The disruptiveness of blockchain technology : an exploratory research conducted in the Dutch payments industry

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Jeroen Custers 10505555 | University of Amsterdam | Amsterdam Business School |

Executive Program in Management Studies, Strategy Track | First Supervisor: Nathan Betancourt |

June 26, 2015: Final version|

The Disruptiveness of

Blockchain Technology

AN EXPLORATORY RESEARCH CONDUCTED IN THE DUTCH

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1 The Disruptiveness of Blockchain Technology

Table of content

Overview of Tables and Figures ... 3

Preface... 4

Abstract ... 5

1. Introduction ... 6

2. Literature review... 8

2.1 Technological Innovation ... 8

2.2 Radical and discontinuous innovations ... 9

2.3 Disruptive innovation ...11

2.4 A revised definition of disruptive innovation ... 13

2.5 Research questions ... 16 3. Methodology ... 17 3.1 Research method ... 17 3.2 Research design ... 18 3.3 Data collection ... 20 3.4 Data analysis ... 22 3.5 Data condensation/reduction ... 22 3.5.1 First-cycle coding ... 24 3.5.2 Second-cycle coding ... 25 3.6 Data display... 27 3.7 Data interpretation ... 28 4. Results ... 30 4.1 Disruptive innovation ... 30

4.1.1 Different set of features ... 30

4.1.2 Different performance ... 32

4.1.3 Different price attributes ... 33

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4.1.5 Different customer segment ... 35

4.2 Role of regulators ... 36

4.3 Business Models and Use Cases ... 37

4.3.1 Real-time -, 24/7 - and Interbank payments ... 39

4.3.2 Remittance and foreign exchange ... 41

4.3.3 Big data ... 42

4.3.4 Digital wallets and identity management ... 43

4.3.5 Documentary trade- and escrow applications ...45

4.3.6 Asset servicing ... 46 5. Discussion ... 48 6. Conclusions ...58 Bibliography ... 59 Appendices ... 64 Appendix A: Questionnaire ... 64

Appendix B: List of codes after first cycle of coding ... 67

Appendix C: List of merged codes before second cycle ... 69

Appendix D: Codes merged into Code Categories ... 77

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Overview of Tables and Figures

Table 1 - Overview of interviews linked to case groups……….………21 Table 2 - Overview of code categories and topic they are related to…….………….26 Table 3 - Code categories and times mentioned by different respondents………28 Table 4 - Overview of code categories related to a disruptive innovation……....30 Table 5 - Overview of business models mentioned by different case groups……39

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Preface

The writing of this thesis was the last step of a journey that started in February 2013. Back then, I could not have imagined that I would enjoy the past two-and-a-half years as much as I did. There have certainly been moments in which the combination of a full time job and a demanding study were challenging, but overall it has been fun for me to meet many new people, to learn from them, and to be in an inspiring environment in which I was challenged over and over again.

Somewhere towards the end of 2014, I started thinking about a topic for this thesis when Blockchain popped-up in my mind, and I have ever since felt exhausted about the topic and the potential it has. Now, six months later, there is an end result of which I am proud. A strong drive to succeed helped me to meet all of the deadlines and kept me working hard. Such work cannot be done without the help of many. First, I want to thank Martine for her help, her patience and her continuous support. Without that support and the motivation she gave to me, I would never have been able to get this job done. Second, I want to thank Nathan Betancourt, my supervisor from the University of Amsterdam, for his help, his critical mind and for the handles he gave to me. His critical questions, his advice and his suggestions provided new insights and constantly forced me to assess the steps that I took. Third, I want to thank my employer, ING, and especially Lodewijk Bonebakker, the Head of ING’s Client Experience Center. Lodewijk gave me the opportunity to work on this thesis under his supervision. Ever since our first conversation, I was convinced that my goals were achievable. Lodewijk supported me by challenging me and the steps I took. Finally, I want to thank the respondents for their openness and their trust in me as a researcher. There would be no end result without their contributions. Jeroen Custers

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Abstract

Blockchain is a novel technology that is used in electronic money transfer. It is based on a decentralized network of participating computers, and is currently best known for its use as the technology behind the cryptocurrency, Bitcoin. Most people with knowledge about the technology agree that it is a disruptive one: it will change the society we are living in and the way in which we have transferred value in the past. But is it a disruptive innovation? And where will it have the highest impact? The aim of this thesis is, first, to assess whether Blockchain technology can be seen as a disruptive innovation, and second, to explore the business models that can be implemented in response to it by incumbents in the Dutch payments industry. Given the exploratory character of this research, semi-structured interviews were conducted with thirteen people from different organizations in the Dutch payments industry. The outcome is that Blockchain technology can be seen as a disruptive innovation and that incumbents can implement different business models in response to it. The relationship between the business models and the different dimensions of a disruptive innovation have been outlined, and differences in the implementation of the business models and market conditions that may exist when they are implemented are discussed.

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1. Introduction

Blockchain is a novel technology used in electronic-money transfer that is based on a decentralized network of participating computers (Nakamoto, 2008). It is currently best known for its use as the technology behind the cryptocurrency, Bitcoin. All participating computers (called nodes) use the Blockchain as a shared transaction database. A full copy of the Blockchain consists of all transactions that ever took place, as a result of which anyone can find out how much value has belonged to any single address at any point in history. Nodes maintain consensus in the Blockchain by solving difficult mathematical problems (known as hashes). Every new block contains a hash of the previous block. As a result, a chain of blocks from the genesis block to the current block is created. Each new block is guaranteed to come after the previous block; otherwise the hash would not be known. Moreover, it is computationally impractical to modify a block once it has been in the chain, because every block after it would also have to be regenerated. Nodes are rewarded with Bitcoins and optional (voluntary) transaction fees for solving the hashes. These rewards are the mechanism that increases the money supply (De Vries, Crutchley, Hwang, and Jevremovic, 2014). In other words, it is possible to transfer value securely without double-spending a transaction. The double-spending problem is unique to digital-value transfer, because digital value or information can be copied relatively easily. With digital currencies, there is a risk that the holder of a currency can copy it and send it to a merchant or another party while retaining the original. Blockchain technology makes the role of a trusted third party to verify the transaction obsolete by solving the double-spend problem; moreover, the technology makes it impossible to forge a transaction or to claim that transferred value back (i.e., a chargeback). Systems with characteristics that include a Blockchain and public/private key infrastructure, such as Bitcoin, allow transactions to be executed in only a few minutes. This is an important potential change, because the processing time of transactions is currently significantly longer in the payments market. Cross-border transfers can take days. From a more local point of view, a Blockchain-based system could enhance security and reduce fraud on an everyday level. In the US in particular, credit cards are used in everyday transactions for convenience; but this

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7 The Disruptiveness of Blockchain Technology leaves both the merchant and the banks at risk of chargebacks. In principle, this is less of a problem with debit cards. However, even then a Bitcoin-like system could provide enhanced security and lower costs by giving users direct control of their funds and a 'private key' that ensures security through encryption. Blockchain technology could therefore be a major threat to banks, as they currently are the trusted third parties that solve the double-spending problem, and their business models are built on generating revenues by fulfilling this specific role and by charging their clients for it.

