Master Thesis Innovation Sciences

Eindhoven University of Technology

MASTER

Financial technology innovation in the financial services industry of Vancouver B.C.

Baltissen, J.

Award date: 2017

Link to publication

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Financial technology innovation in the financial services industry of Vancouver B.C. A thesis submitted in fulfilment of the degree: Master of Science, Innovation Sciences

Jip Baltissen

Supervisors Eindhoven University of Technology Faculty of Industrial Engineering and Innovation Sciences Dr. Bert M. Sadowski Dr. Z.O. Nomaler Prof. dr. F. Alkemade

Consulate-General of the Kingdom of the Netherlands Economic department in Vancouver B.C. Canada Barry Nieuwenhuijs – Deputy Consul General Maarten den Ouden – Trade Officer

Eindhoven, August 2017

Preface From the end of January 2017 until June 2017, I did an internship for five months at the Consulate- General of the Kingdom of the Netherlands in Vancouver B.C., Canada. This internship gave me a unique chance to experience a new country and culture, while getting the opportunity to broaden my professional network, make new friends and gain work experience in an international environment. This experience is an invaluable asset to my career.

I would like to thank the staff of the Consulate-General in Vancouver for selecting me for this internship position. The subjects and tasks fitted very well with what I was looking for in an internship position, given the focus on innovation and technology of this vacancy. Also, the freedom and the large amount of time I received for doing my research during office hours, is something I want to thank the staff for. Adding to that, I am very pleased for the great atmosphere in the office that kept me motivated and gave me energy for doing the research.

Especially, I would like to thank Barry Nieuwenhuijs, deputy Consul General, and Maarten den Ouden, trade officer, from the Consulate-General for providing a great work environment, and for the feedback I received during the writing process of my thesis. I did not only learn a lot from this feedback, but also from their professional approach to work during economic trade activities, which is very useful for my further professional career.

From the University of Technology Eindhoven, I would like to thank dr. B.M. Sadowski for being a great mentor during the whole Innovation Sciences master’s program and during the last part of the bachelor Technical Innovation Sciences. Especially during the internship program, you have been a great mentor and guide through the research process. The feedback I received was very useful and made me push a little harder, which made the research process more interesting and helped me to substantially improve the quality of my thesis. I would like to thank Onder Nomaler as second supervisor for the support and feedback I received for improving the quality of my work.

Also, I would like to thank Nadieh Wesseling, MSc, for being a great colleague and friend during the internship. Your advice and additional feedback on my research helped me a lot with finding new research directions. I also would like to thank Sacha, and my parents who supported me during the internship selection and post-research process.

Executive summary The goal of this master thesis was to understand the dynamics between different stakeholders and their innovation activities in the emerging financial technology (FinTech) industry of Vancouver. In the case of Vancouver, different applications of innovative technologies by new ventures, a strong group of traditional financial services providers, and the involvement of universities and the government come together. The potential of the growing number of new ventures in the financial industry is that it introduces new business models, and improves the quality of service for both the traditional financial services and the FinTech company customers. The problem is that these new ventures should have access to enough resources, like for example public and private financial support and enough qualified personnel, in order to grow as a business. Their success and survival depends on how well they are connected in the inter-organisational network, and whether the resources they need are available to them. To get an overview and understanding of the roles of different stakeholders in the emerging FinTech industry and the types of resources they share, required an analysis to uncover the dynamics that hinder or stimulate the development of the innovative new ventures. The concept of social network analysis (SNA) has been used in which the centrality of different stakeholders in the financial services industry: FinTech companies, traditional financial services providers, universities and colleges, venture capital organisations and the government, has been analysed. This has been done for the complete network and four resource-based networks: knowledge, investment, advice and consultancy, and human resources.

This master thesis research contributed on two fronts, namely by contributing to the inter- organisational network literature with the application of the theory on a new type of industry, and by providing new insights to the Consulate-General of the Netherlands, gaining a better understanding of the activities and status of the innovation and technology sector of Vancouver. The main findings are presented in the section below.

Key empirical findings Venture capital organisations and the government have the most positive influence on the development of FinTech companies by taking a central position in the inter-organisational investment network. However, the lack of private financial support is far more inhibiting the innovation activities compared to public financial support.

From the human resources perspective, although universities and colleges take the most central position and they have the strongest connection with FinTech companies among the analysed stakeholders in the financial industry, a lack of enough qualified personnel is one of the most important factors that is inhibiting the further development and innovation by FinTech companies.

Traditional financial services providers have many connections in the financial technology network, however from a network perspective this stakeholder does not play an important role for the development of FinTech companies. Knowledge sharing is mainly done among FinTech companies. Financial institutions have also many connections in the knowledge network, however they do not have an important or influential role relative to financial technology companies.

Table of Contents

Preface ...... 2 Executive summary ...... 3 Key empirical findings ...... 3 Table of Contents ...... 4 List of Figures ...... 6 List of Tables ...... 6

1. Introduction ...... 8 1.1 Financial Technology Industry ...... 9 1.2 Research scope and question ...... 10 1.3 Research Questions ...... 11 1.4 Outline of the Thesis ...... 12

2. Research Context ...... 14 Introduction ...... 15 2.1 The Digital Revolution ...... 15 2.2 Financial Services ...... 15 2.3 The Global FinTech Landscape ...... 16 2.4 The FinTech Landscape of Canada and British Columbia ...... 18 2.5 The FinTech Landscape of Vancouver ...... 19

3. Theoretical Framework ...... 22 Introduction ...... 23 3.1 The notion of Inter-organisational networks ...... 23 3.2 The inter-organizational network and innovation activities ...... 25 3.3 Inter-Organisational Networks in the Financial Sector ...... 28

4. Methodology ...... 30 Introduction ...... 31 4.1 Unit of Analysis ...... 31 4.2 Conditions for Analysis ...... 32 4.3 Data Collection ...... 33 4.4 Data Analysis ...... 34 4.5 Research Quality ...... 35

5. Results ...... 36 Introduction ...... 37 5.1 Inter-organisational networks ...... 37 5.2 Resource-based Networks ...... 43 5.3 Inter-organizational network interpretation of results ...... 48 5.4 Innovation Activities in the FinTech industry ...... 50

6. Summary & Conclusions ...... 56 Introduction ...... 57 6.1 Summary ...... 57 6.2 Conclusion & Recommendations ...... 59

7. Discussion ...... 62 Introduction ...... 63 7.1 Theoretical Implications ...... 63 7.2 Practical Implications ...... 63 7.3 Limitations of this Research ...... 63 7.4 Suggestions for Future Research ...... 64

8. References ...... 66

Appendix ...... 70 Appendix A ...... 70 Appendix B - Research Survey FinTech ecosystem of Vancouver BC ...... 72

List of Figures

Figure 1: Global FinTech Financing Activity 2010-2015 (CB Insights, 2016) 16 Figure 2: Services export CA (Burt, 2016) - Average annual growth real exports, 2006–2015 (percent) 18 Figure 3: Private equity & Venture capital investment (Burt, 2016) - relative to GDP, 2015 (percent) 19 Figure 4: Overview FinTech companies in the financial services industry of Vancouver B.C (Sofia, 2016). 21 Figure 5: Metaphorical & analytical perspective network research (Bergenholtz & Waldstrøm, 2011) 24 Figure 6: The Digital Finance Cube (DFC) (Gomber, Koch, & Siering, 2017) 28 Figure 7: The core business of FinTech companies that have participated in this thesis 37 Figure 8: FinTech company participant's job titles 38 Figure 9: Network memberships for each type of actor in the FinTech ecosystem 39 Figure 10: Complete network of the FinTech ecosystem of Vancouver B.C. in Canada. 40 Figure 11: Average strength ties between FinTech & others in the networks (0 never) – 5 daily) 41 Figure 12: Knowledge Inter-organisational network 43 Figure 13: Investment Inter-organisational network 45 Figure 14: Advice and Consultancy Inter-organisational network 46 Figure 15: Human Resources Inter-organisational network 47 Figure 16: Innovation activities exploited by sixteen FinTech companies in Vancouver 51 Figure 17: Importance of information sources for innovation activities 52 Figure 18: Reach for Information sources for innovation activities by FinTech companies 52 Figure 19: Factors that inhibit the innovation activities of FinTech companies 53 Figure 20: Ventures in Vancouver B.C. Canada between 2011-2017 (angel.co – Elaborated by the author) 70 Figure 21: Number of ventures per category of core business in Vancouver B.C. Canada between 2011-2016 71

List of Tables

Table 1: Summary Network data 39 Table 2: Whole network characteristics 41 Table 3: Complete network variables per actor type 42 Table 4: Knowledge network variables per actor type 44 Table 5: Investment network variables per actor type 45 Table 6: Advice and consultancy network variables per actor type 46 Table 7: Human Resources network variables per actor type 48

1. Introduction

1.1 Financial Technology Industry During the 21st century a new industry, called FinTech, emerged. FinTech is a combination of the words financial and technology, which originally comprised the application of technology to the back office of established financial institutions. The term has nowadays expanded to include any technological innovation in the financial sector and the innovative use of technology in the design and delivery of financial services. Since the internet and the mobile-internet revolution, the FinTech industry has grown tremendously and is transforming the banking world as we know it. Technologies from artificial intelligence, peer-to-peer lending, Big Data, Blockchain, Crowdfunding, Digital payments and Robot advisors are examples of FinTech or technologies that are applied in this new industry.

FinTech is an example of a technology niche that is transforming the current financial regime. This new industry has become important in the aftermath of the global financial crisis. Historically, as technology evolved, the banking industry was reasonably good at integrating new technologies to provide better services to their customers. However, this has changed during the financial crisis in 2008, when banks were pushed in the direction of prioritizing the integration of new rules and regulatory requirements. During this period, investing in the application of innovations was less prioritised. At the same time, new technological innovations have transformed the way we live and have become part of our everyday life. The iPhone, AIRBNB, UBER, WhatsApp and WeChat are examples of these new technologies and business models. A chasm was created between what banks were offering and what customers came to expect from a user experience and convenience perspective. The FinTech industry developed and gained more traction in the financial services sector in that time, as even non-traditional banking players did. For example, Facebook currently has fifty different regulatory licenses in the US alone (Facebook, 2017). These licenses will allow Facebook users to transfer money through the Facebook Messenger app. Another example is Amazon.com that experimented with offering student loans over its platform in collaboration with Wells Fargo (Lobosco, 2016).

The application of technological innovation in the financial sector has created the opportunity for disintermediation of traditional players in this sector, by reducing costs and expanding the choice of financial services for consumers. Traditional financial hubs, such as London, New York and Hong Kong have a geographical advantage to take a dominant role in the FinTech industry. However, there is also an opportunity for other cities and regions to develop new ecosystems, because the range of technologies and knowledge that can be applied in the financial sector goes beyond exclusively financial knowledge. Additional to that, a developed FinTech industry can be an engine for economic growth, enriching a culture of innovation and entrepreneurship, with all the indirect long-term benefits for a city or country (Iansiti & Levien, 2004). The increased competition in this sector from new entrants is changing the dynamics between the different players in the financial ecosystem. Available concepts from Innovation and strategic management literature are needed to analyse the dynamics between different stakeholders in this changing industry.

1.2 Research scope and question An example of a regional financial industry in which new technology entrants are challenging incumbent stakeholders by applying innovations to the traditional operations of the industry, is the FinTech industry of Vancouver in Canada. For established organisations to succeed in this industry, they need to improve their internal processes for value creation by searching for complementarities available from others. By introducing the notion of inter-organisational networks for analysing this complex, multi-stakeholder innovation, insights on the role of different stakeholders in this innovation ecosystem are expected to be obtained. To analyse the inter-organisational network of Vancouver, available concepts from Innovation and strategic management literature are needed.

Firms should be able to effectively use their relationships with customers, partners or competitors to be more innovative and find solutions to the needs of its customers (Gulati, Nohria, & Zaheer, Strategic networks, 2000). In order to be able to obtain an understanding of these dynamics and interconnections, the factors and mechanisms that govern such networks and change when niches are competing with the established regime need to be analysed. A concept that has been used in several study areas, but mainly in Sociology, is the Social Network Analysis (SNA) concept (Granovetter, 1985). This concept has been applied at distinct levels (individual, inter-organisational, industry) in research to examine the links among different stakeholders, called structural research. The social network approach is grounded in the notion that the pattern of links between different actors has important consequences for those actors. Network analysts seek to uncover various kinds of patterns and they try to determine the conditions under which those patterns arise and to discover their consequences (Freeman, 2004). One of the most cited definitions in the SNA literature defines networks as: “a set of nodes (e.g. people, organizations, institutions) that are linked by a set of social relationships (e.g. friendships, transfer of funds, knowledge sharing) of a specified type, called ties” (Laumann, Galaskiewicz, & Marsden, 1978).

While research on the changes in the financial services industry is fragmented and sometimes even immature (Gomber, Koch, & Siering, 2017), there has been an increasing number of articles about Digital Finance between 2009-2015 which provides new insights for innovation management. Most of the new insights is gained from research about the relation between business functions and technologies, Gomber et al. (2017) reveal that some technologies and technological concepts are regularly connected with certain business functions. For example, research in the dimension of Digital Payments particularly focus on Near Field Communication (NFC) technology, a technology used in credit cards and digital devices to allow users to pay remotely by connecting two surfaces e.g. (Chen, Mayes, Lien, & Chiu, 2011). However, although the increasing number of articles in this subject area, there is still a lack of research that focusses on the interactions between different stakeholders.

This research aims to understand the dynamics between different stakeholders and their innovation activities in the emerging FinTech industry of Vancouver. In the case of Vancouver, different applications of innovative technologies by new ventures, a strong group of traditional financial services providers, and the involvement of universities and the government come together. The potential of the growing number of new ventures in the financial industry is that it introduces new business models, and improves the quality of service for both the traditional financial services and the FinTech company customers. The problem is that these new ventures should have access to enough resources, like for example public and private financial support and enough qualified personnel, in order to grow as a business. Their success and survival depends on how well they are connected in the inter-organisational network, and whether the resources they need are available to them. To get an

10 overview and understanding of the roles of different stakeholders in the emerging FinTech industry and the types of resources they share, requires an analysis to uncover the dynamics that hinder or stimulate the development of the innovative new ventures.

To offer insights in the above stated problems, this thesis aims to contribute in two ways. First, the research context is illustrated to give the reader a sense of understanding of the status of the FinTech industry worldwide and in Vancouver. The main goal here is to create a benchmark for the analysis of the emerging FinTech industry with an overview of the different FinTech ventures in the financial services industry. The second contribution of this thesis is to gain insights from the structural characteristics, innovation activities and obstacles for FinTech ventures in Vancouver. A structured method has been used for the data collection and analysis of this industry, by following the work of (Ricken, Schuler, Grandhi, & Jones, 2010). As a result, the second aim of this thesis is to provide an overview of interactions between different stakeholders in the FinTech industry and provide recommendations for improving the access to resources for new ventures in Vancouver.

This research is conducted in collaboration with the Consulate-General of the Kingdom of the Netherlands in Vancouver B.C., Canada. The Consulate-General strives to gain a better understanding of the activities and status of innovation and technological developments in West-Canada. These insights can be used to inform the Dutch Ministry of Foreign Affairs and Economic Affairs about potential opportunities in West-Canada for businesses in the Netherlands. This thesis specifically contributes to this goal by providing information about the status of an industry that is radically changing world-wide, and provides insights in the strengths and obstacles for innovation in the region. The research aims are translated into the following research question: ‘To what extent are stakeholders in the emerging financial technology industry of Vancouver B.C. Canada, involved in the development of new FinTech ventures’. The next section will describe the sub-research questions that create the context for answering the main research question of the thesis.

1.3 Research Questions To answer the main research question, sub-questions have been derived that form a link between the inter-organisational network concept and the case of the FinTech industry of Vancouver. The aim of these sub-research questions is to create a complete picture or context of the factors that are involved in the development of the FinTech industry in Vancouver.

The main research question

To what extent are stakeholders in the emerging financial technology industry of Vancouver B.C. Canada, involved in the development of new FinTech ventures?

