizations and individuals – the organisms of the business world. This economic community produces goods and services of value to customers, who are themselves members of the ecosystem. The member organisms also include suppliers, lead producers, competitors, and other stakeholders. Over time, they coevolve their capabilities and roles, and tend to align themselves with the direction set by one or more central companies. … [The]

ecosystem leader … enables members to move toward shared visions to align their investments, and to find mutually supportive roles. (Moore, 1996, p. 26)

Furthermore, by reviewing the usage of ecosystems within business terminology, it is clear that multiple understandings exist (Thomas & Autio, 2020). Non-academic use cases often pertain to business actors involved with other actors that are either spatially related, thematically related, or both. For example, industry and business clusters, incubators and other spatially oriented meanings are often implied when referring to ecosystems. However, when discussing business actors interlinked through platforms or similar activities and output, additional classification is required.

Further classification and treatment of the word ecosystem are warranted both in academic and in non-academic fields, as scholars of different business profiles, be it strategy, management, economic geography, or innovation, imply different meanings in the word (Thomas & Autio, 2020, p. 4). For example, strategy scholars define an ecosystem as the influencing actors in creating a value proposition, while economic geography scholars follow purely spatial criteria, and innovation scholars focus on both physical and virtual clusters of innovation activities in related fields (Thomas & Autio, 2020, p. 4-5). Such a lack of conformity arguably confuses non-academic actors as well, when it comes to understanding and treating the term. Resultingly, recent literature has aimed to clear up this confusion (e.g. Adner, 2017; Thomas & Autio, 2020;

Tsujimoto et al., 2018, p. 2; Jacobides et al., 2018, p. 3), which sections 2.2.1 and 2.2.2 present in order to describe what the involved ecosystem concepts are, and what they are not.

2.2.1 Classification by Affiliation or Value Proposition Structure

Adner (2017, p. 39), a strategy scholar, attempts to conceptualize the ecosystem structure and examine relationships among ecosystems. The researcher distinguishes between two approaches, ecosystem-as-affiliation and ecosystem-as-structure. However, both can be present simultaneously in a given setting.

The first approach is an actor-centric ecosystem-as-affiliation approach that describes major parts of the literature, and “sees ecosystems as communities of associated actors defined by their networks and platform affiliations” (Adner (2017, p. 40). This view stresses the growth of independence, the likelihood of symbiotic relationships in fruitful ecosystem and the interruption of traditional industry boundaries. Strategy-wise, this ecosystem approach focuses on increasing its centrality, its expected power and the number of actors with links to a focal platform or actor. Furthermore, the increased number of actors may facilitate new interactions and combinations that lead to improved value creation in the ecosystem (Adner, 2017, p. 41).

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Examples of ecosystems-by-affiliation include Silicon Valley, the Microsoft ecosystem, or the healthcare ecosystem (Adner, 2017, p. 41). Actors in these ecosystems are largely identifiable by either location or the output or end activity. This view coincides with economic geography scholars’ spatial approach, and innovation scholars’ thematic similarities in innovation activities creating affiliation.

The second approach is an ecosystem-as-structure approach that takes an activity-centric view of interdependence and “views ecosystems as configurations of activity defined by a value proposition” (Adner, 2017, p. 40). It is a complementary approach to the ecosystem-as- affiliation and consider interdependent value creation. This approach starts with a value proposition and secondly search for actors that, through interactions, can achieve the value proposition (Adner, 2017, p. 41).

As an example, when grouped by structure, the producers of smartphone chassis, transistors, phone processors, radio antennas, sensors, LCD digitizers, touch screens, batteries, designers, and so on, may end up within the same ecosystem despite undertaking vastly different activities in vastly different areas of the world. The smartphone producer, i.e. whoever puts all these parts together, ends up being the only actor in contact with most, if not all other involved actors, while interlinking a large network of actors influencing the value proposition through non- generic complementarities.

Additionally, Adner (2017, pp. 42-43) built a new definition for ecosystems and contributed four elements of alignment structure, namely activities, actors, positions and links. The definition is as follows: “The ecosystem is defined by the alignment structure of the multilateral set of partners that need to interact in order for a focal value proposition to materialize. (Adner, 2017, p. 42)

The “alignment structure” refers to the existence of a mutual agreement between the actors concerning their flows and positions in the ecosystem. In order to maintain an effective ecosystem, the participants need to be pleased with their positions. However, in environments where actors do not need to be aligned, it is not valuable to call for a logic of ecosystem.

