cloud computing as a sustaining or a disruptive … · 5.4 dropbox ... 8.1 works cited ... 8.2.1...

Cloud Computing as a sustaining or a disruptive technology How can Enterprise Modelling help in analysing the impact of Cloud Computing on business and operating models?

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Jan Van Der Burgt student number : 0604179 Promotor/ Supervisor: Prof. dr. Geert Poels

Master’s Dissertation submitted to obtain the degree of:

Master of Science in Business Economics Academic year: 2016 - 2017

Cloud Computing as a sustaining or a disruptive technology Van Der Burgt Jan

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PERMISSION I declare that the content of this Master’s Dissertation may be consulted and/or reproduced, provided that the source is referenced. name student : Jan Van Der Burgt Signature


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Table of Contents Master’s Dissertation submitted to obtain the degree of: ........................................................ 1

List of used tables ....................................................................................................................... 6

List of used figures...................................................................................................................... 6

1 Part I: Introduction ............................................................................................................. 7

1.1 Preface ......................................................................................................................... 7

1.2 Acknowledgements ..................................................................................................... 8

1.3 Personal motivation .................................................................................................... 9

1.4 Social Relevance ........................................................................................................ 10

2 Part II: Literature review .................................................................................................. 14

2.1 Conceptual delimitation: cloud computing ............................................................... 14

2.1.1 Definition of cloud computing ........................................................................... 14

2.1.2 Different types of cloud computing ................................................................... 14

2.1.3 Characteristics of cloud computing .................................................................... 15

2.1.4 Service models ................................................................................................... 16

2.1.5 Advantages of using cloud computing ............................................................... 17

2.1.6 Weaknesses of cloud computing ....................................................................... 19

2.1.7 Technical explanation of cloud computing ........................................................ 20

2.2 Conceptual delimitation: innovation ......................................................................... 23

2.2.1 General about innovation .................................................................................. 23

2.2.2 Incremental innovation ...................................................................................... 24

2.2.3 Radical product innovation ................................................................................ 25

2.2.4 Incumbents ......................................................................................................... 25

2.2.5 Disruptive innovation ......................................................................................... 25

2.2.6 Different types of disruptive innovations .......................................................... 34

2.3 Enterprise modeling and Business models ................................................................ 36

2.3.1 Enterprise modeling ........................................................................................... 36

2.3.2 General info about Business Models ................................................................. 37

2.3.3 Business model innovation ................................................................................ 38

2.3.4 Business and revenue models used in cloud computing ................................... 39

2.3.5 The Business model Canvas ............................................................................... 43

2.4 Cloud computing and innovation .............................................................................. 45

3 Part III: Research question ............................................................................................... 49

3.1 Research question ..................................................................................................... 49

4 Part IV: Methodology ....................................................................................................... 50


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4.1 Research design ......................................................................................................... 50

4.1.1 The choice of the research strategy ................................................................... 50

4.1.2 Case selection ..................................................................................................... 51

4.1.3 Data selection ..................................................................................................... 52

4.1.4 Data analysis ....................................................................................................... 52

4.1.5 Applied frameworks ........................................................................................... 53

4.2 Validity and reliability ................................................................................................ 57

4.2.1 Construct validity ............................................................................................... 57

4.2.2 Internal validity .................................................................................................. 57

4.2.3 External validity .................................................................................................. 57

4.2.4 Reliability ............................................................................................................ 58

5 Part V: Discussion different cases .................................................................................... 59

5.1 Netflix ........................................................................................................................ 59

5.1.1 General info ........................................................................................................ 59

5.1.2 How Netflix uses Cloud computing technology ................................................. 62

5.1.3 Business model of Netflix explained .................................................................. 64

5.1.4 The Business Model Canvas of Netflix ............................................................... 65

5.1.5 The impact of cloud computing on the business model of Netflix .................... 69

5.1.6 Does it meet the disruptive innovation criteria? ............................................... 70

5.2 Salesforce ................................................................................................................... 73

5.2.1 General info ........................................................................................................ 73

5.2.2 How Salesforce uses cloud computing ............................................................... 74

5.2.3 The business model of salesforce explained ...................................................... 75

5.2.4 The Business Model Canvas of Salesforce ......................................................... 77

5.2.5 The impact of cloud computing on the business model of Salesforce .............. 81

5.2.6 Does it meet the disruptive innovation criteria? ............................................... 82

5.3 Spotify ........................................................................................................................ 86

5.3.1 General info ........................................................................................................ 86

5.3.2 How Spotify uses cloud to offer its service. ....................................................... 88

5.3.3 Business model of Spotify explained.................................................................. 91

5.3.4 The Business Model Canvas of Spotify ............................................................... 98

5.3.5 The impact of cloud computing on the business model of Spotify ................. 103

5.3.6 Does it meet the disruptive innovation criteria? ............................................. 104

5.4 Dropbox ................................................................................................................... 107

5.4.1 General info ...................................................................................................... 107


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5.4.2 How Dropbox uses cloud computing ............................................................... 109

5.4.3 The business model of Dropbox explained ...................................................... 112

5.4.4 The Business Model Canvas ............................................................................. 114

5.4.5 The impact of cloud computing on the business model of Dropbox ............... 117

5.4.6 Does it meet the disruptive innovation criteria? ............................................. 119

6 Part VI: Results ............................................................................................................... 122

6.1 Comparing business models .................................................................................... 122

6.1.1 How can Enterprise modelling help in analysing the impact of cloud computing 122

6.1.2 The impact of cloud computing on the business models ................................ 122

6.1.3 Is cloud computing a disruptive or a sustaining innovation? .......................... 123

7 Part VII: Conclusion ........................................................................................................ 125

7.1 Conclusion ............................................................................................................... 125

7.2 Limitations of the research ...................................................................................... 126

7.3 Further research ...................................................................................................... 126

8 Part VIII: Appendices ...................................................................................................... 127

8.1 Works Cited ............................................................................................................. 127

8.2 Case study protocol ................................................................................................. 150

8.2.1 Overview of the case study project.................................................................. 150

8.2.2 Substantive issues that are investigated .......................................................... 154


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List of used tables Table 1: Proposed assessment framework (Hang, Chen, & Yu, 2011) ..................................... 54

Table 2: Criteria sheet entrant (Keller & Hüsig, 2009) ............................................................. 55

Table 3: Criteria sheet incumbent (Keller & Hüsig, 2009) ........................................................ 56

Table 4: Assessment framework of Hang, Chen and Yu (2011) applied to Netflix .................. 70

Table 5: Assessment framework of Keller and Hüsig (2009) applied to Netflix ...................... 71

Table 6: Assessment framework of Hang, Chen and Yu (2011) applied to Salesforce ............ 82

Table 7: Assessment framework for entrant of Keller and Hüsig (2009) applied to Salesforce .................................................................................................................................................. 83

Table 8: Assessment framework for incumbent of Keller and Hüsig (2009) applied to Salesforce ................................................................................................................................. 84

Table 9: Assessment framework of Hang, Chen and Yu (2011) applied to Spotify ............... 104

Table 10: Assessment framework for entrant of Keller and Hüsig (2009) applied to Spotify ................................................................................................................................................ 106

Table 11: Assessment framework of Hang, Chen and Yu (2011) applied to Dropbox ........... 119

Table 12: Assessment framework for entrant of Keller and Hüsig (2009) applied to Dropbox ................................................................................................................................................ 120

List of used figures Figure 1: Cloud computing architecture (Zhang, Cheng, & Boutaba, 2010) ............................ 21

Figure 2: Basic layered design of data centre network infrastructure (Zhang, Cheng, & Boutaba, 2010) ......................................................................................................................... 22

Figure 3 : Innovation and technological trajectories following Olofsson( 2003) ..................... 24

Figure 4: Performance trajectories (Cantner & Prasetio, 2015) .............................................. 27

Figure 5: The need-satisfaction curve (Norman, 1998) ........................................................... 28

Figure 6: Innovation linked to adopters (Norman, 1998) ........................................................ 29

Figure 7: The Business Model Canvas (Osterwalder & Pigneur, 2010) .................................... 43

Figure 8: Towards computing organised as a public utility...................................................... 45

Figure 9: Diffusion of innovations (Rogers, 1995) .................................................................... 48

Figure 10: Netflix’ cloud architecture (Adhikari, et al., 2012). ................................................. 63

Figure 11: The Business Model Canvas of Netflix .................................................................... 68

Figure 12: The customer success platform (Salesforce, 2017f) ............................................... 74

Figure 13: Salesforce data storage ........................................................................................... 74

Figure 14: The Business Model Canvas of Salesforce .............................................................. 80

Figure 15: Marketshare in streaming services (Mulligan M. , 2017) ....................................... 88

Figure 16: Spotify storage architecture (Yanggratoke, et al., 2015) ........................................ 90

Figure 17: Spotify revenues: ads vs subscription (Ingham, 2016) ........................................... 93

Figure 18: The Business Model Canvas of Spotify .................................................................. 102

Figure 19: An example of the Dropbox protocol (Drago, et al., 2012) .................................. 110

Figure 20: Dropbox free view ................................................................................................. 113

Figure 21: The Business Model Canvas of Dropbox ............................................................... 117


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1 Part I: Introduction 1.1 Preface This thesis was written in order to receive the degree of master in business economics at the University of Ghent. This document contains eight consecutive chapters. The structure follows: Introduction, Literature review, Research question, Methodology, Discussion of different cases, Results, Conclusion and Appendices. A huge part of the time invested in this thesis went to research and gathering the well needed data. As always data and the gathering of data is the cornerstone of a decent scientific research. Therefor I believed that I was only appropriate to invest a huge part of the time in this part. This is also reflected by the voluminous literature study that is provided in chapter two. Literature is only literature and does not always correspond to the real world. This real world is presented in chapter five by offering four well described cases that are studied in the light of this thesis. These are: Netflix, Salesforce, Spotify and Dropbox. In chapter six the results and findings can be found and in chapter seven I wrote a conclusion based on the executed research. Keywords: Disruptive innovation – Innovation – Cloud computing – Enterprise modelling – Business models – Business model canvas – case study


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1.2 Acknowledgements I would like to express my special thanks and sincere gratitude to my promotor Mr. Poels who gave me the opportunity to write this thesis on the topic Cloud computing and disruptive innovation a truly inspiring me to dig into an, until the beginning of this year, a rather unknown subject. Also I would like to express gratitude to Mr. Gailly who voluntarily took the honours of mentoring me as a student in absence of Mr. Poels. Special thanks should go out to my girlfriend as she stood by my side the whole time and she also helped me a lot in two ways. Firstly in pushing me to the level of the high performance and secondly to help me read through the text in order to find any linguistic errors. Thirdly I would also like to thank my parents and colleagues who helped me a lot and gave me the opportunity to finalize this project within the limited time frame. Special thanks should also go to Bob Odenkirk, Alistair Humphreys and Jon Krakaur for their writing always gave me the needed inspiration to continue.


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1.3 Personal motivation This thesis is the second one I wrote in two years’ time. A lot of people asked me what I was thinking when I started doing this. I guess for most people writing one thesis is more than enough. Apparently I am not like all the rest because I was really looking forward to write a thesis, for once on a subject that really had my interest. I admit the one I wrote last year was not quite in line with the things that interest me the most. An awful mistake I will not make again. My motivation to write this thesis can best be described using a cake metaphor because everybody likes cake. The first piece of cake is: the topic. The topic of this thesis consists of several parts that each has a certain appeal to me. These topics are Information technology or cloud computing more precisely. Business models and (disruptive) innovation. Let’s start with Information technology. This has become a big thing in my life. Maybe even more than it was before because let’s face it, spending a day without getting in touch with IT will be harder to do than riding from Virton to Ostend in a 24 hrs window. Knowing how hard that was when I tried the latter last April and had to quite at 100k from the finish, is just setting the scene to confirm the previous bold statement. In my opinion a day without IT can’t be done, unless in outback Australia and even then I have doubts. IT gained a greater place in my life, for a certain reason that I will set out in detail. Last year after graduating from a study career of six years I decided to go and look for a job at an IT consulting Company. Not that I studied anything in the direction of IT however I had and still have a good mind-set that I wanted to learn what was needed to succeed. Thus so I landed my first job in the private sector in six years as a Business Intelligence consultant. Before, I always worked as a public servant during my studies. So this was a new and a fresh start. To make a good impression at my employer it seemed like a very good idea to also write a thesis about something related to IT. So already at the beginning of September I started looking for a subject. After some research and a brief meeting with my promoter everything was arranged and I could start a year full of IT. Looking back at it, this was what it was. The IT component is not the only thing that caught my interest. The subject of the thesis also had a business model side. I have always been a curious guy and always wanted to discover how things or systems work and getting to know business models of some firms and what is behind all the used concepts seemed to me like a very interesting pastime. The last part of the subject is Disruptive innovation. To be fully honest, at the beginning, I only knew a thing or two about innovation, not to mention that there was something like disruptive innovation. However after reading Christensen’s book “The innovator’s dilemma” in September 2016 I was thrilled by the subject and the theories behind it. Truly, after reading the book I was happy that I choose a subject for my thesis in that direction. What also helped is that I could investigate cases I use on an almost daily basis for this thesis. Getting to know how something works or how it had gotten the way it is right know has always been a thing I liked, see the I am a curious guy a couple of lines above. An interesting topic is of course only one part of the cake. The second piece of cake: I wanted a second degree in business economics. Not for the degree per se. I had the feeling I had some place to stuff some extra economical baggage in my backpack of life because to my opinion in my previous academic track, business economics as a broad topic was only handled in a limited way and I felt that I could use some extra foundations of knowledge to be able to take on the world. This being said, I enjoyed doing this research and writing most of this thesis. However this time I will not be tempted to write a third one: Two’s not enough but three is a crowd.


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1.4 Social Relevance In order to convince the reader of the social relevance of this topic I would like to rely on a couple of bold statements. These will be used to clarify why this research is relevant and in what way this thesis can contribute to the already existing literature, which will off course be further discussed extensively afterwards. The main intention here is to briefly demonstrate the relevance of each of the building blocks that together form the basis for the research carried out in this thesis. The cloud is everywhere! If this bold statement would be spread in the case of a weather forecast this could be seen as a national disaster, which would require extra speed limits on the highway and what so on. However luckily this is not the case. The subject of this thesis has little to do with forecasting weather or what so ever. The subject will be cloud computing and this type of information technology appears to be everywhere. It is not only the proverbial reference of describing the technology, like a cloud, it is also literally everywhere and if not used or needed anymore it can disappear. Not that it actually disappears, it just seems like it does from a user’s perspective. When I look at my personal life, as a student, I can confirm that the number of applications using cloud computing is already quite large and it is still growing as applications using this information technology are emerging every day. Cloud computing only recently appeared in the Oxford English Dictionary. However its use is spreading rapidly because it captures a historic shift in the IT industry as more computer memory, processing power, and apps are hosted in remote data centres, or the “cloud.” (Regalado, 2011). There is some controversy about who coined the term cloud for the first time. It could be Eric Schmidt, a former CEO of Google, who introduced the term into common use by stating, at the 2006 search engine strategies conference, that Google’s services belong in a cloud somewhere (Fogarty, 2012). Others refer to the events that occurred inside the offices of Compaq Computer where a small group of technology executives was plotting the future of the Internet business and calling it “cloud computing” (Favaloro & O'Sullivan, 1996) (Regalado, 2011). Their vision was detailed and prescient. Not only would all business software move to the Web, but what they termed “cloud computing-enabled applications” like consumer file storage would become common in our nowadays (business) life (Favaloro & O'Sullivan, 1996) (Regalado, 2011). Also for the concept behind cloud computing there are quite some contenders who could be pointed out as “the first” or one of the first with the idea of what cloud computing is. In the early sixties, John McCarthy, the computer scientist who also coined the term “artificial intelligence,” came up with the theory of time-sharing, which is very similar to today’s cloud computing (McCarthy J. , 1962) (Pullen, 2015a). Back then, computing time was extremely expensive and users wanted to make the greatest use out of a very precious asset. In addition, smaller companies who couldn’t afford a computer of their own also wanted to be able to do the type of automation that larger companies could do, but without making such an expensive investment. So, if users could find a way to “time-share” a computer, they could effectively rent its computational force without having to singularly pay the bill for its massive cost (Pullen, 2015a). Another contender could be Western Union who dreamt in 1965 of a nationwide information utility (Harding & Oswald, 1965).


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It is true that several of the above ideas were indeed present for a long time. Nonetheless their confluence today in an environment where information can be accessed independent of device and location represents a major shift in computing as we know it (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). It is perhaps not the best, however it seems like a very convenient way to illustrate that the usage of cloud computing is fully embedded in ‘our’ day to day life by referring to my own life as an example. I am off course aware that self-experimentation studies can raise questions about whether analyses of just a few individuals are scientifically valid. Self-monitoring experiments are not randomized or blinded like traditional human studies, and the experimenter’s personal involvement and motivations could make the research seem less objective (Landhuis, 2016). Despite these concerns and caveats, there are scenarios where self-experimentation may be not only acceptable but optimal (Landhuis, 2016). I am off course aware of the non-scientific value of the example, however I do believe it has value to show that cloud computing is almost everywhere and that it is available to use at any time by anyone. It is up to the person in question to decide whether or not he or she wishes to use it. The illustration of “the cloud is everywhere” statement comes down to the fact that this thesis was written while using a version of MS Word in a cloud environment, provided by the University of Ghent, called ‘Athena’. To avoid a thorny issue in case of a computer malfunction or crash as a safety measure I also made sure I always saved a copy of a recent version of this document in a Dropbox folder. Often if not always while typing or doing review work I would be listening to music on Spotify and on the rare moments while taking a break I would watch some series on Netflix to get my mind of the subject. As described, it is easy to see that this elementary process of writing a thesis already involved four applications of cloud computing technology. As mentioned before by Landhuis (2016) a single case does not always appears valid. The European Union however provides statistics about the use of cloud computing services by European firms. According to their research 21 % of the European firms are already using this technology in 2016 (European Commission, 2016). Significant differences can be observed across countries. In Finland, Sweden and Denmark, over 40 % of enterprises are using cloud computing. On the other hand, fewer than 10 % are using this technology in Greece, Latvia, Poland, Romania and Bulgaria. Belgium scores a little bit above the European average where 28% of the firms are using Cloud computing technology according to the European statistics (European Commission, 2016). Other research like the 2016 IDG Enterprise Cloud Computing Survey shows a higher figure of up to 70 % of organisations that have at least one application in the cloud (IDG enterprise, 2016). The discrepancy between the two results can be explained by the high degree of North American participants to the last mentioned survey and also that all the participants of this survey are IT professionals. According to the Synergy Research Group 2015 was the year when cloud became mainstream and 2016 was the year that cloud started to dominate many IT market segments because major barriers to cloud adoption were almost a thing of the past (Synergy Research Group, 2017). Cloud technologies are now generating massive revenues for technology vendors and cloud service providers, yet there are still many years of strong growth ahead (Synergy


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Research Group, 2017). Gartner expects the cloud computing market in total to grow to about USD 247 billion worldwide in 2017 (Statista, n.d.) Innovation is participating and Disruptive innovation is Gold Just stating that innovation is important would be like kicking in an open door instead we used the reference to the Olympic catchphrase. The reason for this is quite straightforward: in order to maintain a certain market share a constant ‘innovation’ is needed, this can be linked to the product lifetime value or just as a way to keep customers close to the firm. Disruptive innovation is Gold. This is perhaps a bit too bold to say, however it is true that if a firm succeeds to disrupt a market they have a fair chance at becoming market leader for at least a certain period of time. When Christensen’s book “The innovator’s dilemma “was published in the late nineties, 1997 to be more precise it created a shockwave in the research field for innovation. Suddenly Christensen was seen as some kind of “guru” (Scherreik, 2000). His work was cited extensively by scholars working on diverse topics going from marketing to technology management. Before Danneels’ paper in 2004 there was not really a constructive criticism on the concepts described by Christensen. Until that publication nobody really had challenged the concepts in this top selling book. Discussion (Danneels, 2004) (Christensen C. M., 2006) still exists among both authors on the ex-ante applications of the disruptive innovation theory in predicting whether an early stage disruptive innovation case would succeed subsequently. Since the theory has been largely based on extensive study of empirical evidences of many successful cases in the past (ex post) (Hang, Chen, & Yu, 2011) instead trying to predict possible disruptions (ex ante). In line of this I will provide a contribution to the existing literature in trying to get an understanding of the business models of these firms who were successful in disrupting a market by using cloud computing technology. The goal is to investigate through a number of case studies whether cloud computing can be seen as disruptive innovation. A unique business model is needed to differentiate from competition One of the few ways left for companies to protect their margins is through business model differentiation (Plantes, 2017). An unique or distinctive business model has become the new basis of competition, replacing product features and benefits as the playing field on which companies emerge as dominant or laggards. This is happening because the traditional strategies, like branding and marketing communication, are less effective in maintaining margins. The main reason of their ineffectiveness is that they cannot cope with multiple forces that are accelerating market commoditization which leads to price wars. Some examples of these forces are globalization, copycat competition, price transparency and loss of messaging control. So to avoid this, leadership teams in organisations should be aware and keep their business model up to date to avoid sliding down in a commodity-like market. (Plantes, 2017) A business model should answer five interdependent core strategy questions. To start with “Who is our target market and how to reach and relate to its members?” The second question handles the scope of the offering “What is in the scope and what is out of scope?” the third question is “What promise of value leads customers to us?” The fourth question relates to how you are going to protect this value promise? The last one is about factors that ensure the firms profitability in delivering (Plantes, 2017).


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Business model innovation embraces many different types of changes to an existing business model. These can run the gamut from incremental to transformative. The changes can be proactive or forced by competitors. Keep in mind that in a free market economy, every industry has a Wal-Mart. In other words, a company with a business model that makes them the lowest cost competitor, profitable competes on price. If that’s not you, avoid becoming your industry’s Sears/K-Mart. Differentiate your business models before it is too late. And if you are the Wal-Mart of your industry, watch out for your industry’s version of Amazon, Tesco and Dollar Stores who are coming after Wal-Mart’s low cost position with advantages Wal-Mart can’t easily copy (Plantes, 2017). The previous sentence already combines innovation or even disruptive innovation with the business model concept as I will continue on doing throughout this thesis. The focus will be put on business models of those innovative firms that try to take over the market by offering a different set of values than incumbents are offering. A Merger of three statements In this thesis the goal is to investigate through a number of case-studies whether Cloud Computing presents a sustaining or disruptive technology and what the impact of this technology is on the used business model of the firms in scope. These two goals are further down reflected in the research question. The cases in scope are Netflix, Salesforce, Spotify and Dropbox. At first sight all of these can be seen as successful companies benefitting from cloud computing technology. However we will not blindly believe what is noticed at a first glance. In order to make sure the cases selected are indeed an example of a disruptive innovation they will first be tested by applying a framework developed in the paper by Hang et al (2011). The whole reason for the continuing interest in disruptive innovation is because the impact can be so extreme that virtually non-existing firms can rise to dominance while leading incumbents can cease to exist or largely diminish (Baiyere & Salmela, 2013). Once these cases passed the test the research will continue. If they do not meet the requirements in the framework we will elaborate more on the why not. However this is not what is expected. The impact of Cloud Computing as sustaining or disruptive technology will be modelled and analyzed using various Enterprise Modeling techniques and associated tools (e.g., the Business Model Canvas). The purpose of this thesis research is to gain additional insights into the role of Cloud Computing as disruptive or sustaining technology and its impact on an organization’s business model or business processes. The central research question is: “How Enterprise Modeling can help organizations in dealing with the impact of Cloud Computing on their business?” Research activities included literature research and analysis of published case-studies and experience reports. The sources used are both academic as well as professional business- and IT literature.


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2 Part II: Literature review The aim of this chapter is to explore the theme and literature concerning the main subject dealt within this thesis. In the following paragraphs I am giving an outline of all the crucial concepts that are relevant to the research conducted and that have been already addressed in scientific literature. As already illustrated in the previous chapter the main subject of this thesis consists in fact of three big blocks. These will be clarified elaborately in the subsequent subchapters.

2.1 Conceptual delimitation: cloud computing In this first part of the literature review I will focus further on the concept of the cloud. First of all I will try to clarify what is meant when using the word cloud by offering a solid definition and by explaining the different appearances.

2.1.1 Definition of cloud computing A possible definition of the concept could be: “ Cloud computing is a model for enabling ubiquitous, convenient, on-demand, network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction” (Mell & Grance, 2011). Although this definition is very complete it is hard to understand, this is why another, more understandable, definition is preferred: “It is an information technology service model where computing services (both hardware and software) are delivered on-demand to customers over a network in a self-service fashion, independent of device and location” (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). From the above definition one can derive that cloud computing comes from two major trends in information technology: IT efficiency, where the power of computers is utilized more efficiently through scalable hardware. And business agility whereby IT can be used as a competitive tool that responds in real time to user requirements (Kim W. , 2009). The use of the term ‘cloud’ is metaphorical and typically points to a large pool of usable resources such as hardware and software that is easily accessible via the internet. (Vaquero, Rodero-Merino, Caceres, & Lindner, 2009) (Vouk, 2008). Despite the rather technical phrasing of the definition of cloud computing it can in essence also be seen as an economic model to acquire and manage IT resources (Lewis, 2010).

2.1.2 Different types of cloud computing The cloud has four deployment models: Private cloud, Community cloud, Public cloud and Hybrid cloud (Mell & Grance, 2011). These types are technologically comparable however they differ in terms of ownership, organizations and operations. A private cloud is customer-owned and a self-operating infrastructure. Only authorized stakeholders can access this environment (Kaltenecker, Hess, & Huesig, 2015). It has the same benefits of a public cloud computing environment, like being elastic and service based however it is managed within an organization. On the other side private clouds offer greater control over the cloud infrastructure and are often suited for larger installations (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). The community cloud is more open and allows access for consumers that have shared concerns, such as specific security requirements. The biggest user of a community cloud is the Federal government of The united States (Kundra, 2010). A public


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cloud is in general owned and run by a third party IT service provider offering a selection of highly standardize business processes. The basis here is a pay-per-use. It is a cost-effective way to deploy IT solutions. Lastly the cloud infrastructure of a hybrid cloud is a combination of the afore-mentioned (Kaltenecker, Hess, & Huesig, 2015).

2.1.3 Characteristics of cloud computing The cloud computing model on its own has five essential characteristics: it is on-demand and self-service, it requires a broad network access, it has the option of pooling resources, has rapid elasticity and it is a measured service (Surya, Mathew, & Lehner, 2014). Cloud computing shares some characteristics common to parallel computing, such as: cluster computing and grid computing. The main difference with grid computing is that Cloud computing uses virtualization for resources management (Grandison, Maximilien, Thorpe, & Alba, 2010) especially the virtualization of hardware resources (Vaquero, Rodero-Merino, Caceres, & Lindner, 2009) this enables the actual sharing of resources like on a grid. This gives the impression of a single dedicated resource (Vaquero, Rodero-Merino, Caceres, & Lindner, 2009). Cloud computing, in the way we know it nowadays, was not fueled by Virtualization alone. Multitenancy and Web services are also important enablers. Virtualization can be best described as the technology that actually hides the physical characteristics of a computing platform while instead presenting an abstract emulated computing platform (Vouk, 2008). Virtualization is nothing new it has been prevalent since the sixties however it is only in recent years that computing power and networking resources have caught up to deliver a level that is comparable to the level users had grown accustomed of on personal computers (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). The concept of multi-tenancy is a single instance of an application software that serves multiple clients. This allows better utilization of a system’s resources which otherwise would have been considerable if the software had to be duplicated for each individual client (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). The third concept is a Web service which can be defined as a software system designed to support interoperable machine-to-machine interaction over a network (W3C, 2004). In common usage the term refers to clients and servers that communicate over the HTTP protocol, the foundation of data communication on the World Wide Web. Web services help standardize interfaces between applications making access to server applications over a network easier (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011).


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2.1.4 Service models Initial literature only mentions three service models: Software-as-a-service (SaaS), Platform-as-a-service (PaaS) and Infrastructure-as-a-service (Iaas) (Mell & Grance, 2011). These models refer to different layers of the cloud computing architecture (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011) and the division between them is based on their capabilities (Lewis, 2010). Infrastructure-As-A-Service (abbreviated IaaS) can be seen as the lowest level of the three, a provider that just offers the basic infrastructure like storage or compute on virtual servers and network infrastructure (Kaltenecker, Hess, & Huesig, 2015). IaaS allows organizations and developers to extend their IT infrastructure on an on-demand basis (Lewis, 2010). Amazon Web Services, Microsoft Azure and Google compute Engine can be seen as prominent examples. IaaS is also the largest cloud computing market in terms of revenues together with Platform-as-a-Service (Henderson N. , 2017). Whereas Amazon Web Service is by far the biggest provider accounting for 45 % of the public IaaS market (Henderson N. , 2016). Platform-As-A-Service (abbreviated PaaS) comprises IT services in a middleware layer. On these platforms development and integration of application components are enabled without the cost and complexity of buying and managing the underlying hardware and software layers. In order to create and host applications of a larger scale than the individual business would have been able to handle (Lewis, 2010). This enables developers to access resources for application development and to collaborate with others (Mathur & Nishchal, 2010). Examples of PaaS include Microsoft’s Azure services platform, Googles app engine and Salesforce’s Force.com At the moment that only three service models were used: Software-As-A-Service (abbreviated SaaS) was the highest cloud computing service level offering its users business applications as a standardized service (Kaltenecker, Hess, & Huesig, 2015). In short Software-as-a-Service can be described as an application providing business specific capabilities running in the cloud and thus eliminating the need to install and run the application on the client computer (Lewis, 2010) (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). Examples of SaaS include Salesforce, Netsuite and Gmail. SaaS often involves business processes completely uncoupled from the technology like for example, customer relationship management (CRM) systems (Vaquero, Rodero-Merino, Caceres, & Lindner, 2009). For the usage of SaaS services all the user requires is a browser and internet access. Due to these low requirements it is possible to offer services at previously excluded groups. This combined with the ready provision software, the flexibility and the minimal capital investment makes SaaS ideal for start-ups and smaller business to utilize otherwise not accessible software through the on-premises business model (Benlian, Hess, & Buxmann, 2010). The concept of SaaS is in fact not entirely new. Some scholar compare it as a form of outsourcing (Benlian, Hess, & Buxmann, 2010), others note that application service provision can be seen as the SaaS antecedent (Günther, Tamm, Hansen, & Meseg, 2001) (Leavitt, 2009). SaaS as well as application service provision models both aim to provide trouble free operation for the end users and allow corporate customers to free up their IT resources (Pearlson & Saunders, 2009). What differentiates SaaS from these concepts is that SaaS has a much bigger potential and offers users and providers more opportunities especially due to the development and widespread adoption of internet technologies and standards (Buchegger & Riedl, 2005).