Innovations that have caused many of history's best companies to plunge into crisis and ultimately fail (Christensen, Bohmer, and Kenagy, 2000) are often categorized as disruptive innovations. Extensive research has been conducted to assess the characteristics of different disruptive innovations—most of it ex-post. There is, however, limited knowledge about the disruptiveness of innovations or technologies ex-ante (Govindarajan and Kopalle, 2006b). The main contribution of this research will be to assess how Blockchain technology can be categorized from a strategic perspective: i.e., to determine whether it fits the characteristics of a disruptive innovation and whether it can be seen as such by incumbents in the payments industry (because it solves the double-spend problem and/or because of its ability to provide cheaper and faster payments solutions, for example). In case Blockchain technology can be seen as a disruptive innovation, the incumbents in the Dutch payments industry have to reconsider or reinvent their business models. If Blockchain technology appears not to be a disruptive innovation, it is important to explore what the technology is going to be used for and what parties might end up as the winners by making use of it. Accordingly, the second aim of this thesis is to explore possible business models that can be implemented in response to the new technology. Given the exploratory character of this thesis, and because Blockchain technology is a relatively new phenomenon that has not yet been studied with a focus on its potential disruptiveness to the Dutch payments industry, an inductive, multiple case study is conducted in order to answer the research questions.

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2. Literature review

2.1 Technological Innovation

The contribution of technological innovation to economic growth is well established in the economic literature, both theoretically (Cameron, 1996; David, 1975; Romer, 1986; Solow, 1956) and empirically (Mansfield, 1972; Nadiri, 1993). Innovation has received attention from scholars and scientists ever since Joseph Schumpeter published The Theory of Economic Development (Schumpeter, 1934). Schumpeter (1942)argued that economic growth in a capitalist regime happens through creative destruction: a process in which the old is continuously being destroyed, thereby freeing resources for the new (Sandström, 2010).

Incumbent firms often face problems as a result of creative destruction (Gilfillan, 1935). This has been so in the past, and could definitely be so the case in the future too. A few concrete examples include the shift from typewriters to personal computers, the shift from sailing ships to steam ships, and the minimill technology that emerged in Italy for steel production in the 60’s and the problems that incumbents in the steel industry faced in other Western European countries as a result of it (Jörnmark, 1993). Another concrete example from the past is the shift from vacuum-tube radios to transistor radios that resulted in problems for incumbents like RCA and created massive opportunities for new entrants like Sony (Henderson and Clark, 1990).

In this thesis, we will consider whether Blockchain technology can be seen as a disruptive innovation. Attention will be given to the challenge that a disruptive innovation could have on incumbent firms. Henderson and Clark (1990) describe the difference between radical and incremental innovations and the different competitive consequences they have. These different competitive consequences result from the organizational capabilities that are required in order to deal with disruptive innovations. Hannan and Freeman (1984) and Nelson and Winter (1982) add that organizational capabilities are hard to create and costly, and that reinforcement of capabilities is needed when a company is faced with an

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9 The Disruptiveness of Blockchain Technology incremental innovation. In contrast, when companies are faced with radical innovations, they need to ask themselves a set of questions to draw on new technical and commercial skills (Burns and Stalker, 1961; Ettlie, Bridges, and O’Keefe, 1984; Hage, 1980; Tushman and Anderson, 1986a).

2.2 Radical and discontinuous innovations

Radical innovation and discontinuous innovation are seen as synonyms in academic literature (Veryzer, 1998). Disruptive innovation was a synonym until 1997. Since then, the term has been strongly associated with Christensen’s model, which will be explained further on in this literature section.

Hamilton and Singh (1992) define a discontinuous (or radical) innovation as, “an innovation which creates a discrete and momentous shift related to a firm’s competence base or network.” Such a shift can be created by new technologies, business models or regulatory changes (Hamilton and Singh, 1992). A technological discontinuity can be defined as, “a major technological change resulting in the creation of a substitute character for a particular industry’s products or processes” (Hamilton and Singh, 1992). Academic literature (Anderson and Tushman, 1990; Cooper and Schendel, 1976) supports the hypothesis that incumbent firms encounter difficulties when they are faced with discontinuous innovations or technological discontinuities. The “birth” and domination of digital photography (compared to analog imaging) can be seen as a concrete example of a discontinuous innovation given the fact that it resulted in an immediate shift in the imaging industry, for example where it comes to the competence base of incumbents and the way these incumbents create value (Cooper and Schendel, 1976). Utterback (1994) adds: “Frequently, firms fail to cope with these changes, they lose market shares and the successful firms are found among newcomers.” Henderson (1993) adds that incumbents could historically be seen as incremental innovators: they are able to innovate under stable circumstances. But when new technologies emerge or new business models need to be implemented, the winners might be found under the newcomers in the field because the newcomers are better able to develop and deal with new technologies (Henderson, 1993).

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10 The Disruptiveness of Blockchain Technology Dosi (1982) used the terms technology paradigm and technology trajectories to explain continuous and discontinuous change: technologies display themselves along a trajectory that is featured by a discontinuous change that sets the existing paradigm aside. As a result, when a new technology trajectory is introduced, problems may be encountered by firms that work according to a specific paradigm. Dosi’s standpoint was criticized by Abernathy and Clark (1985), who argued that the impact of discontinuity can be understood by determining the extent to which it has an effect on the existing competencies of an organization and the extent to which it disrupts linkages between market parties. The supply-side that gets attention from Abernathy and Clark (the firm’s resources and capabilities) is in line with Dosi’s arguments. What is new, however, is the demand-side and the possible impact it may have on the market and its environment.