Furthermore, ecosystems are “multilateral” and require critical interactions between these relationships in order for the ecosystem construct to matter (Adner, 2017, p. 42). The “set of partners” entail that actors in the ecosystem have the same value creation effort as a goal, i.e.

their participation relies on that specific proposition. The last part of the definition, “for a focal value proposition to materialize”, refers to the need of materialization since it requires the actors to reach a level of coordination in order to deliver (Adner, 2017, p. 43).

Table 1: The Four Elements for How Value Is Created through an Ecosystem-as- Structure or Ecosystem-as-Affiliation Approach (Adapted from Adner, 2017).

Element Function

Activities Specify the actions to be undertaken to materialize the value proposition.

Actors Entities that undertake the activities. A single actor may undertake multiple activities; conversely, multiple actors may undertake a single activity.

Positions Specify where in the flow of activities across the system actors are located and characterize who hands off to whom.

Links Specify transfers across actors, not necessarily with a direct connection.

Transfer contents can vary between matériel, information, influence, and funds.

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Ecosystem-as-Structure

. Activities Links Actors

Positions Determined

Ecosystem-as-Affiliation

. Actors Links Activities

Positions Determined

Value proposition

Determine required activities

Find actors

Actors Observe

actors' links

Create value proposition

Both approaches, ecosystem-as-affiliation and ecosystem-as-structure, organize themselves according to the elements in Table 1, but do so in opposite order. The ecosystem-as-structure approach starts with a value proposition, followed by the process of determining the required activities for materialization and lastly finds actors to align with positions. The ecosystem-as- affiliation approach, on the other hand, starts with the actors, observes the present links and lastly creates a value proposition (Adner, 2017, p. 44). See Figure 6 for a conceptualization.

As a conclusion, Adner (2017, p. 39) states that it is the “value proposition that creates the … [external] boundary of the relevant ecosystem”.

E

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2.2.2 Classification by Ecosystem Activity

Through applying a more direct lens, one may classify ecosystems by the activity and reason for which it was formed. Ecosystems are often referred to in relation to a number of different other words, including “platform”, “innovation”, “business”, and “knowledge” (Thomas &

Autio, 2020, p. 2). Although these terms for ecosystems are used diffusely and with much overlap, likely due to the confusion stemming from a lack of clear definitions, they do to some extent specify the scope of an ecosystem’s activities. However, while many of the ecosystem

“subcategories” mentioned have diffuse bounds, it is clear that many refer to concepts outside of that which is clearly defined by previous scholars.

To move towards a wholesome understanding of ecosystems, one must realize that they can be classified by different spatial levels of analysis, for example the local, regional, national and international levels (Thomas & Autio, 2020, p. 5). Moreover, a number of criteria can be introduced. Participants of ecosystems tend to display high levels of heterogeneity, interdependence must be present, be it technological, economic, or cognitive, most participants’

ecosystem involvement must take place on a non-contractual basis, and the outputs they collectively produce exceed those of any given ecosystem participant (Thomas & Autio, 2020, pp. 10-15). If these criteria are fulfilled, one can most likely identify an ecosystem’s existence.

Based on the degrees of presence of these criteria, as well as the abovementioned levels of analysis, Thomas & Autio (2020) identified three main ecosystem subcategories: (1) the knowledge ecosystem, (2) the entrepreneurial ecosystem, and (3) the innovation ecosystem.

Figure 6: Order of Priorities in Various Ecosystem Approaches. Adapted from Adner (2017, pp. 42-44).

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Category 1: Knowledge Ecosystem

Level of analysis: Usually regional, due to the emphasis on collectively learning and producing new knowledge through knowledge exchange processes.

Participant heterogeneity: Participants’ commercial interests may diverge, but within the ecosystem, the goal is to create new pre-commercial knowledge, i.e. that which is not embodied products, services or business models, through complementarities. Participants whose commercial interest is contractual with ecosystem participant(s) are generally not regarded as part of the ecosystem.