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Nowadays the mere division in only three service models is in some way a bit outdated. However it still represents the biggest share of cloud computing applications. Due to technological employability it is now more applicable to speak of XaaS where X could stand for almost anything as-a-Service (Kiblawi & Khalifeh, 2015). Services are typically provided on the basis of service level agreements (SLA) and depending on a particular customer needs can meet different kinds of quality of service criteria (Lin & Chen, 2012).Although the above mentioned service types each serve different purposes and target different customers they share a common business model that is that they ‘rent’ the use of their computing resources (Lin & Chen, 2012). This model is similar to the application service provider model (ASP) where a provider provides software, infrastructure or people to run a customize fashion for the customer (Wang, et al., 2010). Other scholars compared cloud computing to what IT outsourcing was before, namely a great innovation that changed the IT landscape. Nowadays Cloud can offer an alternative to existing solutions like IT outsourcing or even in-house models (Yang, 2011).

2.1.5 Advantages of using cloud computing IT in general provides services and applications for business needs and there are many instances where the existing IT infrastructure lacks capabilities to provide the ever increasing business needs (Foster, Zhao, Raicu, & Lu, 2008). The usage of cloud offers firms flexibility over their use of resources (Buyya R. , Yeo, Venugopal, Broberg, & Brandic, 2009). It is one of cloud computing’s essential characteristics. This offers a substantial benefit to smaller users, in a business perspective called SMEs. These users can by using cloud technology easily scale up or scale down service capabilities needed at any time. These extra possibilities also obviate the need for underutilized servers in anticipation of peak demand (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). These services are so called on-demand consumption models (Surya, Mathew, & Lehner, 2014) that are charged in a pay-as-you go manner.


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To indicate the major advantage this flexibility offers we refer to the example used by Grossman (Grossman, 2009) . Assume that you have a requirement to operate 100 servers over the course of three years. One option is to lease them at $0.40 per instance-hour, which would cost approximately:

100 servers * $0.40 instance-hour * 3 years * 8,760 hours/year = $1,051,200

Another option is to buy them. Let’s assume the cost to buy each server is $1,500, that you need two staff members at $100,000 per year to administer the servers, and that the servers require 150 watts each, with the cost of electricity at $0.10 per kilowatt-hour, bringing the yearly cost to operate the 100 servers to $13,140. This option would cost approximately:

100 servers * $1,500 + 3 years * $13,140 electricity/year + 3 years * 2 staff * $100,000 salary/year = $789,420

So, if you were to run the servers at 100 percent utilization, buying the 100 servers is in this case less expensive. However, if you were to run them at 75 percent utilization or less, using an on-demand style of cloud would be less expensive (Grossman, 2009). In fact, this example underestimates the benefits of elasticity, because in addition to simple diurnal patterns, most services also experience seasonal or other periodic demand variation (Armbust, et al., 2010). A couple of other things to consider in this example are that first of all if new equipment needs to be bought to handle those expected spikes and that it takes weeks to acquire and install these servers. Secondly if the provision is wrong it directly means waste of resources in case of an overestimation or loss of clientele if the underestimate (Armbust, et al., 2010). Another thing to take into consideration is that not all firms can immediately pay up the lump sum to buy their own servers. While the sum that has to be payed to lease them via the cloud can be put in to the operation expenses. Another advantage is that the usage of Cloud technologies dramatically lowers the cost of entry for firms trying to benefit from compute-intensive business analytics that were hitherto only available for the largest corporations (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). This opens the door for small and medium enterprises (SME), because buying, running, maintaining their own hardware and software infrastructure is not needed anymore (Miller M. , 2008). Not only on the level of corporations has cloud offered opportunities, also on the level of countries, whereas countries who traditionally lacked the resources for widespread IT deployment can now access this new kind technology. Cloud computing makes it also possible to treat IT as an operational expense (Opex) opposed to the investment sensitive capital expenditure (capex) which was the conventional model. This conversion of costs makes a significant contribution to the long term viability of a firm because IT’s high penetration in today’s companies (T-Systems, 2009) Cloud computing is not only making the purchase obsolete it also leads to reduced infrastructure costs and energy savings as well as reduced updates and maintenance costs (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). The total cost savings are estimated at a five to seven time reduction in the total cost of computing (Armbrust, et al., 2009). Cloud


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computing also has the ability to lower the IT barriers to innovation (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). On the level of security cloud computing offers some advantages as it allows a more secure environment. By using this technology firms can better control when, where and how employees have access to the organization’s computer systems, all managed over a simple web-based interface (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). Other advantages are increased collaboration options, such as working simultaneously on common data and mobility because by using cloud technology, users can access data and applications from around the globe (Lewis, 2010) and this on any device that can be connected to internet (Jeknic & Kraut, 2015). For truly remote workers it offers a great advantage as web-based applications empower them to be truly mobile and still being able to accomplish their work (Jeknic & Kraut, 2015).

2.1.6 Weaknesses of cloud computing In this section we briefly touch the major concerns that are mentioned in literature that were arising with the introduction of cloud computing technology. The risks of using cloud computing can be grouped in four categories (Khajeh-Hosseini, Greenwood, Smith, & Sommerville, 2012): policy and organizational risks, technical risks, legal risks and risks not specific to cloud. In the first category, policy and organizational risks, one can find that organizations wary over the loss of physical control of the data that goes to the cloud. This is mainly because in converting to cloud computing companies hand over their data to a third-part service provider. This provider then stores and processes such data in the cloud (Horrigan, 2008). Also on the business side of things there are some concerns like the fear for a vendor lock-in once a move to the cloud has been executed (Opara-Martins, Sahandi, & Tian, 2016). Once this migration has been completed the cloud provider gains a strong position an can for example raise his prices. Another risk involves what happens if cloud providers go bankrupt (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). The second category, the more technical related risks entail the fact that organizations are wary about entrusting mission-critical applications to a cloud computing paradigm where providers cannot commit to the required high quality (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011) and where failure or the slightest chance for down time is not accepted. Besides reliability, the other concerns are handling performance and security (wired.com, 2009). Much of the concerns about security are related to third party management, where important data is handled by cloud service providers external to the client organization (Dorey & Leite, 2011) (Lin & Chen, 2012) The third category, the legal risk, contains the issue that for a while providers where unable to guarantee the location of a company’s information (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). This brought along some legal issues, especially in mainland Europe where laws on data security, data privacy, data protection and data location requirements could differ from one country to another which could negate some of the benefits of cloud computing (Cloud Security Alliance, 2009). However due to the implementation of the General Data Protection Regulation this issue should disappear (Blackmer, 2016).


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The fourth category entails the fact that an internet connection is needed to take full advantage of the service offered (Miller M. , 2008) (Jeknic & Kraut, 2015). So if internet connection fails no work can be done. However the same could be said if electricity breaks down, which is not a frequent thing to happen. Internet is seen as the new and fifth utility and isnt expected to go down anymore (Buyya R. , Yeo, Venugopal, Broberg, & Brandic, 2009). Linked to this the usage of internet introduces latency into every communication between user and provider (Lewis, 2010).

2.1.7 Technical explanation of cloud computing Cloud computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the data centres that provide those services (Armbust, et al., 2010). Technically spoken the data centre hardware and software is what we will call a cloud. There are three new aspects in cloud computing from a hardware provisioning and pricing point of view. The appearance of infinite computing resources that are available on demand, that are quickly enough to follow load surges and that eliminate the need for cloud computing users to plan far ahead (Armbust, et al., 2010). The elimination of an up-front commitment by cloud users, thereby allowing companies to start small and increase hardware resources only when there is an increase in their needs (Armbust, et al., 2010). The ability to pay for use of computing resources on a short-term basis as needed (for example, processors by the hour and storage by the day) and release them as needed, thereby rewarding conservation by letting machines and storage go when they are no longer useful (Armbust, et al., 2010). Armbust et al. (2010) argue that the construction and operation of extremely large-scale, commodity-computer data centres at low-cost locations was the key necessary enabler of cloud computing, for they uncovered the factors of 5 to 7 decrease in cost of electricity, network bandwidth, operations, software, and hardware available at these very large economies of scale. These factors, combined with statistical multiplexing to increase utilization compared to traditional data centres, meant that cloud computing could offer services below the costs of a medium-sized data centre and yet still make a good profit (Armbust, et al., 2010).


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The architecture of a cloud computing environment can be divided into four layers: the application layer, the platform layer, the infrastructure layer and the hardware layer. This is shown below in figure 1.

Figure 1: Cloud computing architecture (Zhang, Cheng, & Boutaba, 2010)

The application layer can be found at the highest level of the hierarchy, it consists of the actual cloud applications. Different from traditional applications, cloud applications can leverage the automatic-scaling feature to achieve better performance, availability and lower operating cost. Compared to traditional service hosting environments such as dedicated server farms, the architecture of cloud computing is more modular (Zhang, Cheng, & Boutaba, 2010). The platform layer is built on top of the infrastructure layer, it consists of operating systems and application frameworks. The purpose of the platform layer is to minimize the burden of deploying applications directly into VM containers. For example, Google App Engine operates at the platform layer to provide API support for implementing storage, database and business logic of typical web applications (Zhang, Cheng, & Boutaba, 2010) The infrastructure layer is also known as the virtualization layer, it creates a pool of storage and computing resources by partitioning the physical resources using virtualization technologies such as Xen, KVM and VMware. The infrastructure layer is an essential component of cloud computing, since many key features, such as dynamic resource assignment, are only made available through these virtualization technologies (Zhang, Cheng, & Boutaba, 2010).


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The hardware layer is responsible for managing the physical resources of the cloud, including physical servers, routers, switches, power and cooling systems. In practice, the hardware layer is typically implemented in data centres which usually contain thousands of servers that are organized in racks and interconnected through switches, routers or other fabrics (Zhang, Cheng, & Boutaba, 2010).

Proper planning of this network architecture is critical, as it can heavily influence applications performance and throughput in such a distributed computing environment (Zhang, Cheng, & Boutaba, 2010). Further, scalability and resiliency features need to be carefully considered. Currently, a layered approach is the basic foundation of the network architecture design, which has been tested in some of the largest deployed data centres (Zhang, Cheng, & Boutaba, 2010). The basic layers of a data centre consist of the core, aggregation, and access layers, as shown in figure 2.

The access layer is where the servers in racks physically connect to the network. The aggregation layer usually provides important functions, such as domain service, location service, server load balancing, and more. The core layer provides connectivity to multiple aggregation switches and provides a resilient routed fabric with no single point of failure (Zhang, Cheng, & Boutaba, 2010). Another area of rapid innovation in the industry is the design and deployment of shipping-container based, modular data centre (MDC) (Zhang, Cheng, & Boutaba, 2010) because of the ease of deployment of such infrastructure. Each of the above mentioned layers is loosely coupled with the layers above and below, allowing each layer to evolve separately. The architectural modularity allows cloud computing to support a wide range of application requirements while reducing management and maintenance overhead (Zhang, Cheng, & Boutaba, 2010).

Figure 2: Basic layered design of data centre network infrastructure (Zhang, Cheng, & Boutaba, 2010)


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2.2 Conceptual delimitation: innovation

2.2.1 General about innovation For a complete understanding of innovation and even further to be able to tell whether or not this innovation is disruptive or sustaining one must first understand the building blocks of this concept. Over the years content analysis from multiple disciplines yielded nearly sixty unique definitions for innovation which immediately shows the absence of a unique understanding (Baregheh, Rowley, & Sambrook, 2009). To address this issue Baregheh et al. (2009) provided a definition in line with all the important attributes of innovation, like the nature, type, stage, context and aim of innovation. They defined innovation as “the multi-stage process whereby organizations transform ideas into new/improved products, service or processes, in order to advance, compete and differentiate themselves successfully in their marketplace”. The perspective of this definition is an organizational one. Emphasizing the dynamic nature of innovation is essential in a definition and technological innovation addressed this need: “Innovation is an iterative process initiated by the perception of a new market and or new service opportunity for a technology based invention which leads to development, production and marketing tasks striving for commercial success of the invention” (Garcia & Calantone, 2002) Numerous contributions have been made to the field of innovation studying all types of innovation going from incremental changes, sustaining innovation, radical innovation to disruptive innovation. What follows is brief review of the different research lines on innovation as an introduction. In subsequent chapters these will be explained in their full extend. Christensen (1997) explained for the first time the concept of disruptive innovation in his book “The Innovator’s Dilemma”. In short his theory can be explained as follows: a (new) firm outside the market in question develops a new or similar product that could also satisfy the needs of the customers of that market and in some cases other needs as well. Due to the use of a new technology the cost of producing is much lower than that of the existing products in the market. If these new products enter the market they have the opportunity to take over almost the entire market pushing the incumbent firms out of the market or to a higher segment. In the literature also several other views on innovation exist alongside Christensen’s theory, including the diffusion theory (Rogers, 1995) and the concept of radical innovations (Chandy & Tellis, 1998). Whereas the first mentioned focusses on innovations without regard to incumbents. It focusses merely on five variables that have a profound influence on the rate of the adoption of an innovation. These include: perceived attributes of innovation, type of innovation decision, communication channels, nature of social system, and change agents’ promotion efforts (Rogers, 1995). The second one, radical innovations, focusses on the successful introduction of radically new products.


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It is a fact that investments in R&D have been continuously growing through the last years. However it is also true that investments to sustain existing technologies may devalue to a large extent as a result of new (disruptive) technologies that foster radical innovations (Kopetzky, et al., 2013). Figure 2 shown underneath, illustrates how technology trajectories typical improve over time before leveling out in terms of cost per performance unit. Foster (1986) has thoroughly discussed this S shaped performance increase of technologies.

Figure 3 : Innovation and technological trajectories following Olofsson( 2003)

Christensen’s (1997) perspective is marked by the dotted rectangle with the intersection point of two dominant technological trajectories where technological and market disruptions take place.

2.2.2 Incremental innovation An incremental or a sustaining innovation results in the performance improvements of the attributes most valued by mainstream customers (Kaltenecker, Hüsig, Hess, & Dowling, 2013). They provide added features, new versions or extensions to an otherwise standard product line (Tushman & Nadler, 1986). This kind of innovation helps to improve profit margins by exploiting existing processes and cost structure and makes better use of current competitive advantages (Kaltenecker, Hüsig, Hess, & Dowling, 2013). As mentioned above in general most firms are continuously investing in improving their products. This has to do with at least the following two factors: competition and the product life cycle. If a company does not improve its own product in line with what his direct competitors are doing they provide a competitive advantage to this competitor and they will probably lose market share to this competitor. A second factor that should be kept in mind is the Product life cycle. The product life cycle has four phases. During the introductory stage there is usually a substantial amount of product innovation as several forms of the same product compete for dominance (Tushman & Nadler, 1986). The first stage is less applicable in the light of incremental innovation. In the next stage, major product variation gives way to competition based on price, quality and segmentation. In other words this consists of process innovation rather than product innovation (Tushman


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& Nadler, 1986). During the third stage of a product life cycle, the pattern of incremental product and major process innovation continues until the product and its associated production processes are so intertwined that only incremental product and process innovation are possible (Tushman & Nadler, 1986). This period can be very profitable, since small changes in the product or processes can lead to significantly decreased costs or higher quality. This stage evolves in the fourth stage. The fourth stage is the decline stage where the market for a product will start to shrink. This shrinkage could be due to the market becoming saturated i.e. all the customers who will buy the product have already purchased it (Product Life Cycle Stages, sd) or because the consumers are switching due to an external shock such as deregulation, technological change, or foreign competition triggers a new wave of major product innovation (Tushman & Nadler, 1986). While this decline may be inevitable, it may still be possible for companies to make some profit by switching to less-expensive production methods and cheaper markets (Product Life Cycle Stages, sd).

2.2.3 Radical product innovation It is widely believed that only sustaining or incremental innovation can be planned and that radical innovation simply happens, without aim nor intent (Kopetzky, et al., 2013). Radical innovations can however be fostered according to Kopetzky et al (2013) with a super-set of rules, principles and decision-making methods guiding the developers also called an ‘innovation regime’ (Godoe, 2000). Radical product innovations are defined as new products that are based on substantially new technology that delivers substantially better the customers’ benefits in comparison to previous products (Danneels, 2004).

2.2.4 Incumbents Incumbents are firms who also sold the previous generation of products (Danneels, 2004). Or in other words, a firm that manufactured or sold products that belonged to the previous product generation on the introduction date of the disruptive technology (Chandy & Tellis, 2000).

2.2.5 Disruptive innovation The disruptiveness of innovation is most easily explained when talking about technology because in every technology related sector an innovative change to a product or a service can make a huge difference and create competitive advantage for the company that’s driving it. When talking about disruptive technologies one can simply not neglect the worldly appraised work from Christensen (1997). In his book “The Innovators Dilemma” the term disruptive innovation was coined for the first time in history. The first step in understanding disruptive technology is to understand that in a given market the incumbents, firms already active within a certain market, are overserving the customers of that market. Due to competition amongst one another, companies within the same market thrive up specs of their technology or product to performance heights. This leads to an overserving of the consumer of the actual product or technology. Such managerial dilemma is the result from the asymmetric motivation in the high-end, a more lucrative market (Yang, 2011).


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Christensen (1997) explains disruptive technologies as those technologies that initially may underperform established ones in serving the mainstream market. However these will eventually displace the established technologies. In this process entrant firms that supported this technology will displace mostly all of the incumbent firms that where still supporting the prior technology. This off course does not happen over one night sleep, the whole process, as explained underneath, takes some time. Initially the disruptive technology does not succeed in satisfying the minimum requirements along the performance metric most valued by customers in the mainstream segment. Often these new technologies are commercialised in emerging or less significant markets. This is mainly because leading companies’ most profitable customers initially do not want them. Therefore this new technology is seen as inappropriate by incumbents to satisfy the needs of their main customers and therefor they retain their old product line and try to improve this instead of choosing the new technology (Christensen & Bower, 1996). However as mentioned earlier the main market is mostly overserved, meaning that there is a chance that a part of that market, or an, until now, never reached market exists whose needs could be fulfilled by this new technology. Over time as this technology grows in his niche and less significant market, the technology matures to a point it can also satisfy the requirements of the mainstream market. Incumbent firms who only focusses their R&D attention on improving the existing technology and by doing so sustaining it, have a hard time catching up when the lead entrants suddenly emerge in the main market based on the disruptive technology. Subsequently there is a strong likelihood that the new technological innovation pushes the established players out their core business or to a different (higher) market segment (Kaltenecker, Hess, & Huesig, 2015).


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Christensen (1996) uses a concept called performance trajectories to explain when disruption could occur in a certain market. This performance trajectory is the rate at which the performance of a product has improved, and is expected to improve. Market disruption occurs when the performance trajectory of the product based on disruptive technology crosses the performance trajectory valued by the mainstream companies (Christensen C. M., 1997) (Surya, Mathew, & Lehner, 2014).

Figure 4: Performance trajectories (Cantner & Prasetio, 2015)

The above figure (4) is very useful to explain the key attributes of disruptive innovation. In essence it shows the performance increase over time of products or technologies. Where the full line represents an incumbent technology while the dotted line a disruptive technology. According to Christensen (1997) a first key attribute is that the initial value brought by disruptive technologies is inferior to the mainstream service offering. This is clear on the figure because the dotted line does not reach the demands of the low end. The disruptive technology offers new value propositions which can attract new customers or price sensitive parts of mainstream market, due to the lower price. Over time performance increases until entrants service offerings are overlapping with the mainstreams customers’ requirements, in that case disruption occurs (Adner, 2002) (Govindarajan & Kopalle, 2006a) (Yu & Hang, 2010) because incumbents are not prepared for such a surprised attack from below (Hang, Chen, & Yu, 2011).


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Figure 5: The need-satisfaction curve (Norman, 1998)

Above, figure 5 the needs-satisfaction curve of a technology is depicted. New technologies start at the bottom left of the curve: delivering less than the customers require. As a result, customers demand better technology and more features, regardless of the cost or inconvenience (Norman, 1998). A transition occurs when the technology can now satisfy the basic needs. An interesting group of users to focus on are lead users. This are users whose present strong needs will become general in a marketplace months or years in the future. Since lead users are familiar with conditions which lie in the future for most others, they can serve as a need-forecasting laboratory for marketing research (von Hippel, 1986). These are essential for a firm who wants to disrupt a market because they can form their first foothold in and to the market The standard view is that market acceptance starts with early adopters and then, slowly, brings in late adopters. This concept was first formulated by Moore (1991) and Rogers (1995). These two groups of adopters are very different (Norman, 1998) as Moore argued that they were separated by a chasm that could only be bridged by a better product and different marketing (Moore, 1991). The difference is often described by stating that for early adopters, the technological promise is sufficient. For late adopters, human-centred design is essential. These people want easy to understand, effective and enjoyable products (Norman, 1998). This view of the product acceptance cycle could be combined with Christensen's insights of the relationship between technological capabilities and customer needs. As long as the technology's performance, reliability and cost fall below customer needs, the marketplace is dominated by early adopters: those who need the technology and who will pay a high price to get it (Norman, 1998). But the vast majority of customers are late adopters. They hold off until the technology has proven itself, then they insist upon convenience, good user experience, and value. Christensen has continued to expand and elaborate upon his ideas of


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product disruption. In the original formulation of his model in his book ‘the Innovator’s Dilemma’ (1997) the line that depicts "required performance" is horizontal. But as Christensen (2003) later points out, the level should differ for different people and different applications, and so in his later work he has reminded readers that the line is simply the average of the user base.

Figure 6: Innovation linked to adopters (Norman, 1998)

Figure 6 shows the combination of the need-satisfaction curve and the adopters curve. The long grey rectangle depicts the moment or the shift when a product reaches acceptable performance for the masses. This can be seen as the disrupting moment where the new product is preferred above the already existing ones. Danneels (2004) comments on Christensen’s work are: that it seems like a disruptive technology is a specific type of technological change, which operates through a specific mechanism and has specific consequences without actually saying what a disruptive technology is. The fact that Christensen did not establish clear criteria to determine whether or not a technology is disruptive is one of Danneels (2004) main remarks. Together with some other remarks in the same direction. For example is a technology inherently disruptive or is this disruptiveness a function of the perspective of the companies subject to it (Danneels, 2004)?


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To help overcome these issues Danneels (2004) defines disruptive technologies as follows: “A disruptive technology is a technology that changes the bases of competition by changing the performance metrics along which firms compete. Disruptive technologies change the bases of competition because they introduce a dimension of performance along which products did not compete previously.” More recently Godvindarajan, Kopalle and Danneels (2006a) (2011) created an even more extensive definition of the concept of disruptive innovation: “A disruptive innovation 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. Subsequent developments over time, however, raise the new product’s attributes to a level sufficient to satisfy mainstream customers, thus attracting more of the mainstream market.” Other studies (Christensen & Bower, 1996b) (Lyytinen & Rose, 2003) (Christensen, Anthony, & Roth, 2004) identified a list of attributes of disruptive innovation. The most important are here after described. To start with entrants with disruptive technologies provide a different performance dimension compared to mainstream technologies and are initially inferior on a performance level compared to the mainstream technologies. Secondly disruptive technology performance trajectory improves over time and when it intersects the performance trajectory of mainstream performance, technology and market disruption occur. The most recent version of the framework set out by Christensen makes a distinction between low-end disruptions and new-market disruptions where the first one addresses the low-end of existing markets and the latter creates a new value network (Christensen & Raynor, 2003). However Christensen makes a clear distinction between the two. The new-market disruption occurs when existing characteristics of a product limit the potential customers in terms of cost or complexity and are replaced by new products. Low-end disruption on the other side, the basic case in Christensen’s work, is a situation where a ‘good-enough’ product, that satisfies expected performance needs of main customers, are replaced by low-priced a relatively simple products (Christensen & Bower, 1996). Although a mixture of new-market and low-end disruption is also possible. In one of Christensen’s later books (2003) the term disruptive technology is even replaced by disruptive innovation to broaden the theories applicability because the technology factor was continuously being pushed to include other areas. However it seems that the concepts and mechanisms that worked in Christensen’s earlier work become increasingly stretched (Danneels, 2004). Further comments on the disruptive technology framework are pointed at the fact that the framework was built upon cases ex post. The fact that Christensen only withheld cases where the potentially disruptive technology did succeed, presents in fact an analytical problem (Danneels, 2004).Because there are also examples of disruptive technologies which failed e.g. the Network computer (Ford & Garnsey, 2007). This sheds a light on the fact that established companies tend to be sceptical about disruptive technologies.


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However the real challenge here, to be useful managerially, is how the theory performs on predicting the outcome of cases ex ante. Other scholars have delivered further contributions to this prediction issue (Govindarajan & Kopalle, 2006a) (Keller & Hüsig, 2009) (Klenner, Hüsig, & Dowling, 2013) (Kaltenecker, Hüsig, Hess, & Dowling, 2013). These include the general guidance to predict future disruptions by identifying the drivers (Paap & Katz, 2004). Measures of disruptiveness which may be used to make ex ante predictions about the type of incumbent firms best positioned to develop disruptive innovations (Govindarajan & Kopalle, 2006a) (Ganguly, Nilchiani, & Farr, 2010). Research on how incumbents could spot possible disruptive threats (Rafii & Kampas, 2002) and a criteria sheet for comparing relative competitive advantages of incumbent and entrant firms (Keller & Hüsig, 2009). One of Christensen’s most interesting findings based on the case studies he did across several industries, is that incumbents tend to falter when faced with disruptive technologies. A finding that is in line with previous research that states that innovations that ultimately transform an industry often do not originate from industry’s leaders (Cooper & Schendel, 1976) (Foster R. , 1986) (Henderson & Clark, 1990) (Utterback, 1994). Research by King and Tucci (1999) (2002) tends to contradict the finding of Christensen that incumbents fail or go out of business when faced with a new disruptive technology. The researchers actually found that incumbents with experience in prior market segments tend to enter in new niche markets. However these studies did not examine the shifts in industry leadership across the different transitions and therefore did not test Christensen’s initial claim that incumbents lose their leadership position in terms of dominant market share if faced with disruptive technologic change (Danneels, 2004). Therefore it seems that many, but not all, incumbents fail in the face of disruptive technology (Danneels, 2004). More examples of incumbents surviving their meeting with disruptive technology can be found in the research about the Charles Schwab case (Cohan, 2000) and how dominant producers of radios managed to become dominant in producing television products (Klepper & Simons, 2000). In their study on a broad range of radical product innovations in office products Chandy and Tellis (2000) also concluded that the incumbents curse defined by Christensen has been overstated. When to use independent spin out organization: “When a threatening disruptive technology requires a different cost structure in order to be profitable an competitive or when current size of the opportunity is insignificant relative to the growth needs of mainstream organization then and only then is a spin out organization a required part of the solution (Danneels, 2004) (Christensen & Overdorf, 2000) Christensen (1997)uses two types of reasoning for incumbent failure however these can also be used the other way around to explain incumbent success. The first line of reasoning goes along the resource allocation process. If this is a cause of incumbent’s failure due to the wrong resource allocation to harness disruptions then a superior research allocation system could characterize those incumbents which are successful (Danneels, 2004). Earlier research by Tushman and Anderson (1986) showed that competence-destroying technological discontinuities were initiated by new firms and competences enhancing technological


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discontinuities were initiated by existing firms. This thesis is underbuilt by the observation that incumbent firms are often entrenched by a legacy of prior technology and ways of operating in contrast to new firms who are unconstrained by prior competence and history and can take thus advantage of new opportunities (Tushman & Anderson, 1986). In line with this Henderson (1993) (Henderson & Clark, 1990) found that incumbent firms often invest more in incremental innovation while entrants tend to dominate radical innovation. This can be declared by the fact that larger incumbent firms may be saddled with their assets and therefore reducing the efficiency of their attempts at radical innovation (Henderson R. , 1993) They already have effective routines for handling their customers, which lead to organizational inertia (Henderson R. , 2006). On occasion they don’t fall prey to this inertia, it is in particular because the resource advantage that they can survive the disruption (King & Tucci, 2002). Recent research also shows that interorganizational trust can offer a possible explanation for incumbent survival (Obal, 2013). A lot of the earlier executed research focussed on start-up entrants, while innovations introduced by diversifying entrants, in other words already established firms that enter in an industry new to them, are often overlooked (Methé et al (1997). Slater and Mohr (2006) note that the successful commercialization of a disruptive innovation requires a firm not only to develop a break-through innovation but also to reach a mainstream market beyond its niche. When trying to reach this mainstream market a pre-established distribution system becomes a point of competitive advantage (Obal, 2013). In some cases, for example the pharmaceutical industry, incumbents access upcoming firms who may have a technological advantage by creating strategic alliances. In return these starting firms can benefit from the sales and distribution network already in place by the incumbent (Rothaermel, 2001). On the level of business to business relations, trust is a very important concept as it has proven to lead to positive outcomes such as competitive advantage, performance, perceived risk reduction and satisfaction (Pavlou, 2002) (Zaheer, McEvily, & Perrone, 1998). As such incumbents who have already gained trust from their customers have an advantage on more recent entrants who cannot rely on past successes (Doney & Cannon, 1997) (Ganesan, 1994) (Seppanen, Blomqvist, & Sundqvist, 2007). Resources in general have a certain influence on getting a product in the air and on a disruptive track because getting it out there requires time and investment. Empirical research has shown that managers tend to commit insufficient resources to the development of an adequate response if they regard the disruption as an opportunity. This contradicts to when managers view the disruption as a threat, the respective managers end to overreact, allocating significant resources (Clark & Bower, 2002). The focus on resources should not stop at the border of the firm in scope. The technological advantage created should be passed on to their customers so they can receive value from the new product. (Afuah, 2000).