Several explanations of why incumbent firms face difficulties when confronted with discontinuous change have been presented in supply-side-related literature. It has, for example, been suggested that firms build structures and processes so that they can efficiently gather and process knowledge and information. Burns and Stalker (1961) argue that firms become more structured and hierarchical as they grow. They call such firms mechanistic organizations. When an organization shifts to a mechanistic structure, systems are adopted and processes are implemented to ensure that the organization will become more effective. Once these systems are in place and people are working with the new policies and procedures, there is no time or space left to exploit innovative ideas, and the established firm might be vulnerable to profound changes in technologies that threaten their positions. One could argue that this is the situation within banks in general: they are seen as bureaucratic organizations that have policies and procedures in place to avoid risky investments or situations. This makes banks vulnerable to new technologies (such as Blockchain) or to other innovations that threaten their positions.

Other researchers have identified similar patterns. Abernathy and Utterback (1978) found that some firms in the automotive industry were less competitive over time because they strived for increased efficiency. Their efforts to become more efficient reduced their ability to be innovative. Several researchers argue that firms need to

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11 The Disruptiveness of Blockchain Technology be efficient and innovative at the same time if they would remain competitive (Abernathy and Utterback, 1978; Hayes and Abernathy, 1980). This is known as the productivity dilemma. Banks also face this dilemma, as investors and shareholders are interested in optimal returns on their investments. This means that banks have to cut costs and service their clients via direct channels. On the other hand, it could be asked how important return on investment is when a bank ceases to exist because it does not pay enough attention to innovations or to new technologies that may disrupt its current business models.

2.3 Disruptive innovation

In contrast to the above, Clayton Christensen (1992) wrote a doctoral publication about the disk-drive industry and identified a gap that the previous literature did not explain. He found a pattern in the dynamics between entrants and incumbents that was not in line with the conclusions drawn by Tushman and Anderson (1986b) and Henderson and Clark (1990). In the period 1970-1990, several technological shifts were identified in the disk-drive industry; but established firms were not beaten by competence-destroying or architectural discontinuities. Christensen found that incumbents lost market share because new entrants were able to make smaller, cheaper and simpler drives (initially with a lower storage capacity). Incumbent firms lost market share to new entrants after each of the six generations of disk drives that were studied, a pattern for which existing theory could not account. Christensen therefore rejected explanations that pointed to supply-side factors. Rather, he focused at the market and built on resource-dependence theory (Pfeffer and Salancik, 1978; Pfeffer, 1982) and value networks (Christensen and Rosenbloom, 1995) in an attempt to explain the failure of incumbents. Pfeffer and Salancik (1978) argue that previous literature on organizations overlooked the importance of the environment and was too focused on internal issues. They claim that critical resources are needed for organizations to survive, that organizations need to acquire them in the market if they are not available to the organization itself; hence, that organizations are to some extent under control of the actors from whom these resources are purchased. In some situations, it might occur that the required resources are not available in the market. In order to avoid such a situation of

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12 The Disruptiveness of Blockchain Technology uncertainty, firms will seek a relationship with actors known to deliver, thereby possibly reducing their own freedom. These actors can exercise influence on decision making and resource allocation within a firm, and their role is not to be underestimated. The main aims of this thesis are to assess whether Blockchain technology can be seen as a disruptive innovation, and to explore how incumbents can respond to this innovation. Are incumbents able to react quickly enough to market developments themselves, or do they need to start partnerships with actors known to be able to meet the new standards, thereby reducing their own slack? Bower (1970) provided arguments in line with those of Pfeffer and Salancik (1978) in suggesting that the actions of firms are constrained by the demands of existing customers. Christensen calls this the concept of value networks, “the context within which the firm identifies and responds to customer’s needs, procures inputs and reacts to competitors” (Christensen and Rosenbloom, 1995 p. 234). Given the fact that existing clients influence resource allocation, whether or not incumbents will succeed depends largely on the extent to which a new technology answers the demands of existing customers. Firms try to satisfy their existing value network because it largely determines the competitive advantage and provides production resources. At the same time, value networks avoid the development of innovations that are not requested by the existing client base. From this standpoint, Christensen makes a distinction between sustaining and disruptive technologies to explain incumbent failure. Sustaining technologies focus on the improvement of established products along the features that clients value. In contrast, disruptive technologies start with a lower performance along the valued dimensions and add new functions or features. Given the fact that the radical technology is simpler and cheaper than the sustaining technology, the contrast between the two is different from often used terms such as “incremental” versus “radical” or “competence-enhancing” versus “competence-destroying.” The distinction between disruptive versus sustaining terminology is intended to address the extent to which an innovation is the result of a change in demand from existing clients in already established value networks. As a result, radical innovations can be sustaining and incremental innovations can be disruptive, depending on the impact they have for the existing clients.

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13 The Disruptiveness of Blockchain Technology Christensen showed that incumbents are usually the winners in sustaining battles, whereas disruptive battles are often won by new entrants. Both investors and existing clients appear to hold incumbents captive. Therefore, resources are in many cases not allocated to initiatives that are initially less profitable. Christensen called this the innovator’s dilemma: disruptive technologies present a challenge for incumbents given the fact that they require managerial skills that differ from the skills needed to win in sustaining battles (Christensen, 1997).

Christensen (1997) proposed a set of managerial solutions to the problem that established firms are not able to succeed in disruptive innovations. So far, theory on disruptive innovation has been perceived as pessimistic when it comes to the ability of established firms to succeed. The most important solution is that established firms can start up independent organizations or departments in order to develop disruptive innovations. Such a set-up can protect any initiative from the forces of resource dependence and allocation of resources toward innovations. In this way, incumbents avoid the situation in which customers have control over the resource-allocation process and the situation in which no resources are invested in initiatives of disruptive innovation.

Since Christensen’s publications in the nineties, the theory of disruptive innovation has received a lot of attention and has been improved. Christensen and Raynor (2003) started with a distinction between low-end disruptions and new-market disruptions. According to them, the former evolve in lower segments of a market - mostly by having a business model to offer cheaper and initially inferior products-while the latter prosper among clients that have not been in scope previously. Concrete examples of new-market disruptive innovations include portable transistor radios and tablet computers.