Interdependence: The new pre-commercial knowledge created should be a shared resource that no ecosystem participant may manage to produce single-handedly.

Output: The central aspect for knowledge ecosystems is that new knowledge is the activity and output; it is not necessarily commercial nor competitive, and no product needs to be targeted at a defined audience. However, Thomas & Autio (2020) recognize that the insights from knowledge ecosystems by such a definition appear “difficult to distinguish from those in the extensive literature on systems of innovation” (Thomas & Autio, 2020, pp. 8, 11).

Category 2: Entrepreneurial Ecosystem

Level of analysis: Several aspects and actors in entrepreneurial ecosystems have previously been subject to much research in innovation and economic geography, specifically clusters, industrial districts, and regional and national systems of innovation (Thomas & Autio, 2020, p.

20). Much previous research emphasizes regional clusters and geographical proximity, making the regional level of analysis most common for entrepreneurial ecosystems. In the same lieu, the ecosystems build upon similar concepts to that of previous research to create a distinct entity through emphasis on entrepreneurial agents and business model innovation. Thomas & Autio (2020, p. 21) explain this relationship by stating that “[e]xperimentation-driven collective discovery and related knowledge exchange regarding effective business model recipes are facilitated by geographical proximity.”

Participant heterogeneity: Entrepreneurial ecosystems attract a range of actors, including venture funding, venture accelerators and specialized advisors. The ecosystem participants’

roles are organized around the early life cycle of new ventures (Thomas & Autio, 2020, p. 12).

Interdependence: The participants in entrepreneurial ecosystems carry out highly specialized roles, “yet the roles are interdependent in the sense that none of the ecosystem participants alone is able to create the ecosystem output.” (Thomas & Autio, 2020, p. 13).

Output: The output of entrepreneurial ecosystems is business model innovation as carried out by new ventures. This is more aimed at finding new ways to capture value, instead of aiding a value proposition. It differs from ecosystems delivering products and services as this output is not meant for consumption by a defined audience (Thomas & Autio, 2020, p. 20). The output is guided by niches and research-based knowledge spillover.

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Category 3: Innovation Ecosystem

Level of analysis: There are three subsystems to innovation ecosystems. Firstly, there are business ecosystems, which emphasize “broader community within which a focal firm operates” (Thomas & Autio, 2020, p. 17). Examples include industry parks, shopping streets, and larger agglomerations of businesses that may or may not operate with similar focus.

Thereafter, there are modular ecosystems, emphasizing “collective co-production of an ecosystem value offering directed at a defined audience” (Thomas & Autio, 2020, pp. 16-17).

Actors in modular ecosystems have vital roles in the production of a value proposition, and the level of analysis is the individual parts that are active in that value proposition. Lastly, there are platform ecosystems, which are based around the coordination of technological interdependencies, generally through platforms. The level of analysis for platform ecosystems is linked to the platform itself, be it e.g. regional emissions standards or international industry standards.

Participant heterogeneity: Actors interested in value co-production tend to be the participants in innovation ecosystems, through such ecosystems fostering multi-stakeholder relationships around value-creating methods (Thomas & Autio, 2020, p. 16).

Interdependence: The type and degree of interdependence depends on the subcategory of innovation ecosystems studied. For example, business ecosystems are dependent on the community for economies of scope, whereas modular ecosystems depend other on actors in the value proposition, and platform ecosystems are dependent on other actors in the platform for the platform to hold value and attractiveness (Thomas & Autio, 2020, pp. 11, 16-17).

Interdependence differs from conventional supply chains in that not all supplier relationships are contractually involved.

Output: The various subcategories of innovation ecosystems all have one criterion in common, namely a commercially deliverable output at a defined audience. The value of the ecosystem’s offerings can be boosted by complementary products or services from and in the ecosystem (Thomas & Autio, 2020, p. 17).

Table 2: Summary of Ecosystem Classification (Adapted from Thomas & Autio, 2020).