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This value network in which a firm is embedded plays a crucial role in how firms deal with disruptive technologies because the technological change will impact all players in the value chain (Rosenbloom & Christensen, 1994). Failing incumbents are described by Christensen (1997) as firms which lacked the marketing competence. This can be described as the ability to build up new customer competences and to build relationships with customers it has not served yet (Danneels, 2002) . These incumbents often try offering a new product to existing clients who do not always see the achievable added value of it. Further these incumbents lacked the skills to conduct research on possible new markets, to set up new sales and to use the right distribution channel (Christensen & Bower, 1996). MacMilan and McGrath (2000) emphasized that one of the key challenges of emerging technologies is to identify killer applications in early stages. This is done by determining the products attributes created by the new technology and then to find a market which needs can be satisfied by those attributes. In other words find customers who value the new and unique attributes the technology created. As mentioned earlier these customers are often not the existing customers of the incumbent. Therefore Danneels raises the question whether the capacity to survive a technological shift is a function of a firms marketing competence, (Danneels, 2004). Is a wrong marketing policy, inside an incumbent firm, then really at the origin of their decay? In his work Christensen (1997)provides arguments for the case that listening to your customers in the light of disruptive technologies is like signing your own dead warrant. Others go even further saying that the customer is a source of trouble in a smooth running operation (Dwyer & Tanner, 2002). Christensen (1997) pointed out that established firms are in fact being held captive by their current customers and therefore miss the boat on disruptive technologies. In other work together with Bower (1996) it is phrased as companies who listen too carefully to their customers. This interpretation was contradicted by Danneels (2004) for two reasons. First the distinction between current and future customers must be made. Focusing on future customers rather than on current ones, results in a greater degree of radical product innovation (Chandy & Tellis, 1998). Keeping this in mind Danneels (2004) argues that companies should not allocate all their resources to serving current customers. As a second argument against Christensen’s work Danneels highlights the issue that the firms used by Christensen show only a shallow understanding of their customers’ needs (Danneels, 2004). A real customer focused organization should also be able to understand the latent and unexpressed needs of its customers (Slater & Narver, 1998). In the light of inter-industry, comparisons are not fully pertinent; being able to measure the degree of disruptiveness that a new development holds for a given industry is crucial (Kopetzky, et al., 2013). Godvindarajan and Kopalle (2006a) propose an approach whose general methodology for quantifying the disruptiveness of a particular observation addresses both dimensions: ‘low-end’ disruptions and ‘high-end’ (new market) disruptions. When one can establish that a particular observation belongs to one or the other category it would be of further benefit to attempt ex-ante forecasts that either identify future disruptive technologies (Hüsig, Hipp, & Dowling, 2005)or highlight which firms are capable of producing disruptive innovations (Govindarajan & Kopalle, 2006a). In other work of the same


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researchers they published a two stage scale that can be used to compare disruptiveness across markets on a qualitative level (Govindarajan & Kopalle, 2006b) Markides (2006) pointed out the need for making a clear distinction between two theory fields: disruptive technologies and disruptive innovations. For example, both the field of business-model innovation and that of radical product innovation deal with disruptive innovations; however the problems corresponding to these two fields differ drastically. Further comments on Christensen’s framework point at the fact that some aspects of the framework are not only open to unclear definitions but also happen to be entirely hypothetical and fail to offer any real-life solutions (Denning, 2005). Tellis (2006) determined that technologies rarely fit the model of performance increases along technology S-curves, which technologies do not necessarily cross performance thresholds only once and that technology S-curves do not necessarily start below and finish with better performance than former technologies. Together these facts make it according to Tellis (2006) necessary to strongly question the model of disruptive technologies, its vague definition and sampling logic. This critic was countered by Christensen (2006) by unfolding his process of theory building, showing that his sampling is valid and referring to anomalies that do not fit his theory.

2.2.6 Different types of disruptive innovations Baiyere and Salmela (2013)offer a categorization of disruptive innovation by innovation type and by market diffusion. By innovation type they distinguish the following classifications: disruptive technology innovation, disruptive business model innovation and disruptive radical products (Baiyere & Salmela, 2013). The disruptive technology innovations are those whose disruptive tendencies stem from the advancement in the technological component of the innovation (Christensen C. M., 1997) (Markides, 2006). The core of the disruptive business model concept is not the technology but the manner the business model has been employed (Crockett, McGee, & Payne, 2013) (Markides, 2006). In most cases, the business model innovation is at a tangent with the traditional or existing models and gradually results in the eventual disruption of an industry or incumbent organization (Baiyere & Salmela, 2013). Disruptive radical innovations are according to Markides (2006) those innovations that are new-to-the-world that grow in significance to a point that ultimately disrupt an existing product or technology. This category of innovation products are distinctively novel and dissimilar relative to existing products or technologies and they are mostly not demand driven usually tend to have a slow adoption rate (Baiyere & Salmela, 2013).


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By market diffusion they distinguish the following classifications: Low-end Disruption, High-end Disruption and New market Disruption (Baiyere & Salmela, 2013). Low-end Disruptions are the type of disruptions that encroach on an existing market from the bottom of the market. The customers at this point in the market are not considered the most valuable customers by the business. This type of disruptive innovation tends to serve over-served customers by utilizing a new operating or financial approach (Kaltenecker, Hüsig, Hess, & Dowling, 2013). This is the foundational illustration of disruptive innovation as presented by Christensen (1997). High-end Disruption, as would be expected from the name, is the opposite of the Low-end. The innovation that disrupts this market is usually not necessarily cheaper or simpler in comparison with the Low-end (Baiyere & Salmela, 2013). They could be of higher performance and price and yet attract the high paying customers of a market until they gains enough momentum to gradually cause a disruption to an existing market (Baiyere & Salmela, 2013). New market disruption is a unique type of disruption that initially occurs by creating a new market. This could be done by a product or service offering lower performance in traditional attributes if it can provide an improved performance in new attributes. Initially this strategy targets non-consumption, thus customers who historically lacked the money or skill to buy and use the product (Kaltenecker, Hüsig, Hess, & Dowling, 2013). However it could also mean a totally new product or service that no customer had ever thought of before. However, like the other type of market disruptions it also gradually becomes attractive to customers of an existing market. It could gain market share from an existing market from any part of the market high or low end (Baiyere & Salmela, 2013). According to Adner (2012) there is a difference between innovations who fail and those that succeed. He describes the blind spot as failing to see how a company’s success depends on partners who themselves would need to innovate. For successful innovation two things are important: The company’s execution of promises and the partners commitment to the company (Adner, 2012). Thus a company needs an ecosystem supporting the innovation.


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2.3 Enterprise modeling and Business models Enterprise models and business models differ even though they are conceptually relatively close (Osterwalder, Pigneur, & Tucci, 2005). The term enterprise modelling is a collective name for the use of models in enterprise engineering and enterprise operation (Bernus, 2001).Thus, enterprise models are mainly concerned with processes and activities (Wortmann & Hegge, 2001)while business models essentially focus on value creation and customers.

2.3.1 Enterprise modeling An enterprise model (EM) is a computational representation of the structure, activities, processes, information, resources, people, behaviour, goals, and constraints of a business, government, or other enterprise (Fox & Gruniger, 1998). It can also be defined as expressing enterprise knowledge, which adds value to the enterprise or needs to be shared (Vernadet F. , 2002a). From a design perspective, an enterprise model should provide the language used to explicitly define an enterprise (Fox & Gruniger, 1998). The goal of enterprise modelling is to represent or formalize the structure and behaviour of enterprise components and operations in order to understand, engineer or re-engineer, evaluate, optimize and even control the business organization and operations (Vernadet F. , 2002b) (Sandkuhl, Stirna, Persson, & Wibotski, 2014). In scientific literature a lot of business model frameworks exist that aim at facilitating and guiding business modeling. Underneath I will provide an overview of the most relevant of these. Gordijn and Akkermans (2001) present a rigorous conceptual modelling approach for e-business called e3-value. The focus of their e3-value ontology lies on the value creation and exchange within a network of actors. Samavi et al. (2009) defined the strategic business model ontology (SBMO) as a layer on top of the i* goal-oriented modelling framework. The main contribution of the SBMO ontology is its ability to express the overall decision making process by capturing the cooperation and communication between different stakeholders inside and outside a firm (Samavi, Yu, & Topaloglou, 2009). SBMO is one of the two parts that a practical strategic business modelling framework requires: the representation language. The second part is a modelling methodology, that is, a sequence of steps that enable us to build and reason with the states of a business from “as-is” to “to-be” (Samavi, Yu, & Topaloglou, 2009). Burkhart, et al. (2012) designed an ontology consisting of two levels whereby level one focusses on the Meta surroundings of a business model and level two on the core business model ontology. Osterwalder (2004) developed in his PhD thesis business model ontology with the aim to build a foundation for the development of new management tools. In later publications the business model canvas as a management tool came to the forefront (Osterwalder & Pigneur, 2010). In this thesis this business model canvas will be applied to the different cases therefor this model is discussed more into detail in the subsequent chapter 2.3.5.


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2.3.2 General info about Business Models The popularity of the term “business model” is a relatively young phenomenon. Though it appeared for the first time in an academic article in 1957 (Bellman & Clark, 1957) and in the title and abstract of a paper in 1960 (Jones, 1960), the idea behind the concept of a business model was however first expressed by Peter Drucker (1954). However it only rose to prominence towards the end of the 1990s (Osterwalder, Pigneur, & Tucci, 2005) Nowadays most scholars still refer to three main elements expressed by Drucker when talking about a business model: value proposition, value creation and value capture (Drucker, 1954) (Voigt, Bulinga, & Michl, 2017). The value proposition illustrates the potential benefits a company creates for its customers through its products. The value creation discusses the methods used to make and deliver the value proposition to customers (Zott & Amit, 2002) (Chesbrough H. , 2007b) (Abdelkafi, Makhotin, & Posselt, 2013). The value capture defines the conversion of payments by customers into profits (Voigt, Bulinga, & Michl, 2017). In academic literature there is however a lack of consensus around these primary components as some studies also refer to value networks as a primary component (Koen, Bertels, & Elsum, 2011) while others regard this as part of value creation (Abdelkafi, Makhotin, & Posselt, 2013). The term business model is by some equated with the term strategy however this is not entirely correct (Margretta, 2002). Although they are closely related: a business model can be seen as the collection of strategic decisions with respect to scope, governance and competencies. This model should provide answers to the following questions: ‘What will the enterprise do?’, ‘Who is needed to do this’ and ‘ How will this be done’. The motivation for this decision, the why question, can be retrieved in the enterprise strategy (Poels, 2015). However the relationship with strategy formulation is bi-directional. Starting from a strategy a business model can be developed that operationalizes the strategy. Vice versa, starting from a given business model it can be evaluated whether it still operationalizes the current enterprise strategy (Poels, 2015). A business model describes thus the logic of the firm how it creates value and the way it operates (Casadesus-Masanell & Ricart, 2010b). According to Johnson (2010) it consists of a customer value proposition, key resources and processes and a profit formula. Starting from the semantics of the two words, Osterwalder et al. (2005) came up with the following definition: “A business model is a conceptual tool containing a set of objects, concepts and their relationships with the objective to express the business logic of a specific firm. Therefore we must consider which concepts and relationships allow a simplified description and representation of what value is provided to customers, how this is done and with which financial consequences.” Business models help to capture, visualize, understand, communicate and share the business logic (Osterwalder, Pigneur, & Tucci, 2005). The business model concept can also contribute in analysing the business logic of a company. In this way the business model becomes a new unit of analysis (Stähler, 2002). Business models can improve measuring, observing, and comparing the business logic of a company (Osterwalder, Pigneur, & Tucci, 2005)


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A business model can thus serve as a building plan that allows designing and realizing the business structure and system that constitute the company’s operational and physical form (Osterwalder, Pigneur, & Tucci, 2005). The creation of a viable business model remains a challenge for both emerging and existing enterprises. While the first often fails by their inability to define what exactly is their business model, the latter often fails by their inability or unwillingness to change their existing business model (Al-Debei & Avison, 2010) (Casadesus-Masanell & Ricart, 2010a) (Desouza, et al., 2009). After designing a business model, a business plan or business case needs to be developed in order to finance the business model. When financial means are secured, the business model is ready to be implemented. At that point the emphasis shifts from business modelling to enterprise architecture and engineering (Poels, 2015) . A viable business model must thus be aligned with the existing market realities, the expectations of the customer, competitive pressure and off course be suitable from an economic point of view (Braganza, Awazu, & Desouza, 2009) (Christensen C. M., 1997). In the current information technology sector the task of building a decent business model is even tougher than in any other industry, simply because successful products can become superseded at any time by new and improved products (DaSilva, Trkman, Desouza, & Lindic, 2013). However technology in itself has little value as value is generated when technology is commercialized through a business model (Chesbrough & Rosenbloom, 2002). Some studies suggest that success of a product depends as much on the business model innovation as on the underlying technology especially in order to capture the full value from it (Chesbrough H. , 2010).

2.3.3 Business model innovation Long gone are the days when breakthrough technological innovation was considered the only driver of competitiveness (Rayna & Striukova, 2016). Nowadays innovative business models are allowing less technological advanced firms to displace powerful incumbents. Incumbent firms are usually not disrupted by technology per se, but rather by their inability to alter their existing business model (DaSilva, Trkman, Desouza, & Lindic, 2013). Business model innovation concerns the redefinition of existing products or service and how they are provided to customers (Chesbrough H. , 2007a) (Baden-Fuller & Haefliger, 2013). Markides (2006) argues that disruptions to the business model are similar to technological disruptions because both types of change involve potential devaluation of the organization’s key capabilities. It can be seen as one of the few ways left for incumbent companies to protect their margins through business model differentiation (Plantes, 2017). The ability to mobilize and to reorganize dispersed organizational resources play a key role in successfully renewing business models which is in turn also dependent on managements understanding of the need to revise its business model (Khanagha, Volberda, & Oshri, 2014). To explore how the firms in this case study used cloud computing to become disruptive and to compare how different business models based on the same technology yielded different outcomes, the term business models needs to be defined. In the previous section a general definition of this Meta model was already provide (Osterwalder, Pigneur, & Tucci, 2005).


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I could have used the model by Nenonen and Storbacka (2010) they express a business model in terms of five elements or the recently published 360° business model framework (Rayna & Striukova, 2016). However because the business model canvas (Osterwalder & Pigneur, 2010) was deemed more appropriate to compare the business models of the different firms that are discussed in this case study, this model was used.

2.3.4 Business and revenue models used in cloud computing There are many different potential pricing policies available to providers such as, a for free model, a flat fee, freemium, a pay-per-use or a mix of flat and pay-per-use fee (Alford & Morton, 2009).

2.3.4.1 The Pay-as-you-go model The Pay-as-you-go or pay-per-use model is often cited as the revenue model of cloud computing. This model, common for other utility services lets you pay for what you have used instead of a fixed price. For SaaS, the industry already managed to find an acceptable well defined fine-grained charging unit, data size/transfer per Gigabyte (Shadi, Bingsheng, & Hai, 2011). However for PaaS and IaaS the utilized scheme based on virtual machine per hours is subject to unfairness due to possible interference between virtual machines (Wang, et al., 2010) (Shadi, Bingsheng, & Hai, 2011). This unfairness came to the surface because the same task does not always has the same running time (Wang, et al., 2010) (Shadi, Bingsheng, & Hai, 2011). To avoid this unfairness Shadi et al (2011) proposed a pay-as-you-consume model. While this model seemingly reduces the cloud providers’ profit, it urges providers to improve their system design and optimization to provide good services and to gain competitive advantages (Shadi, Bingsheng, & Hai, 2011).

2.3.4.2 The “for free model” Another model that could be used is the “for free model”. A key objective of innovative pricing models is to activate latent demand. To offer utilization of a product or service without being charged seems particularly effective for attracting new customers (Khare, Stewart, & Schatz, 2016). One issue, often pointed out from behavioural economics , is that benefit increases disproportionately during the transition to a zero-price offer (“for free”) (Khare, Stewart, & Schatz, 2016). Ariely (2010) describes this phenomenon as follows: “Zero is almost another world. The difference between two cents and one cent is small, between one cent and zero cents, however, enormous.” One strategy, perfectly executed by Google, is to charge third parties. The first step was to offer an outstanding search engine for free and to generate value to the customer, which led to an enormous flow of traffic (Khare, Stewart, & Schatz, 2016). In 2015, mainly due to advertising, Google earned profits of almost $16 billion after taxes, based on $75 billion revenues (Khare, Stewart, & Schatz, 2016). The same model is also partly used by Youtube to provide free streaming media to the masses.

2.3.4.3 The freemium model Another revenue models that is often used in combination with cloud computing is ‘Freemium’. It comes from the combination of “free” and “premium” (Wilson, 2006).The concept behind this dates back to the eighties when Adobe started to publish software in “light” versions. These did not include all functionalities of the whole product however they were free of charge (Wagner, Benlian, & Hess, 2014) and helped future customers to get to


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know the product. Anderson (2009)describes this revenue model as having a free version that is made available to anyone who wants it in the hope that some user will then choose to upgrade to the premium version. Freemium works on the 5 Percent Rule, where 5% of premium customers support the remaining 95% of free users and also the cost of servicing the 95% is close to zero (Anderson C. , 2009) . Over the last few years freemium has gained popularity and seems to be the long awaited answer to the question of how to earn money from online content as part of a business model. Freemium is a revenue model for internet services that are either free or a paid version that allows access to premium content or features (Veit, et al., 2014). The main concept of freemium is that the paying users finance the non-paying users (Wagner, Benlian, & Hess, 2014).However the use of advertisements to subsidize the free version is also very well assimilated (Anderson C. , 2009) (Dörr, Benlian, Vetter, & Hess, 2010). It is a common practice to use the freemium model in markets with direct networking effects. In these markets free users are of enormous value because they build the critical mass of these networks (Wagner, Benlian, & Hess, 2014). For example in the music business a huge network can increase the value of the service by creating and sharing playlists (Wagner, Benlian, & Hess, 2014). In general these service providers are also interested in high conversion rate. A risk of using this revenue model is customers who keep on freeriding. To reduce the risk of free-riders (Bourreau & Lethiais, 2007), who are generally willing to pay for the service but see no reason to do so, freemium providers could introduce a trial period of sorts (Wagner, Benlian, & Hess, 2014). The freemium revenue model offers both free and paid subscriptions. The free version of the service can be regarded as an advertisement for the premium version with users being persuaded by testing the service and the provider (Wagner, Benlian, & Hess, 2014). An advertisement is in essence aimed at persuading potential customers to buy a product by showing them the product or brands benefits (MacKenzie, Lutz, & Belch, 1986). The main plus-point of the Freemium model is that you can neglect the traditional sales-driven marketing strategy towards the potential customers. They can in fact get to learn by themselves about the benefits of the product by trying it out, before even buying it in doing so the goal is winning their mind share (Balaji, 2016). In addition to this, with the help of the data from your free users’ behaviours, you can easily find out what features of your product are or aren’t their favourites and which segments of the market are getting the most out of your product (Balaji, 2016). Not all business or revenue models suit all businesses alike and choosing the model that works right for a certain business is crucial. The Freemium model is no exception to this rule. Some criteria that should be met before opting for the freemium model are: Is the product offered a high-quality free product which people would want to get their hands on and even better does it solve an immediate need? The cost of duplicating and distributing of this free product should be close to negligible (Balaji, 2016). The basic economics behind this model is that with the advent of SaaS (multi-tenancy), the marginal cost of distributing a software product among a 100 or a 100,000 is nominal. The product will need a large reach where the potential market is huge (Balaji, 2016). The features the free product has, need to be simple enough for the


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customers to educate themselves in order to avoid the need for hands-on training or support from the provider side (Balaji, 2016).The product should preferably promotes repeat usage or even better it should have a certain stickiness (Balaji, 2016). This stickiness is achieved by actually giving everything to your customers and keeping them informed in any kind of way so they become brand evangelists (Templeman, 2014). Another thing that is crucial in applying a freemium model is the balance between the features that a company is offering for free and those requiring a subscription (Vineet, 2014) (Balaji, 2016). If the free features aren’t captivating enough, a company simply will not attract users. If the free features are in abundance, then there’s a good possibility for a company to get the required traffic, but it will fall short in the conversion rate (Vineet, 2014) (Balaji, 2016). Anderson (2009)argues that a company should strive for a 10% conversion rate from free to paid. If the rate falls below that, the cost of serving the freeloaders will make it difficult to make money. On the other hand, a rate above that could signal that a company is offering too little in their free version, which might limit their reach (Balaji, 2016). In order to resolve such issues companies should clearly distinguish the free and the paid plans to help the users see the value in paying more and help them answer the “what’s in it for me?”-question and thus making it more compelling to upgrade (Vineet, 2014) (Balaji, 2016). The baseline is that when using a freemium model the free users should fall into one of these two categories: those who will convert into paid customers or those who will help in acquiring more free customers preferably from the first category (Vineet, 2014) (Balaji, 2016). In his research Vineet (2014) discovered that a free user is typically worth 15% to 25% as much as a premium subscriber, with significant value stemming from referrals and that firms can increase the value of referrals by carefully managing referral incentives and communications. When using a freemium model, it is utterly useful to pay close attention to why and how satisfied users could become brand evangelist and might help your product go viral (Vineet, 2014) (Templeman, 2014). It’s a mistake to see freemium merely as a customer acquisition tool and to drop the free version when new customers stop coming in or when the upgrade rate dives. Users who join late are typically harder to convert; therefore, in order to keep increasing upgrades, you’ll need to keep increasing the value of your premium services. Smart companies view freemium not only as a revenue model but also as a commitment to innovation (Vineet, 2014).


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2.3.4.4 The subscription model The subscriptions model including a yearly or monthly payment does not constitute a truly new form of pricing. Even before the digital age, companies used subscriptions either to generate customer loyalty by offering subscriptions or to achieve a basic utilization of production and marketing capacities (Khare, Stewart, & Schatz, 2016). The underlying price logic of flat price, however, has a significant disadvantage in a non-digital world. Consumption must be limited at all costs. In newspapers and magazines, a given circulation defines this limitation. Corresponding limitations for service branches such as fitness studios or telecommunications would hardly be feasible (Khare, Stewart, & Schatz, 2016). The safety of capacity consumption and/or product use is one advantage of the subscription model while also being an efficient instrument for customer loyalty (Khare, Stewart, & Schatz, 2016). It can be especially targeted at heavy users and users who prefer to have certain price security. At the same time, the model includes an entrance hurdle. Therefore, communicating the key value is one of the main success factors and the customer loyalty is mainly based on a contractual level (Khare, Stewart, & Schatz, 2016). In addition, important elements of emotional and non-rational customer loyalty must be kept in focus and have to be expanded. Finally, there is an ultimate risk for the provider if the consumption growth is stronger than expected, accompanied by an increase in the variable costs (Khare, Stewart, & Schatz, 2016).


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2.3.5 The Business model Canvas The Business model canvas, as shown underneath, was developed by Osterwalder and Pigneur (2010) . It is in fact a conceptual management tool that is part of the Business model Ontology. On the canvas a business model is interpreted as the way in which companies create and capture value or in other words the way the value proposition takes place and how it is monetized (Osterwalder & Pigneur, 2010). The business model canvas is often described as a shared language for describing, visualizing, assessing and changing business models. It is made up by nine building blocks that help focus attention on key attributes of a business and in sum define the business model. These nine building blocks are not a single invention of Osterwalder and Pigneur. In previous research they brought these nine building blocks to the forefront after a synthesis of literature whereby these were at least mentioned by two or more authors (Osterwalder & Pigneur, 2004) (Osterwalder, Pigneur, & Tucci, 2005). Key Partners Who are our Key

Partners? Who are our Key Suppliers? Which Key Resources

are we acquiring from partners? Which Key Activities

do partners perform?

Key Activities What Key Activities do

our Value Propositions require? Our Distribution Channels?

Customer Relationships? Revenue streams?

Value Proposition What value do we

deliver to the customer? Which one of our customer’s problems

are we helping to solve? What bundles of products and services

are we offering to each Customer Segment? Which customer needs

are we satisfying?

Customer

Relationships What type of relationship does each

of our Customer Segments expect us to establish and maintain

with them? Which ones have we established?

How are they integrated with the rest of our business model?

How costly are they?

Customer

Segments For whom are we creating value?

Who are our most important customers?

Key Resources What Key Resources do our Value Propositions require?

Our Distribution

Channels? Customer Relationships?

Revenue Streams?

Channels Through which Channels do our Customer Segments

want to be reached?

How are we reaching them now? How are

our Channels integrated? Which ones work best? Which ones are most

cost-efficient? How are we integrating them with customer

routines?

Cost Structure What are the most important costs inherent in our business

model? Which Key Resources are most expensive? Which Key Activities are most expensive?

Revenue Streams For what value are our customers really willing to pay?

For what do they currently pay? How are they currently paying? How would they prefer to pay?

How much does each Revenue Stream contribute to overall revenues?

Figure 7: The Business Model Canvas (Osterwalder & Pigneur, 2010)

Each of the building blocks contains a part of the total business model. The questions in grey help with pointing out what should or could be filled in. At the center of the model the value proposition is positioned. This not only fulfills the customers demand but also represents the reason why a customer prefers one firm over another (Voigt, Bulinga, & Michl, 2017) and because of its importance it is put central in the canvas. It specifies what is offered (products


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and services) and at what price. The value proposition must be both sustainable for the firm and suitable for the market (Rayna & Striukova, 2016). The three elements on the right side of the schedule describe customer-related aspects: the customer segments or in other words “who are the customers” and the channels. The latter represents “how to reach these customers” both in ways of communication and how to deliver product or value (Osterwalder, Pigneur, & Tucci, 2005) (Abdelkafi, Makhotin, & Posselt, 2013). A channel also has several phases it can go through, depending on what needs to be achieved, going from raising awareness, evaluation, purchase, delivery to after sales. The third one, customer relationships, can be seen as what role a company addresses to its customers, merely as a consumer of service or as a contributor. However the degree of service it wants to provide can also be included here. The underlying motivators here are customer acquisition, retention and upselling. Underneath these three elements the revenue streams are depicted. These represent how the company earns money and to what kind of service or product this is related. Included can be revenues from one time payments or recurring revenues from ongoing payments. On the left side of the schedule three elements are depicted that refer to the internal side of the business model. The key activities ensure a functioning business model. These activities show what a company does in order to create the value proposition, to reach customers and to generate revenue. The key partners are in fact the network on which the firm relies externally. The form of this partnership could take on any form going from strategic alliances to buyer-supplier relationships. The key resources are the internal drivers of the value proposition. Underneath this all the cost structure is located. This is where the company can list all the costs of the above mentioned internal company side of the business model. In an ideal world in order to speak of a “good” business model the value proposition is the perfect balance of the business side and the customers side where the cost of the business side equals the revenue made on the customer side. The fact that all the above described elements are presented on a single-page graphical format makes it easy and practical for practitioners to complete. This is one of the strong points of the canvas, besides the fact that the one page set up makes it a splendid tool for business model representation. Furthermore the business model canvas is stakeholder focused and this makes it valuable for designing and building models where the required actions are intuitively clear (Rayna & Striukova, 2016). On the other side the business model canvas has some downsides making it less applicable for discussing business model innovation as it does not address some of its key drivers associated with value creation, capture and delivery (Rayna & Striukova, 2016). Since the focus of this paper is not in particular on business model innovation per se, these downsides will have no impact on our results due to the usage of the business model canvas.


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2.4 Cloud computing and innovation Cloud computing can be viewed as an innovation in computing as well as a new way of providing IT services (Buyya R. , Yeo, Venugopal, Broberg, & Brandic, 2009) (Rimal, Jukan, Katsaros, & Goeleven, 2011). In fact it has created a whole new market at both industry and firm levels by changing the roles of the traditional computing stakeholders. It also requires regulatory compliance based on the location of the infrastructure of the service provider (Sultan & van de Bunt-Kokhuis, 2012). Cloud computing is not entirely new (Buyya R. , Yeo, Venugopal, Broberg, & Brandic, 2009) nonetheless it fulfils the requirements of being potentially disruptive because of its potential of shaping the information technology for the next few years (Oakey, 2009). In Information technology in specific it is observed that disruptive innovation is often associated with architectural changes (Lyytinen & Rose, 2003) (Surya, Mathew, & Lehner, 2014). One way to describe this is: the act of adopting latent technologies to provide unprecedented user needs (Lyytinen & Rose, 2003) (Christensen & Bower, 1996b) (Abernathy & Clark, 1993). Another way is the reconfiguration of an established system where the core design concepts are re-enforced and by doing so, creating new links between existing components and thus forming a new architecture (Pineda & Izaret, 2013). One of the key requirements of disruptive IT innovation is that it should accompany architectural changes (Lyytinen & Rose, 2003). In the case of cloud computing previously existing technologies like internet and virtualization can be seen as key enablers together with changes in hardware and software components and its interconnections makes cloud architecture distinctly different from previous computing models like grid, cluster, distributed, utility and service computing (Reeves, et al., 2009). Cloud computing can be viewed as an evolution from these previous computing models as the underneath depicted figure shows.

Figure 8: Towards computing organised as a public utility

The above depicted picture shows the development of cloud computing from early utility computing over various related technologies in their estimated time-frames to the recent state-of-art, as well as the usage of internet. The more recent rise of the internet and the web together with the availability of high-speed networks and more powerful computers have


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paved the way of distributed computing leading to the possibility of using networks of computers as a single computing resource (Kopetzky, et al., 2013). Armbrust et al. (2009) describe cloud computing as a new model for a long held vision of computing, as a utility (McCarthy J. , 1962), which now has emerged as a reality. The improvement of technological capabilities allowed for market-oriented cloud computing (Buyya, Yeo, & Venogopal, 2008) combining several advantages of the described technologies such as multitude of services from system infrastructure, e.g. storage services and raw processing as well as applications such as managed exchange and business processes. This is allowing in-house IT-teams to refocus their efforts towards business development (Buyya & Sulistio, 2008)From a pure technical perspective, the driving forces in cloud computing are wireless networks and ubiquity of broadband, progressive improvements in software and falling storage costs (Kopetzky, et al., 2013) Referring to the four value creation drivers in e-Business namely efficiency, novelty, lock-in and complementarity (Amit & Zott, 2001) cloud computing may leverage business value in real world applications with emphasis on start-ups and SME (Mladenow, Fuchs, Dohmen, & Strauss, 2012). It is not only the technological improvements that differentiates the cloud computing model, It is the unprecedented way of offering traditional and new services at reduced risk and cost which have made the innovation a huge challenge for service providers (Surya, Mathew, & Lehner, 2014). Some studies have already suggested that cloud computing could be viewed as a disruptive innovation (Slater & Mohr, 2006) (Krikos, 2011) (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). The disruptive innovation theory as described by Christensen is useful to take aside while studying the phenomenon. The cloud phenomenon involves entrants with new technology and highly innovative business models competing with customer oriented industry leaders who have strong technology competence (Govindarajan & Kopalle, 2006a) (Yu & Hang, 2010). Especially the entry of new players in traditional IT value chain who compete with established IT service providers can be seen as an example of this. Like Amazon who used its existing Data centers with some technological adjustments and offered it as a new product, i.e. Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (Paas), in the mainstream market (Lyer & Henderson, 2010) (Sultan & van de Bunt-Kokhuis, 2012). As mentioned above in conceptual delimitation about cloud computing, cloud has different performance attributes like on-demand IT service provisioning, self-service and elasticity where as the mainstream values improved computing capacity, network capacity, reliable, secured agile resources at reduced cost (Foster, Zhao, Raicu, & Lu, 2008) (Buyya R. , Yeo, Venugopal, Broberg, & Brandic, 2009) (Lyer & Henderson, 2010). Cloud computing initially lacked one or more of this main stream performance attributes like security and reliability (Lyer & Henderson, 2010). As an example one could refer to SaaS that initially provided for personal applications such as Gmail and Twitter, now it includes enterprise level applications such as Salesforce.com, Netsuite or Google apps that indeed confirms that cloud services have invaded mainstream market (Surya, Mathew, & Lehner, 2014). Furthermore cloud also fits the asymmetries of motivation concept, a condition where entrant has the same capabilities as that of an incumbent but introduces new products in a new market that is not valued by the incumbent (Christensen, Anthony, & Roth, 2004). For example one can look at IBM and


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Amazon, both owned datacenters but Amazon initially served only the niche market consisting of customers like SME’s and provided cloud for personal usage like storage of individual files. However later Amazon invaded the mainstream IT market (Reeves, et al., 2009). The first real application of Cloud computing was in fact Salesforce.com who started offering business applications over the internet via their website in 1999, what is now called Software-as-a-Service (SaaS). However Salesforce.com was in this way ahead of the actual cloud foothold that did not start growing until Amazon introduced their Amazon Web Services in 2002. Which was followed by the elastic compute cloud (EC2) in 2006, as a commercial web service providing computing capacity (Surya, Mathew, & Lehner, 2014). The Amazon web service is an example of Platform-as-a-service. Amazon was one of the first to offer a pay-as-you-go service in cloud computing. However with the introduction of Web 2.0, Google and others soon followed (Surya, Mathew, & Lehner, 2014). Some scholars have argued that IaaS should be seen more as sustaining innovation complementary to the current service (Yang, 2011). Such service provides improvements on flexibility however it maintains the same value creating mechanism than used before although it offers a competitive advantage on competition (Yang, 2011). In order to legitimately call cloud computing disruptive, research should prove that cloud computing has the potential to disrupt an established performance trajectory. In their research Kaltenecker et al. (2013) focused on cloud computing technology, in particular the form of SaaS and they found that SaaS changed the trajectory of performance improvement in terms of price and features resulting in ideal conditions for disrupting the whole software market. However it should be highlighted that this research was pointed at the potential and probability of disruption and not at the final result which is disruptiveness. In fact most practitioners and experts agree that cloud computing technology is still at the beginning of its lifecycle (Kaltenecker, Hüsig, Hess, & Dowling, 2013). On its way to disruption, a step that first needs to be taken is that cloud computing as a technology needs to be adopted. In their seminal paper, Davis, Bagozzi and Warshaw (1989) found that perceived usefulness and ease of use are the biggest determinants of a person’s intent to use and adopt a new technology. Another option could be like Marston et al (2011) suggest is the development of a “divide-and-conquer” approach whereby potential customers can be enticed to try some of the novel characteristics of the cloud-based application, with the hope of increasing familiarity would lead to greater acceptance.