2.4 A revised definition of disruptive innovation

Schmidt and Druehl (2008) went on to argue that new market disruptions can be regarded as emerging in fringe markets and in more detached ones. Govindarajan and Kopalle (2006) criticized Christensen’s original definition for considering only cheaper, simpler and initially lower-performing products. They came up with the

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14 The Disruptiveness of Blockchain Technology following definition: “an innovation which introduces a different set of features, performance, and price attributes relative to the existing product, an unattractive combination for mainstream customers at the time of product introduction because of inferior performance on the attributes these customers value and/or a high price -although a different customer segment may value the new attributes.” (Govindarajan and Kopalle, 2006b). Christensen (2006) confirmed that the definition of Govindarajan and Kopalle was better than his own original definition from 1997, since it captures a wider range of identical phenomena.

Other researchers have further developed these theories, for example, by addressing the dynamics in competition (Adner and Zemsky, 2006), by developing a way to measure and assess the disruptiveness of an innovation (Govindarajan and Kopalle, 2006b) or by focusing on the business-model implications (Markides, 2006). Markides (2006) tried to extend the theory about disruptive innovations with theory about business models. Markides argued that business-model innovations are not necessarily technological innovations but are actually business models with characteristics that differ from those described by Christensen (1997). This is the reason not only to assess whether Blockchain technology fits into the definition of Govindarajan and Kopalle but also to explore new business models that can be implemented in response to the technology. Christensen redefined the challenge of a disruptive technology as, “a business model challenge, not a technology problem” (Christensen, 2006 p. 48). As a result, a disruptive innovation is now seen as a technology that does not fit with the business model of an incumbent. It is, however, still unclear what is exactly meant by this, and what the consequences are of this conceptualization (Sandström, 2010).

Amit and Zott (2001) define a business model as, “the content, structure and governance of transactions designed so as to create value through the exploitation of business opportunities.” Chesbrough and Rosenbloom (2002) define it as, “a focusing device that mediates between technology development and economic value creation.” Magretta (2002) argues that a business model is about who the customer is and what the customer values, and that companies have to ask themselves how they can make money in the businesses they are in. Shafer, Smith,

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15 The Disruptiveness of Blockchain Technology and Linder (2005) studied definitions of business models that were defined in academic literature, and present four elementary components of a business model: the strategic choices that have been made, a value network, value creation and value capturing. Creating and capturing value are essential functions that all organizations must perform to remain viable over a certain period of time. Successful firms are able to create substantial and sustainable value by creating capabilities and competences that help them to do things differently than their competitors. Often, incumbent firms fail to manage a change of technology effectively because they have difficulty recognizing and implementing new business models when technological developments require it. This might also be the challenge for incumbents in the payments industry.

Although it is argued in the literature that obtaining a competitive advantage can be realized by changing a business model (Chesbrough, 2010; Teece, 2010), and though the importance of doing so when being faced with disruptive innovations is recognized (Doz and Kosonen, 2010), additional research is needed about how firms can do this. The increased attention on business models that could be observed in the last decade has explicitly focused on value creation and value appropriation (Teece, 2010). It is argued that radical or discontinuous innovations need new business models to succeed (Christensen, 2006). These new business models can help incumbents to benefit from new products and remain competitive in existing businesses.

There is currently little knowledge about whether Blockchain technology could be seen as a disruptive innovation. As noted above, Christensen (2006) and Markides (2006) argue that a disruptive innovation is more a business-model challenge than a technological problem. In addition to determining whether Blockchain technology could be seen as a disruptive innovation, it might therefore be relevant to explore the different business models that incumbents can implement in response to the technology. The aims of this thesis are, first, to determine whether Blockchain technology can be seen as a disruptive innovation based on the definition of Govindarajan and Kopalle (2006b), and second, to explore new business models that incumbents can implement in response to this disruptive technology. Moreover, we

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16 The Disruptiveness of Blockchain Technology will explore what Blockchain technology will be used for (and by whom) if Blockchain technology appears not to be a disruptive innovation to the Dutch payments industry.

2.5 Research questions

Could Blockchain technology be seen as a disruptive innovation (based on the definition of Govindarajan and Kopalle (2006b)?

Furthermore, it is intended that we explore what actions incumbents can undertake or which business models they can adopt to respond to this new technology. The concrete research question here is this:

Which business models can incumbents in the Dutch payments industry implement in response to Blockchain technology, or for what is the technology going to be used if Blockchain technology is found not to be a disruptive innovation?

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3. Methodology

3.1 Research method

The goal of this thesis is to provide insight into the extent to which Blockchain technology could be seen as a disruptive innovation to the Dutch payments industry. Given the fact that Blockchain is a relatively new phenomenon that has not yet been studied for its potential disruptiveness on incumbent’s business models, the author opted for an inductive multiple-case study. According to Miles and Huberman (1994), a qualitative research method enables, “richness and holism, with strong potential for revealing complexity.” Moreover, qualitative research is often preferred when research has an exploratory character (Flick, 2009) and when general information needs to be discovered about a topic that is not yet understood clearly by the researcher. It lends itself particularly well to new phenomena (Saunders, Lewis, and Thornhill, 2012). Multiple case studies of different organizations will be undertaken, because these enforce the external validity of the outcomes (Yin, 2009). Eisenhardt and Graebner (2007) argue that multiple cases lead to a theory that is more robust and generalizable than a single case study. Yin (2009) adds that case studies apply best in studies in which, “how and why questions are to be answered”, “the investigator has little control over events” and “the focus is on a contemporary phenomenon within a real life context.” These three criteria apply for this thesis, since its aim is not only to assess whether Blockchain technology could be seen as a disruptive innovation based on the definition that is formulated by Govindarajan and Kopalle (2006b) but, more importantly, to explore how incumbents can -or need to- respond to this new technology with which they are all confronted.

To get a balanced overview of the different viewpoints in the Dutch payments industry, more than just incumbents were interviewed. In total, four case groups have been identified: incumbents, consultancy firms, new entrants and regulators. By conducting at least three interview within each group, different viewpoints were taken into account. This leads to a more reliable outcome (Saunders et al., 2012). Saunders, Lewis, and Thornhill (2012) add that triangulation leads to a better understanding of a research topic.

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18 The Disruptiveness of Blockchain Technology Good qualitative research should involve a worthy topic, rich rigor, sincerity, credibility, resonance, significant contribution, ethics and meaningful coherence (Tracy, 2010). Attention is given to these topics in every phase of the research. Research validity can be thought of as either internal or external. Internal validity refers to the degree to which the collected data agree with the reality that they try to represent. There are several ways to improve internal validity. In this thesis, internal validity will be achieved by comparing the evidence with a rigorous theoretical framework. Besides, triangulation is used to verify the findings: multiple perspectives are adopted not only from the incumbents, but also from consultancy firms, new entrants and regulators (Gibbert and Ruigrok, 2010; Yin, 2009).