Criteria Knowledge Ecosystem

Entrepreneurial Ecosystem

Innovation Ecosystem Level of Analysis Mostly regional Regional, local

clusters

Industry platform, value chain, or locality Participant

Heterogeneity

Various commercial interests

Various functions to serve new ventures

Multi-stakeholder relationships around value co-production Interdependence Knowledge created

collectively as shared resource

Highly specialized actors all dependent on venture success

Economies of scope, value chain modularity

Output Pre-commercial

knowledge

Business model innovation, new ways to capture value

Commercially

deliverable output with defined audience

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2.3 Innovation Ecosystem Stakeholders

When studying innovation systems in general, it is vital to identify the stakeholders involved, as stakeholders play a vital role in nourishing and sustaining the systems both during their creation and their functional lifetime (Budden & Murray, 2019, p. 2). While some authors, like Budden & Murray (2019) emphasize the presence of investors and support actors, the traditional view of the vital stakeholders for innovative economic growth is the Triple Helix of Innovation model, where government, university and industry involvement are considered (Etzkowitz & Leydesdorff, 2000, pp. 2-3, 7).

As the roles of industry and university actors changed in the 20^th century, overlaps occurred.

Industry actors and businesses changed their investment policies to favor R&D and innovation, entering traditional university research spaces, whilst university actors and academia invest increasingly more resources in industrial research and the “scientification of industrial production” (Etzkowitz & Leydesdorff, 1995, pp. 14, 16). Government involvement is shifting as well, with incentives and pressure placed on academia to contribute to wealth creation and changing governance over economic institutions. The interplay between the actors and the changes they undertake create what is referred to as the Triple Helix of Innovation, revolving around knowledge-based economic development bringing “the knowledge, productive and regulatory spheres of society into new configurations” (Stanford University, n.d.).

The function of the Triple Helix’s industry side is to emphasize “conscious reshaping of the knowledge infrastructure under conditions that theoretical uncertainty adds to the uncertainties of the markets” (Etzkowitz & Leydesdorff, 1995, p. 18). Similarly, academia and universities partake in knowledge-infrastructure-oriented activities, like industry actors. In addition, universities’ roles are supplemented through the rise of the entrepreneurial ecosystem, which proactively aims to apply knowledge and create new knowledge (Stanford University, n.d.).

While academia and industry are somewhat limited in isolation, government involvement in the Triple Helix is largely what separates different innovation system outputs and effectiveness levels. Governments add additional flexibility in the Triple Helix model through organizing thee potential configurations as per the neo-institutional perspective, namely the statist (1), laissez-faire (2) and the balanced (3) configurations.

(1) The statist configuration entails that state and government activities lead the helix, driving academia and industry, whilst also limiting the extent of which industry and academia can develop innovative transformations. Such state-controlled systems typically arise from socialist and communist political orientations, as seen in e.g. Russia, China and some Eastern European countries.

(2) In a laissez-faire configuration, a capitalist orientation is assumed, where state intervention is low and industry actors become the driving force of innovation.

Universities assume the task of supplying skilled human capital, while government actors become social and economic regulators. This is typically seen in the US, and in certain Western European countries.

(3) Balanced configurations tailor a mix of industry, academia and government actors to drive knowledge innovation. Here, academia and related knowledge institutions act together with industry and government actors to form or even lead joint initiatives (Etzkowitz & Leydesdorff, 2000, p. 111). This approach is often argued to be most

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beneficial, as “the most favourable environments for innovation are created at the intersection of the spheres” (Stanford University, n.d.). National and multi-national investment strategies have favored some state involvement in innovation ecosystems, due to the interdependent benefits of joint industry, government and academia innovation initiatives (Stanford University, n.d.).

Table 3: Effect of Various Types of Government Involvement in Triple Helix Innovation System (Etzkowitz & Leyesdorff, 1995; Stanford University, n.d.).

Approach Function State intervention Political orientation Knowledge innovation leader Statist Limiter Yes Socialist/communist State

Laissez-faire Supporter No Capitalist Industry

Balanced Enabler Some Liberal Balanced/Academia

2.4 Innovation Ecosystem Stages

Regarding the result and output of innovation systems, academia agrees that innovation and innovation spillover do promote economic development (Zhao et al., 2019, p. 25313; Etzkowitz

& Leydesdorff, 2000). However, the stage of the innovation system and its activities, seen through innovation value chain theory, influence the extent to which economic development is promoted. As most studies view innovation and subsequent economic output as a dependent variable and knowledge input as the independent variable, it is vital to consider the source of the knowledge and the way in which it is applied (Stanford University, n.d.; Zhao et al., 2019, p. 25313).