Sub-questions

1. What is the status of the development of the financial technology in the region of Vancouver? Chapter 2

2. Why is the notion of inter-organisational networks a useful perspective to analyse the emerging financial technology industry of Vancouver? Chapter 3

3. What types of resources can stakeholders in the financial services industry of Vancouver share and how could the inter-organisational network concept be used to analyse this? Chapter 3

4. Which information sources in the FinTech industry are most important for innovation in the financial services industry and where is that information coming from? Chapter 5

5. Which obstacles do FinTech companies face in their development in the financial services industry of Vancouver? Chapter 5

1.4 Outline of the Thesis

Chapter 2 The research context will be illustrated, the FinTech industry worldwide and in Vancouver B.C. Canada. This chapter will conclude with an overview of the stakeholders that belong to the emerging FinTech industry and the results of a trend analysis conducted prior to the research.

Chapter 3 The theoretical framework, which consists of the inter-organisational network concept and previous research about FinTech are explained. The aim of this chapter is to give an overview of the concepts used in this thesis and provide the foundation for the rest of the report.

Chapter 4 The methodology chapter is used to describe in detail how the data for this research is obtained. The conditions for the Social Network Analysis are described and in the data analysis section the measures are defined.

Chapter 5 The results are described in this chapter by presenting the data that have been obtained with the methods as described in chapter 4. This chapter ends by giving an answer to the last three sub- questions.

Chapters 6 and 7 will conclude the thesis with the overall summary and conclusions and a reflection of the work by highlighting its limitations.

2. Research Context

Introduction This chapter starts by explaining the context in which the financial services industry is changing and in which other industries have changed, called the Digital Revolution. Then the financial technology (FinTech) concept will be described and where it comes from. The rest of the chapter will be devoted to describing the global, Canadian and finally the FinTech industry in Vancouver.

2.1 The Digital Revolution Technological innovations like the steam engine and electrification have been the driving forces for Industrial Revolutions from the 18th century onward. In an equivalent way, digital technologies are reshaping industries as these fundamental innovations did in the past. The emergence of innovations such as the Internet, smartphones, social media and cloud computing have been an important source of large-scale transformations across multiple sectors and industries. However, the role of these technologies is shifting according to a report by the World Economic Forum, because digital technologies will no longer be just drivers of marginal efficiency but enablers of fundamental innovation and disruption across different sectors (Spelman, Weinelt, Lacy, & Shah, 2017).

Organisations are trying to grasp the strategic implications of these fundamental transformations for their own positions and this led to the integration of digital technologies in every aspect of business operations. However, there are still organisations in industries that lag on the digitalisation of their businesses. These organisations are mostly characterised by a combination of embedded cultural and organisational challenges in industries with low entry barriers and traditional business models. According to research by Grossman, the sectors that are expected to be most affected by digital technologies during the 21st century include media, telecom and financial services (Grossman, 2016). Although these sectors will potentially be transformed, they are affected in different ways. The characteristics of industrial resources resolve how vulnerable organisations in an industry are for the changes of their business by digital technologies. Yip et al. (2003) define different drivers that characterise how industries can be affected, such as information intensity, electronic deliverability, network effects and standardization benefits. These internal characteristics of organisations and industrial resources are one side of the story, because the strength of the external landscape, the ability of niches to develop in a protective space, and access to the right skills, are among other things, external factors that also determine how fast and in which way industries transform. Therefore, analysing industries and economic interdependencies is needed due to the radical change in industrial contexts as we know them today.

2.2 Financial Services The financial services sector has historically been one of the sectors most resilient to technological disruption. Banks have built a robust regime with unique expertise and consumers have generally been slow to change financial services providers. The last decades of significant technological disruption, which was driven by the advent of the Internet and new business models, provided further evidence of the resilience of the players in the financial regime.

The portmanteau “FinTech”, conceived by a New York banker in 1972, refers to the intersection of technology and financial services. There is no widely accepted definition of what qualifies as a FinTech yet, nonetheless companies considered to belong to this sector include among others (mobile) payments, lending platforms, distributed ledgers like Blockchain, digital or crypto-currencies and artificial intelligence and robotics in finance. With an expanded definition considered to include

15 biometrics, digital identity, wearables, internet of things and technology to assist with regulations (“Regtech”), this sector also faces the challenge to align all these technologies.

Established and new technology firms can enter the financial sector and compete with the established financial institutions. Technology firms have the in-house technological expertise, daily existing touch points with their customers and to a certain extend they also have the customer’s trust and confidence. A young child is probably going to open a first bank account in the future, not at HSBC or ING, but rather with Facebook or Apple. If customers are confident enough to share their children’s’ photos on Facebook, people will probably also use these platforms to transfer money to friends and family. The FinTech industry is working on transforming how financial services are being delivered. Artificial Intelligence (AI) powered chat-bots can for example mimic human conversations in messaging apps and might have the potential to replace the current call centres. Start-ups and technology firms are connecting FinTech to the Internet of Things (IoT) and wearable technologies, embedding banking and insurance in the day-to-day life, so in the future consumers do not even need to worry about it (IBM Institute for Business Value, 2016). For example, car insurance premiums that automatically go down, because your car knows that you have been driving safely and or the other way around.

The next paragraphs will describe the current situation of the FinTech industry on different geographic scales from a Global to a Canadian perspective, followed by a description of the Vancouver FinTech landscape.

2.3 The Global FinTech Landscape During the aftermath of the financial crisis that started in 2008, the FinTech sector gained a considerable amount of momentum with a lot of investments from different players in the start-up world. For example, venture capitalists, private equity firms and corporates have invested a massive amount of money into financial technology start-ups. More than $50 billion has been invested in almost 2.500 companies since 2010 (CB Insights, 2016). Figure 1 depicts the increasing amount of investments and deal volumes between 2010 and 2015. The global FinTech investment in 2015 grew by 75% to $22.3 billion against $12.7 billion in 2014.

Figure 1: Global FinTech Financing Activity 2010-2015 (CB Insights, 2016)

Traditional financial services providers and the current financial regime remain important to the economy and continue to be the gateways to the most important payment systems. However, some things have changed in the external landscape during the last decade. First, the financial crisis in 2008 had a negative impact on the consumer trust in the banking system. EY’s Global Consumer Banking Survey, with 55.000 consumers from 32 countries, reveals that trust in traditional banks is lower than for other players in the financial sector, such as digital banks without physical branches (Ernst & Young, 2016). Second, the unprecedented applications of mobile devices have begun to attenuate the advantages of the distribution of offline branches of banks. Smartphones enable new payment methods as well as personalized customer experiences and services. There has also been a significant demographic shift with a new generation of customers, ‘millennials’, who ask for better online experiences and functionality.

The banks are realizing that the landscape is changing and to be able to evolve with this change they must devote more resources to providing better services that suit the needs of their customers. Some banks will succeed in this evolution, and being able to embed a new culture of innovation and entrepreneurship across the organisation, but many might not. This shakeout of traditional financial institutions will have its consequences. Citi bank estimates that over the next ten years, 40% of all banking jobs will disappear (Citi Bank, 2016). This will have serious consequences for many financial service centres. It’s not only the direct job losses, but also the related economy around will be affected, from law to accounting firms and from hotels to restaurants (Citi Bank, 2016). Some new jobs will be created in the FinTech industry, but a substantially smaller number.

Probably the most important change is how the next generation of talent is trained. For example, students in finance programs will need courses that teach them about FinTech. Besides teaching the next generation of student’s core courses like economics, corporate finance and strategy, finance or business schools need to incorporate in each curriculum courses on design thinking, coding and product development. This is very important, because the bankers of the future and those who will shape the future of this industry are likely not going to be traditional bankers, but rather designers, programmers and creative thinkers (Ernst & Young, 2016).

Instead of becoming victims of intense competition from technologically strong players, the most dynamic banks are seizing the opportunity to position themselves at the epicentre of a rapidly evolving industry ecosystem. Possessing an enormous customer base, banks that can succeed in the FinTech revolution can reposition themselves to orchestrating a broad range of services for the benefit of their customers (IBM Institute for Business Value, 2016). These established institutions that actively partner to build ecosystems around their customers will be better positioned to offer a wider range of services and better experiences because of innovation from FinTech and others.

FinTech is also bringing a lot of positive developments and one of the most important ones is financial inclusion. According to research by the World Bank, more than two billion people worldwide had no access to financial services in 2014 (World Bank, 2015). These are individuals who have no access to a bank account, alternatives to borrow money and for whom the only way to save money is to keep the money themselves. The lack of access to these services maintains a vicious cycle of poverty. However, this is not only a problem in developing countries, but in developed countries as well. In the US for example, only 68% of the households are fully banked (Federal Deposit Insurance Corportation, 2016). The good news is that for the first time in modern history, FinTech and especially mobile money, is offering these individuals financial services. According to the World Bank, the number of people worldwide having an account grew by 700 million between 2011 and

2014, and 62 percent of the world’s adult population currently owns a bank account (World Bank, 2015).

From a regulation and legislations point of view, governments should formulate new policies and regulations that are aligned with the needs of the future industry. This is important to make sure that everybody can adapt to a new reality, and innovation and growth in the financial industry is fostered rather than hampered. Next paragraph will focus more on the role of the Canadian government and how policymakers can actively support transformations in the financial services sector.

2.4 The FinTech Landscape of Canada and British Columbia The financial services sector is a vital part of the Canadian economy. In 2014, the sector accounted for approximately 10% of Canada’s gross domestic product (GDP). And in 2015, the sector grew at five times the rate of Canada’s overall GDP (Burt, 2016). As depicted by Figure 2, financial services are also Canada’s largest and fastest-growing source of services exports between 2006 and 2015.

Figure 2: Services export CA (Burt, 2016) - Average annual growth real exports, 2006–2015 (percent)

The Competition Bureau of the Government of Canada launched a market study about innovation in the Canadian financial services sector (Jokic & Briere, 2017). During a stakeholder consultation as part of this study across all segments of the financial services sector, they conclude that new regulations in this sector are essential. This maintains important public policy goals, such as safeguarding privacy and copes with financial crime. However, there are different perspectives between stakeholders, for example investors and new industry entrants share an argument in the certainty that regulation provides, while customers tend to place more trust in regulated businesses. Existing regulations in the financial services sector are diverse and fragmented though, which can be difficult for a start-up to navigate through. Another outcome is that regulations need to be flexible and ‘technology neutral’, which means that technology-specific regulations can exclude innovative new business models and all their associated benefits and growth potential for the Canadian economy (Jokic & Briere, 2017).

The FinTech industry in Canada has been gaining substantial momentum in recent years. Toronto possesses the largest financial services sector in Canada, followed by Montreal and Vancouver. The amounts of money spend in technology in this sector is growing rapidly, with nearly 15 billion Canadian Dollars (CAD) by 2018 (All Street Research, 2016). Canada is ranked third worldwide in the 2015 rankings for venture capital investments relative to GDP, with 0.08 percent of total GDP. Canada is ranked fourth in the private equity investment ranking, with 1.1 percent of total GDP

(Figure 3). Private equity investments, traditionally target established firms, while venture capital is a type of private equity that generally targets start-ups. Venture capital financing is often used as a metric for innovation and entrepreneurship activities in a country. Canada ranks high for both investment types and shows that Canada is a relatively attractive country as an investment destination. The key factors that contribute to this are the few burdens to starting a new business and strong corporate governance (Burt, 2016). However, there is a substantial difference between the top countries in these rankings, especially the United States and Israel.

Figure 3: Private equity & Venture capital investment (Burt, 2016) - relative to GDP, 2015 (percent)

The government is realising that a new industry is starting to evolve and therefore new regulations and policies are needed to foster innovation and growth in the FinTech industry. Also, the national private equity and venture capital investment climate belongs to the best in the world and therefore Canada is a good place to start a FinTech business. However, in Canada the political and investment climates can differ from province to province, therefore the next paragraph will draw upon the financial services and FinTech landscape in British Columbia and the city of Vancouver BC. The federal government is offering different funds for new businesses. For example, up to $1 million in federal government loans for individuals to create new businesses, or expand and make improvements on existing enterprises; and up to $4,000 for costs related to travel and accommodation for Canadian start-ups and SMEs to gain access to foreign markets.

2.5 The FinTech Landscape of Vancouver Vancouver is consistently named as one of most liveable cities in the world over the past decade, while simultaneously climbing the ranks as a global start-up hub (The Economist Data Team, 2016). The city of Vancouver has more start-ups per capita than any other city in Canada, and is leveraging its unique combination of assets: a strong industrial foundation, a diverse talent pool, with over half of its residents speaking a different language than English, and is ranked as one of the world’s top twenty Global Financial Centres (Yeandle, 2016).

Ambitious incubators and accelerators in Vancouver collaborate and are committed to growing the technology start-up scene in this city. Vancouver is ranked fifteenth overall in 2016, moving up the ranking by three compared to the Global Start-up Ecosystem rankings in 2015 (Gauthier, Penzel, & Marmer, 2017). The city has the fewest number of start-ups in the rankings, although the individual company valuations are high. Market reach is one of the metrics used in the index, which is the strongest factor of the Vancouver start-up ecosystem, due to a strong global connectedness and the best reach to foreign customers compared to other ecosystems worldwide. A key factor in differentiating Vancouver from other finance and business centres such as London, New York, Silicon

Valley, Singapore and Hong Kong, is its location within the North American trading zone being within reach of both the Asian countries and the US market (Gauthier, Penzel, & Marmer, 2017).

Start-ups can benefit from different regulatory and tax advantages by the provincial government of British Columbia (BC). For example up to $150,000 in loans to assist community based entrepreneurs in BC with expenses related to starting up, purchasing or growing a business; up to $20,000 in wage subsidies for businesses to employ recent college or university graduates; access to expert advice to help entrepreneurs develop their innovative ideas; and free access to immediate technical information on standards, technical regulations and certification programs used around the world for businesses involved in importing and exporting goods or services (British Columbia Innovation Council (BCIC), 2017).

The Digital Finance Institute’s headquarters are in Vancouver, which is a think tank created for the next generation of financial services. They address issues in respect of the nexus between financial innovation, digital finance policy and regulation, financial inclusion and women in financial technology. Their goals are to develop partnerships for balanced regulation of digital payments and remittances. They support research and the use of FinTech innovation to solve financial inclusion problems and participate in emerging digital finance market areas, including climate finance and artificial intelligence for digital finance. This organisation is important for the support of the growth of the FinTech ecosystem. Building a FinTech community that is vibrant and inclusive, and supportive of start-ups, in a manner that involves all stakeholders and provides networking and educational opportunities for participants is their function in the FinTech ecosystem of Vancouver. As a preparation for this research thesis, a trend analysis has been conducted to obtain information about the industries that are developing in a fast pace in the region of Vancouver. The results of the trend analysis can be found in appendix A section. There has been a rise in the number of financial technology ventures in the greater region of Vancouver between 2011-2016. Figure 4 depicts an overview of FinTech companies in the financial technology ecosystem of Vancouver B.C. classified by their business segment. Most of these companies operate in the payments followed by the lending segment. Figure 4 also shows how diverse the financial technology industry is within the financial services sector of one particular region, with more than ten different business operation segments.

Figure 4: Overview FinTech companies in the financial services industry of Vancouver B.C (Sofia, 2016).

The gaining momentum of the FinTech industry, the emerging ecosystem and its geographical position, makes the financial technology industry of Vancouver an interesting research area. In addition to that, the ecosystem differs from others, since there is no government or global bank subsidized centre or innovation lab part of this ecosystem yet. However, for an ecosystem to function properly and sustain itself, a stable level of sustained collaboration is necessary among governments, financial institutions, and entrepreneurs. The next chapter will describe the theoretical framework for this research that will be the guidance for the analysis of the FinTech industry of Vancouver.

3. Theoretical Framework

Introduction This chapter starts with a discussion of the research field of inter-organisational networks. Next the concept of Network-centric innovation will be explained and the different models for inter- organisational innovation will be described. The chapter ends with a discussion of literature applied on digital finance. Insights gained from this chapter will help to understand what this concept means, how it’s used in literature and applied on the emerging FinTech industry.

3.1 The notion of Inter-organisational networks The neo-classical perspective has dominated the field of economics in the past century. This perspective takes the firm as an autonomous and isolated entity, striving to use its resources to compete with other entities (Gulati, Nohria, & Zaheer, Strategic networks, 2000). In the twentieth century, companies coped with technological change by building in-house research and development (R&D) facilities to stay ahead of technological developments (Iansiti & Levien, 2004). This autonomous and isolated approach for dealing with technological change. A different perspective has gained momentum in the late twentieth century, where firms access resources and capabilities through their networks, and in which organisational actions and outcomes are approached in a more relational rather than individual way (Gulati, 1999). This ‘network lens’ is used to analyse and view the world in a dynamic and structural sense.