External validity concerns the ability to draw more or less generalizable conclusions from the research that is conducted. Yin (2009) argued that case studies are limited with regard to external validity, given their focus on specific events. It should be pointed out here that the presented work aims to develop new theory rather than to test existing theory. According to Eisenhardt (1989), a case study is the appropriate research strategy when, “little is known about a phenomenon and existing theories seem inadequate or insufficient” (Eisenhardt, 1989). This paper does not aim to provide an exhaustive set of answers. The purpose is rather to add new insights about the potential disruptive character of Blockchain technology and the possible responses of incumbents or new entrants to this innovation.

3.2 Research design

Having defined the research method, the research design deserves attention. As stated above, a multiple-case-study approach is chosen, as it enables a detailed and intensive analysis of the Blockchain technology and of the possible impact it might have on incumbents in the Dutch payments industry. Case studies constitute a way to pay attention to the complex, specific and dynamic nature of a case rather than to overlook these characteristics. Despite the drawback concerning the generalizability of the findings in case-study research, case study is assumed to be the best research design here given the exploratory character of this study. Moreover, case studies are used when new theories are being developed rather than

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19 The Disruptiveness of Blockchain Technology when existing theories are being tested (Eisenhardt, 1989). The aim of this study is to further develop the relatively new theory of disruptive innovation. For all of these reasons, a multiple-case-study approach is chosen. It enables the kind of nuanced documentation that can be of help in defining concrete answers to the formulated research question.

Thomas (2011) argues that case studies must comprise two elements that are the subjects of a case study -which is described as “a practical, historical unity”- and the object of the study, which is the analytical and theoretical frame. The subject in this thesis is the Dutch payments industry as confronted with a new technology that might require it to reinvent itself. The objects are incumbents, new entrants, consultancy firms and regulators in the industry who are able to determine whether there is a need to implement new business models. By interviewing people with different interests and varying motives, we will answer the research question based on a broad spectrum of visions, which is good for the external validity of this research.

Respondents need to meet certain criteria to ensure a rigorous design. Most important here is that the respondents have a background in the payments and cash management industry: i.e., that they have a thorough understanding about the way in which cross-border transactions and credit-card transactions are processed, for example. Given this background, they are likely to be familiar with the (dis)advantages of the current system and able to assess the (dis)advantages of new technologies (such as Blockchain) that might resolve some of the existing disadvantages. Moreover, understanding of Blockchain technology is a requirement, because we are to determine to what extent this technology could disrupt the current business models of the incumbents in the Dutch payments industry. Lastly, the respondents have to be based in the Netherlands. Given a limited timeframe and limited financial resources, the author is not able to go abroad to do interviews. The number of people who meet these requirements is very limited and hard to estimate. Snowball sampling is, “a type of non-probability sampling in which, after the first sample member, subsequent members are identified by earlier sample members”

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20 The Disruptiveness of Blockchain Technology (Saunders et al., 2012). It is used when it is hard to identify the members of a population. By making use of the network of the respondents, the author was able to conduct 13 interviews with individuals who meet the requirements. Initially, the author had access to only three (former) colleagues that met the requirements. These respondents had a broader network in the Blockchain/Bitcoin community, and they shared the contact details of relevant people in their networks. This was done by the newly targeted people as well. Interestingly, similar names kept coming back over and over again, which is an indication for the limited number of people that fit into the profile. This process nevertheless helped the author contact 13 respondents with different viewpoints and to conduct interviews with them.

3.3 Data collection

Respondents working for Dutch firms that fall under the supervision of the European Central Bank and the Dutch Central Bank are targeted in this research. Moreover, because they have the knowledge needed to explain the possible disruptive character of this technology and can delineate the future banking landscape from various points of view, people who work for companies that are active in cryptocurrencies were interviewed: e.g., Blocktrail and Ethereum. Additionally, payment-service provider Mollie, transaction processor Equens, an independent Consultant and Consultancy firms such as Innopay, KPMG and Deloitte were interviewd. In order to get insights from a regulatory perspective as well, a respondent from Europol and two respondents from the Dutch Central Bank were interviewed. In total, 13 people in the abovementioned organizations were interviewed, which should limit bias (Eisenhardt and Graebner, 2007). To determine whether differences or commonalities can be observed based on the roles that companies have in the Dutch payments industry, the intention is to present the data on the basis of a shared perspective among the interviews that have been undertaken. This is achieved by defining four different case groups: “consultancy firms”, “incumbents”, “new firms” and “regulators.” The main advantage of this approach is that homogeneity within a category is ensured whereas there is heterogeneity between code groups. This ensures the external validity and gives more theoretical certainty to the research design. The four case groups are defined

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21 The Disruptiveness of Blockchain Technology as follows. Consultancy firms are made up of experts who work for a company that provides professional advice to an organization or an individual for a fee. An incumbent is an individual who works for a firm that currently fulfills a major role in the processing of payments. New firms are comprised of people who work with different business models than the incumbents and started to do so after 2005 (less than 10 years ago). Regulators are people who work for companies with the aim of defining regulation or striving to enforce it.

Table 1 gives an overview of the companies for which the interviewed people work and of the case groups they represent.

Interview number Consultancy Incumbents New Firms Regulators

Interview 1 X Interview 2 X Interview 3 X Interview 4 X Interview 5 X Interview 6 X Interview 7 X Interview 8 X Interview 9 X Interview 10 X Interview 11 X Interview 12 X Interview 13 X

Before the start of each interview, an introduction was given to set the scene and to explain the goal of the interview. The interviewees were targeted with introductory questions, followed by grand-tour questions (Leech, 2002) about what Blockchain technology is, if and why it could be seen as a disruptive innovation, why it forces incumbents to reconsider their business models, what it would mean if incumbents do not pay attention to it, what the role of the incumbents will be when they do not pay attention to it and how the banking landscape will look if central banks fail to

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22 The Disruptiveness of Blockchain Technology see the potential of Blockchain and remain focused on the shortcomings that a cryptocurrency like Bitcoin has. An overview of the interview guide can be found in Appendix A. The interviews were limited to between 40 and 60 minutes so that the respondents would not lose focus. All semi-structured interviews were recorded, transcribed and coded afterwards. The transcriptions were sent to the respondents for proofreading, and the opportunity was given to make cases anonymous. The different cases were compared and contrasted to each other to look for patterns across them.