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Another theory that helps in predicting the adoption of a new innovation is the diffusion of innovation theory (Rogers, 1995). As depicted below in figure 9 one can see a combination of two graphs. The first one is the S-curve with provides us with insides on the saturation level of the product in question. The second one, a normal distribution, shows the successive groups of consumers adopting the new technology. By combining these two, one can truly get insights on the adoption level of a product.

Figure 9: Diffusion of innovations (Rogers, 1995)

The diffusion theory further discusses five variables that influence technology adoption: perceived attributes of innovation, type of innovation decision, communication channels, nature of social system and change agents promotion efforts (Rogers, 1995). The perceived attributes of innovation are by far the most important predictors of innovation adoption intentions of possible users (Rogers, 1995). The relative advantage is the degree to which using an innovation is perceived to make one better than when not using it (Lin & Chen, 2012). For cloud computing the relative advantages include: capital costs, capacity, agility of implementation, reliability, compatibility, ease of use and flexibility (Grossman, 2009) (Leavitt, 2009) (Melvin & Greer, 2009) (Miller M. , 2008). Other scholars also mention compatibility, trialability and observability as predictors for adoption of innovations (Teo, Tan, & Wei, 1995) (Lin & Chen, 2012). However because disruptive technologies tend to displace existing technologies, even though they might not have reached their diffusion target, a second S-curve that radically changes the diffusion curve of the former technology starts (Kaltenecker, Hüsig, Hess, & Dowling, 2013).


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3 Part III: Research question 3.1 Research question “How can Enterprise Modelling help in analysing the impact of Cloud Computing on business and operating models?” Sub question: Is Cloud Computing a sustaining or a disruptive technology? The above mentioned main research questions served as guidance through the whole process of researching, reviewing and writing. In order to provide a decent answer to the main research question the main research question was further broken down in to the following questions:

How does cloud computing impact business models?

How can Enterprise modelling help in analysing this impact?

Together with the above already formulated sub question this meant that in this thesis I will try to provide an answer on these three questions all together and by doing so offering an answer to the main research question that served as an initiator for this thesis.


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4 Part IV: Methodology

4.1 Research design As mentioned in the previous chapters, a good literature review is of paramount importance for the further empirical work that needs to be done. Almost as important as this solid literature review is the research question which obviously has an influence on the conducted research. It serves as a guideline.

4.1.1 The choice of the research strategy In scientific research one can use different research strategies, methodologies and techniques. In order to avoid mixing up the terms I will immediately refer to the definition of ‘t Hart et al (1998) to clarify the different activities. A strategy can be described as an overarching logic within the research. Within this strategy one can use several methods; these are ways or manners to gather data. Techniques imply how data will be analyzed (Van Thiel, 2007). For this master thesis I have chosen to use the case study as a research strategy. This way of conducting research is well established in the information system literature (Tsang, 2014) and a preferred way of investigating real life phenomena beyond the control of the researcher (Yin, 2014). According to Yin (2014) there are three conditions that need to be met before we can speak of a case study research. As a starter the research question at the base of the research should be a ‘How’ or a ‘Why’ question. The question that is the subtitle of this thesis is such a question: “How can Enterprise Modelling help in analysing the impact of Cloud Computing on business and operating models?” there for the first condition is met. A second condition is that as a researcher you are not in control of the events (Yin, 2014). Since the case studies that will be discussed are all been looked into on an ex post basis there will be no amount of control for the researcher. With ex post is meant that all cases are discussed after they showed their breakthrough or their disruptive nature. A Final and third condition is that a case study must focus on a contemporary problem (Yin, 2014). In this research we focus on a couple of applications using cloud technology and by doing so these where able to change or create the market in their own separate sector. The research fulfils all of these conditions of a case study as outlined by Yin (2014). To meet these criteria is one thing, a formal and good execution of this is what we intend. The same author also provided five components which are crucial for the good execution of a case study. Identical as the requirements that should be met in order to speak of a true case study is the importance of the research question. In a case study research this question should start with ‘How’ or ‘Why’ (Yin, 2014). As shown above our research question is in line with this requirement. A second component handles the hypotheses. In this thesis I choose not to use hypotheses although this is a requirement as described by Yin. However this does not undermines the value of the research that was executed based on the existing literature. Instead of being led by hypothesis and testing these I used the sub questions to form an answer on the main research questions as a guideline for the investigations needed in this thesis. A third component treats the unit of analysis of the case. Hereby is meant that that


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case or the subject should be clearly delimited. In this thesis four cases will be investigated in particular: Netflix, Salesforce, Spotify and Dropbox. A fourth element addresses the link between the gathered data and the sub questions. To achieve this, different techniques can be used. In this research the technique of pattern-matching will be used. What means as much that I, as a researcher, will verify the sub questions answers for they occur in the real word (Yin, 2014). The last element in order to rightfully speak of a case study is the criterion that will be used to interpret the results. As mentioned by Yin (2014) this is one of the most difficult things to do. Because it is so difficult to do I decided to use a combination of criteria and frameworks used in previous academic research by Hang et al (2011). In this research we will investigate a selected group whom are possible candidates to be labeled as disruptive innovations using cloud computing technology. Gibbert and Ruigrok (2010) suggest that best practice case studies report the research actions taken to ensure methodological discipline. This process is also called “talk the walk”. In this study the technique of talking the reader through the applied procedures will be used. In this walk research setting, data selection, data collection and data analysis will be included (Pratt, 2008) (Kaltenecker, Hess, & Huesig, 2015). This case study is what one might call instrumental because the cases selected will advance my understanding of business models of firms with a disruptive nature by using cloud computing technology (Yin, 2014) (Keil, 2002). Multiple cases are used to ensure a reliable data analysis and a general understanding of the overall case context.

4.1.2 Case selection The cases that are being researched in this thesis are all examples of cases that at first sight can be seen as being innovative. The goal of this research is to determine whether or whether not they can be seen as truly a disruptive innovation. Another goal is to get a closer view on their respective business models. The cases selected are in random order: Netflix, Salesforce, Spotify and Dropbox. The reason for selecting these cases in particular is that they have all succeeded in reaching a market that was previously not there or that was served by other incumbent firms. A second reason is that all these firms use cloud technology in order to serve their customers or deliver their product. A third reason is that these firms are all relatively young. This is important because in the disruptive innovation framework disruption is mostly expected from new entrants or existing firms who differentiate to a new market and can be seen as new for that specific market. As mentioned before the idea in this thesis is to extensively investigate the four cases as mentioned in order to draw conclusions on how cloud computing as a technology helped these firms in becoming a disruptive innovative firm. A first step that needs to be taken is to ascertain that the selected cases can be seen as disruptive firms. This evaluation is described at the end of respectively each discussion about the individual cases. The second step


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4.1.3 Data selection A first step that was taken for the gathering of information about the subject was an extensive research on the web of science database, on Google scholar and the general Google search engine. Starting from the basic terms that can be found in the research question several key words could be derived and this led me to start looking for articles that could be useful for the writing of this thesis. The results of this research are reflected in the structure of the literature review in part II. Basic search terms included: Cloud , Cloud computing, Disruptive Cloud computing, Disruptive innovation, Disruptive business models, innovation, Incumbents survival, Business model canvas, Enterprise modeling, Netflix history, Netflix cloud computing, Netflix business model, innovation Netflix Spotify history, Spotify cloud computing, Spotify business model, innovation Spotify, Salesforce history, Salesforce Cloud Computing, Salesforce business model, innovation Salesforce, Dropbox history, Dropbox Cloud Computing, Dropbox business model, innovation Dropbox. When interesting academic articles where found the basic way to go was to read through the abstract and key words to check if these would be interesting enough to fully examine. If yes I would save them on my personal Dropbox account and if not they were disregarded.

4.1.4 Data analysis The data analysis that was performed in the light of this thesis was organized as follows: A big part of the thesis consisted of literature review however also four case studies were discussed. To conduct these case studies in decent manner the underneath shown way of work was used. According to Huberman and Miles the researcher should begin with the within-case analysis. This is also shown in the underneath figure and referred to as write individual case report. These reports form than later on the basis for cross case analysis and conclusions. a most similar system design the underneath shown procedure is used.:

Figure 6: Procedure case study (Yin, Case study research: design and methods, 2014)


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4.1.5 Applied frameworks The framework created by Hang, Cheng and Yu (2011) consists of three main parts: Marketing position, technology and other drivers Christensen and Raynor (2003) have focused on two appropriate market segments to create an initial foothold for a disruptive business. One is the low-end market which offers a basic product that does the job for a lower price. The other is a new, niche market in which the disruptive product has a good enough performance for the non-consumers (Hang, Chen, & Yu, 2011). The main difference between these two is that developments in one or the other have a different effect on incumbents. In case of the new market, the incumbent would most likely ignore the new entrants as their existing business is not initially threatened. In the other case incumbents have two options, move up to a higher segment in line with the theory that draws on the asymmetry of motivation or go for a head-on encounter for which they often have enough resources (Hang, Chen, & Yu, 2011). On the technology part a difference can be observed between the disruptive one and the sustaining one. The former technology might be not meeting the demand of the mainstream market, it has certain features that are attractive to low-end or new market consumers who were previously non-consumers (Hang, Chen, & Yu, 2011). If the disruptive innovation can be further developed by adding a R&D dimension it can improve its features to meet mainstream market demands. Other significant drivers that can influence the pace of innovation over time are: lifestyle change, legislation change, network effects (Gaullaugher & Wang, 2002) (Wagner, Benlian, & Hess, 2014), bottom-of-the-pyramid opportunities (Prahalad, 2004), the growing importance of developing countries with their corresponding growing markets and the greying population which has a known effect on innovation (Drucker, 1985) (Osterwalder A. , 2007). It is indeed not a simple and straightforward exercise to complete the assessment form however once the form is completed, it is easy to make assessments as follows:

If all the answers are ‘‘yes’’, the framework indicates that both low-end and new market

disruptions are progressing simultaneously.

If all the answers are ‘‘yes’’, with only two ‘‘no’’ being ticked for low-end market (in market

positioning and technology), then the framework indicates that a new market disruption is

on its way. On the contrary, if two ‘‘no’’ are ticked for new market, then it indicates that a

low-end disruption is on its way.

If there are other ticks of ‘‘no’’, the framework indicates that there exist doubts about the

eventual success of the disruption.


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Question Yes No

A. Market positioning

Viable business in the low-end market

Viable business in a new, niche market

are incumbents in the main-stream market willing to run away or ignore the initial disruptors?

B. Technology

There exists a performance overshoot in the main-stream market

Adequate for a foothold in the low-end market

Adequate for a foothold in a new, niche market

Could be further improved in performance, price/ performance, etc

R&D needed to improve the disruptive technology is feasible, affordable and well executed

C. Other Favourable Drivers

Table 1: Proposed assessment framework (Hang, Chen, & Yu, 2011)

In order to be able to fill out the above shown table in a correct manner, an in-depth study of the case that needs to be assessed is needed in order to answer “yes” or “no” with sufficient confidence. For each of the cases selected this in-depth study has been fully executed. For all the cases this was done according the same logic. First some general info is provided followed by an explanation on the usage of the cloud computing technology in this specific case. The third and the fourth part of the case study handles the business model of the firm in question, first a general approach that is followed by an application of the business model canvas. The last part of the discussion of each consecutive case consists of the application of the above described model of Hang, Chen and Yu (2011). The reason why this framework is chosen is that up to now in literature there is an ongoing discussion about the theoretical framework as initiated by Christensen (1997). Danneels (2004) for example criticised specifically the fact that only ex post verifications were done and no ex ante framework was used. The value of the framework used is that it can be further developed into a systematic tool for answering the question whether the disruptive innovation theory indeed could be used to provide ex ante predicting of the success of a new disruptive innovation (Hang, Chen, & Yu, 2011). In order to verify if this framework it is used in this thesis on four cases that, to our knowledge, previously have not been researched in the light of the disruptive innovation theory. Because of the crucial value of determining whether or whether not a firm is disruptive in the light of this thesis and as a double check of the framework used, a cross examination is also executed by applying another framework. The framework used for cross validation is explained below.


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The second framework for ex ante identification of disruptive innovations that will be used for cross validation is the framework designed by Keller and Hüsig (2009). It is based on the following approaches: the disruptive threats for incumbents (Rafii & Kampas, 2002), industry change due to innovation (Christensen, Anthony, & Roth, 2004) and the disruptive potential of technology (Hüsig, Hipp, & Dowling, 2005). Based on these previously developed approaches Keller and Hüsig (2009) developed two criteria sheets for what indicates a disruptive innovation as well as a trajectory map of the technologies performance attributes. They respectively developed a criteria sheet for entrants as well as for incumbents. A rating system for these criteria was used as follows: fulfilled/not-fulfilled/unknown. Further they distinguish three phases: foothold market entry, main market entry, failure of incumbents. By doing so, they give credit to the dynamic character of the disruption and help to assess the potential threat in advance (Keller & Hüsig, 2009). All criteria are noted in their disruption-positive form. That is, if the criterion is fulfilled, a disruption is more likely to occur Below the criteria sheet is displayed, if the criterion is fulfilled, a disruption is more likely to occur.

entrant

Phase Criterion Fulfilled Not fulfilled Unknown

Foothold market entry

Products perform worse based on established attributes

Products are cheaper, simpler, more comfortable or more reliable

Products address current non-consumers

Profitable business model targeting over-satisfied customers

Investors allow experimentation

Phase total

Main market entry

Products are based on standard components

Strategic resources (licenses, capital, etc.) are accessible

Network for Potential disruptive innovation (PDI) is expected to be large

Potential disruptive innovation (PDI) is compatible with existing network

Phase total

Failure of incumbent

Business model is significantly different

Processes are significantly different

Value network has a low overlap

Phase total

Total Table 2: Criteria sheet entrant (Keller & Hüsig, 2009)


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incumbent



Some customers are over-satisfied

Main customer segment does not appreciate entrants products

Market for products based on Potential disruptive innovation (PDI) appears small and irrelevant

Phase total

Main market entry

Established performance attributes are shifting

Customers are unwilling to pay for further improvements along established attributes

Switching costs are low

Coordination costs are low

Phase total


Products matching entrant's offer are not added

Incumbent is fleeing to premium customer segments

Potential disruptive innovation (PDI) is not implemented in separate organization

Phase total

Total Table 3: Criteria sheet incumbent (Keller & Hüsig, 2009)

The second part of the methodology created by Keller and Hüsig (2009) consists of trajectory maps. Ideally a disruptive trajectory moves from disruptive technology intersects the lower market demand, incumbent technology’s performance overshoots market demand, price trajectory of disruptive technology intersects the price trajectory of established technology from above or always stays below (Keller & Hüsig, 2009). Trajectory maps are considered very useful for the ex-ante analysis of a potentially disruptive innovation (Danneels, 2004) (Hüsig, Hipp, & Dowling, 2005). A trajectory map tracks the performance of the existing technology, the new technology and market demand along established performance attributes. A disruption can only occur, if the new technology is capable of meeting performance demanded in the mainstream market (Christensen C. M., 1997). Price trajectories are an enhancement of this concept. They are based on Adner's (2002) demand-based view of disruptions. The disruptive potential is increased, if the disruptive technology offers a smaller unit price (Adner, 2002). The first part of framework of Keller and Hüsig (2009) will be used to cross validate the results and insights gained by using the framework of Hang, Chen and Yu (2011).


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Once the framework of Hang, Chen and Yu (2011) is applied, the goal is to compare the business models of the different firms. To facilitate this, a business model canvas for each of these companies has been put together.

4.2 Validity and reliability When conducting a case study one needs to take into account four possible problems which can occur while using this way of working. These possible issues are construct validity, internal validity, external validity and reliability.

4.2.1 Construct validity The main issue here is that the observed measurements are indeed an actual indication for the concept whereof you want to make a statement. A critic that is often heard against the use of case studies is that researchers do not succeed in collecting an operational valid mix of measurements and that often subjective judgements are used to collect data. To counter this Yin (2014) sums up some actions like the use of several sources of evidence, the use of a member check or the use of key informants who read the case study report, formulate comments and focus especially on the chain of evidence. Because this master thesis was written on my own, meaning it was not a duo master thesis, I did not have the luxury to use the member check option. However this should not be something insurmountable. However to make sure that no issues could arise on a construct validity level I used several different sources where most of them have been reviewed by many other researches before I used them.

4.2.2 Internal validity Internal Validity is the approximate truth about inferences regarding cause-effect or causal relationships (Trochim, 2006). All that internal validity means is that you have evidence that what you did in the study (i.e., the program) caused what you observed (i.e., the outcome) to happen (Trochim, 2006)..

4.2.3 External validity The term external validity is used to determine to what extent the findings about one case can be generalized. The main issue here is caused by too few units of research (Yin, 2014). One way to bypass this sort of trouble is triangulation. In this research this is done by using different sources, where some have been reviewed before being published into international scientific journals, if not they are double checked on accuracy. Analog to the techniques used by other scholars on previously executed research, I searched for qualitative as well as quantitative data from multiple sources. Another technique was described by Yin in earlier published work (1994) where he discussed the usage of sub units within each case. This is often called a layered design. In this study this is also done in this way: in total I discussed four cases. All these cases together compose the case of a firm using cloud computing in a disruptive way


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4.2.4 Reliability The term reliability is used in this case to guarantee that the research and the way of data collection can be replicated. The reliability of a research is subject to two factors: the accuracy and the consistency whereby variables are measured (Van Thiel, 2007). In order to achieve this as good as possible my intent is to document the evaluations made as transparently as possible in the text and by the usage of public data sources.


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5 Part V: Discussion different cases In this chapter each of the aforementioned cases will be discussed in depth. For each case the same structure is used. Starting with some general info followed by how this firm uses cloud computing. This is followed by a general section on the business model and a discussion on the business model using the business model canvas. After this a section discussing the impact of cloud computing on the business model as applied in the business model canvas follows and the concluding section handles the question: does the case meet the disruptive criteria.

5.1 Netflix 5.1.1 General info

5.1.1.1 The pre Cloud years Netflix was launched by Reed Hastings and Marc Randolph on August 29, 1997. They both admired the concept of e-commerce like Amazon and wanted to find a large category of portable items to sell over the internet using a similar model (Keating, 2012). Netflix opened its first distribution facility in 1998, when DVD players where still sold for $700 each. So this was a small market, hardly even served. The small number of household DVD players held Netflix back initially (O'Brien, 2002). At first they did consider the VHS tape rental market; however this was too expensive to stock and too delicate to ship. Although this would have been a greater market to address they chose to go for DVDs, which were available in only a few markets in 1997, they tested the concept of selling or renting DVDs by mail by mailing a compact disc to Hastings' house in Santa Cruz. When the disc arrived intact, they decided to take on the $16 billion home video sales and rental industry (Keating, 2012). Over time the price for DVD players dropped , by Christmas 1998, DVD players fell below $200 a unit and became “the fastest-selling-single-electronic product” in history (Ebersole, 2013) and the popularity of the DVD player rose in that manner that by 2006 it had surpassed the VCR (CNN Money, 2006). At that time when Netflix just started the market of renting videos in the United States was dominated by Blockbuster. This is a firm who owned a large number of physical assets and used the physical landlord business model. This model is based on the following logic. A Landlord sells the right to use, but not to own, an asset for a specified period of time. (Weill, Malone, D'Urso, Herman, & Woerner, 2005). Netflix was in fact already a disrupter from the start by going beyond the traditional bricks and mortar rental store and the use of the pay-per-rent model (see further down). For its most popular service, Netflix charged users $17.99 a month for an unlimited number of DVD rentals. Titles could be ordered via the company's website and were dispatched overnight. Customers could then mail them back in a pre-paid envelope, which releases the next movie on a personalised list of films to see on the Netflix website. Subscribers could have up to three DVDs out at any time. Needless to say, there were no late fees as with Blockbuster’s model (The Economist, 2005). From the beginning on, the website of Netflix played a crucial role. With their website they aimed to create an interface that offered a unique experience for each subscriber tailored to individual tastes and preferences and it had to be easy to use. The website had three main


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“intelligent” features distinguishing the Netflix interface that allowed user interaction and customization: the FlixFinder, the Cinematch, and the Queue. The FlixFinder was a search engine that helped in finding the correct title, the Cinematch gave suggestions customers would also like based on ratings and the Queue was the list a customer could put together individually and offered serialized delivery that mailed the movies at the top of their Queue automatically (Keating, 2012). An important function of Cinematch was that it led customers deep into the catalogue and away from newly released titles and guiding people to films that were in stock, as opposed to showing them everything that was available (Keating, 2012). Another important item that helped pave the way for Netflix was their ability to amass an inventory that goes far beyond what any high street operation can hope to sustain. Netflix has what some people fashionably call a “long tail” business. Its catalogue of more than 45,000 titles means that it can cater to almost any interest instead of just focussing on best sellers as the likes of Blockbuster (Noren, 2013).

Over the years Netflix’ popularity rose and at the peak they used more than 35 distribution centres within metropolitan markets to help and meet the aim of overnight delivery. Yet when online delivery of movies, in whatever form it takes, eventually starts to take off, that legacy infrastructure will count for nothing: as warehouses will be replaced by huge computer servers that can be based anywhere (The Economist, 2005). The impending death of Netflix, the company with its online system for renting DVDs delivered by mail, was already predicted in late 2002, when Wal-Mart said it would enter the business. Then again in 2006, when Apple and Amazon announced movie-downloading services and again in 2007, after the introduction of a series of products and services intended to bring Internet video to television sets (Helft, 2007) (Anderson N. , 2007). It was expected that after a while overnight delivery was not going to be fast enough anymore for a video on demand provider. However in 2007 Netflix reached the milestone of sending out the billionth DVD (The Victoria Advocate, 2007)

5.1.1.2 Streaming via the cloud In 2007 Netflix started introducing a service to deliver movies and television shows directly to users’ PCs, not as downloads but as streaming video, which is not retained in computer memory (Helft, 2007) (Anderson N. , 2007). The service, which at the beginning was free to Netflix subscribers, was meant to give the company a toehold in the embryonic world of

Internet movie distribution (Helft, 2007). By letting their customer base have a free taste of this type of service, they created a customer segment that liked this or at least got used to this new kind of service. At the time of the launch of this new kind of service Netflix knew that what they actually wanted was not technologically possible just yet, because it was back then still difficult to deliver various Internet video formats to a TV screen (Helft, 2007). Mainstream consumer adoption of this new introduced online movie watching will take a number of years due to content and technology hurdles, the time is right for Netflix to take the first step," said Netflix CEO Reed Hastings at that time (Anderson N. , 2007). Over the coming years Netflix will expand their selection of films (Anderson N. , 2007). In the following years a lot of new devices came out that were able to connect to the net and thus could be used for streaming. This multi-platform streaming changed how, when, and where consumers watched media, allowing them to tune-in and turn-on whenever and wherever, piping movies through broadband fibre optics across high-speed DLS data lines or digital subscriber lines (Ebersole, 2013). What once


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was a medium dominated by a person in the domestic space (typically a male) who had the “power of the dial,” now can be watched across a wide array of screens in multiple geographical locations (Spigel, 2013). By 2006, around “50 percent of U.S. households had access to broadband service and those that had fast Internet access still had to wait a couple hours to download a near DVD-quality movie onto a laptop or computer” (Keating, 2012). As the speed of broadband was always increasing global digital movie piracy threatened studio profits, the industry sought a legal alternative for offering instantaneous digital content. The alternative was Netflix (Keating, 2012). There were also many benefits of digital delivery over physical media. Other than cutting send-by-mail costs and spending less on distribution warehouses, digital media was the “holy grail” of customer feedback (Keating, 2012). By using this way of delivering content Netflix can track in real-time input about what customers thought about the movies they watched, based on how they behaved as they watched them. The system watched viewers as they screened films, noting the scenes where they stopped and rewound, how long it took them to abandon a film they didn’t like, where they paused, what scenes they skipped (Keating, 2012). According to industry magazine TechCrunch, Netflix spends approximately $2 billion a year primarily on licensed content (Kumparak, 2013). Yet, these licensing deals have been expiring and media providers started teaming up to rival Netflix. Other contract prices started ballooning (Ebersole, 2013) and HBO and Universal signed a renewed deal in 2013 to withhold their content from Netflix for ten years (Isaacson, 2013). So at a company low point and as licensing deals expired, Netflix needed to find a new way to boost subscribers. With access to the large collection of Internet data, the moment had come for Netflix to move from a content distributor to a content creator. This is where ‘House Of Cards’ enters the picture (Ebersole, 2013). Original production is one means for channels to assert their brand and extend their revenue streams. House of Cards presented the Netflix brand as “hip yet serious and outside the mainstream … the top underdog brand, cool and mighty” (Keating, 2012). This brand is similar to that of HBO, as it was the first to start asserting its brand in the world of quality television production. These branding strategies are important for Netflix as they provide a way for it to distinguish itself from other major digital entertainment production and streaming competitors such as Hulu PLUS, Amazon Instant Video, iTunes, YouTube, HBO GO, and now Redbox instant (Ebersole, 2013). The creation of original content like House of Cards caused a ripple across the digital television world as other such digital content distributors mirrored this business strategy. Netflix’s massive amounts of data spit out information on its audience’s favourite actors, genres, and directors. Netflix used this data to produce a series, House of Cards, that could mathematically be guaranteed to be a success (Ebersole, 2013).


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5.1.2 How Netflix uses Cloud computing technology The journey to the cloud for Netflix began in August of 2008, when they experienced a major database corruption and could not ship DVDs to our members for three days to our members (Izrailevsky, Vlaovic, & Meshenberg, 2016). After this security breach Netflix decided to stop using its relational databases in their datacentre and would move towards highly reliable, horizontally scalable, distributed systems in the cloud (Izrailevsky, Vlaovic, & Meshenberg, 2016). When companies require massive technology resources, they have two choices: Build it in-house, or outsource it. And if they outsource it, there are only so many places where companies the size of Netflix can get instant access to the hundreds and thousands of servers it uses every day. Amazon is one of those. Netflix chose Amazon Web Services (AWS) as cloud provider because it provided them with the greatest scale and the broadest set of services and features. The majority of their systems, including all customer-facing services, had been migrated to the cloud prior to 2015 (Izrailevsky, Vlaovic, & Meshenberg, 2016). The reason this migration took so long is because Netflix chose a cloud-native approach, rebuilding virtually all of their technology and fundamentally changing the way they operated the company. Architecturally Netflix migrated from a monolithic app to hundreds of micro-services (Izrailevsky, Vlaovic, & Meshenberg, 2016). But by providing the massive infrastructure resources, Amazon is in fact enabling its competition (Butler, 2013). “The fact is: Amazon’s making money off Netflix.” And Netflix is running its business on Amazon infrastructure. Welcome to the newfound world of cloud computing that blurs the lines of business relationships (Butler, 2013) Just like Netflix doesn’t own power plants to produce its own electricity, its senior executives don’t want to own the data centres to run the company if it can be just bought online. Building out data centre infrastructure is not where the company can create competitive advantages, like Amazon’s streaming business. Hardware resources are becoming commoditized, virtual machines are spun up and down in seconds, and anyone with a hypervisor can do it. Where Netflix can make its hay is in creating original content. (Butler, 2013) The Video streaming is served out of multiple content delivery networks (CDNs), UltraDNS which ensures 100 % website availability and a public DNS service is used as its authoritative DNS servers. The end result is that Netflix manages to build its internet video delivery service with little infrastructure it actually owns (Adhikari, et al., 2012).


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The underneath shown picture (figure no 10) shows the basic architecture for the Netflix video streaming platform. It consists of four key components: the Netflix data centre, the Amazon cloud, the content delivery networks (CDNs) and the HTML5 video extensions (Adhikari, et al., 2012) (Keizer, 2013).

Figure 10: Netflix’ cloud architecture (Adhikari, et al., 2012).