A common problem across research areas is to reach consensus on theories and definitions. The field of inter-organizational research is fragmented and a clear definition does not exist, due to the wide number of research fields applying the network concept (Borgatti, Mehra, Brass, & Labianca, 2009). “Networks” are also emphasized in very different theoretical approaches, for example, power (Cook, 1977), trust (Zaheer, McEvily, & Perrone, 1998), access to resources (Laursen & Salter, 2006) and status (Jensen & Roy, 2008). Although research on inter-organizational networks is fragmented, this field can be divided in two approaches, a metaphorical description of interactions across organizational boundaries, and an analytical perspective on the specific ‘social structures’ between organizations (Wasserman & Faust, 1994). The former qualitative approach has dominated research in this field. A concept used in this research was first introduced by Moore (1993) in strategic management literature, called business ecosystems. The ‘business ecosystem’ perspective is analogous to biological ecosystems, to describe a complex network of entities and their relations. This concept provides information on networks and networking on a general level, and is a method to depict how organisations are operating across organisational boundaries in an interconnected industry landscape.

Other research in the field of inter-organisational networks takes an analytical perspective and structural approach to analyse the interactions between different entities. This research is grounded in the notion that the pattern of links between different organisations has important consequences for their performance and outcomes. Network researchers seek for example to uncover various kinds of patterns and try to determine the conditions under which those patterns arise (Freeman, 2004). There has been an increase in the use of this structured and quantitative concept in network research, called social network analysis (SNA). This concept differs from the purely metaphorical notions such as connectedness, interdependence or embeddedness. Research in this field of study has a history in several study areas (e.g. political science, strategy and sociology) at distinct levels of analysis (intra- organisational, firm or industry). One of the most cited definitions in the SNA literature defines networks as: “a set of nodes (e.g. people, organizations, institutions) that are linked by a set of social relationships (e.g. friendships, transfer of funds, knowledge sharing) of a specified type, called ties” (Laumann, Galaskiewicz, & Marsden, Community structure as interorganizational linkages, 1978).

This definition describes both that a network could have different types and numbers of entities, and content of the ties. The SNA concept is used to analyse the pattern or structure of relations and interactions among the nodes. The relations or interactions are mapped in a graph as a tie and can have different strengths and content characteristics. The difference between the metaphorical and social network perspective on networks is depicted by Figure 5.

Figure 5: Metaphorical & analytical perspective network research (Bergenholtz & Waldstrøm, 2011)

There are several challenges for inter-organisational network studies, one of them is the types of relations to be studied, for example patent licensing, joint ventures and strategic networks. The difficulty to determine the ‘network boundaries’ is one of the hardest challenges for network researchers (Laumann, Marsden, & Prensky, 1989). The boundaries of the studied network are in most cases determined by the network research methodology, the available resources (such as time) and specific research subject used. For example, a researcher may only consider the entities in a certain network that are within a specific geographical space. The choice between an industry, organisational or individual level of analysis is also an important characteristic of a network study that’s needs to be determined (Sedita, 2008).

A paper by Powell (1990) in the field of inter-organisational networks, generated additional attention on this research area by creating a chasm between the neo-classical and the network-based perspective. This study showed that neither markets nor hierarchies are explaining organisational performance and outcomes as networks do. Combined with research by Granovetter et al. on the embeddedness of business transactions (1985), this new perspective led to more research using the analytical social network approach. SNA became a useful perspective to explain network formation and organizational performance (Ahuja, 2000). Early research that was using the analytical approach for analysing inter-organisational networks, applied SNA in a rather narrow way. For example, research by Ahuja (2000) relied on ego-networks for explaining how an organization can be most innovative in an inter-organisational network space. Later research by (Owen-Smith & Powell, 2004) shows that using richer data, or multiplex relational data such as institutional and empirical information of entities in the analysed network, leads to more valuable information. For example, a

24 study that would have used uniplex relational data on an ego-network, would not have been able to come up with the same in-depth results, because the networks beyond the ego-network level influence the findings. During the first decade of the twentieth-first century, more studies have emerged that applied the SNA concept in a wider sense than the first publications did in the inter-organisational network research field (Owen-Smith & Powell, 2004); (Sorenson & Stuart, 2008). These studies came up with more valuable insights of the network activities, which creates the opportunity of analysing multiple layers of information and therefore, research in this field does not rely solely on the social network approach. Hence, this approach is used in research together with statistical tools and combined with other theoretical explanations, for example the resource-based view (Ahuja, 2000). However, combining data from different types of datasets leads to a challenge, because SNA is based on interdependence of data (SNA) and a statistical approach assumes independence of data.

3.2 The inter-organizational network and innovation activities As organisations form collaborations with each other, they weave a network consisting of different types of ties with diverse strengths of communication (Ynalvez & Shrum, 2011). Collaboration and the establishment of a knowledge base are very important for innovation processes in businesses (Hölzl & Janger, 2004). Central organisations have more opportunities for knowledge transfer and learning, because they are among others often closer and between other organisations in the transfer of knowledge and other resources. However, the benefits from the central position in inter- organisational networks would be influenced by its network characteristics. Prior research did not explore the effect of an organisation’s position in different types of networks, that are characterised by the type of resources shared in their connections, on its innovation activities.

One of the goals in this thesis is to analyse an industry from four resource perspectives: knowledge, investment, advice and human resources. Prior studies demonstrated that internal and external knowledge acquisition activities are complementary for innovation (Cassiman & Veugelers, 2006). Research by Chesbrough (2003) describes an innovation paradigm shift from a closed to an open innovation model, which is characterized by using inflows and outflows of knowledge to both accelerate internal innovation and expand the markets for external use of innovation. It is noted that collaboration by organisations in its network increases the external information flows which has a positive influence on the internal innovation activities (Chesbrough, 2003). This can be explained by the fact that inter-organisational ties act as channels of interactions that provides knowledge spill overs from other entities in the network. Furthermore, the influence of external information flows on its internal network relies on the amount of spill overs coming from the inter-organisational network, which in turn depends on the organisation’s position within the network. These flows within the organisation influence the ability to assimilate and process information, thereby changing their innovation activities. Thus, in this research an inter-organisational collaboration network and the different types of resources shared within these ties are analysed at the network level.

In inter-organisational networks, each actor has a unique surrounding network of connections, called the ego-network. Analysing the ego-network can show the opportunities for an actor to acquire and share new knowledge and other resources (Acs, Anselin, & Varga, 2002). This relation has been described in previous studies, in which a close relation exists between the actor’s network characteristics and the innovation performance (Phelps, 2010). A common used measure in network research, is the centrality of an actor in its network. This network measure captures the patterns of exchanges of resources depending on the structural position of an actor in its network (Breschi & Catalini, 2010). Centrality can reflect the extent to which an organisation acts as a knowledge broker

25 or intermediary, the degree of access to resources, the way it controls them and the importance or influence in the network (Brass, 1984). Organisations in a network that have a central structural position have an advantage for sharing, integrating and utilizing resources such as information, technology and knowledge (Borgatti, Mehra, Brass, & Labianca, 2009). Such actors have access to unique information and become a central point in the network for knowledge sharing. Previous research about network centrality and the exploration of novel technologies supports this relation that actors with high centrality have an information advantage (Gilsing, Nooteboom, Vanhaverbeke, Duysters, & van den Oord, 2008). A central organisation also provides opportunity to easily bridge the information gap between other entities, which is called a brokerage position and has been analysed with the betweenness centrality metric in network literature (Gilsing, Nooteboom, Vanhaverbeke, Duysters, & van den Oord, 2008). Central organisations also have a greater capacity to reach a high status within the network, which can be analysed with a specific type of centrality measure, called the eigenvector centrality (Ahuja, 2000). Organisations with a high eigenvector centrality measure can attract other entities, for example new ventures, to have access to a more diverse set of resources and a denser network (Sytch, Tatarynowicz, & Gulati, 2012).

Betweenness centrality A network centrality metric that has often been used in network research to determine the ability of the node to act as an intermediary and controls information or other resource flows in a network, is the betweenness centrality measure (Cantner & Rake, 2014). This centrality measure is calculated by analysing how often a node appears on a shortest path between other nodes in the network. Nodes with high betweenness centrality control the information flow, grasp business opportunities, and have access to intermediary benefits. Equation (1) shows how the betweenness centrality measure (CB) for node i (ni) is calculated. The symbol Spjk denotes the total number of shortest paths from node j to node k. Spjk(ni) refers to the number of paths that go through node ni and V is the number of nodes (vertices) in the network.

( )/ � � = (1) ()()

Closeness centrality A common centrality measure in network research is closeness. This measure gives an indication about the average (normalised) length of the shortest paths between a node and the other nodes in the network. Closeness measures have been developed by Bavelas (1950), Beauchamp (1965), Sabidussi (1966), Moxley and Moxley (1974) and Rogers (1974) as the reciprocal of the farness (Freeman, 2004). Thus, the more central a node is, the closer it is to all other nodes in the network. A benefit of this, for example, is faster access to knowledge or investment organisations. Equation (2) shows how the closeness centrality measure (CC) is calculated. The symbol d(k, ni) denotes the distance between the node i (ni) and the rest of the nodes k (nk) in the network. V is the number of nodes (vertices) in the network.

� � = (2) (, )

Eigenvector centrality The eigenvector centrality is used in research as a measure of the influence of a node in a network. It assigns relative scores to all nodes in the network based on the concept that connections to nodes with higher importance in the network contribute more to the score of the node measured than equal connections to low-scoring nodes. In many cases, a connection to a popular actor is more important than a connection to less important entity in the network. The eigenvector centrality metric takes into consideration not only how many connections a node has (i.e. its degree), but also the degree of the vertices that it is connected to. Equation (3) describes for a network G := (V, E) with V as the number of nodes in the network (vertices), with A = (ai,t) the adjacency matrix (square matrix used to represent a finite graph). For example, ai,t = 1, if node i has a connection with node t, otherwise ai,t = 0. The relative centrality value for node i can be defined as:

1 1 � � = � � = � � (� ) (3) l �∈�(��) � � l �∈� �,� � �

In equation (3), M(ni) is a set of the neighbours of node i and l is a constant, called the eigenvalue.

Clustering coefficient A measure used in network research which is an indication of the degree of connectivity in the neighbourhood of an actor, is the clustering coefficient (Watts & Strogatz, 1998). The definition of the clustering measure is formulated as “the probability that two randomly selected connections of an actor are also connected with each other” (Easley & Kleinberg, 2010). The clustering coefficient indicates whether an actor’s direct connection in a network also relate to each other (Gulati, Sytch, & Tatarynowicz, 2012). A high coefficient influences an easier diffusion of resources, and more repeated connections provide trust in a group explained by the concept indirect reciprocity or reputation (Granovetter, 1985). A downside of a high clustering coefficient, is the amount of repeated information within the cluster which leads to more homogenous information sharing and may harm innovation performance. Such clusters are filled by traditional ideas rather than fresh opinions. A high clustering coefficient means that the organisation’s connections can interact with each other without the need of the organisation itself to be part of the interaction, resulting in a loss of its brokerage role.

A cluster in a network is a dense group of nodes that relate to each other. The cluster coefficient is a measure of the degree to which nodes in the surrounding network of a particular node in the network are connected, also called cliques. The maximum value of this measure is 1, which means that all nodes a node relates to, also are connected with the nodes that node has a connection with.

2∗(�� ) �� (�) = (4) �� ∗(�� −1)

3.3 Inter-Organisational Networks in the Financial Sector This paragraph will start with a general overview of the available literature on FinTech and Digital Finance, followed by an overview of literature inter-organisational networks in the financial sector. Finally, this chapter will conclude with the position of this research relative to other research in this area of study.

3.3.1 Digital Finance and FinTech Literature Gomber et al. (2017) have conducted a meta-analysis of literature to identify the areas that are studied most on digital finance and FinTech, and those areas that have not yet been addressed by academic literature so far. This literature analysis used a systematic approach to identify the most relevant papers by applying a multi-stage process and developing a concept to position the 83 articles in the intersection between different research subjects (Gomber, Koch, & Siering, 2017). This literature review observes an increasing number of articles about Digital Finance between 2009-2015 and most articles are mainly published in Information systems journals, which shows that most research on Digital Finance is focussed on the Digital part of this subject area.

Figure 6: The Digital Finance Cube (DFC) (Gomber, Koch, & Siering, 2017)

The concept Gomber et al. (2017) used to position literature and identify untapped research areas is a useful tool, called the ‘Digital Finance Cube’ (DFC). The concept of the DFC is depicted by Figure 6, and has three dimensions to structure this field of study. These dimensions are: (a) Digital Finance business functions, (b) technologies and technological concepts, and (c) institutions providing Digital Finance services.

Within the dimension of business functions, there are no articles found concerning Digital Insurance that can be classified as qualitatively good research. Almost half of the articles contribute to the business function of Digital Financing, concerning crowdfunding as research subject. This shows that the other business functions have been studied sparsely compared to Digital Financing and there is still a high opportunity here for future research. Concerning the relation between business functions and technologies, Gomber et al. (2017) reveal that some technologies and technological concepts are regularly connected with certain business functions. For example, research in the dimension of Digital Payments particularly focus on Near Field Communication (NFC) technology, a technology used in credit cards and digital devices to allow users to pay remotely by connecting two surfaces e.g. (Chen,

Mayes, Lien, & Chiu, 2011). The specific focus of business functions on particular technologies or technological concepts, creates a lot of research opportunity to investigate how technologies could be applied in other business functions in finance. This meta-analysis also found articles in the Digital Finance institutions dimension of the DFC. Although most business functions have been subject of research in both components of this dimension, research about the business functions used by FinTech or traditional financial service providers and the application of technologies is not studied in academic literature so far.

An important conclusion of the literature review by Gomber (2017), which is in line with this research, states that the potential of co-operation among FinTech and traditional financial service providers is often debated. They point out that research could contribute by case-studies focusing on the success of such co-operations between different stakeholders. There is also research potential about FinTech or Digital Finance beyond de concept of the DFC, especially concerning regulations. For example, a comparative academic analysis of national FinTech ecosystems that are supported by initially lighter regulation for FinTech companies and ecosystems that do not allow for regulatory niche areas, is also a relevant untapped research area for future research (Gomber, Koch, & Siering, 2017). Research has not yet successfully revealed the specific roles of FinTech companies and traditional financial service providers in Digital Finance. Gomber et al. (2017) state that research could investigate the potential for both stakeholders in the FinTech ecosystem and reveal how they will compete and could co-operate, respectively.

This paragraph gave a general overview of the available literature on the area of Digital Finance and FinTech, contributed by a profound meta-literature analysis by Gomber et al. (2017). They concluded that future research should focus more on the meso- or macro level perspective on the digital transformation of the financial sector. This is exactly what this research is about, which answers to this statement. Next paragraph will give an overview of the available literature concerning this aspect of research.

4. Methodology

Introduction The goal of this research is to derive the structural characteristics of an inter-organisational network to understand the dynamics between different stakeholders. To achieve this goal, the characteristics of interactions in the inter-organisational network need to be identified. A mixed methods inductive approach has been used, because variable-oriented techniques would not allow, for example, to address questions about the context for collaboration or to observe causal processes, particularly regarding sensitive issues such as inter-organisational relationships and factors that hamper innovation activities. Moreover, the emerging FinTech industry is a complex, novel, and little studied research area, and therefore the inductive approach made it possible to derive details and interactions in this case. To do this, a Social Network Analysis (SNA) has been performed, which is a systematic approach for illustrating an inter-organisational network and can be used to perform an analytical analysis of the network. This approach consists of three general steps. The first step entails the identification of the boundaries of the network and the members that belong to it (Laumann, Marsden, & Prensky, 1989). Then, the data is collected from the identified members of the network. As a third step, the data is analysed with a software package and this model is validated by experts and by using documentation. This chapter discusses how the SNA is performed and the conditions for analysis that have been set (Ricken, Schuler, Grandhi, & Jones, 2010). Finally, the data validation and reliability will be discussed in the last section of this chapter.