3.4 Data analysis

Michael Patton (1990) argues that qualitative research and data analysis is a highly creative exercise, but also has to be analytically rigorous, mentally replicable and explicitly specific. The aim here is to describe how the data collected for this thesis was rigorously analyzed in its specific context. Miles and Huberman (1994) suggest that qualitative data analysis embraces three linked sub-processes: data condensation/reduction, data display and data interpretation.

3.5 Data condensation/reduction

The first step in qualitative data analysis, as described by Miles and Huberman (1994), is data condensation/reduction. This process generally consists of two cycles of coding: first-cycle coding, in which codes are assigned to data chunks; and second-cycle coding (also called pattern modelling) in which similar codes are clustered together in smaller numbers of categories. These cycles are followed by a final step in which the interrelationships of the categories are constructed.

All of the interviews were recorded and subsequently transcribed. The transcription of the interviews took place within 48 hours after they were conducted. During the interviews, detailed notes were made on paper, and some of these notes ultimately led to a specific code. A concrete example is a note that was made in all the interviews and which was the answer to a question about the fact that it takes several days for a cross-border transaction to arrive in the account of the beneficiary: all

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23 The Disruptiveness of Blockchain Technology respondents answered that this is outdated in the 24-7, Internet-based economy in which we live. This led to the code, “Outdated.”

For the researcher to be aware of the general context in which the interview took place when using quotes and drawing conclusions, general findings were written down immediately after the interview: e.g., the location of the interview, background noise, the mood of the respondent, and his or her ability to speak freely. More diplomatic and general answers are to be expected from a respondent who did not feel at ease from one who did. Within twenty-four hours after finalizing the transcript, the recorded interview was listened to again while reading the transcript and checking the hand-written notes to ensure that the transcript was free of typos and errors. This gave the author another opportunity to draw some preliminary conclusions before the in-depth analysis and coding started.

After this step, the interviews were sent back to the interviewees, and they were asked to add or remove data: this opportunity was explicitly mentioned at the start of the interview. Several interviewees made use of this opportunity because of the highly sensitive character of the topic. Moreover, in the mail in which the transcript was sent, some of the respondents were asked to share contact details of other experts. It was mentioned before that there is a very limited group of experts on this relatively new topic and it was very challenging to get in contact with them. By making use of the network of the people who were interviewed, access to these specialists was obtained. This process is called snowball-sampling (Marshall 1996). It is important to note here that the interviews were analyzed and coded in groups: the first three interviews were transcribed and coded within several days. These interviews yielded insights that were used in the conversations that followed. This process gave the author an opportunity to learn from mistakes and to use best practices. A concrete example is “remittance” and the added value that Blockchain technology or Bitcoin transactions could have on this specific transaction type. Questions about remittance were not included in the initial interview guide; but the topic was mentioned by the first three respondents in answer to a question which business models incumbents could adopt to respond successfully to this new technology. Therefore, questions about remittance and the added value of

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24 The Disruptiveness of Blockchain Technology Blockchain technology were added to the interview guide after the third interview. Another concrete example is the question about the costs for cross-border transactions. The respondents started to elaborate on the costs for the banks, though it was the intention to gain insight in the price that users pay when initiating these types of payments. Interviews 4 - 8 yielded comparable insights: they were conducted, transcribed and analyzed within a short timeframe and led to additional insights that were added to the interview guide for the last interviews. These insights include the fact that respondents do not think Blockchain or Bitcoin will have a high impact in the short term in countries in which the payments systems is already cheap and efficient (as in the Netherlands). In contrast, in countries where people do not have access to a payments system or where it is expensive and time-consuming to send or receive payments, it might be easier to add value.

3.5.1 First-cycle coding

After interviews were received back with comments from the interviewees, their changes were made in the transcripts by the author and imported into RQDA and RStudio. Then the first-cycle coding was performed. The program is available for free on the Internet. It was very helpful in the phase of data reduction, and ensures the richness of the data. The initial interview guide was divided into seven topics: general questions, cross-border transactions, credit-card transactions, the payments industry in general, the vision of Blockchain technology, the role of regulators, and the assessment of the definition of a disruptive innovation. These general topics formed the initial framework from which the detailed transcript analyses was conducted and, as a result, the most important element of the analysis was coding the data under the seven topics (O’Dwyer, 2004). During this process, it became clear that it is very difficult to place a very specific chunk of text under a general code: stand-alone themes emerged, and new stand-alone codes were created during the coding of the interviews as a result. The advantage here is that the codes were kept closely to the terms they initially represented. In the second cycle of coding, the seventy-five codes and sub-codes were combined in order to narrow down the specifics and move slowly towards more generalizable conclusions.

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25 The Disruptiveness of Blockchain Technology 3.5.2 Second-cycle coding

After the first cycle of coding, a total of seventy-five codes were defined, of which an overview can be found in Appendix B. Immediately after the first cycle of coding, a critical one-page mind map of every interview was made in which the hand-written notes, general findings and most-used codes were combined. This process gave a good overall impression of every individual interview.

The seventy-five codes together represent a relevant and meaningful piece of data that can give guidance towards the desired answers to the research questions. In order to assess the most relevant and most-used codes, the code-list in RQDA was sorted from most to least used. The author started to focus on the least-used codes, and looked for an opportunity to combine them with codes that were more often used. This might sound as an easy exercise, but to ensure rich rigor -which is referred to as one of the eight critical subjects of qualitative research by Tracy (2010)- it is important to describe the steps that are undertaken in order to combine the clustering of codes. 27 codes were used ten times or more, and 25 codes were used three times or less. The author tried to combine the codes that were used three times or less with the codes that were more often used. For example, the code “Escrow” was used in interviews 5 and 7 as a concrete example of something that firms can do with Blockchain technology. These are possible-use cases as a result of the use of Blockchain technology; therefore, the code “Escrow” could be merged into the code “Use_Case.” This exercise was done for the 25 least-used codes. Appendix C describes the manner in which the codes were merged and identifies the specific quotes that were added to other codes. After this round of clustering codes, 50 codes remained.

To ensure that the research questions can be answered properly, the next step was to create code categories that make it possible to direct the outcomes of the conversations and the codes back to research question and the most important characteristics of a disruptive innovation and the business models that can be implemented in answer to the innovation. The code categories are presented in Table 2 in combination with the research question they represent.