Adhikari et al (2012) revealed in their analysis that Netflix uses its own IP address space for the hostname “www.netflix.com”. The Netflix data centre primarily handles two key functions: (a) registration of new user accounts and capture of payment information (credit card or Paypal account) and (b) redirect users to movies.netflix.com or signup.netflix.com based on whether the user is logged in or not. This server does not interact with the client during the movie playback. Except for “www.netflix.com” which is hosted by Netflix, most of the other Netflix servers such as “agmoviecontrol.netflix.com” and “movies.netflix.com” are served off the Amazon cloud. Cockcroft (2011) indicated that Netflix uses various Amazon cloud services, ranging from EC2 and S3, to SimpleDB and virtual private cloud. Key functions, such as content ingestion, log recording/analysis, DRM, CDN routing, user sign-in, and mobile device support, are all done in Amazon cloud (Adhikari, et al., 2012).. Netflix employs multiple CDNs to deliver the video content to end users. The encoded and DRM protected videos are sourced in Amazon cloud and copied to CDNs. Netflix employs three CDNs: Akamai, LimeLight, and Level-3. For the same video with the same quality level, the same encoded content is delivered from all three CDNs (Adhikari, et al., 2012).. Netflix used to use Silverlight to download, decode and play Netflix movies on desktop web browsers. The run-time environment for Silverlight used to be available as a plug-in for most web browsers (Adhikari, et al., 2012). However because Microsoft announced the end of


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Silverlight 5 by 2021 and it never publicly committed to a Silverlight 6 Netflix switched to a trio of HTML 5 extensions (Keizer, 2013). The switch to HTML5 was not unexpected. Netflix already relies on HTML5 to stream video through its mobile apps, such as the one for iOS on the iPhone and iPad (Keizer, 2013). The HTML 5 video extension consists of three types: Media Source Extensions (MSE), Encrypted Media Extensions (EME) and Web Cryptography API (WebCrypto) (Park & Watson, 2013). The MSE specification allows a JavaScript to generate media streams for playback (Park & Watson, 2013). The EME specification provides APIs to control playback of protected content. The video content Netflix streams to customers is protected with Digital Rights Management (DRM). This is a requirement for any premium subscription video service. The Encrypted Media Extensions allow Netflix to play protected video content in the browser by providing a standardized way for DRM systems to be used with the media element (Park & Watson, 2013). The WebCrypto specification defines an API for basic cryptographic operations in web applications, such as hashing, signature generation and verification, and encryption and decryption. This API allows Netflix to encrypt and decrypt communication between the JavaScript and the Netflix servers. This is required to protect user data from inspection and tampering and allows Netflix to provide a subscription video service on the web (Park & Watson, 2013). Netflix uses the DASH (Dynamic Streaming over HTTP) protocol for streaming which is enabled by the HTML5 media source extensions (Lederer, 2015). In DASH, each video is encoded at several different quality levels, and is divided into small ‘chunks’ , video segments of no more than a few seconds in length (Adhikari, et al., 2012). The client requests one video chunk at a time via HTTP. With each download, it measures the received bandwidth and runs a rate determination algorithm to determine the quality of the next chunk to request. DASH allows the player to freely switch between different quality levels at the chunk boundaries (Adhikari, et al., 2012).

5.1.3 Business model of Netflix explained Netflix gained its initial competitive advantage by combining existing models like the physical landlord model with the subscription business model and the all-you-can-eat business model to allow customers to rent all the DVDs they could in a month for a flat fee (Noren, 2013). Customers were allowed to keep the DVDs for an unlimited period with a maximum of three at a time and in return the agreed to pay a recurring monthly subscription fee. The delivery by mail tackled one of the issues from the physical landlord model (Noren, 2013). The all-you-can-eat model deleted the need and use of returning in late fees and the monthly subscription provided a consistent cash flow for the company (Noren, 2013). Before Netflix no firm had ever used the technique of asking a subscription fee for the videos on demand service (Noren, 2013). Today, inspired by this success, many services are adopting this model. You can find everything from opera to BBC classic shows available online in exchange for a subscription fee. However, Netflix, the video on demand giant is still dominating the market, with more than 89% of total shows streamed online during the first quarter of 2013 coming from this service alone (Radak, 2016). Competitors for hosting and buying rights of content are Hulu (other business model, still shows adds every 20 minutes) and Amazon prime. Netflix had a revenue of $6.7 billion in 2015 so no problem with the operational Cash flow. Netflix offers three types of streaming membership plans, starting from 7.99 euro per month


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in Belgium for the Basic version, 9.99 euro for the standard version and 11.99 euro for the premium (Netflix, 2017). The main difference between the different versions are the amount of screens that can be used at the same time and that only in the premium version ultra HD is available (Netflix, 2017). For each of those subscriptions a free trial period of one month is possible to get to familiar with the product. However Netflix needs a lot of money for buying the licensing rights of movie titles, television series and also marketing expenses (Unicorn Economy, 2016b). The basics of their business model are quite simple: Buy or license the commercial rights to broadcast or stream content from production houses which are the real owners of the content (Unicorn Economy, 2016b). Prepare and maintain a robust platform to broadcast these pieces of content. Attract as many users as possible to view this content and get used to the product by using a trial version (Unicorn Economy, 2016b). Convert these free users to paid recurring subscribers and charge a monthly fee for viewing the content. Maintain this subscriber base by continuously buying new and fresh content or by releasing original content (Unicorn Economy, 2016b).

5.1.4 The Business Model Canvas of Netflix In the previous section I tried to provide a general overview about the characteristics of the business model that is used by Netflix from the beginning on and how and why this model was applied. In this section I want to dissect the model further and link it to the business model canvas in order to make it more comparable to the other cases in this research.

5.1.4.1 Key Activities Netflix’ video-on-demand offer relies on two key activities: providing content and improving the platform. Netflix provides value via its own website, which acts as a content delivery platform. The company dedicates effort for improving audio and video quality, reducing re-buffering times and ensuring permanent availability of its service to customers, particularly in peak times (Voigt, Bulinga, & Michl, 2017). Licensing content from broadcast networks, cable network providers and studios represents one of the key activities, alongside with original content creation. For differentiation reasons, the goal is to secure exclusive content rights against other streaming providers (Voigt, Bulinga, & Michl, 2017). Licensing agreements only last for a limited time: when license renewal is necessary, Netflix evaluates which titles are being viewed most often and only in case of frequent streaming the license for the specific title is renewed (Voigt, Bulinga, & Michl, 2017). In addition, Netflix also successfully produces and broadcasts original content, such as the TV shows “House of Cards” or “Orange Is the New Black”.


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5.1.4.2 Key Resources The streaming business relies on the database of content available for streaming. Content streaming is secured by the content delivery network Open Connect, a global network of storage servers that cache content close to where it will be viewed. That local caching reduces bandwidth costs and makes it easier to scale the service over a wide area (Niccolai, 2014). Servers have a capacity of about 100 terabytes of data, storing between 10,000 and 20,000 movies (Niccolai, 2014). Moreover, the online platform is still an essential resource for the company’s success. In order to attract and retain customers, Netflix relies heavily on recommendation technology, CineMatch, first introduced in 2000 (O'Brien, 2002). Additionally, Netflix patented its rating algorithms and its movie queue, which saves movies selected by customers for later viewing (Voigt, Bulinga, & Michl, 2017).

5.1.4.3 Key Partners Netflix heavily relies on content providers such as DreamWorks Animation, TimeWarner Company or the Walt Disney Company. To support outbound streaming traffic, Netflix partners with internet service providers (ISPs) such as COX or Verizon (Voigt, Bulinga, & Michl, 2017). Internet service providers ensure trouble-free broadband access, and guarantee a high audio and video quality with reduced buffering times. Netflix also partners with Amazon Web Services (AWS) since 2010, in order to provide a globally seamless service (Voigt, Bulinga, & Michl, 2017). Amazon Web Services provides storage and servers, which enable customers to stream Netflix content anywhere and anytime. In order to make streaming available to the broad array of different devices supported by Netflix, such as Play station, Xbox, Apple devices, smart TVs, tablets, smartphones and PCs, partnerships with consumer electronic companies are required. This device variety allows the company to substantially increase customer numbers (Voigt, Bulinga, & Michl, 2017). Netflix uses consumer data mining to determine which content viewers pay to see and relies heavily on this information to determine the total cost of each licensing agreement (Anastasia, O'Keefe, Boldog, & Tinoco, 2014) (Investopedia, 2015).

5.1.4.4 Customer Segments In its streaming business, Netflix focuses mostly on younger generations, since these are not only familiar with online streaming, but also use a broader variety of devices, such as smartphones or tablets (Voigt, Bulinga, & Michl, 2017). Device variety increases streaming likelihood, regardless of the individual’s location. In fact there is only one big segment being the mass market. However certain categorization is possible. A division can be made between cable or satellite watchers who can see Netflix as a premium serving them and binge watchers. The latter being an ideal customer of Netflix because they seem addicted to the main product that Netflix offers: all-you-can-eat video streaming. With the start of the company’s international expansion in 2010, Canada, Latin America or Germany also became part of the customer base. However, as of 2014, among the top five Netflix user countries, the U.S. ranks number one, with 67.2% of total Netflix customers, followed by Canada with 4.6 %, and Mexico with 4.0 % (Voigt, Bulinga, & Michl, 2017). During the same year, the streaming service reported more than 63 million members, an increase of more than 23% in comparison to 2013. International streaming members increased from almost 11 million in 2013 to 18 million in 2014. During the same one-year period, domestic streaming members increased from 33 to 39 million (Voigt, Bulinga, & Michl, 2017).


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5.1.4.5 Customer Relationships Although customers stream content online without direct interaction with Netflix employees, the company makes an effort towards establishing direct customer contact, by providing a hotline as well as the option of live chat with its service staff (Voigt, Bulinga, & Michl, 2017). This can be seen as a self-service model. Personal assistance and support are designed as a radar for better observing not only customer behaviour, but also emergent problems with the platform. Also, by recording and analysing customers’ viewing behaviour and search queues, the company is able to predict and recommend movies that customers are likely to enjoy (Voigt, Bulinga, & Michl, 2017). Netflix’s massive amounts of data spit out information on its audience’s favourite actors, genres, and directors. Netflix used these data to produce a series, House of Cards, that could mathematically be guaranteed to be a success in pleasing its customers (Ebersole, 2013). This helped in differentiation from imitators.

5.1.4.6 Revenue Streams While other streaming services implement a combination of member subscriptions and the sale of advertising space to outside companies, Netflix stands out from the crowd in its approach toward generating revenue (Investopedia, 2015). Netflix has from the start on always used the subscription model, first for the DVD mail-order service and later on to the streaming service. The basics of it are quite simple, customers pay a flat recurring fee each month and they get unlimited access to streaming. Currently, Netflix offers three types of streaming membership plans, starting from 7.99 euro per month in Belgium for the Basic version, 9.99 euro for the standard version and 11.99 euro for the premium (Netflix, 2017). The main difference between the different versions are the amount of screens that can be used at the same time and that only in the premium version ultra HD is available (Netflix, 2017). Netflix attempts to mitigate losses by moderate increases in monthly membership fees (Owens, 2016), continuing to pursue an add-free value proposition. Another revenue stream that recently was created involves the Netflix originals. The rights to broadcast these series are sold to television networks.

5.1.4.7 Cost Structure In the streaming business, content costs form the largest cost block, and are increasing. Between 2010 and 2013, Netflix’s streaming content obligations surged by seven times (Voigt, Bulinga, & Michl, 2017). Securing licensing agreements with TV networks, filmmakers, and other content owners is arguably the greatest expense for Netflix. For example, the company spent nearly $200 million in 2011 for access to Disney films and TV programming for a one-year period (Investopedia, 2015). The full series of "Lost" cost the company $45 million, "Scrubs" came in at $26 million and "Desperate Housewives" totalled $12 million for a single year. The growth of Internet-based television has made it more difficult to purchase licensing inexpensively and the company's current content licensing budget reflects this truth (Investopedia, 2015).


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5.1.4.8 Channels The main communication channel is Netflix’ website; as well for content delivery, as for communication towards the customers. Netflix itself also invested immense to get feedback from its customers on how their service is used by the implementation of a datamining tool. This mining system goes further than just relying on past consumption but also on cross reference usage patterns (Anastasia, O'Keefe, Boldog, & Tinoco, 2014) (Investopedia, 2015).

5.1.4.9 Value proposition Netflix offers streaming services in over 50 countries. Within its streaming business, the company provides movies and TV shows for subscribers anywhere, anytime and without commercials. Another important value proposition is that Netflix offers exclusive content the Netflix original series.

5.1.4.10 The business model canvas of Netflix The above discussed subsections can be all found together in the underneath depicted business model canvas (Osterwalder & Pigneur, 2010):

Key Partners

Movie and television studios

Internet service providers

Amazon Web Services

Key Activities

Video player software

Content licensing

Creating content

Value Proposition

Providing movies and TV shows for subscribers anywhere, anytime, on any device and without commercials

Netflix original content

Customer Relationships

Self-service

Automated services

Customer Segments

Mass market

Cable/satellite replacers

Binge watchers

Key Resources

Content (virtual)

Cinemath

Servers (physical or in cloud)

Channels

Netflix.com

Cost Structure

Fixed costs server, website

Variable cost (per user licensing, streaming, capacity)

Creation of original content

Revenue Streams

Monthly subscription revenue

Advertising of content pushing series movies to more views..

Selling Netflix original content

Figure 11: The Business Model Canvas of Netflix


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5.1.5 The impact of cloud computing on the business model of Netflix In the business model canvas of Netflix that was presented on the previous page some of key parts were put in bold to signal that in these parts cloud computing has an clear impact. In this part I will briefly go in to detail on how cloud computing has an impact on several concepts that immediately have impact on the business model of Netflix. Starting from the left on, in the Key partners concept. Netflix for its service delivery as much as any other service provider using cloud computing is dependent on a quick delivery of internet. If there is no connection to the internet, Netflix simply cannot provide our deliver. This confirms the fact that internet is more and more seen as the fifth utility (Buyya R. , Yeo, Venugopal, Broberg, & Brandic, 2009). In the partners concept Amazon Web services is also mentioned because Netflix uses this service to store and distribute its content around the world. Netflix chose Amazon Web Services (AWS) as cloud provider because it provided them with the greatest scale and the broadest set of services and features (Izrailevsky, Vlaovic, & Meshenberg, 2016). By doing so they committed to the cloud instead of keeping on using their on servers and databases. This partnership with Amazon is also reflected in the cost structure as Netflix owes Amazon a certain amount depending on the usage of storage and computational resources. This provides the opportunity for Netflix to utilise several advantages of cloud computing like elasticity and scalability in its own advantage (T-Systems, 2009) (Armbrust, et al., 2009) (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011) (Surya, Mathew, & Lehner, 2014). Because building up a server park and maintaining it would come down to a higher cost than the current setup. The usage of storage and the computational resources via the cloud are also key resources of Netflix as they offer a library of movies and series to their customers. The computational resources come to play with the application of Cinemath that provides users suggestions on what to watch based a highly powerful data mining system that goes further than just relying on past consumption but also on cross reference usage patterns. The recommendation engine eventually provided Netflix with data for another innovation: the creation of original content (Anastasia, O'Keefe, Boldog, & Tinoco, 2014) (Investopedia, 2015). This datamining tool is also used its customer relationships. Cloud computing is also intensely entwined with the value proposition Netflix offers: that is to provide movies and TV shows to subscribers anywhere and anytime on any device. Without the usage of cloud computing technology this would not be possible


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5.1.6 Does it meet the disruptive innovation criteria? In order to be able to say whether or whether not Netflix can be categorized as a firm using disruptive innovation the framework of Hang, Chen and Yu (2011) needs to be applied. Hereafter the framework is completed based on the previously gathered and discussed information about Netflix.

Question Yes No


Viable business in the low-end market x

Viable business in a new, niche market x

are incumbents in the main-stream market willing to run away or ignore the initial disruptors? x

B. Technology

There exists a performance overshoot in the main-stream market x

Adequate for a foothold in the low-end market x

Adequate for a foothold in a new, niche market x

Could be further improved in performance, price/ performance, etc x

R&D needed to improve the disruptive technology is feasible, affordable and well executed x


New value proposition x

Table 4: Assessment framework of Hang, Chen and Yu (2011) applied to Netflix

From the above shown table can be derived that only two times “no” has been ticked. In this case for the viable market position in the low-end market and the existing performance overshoot in mainstream market. Both are in fact correlated in the way that a viable business in a low end market cannot exist without a performance overshoot in the main market, otherwise both would be mainstream. Further for the case of Netflix the framework indicates that a new market disruption is the case. This can be substantiated by the fact that by offering movies and series over the internet, by streaming, was indeed a new market that was addressed and created by Netflix for the first time. It is also interesting to add some context to some of the questions ticked “yes”. On the market positioning one can clearly see that from the beginning of Netflix’s streaming service, this was a new and initial niche market. Another thing that could be observed is the fact that the, at the time of introduction, main competitor of Netflix, blockbuster, was initially ignoring this disruption of moving to streaming movies delivered over internet. Only in a later stage this firm and other started offering a similar service. On the technology side the initial foothold was created by offering the streaming service on the side of the, at that time, main operating model of DVD delivery per mail. By doing this Netflix could grow the foothold for this new service and in the meantime it could further


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continue in improving this service. Because at the time of the launch of this new kind of service Netflix knew that what they actually wanted was not technologically possible just yet, because it was back then still difficult to deliver various Internet video formats to a TV screen (Helft, 2007). Also improvement of the product has always been important for Netflix. One of their initiatives to achieve this was to organize a competition, The Netflix Prize. This three-year contest challenged developers around the world in order to improve the company’s recommendation algorithm, the code that tells you what movies Netflix’s computers think you’ll like based on movies you and others have viewed and rated (Greenberg, 2009). The other favourable drivers are that a new value proposition was offered to their customers. This fact of offering an additional, different than in mainstream market, set of attributes that is appreciated by the customer is no initial part of the framework although it is a driver of the adoption of such a new technology. Especially the fact of the unlimited viewing anywhere at any time was a serious contribution. As mentioned in the methodology chapter a cross validation of the results of the application of the framework of Hang, Chen and Yu simultaneously (2011) is also executed by using the criteria sheets of Keller and Hüsig (2009):

entrant



Products perform worse based on established attributes x

Products are cheaper, simpler, more comfortable or more reliable x

Products address current non-consumers x

Profitable business model targeting over-satisfied customers x

Investors allow experimentation x

Phase total 1 1 3

Main market entry

Products are based on standard components x

Strategic resources (licenses, capital, etc.) are accessible x

Network for PDI is expected to be large x

PDI is compatible with existing network x

Phase total 3 0 1


Business model is significantly different x

Processes are significantly different x

Value network has a low overlap x

Phase total 4 0 0

Total 8 1 4 Table 5: Assessment framework of Keller and Hüsig (2009) applied to Netflix


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The application of the framework developed by Keller and Hüsig (2009) confirms the result that was found by applying the framework by Hang, Chen and Yu (2011). Netflix can be seen as a firm that is using Cloud computing as a disruptive innovation. The second framework focusses not entirely on the same factors that can enable disruption so this double check serves as a worthy addition. The main difference with the model of Hang, Chen and Yu (2011) that can be noted: is the fact that also business model, processes and value network are also taken into account. For Netflix the business model, the new processes and the value network have been drivers of their success. In particular the subscription model has been a true bonanza. Before Netflix no firm had ever used the technique of asking a subscription fee for a videos on demand service (Noren, 2013). One of the processes to create value for their customers is to secure exclusive content rights against other streaming providers (Voigt, Bulinga, & Michl, 2017). Netflix does this on two fronts. First it competes with other streaming providers to win gain exclusive rights on content provided by production houses and secondly, perhaps even more successful, by creating their own content: Netflix original series. For the discussion of the framework used for cross validation our initial result that Netflix can be seen as firm using disruptive innovation only the first criteria sheet of Keller and Hüsig (2009) is used. The reason for this is that when Netflix started providing their new service: video streaming over the internet the market still had to be developed and no incumbents could be selected as such. A difference that can be noted to the framework of Christensen (1997)is that in this case the firm that is disruptive in this case was not a new entrant or a start-up. Netflix was already an established firm in another market.


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5.2 Salesforce 5.2.1 General info Founded in 1999 in Delaware, USA, Salesforce currently has more than 100.000 customers and total revenues of 8.4 billion USD in 2017 (Salesforce, 2017b). The SaaS vendor focuses on CRM software however it does not only provide SaaS, but also runs the IT-infrastructure for SaaS and it also offers a PaaS service via force.com. The latter is an integrated set of tools and application services that customers can use to build any business application and run it on the same infrastructure that that delivers the Salesforce CRM applications (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). The first real application of Cloud computing as we know it, was in fact Salesforce.com who started offering their business applications over the internet via their website form their start-up back in 1999. The goal of the four pioneers who started Salesforce was to create business software applications in a completely new way, to deliver software through a model known as Software-as-a-Service (McCarthy B. , 2016). This would completely eliminate the need for multimillion dollar upfront costs, implementations that could take years and for the ongoing complexities of maintenance and constant upgrades. They had the first prototype working within a month of development; it was basic, barebones and was modelled to a similar look of Amazon.com with tabs across the top. Amazon has served as an inspiration to think: why business applications until then could not be delivered through a website that was as easy to use as Amazon.com (McCarthy B. , 2016). In 2000 salesforce officially launched its product together with a controversial negative brand advertising that has become synonymous with Salesforce: “No Software” (McCarthy B. , 2016). In 2003 the first ever Dreamforce was organised, an event over a few days that showcased the latest Salesforce features and roadmap. This event allowed customers to network and talk about how to get more out of the product (McCarthy B. , 2016). In 2005 Salesforce developed a service that would change business software forever: the AppExchange platform (Salesforce, 2017a). It was often compared to “eBay for business software” or “iTunes of business software” (McCarthy B. , 2016). In 2008 Force.com was released during that year’s Dreamforce event. Force.com is based on a technology that allowed users to create any user interface they wanted, they could build forms, buttons, links and embed anything they liked. This paved the way for the logical extension of the Salesforce SaaS platform, Platform-as-a-Service (McCarthy B. , 2016). In 2013 Salesforce rolled out the Salesforce1 platform, with the goal to open up access to as much information that you can access on a computer. This not only let customers access their favourite Salesforce Apps, but also custom applications and integrations, as well as AppExchange Apps that customers may have downloaded from the App store (Williams A. , 2013) (McCarthy B. , 2016) In 2016 Salesforce announced their newest product, Einstein. Einstein has been developed across every Salesforce product and allows all Salesforce customers to access the latest insights about their platform and customers. It delivers advanced Artificial Intelligence capabilities to sales, service and marketing (McCarthy B. , 2016) (Miller R. , 2017).


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5.2.2 How Salesforce uses cloud computing The Salesforce Cloud architecture can be best explained by using the below shown figure:

Figure 12: The customer success platform (Salesforce, 2017f)

The Salesforce customer success platform consist of several parts: API’s, complete CRM, a scalable metadata platform and a trusted multitenant cloud. The API’s are in essence a contact between two pieces of software that enable communication (Salesforce, 2017f). This enables a lot of the Salesforce functions on which they are so proud. The complete CRM are in fact al the typical apps that customers use. The metadata platform is the actual structure of Salesforce org and contains data about the data in a metadata-driven architecture. In Salesforces’ architecture, they keep the metadata layer separate from their services layer, which allows seamless and easy upgrades (Salesforce, 2017f). Multitenancy cloud means that all of Salesforce’s customers, from small businesses to enterprise companies, are on the same

code-base and all get the benefits of the same features, functionality and automatic upgrades (Salesforce, 2017f). However each customer gets its own customized service. There is one primary data store per instance. Salesforce only has one primary data storage in total. The data from each customer is kept separate by the customer itself, by their org ID. These are used as primary keys in this one big data table (Merret).

Further Saleforce uses aproximately 50 pods to group al their customers. A pod, or a Point of Development, is a self contained unit that contains all that is required to run Salesforce, including the application server, database server etc. Each customer is appointed to only one pod (focusonforce, 2017).

Figure 13: Salesforce data storage


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Salesforce maintains a pool of servers to handle login traffic for all instances. An instance or a pod, is a complete set of systems, network and storage infrastructure, both shared and non-shared, that provides the salesforce.com service to a subset of their customers (Johnson C. , 2013) (focusonforce, 2017). It also consists of a huge Meta data cache to make the service run efficient (Merret) because Salesforce runs every page on the fly. Customer traffic starts with salesforce’s external DNS. Once a lookup has successfully returned the IP address for an instance, standard Internet routing directs it to the appropriate datacentre (Johnson C. , 2013). Once the traffic enters the network in that datacentre, it is directed to the load balancer pair on which that IP lives. The load balancer directs the traffic to the application tier of the given instance (Johnson C. , 2013). At this tier, Salesforce services both standard web page traffic as well as API traffic. Depending on the needs of the customer's request, it will be directed to additional server tiers for various types of backend processing (Johnson C. , 2013). Salesforce owns back-end infrastructure runs on only about 1,000 servers and that is mirrored, so it is really only 500 (Schonfeld, 2009).

5.2.3 The business model of salesforce explained Salesforce has focussed from the beginning on reaching a blue ocean, by targeting e.g. SMEs that until then had been ignored by incumbents. This allowed Salesforce to gain credibility and grow its client base. This led to large corporations who started implementing Salesforce in some departments and eventually spreading throughout these whole companies (DaSilva, Trkman, Desouza, & Lindic, 2013). Another thing that differentiated them from the start on was their focus on end users instead of CIO. The revenue model used by Salesforce is a subscription based pricing model in different versions (Salesforce, 2017e). For each of these options, a prospect can also chose to try the product first before subscribing. By allowing a trial users to insert their data users not only became familiar with the platform but also committed themselves by doing this. As a result when the trial period ends satisfied users will be inclined to keep the service given the non-monetary resources already invested (DaSilva, Trkman, Desouza, & Lindic, 2013). While innovative technology usually reduces transaction and switching costs, companies must devise creative ways to lock customers into their products through ways that go beyond financial commitment (DaSilva, Trkman, Desouza, & Lindic, 2013).Given these lower switching costs and high levels of differentiation offered by various players in the SaaS layer of the cloud, Salesforce understood that marketing plays viable role in securing and keeping early adopters (DaSilva, Trkman, Desouza, & Lindic, 2013). Salesforce‘s CRM value proposition equates to the general promises of SaaS: Cost reduction, decreased complexity, accessibility, and usability (Buxmann, Hess, & Lehmann, 2008). As the company offers an on-demand product, clients can control costs and pay only for the applications they need. New and updated applications are easily provided via the AppExchange platform (Salesforce, 2017a). The business model applied by salesforce in the beginning of their existence mainly addressed new customers in the unsaturated market. Salesforce saw itself as a visionary and a market leader, which increased its profile and facilitated the acquisition of this specific costumer segment (Maos, 2011). This attitude allows for experimentation. The founder and CEO Marc


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Benioff fosters innovating from within as well as acquiring external know-how (Kaltenecker, Hüsig, Hess, & Dowling, 2013). Furthermore, small and medium sized companies are choosing to switch from established on premises players to Salesforce as they do not require extremely complex solutions for their businesses (Barret, 2012). This showed that over-served customers consider Salesforce to be an adequate alternative for incumbents in the market. Salesforce was the first in the cloud computing space to dream up a business model built on partnership (Fagan, 2015). This intersection of innovation and customer success has created tremendous opportunity for everyone in the Salesforce ecosystem (Fagan, 2015). Developers select users to test beta versions of a package before rolling it out across all users (Salesforce, 2011) (Büst, 2010). Only if the customers are satisfied with new features will they be adapted in the release version. New features normally come out three times per year (Salesforce, 2010). Salesforce exclusively uses the internet as its distribution channel and consequently avoids any potentially restricted access from subcontractors (Berg, 2012). The business model is significantly different compared to that of incumbents because of their pay-as-you-go service, the innovative and forward-looking attitude as well as the fan-like community (Berg, 2012). Salesforce has been able to pull both larger companies as well as growing start-ups away from their current CRM supplier (Berg, 2012). One of the many advantages of using Salesforce is that it provides upgrades and maintenance over the internet. Especially for smaller but fast growing enterprises this is an advantage (Kaltenecker, Hüsig, Hess, & Dowling, 2013). Looking at its CRM product one can see that programming, infrastructure and all user activities are located in the cloud. For standard solutions, a large number of prefabricated modules that are connected in a "point and click" process in a complete system are offered (Kaltenecker, Hüsig, Hess, & Dowling, 2013). Salesforce guarantees via Web API‘s (application programming interface) the trouble-free integration of additional programming and in-house software upgrades for cloud computing (Kaltenecker, Hüsig, Hess, & Dowling, 2013). The system is compatible with Windows PCs, mobile operating systems and mobile devices. Support and apps for Mac computers are provided by the developer community and the system also supports social media (Kaltenecker, Hüsig, Hess, & Dowling, 2013). Salesforce CRM software is thus compatible with all standard browsers and operating systems. With no specific software or hardware to install, customers can use their own equipment. The product is available online to anyone. Furthermore, no matter which edition of Salesforce CRM is selected, the consumer joins more than 150,000 customers (Salesforce, 2017b) Salesforce CRM simultaneously offers a project-related platform in order to work with external partners (Kaltenecker, Hüsig, Hess, & Dowling, 2013). In the cloud, safe and confined spaces are definable (Salesforce, 2017a). Another thing Salesforce did was that they allowed prospective clients to experiment with their product before actually committing to it. By doing so Salesforce wanted to get prospective customers to get used to its product by giving them full access to all possibilities. At the end of the trial period prospective clients can still walk away or chose to purchase the


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application (DaSilva, Trkman, Desouza, & Lindic, 2013). Because the time and effort invested in getting to know the program, most of them go over to paying a subscription.

5.2.4 The Business Model Canvas of Salesforce In the previous section I tried to provide a general overview about the specific characteristics of the business model that is used by Salesforce from the beginning on and how and why this model was applied. In this section I want to dissect the model further and link it to the business model canvas in order to make it more comparable to the other cases in this research. I will do this by first elaborating on all the subsets from the canvas and by providing links what was already discussed in the above sections.

5.2.4.1 Value Proposition of Salesforce Salesforce offers its customers a service that requires little start-up investment, practically no installation burdens, no need for maintenance or a skilled IT department, no requirement to buy additional hardware and no minimum subscription period (DaSilva, Trkman, Desouza, & Lindic, 2013). By doing so, salesforce is addressing customers who are looking for an easy to use service with high benefits and low costs. Salesforce offers a pay-per-month solution operated through the internet (Weinhardt, Anandasivam, Blau, & Stosser, 2009) and is the fastest growing CRM provider in the United States (Wardley & Shirer, 2012). Salesforce was at the start just a CRM solution in the cloud. At the beginning there were some who were concerned about the off-premises data storage solution offered by Salesforce however these were soon outweighed by the advantages of using salesforce.com (DaSilva, Trkman, Desouza, & Lindic, 2013). Nowadays Salesforce offers more than just a single product; it offers a complete integrated solution to their customers for managing all interactions with customers and prospects (Salesforce, 2017d).