4.1 Unit of Analysis The first step in a SNA study is to identify the members of the inter-organisational network. This may be difficult if members are frequently moving in or out of the network. The study applies the principle of data triangulation, whereby the unit of analysis is studied from different perspectives using different data sources (Ricken, Schuler, Grandhi, & Jones, 2010), e.g. different types of organisations, which vary in terms of size, locality, or industrial background, in order to achieve validity of interpretation, explanation and generalization. The participants in this study are coming from a diverse industry, in which different kinds of innovations or technologies, e.g. cryptocurrencies, artificial intelligence or distributed ledger technology, are applied in the financial sector. These FinTech companies have a diverse set of employees with different backgrounds, from business administration to software development. It is often difficult for a researcher to identity key informants who can provide the most relevant information. Consistent with the logic of Huber and Power (1985), who argue for selecting knowledgeable informants within organisations (Huber & Power, 1985). The respondents for this study were purposely selected to represent the innovation division of the organisation: Chief Executive Officers, Chief Operation Officers, Chief Technology Officers and Business Development managers (more descriptive statistics about the respondents in chapter 5). This approach allowed examination of the perspectives of a diverse selection of participating organisations who are directly involved with the studied area. The selection of the participating organisations has been done by using different sources of information. First documentation on the Internet has been used to find lists of companies that are tagged as FinTech companies, and the SNA data collection method used in this research allowed for the addition of participants that were not yet been identified with the other information sources. The FinTech companies are the main units of analysis in this study, other organisations and institutes in the financial ecosystem have been added as control groups in the network: traditional financial services providers, universities and colleges, venture capitalists or investor associations, government, and additional organisations or entities that belong to the emerging financial ecosystem.

4.2 Conditions for Analysis Social network actors are in most studies concrete and observable, however this study relies on an inductive inter-organisational network analysis. There are a few conditions that should be formulated before the SNA can be performed (Ricken, Schuler, Grandhi, & Jones, 2010). The first condition is the level of the network analysis, which gives the direction and perspective of the inter-organisational network analysis. Different perspectives can be applied, a micro-, meso- or macro-scale, and with each of these perspectives a different level of detail can be analysed. The next condition for the SNA, is a clear description of the boundary of analysis. This condition is important, as it determines the threshold or scope of the data collection phase, and defines the scope of analysis. The boundary can be determined in several ways, for example by choosing a certain method of data collection that allows to track the amount of new data sources added to the sample. One of these methods is the snowball effect, which is a way to generate a network by asking participants to add the entities they interact with and when there are no new entities added by new participants, the data collection stops. Another example is to use a geographical boundary of analysis, by selecting all entities in a geographic space. The last condition is called multiplicity, a condition that is about the amount of information that should be attached to the ties between the entities of analysis. The next sections will describe the different conditions that have been used in this research and how these conditions have been set.

Level The level of analysis for this research has been determined when the unit of research was identified, which is the emergence of FinTech companies in the financial sector. Because this unit of research is a very broad topic, different levels can be used for research. For example, a micro-level perspective would focus on the internal social network between entities within an organisation. The goal of this research is to analyse the interactions between different organisations and institutions, which is why the level of the industry is used as the level of analysis in this research.

Boundary To study an inter-organisational network, an explicit network boundary should be determined as a condition for the network analysis. Previous research has formulated the problem of spatial boundary specification (Laumann, Marsden, & Prensky, 1989), in literature different perspectives have been used for boundary setting, for example that the boundary-setting problem should be based on the objectives of the research (Fombrun, 1982). Other research has used another strategy that emphasizes ‘the core technology or knowledge’ as the key defining characteristic of a network boundary (Silverman & Baum, 2002). In this study, a combination of both boundary specification strategies has been used. To determine the boundary of analysis, the core technological application of technology by new ventures in the financial sector has been one of the key characteristics of the network boundary. The goal of this research is to determine the roles of different organisations in the financial services sector, however this sector is a globally operating sector that is not taking any geographical boundaries of nation states into account. Therefore, the focus for the inclusion of actors, as defined by Conway and Steward (1998) is based on the actor’s activities in the specified spatial boundary of the FinTech ecosystem of Vancouver B.C. in Canada (Conway & Steward, 1998).

Multiplicity Data collection can be done based on different ranges of information, from general to in-depth information about the type, strength and duration of the interactions between different entities in the network. A trade-off should be made when the multiplicity of the data collected is determined, between what information is needed within the scope of the researched unit of analysis and the amount of resources (time, money, etc.…) that are available for the research. This thesis focusses on

32 the interactions between different organisations in the financial technology industry of Vancouver, therefore the multiplicity is based on the different types of information, the type and strength of the interactions between stakeholders.

4.3 Data Collection To account for data credibility, multiple data sources have been used in this research. The data collection was completed over a three-month period (April–June 2017). In the next sections the different data sources used in this study will be described: survey, social network survey and interviews.

Survey A survey method was used to collect quantitative data from a sample of organisations within the boundaries of analysis as specified before. This method allows to collect data from a large sample in a structured way. A template has been used for the questions about the innovation activities of the FinTech companies and financial institutions, called the Community Innovation Survey (CIS) (Eurostat, 2014). The CIS innovation statistics are part of the EU science and technology statistics and are carried out with two years' frequency by EU member states. The CIS is a survey of innovation activities in enterprises, which has been refined based on previous survey versions which makes the questions asked more reliable and validated by experience. The harmonised survey is designed to provide information on the innovativeness of sectors by type of enterprises, on the different types of innovation and on various aspects of the development of an innovation, such as the objectives, the sources of information, the public funding, the factors hampering innovation etc. The questions in the survey have been adjusted to fit the research subject of this study, and has been designed and distributed with the online Qualtrics software, that can easily be shared through different communication channels: Social Media, Email, SMS, etc. The survey questions used in this research are depicted in appendix B.

Social Network survey A network is a set of vertices (nodes) connected by edges (links), representing entities (individuals, organisations, groups or other entities) and the interactions among them. This study focuses on networks with vertices (organisations and institutions) differentiated by different types of edges and their strengths. Within the survey a separate section has been devoted to the social network data collection. This part of the survey has been divided in four different sections for each group of organisations or institutions that are part of the analysis. The SNA data collection method used in this research, is a combination of both a roster and a name generator method. First, the participating companies were asked for each group to select the companies they interact with from a pre-defined list of all organisations within the network. The next section presented the selected contacts and asked to define the ‘type’ of interaction their company has with them. They could choose more than one option for each contact from the following types: 1. Knowledge/ data/ technology This type of interaction with another business can be characterised as a relation or connection in which knowledge, know-how, data, technology, etc... are provided, received or shared. 2. Investment/ funding This type of interaction with another business can be characterised as a relation or connection in which an investment, funding, money etc... are provided, received or shared. 3. Consult/ advice This type of interaction with another business can be characterised as a relation or connection in which advice, consultancy, mentorship, etc... are provided, received or shared.

4. Human Resources/ employees This type of interaction with another business can be characterised as a relation or connection in which human resources, employees, tacit knowledge, etc... are provided, received or shared.

In the third section of each group of contacts, the participants were presented with the list of chosen contacts within the specific group, and they were asked to define how often they interact with them. In this section only one option could be chosen and they were presented with the following five options: 1. Interacted ones 2. Yearly interactions 3. Monthly interactions 4. Weekly interactions 5. Daily interactions

The last section of the social network questions was a so called ‘name generator’, in which the participants were asked to add the names, type of interaction and how often they interact with contacts that were not present in the lists of proposed organisations in the network. This question was added to the survey to make sure that the list of organisations was up-to-date when presented to the participants and organisations and institutions within the specified boundary of analysis.

Interviews Interviews have been used as a qualitative method to collect the context of different connections and activities in the studied inter-organisational network. The interview was semi-structured and open- ended for its potential to generate rich and detailed accounts of the interviewed participating organisations. This research method allows the discussion to lead into areas which may not have been considered prior to the interview in the surveys, but may be potentially relevant. This flexibility was particularly important in this study due to the different types of new ventures that belong to the FinTech group of companies with differences in structural positions in the inter-organisational network, while ensuring consistency and comparability across the interviews. In the interviews the answers from the surveys were used as a guideline for the interviewed topics. This style of interviewing allowed for the collection of data which was comparable across the participating organisations in this study. First the surveys were completed by participants and randomly chosen to participate in an additional interview session. During the interviews the goal was to derive the context of the answers given in the surveys. This resulted in a richer dataset and even lead to additional information that the surveys did not capture. The specific questions and their order varied between interviews depending on the answers given in the survey while the common topics ensured comparability across interviews. Five interviews were conducted in total, of which four different FinTech companies and one financial institution with headquarters in Vancouver. The interviews ranged in length from 15 to 60 minutes, and were not taped as it allowed the interviewees to share more information.

4.4 Data Analysis The collected multiplex network data with four types of undirected connections, has been cleaned, transformed and imported into de SNA software package NodeXL as an edge-list. This software enables researchers to examine the network data visually through sociograms and statistically through a variety of metrics. Sociograms are graphical representations of social interactions that conceptualize individuals or organizations as points, called "nodes or vertices", and their relationships as lines

34 between the nodes, which are called "ties or edges". Two organisations with a connection are represented by a tie between them in the sociogram, whereas two nodes without a tie indicate that a relationship does not exist. Nodes can be symbolized by colour, size, and shape according to characteristics derived from the collected data. Similarly, ties can be symbolized by any characteristic of the relationship such as frequency of communication, strength of the relationship, or the type of resources shared within the relationship. Numerous SNA metrics are available for analysing networks and organisations within networks. The metrics used in the data analysis have been described in the theory and hypotheses chapter. In the next chapter, the results of the data analysis are summarised and described. After the networks have been constructed, the different networks have been analysed to test the proposed hypotheses in the previous chapter.

4.5 Research Quality In this section of the methodology chapter the validity and reliability of the findings are being solidified.

Validity The internal validity of the research findings was secured by following the guidelines for data triangulation summarized by Creswell (1994). Therefore, the following steps have been performed: First, the research survey has been presented to experts within the industry and the questions have been fine-tuned based on their feedback. Secondly, during the interviews with a selection of the participating organisations, their data has been discussed to derive more context and to test the validity of the provided information. These steps prevented unfounded conclusions and secured validity of the findings. Finally, the findings have been triangulated with other data sources such as social network information from the different participants confirming the ties in the network, and from conferences and meet ups in which the emergence of the financial technology industry was the main topic.

Reliability Creswell (1994) defines reliability as “the issue of generalizability, the uniqueness of a study within a specific context” (Creswell & Miller, 2000). The analysis of the inter-organisational network is based upon a specific geographical region with its unique characteristics. The pitfall of such studies is that the findings are limited to the studied network. However, by concentrating on a specific inter- organisational network, a rich amount of data can be obtained and a detailed understanding of the context can be derived. The work of this research provides cutting edge details about the structural characteristics of an understudied research area. Interviews with CEO’s of the FinTech companies that operate worldwide, made it possible to validate and test the answers provided in the survey. Using the Community Innovation Survey (CIS) as a template for the survey questions, makes it possible to compare the results of this survey with previous surveys by the EU science and technology statistics department. The results of this research can be compared with other databases that used the same survey template, which increases the generalisability of this study.

5. Results

Introduction In this chapter, the key findings of the study are summarised as follows. First, the general findings will be discussed about the overall inter-organisational network of the FinTech sector in Vancouver B.C. The sections after this, will describe the findings concerning the knowledge, investment, advice, and human resources network. Finally, the role of intermediaries in supporting innovation are discussed.

5.1 Inter-organisational networks This section starts with an overview of the results based on the complete network data. The whole network structure will be depicted and the structural characteristics are displayed and explained for each type of actor in the network. The following sections will focus on the different types of interactions in the inter-organisational network. The analysis of different types of ties or links as described in the methodology chapter, leads to the opportunity to analyse the complete network from different perspectives: knowledge transfer, investment interactions, advice or consultancy connections, and human resources interactions. By focussing on these network types the different actors and their structural characteristics within a specific type of interaction network can be analysed, which gives valuable information about the roles of different organisations within the inter- organisational network. The network software package NodeXL has been used to construct this picture and to derive the different network variables of the different networks in this chapter.

Overview of the Data The results of this research are based on data from 19 organisations (16 FinTech companies; 3 financial institutions) within the FinTech ecosystem of Vancouver (Canada). Figure 7 depicts the core business of the FinTech companies that have participated in this research. Most the participating companies operate in the payments business (40 percent), followed by investment companies (20 percent), and banking (13 percent). As described in the chapter about the context of this thesis, there are thirty-six FinTech ventures in the region of Vancouver of which sixteen (45 percent) have participated in this study.

7% Payments 7% Investments 7% 40% Banking 6% Blockchain 13% Cryptocurrency Identity Verification, Security 20% Accounting

Figure 7: The core business of FinTech companies that have participated in this thesis

For the validity of the thesis it’s important to identity key informants who can provide the most relevant information about the participating companies. Figure 8 depicts the job titles of the informants of the participating companies in this research. To be able to answer the questions about the organisation’s collaboration and interaction network, and innovation activities, someone within the participating sample of companies was approached who could provide this information. The participating informants within the sample were in most cases Chief Executive Officers (57 percent), because these people have the highest position within the companies and therefore the best overview over its operations. The second largest group with 19 percent of the participating sample, are Vice Presidents who were also able to provide valuable information about the company’s operations.

Chief Executive Officer 6% 6% Vice President 6% 6% Global Director of Operations

57% Consulting Intern 19% Director of software development

Director of Customer Care

Figure 8: FinTech company participant's job titles

These organisations have provided social network data which is used to construct a network graph of 55 organisations (nodes) within the FinTech ecosystem with a total number of 285 connections (ties). Table 1 summarises the number of ties for each type of organisation within the specific networks studied: complete, knowledge, investment, advice and the human resources network. The network consists of twenty-three FinTech companies with 160 unique ties in the complete inter-organisational network. Almost a hundred connections more than the second largest group in this inter- organisational network, the sixteen financial institutions. These organisations have 69 ties in this network. The three largest Universities in the region of Vancouver (Canada) and one college are part of the FinTech ecosystem: University of British Columbia, Simon Fraser University, British Columbia Institute of Technology, and Langara College. This group has 14 unique ties in the FinTech network and later in this chapter the specific network will be described in which this group of actors has a strong presence. Also, ten Venture Capitals belong to the network, with a total number of 32 ties with the other types of organisations. The final group consists of the Provincial government of B.C. and the Federal government of Canada, which have ten ties in total in this network.

Table 1: Summary Network data

Number of Connections/ Ties Type Number of Unique ties Knowledge Investment Advice Human Organisations Resources FinTech companies 23 160 92 52 54 29 Financial Institutions 16 69 61 20 9 6 Universities & Colleges 4 14 3 1 4 9 Venture Capital 10 32 8 25 15 2 Government 2 10 2 9 0 0 Total: 55 285 166 107 82 46

180 160 140 120 100 80 60 40 Number of network ties networkof Number 20 0 Complete Knowledge Investment Advice Human Resources Network

FinTech Financial Institutions Universities Venture Capitals Government

Figure 9: Network memberships for each type of actor in the FinTech ecosystem

Not only the complete network has been analysed, but as described in the previous chapter, also different types of connections have been analysed. Table 1 and Figure 9 show the number of ties for each type of interaction network: knowledge, investment, advice and human resources. FinTech companies belong to all these specific networks, and have the most ties compared to the other groups of actors. The Universities belong to all networks except the investment network. This group of actors has most of its ties with other organisations in the human resources network, providing human resources, new employees or tacit knowledge to other organisations. Both financial institutions and venture capitals belong to each specific network type, however both actor types differ in the number of ties per inter-organisational network. Financial institutions have most of their ties in the knowledge network, in contrast, the venture capital organisations have most of their connections in the investment network. The government only connects with other organisations in the knowledge network and mainly in the investment network, providing public financial support.

In the next sections, first the complete network will be depicted with its specific characteristics, followed by the results of the resource-based networks.

Complete network The whole network studied in this research is described in this section. Figure 10 depicts an overview of the inter-organisational network. Each node represents an organisation in the network and each tie is a connection between two nodes. The colour of a node represents the type of actor in the network, red for FinTech companies; blue for financial institutions; green for venture capitals; yellow for university and colleges, and purple nodes represent the government. The size of a node is an indication of its betweenness centrality, the larger the diameter the higher the betweenness of a node in the inter-organisational network.

Figure 10: Complete network of the FinTech ecosystem of Vancouver B.C. in Canada.