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Code Category Disruptive Business Model Regulators

01_Different_Set_Features X 02_Different_Performance X 03_Different_Price_Attributes X 04_Unattractive_Combination X 05_Different_Segment X 06_Business_Models X 07_Credit_Card_Companies X 08_Regulators X 09_Reliability_Safety X

The first five code categories represent the different characteristics mentioned in the definition of Govindarajan and Kopalle (2006b). The business models comprise all quotes and codes related to any business model or use case that the respondents referred to during the interviews. In Appendix D, an overview is presented in which the codes that were merged into these code categories are shown. The hand-written mind maps that were made after listening to the recordings were used, because they gave insight into the most frequently mentioned business models and answers to the different components of the definition of a disruptive innovation. Based on these mind maps, it was the intention to create seven code categories: the five characteristics that define whether something can be seen as a disruptive innovation, business models, and regulators. During the exercise of merging codes under these code categories, it became clear that credit cards, reliability and safety were more-or-less independent codes. This means that they are not closely related to other codes and hence difficult to link to the other code categories. Therefore, these different code categories were made to preserve as much relevant data as possible for analysis. A graphical overview of the codes that were merged into these code categories and the interrelationships between them can be found in figure 1.

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27 The Disruptiveness of Blockchain Technology To ensure rich rigor and credibility, the nine different code categories were given a specific definition so that it was clear that codes could only be linked to -and merged into- a code category when they were in line with the definition. An overview of these definitions can be found in Appendix E.

3.6 Data display

The gathered data is interesting in itself; the display and interpretation of the data, however, are what make this dissertation valuable. By clustering the different codes into different code categories, an overview was created in which it was easy to determine what the answers to specific questions are. A concrete example is provided by the set of features which is one of the five components of the definition of a disruptive innovation. The codes related to different features were made visible in RQDA and RStudio by selecting the code category 01_Different_Set_Features. This gave the author the possibility to assess whether there were different features relative to the existing products and if so, what these different features were. In Table 3, an overview is given of the different code categories and the times the respondents mentioned something related to the code category during the interview. The different colors represent the different case groups. Red is used for

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28 The Disruptiveness of Blockchain Technology the consultancy firms, green for the incumbents, blue for the new entrants and black for the regulators.

The most important display of the data in this thesis is in the form of quotes. Quotes from different interviews have been used for each of the components that were relevant to the research questions,

3.7 Data interpretation

Tracy (2010) argues that resonance is important when conducting qualitative research. This means that different insights have to be compared with each other. For triangulation purposes, insights from different case groups have been gathered. For the interpretation of the outcomes, it is relevant to assess different perspectives or visions from these different case groups.

Display of the data in the results chapter was done mostly by making use of quotes from different interviews. To ensure resonance, viewpoints from other case groups could support or undermine the initial conclusions. This way of analyzing and interpreting the data proved to be very helpful in analyzing the different business models and the different components of a disruptive innovation that were assessed. Table 3 shows that all respondents mentioned the different business models that can be implemented in response to Blockchain technology several times. It could be argued that consultants come up with different business models because they are

Code category _I1 _{I2 I3 I4 I5 I6 I7 I8 I9 I10 I11 I12 I13} 01_Different_Set_Features ₂₃ ₉ ₁₄ ₁₄ ₉ ₁₇ ₁₅ ₂₁ ₁ ₁₀ ₇ ₆ ₄ 02_Different_Performance 29 18 26 20 15 22 20 27 2 13 9 7 6 03_Different_Price_Attributes 18 13 20 9 9 12 10 21 1 7 6 6 8 04_Unattractive_Combination 12 7 12 6 7 7 7 12 0 3 4 3 3 05_Different_Segment 25 17 21 17 12 8 12 12 0 4 5 5 7 06_Business_Models 107 88 35 76 48 67 43 32 16 13 19 13 16 07_Credit_Card_Companies 5 11 3 3 2 2 6 6 2 3 0 1 0 08_Regulators 15 2 3 8 3 7 7 1 2 2 14 16 15 09_Reliability_Safety ₇ ₂ ₁₁ ₃ ₄ ₄ ₆ ₃ ₃ ₁ ₆ ₇ ₇

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29 The Disruptiveness of Blockchain Technology paid to give advice to incumbents and new firms. Hence, they are open to new initiatives and prone to spot opportunities that they are willing to share with their clients. In contrast, incumbents and regulators have a more defensive position and might try to defend their current positions and business models. The truth will be somewhere in the middle, and it is the task of the author to define what that truth is. This will be done by approaching from different angles to ensure a discussion that is based on insights from people with different motives and to thereby ensure sincerity, credibility, resonance, significant contribution, and meaningful coherence (Tracy, 2010).

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4. Results

4.1 Disruptive innovation

To answer the research question, the different characteristics of a disruptive innovation mentioned in the definition are treated separately. These characteristics are, i) a different set of features relative to the existing product, ii) a different performance relative to the existing product and iii) different price attributes relative to the existing product, iv) the unattractive combination for mainstream customers at the time of introduction (because of a higher price or an inferior performance on the attributes the customers value), and v) an different customer segment that may value the new attributes. These different characteristics were defined as code categories during the analysis of the data, and will be handled subsequently in this chapter. Moreover, attention will be paid to the role that regulators have and to the possible business models and use cases that can be implemented in answer to the new technology. Table 4 provides an overview of the code categories that are related to the definition of a disruptive organization. It is shown there how often respondents from the various case groups referred to one of the codes that were merged into these categories.

Disruptive innovation Consultancy Incumbents New Firms Regulators

01_Different_Set_Features ₇₃ ₂₄ ₃₆ ₁₇ 02_Different_Performance ₉₆ ₄₆ ₅₀ ₂₂ 03_Different_Price_Attributes ₅₈ ₃₄ ₂₈ ₂₀ 04_Unattractive_Combination ₃₇ ₁₉ ₁₇ ₁₀

05_Different_Segment ₆₆ ₃₈ ₂₄ ₁₇

4.1.1 Different set of features

All respondents in this research said that Blockchain technology provides a different set of features than existing products or services. Blockchain technology is applied in cryptocurrencies such as Bitcoin and other altcoins and -more importantly- to

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31 The Disruptiveness of Blockchain Technology create an associated own-transfer mechanism by which the widely used payments system can be set aside, which can be defined as a different feature relative to the existing product. By making use of cryptography, the double-spend problem is solved, and peer-to-peer transactions are made possible without the involvement of a trusted third party.