5.2.4.2 Revenue streams of Salesforce One of the things that enabled Salesforce to disrupt the CRM sector was that it changed the earnings logic: The introduction of the rental model which entailed a monthly payment based on the number of users instead of the customary lump sum licensing fee, for a company as a whole (DaSilva, Trkman, Desouza, & Lindic, 2013). The usage of this model made it also possible to address small and medium firms that before were left aside as being unattractive by incumbents. This previously unaddressed market appeared to be a blue ocean (Kim & Mauborgne, 2004) which helped fuel the success of Salesforce. This rental model generates monthly subscription fees. Salesforce provides a straightforward, per-user pricing scheme for all of their editions: Lightning Unlimited CRM power and support for $300, Lightning Enterprise which is deeply customisable CRM for $150, Lightning Professional which offers a complete CRM for any size team for $75 and Salesforce IQ CRM Starter (up to 5 users) for $25 (Salesforce, 2017e) . All the previous prices are in USD per user per month and are billed annually because Salesforce has a minimum subscription term of one year (Salesforce, 2017e). A second stream of revenue consists of related professional services such as process mapping, project management, implementation services and other revenue e.g. training fees (Stock analysis on net, sd).


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5.2.4.3 Key Resources of Salesforce The key resources of Salesforce.com is its CRM platform and this is built upon its scalable technology and products (Schonfeld, 2009). To enforce this Salesforce did a huge investment in order to build a unique state-of-the-art datacentre compatible with their clients’ requirements (Tzuo, 2007). Salesforce has more than just technological resources, over the years they managed to build up a strong team of highly competent employees (Trefis, 2011). These employees were on one side motivated to build an exceptional product and on the other side they created a highly marketable product. Another Key resource are the “lead qualifiers” which are persons responsible for contacting free trial users and identifying future paying customers (DaSilva, Trkman, Desouza, & Lindic, 2013).

5.2.4.4 Key partners of Salesforce Salesforce organised the Salesforce Partner Program to take advantage of the unique customer engagement models, economics and potential to deliver unparalleled customer success offered by cloud computing. Salesforce actually calls it a cloud ecosystem that consists of the AppExchange partner programme (ISV) (Salesforce, 2017a) and the partner community for consulting partners (Salesforce, 2017c). The introduction of Force.com, a customization platform for corporations, was a real extension of Salesforce’s services. By opening its infrastructure to external developers allowed these to implement their own inputs. Because the platform is compatible with all major development environments independent developers could integrate all kinds of services that could ultimately serve customers better. Up to date force.com has more than 3.0 million developers (Salesforce.com, sd) whom all reside under the AppExchange partner programme (Salesforce, 2017a). Salesforce.com and Oracle have a comprehensive nine-year partnership encompassing all three tiers of cloud computing: Applications, Platform and Infrastructure (Hellinger, 2013). Salesforce has also signed a major deal with Amazon in 2016 in order to start using their public cloud infrastructure, not for all their services as they will still keep on using their own Oracle database systems (Darrow, 2016b). On the other hand Amazon is using a lot more of Salesforce software as well. Salesforce president Keith Block said that Amazon now uses Salesforce software company wide (Darrow, 2016a).

5.2.4.5 Key activities of Salesforce Salesforce had always leveraged technological developments in order to improve its service to be faster, safer and more reliable (DaSilva, Trkman, Desouza, & Lindic, 2013). However with building bigger, better and stronger systems that incorporate the ability to change as new developments come along contains a tricky balance: be resilient but adaptable (Park I. , 2016) Continues innovation is not only initiated by Salesforce’s own team but also by organising Hackathons and collaborations through AppExchange (Park I. , 2016). Salesforce used its Salesforce University, cross-training and other more traditional types of educational systems however this was still not enough to provide a decent formation to as many people as possible. At Dreamforce ‘15, the team launched Trailhead, a platform that gets the trainer out of the way and allows trainees to learn at their own pace to get certified. Which is in effect, another kind of self-service system this one designed for education (Park I. , 2016).


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5.2.4.6 Customer Relationships of Salesforce Salesforce offers each of its customers a totally customized customer service. Salesforce does this by utilizing Service Cloud (Mobile). This is a package containing features like (mobile) co-browsing, in-line community support for game apps and a (mobile) chat capability. A fourth feature, designed specifically for customer service reps, allows agents to use mobile devices to give personalized support by connecting internally to get questions answered through a lightweight activity stream experience (Williams A. , 2013). Internally to achieve better performance with their service request ticketing service Salesforce introduced Concierge. Concierge is designed to be searchable and scalable. It offers a Google-like search functionality of hundreds of Service Cloud Knowledge articles, aligned with the way users are accustomed to searching for information (Reynolds, 2016). Crucially, it recognizes the users, their employment history with the company, their records and their function. Tickets are routed to the proper queue across many different organisational and service teams down to the article level (Reynolds, 2016). Salesforce latest addition in order to achieve this is Einstein, an artificial intelligence initiative, that makes life easier for customer service reps and their managers. For the reps, it gives information that is supposed to help them better understand the needs of the customer they’re dealing with (Miller R. , 2017).

5.2.4.7 Customer Segments of Salesforce Salesforce started life with the right strategy, namely attack just one segment and dominate the field. Salesforce first focussed on SME’s who were until then not served by the major software companies. After growing its customers in the SME segment they gradually started to take institutions and larger enterprises as customers (DaSilva, Trkman, Desouza, & Lindic, 2013). CRM, the software solution, was the field and was ripe for the taking because it was always somewhat detected from other centralized applications (Smith G. , 2009). Next to selling to a customer, supporting a customer is key to any business. Thus Salesforce has picked customer service as a second field to focus on (Smith G. , 2009).

5.2.4.8 Cost structure of Salesforce Although salesforce at the start began as a small firm consisting of four employees by now already 25.000 people are working for the firm making the wages of employees an important cost factor (Fortune, 2017). Also recently Salesforce invested another $3 million to eliminate statistically significant differences in pay in their first-ever equal-pay assessment (Robbins, 2017). New offerings and constant upgrades of existing offerings have driven Salesforce's R&D expenses which, in absolute dollar terms, have increased year over year (Trefis Team, 2016). Additionally, the integration of newer acquisitions into the existing offerings has also impacted R&D expenses. Despite the increase the R&D expenses, as a percentage of gross profit these expenses have remained relatively consistent (Trefis Team, 2016). Salesforce, is in the business of marketing and sales management software. Salesforce invests 53% of their revenue into sales and marketing. That’s just over half of the $4.1 billion in revenue generated in 2014. In return for such a hefty investment they get growth. In 2014 Salesforce grew by 33% over the previous year (Brady, 2016).


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5.2.4.9 Channels of Salesforce The main channel used by Salesforce is their main website: Salesforce.com. However they also have several affiliate websites each referring to a singular product or service they are offering e.g. Force.com, Data.com and Desk.com. Salesforce thus exclusively uses the internet as its distribution channel and consequently avoids any potentially restricted access from subcontractors (Berg, 2012). Another reason for Salesforce success is that through its channels Salesforce targets its end-customers directly making the IT departments often less indispensable or even redundant (Irwin, 2012).

5.2.4.10 The Business Model Canvas of Salesforce The above discussed subsections can be all found together in the underneath depicted business model canvas (Osterwalder & Pigneur, 2010):

Key Partners

Partner network: developers and consulting

Oracle

Amazon web services


Key Activities

Continuous innovation of the platform

Training

Value Proposition

Easy to use

Cheap business process software

Access to their software from everywhere


Totally customized through service cloud

Customer Segments

SME, Institutions, Large enterprises

CRM customers

Service customers

Key Resources

Salesforce CRM

Competent employees

Lead qualifiers

datacentre

Channels

Salesforce .com

Other affiliate websites

Cost Structure

Wages of employees

R&D

Marketing & sales

Server hosting

Revenue Streams

Recurring flat fee for monthly or yearly usage

Fees for professional service (consulting)

Figure 14: The Business Model Canvas of Salesforce


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5.2.5 The impact of cloud computing on the business model of Salesforce In the business model canvas of Salesforce that was presented on the previous page some of key parts were put in bold to signal that in these parts cloud computing has an clear impact. In this part I will briefly go in to detail on how cloud computing has an impact on several concepts that immediately have impact on the business model of Salesforce. Starting from the left on, in the Key partners concept. Salesforce for its service delivery as much as any other service provider using cloud computing is dependent on a quick delivery of internet. If there is no connection to the internet, Salesforce simply cannot provide our deliver. This confirms the fact that internet is more and more seen as the fifth utility (Buyya R. , Yeo, Venugopal, Broberg, & Brandic, 2009). In the partners concept Amazon Web services and Oracle are both mentioned because Salesforce.com and Oracle have a comprehensive nine-year partnership encompassing all three tiers of cloud computing: Applications, Platform and Infrastructure (Hellinger, 2013). Salesforce has also signed a major deal with Amazon in 2016 in order to start using their public cloud infrastructure, however not for all their services as they will still keep on using their own Oracle database systems (Darrow, 2016b). Cloud computing also comes to the forefront in the key resources of Salesforce for example in the CRM platform that is built upon its scalable technology and products (Schonfeld, 2009). The pricing of its CRM is also linked to what the customers wants to use and nothing more. Making it a standard-bearer for the advantages the usage of cloud computing has to offer.This partnership with Amazon and Oracle is also reflected in the cost structure as Salesforce owes Amazon and Oracle a certain amount depending on the usage of storage and computational resources. This provides the opportunity for Salesforce to utilise several advantages of cloud computing e.g. elasticity and scalability in its own advantage (T-Systems, 2009) (Armbrust, et al., 2009) (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011) (Surya, Mathew, & Lehner, 2014). Because building up a server park and maintaining it would come down to a higher cost than the current setup. Also on the customer relations cloud computing has a certain impact as new technologies based on cloud computing are continuously applied. For example: the application of Concierge. It offers a Google-like search functionality of hundreds of Service Cloud Knowledge articles, aligned with the way users are accustomed to searching for information (Reynolds, 2016). Crucially, it recognizes the users, their employment history with the company, their records and their function. Tickets are routed to the proper queue across many different organisational and service teams down to the article level (Reynolds, 2016). Cloud computing is also intensely entwined with the value proposition Salesforce offers: Access to their software from everywhere. Without the usage of cloud computing technology this would not be possible.


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5.2.6 Does it meet the disruptive innovation criteria? In order to be able to say whether or whether not Salesforce can be categorized as a firm using disruptive innovation the framework of Hang, Chen and Yu (2011) needs to be applied. Hereafter the framework is completed based on the previously gathered and discussed information about Salesforce.

Question Yes No




Are incumbents in the main-stream market willing to run away or ignore the initial disruptors? x

B. Technology


Adequate for a foothold in the low-end market x

Adequate for a foothold in a new, niche market x




Table 6: Assessment framework of Hang, Chen and Yu (2011) applied to Salesforce

From the above shown table can be derived that only one “no” has been ticked. In this case for the incumbents in the main-stream market are willing to run away or ignore the initial disruptors. This might have been the case in the first years of Salesforce existence however nowadays these incumbents have also adapted to the new paradigm of serving their customers over internet. Only one “no” ticked means that the frame work indicates that both low-end and new market disruptions are progressing simultaneously (Hang, Chen, & Yu, 2011). Some of the questions ticked “yes” are also interesting to add some context. For example the Viable business in the low-end and both a new niche market. This can be clarified by the fact that Salesforce has focussed from the beginning on, on reaching a blue ocean, by targeting e.g. SMEs that until then had been ignored by incumbents, new market. This allowed Salesforce to gain credibility and grow its client base and gain trust of the firms in the low-end of the market. On the performance overshoot can be noted that incumbent CRM providers often provided general software to a whole firm for a fix price contains all the functions they are offering. In the case of Salesforce they offer for each customer a customized service or package depending on the needs of the end users. Improvement of the technology is feasible and improvement in performance, price and performance has always been important. Salesforce saw itself as a visionary and a market leader, which increased its profile and facilitated the acquisition of this specific costumer segment (Maos, 2011). This attitude allows


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for experimentation. The founder and CEO Marc Benioff innovating from within as well as acquiring external know-how (Kaltenecker, Hüsig, Hess, & Dowling, 2013). As mentioned in the methodology chapter a cross validation of the results of the application of the framework of Hang, Chen and Yu simultaneously (2011) is also executed by using the criteria sheets of Keller and Hüsig (2009):

Entrant Salesforce








Phase total 4 1 0

Main market entry





Phase total 4 0 0





Phase total 3 0 0

Total 11 1 0 Table 7: Assessment framework for entrant of Keller and Hüsig (2009) applied to Salesforce

The application of the framework developed by Keller and Hüsig (2009) confirms the result that was found by applying the framework by Hang, Chen and Yu (2011). Salesforce can be seen as firm using Cloud computing as a disruptive innovation. Almost all criteria are fulfilled. Only one can be rated as not fulfilled: Products perform worse based on established attributes. This is mainly because the performance of the salesforce product is not on a lower level. The second framework focusses not entirely on the same factors that can enable disruption so this double check serves as a worthy addition. To avoid too much duplication of the same arguments I mainly focus on these extra attributes: The main difference with the model of Hang, Chen and Yu (2011) that can be noted, is the fact that also business model, processes and value network are also taken into account.


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The business model is significantly different compared to that of incumbents because of their pay-as-you-go service, the innovative and forward-looking attitude as well as the fan-like community (Berg, 2012). Specific processes in on-demand companies differ to those of on premise companies e.g. the high maintenance costs of the on premise software and updates (Howlett, 2010). Salesforce provides three times a year an upgrade and maintenance over the internet. This is an advantage, in particular for smaller enterprises (Kaltenecker, Hüsig, Hess, & Dowling, 2013) In the case of Salesforce and the CRM market, the criteria sheet for the main incumbent, In this case SAP, can also be completed.

incumbent e.g. SAP



Some customers are over-satisfied x

Main customer segment does not appreciate entrants products x

Market for products based on PDI appears small and irrelevant x

Phase total 2 1 0

Main market entry

Established performance attributes are shifting x

Customers are unwilling to pay for further improvements along established attributes x

Switching costs are low x

Coordination costs are low x

Phase total 1 1 2


Products matching entrant's offer are not added x

Incumbent is fleeing to premium customer segments x

PDI is not implemented in separate organization x

Phase total 3

Total 3 5 2 Table 8: Assessment framework for incumbent of Keller and Hüsig (2009) applied to Salesforce

When going over the above depicted table the first criteria and a corner stone of Christensen (1997)‘s innovation theory is that customers are over satisfied. This is also the case for SAP that also implemented incentives to even exceed customer expectations. This will lead to some customers being over-satisfied in the future (Kaltenecker, Hüsig, Hess, & Dowling, 2013). On the main market entry level one can see that the switching costs are low is checked as not fulfilled. This is because here it forms a fundamentally different to the on-premises world. In the SaaS world this is seen as a crucial factor. If customers do not like a service, they can stop paying and move on (Kaltenecker, Hüsig, Hess, & Dowling, 2013).


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On the failure of the incumbent level: the following observations can be made. SAP has jumped on the bandwagon and invested in cloud computing. By doing so SAP has recently begun moving towards the SaaS model. With SAP CRM OnDemand, SAP has entered into the new market, being joined along the way by even more competitors (Kaltenecker, Hüsig, Hess, & Dowling, 2013). This explains the incumbent fleeing to premium customers is checked as not fulfilled. The company is not fleeing to premium segments but is keeping its eyes open for new trends and ideas. According to Maisto (2012) SAP has branded itself globally as the powerhouse solutions provider behind powerhouse companies. But aware of the small- and midsize-enterprise (SME) opportunity that's growing with the popularity of flexible cloud based solutions, SAP hosted an SME Summit at its New York office to get out a new message: “We do small, too” (Maisto, 2012). This brings us to the next item that is check as not fulfilled: potential disruptive innovations are not implemented in separate organization. This is because in this case, SAP has consolidated all cloud-related supplier assets. They will operate as an independent business under the name: Ariba, an SAP company (SAP news, 2012). Thus, SAP is an incumbent offering the potentially disruptive technology itself. This reaction can be interpreted as a response strategy. The question is whether this response strategy is in line with theory (Kaltenecker, Hüsig, Hess, & Dowling, 2013). Incumbent companies almost always invest to morph the innovation into a product that better suits the needs of their current customers rather than target a new set of non-consumers. In this case it is not different; the SAP on-demand software mainly targets the already existing SAP customers (Kaltenecker, Hüsig, Hess, & Dowling, 2013). Apart from this strategy, SAP tries to pursue medium-sized organizations (Online-crm, 2013) however the main focus remains for current customers to get used to the SAP on-demand product and gain a foothold in the market. Thus, SAP does not particularly target new customers. According to the existing theory, this is a logical (and predicted) step (Kaltenecker, Hüsig, Hess, & Dowling, 2013). As a last point of comparison between the two firms (entrant and incumbent) the prices are compared. The monthly subscription fee for SAP AG, salesforce biggest on premise competitor is 1.000€ (SAP AG, 2013). Salesforce provides a straightforward, per-user pricing scheme for all of their editions: Lightning Unlimited CRM power and support for $300, which is the most extensive version (Salesforce, 2017e). thus, the price curve is far beyond Salesforce‘s price curve. According to Christensen (2006), the price of the entrant is always below the incumbent‘s price. This is true for the current situation. In this respect, we can state that the observation above is in line with Christensen theory.


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5.3 Spotify 5.3.1 General info Spotify is a music player that can be used online or downloaded as an app on desktop or mobile. It had been in development since 2006, by a company in Sweden called ‘Spotify AB’. It was founded by Daniel Ek, who was formerly the CTO of Stardoll, and Martin Lorentzon, co-founder of TradeDoubler (Sawers, 2011). The name Spotify is a combination of the words “spot” and “identify”. The company’s goal is to help you spot and identify the favourites you forgot about, or maybe didn’t even know you had (Spotify, 2008). The company can be seen as a Music-as-a-Service (MaaS) provider (Wagner, Benlian, & Hess, 2014). Part of the allure for Spotify's users is that the service (currently) boasts more than 30 million tracks, exempted some high-profile holdouts like The Beatles, it has pretty much every song you would ever want to listen to (Pullen, 2015b). The actual launch of Spotify was in 2008 and has completely changed the way people listen to music ever since. By allowing users to play music directly from the cloud, rather than by downloading it first, Spotify became wildly popular (Pullen, 2015b). According to Daniel Ek, one of the founders of Spotify, their platform needed to be something that works for all parties. The difference between Spotify and the main music platform he used when growing up, Napster, is that this only worked for the consumer. What eventually killed it was that it didn’t work for the people participating with the content. The challenge here is about solving both of those things (Sawers, 2011). Back in 2008 Spotify kept its free service invitation only, something that had been in place whilst it was in the final stages of development prior to public launch (Sawers, 2011). While free accounts remained available by invitation only in order to manage the growth rate of the service, the launch meant that paid subscriptions were opened to everyone (It All Makes Sense, sd). The invitation-only element was a vital part of the platform’s rise. Not only did it help manage the growth level of Spotify, but it also helped to create a viral element to the service, with users each having five invites at first to share with their friends. As mentioned above there was a paid service to. It was this paid element of the service that was perhaps a little slower on the uptake, something that Spotify has been working hard to remedy over the past year (Sawers, 2011). On 25 October 2010 Spotify released a mobile version on smartphones. However it was only available in select regions and on certain cell phone carriers (History of spotify, sd). On 14 April 2011, Spotify announced via a blog post that they would drastically cut the amount of music a free member could listen to. By doing this Spotify tried to push users to the paying alternative by restricting the number of hours users can listen to the free version each month, as well as introducing limits on the number of times a track can be listened to (Sawers, 2011). All ‘Spotify Open’ and ‘Spotify Free’ members would be moved onto a new product which limits the amount of streaming to ten hours per month. In addition, a user can only listen to a track a maximum of five times (The Guardian, 2011) . ‘Spotify Unlimited’ and ‘Spotify Premium’ members would not affected by this change. New users were exempt from these changes for six months (It All Makes Sense, sd) (The Guardian, 2011). On 14 July 2011, Spotify launched its US service, which was a major milestone after delays and years of negotiation with the four major record companies (It All Makes Sense, sd) (Ek, 2011). Prior to this milestone, Spotify was only available in Finland, France, the Netherlands, Norway, Spain, Sweden and the UK, meaning that the US is the service’s first foray into non-European territory (Sawers, 2011). Partnerships with Facebook, Coca Cola, Klout and many others (Butcher, 2011) (Sawer, 2011) (Spotify, 2012) led to more real-time social listening (Van


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Buskirk E. , 2012a). The combination of Facebook and Spotify appeared to be very powerful (Van Buskirk E. , A brief History of Spotify's Attempt to Become the 'OS of Music', 2012b). As Spotify allowed users to create and seamlessly share playlists, as well as to exchange music on social networks. Furthermore, Spotify made it easy for third-party developers to create apps that, once integrated on the platform, offered users increased possibilities for discovering and sharing music (Voigt, Bulinga, & Michl, 2017). In real life this comes down to the following example: Someone who’s not a Spotify user will see on Facebook that a friend is listening to a track. If that person clicks on this update he will immediately download Spotify and become a Spotify user (Parks, 2012). By becoming a user he will see that there’s something like Songkick, an app available on the Spotify platform that lists concerts of your favourite artists. As that person is listening to the same track as his Facebook friend, he or she will see that the artist is going to be playing in their town next week, and has the option to click on a link to buy tickets (Parks, 2012). Reaching 60 countries worldwide from Andorra to Uruguay, Spotify has more than 100 million users, 50 million of whom pay for the service (McIntyre, 2017) (Smith C. , 2017). Spotify’s vast collection (more than 30 million songs) still manages to cater to almost everyone’s musical taste (Smith C. , 2017) and has from the beginning on been a way to distinguish them from the competition (Voigt, Bulinga, & Michl, 2017). This is never more evident than when you're paying attention to Spotify’s social media feed. A major part of the service is letting users share everything from favourite playlists to the track they’re currently listening to with friends. This, in turn, helps with music discovery (Pullen, 2015b). According to data polled by Spotify and The Echo Nest, the age of when people stop listening unselectively is 33 and not 24 as Levity discovered previously (Kalia, 2015).


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Among Spotify’s major competitors are Deezer, Apple music, Rhapsody, Napster and Google Play (Sisario, 2015) (Mulligan M. , 2017). Spotify has consistently led the streaming charge and despite a continually changing competitive marketplace it has held determinedly onto pole position since it first acquired it. Even more impressively, it has also maintained market share. Not bad for a service facing its fiercest competitor yet in Apple, a resurgent Deezer and an increasingly significant Amazon (Mulligan M. , 2017).

Figure 15: Marketshare in streaming services (Mulligan M. , 2017)

In their research Wlömert and Papies (2016) revealed that given the current number of adopters of free and paid streaming service, streaming services are net positive for the industry. However they point out that this is mainly due to the strong positive revenue contribution by paid streaming that offsets the negative impact of free streaming (Wlömert & Papies, 2016). Earlier research on the same topic has also revealed that free streaming had no effect on CD sales while it did have a positive effect on live music attendance (Nguyen, Dejean, & Moreau, 2014). Aguiar and Waldfogel (2015) also showed in their analysis that interactive streaming is revenue-neutral for the recorded music industry. They found that while streaming does hurt the final numbers of digital downloads, it offsets the negative consequences by also giving people a better option than illegally pirating the music they want to listen to (Aguiar & Waldfogel, 2015).

5.3.2 How Spotify uses cloud to offer its service. The core function of Spotify is audio streaming. Different from many commercial music streaming services is that it is not web-based, but instead uses a proprietary client and protocol (Kreitz & Niemelä, 2010). Spotify transfers data from both its servers and a proprietary Peer-to-Peer (P2P) network. Using P2P technology considerably increases Spotify’s scalability and reduces server workload and bandwidth requirements (Zhang, et al., 2013). Spotify‘s storage system has back end servers located at three sites: Stockholm, London and Ashburn (Yanggratoke, Kreitz, Goldmann, Stadler, & Fodor, 2015). Clients are directed to the closest data centre by GeoDNS, detecting the country of the client making a DNS query. The client receives a pool of servers to connect to via DNS and randomizes the order in which it attempts to contact them (Zhang, et al., 2013).


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Spotify uses a proprietary network protocol designed specifically for on-demand music streaming (Zhang, et al., 2013). The Spotify service is a peer-assisted system, streaming from Spotify’s data centres. This means that it has a peer-to-peer (P2P) component running on the clients computer to offload backend servers (Zhang, et al., 2013) (Yanggratoke, Kreitz, Goldmann, Stadler, & Fodor, 2015). Smartphone clients do not use the P2P component, they are client-server only applications (Kreitz & Niemelä, 2010). Spotify clients use local storage to cache content the client has accessed, to decrease the network load of data transmission (Zhang, et al., 2013). When a client plays a music track, its data is obtained from a combination of three sources: the client local cache (if the same track has been played recently), by other Spotify clients through peer-to-peer technology or the Spotify storage system in a backend site (Kreitz & Niemelä, 2010). The importance of caching is thus twofold: firstly it is common that users listen to the same track several times and caching the track obviates the need for it to be re-downloaded. Secondly, cached music data can be served by the client in the peer-to-peer overlay (Kreitz & Niemelä, 2010). On the downside the cache cannot be used for playback when the client is offline. A simplified overview of the part of the Spotify backend architecture responsible for music delivery to the clients is captured in Figure 16, shown below. Each Spotify backend site has the same layout of storage servers, but the number of servers varies (Yanggratoke, Kreitz, Goldmann, Stadler, & Fodor, 2015). The master storage component is shared between the sites. When a user logs in, the client connects to an Access Point (AP) using a proprietary protocol. Through the access point, the client can access the backend services including storage (Yanggratoke, Kreitz, Goldmann, Stadler, & Fodor, 2015). While the User Datagram Protocol (UDP) is the most common transport protocol in streaming it is not used by Spotify (Kreitz & Niemelä, 2010) because this does not offer a guarantee of delivery it also does not provide error-correction facilities. Therefor Spotify clients maintain a long-lived Transmission Control Protocol connection (subsequently abbreviated to TCP) to the access point and requests to backend services are multiplexed over this connection (Zhang, et al., 2013). The connection is kept open while the client session is running to keep down playback latency. In the connection, the client authenticates the user, and we refer to the establishment of a TCP connection as logging in, and the closing of the connection as logging out (Zhang, et al., 2013). TCP is a standard that defines how to establish and maintain a network conversation via which application programs, like Spotify, can exchange data. This has some advantages. To start TCP is a reliable transport protocol that simplifies protocol design and implementation (Kreitz & Niemelä, 2010). Secondly, TCP’s congestion control is friendly to itself, thus other applications using TCP and the explicit connection signalling helps firewalls. Thirdly, as streamed tracks are shared in the peer-to-peer network, the re-sending of lost packets is useful to the application (Kreitz & Niemelä, 2010) (Zhang, et al., 2013). Spotify’s storage is two-tiered. A client request for an object goes to Production Storage, a collection of servers that can serve most requests. The protocol between the access point and Production Storage is HTTP, hypertext transfer protocol. In fact the Production Storage servers run software based on the caching Nginx HTTP proxy (Sygoev, 2002). Nginx is a server written to address the C10K problem, where web servers try to handle ten thousand clients simultaneously (Kegel, 2014).


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Unlike traditional servers, Nginx does not rely on threads to handle requests. Instead it uses a much more scalable event-driven (asynchronous) architecture (Sygoev, 2002). In fact it uses whatever high-efficiency network event mechanism is available on the target operating System (Kegel, 2014).

Figure 16: Spotify storage architecture (Yanggratoke, et al., 2015)

The objects are distributed over the production service machines using consistent hashing of their respective keys (Karger, et al., 1997). Each object is replicated on three different servers, one of which is identified as the primary server for the object. Access points route a request for an object to its primary server. When the primary server fails or does not store the requested object, the server will request it from one of the replicas. If they do not store it, the request will be forwarded over the Internet to Master Storage, which is based upon a third party storage service. The retrieved object will subsequently be cached in Production Storage. The third party storage is used because Spotify faces the perpetual challenge of cataloguing not only yesterday and today’s popular tracks, but also all those to be released in the future. Spotify adds over 20,000 tracks a day to its catalogue (Amazon Web Services, 2017). Because of this Spotify needed a storage solution that could scale very quickly without incurring long lead times for upgrades. This led them to cloud storage, and in that market, Amazon Simple Storage Service (Amazon S3) was the most mature large-scale product (Amazon Web Services, 2017). In addition, Amazon Cloud Front delivered the Spotify application and software updates to users. Just as music trends perpetually change, Amazon Web Services (AWS) helped Spotify continuously evaluate its infrastructure in order to meet evolving business goals (Amazon Web Services, 2017). While establishing new storage previously required several months of preparation, by using cloud storage it can now be obtained instantly. While the company cannot always predict the next overnight music sensation, its infrastructure can spontaneously adjust to any alterations in user demand (Amazon Web Services, 2017). Spotify was aware of the work and lengthy preparation involved in provisioning capacity, whether it was storage, servers, or networks. Therefore, Spotify is now able to reduce all of that to an AWS API call (Amazon Web Services, 2017).


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The key performance metric on this level for Spotify is playback latency, as already mentioned above. It is best described as when a user presses ‘‘play’’, the selected track should start ‘‘instantly.’’ To achieve this, the client generally fetches the first part of a track from the backend and it starts playing as soon as it has sufficient data, by doing so it avoids that buffer underrun appears, also known as stutters (Yanggratoke, Kreitz, Goldmann, Stadler, & Fodor, 2015). The system design is intended to provide low latency for the vast majority of requests coming from a large, and growing, user base. By using a proprietary protocol with long-lived connections. The design with a two-tiered storage arises from the engineering problem of providing low-latency access for most requests to a growing catalogue of music, with a popularity distribution close to Pareto. Where approximately 80% of the traffic handles the same 20 % of data. The Production Storage tier gives low-latency access to almost all requests, using high performance servers, while the Master Storage tier provides a large storage capacity for all information at a higher latency (Yanggratoke, Kreitz, Goldmann, Stadler, & Fodor, 2015).