The ties differ in width and darkness of colour, which is an indication of the strength between two ties. The darker and thicker a tie between two nodes, the stronger the connection is. In the methodology chapter a description of this measure is given, in which the strength of the tie is an indication of how often two organisations interact. Figure 11 depicts the average strength between FinTech companies and other actors in the different networks. An average strength of zero, means that there is no interaction between FinTech companies and another actor in a specific network; a strength of 1, interacted ones; strength of 2, interact each year; strength of 3, interact each month; strength of 4, interact each week; and a strength of 5, interact on daily basis. In the complete network, FinTech companies interact with the other actors on average between the yearly (2) and monthly (3) basis. Universities have on average the strongest connections with FinTech companies followed by other FinTech, venture capitals, government, and least with financial institutions. Within the knowledge network, FinTech companies interact most with venture capitals on average on a weekly basis. The strength of the ties between financial institutions and FinTech in the knowledge network is also on average the weakest connection among different actors, as it is in the complete network. However, in the investment network the financial institutions have the strongest relation with FinTech, followed by the government and venture capitals. In this network, the universities do not have any connections with FinTech companies. Venture capital organisations have the strongest relations with FinTech

40 companies in the advice and consultancy network. On average these two actors interact each month. The human resources network is characterised by a strong connection between financial institutions and FinTech companies, followed by the universities. Within this network these two actor types interact on a weekly basis, which means that they share human resources and tacit knowledge.

5.00

4.00

3.00

2.00

1.00 Average strength of connectionsof strength Average 0.00 Complete Nework Knowledge Investment Advice Human Resources

FinTech Financial Institutions Universities Venture Capitals Government

Figure 11: Average strength ties between FinTech & others in the networks (0 never – 5 daily)

Table 2 summarises the whole network characteristics. The network consists of 55 nodes or organisations with 145 unique ties. The density of the network is 0.094, which means that 9.4 percent of the total number of possible ties between all nodes in the network are present in this network. The average degree of this network is an indication that nodes in the network have an average of 5.091 connections. The longest path between two nodes in the network is six ties, and the average path length between two nodes in this network is 2.458 ties. The average clustering coefficient, 0.168, is the average of the local coefficients of nodes in the network, which is a measure of the degree to which nodes in the network tend to cluster together (Watts and Strogatz, 1998).

Table 2: Whole network characteristics

Nodes 55 Ties 140 Density 0.094 Average Degree 5.091 Network diameter 5 Average Path Length 2.458 Average Clustering Coefficient 0.168

The remaining part of this paragraph is devoted to a description of the characteristics of the general inter-organisational network. Table 3 summarises the network variables for each type of organisation.

Table 3: Complete network variables per actor type

Actor Number Degree Closeness Betweenness Clustering Eigen Centrality Centrality Centrality FinTech 23 6.96 0.0078 64.17 0.24 0.022 Financial Institutions 16 4.06 0.0074 36.41 0.11 0.015 University 4 3.50 0.0076 9.79 0.22 0.019 Venture Capitals 10 3.20 0.0073 10.49 0.05 0.014 Government 2 4.50 0.0079 14.73 0.27 0.020

FinTech companies have the highest average degree of 6.96, which means that these companies have an average almost seven connections with other actors in the network. The government and financial institutions have respectively the second and third highest degree (4.50; 4.06). The closeness centrality is a measure which indicates how close actors are to other nodes in the inter-organisational network. A higher measure means more distance to other actors in the network, therefore the governmental organisations have on average a longer distance from the rest of the network (0.0079). The FinTech companies have an average closeness centrality of 0.0078, slightly higher than the universities (0.0076). Financial Institutions and Venture Capitals have the closest distance to the rest of the actors in the network with respectively the following centrality measures: 0.0074 and 0.0073. Betweenness centrality is another centrality measure, which indicates the number of shortest paths between any two nodes that pass through a node. Nodes that are more central in the network have a higher betweenness measure, compared to nodes in the periphery of the inter-organisational network, because they connect more nodes from different parts of the network. The FinTech companies have the highest average betweenness centrality measure (64.17), which indicates that these companies have a bridging position between different parts of the network. Universities have the lowest average betweenness centrality, 9.79, which means that these actors are mostly positioned at the periphery of the network. The clustering variable in the fifth column of table 3, indicates for each type of actor in the network the completeness of the surrounding network. The clustering coefficient can have values between 0-1. A maximum clustering value means that the nodes in an actor’s direct network are also connected with each other. The surrounding network of the government agencies are on average the best connected, and therefore this actor has the highest average clustering coefficient (0.27). Venture capital organisations and financial institutions however, have a low average clustering coefficient, indicating a less complete network surrounding these actors. The final network measure in table 3, is the eigenvector centrality, which is a measure of the influence or importance of the actors in the network. The FinTech companies have the highest average eigenvector centrality score (0.22), which means that this actor has the most influence in the network, followed by the Canadian governmental agencies (0.20) and the Universities (0.19).

In the next four sections the results of the networks based on the types of interactions will be described. First the inter-organisational network in which knowledge is transferred between the organisations is described, followed by the investment, advice and consultancy, and the human resources network. Each network is characterised by its network properties and the types of organisations that belong to these networks.

5.2 Resource-based Networks

Knowledge network In the survey the participating organisations were asked to indicate for each interaction within the FinTech ecosystem of Vancouver, the type of information that is transferred between these two nodes: knowledge, investment, advice and human resources. Followed by this question the participants were asked to indicate how often the organisation is interacting with a particular organisation. This resulted in an inter-organisational network for each type of interaction, of which the results of the knowledge network will be described in this section. Figure 12 depicts the inter-organisational knowledge network with the different types of organisations as described in the previous section. The colour of the nodes indicates the type of organisation (see legend), and the diameter of the nodes depicts the betweenness centrality of the organisation in the network. This is a measure indicating how often the organisation takes a position between other nodes in the network. The width of the ties varies together with the darkness of its colour, which are measures for how often two nodes in the network interact (daily, weekly, monthly, yearly, interacted ones).

Figure 12: Knowledge Inter-organisational network

The characteristics of the knowledge network are summarised by Table 4. This network is the largest network of the four resource-based networks in this study, which consists of 23 FinTech companies, 15 financial institutions, 2 University, 6 venture capitals, and two levels of government.

Table 4: Knowledge network variables per actor type

Actor Number Degree Closeness Betweenness Clustering Eigen Centrality Centrality Centrality FinTech 23 4.00 0.0081 56.23 0.19 0.025 Financial Institutions 15 3.80 0.0078 53.78 0.07 0.023 University 2 1.50 0.0071 0.00 0.50 0.015 Venture Capitals 6 1.33 0.0066 1.83 0.00 0.008 Government 2 1.00 0.0059 0.00 0.00 0.003

The FinTech companies have the highest average degree of 4.00, which means that these organisations have an average of four connections with other actors in the network. The financial institutions have the second highest degree, 3.80. The closeness centrality is a measure which indicates how close actors are to others in the inter-organisational network. Also for this measure, the FinTech companies have the highest average measure (0.0081). This actor type therefore has the largest distance to the rest of the network compared to the other actors. The government and venture capitals have the closest average distance to the rest of the actors in the knowledge network (0.0059; 0.0066). Betweenness centrality is another centrality measure, which is an indication for the number of shortest paths passing through the actor type. Nodes that are more central in the network have a higher betweenness measure. Again, the FinTech companies have the highest average centrality measure (56.23), which indicates that these companies have a bridging position between different parts of the knowledge network. In contrast, Universities and the government have the lowest average of 0.00 betweenness centrality, which means that these actors are positioned at the periphery of the network. However, both actors differ in their structural position, as the government has the lowest closeness centrality measure and the Universities are structurally less close to the rest of the network. The clustering variable in Table 4, indicates per actor group the completeness of the surrounding network. The universities in the network have the highest possible average clustering coefficient (1.00), which means that its surrounding network is complete, all its connections are also connected. Venture capital organisations and the government agencies have however, the lowest possible cluster coefficient (0.00), indicating that the cluster network they belong to are not connected. The eigenvector centrality measure in the last column of Table 4 gives an indication of the influence or importance of the actors in the network. The FinTech companies have the highest average eigenvector centrality score (0.25), which means that this actor has the most influence in the knowledge network, followed by the financial institutions (0.23). The government agencies have the second highest eigenvector centrality measure in the complete network, however this actor group has the lowest average score in the knowledge network (0.03).

Investment network The next network that will be described in this section consists of organisation and institutions that interact based on investments or funding. Figure 13 depicts the inter-organisational investment network, which is the second largest network after the knowledge network. It consists of 14 FinTech companies, 12 financial institutions, 1 University, 10 venture capitals, and two levels of government.

Figure 13: Investment Inter-organisational network

Table 5: Investment network variables per actor type

Actor Number Degree Closeness Betweenness Clustering Eigen Centrality Centrality Centrality FinTech 14 3,71 0,01 61,16 0,05 0,030 Financial Institutions 12 1,67 0,09 21,69 0,00 0,020 University 1 1,00 1,00 0,00 0,00 0,000 Venture Capitals 10 2,50 0,01 23,64 0,00 0,032 Government 2 4,00 0,01 103,09 0,17 0,034

Table 5 summarises the structural characteristics of the investment and funding network. The government has the highest average degree of 4.00 connections, which means that the average number of connections in this network is four per government level. The FinTech companies have the second highest degree (3.71) in this specific network. The single university in this network has a maximum closeness centrality of 1.00. Figure 13 shows that this university is part of a network component that consists of only two nodes with one financial institution. Venture Capitals and the FinTech companies have on average the closest distance to the rest of the actors in the knowledge network (0.009). The next structural centrality measure in table 5 of the different actors in the investment network, is the betweenness centrality measure. The government has on average the highest betweenness centrality measure (103,09), which indicates that it has an important central position between different parts of

45 the investment network. In contrast, the university and the financial institutions have the lowest average betweenness centrality (0.00; 21.69), which means that these actors are positioned more at the periphery of the network. The next variable in Table 5, is the clustering coefficient, which indicates how complete the network surrounding the actors is. The government agencies had the lowest clustering coefficient in the knowledge network in the previous section of this chapter, however in the investment network this actor type has the highest average clustering coefficient (0.17). Venture capital organisations, the university and the financial institutions have the lowest possible cluster coefficient (0.00), indicating that they do not belong to a cluster within the investment network. The influence or importance of the actors in the network is given by the eigenvector centrality measure. The government has the highest average eigenvector centrality score (0.034). This actor has the most influence in the network, because it has a brokerage position connecting two larger clusters in the investment network. The venture capital organisations have the second highest value (0.032), and the university has the minimum value of 0.00 indicating a low importance or influence in this specific network.

Advice and Consultancy network The third network that will be described in this section is depicted by Figure 14, inter-organisational advice and consultancy network. It consists of 16 FinTech companies, 8 financial institutions, 3 University, 8 venture capitals, and no government nodes.

Figure 14: Advice and Consultancy Inter-organisational network

Table 6: Advice and consultancy network variables per actor type

Actor Number Degree Closeness Betweenness Clustering Eigen Centrality Centrality Centrality FinTech 16 3.38 0.082 49.22 0.08 0.033 Financial Institutions 8 1.13 0.009 0.25 0.00 0.015 University 3 1.33 0.012 33.61 0.00 0.034 Venture Capitals 8 1.88 0.011 19.71 0.13 0.032 Government 0 - - - - -

The structural characteristics of this network are summarised in Table 6. The government has not been identified to be part of the advice and consultancy network. The FinTech companies have the highest average degree of 3.38, which means that the average number of connections these actors have in this network is a little more than three. Venture capital companies have the second highest degree (1.88) in this network. The FinTech companies score highest for closeness centrality (0.082), which means that this actor is structurally located far from the rest of the nodes in this network. Financial institutions and venture capitals have the lowest average values for the closeness centrality measure, respectively 0.009 and 0.011. Another centrality indicator for network structures, the betweenness centrality measure, shows that the FinTech companies take on average the most intermediate position in the network (49,22). These companies have an important brokerage position in the advice and consultancy network between other actors. In contrast, the financial institutions have by far the lowest average betweenness centrality (0.25), indicating that this actor does not have a brokerage position in this network. The next variable in table 5, is the clustering coefficient, an indicator for the completeness of the surrounding network of the actor. The venture capital organisations have the highest average clustering coefficient (0.13), followed by the FinTech companies (0.08). Financial institutions and the universities have the lowest possible clustering coefficient (0.00) in this network, indicating that their surrounding network is not complete and do not belong to a clique of actors. Both the universities and FinTech companies are the most influential actors in the advice and consultancy network, with the highest average eigenvector centrality scores (0.034; 0.033). The financial institutions have on average the least influence in this network with an eigenvector centrality value of 0.015.

Human Resources network The fourth and last network that will be described in the results chapter, is the Human Resources network. Figure 15 depicts this network with the different types of organisations in it. The colour of the nodes indicates the type of organisation (see legend), the diameter of the nodes is an indication for the betweenness centrality of the organisation in the network. The width of the ties is shows how often two nodes in the network interact (daily, weekly, monthly, yearly, interacted ones). The network as depicted below consists of 11 FinTech companies, 5 financial institutions, 3 university, 2 venture capitals, and no government nodes.

Figure 15: Human Resources Inter-organisational network

Table 7: Human Resources network variables per actor type

Actor Number Degree Closeness Betweenness Clustering Eigen Centrality Centrality Centrality FinTech 11 2.64 0.016 38.12 0.018 0.049 Financial Institutions 5 1.20 0.013 0.13 0.000 0.032 University 3 3.00 0.018 34.00 0.033 0.088 Venture Capitals 2 1.00 0.013 0.00 0.000 0.020 Government 0 - - - - -

Table 7 summarises the human resources network in which the universities have the highest average degree of 3.00 connections on average, followed by the FinTech companies with an average degree of 2.64 connections. The government has not been identified to be part of this network. The universities have also the highest average value for closeness centrality (0.018). This can be explained with the other centrality measure, betweenness, in which the universities have the second highest value (34.00) after the FinTech Companies (38.12). Figure 15 depicts the human resources network which is linear, and shows that all universities are close to the centre of this network. This explains the high betweenness and closeness centrality values, because nodes that are central in linear networks have longer distances to other nodes that are positioned at the periphery of the network. The Venture Capitals and financial institutions have the shortest average distance to the rest of the actors in this network (0.013), because they are more diffused in the linear human resources network. The clustering coefficient values in Table 7 are very low, which is an indication of a low completeness of the surrounding networks of the actors. The universities have the most complete surrounding networks with the highest average clustering coefficient (0.033), followed by the FinTech companies (0.018). Nonetheless, these values are very low which is an indication for a low cliquishness in the network which can also be explained by its linearity. The financial institutions and venture capitals have the lowest possible cluster coefficient (0.00), because they have missing connections with other nodes in their surrounding network. The eigenvector centrality values indicate that the universities are the most important or influential actor in the human resources network, with an average value of 0.088. The FinTech companies and financial institutions have respectively the second and third highest eigenvector centrality values (0.049; 0.032).

5.3 Inter-organizational network interpretation of results

Complete network In this section, an interpretation of the inter-organisational network results will be given. First, the data from the complete network has been depicted with its structural characteristics for each type of organisation. These results showed that the FinTech companies have the most ties in the financial ecosystem of Vancouver, although the number of connections is not an indication for the strength of an interaction with another actor type. In the general network, FinTech companies have less than twenty ties in total with the universities in the region, however these companies have a strong connection (almost monthly interactions) on average with this group of organisations. FinTech ventures and financial institutions have on average the least amount of interactions, which is an indication that there is not a strong connection between these two groups of actors in the ecosystem with an average frequency of interactions on a yearly basis. The structural characteristics of the general network showed that the FinTech companies have the highest average degree (ties),

48 betweenness centrality and eigenvector centrality measures. These centrality measures show that the FinTech companies have the most central positions in the inter-organisational network, which is a great benefit for the transfer of knowledge and other resources that have been measured in this thesis. These companies have an important role as a broker between different parts of the network. The eigenvector centrality of FinTech companies is also the highest, which means that these companies have a lot of influence in the network. This influence and importance measure is calculated based on the concept that connections to nodes with higher importance in the network contribute more to the score of the node measured than equal connections to low-scoring nodes. The high importance of these companies in the financial sector is an indication that FinTech companies have many connections with other important actors in the network and increases its influence. However, compared with the other types of actors in the network, FinTech companies have the second highest average closeness centrality measure. This means that after the government, these companies have less central position on average in the network and this can be explained by its variance. The high influence of FinTech in the ecosystem can also be explained by relating it to the second highest clustering coefficient (0.022), because FinTech companies belong to more clustered sub-networks than other types of actors and this influences its influence when other actors in the same sub-networks also have a higher eigenvector centrality score. On contrary, the financial institutions have the second lowest clustering coefficient (0.11) after the venture capital organisations (0.05), and the second lowest eigenvector centrality measure (0.015). This means that the financial institutions have less complete surrounding networks and are linked to less influential actors in the network.