“The ability to process payments completely decentralized, without a single point of failure, completely peer-to-peer” (Interview 7)

“It was immediately: this is fantastic. This is a solution for all the problems that I tried to solve. Without a central party that manages it, without servers that can be down or without complex processes” (Interview 5)

But different or new features are not only added to currencies. Public ledgers are also used to register other assets than coins. The ownership of these assets (e.g. shares, vehicles, real estate, chamber of commerce) can also be handed over by making use of a digital ledger. In this way, the ownership of these assets is registered in a peer-to-peer network without a trusted central authority to monitor this registration. The owner of the private digital key to that public record is then the owner of that asset. Asset registries have a potential for reducing governance and auditing costs. This type of application is commonly referred to as ‘Bitcoin 2.0’. More attention to this specific application will be paid in chapter 4.3.6.

“I really think, in order to get it to the Blockchain or Bitcoin 2.0 world, with smart contracts. There will be the real potential, that is going to be “it.” But that can take some years” (Interview 5)

“As an entrepreneur, I had to deal with the Chamber of Commerce. I really ask myself the question what their added value is and whether we can do that on a Blockchain. The same applies to insurances, a concrete example is the crop-insurance: the insurance of crops for whatever weather damage. You could set that up as a smart contract and if there is a certain outcome: there has been loss, there has been deception, what was the weather? You could use data feeds, a kind of a peer-voting system or a prediction market. If you could connect these things with each other by making use of a decentralized market, you could close various complex financial- or insurance products” (Interview 5)

Peer-to-peer transactions or asset registrations are made possible, costs are reduced, and speed and availability are increased. These developments force incumbents to

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32 The Disruptiveness of Blockchain Technology reconsider their business models, because these new features can make their roles superfluous.

“I would say that the real winners on the financial institution front are going to be the people who look at their current infrastructures at the moment. And with a serious eye and in a non-politicized way. Look at it and then see what parts of the Blockchain technology are there. What type of vendors are there that can help them start experimenting with it” (Interview 4) Table 4 shows that regulators spoke less about these features than consultancy firms or new entrants. The quotes below show that the first group also recognizes these different features.

“A different feature would be the international peer-to-peer possibilities” (Interview 12) “I think the basic features. The processing time, the costs, the possibility to do it peer-to-peer” (Interview 11)

4.1.2 Different performance

The second characteristic mentioned in the definition of a disruptive innovation is a different performance relative to the existing product. The performance of Blockchain technology is not only different; it is said to be significantly better in a key attribute: processing time. In the questionnaire, attention was paid not only to the products or services that banks offer but also to those of credit-card companies and other payment-service providers.

When the respondents were asked about the fact that it takes days before a cross-border transaction reaches its destination in today’s 24/7 economy, they answered that these processing times are outdated and that the problem could be solved by using a system based on Blockchain technology.

“No, that makes absolutely no sense. People can send me photos. I speak to friends in South Africa. I’ve got friends in, name a few countries in different continents, we can send each other data packages, completely securely via Facebook, Whatsapp, you name it. I can talk to them but I can’t pay them in that amount of time” (Interview 4)

“That is of course completely outdated. It is based on the infrastructure from decades ago. Someone recently mentioned that a piece of unstructured data-transfer can be done real-time,

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while a piece of structured data like a payment file takes days to arrive. That doesn’t make sense” (Interview 1)

“We do have the systems with which we can do it differently, for example Blockchain. It is unbelievable that it takes so much time these days” (Interview 6)

4.1.3 Different price attributes

A third characteristic of the assessed definition of disruptive innovations has to do with the different price attributes relative to the existing products. The costs for cross-border transactions or remittance are high and are usually paid by the initiating party (a corporate client or a private person doing a remittance transaction). These high costs are associated with the clearing and settlement mechanisms behind these transactions in which banks and banking systems play a role. For such transactions, different price attributes are undeniably possible. A system based on a decentralized Blockchain technology will make it possible to send these transactions instantly and for free or for only a few cents to every corner in the World.

“That could be done differently: more efficient and cheaper and Blockchain can definitely do that. I think that the current Bitcoin technology shows that it is possible to transfer value again low costs, with a good speed in a trustworthy way” (Interview 9)

“The biggest potential here is that cross border payments will become way, way cheaper” (Interview 1)

In chapter 4.3.2 “Remittance and Foreign Exchange”, the author will go into greater depth about the business models that incumbents can implement in response to these different price attributes.

4.1.4 Unattractive combination

The fourth characteristic mentioned in the definition of Govindarajan and Kopalle (2006b) is that the innovation is, “an unattractive combination for mainstream customers at the time of product introduction because of inferior performance on the attributes these customers value and/or a high price.” What has become clear in this research is that users find the Dutch payments industry to be effective and cheap. The Dutch population can use their debit cards or even cellphones to make

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34 The Disruptiveness of Blockchain Technology (instant) payments for very little money. The same applies to transactions on the Internet for which iDEAL could be used.

“It is unknown, it is complicated, it is volatile and it is not safe yet. These kind of things do not make it attractive” (Interview 12)

It is also important to mention that banks are trusted despite the negative publicity they have received for their role in starting the recent financial crisis. That trust is one result of the role that Central Banks and regulators play in monitoring the activities of the financial institutions under their supervision.

“In the end, payment processing is a service that is offered by banks, but they fulfill a very important role in society. When people lose their faith in payment services, that would not only harm banks offering the service, it would impact society as a whole” (Interview 13)

“The Dutch Central Bank and the European Central Bank are there to protect you as a consumer. I would not stimulate my parents to buy Bitcoins and store them in a wallet with one of the Bitcoin companies: If there would be a hack, the money can be gone and they cannot get it back” (Interview 2)

The same applies to credit-card companies or payment-service providers like PayPal. The main advantage of credit-card transactions mentioned by the respondents in this thesis is that they enable chargeback. A Blockchain transaction, on the other hand, is irrevocable once it is added to the Blockchain.

“From a consumer perspective, I prefer a credit-card transaction because the costs are taken by the Merchant and my transaction is insured. I can do a chargeback whenever I want to” (Interview 2)

In contrast to the above, respondents also mentioned possible downsides when a decentralized system would be implemented to replace the current one. The most important advantage for users -which is the ability to do a chargeback- would be taken away, and it would cause ambiguity because challenges to reliability and safety will arise once transactions can be done anonymously and peer-to-peer.

“Another thing that is important in that sense is that you do have these use cases, but for example credit card payments are expensive, but shifting the thing, shifting the money to let’s say Bitcoin network or maybe another network, there are problems with compliancy” (Interview 4)