5.3.2.1 A change in the cloud However a big change to Spotify’s way of working occurred in February 2016 when Spotify choose to move its infrastructure to Google cloud platform. Spotify has always been obsessed with providing a streaming experience that feels as though customers have all the music in the world on any device. Historically, Spotify took a traditional approach to doing this: buying or leasing datacentre space, server hardware and networking gear as close to their customers as possible (Harteau, 2016). However time and again the engineers at Spotify asked whether the trade-off of resources that could otherwise focus on innovative features and software, was worth it (Leygues, 2016). As Spotify realized its increasing global business would require expensive investments in data centres around the world, the company also decided the economics and quality of cloud services had reached a point at which Spotify wouldn't lose much by making a switch (Konrad, 2016) Thus Spotify decided it didn’t want to be in the data centre business and chose Google Cloud Platform over the public cloud competition after careful review and testing (Leygues, 2016). Because good infrastructure isn’t just about keeping things up and running, it’s about making all of Spotify’s teams more efficient and more effective and Google’s data stack does that (Harteau, 2016). Spotify had spent several years looking at cloud offerings, using Amazon for some needs but keeping the bulk of its infrastructure on premise (Konrad, 2016). Google, however, offered data tools that Spotify found more intriguing. Especially because its services for analysing large amounts of data, including a tool called Big Query, are more advanced than data services from other cloud providers (Metz, 2016b). Rumours say that Google cut Spotify a deal, just so it could add such a high-profile name to its client list (Metz, 2016b). Spotify is transitioning from an on premise to an all-cloud infrastructure model gradually, a process it expects will take months to complete (Sverdlik, 2016). While Spotify stumbled over a few gotchas when shifting the 3PB of data from Amazon simple storage services (S3) to google cloud storage, the process was completed by the end of October 2016 and without major setbacks (Heath, 2016).

5.3.3 Business model of Spotify explained Until the launch of Spotify, there were two economic models for streaming services: all free or all paid, there was nothing in between, and both models had a fatal flaw. The paid-only


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services never took off, despite huge investments like spending hundreds of millions of dollars on marketing, because users were being asked to pay for something that they were already getting for free on piracy sites (Ek, 2014). The free services on the other hand paid next to nothing back to artists and labels and were often just a step away from piracy, implemented without regard to licensing. They also failed to offer a path to convert their free users into paying customers (Ek, 2014). The Paid model provided monetization without scale while the free model reached scale without monetization. Neither produced anywhere near enough money to replace the ongoing decline in music industry revenue (Ek, 2014). The hardest part about selling a music subscription or even music in general is that most of the competition comes from the tons of music that is available for free. The three most popular ways to listen to free music are radio, YouTube and piracy (Ek, 2014). The above mentioned leads to the basic assumption: the vast majority of music listening is unpaid and if a firm wants to drive people to pay for music they have to compete with what is available free to get the people their attention in the first place (Ek, 2014). Ek's theory was that people were willing to do the right thing but only if it was just as rewarding, and much less a hassle, than doing the wrong thing (Lynskey, 2013). He argues that Spotify subscribers don't pay for content, they can get that for free through piracy, they pay for convenience (Lynskey, 2013). Spotify also succeeded in fulfilling unmet customer needs, by offering a large music palette, from which customers could freely choose. This idea was not totally new. Deezer had come up with this kind of freemium business model prior to Spotify however the latter was faster, easier to install and use, and more social than all previous platforms that were available (Voigt, Bulinga, & Michl, 2017). On Spotify, free music is supported by ads, and by doing so every play is actually paid (Ek, 2014). Furthermore Spotify from the start on believed in the blended option, or in other words the “freemium” business model. This model uses the combination of “free” and “premium” (Vineet, 2014). At Spotify they believed that a choice for this model would build scale and monetization at the same time and thus ultimately create a new music economy that gives fans access to the music they love and pays artists fairly for their amazing work (Ek, 2014). This led Spotify to build a terrific free tier, supported by advertising, as a starting point to attract fans and get them in the door. Unlike other free music options they payed artists and rights holders every time a song is played on their free service. The big down side is that a freemium membership is not as flexible or uninterrupted as a Premium membership (Ek, 2014). The use of Spotify’s free service on mobile is more or less like listening to the radio. You can pick the kind of music you want to hear but can’t control the specific song that’s being played, or what gets played next and you have to listen to ads (Ek, 2014). Several factors contribute to the appeal of a freemium strategy. Because free features are a potent marketing tool, the model allows a new venture to scale up and attract a user base without expending resources on costly ad campaigns or a traditional sales force (Vineet, 2014) Spotify has always been certain that as fans got used to its application by listening to their favourite music, discovering new music and sharing it with their friends they would eventually want the full freedom offered by the premium tier and they’d be willing to pay for it (Ek, 2014). As it seems Spotify was more or less right. Their free service drives their paid service as 80% of their paid subscribers used to be a freemium user (Ek, 2014).


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Spotify had already reached more than 20 million paying subscribers and more than 75 million active users in 2015. That is 10 million subscribers in their first five and a half years, up to 2014 and another 10 million subscribers in just a single year (Spotify, 2015a). In the last two years numbers doubled again to a dazzling 50 million paying subscribers in 2017 (McIntyre, 2017). When it comes to the total number of people listening on Spotify, a representative for the company is still holding to the 100 million figures announced in June 2016 (McIntyre, 2017). Spotify offers a unique selling proposition (Reeves R. , 1961). The purchasers of unique products will obtain specific unique benefits from consuming those particular products (Bao & Shao, 2002). In the case of Spotify these benefits are: enjoying music in a legal way, a database of 30 million songs, very easy access to this immense library, listening to songs while having the feeling you actually own all of those songs (De Baere, De Groote, & Szeridi, 2013a). Also Spotify can be taken everywhere as long as there is a network connection and with one account you can listen to songs on your smartphones, tablets, computers and television. As a paying customer you can even use one account at multiple devices at the same time, which makes it quite a cheap alternative for radio or buying music online (De Baere, De Groote, & Szeridi, 2013a). Besides the previously mentioned paying customers have some other advantages like no advertising, a better sound quality and offline availability on mobile devices (Dörr, Benlian, Vetter, & Hess, 2010). The monthly subscription fees typically charged are proving to be a more sustainable source of revenue than the advertising model prevalent among other online firms in the early 2000’s (Vineet, 2014). The below shown division of revenues illustrates that perfectly. Although a small growth of the revenue share by ads can be seen, it is more than obvious that the biggest piece of the revenue comes from Spotify’s paying subscribers (Ingham, 2016).

Figure 17: Spotify revenues: ads vs subscription (Ingham, 2016)

As the above figure shows Spotify has three big incoming revenue streams. The third, although in comparison to the other two streams, is almost non-existing. The third stream is mainly driven by what Spotify earns on artist wants to promote a new album (Unicorn Economy, 2016a). One of the main purposes of a freemium business model is to attract new users. If a firm is not succeeding with that goal, it probably means that the free part a firm is offering is not compelling enough and then it should provide more or better features for free (Vineet, 2014).

10,07%

89,67%

0,26%

Spotify 2015 revenue: ads vs subscriptions

advertising subscriptions Other

9,14%

90,47%

0,39%

Spotify 2014 revenue: ads vs subscriptions

advertising subscriptions Other


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If a firm notices they are generating a lot of traffic but few people are paying to upgrade, they may have the opposite problem: in that case the free offerings are too rich and it’s time to cut back (Vineet, 2014). These adjustments in both directions were done by Spotify over the course of years. Back in 2011 they imposed that freemium users could only listen up to ten hours a month after the introductory grace period of six months of unlimited usage had passed (The Guardian, 2011). Later in 2014 they lifted this restriction again (Spotify, 2014a). Right now it seems like Spotify is still attracting plenty of traffic and new users, and their conversion rate is currently about 50% (McIntyre, 2017). The conversion rate is calculated by dividing the amount of paid subscriptions by the total number of Spotify users (premium + freemium). The percentage at this point is extremely high because the last publication of the total amount of Spotify users dates from 2016 already (Shanley, 2016a). Compared to the number representing the amount of subscribers for premium which was recently updated (McIntyre, 2017). It seems more than reasonable that this percentage in reality is a bit lower due to a higher amount of total Spotify users by now, due to normal growth over the past year. However these numbers do not guarantee correct forecasting of growth and revenue. This remains difficult because one cannot simply draw a couple of straight lines, on the assumption that the rate will hold steady (Vineet, 2014). There are some things that should be kept in mind. For example early adopters are less price-sensitive than others, so they are more likely to upgrade. Often they are people for whom the value proposition is unusually compelling (Vineet, 2014). In that light it is expected that conversion rates typically dip over time as the user base expands to include people who are more price-sensitive or who see less value in the service. However until now it seems like Spotify has experienced smooth sailing on that issue. Their subscribers for premium and freemium are increasing at more or less the same pace. It’s important to recognize the full value of free users, which takes two forms: Some of them become subscribers and some draw in new members who become subscribers. A free user is typically worth 15% to 25% as much as a premium subscriber, with significant value stemming from referrals. We have also found that firms can increase the value of referrals by carefully managing referral incentives and communications. If you’re considering a freemium model, pay close attention to why and how satisfied users might help your product go viral (Vineet, 2014). The Spotify business model is dependent on buying and curating more and more content. The catalogue of songs at this moment contains more than 30 million songs and more music is added on a daily basis. This is similar to the business model of Netflix which is also reliant on spending more and more on content (Unicorn Economy, 2016a). Every stream of a song via the paid route pays three parties: the artist, the publisher and Spotify (Unicorn Economy, 2016a). Although if the latter really earns from streaming up to now is up to discussion considering the losses reported in their financial reports from the last few years (Sisario, 2015) (Ingham, 2016). Because Spotify makes its deals with the record labels, everyone should get a cut along the way, leaving little for the people who actually perform the music. This lead some artists to have mixed feelings about Spotify. At the beginning, Spotify boasted about the revenue it shared with musicians, but eventually it was


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revealed that these pay-outs were much lower than expected (Pullen, 2015b). Spotify however felt this was a sensible topic that could harm its business and shed some light on this topic to define what a single stream, or listen, actually is: it’s one person playing one song one time. So people throw around a lot of stream counts that seem big and then tell you they’re associated with pay-outs that sound rather small. Daniel Ek (2014) explains what those counts really represent: “If a song has been listened to 500 thousand times on Spotify, that’s the same as it having been played one time on a U.S. radio station with a moderate sized audience of 500 thousand people. Which would pay the recording artist precisely … nothing at all. But the equivalent of that one play and its 500 thousand listens on Spotify would pay out between three and four thousand dollars”. On the other side lesser-known artists have justified this by valuing the exposure that Spotify’s large user base brings (Pullen, 2015b). In any case their business model launched the streaming service off to a rocky start, as it posted losses of $197 million (€179.1 million) in 2014, despite having raked in $1.3 billion (€1.18 billion) in revenue (Sisario, 2015). In 2015 Spotify brought in an astonishing $2.18 billion (€1.95billion) in revenues, however net losses still stood at a painful $194 million (€173.1 million), but these grew much slower compared to the growth of revenues (Ingham, Spotify revenues topped 2 billion last year as losses hit 194 million, 2016). Spotify persists that it'll turn a profit when it gains a certain amount of subscribers (Wong, 2016). Until now cost of growth for Spotify had been very high because, as it expanded to new markets, it had to pick up the initial cost for music royalties for new users and it takes time before it can generate advertising or subscription revenues from each additional subscriber (Shanley, 2016b). From the moment that Spotify can start to optimize on profitability rather than on growth, then these unit economics will kick in right away, and they are really solid, and have been for quite some time (Shanley, 2016b). Some analysts have argued Spotify's business model is flawed and it is held hostage by the record labels. But with 50 million paid users (McIntyre, 2017), the company could be near a tipping point and may have greater leverage in its negotiations with the music industry (Shanley, 2016b).

5.3.3.1 Going viral? Going viral is closely linked with the above mentioned conversion rate of for now an astonishing 50 %. Spotify has some conversion triggers and ways it ensures their crazy-high conversion and retention (Brandall, 2016). To start with they reduce friction and increase virality with a simple Facebook sign-up, which means you don’t have to fumble for your email address and password. By this one click sign up actually two things happen: the user’s superior taste is displayed to their friends and it acts as a way to familiarize the user’s friends with Spotify and also prompts them to sign up for the service as well (Brandall, 2016). The Facebook integration played thus far a huge part in the virality of Spotify. The whole point of Spotify is to get you to discover music and Spotify initially encourages you to do this is through curated playlists. A curated playlist is one where you already know a few songs, or a theme that matches your taste, Spotify than cycles through and mixes songs you know with songs you didn’t based on their algorithms (Brandall, 2016). The natural reaction to a song you like but don’t know is to explore the artist, album and other playlists which feature it. This is made extremely easy and a focal point of the first screen you see when opening the app or desktop version (Brandall, 2016). But here’s the catch. With a


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free account, you aren’t in full control of what you listen to at any time, especially on the mobile app. Even if you make your own playlists, they’re only available on shuffle (Brandall, 2016).So, one of the main reasons Spotify gets subscribers to upgrade is because subscribers naturally developed a refined taste thanks to its spookily on-point recommendations algorithms (Brandall, 2016). And when those tastes developed, shuffle in most cases does not cut it anymore. Another thing is that it is only possible to skip so many times before I was stuck with whatever Spotify destined for you (Brandall, 2016). If you are pushing your limits, especially on mobile a pop up comes up and at that point you’re hit with the call-to-action: ‘You discovered a premium feature upgrading to Spotify premium will unlock it or click not now’. On desktop these call to actions are more incorporated like adds in the starting screen of the app. Another thing Spotify does is empathizing with its subscribers. This is done through the suggested playlists on the first-use screen as a list of moods, situations and genres because a core reason people listen to music is to intensify their mood (Brandall, 2016). Spotify taps in to the ‘music as a soundtrack to my life / how I’m feeling right now’ vibe by giving descriptive categories with hyper-focused playlists inside (Brandall, 2016). By encouraging you to get acquainted with their playlists and linking it to a tangible, existing habit in your life, Spotify entrenches itself as a soundtrack machine able to generate fitting music for any situation (Brandall, 2016). Building a product into the habits and routines of users is a powerful way to increase retention. Spotify does this in such a personal way by understanding the modes and means of music consumption for example at a party, while running, focusing on your studies, etc. Once you’ve used the playlist once for this purpose, you’ll start to miss it when it’s gone (Brandall, 2016). Another reason why Spotify’s conversion tactics manage to disguise themselves as being non-aggressive (while being aggressive as hell) is that you don’t realize how much the bad parts grate on you. Another option Spotify offers is letting users save music for offline listening during the 30-day free trial just after signing up for the first time and this is in fact devilishly brilliant (Brandall, 2016). By letting users save music for offline use but then slapping them with stream-only mode afterwards, those 30 days of freedom will seem amazing in comparison to the limited state of existence that comes along with a normal freemium account (Brandall, 2016). It gives users a taste of what they could have if they converted in such a profound way because a month is quite a while to build a collection. And as mentioned before Spotify makes it very easy to build a big collection of personally saved music because of its ‘Discover’ feature that recommends up to twenty albums per day and skilfully evolves based on your current tastes and habits (Brandall, 2016). This concept ‘free drives paid’ is fully understood by Spotify and fully integrated in their business model (Businessmodelsinc, 2012). The graph (1) on the following shows the number of Spotify subscribers over the time period 2012-2017. The graph shows that Spotify managed to attract a vast amount of users over the years. In also shows that nearly half of all the users are paying for the service. The second graph (2) shows the percentage of paying Spotify subscribers over the same period. Here one can see that over the last two years the percentage of paying subscribers has almost doubled.


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Graph 1: Spotify subscribers 2012-2017

Graph 2: Percentage of paying Spotify subscribers 2012-2017

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5.3.4 The Business Model Canvas of Spotify In the previous section I tried to provide a general overview about the characteristics of the business model that is used by Spotify from the beginning on and how and why this model was applied. In this section the purpose is to dissect the model further and link it to the business model canvas in order to make it more comparable to the other cases included in this research. I will do this by first elaborating on all the subsets from the canvas and by providing links what was already discussed in the above sections.

5.3.4.1 Key Resources of Spotify Several resources made Spotify’s business model possible: First, licensing agreements with rights holders and with record labels allowed content provision. Second, the online platform ensured not only content delivery, but also speed and ease of access. The distinguishing mark of Spotify’s platform laid in the combination of two different approaches for ensuring scalable music listening, partly streaming music from a central server, and partly using peer-to-peer (P2P)technology (Voigt, Bulinga, & Michl, 2017). As regard to employees, their numbers grew substantially in a six-year period, from 311 in 2011 to over 1,600 in 2015 (Statista, 2016a), following the company’s international expansion and value proposition diversification (Voigt, Bulinga, & Michl, 2017). The Intellectual resource is the brand name which Spotify created for itself among the customers (De Baere, De Groote, & Szeridi, 2013b).

5.3.4.2 Key Activities of Spotify Spotify’s main activity is ensuring music content rights and delivery of music via multiple channels (Unicorn Economy, 2016a) (Voigt, Bulinga, & Michl, 2017). Another key activity revolves around analysing customer preferences as the company attempts to build the best music recommendation service. This is mostly used to create curated playlists (Brandall, 2016) or to recommend new artists. Its efforts in this area are reflected in recent acquisitions, such as that of the music intelligence company Echo Nest (Voigt, Bulinga, & Michl, 2017) by which they try to gain more insights on the behaviour of music consumers as example see the study of Kalia (2015).

5.3.4.3 Key Partners of Spotify Spotify relied on the support of the dominant rights holders and record labels in the market, such as Universal Music Group, Sony Music Entertainment, EMI Music and the Warner Music Group. Without the support of these parties, the advent of Spotify’s business model would not have been possible (Voigt, Bulinga, & Michl, 2017). Additionally, substantial financial backing came from private investors and venture capital groups. In total the company raised over a one and a half billion US dollars between 2008 and 2016, through eight major financing rounds (Sisario, 2015) (Ingram, 2016) (Ingham, 2017) (Chrunchbase, 2017). Spotify has also entered in several partnerships with companies from various industries, primarily in order to increase its user base (Voigt, Bulinga, & Michl, 2017) for example Coca Cola (Spotify, 2012) and Facebook (Sawer, 2011) (Van Buskirk E. , 2012a). This last one is the most prominent example. The cooperation started in 2011 when Spotify was integrated within the social network’s platform, allowing Facebook users to stream music directly from the Facebook page and share songs and playlists with Facebook friends (Sawer, 2011) (Van Buskirk E. , 2012a). although this was not the only partnership in order to attract new members and media attention, another one Spotify initiated was the one with Klout in 2011 (Butcher, 2011),


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a website that ranks users according to their online social influence. Additionally, cooperation’s were started with other well-known names such as Uber (Shontell, 2014), SoundHound (Wauters, 2011) and Starbucks (Spotify, 2015b) to name a few. These new partnerships also improved the value proposition primarily in terms of personalization and social interaction (Voigt, Bulinga, & Michl, 2017). Also essential for the growth of the streaming services were partnerships with telecommunication companies. For example, in the German market, Deutsche Telekom, became Spotify’s key telecommunications partner (Lunden, 2012a). As Spotify released its own application programming interface (API) in 2012 (Van Buskirk E. , 2011), app developers became an additional group of partners, as these create complementary platform applications (Voigt, Bulinga, & Michl, 2017). Such applications invite users to discover new features and to share their experience with friends; this in turn leads to an increasingly personalized music experience. For instance, the application Moodagent enables users to search for appropriate music to one’s mood (Widder, 2013), because music after all soothes even the savage beast (Holland, 1994) or can intensify a mood (Brandall, 2016) . In 2016 Spotify decided it didn’t want to be in the data centre business and chose Google Cloud Platform over the public cloud competition after careful review and testing (Leygues, 2016). Because good infrastructure isn’t just about keeping things up and running, it’s about making all of Spotify’s teams more efficient and more effective and Google’s data stack does that (Harteau, 2016). Spotify had spent several years looking at cloud offerings, using Amazon for some needs but keeping the bulk of its infrastructure on premise (Konrad, 2016).

5.3.4.4 Customer Segments of Spotify In order to make the freemium business model work, Spotify relied on a three-sided customer base: music and technology enthusiasts, artists and music labels, as well as advertising companies (Voigt, Bulinga, & Michl, 2017). Without the last customer segment comprising of advertisers, the freemium business model would have not been possible (Ek, 2014). With regard to the first customer group changes can be observed: while in 2010 the number of free users was fifteen times higher than the number of paying users, the ratio steadily decreased from seven in 2011, to five in 2012 , to three in 2014 (Voigt, Bulinga, & Michl, 2017) and most recently to two in 2017 . At the beginning of 2017, Spotify had 50 million paying users and over 100 million free users in 60 countries across the world (McIntyre, 2017), the largest of which, by subscriber numbers, are the U.S. and the UK.

5.3.4.5 Value Proposition of Spotify The main value proposition is and has always been from the start on to provide a vast online music library that is easy and fast to access. Together with helping you spot and identify the favourites you forgot about (Spotify, 2008). In some geographical regions, Spotify’s initial value proposition for non-paying customers was only a fraction of the value proposition for paying ones. In some cases listeners using the free Spotify version were limited to a number of hours of music listening each month, and were only allowed to replay a song for a few times during the same period (Sawers, 2011) (The Guardian, 2011) (It All Makes Sense, sd). This strategy was, however, ill-suited for a start-up facing increased competition (Vineet, 2014). Spotify soon understood the frustrating effect of time limits on its non-paying customers,


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resulting in their reluctance to subscribe to the service. In turn, the company eliminated time restrictions and slightly increased the number of advertisements to be played on its platform (Spotify, 2014a). In essence, Spotify’s initial value proposition of on-demand music access did not change over time, yet the company went a long way to improve it, for instance through add-ons (Voigt, Bulinga, & Michl, 2017). For example by a mobile app allowing multiple devices for music listening (History of spotify, sd) and a family subscription plan, which made several individual subscriptions among family members obsolete (O'Hear, 2014) (Spotify, 2014b). Additionally, Spotify increased its efforts to personalize the music experience, for example through mood-adjusted playlists (Brandall, 2016) (Widder, 2013). Spotify created a blue ocean (Kim & Mauborgne, 2004) for themselves by creating a new market for customers by offering access to a whole database of songs at a low price instead of offering songs or albums at a higher price compared to for example iTunes. The value for costumers is that they don’t have make choices between their favourite songs they can listen to all of them, although they trade of their owner rights to any of the songs for access to all of them. However there is also a Trade-off in that the customer are not owning any of the songs, they only buy rights to stream from the database for a period of one month if they pay or they are forced to listen to advertisement if they go for the free account (De Baere, De Groote, & Szeridi, 2013b).

5.3.4.6 Customer Relationships of Spotify The customer relationship is an automated service which is a more sophisticated form of self-service. Customers are able to buy the software online and have the possibility to customize it to their specific needs (De Baere, De Groote, & Szeridi, 2013b). For ensuring a prime customer experience and long term customer binding, Spotify introduced substantial personalization options, particularly with respect to music choice and selection (Brandall, 2016). The company initially only permitted new users to join the platform only via invitation by a current user (Sawers, 2011). Over time the process of reaching new customers shifted to social media like Facebook (Sawer, 2011) (Van Buskirk E. , 2012a) and Klout (Butcher, 2011). By focusing on individualization and personalization, Spotify fuelled an inter-industry shift from consumers to prosumers (Gunelius, 2010) (Marketingmag, 2015), who simultaneously consume, produce and distribute media. The Web 2.0 logic and the apps, which accompany the Spotify platform, made this possible. Users are no longer part of an anonymous mass, but became active participants on Spotify’s own platform and on those of its partners (Voigt, Bulinga, & Michl, 2017).

5.3.4.7 Channels of Spotify Spotify initially employed a single platform as channel for music distribution and customer communication (Voigt, Bulinga, & Michl, 2017). There have been significant developments to this initial state. As now more than half of Spotify users stream music via mobile devices, such as smartphones or tablets (Constine, 2015). Additionally, since Spotify cooperates with social media platforms, it is able to reach users not only through its own channel, but through the channels of its media partners, such as Facebook (Brandall, 2016).


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5.3.4.8 Revenue Streams of Spotify Spotify created value by bringing together record labels and listeners, without itself holding music ownership (Voigt, Bulinga, & Michl, 2017). Since the beginning, the company had two big revenue streams: subscription payments from premium users and advertising fees (Ingham, 2016). Revenues generated from subscriptions have always been bigger than the amount of revenues from advertisements. Based on the last available numbers the company’s advertising-related revenues currently only make up for around 10% of its revenues (see figure 17 in the previous section page no 92). If a song is played by a premium subscriber, the record label receives a higher fee from Spotify, than when the same song is played by an ad-sponsored Spotify user (Voigt, Bulinga, & Michl, 2017). As a result, the company’s long-term goal was and until today still is to convert as many free users as possible into premium subscribers which logical outcome of the usage of the freemium business model (Vineet, 2014). The paid subscribers do not only provide higher and more stable revenues for Spotify itself, but also for record labels, which in turn ensures a better bargaining power for Spotify (Voigt, Bulinga, & Michl, 2017). While the company’s total revenues are increasing from $1.3 billion (€1.18 billion) in 2014 (Sisario, 2015) to $2.18 billion (€1.95billion) in 2015 (Ingham, 2016) their losses also increased, although at a smaller rate, from $197 million (€179.1 million) in 2014 (Sisario, 2015) to $194 million (€173.1 million) in 2015 (Ingham, 2016).

5.3.4.9 Cost Structure of Spotify The largest cost blocks comprised of licensing expenses or royalties paid to rights holders, as well as bandwidth and additional operating costs. However licensing costs are not necessarily the reason for Spotify’s yearly losses, as this cost block remained proportionately stable, amounting to about 70% of revenues in 2014 (Voigt, Bulinga, & Michl, 2017). The losses can be explained by the high investments in service development, and by an eager international expansion strategy. The company accepted this trade-off, following its mission statement of making music available for an assortment of markets, instead of aiming only for short-term profits in mature, flourishing markets (Shanley, 2016b).


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5.3.4.10 The business model canvas of Spotify The above discussed subsections can be all found together in the underneath depicted business model canvas (Osterwalder & Pigneur, 2010):

Key Partners

Major record labels and rights

holders

Investors and venture capital groups

Artists

App developers

Partnerships (Facebook, Starbucks,..) for increasing customer reach


Google cloud platform

Key Activities

Ensuring music content rights and delivery of music via multiple channels

Analyzing customer

preferences,

and

building a

world-class

recommendati

on

system

Value Proposition

Simple and fast online access to a vast musical library

anywhere and anytime on any device

Spot and identify


Automated online customer relationship, with personalization options

Encouraging prosumers

Customer Segments Three-sided customer base:

Music and technology enthusiasts

Artist and record labels

advertisers

Key Resources

Licensing agreements

IT capabilities and know-how for platform development and music content provision

Employees

Brandname

Channels

Company owned platforms

Social media

Via sponsors(Coca cola)

Partner websites (Facebook, Klout)

Cost Structure

Royalties to record labels and rights holders (around 70% of total costs)

Bandwidth and additional operating costs

Wages of employees

Revenue Streams

Subscription payments from premium users(89,67 %)

Advertising fees( 10, 07%)

Others (0.26%)

Figure 18: The Business Model Canvas of Spotify


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5.3.5 The impact of cloud computing on the business model of Spotify In the business model canvas of Spotify that was presented on the previous page some of key parts were put in bold to signal that in these parts cloud computing has an clear impact. In this part I will briefly go in to detail on how cloud computing has an impact on several concepts that immediately have impact on the business model of Spotify. Starting from the left on, in the Key partners concept. Spotify for its service delivery as much as any other service provider using cloud computing is dependent on a quick delivery of internet. If there is no connection to the internet, Spotify simply cannot provide our deliver. This confirms the fact that internet is more and more seen as the fifth utility (Buyya R. , Yeo, Venugopal, Broberg, & Brandic, 2009). In the partners concept Google cloud platform is also mentioned because in 2016 Spotify decided it didn’t want to be in the data centre business and chose Google Cloud Platform over the public cloud competition after careful review and testing (Leygues, 2016). Because good infrastructure isn’t just about keeping things up and running, it’s about making all of Spotify’s teams more efficient and more effective and Google’s data stack does that (Harteau, 2016). Before this, Spotify had spent several years looking at cloud offerings, using Amazon for some needs but keeping the bulk of its infrastructure on premise (Konrad, 2016). This partnership also has its influence on the cost structure of Spotify as it now will have to pay for the usage of Google’s services. However as mentioned before due to several advantages delivered by cloud computing this is expected to be less in comparison to organising e.g. own data centres around the world (T-Systems, 2009) (Armbrust, et al., 2009) (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011) (Surya, Mathew, & Lehner, 2014). On the Key resource level Spotify’s main resource is in fact their platform and how this works. The distinguishing mark of Spotify’s platform laid in the combination of two different approaches for ensuring scalable music listening: partly streaming music from a central server and partly using peer-to-peer (P2P) technology (Voigt, Bulinga, & Michl, 2017). So in fact cloud computing is double used in this. First: in streaming from a server in the cloud and secondly: to allow peer-to-peer connections to provide a faster service and to relieve the server. Combining both optimises the speed of music delivery of the platform. Also in customer relationships cloud technology is used. In their relationships with customers Spotify focused on individualization and personalization, Spotify fuelled an inter-industry shift from consumers to prosumers (Gunelius, 2010) (Marketingmag, 2015), who simultaneously consume, produce and distribute media. The Web 2.0 logic and the apps, which accompany the Spotify platform, made this possible to provide this on any device that is used to log in. Users are no longer part of an anonymous mass, but became active participants on Spotify’s own platform and on those of its partners (Voigt, Bulinga, & Michl, 2017). Cloud computing is also intensely entwined with the value proposition Spotify offers: that is to provide simple and fast online access to a vast music library anywhere and anytime on any device. Without the usage of cloud computing technology this would not be possible


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5.3.6 Does it meet the disruptive innovation criteria? In order to be able to say whether or whether not Spotify can be categorized as a firm using disruptive innovation the framework of Hang, Chen and Yu (2011) needs to be applied. Hereafter the framework is completed based on the previously gathered and discussed information about Spotify.