Resource-based networks After the complete network was described, the chapter continued with an overview of the specific networks that were based on the type of interactions between actors in the inter-organisational network. This section will elaborate more on the interpretations of these results. Figure 3 showed the average strength of connections between FinTech companies and the rest of the types of actors in the network. It showed that FinTech companies have strong connections with financial institutions mainly in the human resources network (weekly interactions) and the investment network (monthly interactions). Comparing the strength of interactions in the knowledge network, shows that FinTech companies have the least strongest interactions with financial institutions and the most with universities and venture capital organisations. Financial institutions do on average not interact much with FinTech ventures in the knowledge network (yearly interactions). As described in the interim conclusions part of the complete network, universities have the strongest connection with FinTech companies. This actor type interacts on average every month with FinTech in the knowledge and human resources network, which is an indication that there is a strong link between these two actor types in the emerging financial technology network of Vancouver. The government plays a role in the knowledge and investment networks, however this actor has a more prominent role as a public funding provider with interactions monthly. Venture capital organisations are represented in all types of networks, but they have the strongest interactions with FinTech companies in the knowledge (weekly interactions) and the advice network (monthly/ weekly interactions).

The structural characteristics of the different actor groups in the resource-based networks show that FinTech companies are the most influential and central actors in the knowledge network with high degree (4.00), betweenness (56.23) and eigenvector centrality (0.025) measures. In the investment network, mainly the government has the most important and central position with the highest betweenness centrality (103.09) and eigenvector centrality (0.034) measures. This actor also has the highest clustering coefficient (0.17), degree (4.00), and one of lowest closeness centrality (0.01)

49 measures in the investment network. The government is the most important source for investments and funding in the financial technology ecosystem of Vancouver. Although the FinTech companies in the advice and consultancy network have the best brokerage positions (49.22) for transferring advice to different parts of the network, the universities have the highest eigenvector centrality (0.034) measure. Universities therefore have more influence and are connected to more important actors in this network. Financial institutions are located on average closest (0.009) to the centre of the advice network and as figure 7 depicts, they are mainly connected with only one FinTech company. The last resource-based network analysed in this thesis, is the human resources network. In this network, the universities have the most important positions indicated by the highest an eigenvector centrality measure (0.088). Although this strong position in the HR network, comparing the structural measures with the strength of the interactions, FinTech companies have on average more interactions in this network with financial institutions (weekly versus monthly interactions). However, the structural measures show that financial institutions do not have a central position in this network.

In this section, the results of the social network analysis have been summarised and described. The next paragraphs will be used to describe the data which have been captured by the rest of the research survey. These questions in the survey focussed on: the types of innovation activities; information sources; factors that hamper the innovation activities; and the geographic reach of the participants’ information sources for innovation activities. The data about these topics will also be described in the next sections of this chapter. These results are based on data from sixteen participating FinTech ventures in the city of Vancouver in Canada.

5.4 Innovation Activities in the FinTech industry

Innovation activities & Information Sources for Innovation In this section, the results are described from the survey questions that focussed on the innovation activities performed by FinTech companies to produce new products, processes or services in the financial sector. The same categories of innovation activities have been used as in the community innovation survey (CIS) by the EU science and technology statistics department. Doing in-house research and development (R&D) is the most basic innovation activity performed by businesses, in which creative work is undertaken within the company to increase the stock of knowledge and to devise new and improved products and processes. Some companies rather purchase the output of internal R&D activities performed by other businesses or public or private research organisations. To be able to produce new or significantly improved products or processes, companies can acquire new resources that enable them to perform better during their operations, for example the acquisition of equipment or software. Acquisition of other external knowledge is another activity that businesses can exploit as an innovation activity, in which they for example license patents or purchase non-patented inventions, know-how, and other types of knowledge from other organisations. A more indirect category of activities is the internal or external training for personnel specifically for the development or introduction of products or services. A post-innovation activity, is the eventual market introduction of innovations. These are activities for the market introduction of new or significantly improved products and services, including: market research and launch advertising. The final category consists of all other activities that do not belong to one of the above described activities, for example procedures and technical preparations to implement new or improved products and processes. Figure 16 depicts the results of the data received from sixteen FinTech companies and their innovation activities. The results show that 27 percent of these companies exploit in-house R&D activities in which they produce new knowledge, products and services, compared to only 8 percent that purchase

50 external R&D from other businesses. The second largest category of innovation activities is the market introduction and preparation of new products and services. Twenty-five percent of the FinTech companies said they are doing market research and launch advertising when they develop new or significantly improved products and services. When the participating companies develop, or introduce innovations, 14 percent also train their personnel to increase their knowledge. Both the acquisition of other types of knowledge and activities that are not covered by the previous categories, are performed by only 7 percent of the participating companies. So, only a small proportion of companies license patents or purchase non-patented inventions, know-how or other types of knowledge that do not belong to the external R&D activities of other companies.

7% In-house R&D

27% External R&D purchased 25% Acquisition equipment, software, etc… Other Knowledge acquisition

Training personnel 8% Market introduction preparation 14% 12% Other 7%

Figure 16: Innovation activities exploited by sixteen FinTech companies in Vancouver

Companies can use different types of information sources when they develop new or significantly improved products or services. Figure 17 depicts the results from the survey about the information sources these participating companies use for their innovation activities. The same categories of information sources have been used as in the community innovation survey (CIS) by the EU science and technology statistics department, because these categories cover most of the available sources for companies in their innovation activities. An information source can also differ in level of importance as a source can have a slight impact or an extremely high impact on the success of a market introduction of a new product or service. Therefore, each participating company also gave an indication of the importance of an information source for their innovation activities. Figure 10 shows that customers play an extremely important role as an information source for almost 70 percent of the companies, and for more than 90 percent this information source is used during innovation activities. On the second and third place are internal information and competitors in the market positioned as most important information sources, both used as a source by slightly more than 80 percent of the participants. However, almost 45 percent of the companies indicate that their internal information is extremely important, compared to less than 20 percent of information from competitors in the market. The universities and the government take the last two places in the list as information sources for innovation activities, with respectively 19 and 13 percent. However, both groups of information sources have different levels of importance, because no participating company indicated information from universities as extremely important, but 5 percent did when asked how important information from the government is.

Percentage of FinTech companies (%) 0 10 20 30 40 50 60 70 80 90 100 Customers Internal information Competitors Conferences Industry associations Suppliers Scientific Journals Commercial labs Information Sources Information Universities Government

Extremely Very Moderately Slightly

Figure 17: Importance of information sources for innovation activities

The reach of the FinTech companies in Vancouver for the information sources has been depicted by Figure 18. Information from the most important source, the customers, is mostly coming from both Canada and the United States of America (USA). The reach of FinTech companies spans the whole North American continent. From the interviews with different participating companies, they said that “they must scale up their customer pool by also tapping into the US market, because the Canadian market is relatively small”. The unit of analysis for this research has been the FinTech industry of Vancouver, therefore most of the participants get their internal information from this area. Information for their innovation activities from competitors is as from the customers, coming from both Canada and the USA. This means that these companies compete on a higher scale than just within the region or country.

Figure 18: Reach for Information sources for innovation activities by FinTech companies

Obstacles for innovation For companies to deploy innovation activities they not only depend on internal resources, but they also need to operate within an ecosystem in which they depend on interactions with other organisations and institutions, and the availability of different types of resources from the external landscape. This dependence creates obstacles for companies when they deploy their innovation activities, as described in the theory chapter of this report. Obstacles or factors that hamper innovation activities in an ecosystem, like the financial sector of Vancouver, can be both internal or external. The lack of funds within a business or enterprise group is an example of an internal factor that could be an obstacle for a company to develop new or significantly improved products and services, but also when the innovation costs are too high. Obstacles that can be both an internal and external factor are, a lack of available qualified personnel; lack of information about technology; and, lack of information about (new) markets. However, a company could also face obstacles from the external landscape, for example a lack of private financial support; lack of public financial support from; difficulty in finding cooperation partners for innovation; when the market is dominated by established organisations; when there is an uncertain demand for innovative goods or services; there is no need to innovate due to prior innovations; and, when there is no need to innovate because of no demand for innovation. Each of these factors can have an impact on the efficiency and effectiveness of a company that is exploiting innovation activities, and these factors also differ in how strong they hamper the ability of a company to develop new or improved products and services.

Percentage of FinTech companies (%) 0 10 20 30 40 50 60 70 80 90 100 Lack of Qualified Personnel Lack of private financial support Lack of internal financial resources Difficult to find partners for innovation Market dominated by others Uncertain demand for innovation Lack of public financial support Lack of information about markets High innovation costs Lack of information about technology

Obstacles Innovation Activities Innovation Obstacles No need due to prior innovations No Demand for innovation

High Medium Low

Figure 19: Factors that inhibit the innovation activities of FinTech companies

Figure 19 depicts the results of factors that create obstacles for sixteen participating companies in their innovation activities. Each participant also gave an indication of how strong a factor inhibits their activities: low, medium or high. The availability of qualified personnel is the most important factor that is inhibiting innovation in the FinTech ecosystem of Vancouver. This could be both the availability of new talent in the region and the availability of in-house qualified personnel. More than 60 percent of the participating companies indicated that this is an obstacle for further innovation, with 25 percent of the companies saying it’s a high obstacle. From interviews with different participants, the most important reasons they discussed for this are,

“The high costs of living in downtown Vancouver”, and

“A lack of enough new technical graduates from the universities in the region”

The availability of private financial support for companies, like new FinTech ventures, is according to the participating companies the most (High) inhibiting factor for the development of new products and services in this ecosystem. Within this group of financial sources belong private investors, angels and other companies, and almost 40 percent of these companies indicate that the lack of private financial support is a highly inhibiting factor. Participants said in interviews that,

“Canada has compared to the US a more risk averse culture and that’s one part of the explanation why there is a lack of private capital invested in new ventures”, and

“The number of investors that are capable of investing great amounts of money in new ventures is small compared to other regions like Silicon Valley in the US”

The third factor inhibiting the innovation activities is a lack of internal financial resources (50 percent), with a little more than 30 percent of the companies saying it is highly inhibiting their innovation activities. The next factors in the list, difficult to find partners for innovation and market is dominated by established organisations, are obstacles that characterise the external landscape. More than 40 percent of the participants indicate that finding partners for innovation is difficult, and more than 30 percent say the market is dominated by established actors in the ecosystem. Both factors are highly inhibiting a little less than 20 percent of the companies in the sample. No participating company indicated that there is no need to innovate due to prior innovations, and the same results are found for the factor that there is no demand for innovation in the market.

6. Summary & Conclusions

Introduction This section provides an overview of the work that has been done in this thesis. This chapter ends with a conclusion in which the overall research question will be answered by using the input from the sub-research questions that are first summarised in the summary section of this chapter.

6.1 Summary What is the status of the development of the financial technology in the region of Vancouver? This sub-research question aims to describe the status of the development of FinTech ventures in Vancouver.

In chapter two the research context has been provided in which the current status of the financial technology developments worldwide, in Canada and the province of British Columbia, and the city of Vancouver are described. The city of Vancouver has more start-ups per capita than any other city in Canada, and is leveraging its unique combination of assets: a strong industrial foundation, a diverse talent pool, with over half of its residents speaking a different language than English, and is ranked as one of the world’s top twenty Global Financial Centres (Yeandle, 2016). The trend analysis (appendix A) which has been conducted as a preparation for this research thesis, showed that there has been an increase in the number of new ventures in different industries and especially in the financial industry. This has been a source and motivation for conducting this research thesis. Start-ups can benefit from different regulatory and tax advantages by the provincial government of British Columbia (BC), which is creating an incentive for entrepreneurs to start a business.

Why is the notion of inter-organisational networks a relevant theoretical perspective to analyse the emerging financial technology industry of Vancouver? The aim of this sub-question is to discuss the usefulness of the theoretical approach for analysing the developments in the financial technology industry in Vancouver. Answering this sub-question creates an understanding of why this concept has been used for the analysis and in chapter 3 the gaps in research made this clear.

Why inter-organisational network theory is relevant for the analysis of the emerging financial technology industry and innovation relates to how companies, research facilities and government are interconnected with each other. The network approach is grounded in the notion that the pattern of links between different organisations has important consequences for their performance and outcomes. For example, in a Digital Finance literature review by Gomber et al. (2017), they state that research could investigate the potential for different stakeholders in the FinTech industry and reveal how they will interact and could co-operate, respectively. In sum, ‘why’ the notion of inter- organisational network thinking is a useful perspective for the analysis in this thesis also relates to one of the aims of this research, to provide an overview of interactions between different stakeholders in the FinTech industry of Vancouver and provide recommendations for improving the access to resources for new ventures in Vancouver. The analytical concept called social network analysis makes it possible to analyse the connections between different stakeholders and their structural characteristics in an analytical way.

What types of resources can stakeholders in the financial services industry of Vancouver share and how could the inter-organisational network concept be used to analyse this? This question directs the thesis more in the direction of the link between different stakeholders in the industry. Answering this question will create an understanding of the types of resources that are shared between stakeholders and how the inter-organisational network concept can be used for analysing this.

In chapter two and three the types of resources that new ventures need to be able to develop have been described: knowledge, money, advice and qualified personnel. These types of resources have been used as a basis for the inter-organisational network analysis in which different networks have been created based on these types of resources. These categories have been identified in the interviews and from the Community Innovation Survey, as the major resources that new ventures need to be able to develop. The inter-organisational network concept is a useful way to analyse the types and strengths of the connections between stakeholders in a network level such as the financial technology industry. It is useful because the network theory enables the researcher to analyse multiple layers of information. Hence, this approach is used in research together with statistical tools and combined with other theoretical explanations, for example the resource-based view (Ahuja, 2000).

Which information sources in the FinTech industry are most important for the company’s innovation activities in the financial services industry and where is that information coming from? This question aims to derive what type of information is most valuable to FinTech ventures for their innovation activities. Not only the information types are derived, but also the ‘reach’ (distance) of their search for valuable information for their innovation activities is determined. This information provides important insights about where the information is coming from and what the most important sources for innovation activities are in the region of Vancouver.

In the innovation activities section of the results chapter, the different types of innovation activities and information sources have been described from the perspective of FinTech companies. More than a quarter (27 percent) of the participating companies is doing in-house research and development activities themselves. The most important information sources for their innovation activities are the customers (> 90 percent of participants), internal information (>80 percent of participants), and competitors (> 80 percent of participants). The reach of the FinTech companies in Vancouver for these information sources has been depicted by Figure 18. Customers information for their innovation activities is coming from both Canada and the United States of America (USA). Because these companies have their headquarters in Vancouver, the internal information is also coming for the main part from this area. Information for their innovation activities from competitors is as from the customers coming from both Canada and the USA.

Which obstacles do FinTech companies face in their development in the financial services industry of Vancouver? An answer to the final sub-question of this thesis aims to provide insights about the factors that are hampering the innovation activities and development of FinTech companies in the financial technology industry of Vancouver. These factors can be both internal organisational obstacles and factors that influence the development and hamper the innovation activities of FinTech companies from outside the businesses.

The final section of chapter five showed that a lack of available qualified personnel (> 60 percent of participants) is the number one factor that is creating an obstacle for innovation in the FinTech

58 industry of Vancouver (Figure 19). From interviews with different participants, the most important reasons they discussed for this are, “the high costs of living in downtown Vancouver”, and “a lack of enough new technical graduates from the universities in the region”. The second factor that is hampering the development and innovation activities of FinTech companies, is the lack of private financial support (50 percent of participants). Participants said in interviews that, “Canada has compared to the US, a more risk averse culture and therefore there is a lack of private capital invested in new ventures”. Also, “the number of investors that are capable of investing great amounts of money in new ventures is small compared to other regions like Silicon Valley in the US”.

6.2 Conclusion & Recommendations In this section, the main research question will be answered with the insights obtained by answering the sub-research questions. At the start of this thesis two goals have been formulated. The first goal of this thesis was to create a benchmark for the analysis of the emerging FinTech industry with an overview of the different FinTech ventures in the financial services industry. The second objective was to provide an overview of interactions between different stakeholders in the FinTech industry and provide recommendations for improving the access to resources for new ventures in Vancouver. In doing so, this thesis is positioned in a unique way within the field of inter-organisational network theory. The two goals are represented in the overall research question of this thesis and will be answered below.

To what extent are stakeholders in the emerging financial technology industry of Vancouver B.C. Canada, involved in the development of new FinTech ventures?