Zhao et al. (2019, pp. 25313-25314) identify three non-mutually exclusive stages that one may classify innovation by, based on commercial focus and economic development. They are:

(1) Knowledge Innovation: Mainly undertaken by local knowledge-producing institutions, such as universities and other members of academia, usually measured through research paper output. No commercial focus. Aligns with knowledge ecosystem output.

(2) R&D Innovation: Primarily undertaken by enterprises and research institutes, measured through patent output. Some commercial focus, innovation helix confluence.

(3) Product Innovation: Primarily undertaken by enterprises, measured through new or increased product turnover of regional enterprises. Can be incremental, disruptive, etc.

High commercial focus. Aligns with commercial innovation ecosystems’ output.

Through relating these innovation stages to Innovation Helix theory, it becomes apparent that any innovation-producing system actor, be it e.g. entrepreneurial universities or industry actors, may partake in any of the innovation stages (Stanford University, n.d.; Etzkowitz &

Leydesdorff, 1995, pp. 14, 16). The leader of the innovation system can thus be said to influence the main output of the innovation system, with accompanying effects to economic performance.

Although Zhao et al. (2019, p. 25317) find that producing knowledge and high degrees of knowledge spillover may make economies more cost-effective, they also find that knowledge innovation may not directly be beneficial to economic development, as opposed to R&D and product innovations (Zhao et al., 2019, pp. 25322-25323).

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2.5 Peripheries

Innovation varies over time and space (Mowery et al., 2005, p. 14), leading to a great amount of research regarding innovation performance between regions, and the innovation practices and approaches of firms in different regions (Cooke, 2002b). Much research attention has streamed towards the Regional Innovation Systems (RIS) in knowledge-intensive sectors and in core regions (Doloreux & Dionne, 2008, p. 259). A reasonable justification for that trend is related to the fact that innovation requires basic conditions such as different types of knowledge, skills, resources and capabilities (Mowery et al., 2005, p. 5) mainly found near or within core regions (Doloreux & Dionne, 2008, p. 259). While peripheral areas somewhat lack basic conditions such as knowledge infrastructure and social capital (Tödtling & Trippl, 2005, p.

1213), they may also offer favorable conditions for industries that utilize large areas or have other concerns related to spatiality. Despite their differences, research is applied similarly on the majority of regions. The result is that research neglects the regions’ strengths and weaknesses regarding innovation potential and problems, regarding industries and knowledge institutions (Tödtling & Trippl, 2005, p. 1204). This is problematic since “there is no ‘ideal model’ for innovation policy as innovation activities differ strongly between central [and]

peripheral . . . areas” (Tödtling & Trippl, 2005, p. 1213).

2.5.1 Ways of Classifying Peripheries

Even within peripheries, various different forms of peripheries exist. For example, by applying basic world systems theory to the term, peripheries are countries that “possess a disproportionately small share of the world's wealth” (Migiro, 2018). However, within the aspect of regional development and organizational theory, peripheries are more generally treated as regions within countries, and have three typically characterized “shapes” that the literature stream discusses; spatial, i.e. . These peripheries typically assume one of three shapes;

(1) spatial, i.e. “non-core” peripheries (Lagendijk & Lorentzen, 2007, p. 459), (2) knowledge peripheries, i.e. those that see less competence and fewer universities (cf. Grillitsch & Nilsson, 2015, p. 309; Tödtling & Trippl, 2005, p. 1204), and (3) economical peripheries (Lagendijk &

Lorentzen, 2007, p. 463), lacking in social and economic development when compared to core areas, while not necessarily being economically marginal nor underdeveloped.

Knowledge, economic and geographical/spatial peripheries can be studied using several indicators. Tödtling & Trippl (2005, p. 1209), for example, studied geographical differences affecting firms and industry development, and the ways in which academia tends to differ in different regions, amongst other things. The various outlined indicators consider geographic and organizational factors, such as clusters, networks, firm structures, sizes, and more. While knowledge ecosystem manifestations are studied through universities and knowledge transfer, and economic development can be studied through R&D and product innovation (Tödtling &

Trippl, 2005, p. 1209; Zhao et al., 2019, p. 25322).