The answer to this question is useful to derive the importance of different stakeholders in the financial industry for the development of financial technology companies. The aim of this question is to obtain information about the influence and importance of different types of actors in the inter-organisational network. This information is useful for the Consulate-General of the Netherlands in Vancouver to gain a better understanding of the activities and status of the innovation and technology sector of West-Canada. These insights can be used to inform the Dutch Ministry of Foreign Affairs and Economic Affairs about potential opportunities in West-Canada for businesses in the Netherlands. This thesis specifically contributes to this goal by providing information about the status of an industry that is changing radically worldwide, and provides insights in the strengths and obstacles for innovation in the region of Vancouver.

The results showed that stakeholders in the financial services sector differ in the resources they share and the impact they have on the development of financial technology companies. FinTech companies interact most among each other in the inter-organisational network and in the four-separate resource- based networks. Universities interact most often with FinTech companies and this happens mainly in the human resources and knowledge network. When this information is combined with the information sources for innovation activities of FinTech companies, it shows that although there is a strong connection between FinTech companies and Universities, knowledge from the universities and colleges is for less than 20 percent of the participants valuable for their innovation activities. When the same is done for the factors that inhibit the innovation activities of FinTech companies, the factor that hampers innovation most according to more than 90 percent of the participants is a lack of qualified human resources. To conclude for the effect of universities on the development of FinTech companies, from the network perspective the universities play a central role in the human resources, and advice and consultancy network, however this stakeholder also plays an important role in the

59 factor that inhibits innovation in this industry the most. Also, it doesn’t play an important role for innovation in the emerging financial technology industry.

The traditional financial services provider or financial institution (FI), is most affected by the rise in number of new FinTech companies in the industry. From an inter-organisational network perspective, this stakeholder has least often interactions with FinTech companies compared with other stakeholders in the general network. However, in the investment and human resources network they interact most often, even on a weekly basis on average in the human resources network. After the number of connections between FinTech companies among each other, they have the most interactions with FI in the knowledge network although they interact only ones a year on average. To conclude for the effect of FI on the development of FinTech companies, from the network perspective this actor type only has a central position in the knowledge network. This is also supported by the results about the information sources for innovation activities, in which competitors of FinTech companies take the third place of most important information sources for their innovation activities. However, this actor type is also part of the factors that form an obstacle for innovation in the industry. Participants indicated that it’s difficult to find partners for innovation in the market (> 40 percent) and the market is dominated by established organisations (> 30 percent).

Venture capitalists are present in all networks, but mainly in the investment network. FinTech companies interact most with this stakeholder in the knowledge network, almost every week on average. This knowledge is mainly in the direction of the ventures in which they receive information about business operations and not for innovation activities. The centrality measures show that venture capital organisations have a high value for the eigenvector centrality in the investment, and advice and consultancy networks. This means that they are of great importance and influential in the development of new ventures like FinTech companies. Although these organisations are important for the development, there is still a lack of available private financial support for FinTech companies and this is inhibiting innovation by these companies in the emerging financial technology industry.

The final stakeholder that has been analysed in relation with FinTech companies, is the government. Although this stakeholder has the least amount of total connections with FinTech companies, they interact most often (monthly) on average in the investment network after the FI. The network centrality measures show that this stakeholder has the second highest eigenvector centrality measure in the general network, but the closeness measure shows that they are on average positioned far from the rest of the network. As the government is also part of the knowledge network of FinTech companies, however they do not play an important role in the development of these companies indicated by the lowest eigenvector centrality and betweenness centrality measures. Also, just a little more than 10 percent of the FinTech companies said that this information is useful for their innovation activities. Nonetheless, the government takes the most influential position in the investment and funding network which is also supported by the results that only 25 percent of the participating companies indicated that a lack of public financial support is inhibiting their innovation activities. This is far less than the lack of private financial support. The government thus plays a central role in the development of FinTech companies by providing public financial support.

Concluding the overall thesis research, the following can be stated: The influence and importance of different stakeholders has been analysed for the development of FinTech companies in the city of Vancouver. From the point of view of the Consulate-General of the Netherlands this information also provides insights in how well the different stakeholders are participating in the development of the innovation and technology sector. First, venture capital organisations and the government have the

60 most positive influence on the development of FinTech companies by taking a central position in the inter-organisational investment network. However, the lack of private financial support is far more inhibiting the innovation activities compared to public financial support. From the human resources perspective, although universities and colleges take the most central position and they have the strongest connection with FinTech companies, a lack of enough qualified personnel is one of the most important factors that is inhibiting the further development and innovation by FinTech companies. FI also have many connections in this network, however from a network perspective this stakeholder does not play an important role for the development of FinTech companies. Knowledge sharing is mainly done among FinTech companies. Traditional financial services providers have also many connections in this network, however they do not have an important role and from the interviews this actor is mainly a knowledge taker than one that provides knowledge to the new ventures. This is mainly because, FinTech have specific knowledge about technology that FI do not have and are interested in.

7. Discussion

Introduction In this chapter, a reflection on the research conducted in this thesis is provided. This work has both theoretical and practical implications and therefore both will be discussed separately. The chapter will conclude with the limitations of this research and suggestions for future research are provided.

7.1 Theoretical Implications The theoretical implication for research is that the findings in this research thesis have contributed to the stock of knowledge about a new and timely development in the financial services industry. It provided new insights in the interactions between traditional financial services providers and financial technology ventures. There has not been a lot of research yet in literature in which inter-organisational network theory has been applied to the study area of digital finance. This is supported by a meta- analysis of literature by Gomber et al. (2017) in which they revealed that that future research should focus more on the network level of the financial industry. In addition to that, this thesis has given insights in the importance of different types of resources stakeholders share and the strength of interactions among them.

7.2 Practical Implications The theory of inter-organisational networks has been used to analyse innovation in an industry that is in a stage of transformation provides new insights for policy makers and managers. Policy makers can use these insights to develop new policy agendas that foster the development of the innovation and technology landscape and remove the obstacles that have been identified as factors that hamper innovation in the region of Vancouver. Policy should be focussed first on improving the supply of more qualified (technical) personnel and on keeping talent in the region. The high costs of living and especially the high priced real estate market in Vancouver is a factor that is forming an obstacle for keeping talent in the region.

For managers, the recognition that business leverage on each other complementarities for the creation of new business models and innovations is crucial. As a result, practical management implications should for example, be focused on achieving a good cooperation between FinTech companies and traditional financial services providers. The former group of companies has strong culture of innovation and developing new products and services in a fast pace, as the FI have a strong customer base and stock of knowledge about the market. Both sides can benefit from each other by increasing the interactions, which will be both beneficial for their businesses as for the customer.

7.3 Limitations of this Research The FinTech industry is developing on a global scale and therefore these companies are not bounded to collaborating with only local or regional organisations and institutions. This research thesis focussed on the inter-organisational network of Vancouver and therefore many organisations FinTech companies interact with outside of this boundary have not been considered in the analysis of the inter- organisational networks. This would however create a more complete picture of the interaction and collaboration network of this industry.

By using social network analysis (SNA) concept as an analytical perspective for the analysis of the FinTech industry, the results become more useful when more stakeholders in the network are participating in the research. Due to a lack of time, eventually only the FinTech companies have been

63 selected as data in this study and the other stakeholders are added as control groups. Although almost 50 percent of all the FinTech companies in the region of Vancouver have participated in this research thesis, there is still room for improvement here. The depicted networks in the results chapter are not complete, as not all FinTech companies have participated in this study.

A second methodological limitation is that this study has several specific characteristics related to the FinTech industry in a single region. As a result, findings on centrality measures or the identified obstacles for innovation activities cannot be generalized into other industry sectors. Conducting more case studies into different industries will create a more general overview of these characteristics.

7.4 Suggestions for Future Research The reflection on this research by highlighting its limitations already revealed sufficient suggestions for future research. First, more empirical evidence is needed by repeating inter-organisational network research on different industries and on a broader level, for example provincial, which could result in more generalizable results, and this could also be more beneficial for policy implications.

A longitudinal study of the inter-organisational networks, which means that the changes in interactions among stakeholders in the industry are measured, should reveal and provide information about the importance of specific types of resources at different points in time. This will enable policy makers and managers to pinpoint in a more precise way what new ventures need at a certain stage of their development.

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Appendix

Appendix A Figure 20 and Figure 21 depict an overview of ventures distributed over the core business in the region of in Vancouver B.C. Canada between 2011-2017.

Number of Ventures 0 10 20 30 40 50 60 70 Finance Software Media Health Marketing Ecommerce Technology Human Resources Travel Platform Food and Beverages Real Estate Startups Gaming B2B Education Consulting Design Events Virtual Reality Energy Location based services Internet of Things Hospitality Beauty Bookkeeping Security Lifestyle Groceries Legal Dating Robotics Biotechnology Insurance Construction Chemical 3D printing Drones Entertainment Aerospace Logistics Farming

Figure 20: Ventures in Vancouver B.C. Canada between 2011-2017 (angel.co – Elaborated by the author)

100% Platform 90% 80% Travel 70% Human Resources 60% Technology 50% Ecommerce 40% businesseses Marketing 30% 20% Health 10% Media Percentage of ventures in top 10 coretop in venturesof Percentage 0% Software 2011 2012 2013 2014 2015 2016 Year venture is founded Finance

Figure 21: Number of ventures per category of core business in Vancouver B.C. Canada between 2011-2016

Appendix B - Research Survey FinTech ecosystem of Vancouver BC

Q1.1 Welcome to the FinTech innovation research survey! Thank you for agreeing to take part in this survey about innovation and co-operation in the emerging FinTech ecosystem of Vancouver BC. This study aims to understand the interactions between different stakeholders and the innovation activities in this ecosystem, which will contribute to the research area of innovation and strategic management literature. You have been selected to take part in this survey, because your organisation has been identified as a stakeholder in this ecosystem.

This is an online survey and will take no longer than 15 minutes to complete. For your information, only the researchers of this study will be allowed to use and see the raw information you are providing. (1) All information will be held confidential and (2) your participation is voluntary.

If you encounter any problems during the survey, please inform the researcher about this: T: E:

Please click 'Next' to begin.

About the researcher: This is a Master's thesis study as part of the M.Sc. Innovation Sciences program at the University of Technology Eindhoven (The Netherlands). The researcher is doing a graduate internship in Vancouver BC at the Consulate General of the Kingdom of the Netherlands. Additional to the university-program, the researcher took courses during an exchange program at Bocconi University in Milan (Italy).

Q2.1 General information The survey starts with a couple of general questions about you and your business.

Q2.2 What is the full name of the business you are working for?

Q2.3 What is your job title?

Q2.4 Is ${q://QID5/ChoiceTextEntryValue} part of an enterprise group? Information: A group consists of two or more legally defined enterprises under common ownership. Each enterprise in the group may serve different markets, as with national or regional subsidiaries, or serve different product markets. The head office is also part of an enterprise group. m Yes m No Condition: No Is Selected. Skip To: End of Block.

Q2.5 What is the name of the enterprise group ${q://QID5/ChoiceTextEntryValue} is part of?

Q2.6 In what city is the enterprise group currently headquartered?

Q3.1 Product Innovation Information: A product innovation is the market introduction of a new good or service, (e.g. software, user friendliness, components or sub- systems). The innovation (new or improved) must be new to your enterprise, but it does not need to be new to your sector or market. It does not matter if the innovation was originally developed by your business or by other enterprises.

Q3.2 Did ${q://QID5/ChoiceTextEntryValue} introduce new or significantly improved products or services during the past 2 years? m Yes m No Condition: No Is Selected. Skip To: End of Block.

Q3.3 Who mainly developed these innovations? * Choose one of the three option m Mainly ${q://QID5/ChoiceTextEntryValue} or the enterprise group m ${q://QID5/ChoiceTextEntryValue} together with other businesses or institutions m Mainly other businesses or institutions

Q3.4 Were these innovations mainly? * Choose one of the two options m New to the market (before your competitors introduced these) m Only new to your business (already available for competitors)

Q4.1 Process Innovation Information: A process innovation is the implementation of a new or significantly improved production process, distribution method, or support activity for your goods or services. The innovation (new or improved) must be new to your business, but it does not need to be new to your sector or market. It does not matter if the innovation was originally developed by your business or by other businesses. Exclude purely organisational innovations.

Q4.2 Did ${q://QID5/ChoiceTextEntryValue} introduce new or significantly improved process innovations during the past 2 years? m Yes m No Condition: No Is Selected. Skip To: End of Block.

Q4.3 Who mainly developed these process innovations? * Choose one of the three options m Mainly ${q://QID5/ChoiceTextEntryValue} or the enterprise group m ${q://QID5/ChoiceTextEntryValue} together with other businesses or institutions m Mainly other businesses or institutions

Q4.4 Which category applies most to these process innovations? * Choose one of the two options m New or significantly improved logistics, delivery or distribution methods for your inputs, goods or services m New or significantly improved supporting activities for your processes, such as maintenance systems or operations for purchasing, accounting or computing

Q53 Organisational Innovation Information: An organisational innovation is the implementation of new or significant changes in firm structure or management methods that are intended to improve your firm’s use of knowledge, the quality of your goods and services, or the efficiency of work flows.

Q54 Did ${q://QID5/ChoiceTextEntryValue} introduce new or significantly improved organisational innovations during the past 2 years? m Yes m No Condition: No Is Selected. Skip To: End of Block.

Q55 Select the organisational improvements or innovations ${q://QID5/ChoiceTextEntryValue} introduced during the past 2 years: q Knowledge management systems - New or significantly improved, to better use or exchange information, knowledge and skills within your enterprise q Significant changes to the organisation of work in your enterprise Employee decision making & responsibility for their work q Change of the management structure - E.g. Creating new divisions or departments, integrating different departments or activities, adoption of a networked structure, etc. q Changes in relations with other firms or public institutions - E.g. Through alliances, partnerships, outsourcing or sub-contracting q Set up an Innovation & Technology Lab, division or department q None of the above, but: ______

Q5.1 Innovation activities Did ${q://QID5/ChoiceTextEntryValue} participate in the following innovation activities during the past 2 years? * Select all innovation activities that apply for your business q In-house R&D: Creative work undertaken within your business to increase the stock of knowledge and its use to devise new and improved products and processes (including software development) q External R&D: Same activities as above, but performed by other businesses (including other businesses within your group) or by public or private research organisations and purchased by your enterprise q Acquisition of equipment, software, etc... Used to produce new or significantly improved products or processes q Acquisition of other external knowledge: Purchase of licensing of patents and non-patented inventions, know-how, and other types of knowledge from other organisations q Training: Internal or external training for your personnel specifically for the development and/ or introduction of new/ improved products or processes q Market introduction of innovations: Activities for the market introduction of your new or significantly improved goods and services, including: market research and launch advertising q Other: Procedures and technical preparations to implement new or improved products and processes that are not covered elsewhere q None of the above

Q5.2 Public Financial Support Did ${q://QID5/ChoiceTextEntryValue} receive any public financial support for innovation activities from the following levels of government during the past 2 years? * Multiple answers are possible q Local or municipal q Provincial government q Federal government q Other, namely: ______q None Condition: None Is Selected. Skip To: End of Block.

Q5.3 If ${q://QID5/ChoiceTextEntryValue} received public financial support for innovation activities, please write the name of the institution(s) that provided this support:

Q6.1 1. Interactions with FinTech companies Did ${q://QID5/ChoiceTextEntryValue} have significant interactions with financial technology (FinTech) companies during the past 2 years? m Yes m I'm not sure m No Condition: No Is Selected. Skip To: End of Block.

Q6.3 Identify the FinTech companies from the list with which ${q://QID5/ChoiceTextEntryValue} had significant interactions over the past 2 years. Information: "Significance" means to what extent your business has invested resources (time, attention, care, favours, etc.) in them. * Do not select your own business if it's listed below q Agility Forex q ModernAdvisor q Beanworks Solutions Inc. q MOGO Corp. q Bench q Netcoins q Blockchain Tech Ltd q nTrust q bluzelle q PayByPhone q Cassia Research q Payfirma q Coinpayments, Inc. q PayWith q Control q Peotic q E-xact Transactions q PiALGO q FI.SPAN q QuadrigaCX q Finn.ai q RentMoola q FrontFundr q TIO Networks q Grow q Trippeo q HyperWallet Systems Inc. q Trulioo q INETCO q Trustatom Inc. q InvestX q Voleo q Koho q Wealthbar q Lendful q Zafin q MatchSpread

Condition: Identify the FinTech c... Is Equal to 0. Skip To: Please enter the FinTech company name....

Q6.4 Indicate the 'types' of interactions for each of the selected companies: 1. Knowledge/ data/ technology This type of interaction with another business can be characterised as a relation or connection in which knowledge, know-how, data, technology, etc... are provided, received or shared. 2. Investment/ funding This type of interaction with another business can be characterised as a relation or connection in which an investment, funding, money etc... are provided, received or shared. 3. Consult/ advice This type of interaction with another business can be characterised as a relation or connection in which advice, consultancy, mentorship, etc... are provided, received or shared. 4. Human Resources/ employees This type of interaction with another business can be characterised as a relation or connection in which human resources, employees, tacit knowledge, etc... are provided, received or shared.

* More than one interaction type can be checked per business/ row

1. Knowledge 2. Investment 3. Advice 4. Human Resources Agility Forex q q q q Beanworks Solutions Inc. q q q q Bench q q q q Blockchain Tech Ltd q q q q bluzelle q q q q Cassia Research q q q q Coinpayments, Inc. q q q q Control q q q q E-xact Transactions q q q q FI.SPAN q q q q Finn.ai q q q q FrontFundr q q q q Grow q q q q HyperWallet Systems Inc. q q q q INETCO q q q q InvestX q q q q Koho q q q q Lendful q q q q MatchSpread q q q q ModernAdvisor q q q q MOGO Corp. q q q q Netcoins q q q q nTrust q q q q PayByPhone q q q q Payfirma q q q q PayWith q q q q Peotic q q q q PiALGO q q q q QuadrigaCX q q q q RentMoola q q q q TIO Networks q q q q Trippeo q q q q Trulioo q q q q Trustatom Inc. q q q q Voleo q q q q Wealthbar q q q q Zafin q q q q

Q6.5 Indicate how often ${q://QID5/ChoiceTextEntryValue} interacts with these companies:

Daily Weekly Monthly Yearly Interacted ones Agility Forex m m m m m Beanworks Solutions Inc. m m m m m Bench m m m m m Blockchain Tech Ltd m m m m m bluzelle m m m m m Cassia Research m m m m m Coinpayments, Inc. m m m m m Control m m m m m E-xact Transactions m m m m m FI.SPAN m m m m m Finn.ai m m m m m FrontFundr m m m m m Grow m m m m m HyperWallet Systems Inc. m m m m m INETCO m m m m m InvestX m m m m m Koho m m m m m Lendful m m m m m MatchSpread m m m m m ModernAdvisor m m m m m MOGO Corp. m m m m m Netcoins m m m m m nTrust m m m m m PayByPhone m m m m m Payfirma m m m m m PayWith m m m m m Peotic m m m m m PiALGO m m m m m QuadrigaCX m m m m m RentMoola m m m m m TIO Networks m m m m m Trippeo m m m m m Trulioo m m m m m Trustatom Inc. m m m m m Voleo m m m m m Wealthbar m m m m m Zafin m m m m m

Q6.6 Please enter the FinTech company names that are NOT listed above and with which ${q://QID5/ChoiceTextEntryValue} had significant interaction over the past 2 years. Also indicate the type of interaction and how often ${q://QID5/ChoiceTextEntryValue} interacts: E.g. Company name 1 3 4 weekly

Q7.1 2. Interactions with Financial Institutions Did ${q://QID5/ChoiceTextEntryValue} have significant interactions with Financial Institutions during the past 2 years? m Yes m No Condition: No Is Selected. Skip To: End of Block.

Q7.3 Identify the Financial Institutions from the list with which ${q://QID5/ChoiceTextEntryValue} had significant interactions over the past 2 years. Information: "Significance" means to what extent your business has invested resources (time, attention, care, favours, etc.) in them. * Do not select your own business if it's listed below q Bank of Canada q Laurentian Bank q BDC Business Development Bank Canada q Manulife Financial q BlueShore Financial q Peoples Group q BMO Bank of Montreal q Prospera Credit Union q Canadian Western Bank q RBC Royal Bank q Central 1 q Scotiabank q CIBC q Tangerine q Coast Capital Savings Credit Union q TD Canada Trust Bank q G&F Financial Group q Vancity Credit Union q HSBC

Condition: Identify the Financial... Is Equal to 0. Skip To: Please enter the Financial Institutio....

Q7.4 Indicate the 'types' of interactions for each of the selected institutions: 1. Knowledge/ data/ technology This type of interaction with another business can be characterised as a relation or connection in which knowledge, know-how, data, technology, etc... are provided, received or shared. 2. Investment/ funding This type of interaction with another business can be characterised as a relation or connection in which an investment, funding, money etc... are provided, received or shared. 3. Consult/ advice This type of interaction with another business can be characterised as a relation or connection in which advice, consultancy, mentorship, etc... are provided, received or shared. 4. Human Resources/ employees This type of interaction with another business can be characterised as a relation or connection in which human resources, employees, tacit knowledge, etc... are provided, received or shared. * More than one interaction type can be checked per business/ row

1. Knowledge 2. Investment 3. Advice 4. Human Resources Bank of Canada q q q q BDC Business q q q q Development Bank Canada BlueShore Financial q q q q BMO Bank of Montreal q q q q Canadian Western Bank q q q q Central 1 q q q q CIBC q q q q Coast Capital Savings q q q q Credit Union G&F Financial Group q q q q HSBC q q q q Laurentian Bank q q q q Manulife Financial q q q q Peoples Group q q q q Prospera Credit Union q q q q RBC Royal Bank q q q q Scotiabank q q q q Tangerine q q q q TD Canada Trust Bank q q q q Vancity Credit Union q q q q

Q7.5 Indicate how often ${q://QID5/ChoiceTextEntryValue} interacts with these companies: Daily Weekly Monthly Yearly Interacted ones Bank of Canada m m m m m BDC Business Development Bank m m m m m Canada BlueShore Financial m m m m m BMO Bank of Montreal m m m m m Canadian Western Bank m m m m m Central 1 m m m m m CIBC m m m m m Coast Capital Savings m m m m m Credit Union G&F Financial Group m m m m m HSBC m m m m m Laurentian Bank m m m m m Manulife Financial m m m m m Peoples Group m m m m m Prospera Credit Union m m m m m RBC Royal Bank m m m m m Scotiabank m m m m m Tangerine m m m m m TD Canada Trust Bank m m m m m Vancity Credit Union m m m m m

Q7.6 Please enter the Financial Institutions that are NOT listed above and with which ${q://QID5/ChoiceTextEntryValue} had significant interaction over the past 2 years. Also indicate the type of interaction and how often ${q://QID5/ChoiceTextEntryValue} interacts: E.g. Company name 1 3 4 daily

Q8.1 You are over 60% of the survey now!

Q8.2 3. Interactions with Universities and Colleges Did ${q://QID5/ChoiceTextEntryValue} have significant interactions with universities, colleges, researchers or graduates from these institutes during the past 2 years? m Yes m No Condition: No Is Selected. Skip To: End of Block.

Q8.4 Identify all Universities and Colleges from the list with which ${q://QID5/ChoiceTextEntryValue} had significant interactions over the past 2 years. Information: "Significance" means to what extent your business has invested resources (time, attention, care, favors, etc) in them. * Do not select your own business if it's listed below q British Columbia Insititute of Technology q Capilano University q Columbia College q Kwantlen Poyltechnic University q Langara College q Simon Fraser University q Stenberg College q University of British Columbia q Vancouver Community College Condition: Identify all Universities a... Is Equal to 0. Skip To: Please enter the universities, colleg....

Q8.5 Indicate the 'types' of interactions for each of the selected institution: 1. Knowledge/ data/ technology This type of interaction with another business can be characterised as a relation or connection in which knowledge, know-how, data, technology, etc... are provided, received or shared. 2. Investment/ funding This type of interaction with another business can be characterised as a relation or connection in which an investment, funding, money etc... are provided, received or shared. 3. Consult/ advice This type of interaction with another business can be characterised as a relation or connection in which advice, consultancy, mentorship, etc... are provided, received or shared. 4. Human Resources/ employees This type of interaction with another business can be characterised as a relation or connection in which human resources, employees, tacit knowledge, etc... are provided, received or shared. * More than one interaction type can be checked per business/ row

1. Knowledge 2. Investment 3. Advice 4. Human Resources British Columbia Insititute of Technology q q q q Capilano University q q q q Columbia College q q q q Kwantlen Poyltechnic University q q q q Langara College q q q q Simon Fraser University q q q q Stenberg College q q q q University of British Columbia q q q q Vancouver Community College q q q q

Q8.6 Indicate how often ${q://QID5/ChoiceTextEntryValue} interacts with these institutes: Daily Weekly Monthly Yearly Interacted ones British Columbia Insititute of Technology m m m m m Capilano University m m m m m Columbia College m m m m m Kwantlen Poyltechnic University m m m m m Langara College m m m m m Simon Fraser University m m m m m Stenberg College m m m m m University of British Columbia m m m m m Vancouver Community College m m m m m

Q8.7 Please enter the universities, colleges or research institutes that are NOT listed above and with which ${q://QID5/ChoiceTextEntryValue} had significant interaction over the past 2 years. Also indicate the type of interaction and how often ${q://QID5/ChoiceTextEntryValue} interacts: E.g. Company name 1 3 4 monthly

Q9.1 4. Interactions with Venture Capitalists, Investor or Angel groups Did ${q://QID5/ChoiceTextEntryValue} have significant interactions with Venture Capitalists, Investors or Angel Groups during the past 2 years? m Yes m No Condition: No Is Selected. Skip To: End of Block.

Q9.3 Identify the Venture Capital, Investment or Angel groups from the list with which ${q://QID5/ChoiceTextEntryValue} had significant interactions over the past 2 years. Information: "Significance" means to what extent your business has invested resources (time, attention, care, favors, etc) in them.

q BDC Venture Capital q Omers Ventures q Bootup Labs q Sora Capital corp q Chrysalix q Stanley Park Ventures q Cypress Hills q Vancouver Founder Fund q Discovery Capital q Vanedge Capital q Goal Holdings q Ventures West q HighLine q Version One q Lighthouse equity partners q Yaletown Partners

Condition: Identify the Venture C... Is Equal to 0. Skip To: Please enter the Venture Capitalists,....

Q9.4 Indicate the 'types' of interactions for each of the selected organisations: 1. Knowledge/ data/ technology This type of interaction with another business can be characterised as a relation or connection in which knowledge, know-how, data, technology, etc... are provided, received or shared. 2. Investment/ funding This type of interaction with another business can be characterised as a relation or connection in which an investment, funding, money etc... are provided, received or shared. 3. Consult/ advice This type of interaction with another business can be characterised as a relation or connection in which advice, consultancy, mentorship, etc... are provided, received or shared. 4. Human Resources/ employees This type of interaction with another business can be characterised as a relation or connection in which human resources, employees, tacit knowledge, etc... are provided, received or shared.

* More than one interaction type can be checked per business/ row

1. Knowledge 2. Investment 3. Advice 4. Human Resources BDC Venture Capital q q q q Bootup Labs q q q q Chrysalix q q q q Cypress Hills q q q q Discovery Capital q q q q Goal Holdings q q q q HighLine q q q q Lighthouse equity partners q q q q Omers Ventures q q q q Sora Capital corp q q q q Stanley Park Ventures q q q q Vancouver Founder Fund q q q q Vanedge Capital q q q q Ventures West q q q q Version One q q q q Yaletown Partners q q q q

Q9.5 Indicate how often ${q://QID5/ChoiceTextEntryValue} interacts with these companies: Daily Weekly Monthly Yearly Interacted ones BDC Venture Capital m m m m m Bootup Labs m m m m m Chrysalix m m m m m Cypress Hills m m m m m Discovery Capital m m m m m Goal Holdings m m m m m HighLine m m m m m Lighthouse equity partners m m m m m Omers Ventures m m m m m Sora Capital corp m m m m m Stanley Park Ventures m m m m m Vancouver Founder Fund m m m m m Vanedge Capital m m m m m Ventures West m m m m m Version One m m m m m Yaletown Partners m m m m m

Q9.6 Please enter the Venture Capitalists, investor or Angel groups that are NOT listed above and with which ${q://QID5/ChoiceTextEntryValue} had significant interaction over the past 2 years. Also indicate the type of interaction and how often ${q://QID5/ChoiceTextEntryValue} interacts: - E.g. Company name 1 3 4 interacted ones

Q10.1 5. Additional interactions Are there any other organisations, institutions or stakeholders in the FinTech ecosystem of Vancouver BC that are NOT listed in the questions above? Please provide their names, type of interaction and how often ${q://QID5/ChoiceTextEntryValue} interacts: 1. Knowledge/ data/ technology This type of interaction with another business can be characterised as a relation or connection in which knowledge, know-how, data, technology, etc... are provided, received or shared. 2. Investment/ funding This type of interaction with another business can be characterised as a relation or connection in which an investment, funding, money etc... are provided, received or shared. 3. Consult/ advice This type of interaction with another business can be characterised as a relation or connection in which advice, consultancy, mentorship, etc... are provided, received or shared. 4. Human Resources/ employees This type of interaction with another business can be characterised as a relation or connection in which human resources, employees, tacit knowledge, etc... are provided, received or shared. e.g. Company name 1 4 yearly

Q11.1 Sources of Information for Innovation Activities Select all sources of information for ${q://QID5/ChoiceTextEntryValue}'s innovation activities from the list below? q Internal information or within enterprise group q Supplier of equipment, materials, components, or software q Clients or customers q Competitors or other organisations in the financial sector q Commercial labs, private R&D institutes or Consultants q Universities or other higher education institutions q Government or public research institutes q Conferences, trade fairs, exhibitions q Scientific journals and trade/ technical publications q Professional and industry associations Condition: Sources of Information for ... Is Equal to 0. Skip To: End of Block.

Q11.2 How important are the sources of information for ${q://QID5/ChoiceTextEntryValue}'s innovation activities? Extremely Very Moderately Slightly Not at all important important important important important Internal information or within enterprise m m m m m group Supplier of equipment, materials, m m m m m components, or software Clients or customers m m m m m Competitors or other organisations in m m m m m the financial sector Commercial labs, private R&D m m m m m institutes or Consultants Universities or other higher education m m m m m institutions Government or public research institutes m m m m m Conferences, trade fairs, exhibitions m m m m m Scientific journals and trade/ technical m m m m m publications Professional and industry associations m m m m m

Q11.3 Please indicate for each information source its main location: * Only select the location of the office your business co-operated with

Vancouver & British Canada USA Other No geographic direct region Columbia country location (e.g. Internet) Internal information or within m m m m m m enterprise group Supplier of equipment, materials, components, or m m m m m m software Clients or customers m m m m m m Competitors or other organisations in the financial m m m m m m sector Commercial labs, private m m m m m m R&D institutes or Consultants Universities or other higher m m m m m m education institutions Government or public m m m m m m research institutes Conferences, trade fairs, m m m m m m exhibitions Scientific journals and trade/ m m m m m m technical publications Professional and industry m m m m m m associations

Q12.1 Factors hampering Innovation Select the factors that were hampering your innovation activities during the past 2 years: * Select all factors that apply for ${q://QID5/ChoiceTextEntryValue} q Lack of funds within your organisation or enterprise group q Lack of private finance from sources outside your enterprise q Lack of public finance from sources outside your enterprise q Innovation costs were too high q Lack of qualified personnel q Lack of information about technology q Lack of information about markets q Difficulty in finding cooperation partners for innovation q Market dominated by established organisations q Uncertain demand for innovative goods or services q No need to innovate due to prior innovations q No need to innovate because of no demand for innovation Condition: Factors hampering Innovatio... Is Equal to 0. Skip To: End of Block.

Q12.2 Indicate to what extent the following factors were hampering your innovation activities during the past 2 years? High Medium Low Lack of funds within your organisation or enterprise group m m m Lack of private finance from sources outside your enterprise m m m Lack of public finance from sources outside your enterprise m m m Innovation costs were too high m m m Lack of qualified personnel m m m Lack of information about technology m m m Lack of information about markets m m m Difficulty in finding cooperation partners for innovation m m m Market dominated by established organisations m m m Uncertain demand for innovative goods or services m m m No need to innovate due to prior innovations m m m No need to innovate because of no demand for innovation m m m

Q13.1 Intellectual Property During the past 2 years, did your business: *Select all options that apply for your business q Apply for a patent q Register for an industrial design q Register a trademark q Claim copyright q None of the above

Q14.1 Thank you for answering the questions of this survey. If you have any questions, remarks or recommendations, please feel free to contact the researcher: E: [email protected]

Q14.2 If you wish to receive the final report of this research, please enter your email address below, otherwise click 'Next' to finish the survey.