Question Yes No




are incumbents in the main-stream market willing to run away or ignore the initial disruptors? x

B. Technology


Adequate for a foothold in the low-end market x Adequate for a foothold in a new, niche market x




Network effects x

Unique value proposition x

Table 9: Assessment framework of Hang, Chen and Yu (2011) applied to Spotify

From the above shown table can be derived that only one times “no” has been ticked. In this case for the existing performance overshoot in mainstream market. The non-existence of this performance overshoot stems from the specificity of the music market and the developments in the market happened previously. The three most popular ways to listen to free music are radio, YouTube and piracy (Ek, 2014). This leads to the basic assumption: the vast majority of music listening is unpaid and if a firm wants to drive people to pay for music they have to compete with what is available free to get the people their attention in the first place (Ek, 2014). This assumption prohibits a performance overshoot from existing. Further for the case of Spotify the framework indicates that a new market disruption is the case. Some of the questions ticked “yes” are also interesting to add some context. On the market positioning on can clearly see that from the beginning of Spotify’s streaming service this was a new and initial niche market. Spotify created a blue ocean (Kim & Mauborgne, 2004) for themselves by creating a new market for customers by offering access to a whole database of songs at a low price instead of offering songs or albums at a higher price compared to for example iTunes. To grow this new niche market Spotify kept its free service invitation only, something that had been in place whilst it was in the final stages of development prior to public launch (Sawers, 2011). While free accounts remained available by invitation only in order to manage the growth rate of the service, the launch meant that paid subscriptions were opened to everyone (It All Makes Sense, sd). The existence of the free usage of the service


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was also a way to get the attentions of the lowest segment of the existing digital music market. The invitation-only element was a vital part of the platform’s rise. Not only did it help manage the growth level of Spotify, but it also helped to create a viral element to the service, with users each having five invites at first to share with their friends. The latter is hereby immediately linked to the positive Network effects. More specific the combination of Facebook and Spotify appeared to be very powerful (Van Buskirk E. , 2012b). As Spotify allowed users to create and seamlessly share playlists, as well as to exchange music on social networks. Spotify also continuously worked on the criterion of the further improvement in performance by releasing its own application programming interface (API) in 2012 (Van Buskirk E. , 2011). By doing so, app developers became an additional group of partners, as these create complementary platform applications (Voigt, Bulinga, & Michl, 2017). Another important factor, and therefor added as other favourable driver, is Spotify’s unique value proposition, to provide a vast online music library that is easy and fast to access. Together with helping you spot and identify the favourites you forgot about (Spotify, 2008). To keep on facilitating the need to improve the disruptive technology Spotify organised itself by dividing up its business into small clusters which it calls ‘squads’ and they are all running like a start-up in its own right to stay fresh and agile (Lunden, 2012b). Each focuses on a specific function and iterates on minimum viable product, releasing updates early and often (Lunden, 2012b). This way of working is in fact one of the solutions Christensen (Christensen C. M., 1997) advised incumbents to use, to give disruptive innovations a viable chance in an existing firm. As mentioned in the methodology chapter a cross validation of the results of the application of the framework of Hang, Chen and Yu simultaneously (2011) is also executed by using the criteria sheets of Keller and Hüsig (2009):


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entrant








Phase total 2 2 1

Main market entry





Phase total 4 0 0




Value network has a low overlap 1

Phase total 2 0 1

Total 8 2 2 Table 10: Assessment framework for entrant of Keller and Hüsig (2009) applied to Spotify

The application of the framework developed by Keller and Hüsig (2009) confirms the result that was found by applying the framework by Hang, Chen and Yu (2011). Spotify can be seen as firm using Cloud computing as a disruptive innovation. The second framework focusses not entirely on the same factors that can enable disruption so this double check serves as a worthy addition. The main difference with the model of Hang, Chen and Yu (2011) that can be noted is the fact that also business model, processes and value network are also taken into account. For Spotify the business model, the new processes and the value network have been drivers of their success. In particular the freemium model has been a true bonanza. By implementing a freemium model Spotify managed to combine for the first time the best of the two existing models in the industry. Before Spotify’s launch, there were two economic models for streaming services: all free or all paid, there was nothing in between, and both models had a fatal flaw. The paid model provided monetization without scale while the free model reached scale without monetization. Spotify offered a solution for this in combination with fulfilling an unmet customer need, by offering a large music palette, from which customers could freely choose. Above all Spotify’s platform was faster, easier to install and use, and more social than all previous platforms that were available (Voigt, Bulinga, & Michl, 2017).


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5.4 Dropbox 5.4.1 General info The romanticised story about the idea for Dropbox says it was born in 2006 on a bus to New York. Co-founder Drew Houston planned to work during the four-hour ride from Boston but forgot his USB memory stick, leaving him with a laptop and no code to mess with (Barret, 2011). Later in June 2007 he founded a company together with Arash Ferdowsi based on this idea and in September 2008 the product ‘Dropbox’ was launched publicly (Crunchbase, 2017). The idea behind Dropbox is that each user is provided with a free 5GB of storage and that little to no effort should be put into keeping your desktop files synced with “the cloud” (Hendrickson, 2008). So the founders of Dropbox have built a Python-based desktop client available for both PCs and Macs that acts like a regular folder on your machine. You can manage files within this folder just like elsewhere on your machine (add, edit, copy, and delete them) and changes will be automatically synced to Dropbox’s Amazon S3-backed storage (Hendrickson, 2008). Another advantage of using the Python language to program this application was that it allowed the team to scale much more quickly than if it had used another language or group of languages as its base (Warren, 2013). In the early days, when only two engineers were focused on scaling, limiting complexity was an important part of keeping the project growing (Warren, 2013). In a similar vein, by using popular software stacks including MySQL and Amazon's S3 and EC2 infrastructure, the team was able to ensure that (Warren, 2013). A first challenge that Dropbox had to overcome was marketing a product to solve a problem people didn’t realize they had and thus weren’t searching for (Barret, 2011). Dropbox offered an answer to vexing problem for a world where people carry a phone or two, and perhaps a tablet, but have files and photos stuck on multiple PCs, laptops and mobiles (Barret, 2011). At the very least, you can use Dropbox to automatically backup a subset of your files, and to access them when traveling. You can also use the service to easily share files with friends and co-workers (Hendrickson, 2008). Just right click on a folder and select “Share”. You’ll be taken to a webpage where you can enter the email addresses of who you want to share the folder with. When your friends add files to that shared folder, they will automatically get downloaded to your machine in addition to getting backed up online (Hendrickson, 2008). Co-founder Arash Ferdowsi from the start insisted that the Dropbox’s homepage displayed a simple stick-figure video showing what the product does. No table of features and pricing, instead, a story about a guy who loses stuff and goes on a trip to Africa (Barret, 2011). So rather than to turn to advertising, they turned their small but loyal customer base into salespeople (Barret, 2011). A second milestone occurred in 2009 when Dropbox released a separate video on Digg.com during its private beta launch (Zelman, 2011). In that second video co-founder Drew Houston said his team layered “Easter eggs… aimed at the Digg audience” into the otherwise mainstream presentation. The splash of creativity worked. Within 24-hours Dropbox had 75,000 people signed up for the waiting-list. Where initially they were expecting only 15,000 subscribers tops (Zelman, 2011).Not wanting to risk testing a buggy product on all 75,000, Dropbox carefully screened who could kick around early versions by extending invites through a Gmail style closed beta (Zelman, 2011). This is however the only thing that can be noted about this milestone. Dropbox showed a minimum viable product (MVP), which is basically a


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prototype with sufficient core features to interest early adopters, to a select group of subscribers (Business.com, 2017). Afterwards they encouraged users to make comments via Votebox about what they liked or didn’t like (Hulme, 2010). Enthusiasm among early adopters persuaded Dropbox to persevere (Business.com, 2017). By doing so, Dropbox became a perfect example of the “Lean Startup Methodology” (Ries, 2011). Either way Dropbox’ strategy paid off. Just seven months after public launch Dropbox hit 1-million users (Kincaid, 2009). They learned to continue doing what they were doing right in the first place (developing a committed user community and providing it with influence on product development) and not to worry about more traditional marketing approaches. The firm continued to have an enormous success rate, and, after officially being introduced at TechCrunch50, broke records gaining 50 million users in just under three years. In November 2012, only five years after their start up, Dropbox already reached 100 million users (Crunchbase, 2017) and by the end of 2013 Dropbox had gained over 200 million users (Williams M. , 2014). The amount of users kept on growing as can be observed in the underneath depicted graph (No 3) until the most recent astonishing number of 500 million users in 2016 (Statista, 2016b).

Graph 3: Number of registered Dropbox users from April 2011 to March 2016 (in millions) (Statista, 2016b)

This growth was fuelled by several initiatives initiated by Dropbox. For example the ‘Great Space Race’ offered College Students the opportunity to Win up to 25GB of free storage space for two years (Lardinois, 2012). To qualify for the extra space, students had to register here with their school email addresses and the more students at each school sign up, the more storage space they will get. This program was obviously meant to increase Dropbox’s footprint among college students (Lardinois, 2012). Another example was Dropquest, a game where participants could gain extra storage (Cooper D. , 2012). Dropbox also offered his users the opportunity to receive more free storing space by connecting their Facebook account, their twitter and by downloading the mobile app (Bulygin, 2015). Another option and the best rewarded way to gain extra space on Dropbox without converting to premium or business was


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to add your friends. Dropbox made it possible to share a unique link, that you could get on your own account page, with your friends. Dropbox had learned to build on the enthusiasm of its user base by offering a two-sided incentive referral program (Business.com, 2017). Dropbox offered additional free storage to both new subscribers as well as those who referred users. Friend referrals instil more trust than an advertisement ever could. Furthermore, the sender has an incentive to spread the word about Dropbox, getting extra space. The referee also has an incentive for signing up, more space than if they just signed up through the normal process (Bulygo, 2012). By doing so users had the chance to get an extra 500MB for each friend that signs up and installs Dropbox. This option could get deliver up to 16GB of space by inviting 32 people (Bulygin, 2015). One-quarter of all new customers still come to Dropbox this way (Barret, 2011). Dropbox however is not the only player offering this kind of service. Apple has released the iCloud in a successful attempt at connecting all devices for better file sharing (Williams M. , 2014). With this attack from such a major player Co-founder of Drew Houston was shaken with the fear that Dropbox would fall alongside other names such as MySpace, Netscape, or Palm. iCloud is expected to be pushed upon the 222 million people who’ve bought iPhones, iPods and iPads. This is the fear that drove him to keep on improving the product and quite possibly the deciding factor between his success instead of his failure (Williams M. , 2014). The competition is off course not limited to Apple alone among the other contenders are Google, Microsoft and Amazon in a way then there’s IDrive, YouSendIt, Box.net even e-mail as people are sending themselves everything. Another fierce competitor is Googles Drive product. Predominantly because 1 billion people visit Google sites monthly, according to Comscore, and 190 million worldwide now have an Android device (Barret, 2011). So Dropbox must combat a MySpace-like implosion by spending a lot of his war chest on ubiquity. He’s protecting his flank against Google via a new deal with phone maker HTC, which will make Dropbox the default cloud storage option on every one of its Android phones. Deals with six other phone firms are almost inked. Also a couple of hundred outside developers are making apps for Dropbox (Barret, 2011).

5.4.2 How Dropbox uses cloud computing Accessing Dropbox’s website requires one to have the necessary credentials: an email address and password. The same credentials are also required in order to link or register a device with a Dropbox account (Kholia & Węgrzyn, 2013). In this registration process, the end-user device is associated with a unique “host_id” which is used for all future authentication operations changes and it is stored locally on the end-user device. In other words, Dropbox client doesn’t store or use user credentials once it has been linked to the user account (Kholia & Węgrzyn, 2013). Dropbox client has a handy feature which enables a user to login to Dropbox’s website without providing any credentials. This is done by selecting "Launch Dropbox Website" from the Dropbox tray icon. Dropbox client accomplishes this by involving two values, “host_id” and “host_int” in this process (Kholia & Węgrzyn, 2013). The Dropbox native client is implemented mostly in Python and the application is available for Microsoft Windows, Apple OS X and Linux.2. The basic object in the system is a chunk of data with size of up to 4MB. Files larger than that are split into several chunks, each treated as an independent object (Drago, et al., 2012). Each chunk is identified by a SHA256 hash value which is a cryptographic hash or in other words a unique signature for a text or a data file.


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SHA-256 algorithm generates an almost-unique, fixed size 256-bit (32-byte) hash (Veness, 2017). Dropbox reduces the amount of exchanged data by using delta encoding when transmitting chunks. It also keeps locally in each device a database of meta-data information (updated via incremental updates) and compresses chunks before submitting them. In addition, the Dropbox native client offers the user the ability to control the maximum download and upload speed. Two major components can be identified in the Dropbox architecture: the control for client management and the data storage servers (Drago, et al., 2012). The former are under direct control of Dropbox Inc. on its own servers in the San Jose area, while Amazon Elastic Compute Cloud (EC2) and Simple Storage Service (S3) are used as storage servers committed to Amazon in Northern Virginia (Drago, Bocchi, Mellia, Slatman, & Pras, 2013). In both cases, subdomains of dropbox.com are used for identifying the different parts of the service offering a specific functionality; HTTPS is used to access all services, except the notification service which runs over HTTP (Drago, et al., 2012). Figure 13, depicted underneath, illustrates the messages observed while committing a batch of chunks. After registering with the Dropbox control centre via a clientX.dropbox.com server, the list command retrieves meta-data updates. As soon as new files are locally added, a commit batch command (on client-lb.dropbox.com) submits meta-data information. This can trigger store operations, performed directly with Amazon servers. Each chunk store operation is acknowledged by one OK message. Finally, as chunks are successfully submitted, the client exchanges messages with the central Dropbox servers (again on client-lb.dropbox.com) to conclude the transactions. Note that these messages committing transactions might occur in parallel with newer store operations.

Figure 19: An example of the Dropbox protocol (Drago, et al., 2012)

The Dropbox client exchanges control information mostly with servers managed directly by Dropbox Inc. For example for the notification protocol the Dropbox client keeps a TCP connection to a notification server (notifyX.dropbox.com) continuously open. This is used for receiving information about changes performed elsewhere. In contrast to other traffic, notification connections are not encrypted (Drago, et al., 2012). Delayed HTTP responses are used to implement a push mechanism: a notification request is sent by the local client asking for eventual changes, the server response is received periodically about 60 seconds later in


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case of no change. After receiving the client immediately sends a new request. Changes on the central storage are instead advertised as soon as they are performed (Drago, et al., 2012). As illustrated above in Fig. 19 all data storage flows, store and retrieve operations, are handled by virtual machines in Amazon EC2. More than 500 distinct domain names (dl-clientX.dropbox.com) point to amazon servers (Drago, et al., 2012). Subsets of those aliases are sent to clients regularly. Clients rotate in the received lists when executing storage operations, distributing the workload. Typically, storage flows carry either store commands or retrieve commands (Drago, et al., 2012). Content stored in Dropbox can also be accessed through Web interfaces. A separate set of domain names are used to identify the different services and can thus be exploited to distinguish the performed operations (Drago, et al., 2012). For example, URLs containing dl-web.dropbox.com are used when downloading private content from user accounts. The domain dl.dropbox.com provides public direct links to shared files (Drago, et al., 2012). In their paper Drago et al. (2013)presented a methodology to check both capabilities and system design of personal cloud storage services. They then evaluated the implications of design choices on performance by analysing five services: Dropbox, Microsoft Sky Drive, Google drive, LaCie Wuala and Amazon Cloud drive. Their analysis showed the relevance of client capabilities and protocol design to personal cloud storage services. Dropbox implements most of the checked capabilities like chunking, file-bundling, server data duplication and delta encoding as already mentioned above. Dropbox’ sophisticated client clearly boosts performance as it appeared to be the fastest service to start synchronizing single files (Drago, Bocchi, Mellia, Slatman, & Pras, 2013). Dropbox has shifted billions of its U.S. customers’ files away from Amazon’s platform to three of its own data centres. That way, Dropbox can tweak its network to cut the time it takes to store and sync traffic (Burrows, 2011). This was only a first step towards a new stage of existence.

5.4.2.1 A change in the cloud From 2014 on Dropbox started building its own vast computer network and in 2016 the firm shifted its service onto a new breed of machines designed by its own engineers, all orchestrated by a software system built by its own programmers with a brand new programming language (Metz, 2016a). Drawing on the experience of Silicon Valley veterans who erected similar technology inside Internet giants like Google and Facebook and Twitter, it has successfully moved about 90 percent of those files onto this new online empire (Metz, 2016). In order to achieve this Dropbox created a sweeping software system that would allow to store hundreds of petabytes of data. Also store it far more efficiently than company ever did on Amazon S3. They called this system “Magic Pocket” (Metz, 2016a). Not only the software component needed an adjustment for this data migration. Dropbox stores enormous amounts of data, so it needed machines suited to that task that fit its unique needs. The engineers of Dropbox achieved to build this and named it “Diskotech” (Metz, 2016a). Measuring only one-and-half-feet by three-and-half-feet by six inches, each Diskotech box holds as much as a petabyte of data, or a million gigabytes. Just 50 of these machines could store everything human beings have ever written (Metz, 2016a).


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A project like this, needless to say, is a technical challenge. But it’s also a logistical challenge. Moving that much data across the Internet is one thing, moving that many machines into data centres is another. And they had to do both, as Dropbox continued to serve hundreds of millions of people. One of the best measures of success for this massive undertaking would be that users wouldn’t notice it had happened at all (Metz, 2016a). The whole changing process did not go as smoothly as planned because the magic pocket software didn’t quite fit this new hardware. So Dropbox had to rebuild Magic Pocket in an entirely different programming language. First they used Google’s Go programming language, however Go’s memory footprint was to high so they switched to Rust (Mozilla) on the Diskotech machines. And that’s what Dropbox is now pushing into its data centres (Metz, 2016a). Dropbox has it reasons for creating its own cloud. The first is size and growth. Dropbox has 500 million users and is storing 500 petabytes of data (Butler, 2016). The second reason is that Dropbox wanted to have end-to-end control of the infrastructure so that they could control the performance, reliability and overall user experience (Butler, 2016) The explanation above on how Dropbox uses the cloud however is still relevant as the company continues to use the Amazon cloud in Europe. The main reason for this is that the business is growing in a less predictable way in Europe (Metz, 2016a). The technological architecture in total hasn’t changed that much. In the discussed explanation Dropbox already used its own servers. The only thing that changed is that ninety percent of all files moved from Amazon’s cloud storage into Dropbox own cloud data centres (Metz, 2016a).

5.4.3 The business model of Dropbox explained Dropbox is one of those valuable companies that has a very straightforward business model: it charges customers directly. No advertising or cookie gimmicks. Just good old fashioned “you want our service? Pay for it” logic (Merino, 2014). To be clear, the company utilizes a freemium model: customers can get the basic service for free, and they can upgrade for a fee (Merino, 2014). Dropbox has attracted over 500 million users with a simple proposition: everyone who enters a username and a password got two gigabytes of cloud-based storage free (Wagner, Benlian, & Hess, 2014). Since 2012 this is increased to 5GB (Ionescu, 2012). If people run out of space, they can choose a subscription of 8.25 euro a month for 1 TB of space for secure storage billed on a yearly basis or 9.99 euro in the case monthly billing is preferred. For business or people that prefer working in teams there are other options, here customers can choose between a standard subscription of 10 euro a month for 2 TB of space for secure storage billed on a yearly basis (Dropbox, 2017a). The price increases to 12 euro if monthly billing is preferred. Another option is the advanced subscription. The price starts at 15 euro a month again billed on a yearly basis for three users and in this case users get as much space as they need with sophisticated admin, audit, and integration features. If the user would prefer a monthly billing the pricing starts at 18 euro (Dropbox, 2017a). Lastly, Dropbox's Enterprise edition is priced on a company-specific basis. As you should expect, Dropbox adds more features and customer support with each paid tier (Tonner, 2016). For all the business or team options also a 30-day free trial is offered.


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When it launched, in 2008, it was primarily a service for backing up files. It then began offering shared folders, making it a collaboration tool (Vineet, 2014). Newer features allow for automatic syncing of smartphones and other devices and for automatic uploading of photos. Over time the user interface has improved as well. Each new feature has increased the value of the premium offering (Vineet, 2014). The company’s initial success cannot be attributed to its unique insight into productivity but merely because it wrote better software than its peers (Newton, 2015). Lots of apps let you store and synchronize your files. Dropbox was simply superior (Newton, 2015). It benefited from tremendously clever software engineering and product design. That bought the company years to explore new products and businesses. And ignoring competitors was a strategy that served the firm well as most of them simply weren’t very good and Dropbox’s laser focus on perfect syncing allowed it to vault far ahead of them (Newton, 2015). Over the years Dropbox has raised $507 million to date from investors like Sequoia Capital, Accel Partners, Index Ventures, Greylock Partners, Goldman Sachs, and more (Merino, 2014).

Figure 20: Dropbox free view

In the right downside corner of figure 20 which shows the view from a Dropbox free version. A free version user is constantly urged to convert to premium subscription or at least to try it for a period of 30 days. A successful push into productivity and collaboration software could give corporate customers much more to buy from Dropbox. The first example is “Paper”, which provides a kind of virtual white space where employees and contractors can share Excel spreadsheets, Google Docs, and other digital assets regardless of what device they are using (Burrows, 2011).. The idea is to tie together scores of different productivity tools and fold in management tools to help teams keep projects on track. Dropbox is far from the only company looking to change the way work is done. Competitors are on the same track (Burrows, 2011).Yet even rivals see Dropbox as a likely survivor of the inevitable consolidation. The overall market opportunity for productivity and collaboration tools is $30 billion, if you replace all those PC disk drives and traditional Windows or Mac programs with cloud-based alternatives. “That’s an order of magnitude more than the combined revenue of all the players today,” (Burrows, 2011).


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5.4.4 The Business Model Canvas In the previous section I tried to provide a general overview about the characteristics of the business model that is used by Dropbox from the beginning on and how and why this model was applied. In this section I want to dissect the model further and link it to the business model canvas in order to make it more comparable to the other cases in this research. I will do this by first elaborating on all the subsets from the canvas and b providing links what was already discussed in the above sections.

5.4.4.1 Key Resources of Dropbox On one side Dropbox has intellectual assets being the brand that it was able to build up over the last nine years. However the most prominent resources is the code the build to make their application work as good as it does (Newton, 2015). It is partly because of this that they as a firm stood out from the rest of the market as the best of class. More recently Dropbox started building its own vast computer network and in 2016 the firm shifted its service onto a new breed of machines designed by its own engineers, all orchestrated by a software system built by its own programmers with a brand new programming language (Metz, 2016a).They called this system “Magic Pocket” (Metz, 2016a). Not only the software component needed an adjustment for this data migration. Dropbox stores enormous amounts of data, so it needed machines suited to that task that fit its unique needs. The engineers of Dropbox achieved to build this and named it “Diskotech” (Metz, 2016a). Measuring only one-and-half-feet by three-and-half-feet by six inches, each Diskotech box holds as much as a petabyte of data, or a million gigabytes. Just 50 of these machines could store everything human beings have ever written (Metz, 2016a).

5.4.4.2 Key Activities of Dropbox The engineers at Dropbox are continue working on developments to improve the Dropbox platform. Another that engineers at Dropbox try to do is solving troubles that customers are facing or improving attributes to better serve customers because they fear from bad publicity when their platform doesn’t work perfectly.

5.4.4.3 Key Partners of Dropbox Over the years partners have played a vital role in Dropbox’s success. They’ve helped bring Dropbox to millions of users around the world, and worked hard integrating their solutions to create a strong ecosystem (Humphreys & Blau, 2015). The Dropbox Partner Network is a way for companies of all sizes to share in Dropbox’s success. Companies can benefit by reselling our solutions, or integrating their product into our ecosystem of connected applications (Humphreys & Blau, 2015). Dropbox organizes its partners in two categories: technology and channel partnerships. The technology partners focus on developing applications that connect to Dropbox products, enhancing the experience for our customers. These partners leverage extensive API documentation and resources to create seamless integrations between their products and Dropbox (Humphreys & Blau, 2015). Technology partners include API developers, a program open to any company looking to integrate their products with Dropbox, as well as Premier partners who receive enhanced technical resources, marketing development funds, and lead referral bonuses (Humphreys & Blau, 2015).


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The channel partnerships focus on getting Dropbox Business available wherever customers purchase their technology, whether it’s buying direct, through a reseller, or a managed service provider. That’s where our channel partners come in. We support our partners every step of the way with comprehensive training materials, co-marketing resources, and more. Our customized partner portal gives them everything they need to successfully represent a product already used by over 400 million people and 150,000 businesses. Since Dropbox integrates with big names like Microsoft, Adobe, and Autodesk (Humphreys & Blau, 2015). The Dropbox Partner Network already includes trusted companies such as Dell, Adobe, Hewlett Packard, VMware, Insight, Microsoft, and more (Humphreys & Blau, 2015). Another very important partner over the years was off course Amazon. Because Dropbox used Amazon their cloud storage system for the first eight years of Dropbox its existence (Metz, 2016a). The Amazon partnership has recently been replaced by a partnership with HP Enterprise to build Dropbox’s own datacentres. The partnership could help both companies expand in their own respective markets (Kim E. , 2016). Having HPE as a partner should help Dropbox reach big enterprise customers more easily, a market segment it's been trying to penetrate over the past couple years. HP Enterprise could leverage its deal with Dropbox to prove its market appeal versus low-cost Asian vendors (Kim E. , 2016)

5.4.4.4 Value Proposition of Dropbox Online storage available everywhere on any device and automatic synchronization this is the main idea behind Dropbox. In order to achieve this each user is provided with a free 5GB of storage and the promise that little to no effort should be put into keeping your desktop files synced with “the cloud” (Hendrickson, 2008). Dropbox also offers risk reduction through versioning and backups (Dropbox, 2017b). Dropbox keeps snapshots of all changes made to files in your Dropbox within the past 30 days (or longer with Extended Version History or Dropbox Business). This function allows to recover older versions of files, restore deleted files or undo deletion events (Dropbox, 2017b). Another very interesting option made available by Dropbox is collaboration over internet with team members. At its launch Dropbox was primarily a service for backing up files. It then began offering shared folders, making it a collaboration tool (Vineet, 2014). Newer features allow for automatic syncing of smartphones and other devices and for automatic uploading of photos (Vineet, 2014). Dropbox extended this option into “Paper”, which provides a kind of virtual white space where employees and contractors can share Excel spreadsheets, Google Docs, and other digital assets regardless of what device they are using (Burrows, 2011).

5.4.4.5 Customer Segments of Dropbox Dropbox focusses his business on two categories. The first one can be described as everybody who uses a memory stick for storage or any other way of storage and is satisfied by the adequacy of the free version (Khare, Stewart, & Schatz, 2016). A second customer segment needs more space since customers use Dropbox professionally or back up large files ( music, photos) (Khare, Stewart, & Schatz, 2016).


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5.4.4.6 Customer Relationships of Dropbox From the beginning on Dropbox aimed at a good relationship with their customers by utilizing their feedback after showing their minimum viable product that was introduced by a short movie (Business.com, 2017). Afterwards they encouraged users to make comments via Votebox about what they liked or didn’t like (Hulme, 2010). This technique is a perfect example of the “Lean Startup Methodology” (Ries, 2011). On their website Dropbox also offers a very convenient help function (Dropbox, 2017c) that can help customers with almost any kind of problem. If the help function doesn’t succeed in giving a satisfying answer Dropbox also offers a platform for the community to help each other (Dropboxforum, 2017).

5.4.4.7 Channels of Dropbox The main channel used by Dropbox is their homepage ( www.dropbox.com). Dropbox’s home page displayed a simple stick-figure video showing what the product does by showing a story about a guy who loses stuff and goes on a trip to Africa (Barret, 2011). The number of options on the homepage are limited. It’s clear what Dropbox wants people to do when a visitor comes to the site: get to know the product and sign up (Bulygo, 2012). One of the main drivers for Dropbox’s gain in popularity is based on referral by users (Bulygin, 2015). Dropbox had learned to build on the enthusiasm of its user base by offering a two-sided incentive referral program (Business.com, 2017). Friend referrals instil more trust than an advertisement ever could. Furthermore, the sender has an incentive to spread the word about Dropbox, getting extra space. The referee also has an incentive for signing up, more space than if they just signed up through the normal process (Bulygo, 2012). Other channels that were used are competitions like the ‘Great Space Race’ and ‘Dropquest’ (Lardinois, 2012) (Cooper D. , 2012). Partner channels: e.g. online tech forum www.digg.com, on which Dropbox launched a video that catapulted the popularity of the service back in 2008. Within 24-hours Dropbox had 75,000 people signed up for the waiting-list. Where initially they were expecting only 15,000 subscribers, tops (Zelman, 2011)

5.4.4.8 Revenue Streams of Dropbox The revenue streams of Dropbox consist of recurring flat fees for monthly or yearly usage; The greatest part of Dropbox users use in the free tier version of the service however if people run out of space, they can choose a subscription of 8.25 euro a month for 1 TB of space for secure storage billed on a yearly basis or 9.99 euro in the case monthly billing is preferred. For business or people that prefer working in teams there are other options, here customers can choose between a standard subscription of 10 euro a month for 2 TB of space for secure storage billed on a yearly basis (Dropbox, 2017a). The price increases to 12 euro if monthly billing is preferred. Another option is the advanced subscription. The price starts at 15 euro a month again billed on a yearly basis for three users and in this case users get as much space as they need with sophisticated admin, audit, and integration features. If the user would prefer a monthly billing the pricing starts at 18 euro (Dropbox, 2017a).


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5.4.4.9 The Cost Structure of Dropbox The cost structure of Dropbox consist of the following elements: the wages of their employees, storage costs at Amazon, Amazon’s S3 per request cost and the cost of its own maintaining its own servers. Recently a change in this was initiated by the migration of data from Amazon’s cloud service to Dropbox own servers. This migration was initiated in order to reduce costs.

5.4.4.10 The Business Model Canvas of Dropbox The above discussed subsections can be all found together in the underneath depicted business model canvas (Osterwalder & Pigneur, 2010):

Key Partners

Technology partnerships

Channel partnerships

HP Enterprise

API developers


Amazon web services

Key Activities

Continues development of the platform

Troubleshooting for customers

Value Proposition

Online storage available everywhere at any time on any device

Automatic synchronization

Risk reduction through versioning and backups

Collaboration options over internet


Vote box

Help centre

community

Customer Segments

Free version users: everybody who used a memory stick before

Paying users requiring a lot of space

Key Resources

Physical assets ( own servers)

Programming code

Intellectual assets ( brand, customer data)

Channels

www.dropbox.com

User referral, university challenges

Partner channels

Cost Structure

Wages of employees

Storage costs at amazon & Amazon’s S3 per request cost

Cost of maintaining its own structure

Revenue Streams

Recurring Flat fee for monthly or yearly usage

Figure 21: The Business Model Canvas of Dropbox

5.4.5 The impact of cloud computing on the business model of Dropbox In the business model canvas of Dropbox that was presented above some of key parts were put in bold to signal that in these parts cloud computing has a clear impact. In this part I will briefly go in to detail on how cloud computing has an impact on several concepts that immediately have impact on the business model of Dropbox. Starting from the left on, in the Key partners concept. Dropbox for its service delivery as much as any other service provider using cloud computing is dependent on a quick delivery of internet. If there is no connection to the internet, Dropbox simply cannot provide our deliver. This confirms the fact that internet is more and more seen as the fifth utility (Buyya R. , Yeo, Venugopal, Broberg, & Brandic, 2009).


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In the partners concept Amazon Web services is also mentioned because Dropbox used their cloud storage system for the first eight years of their existence (Metz, 2016a) and still uses the Amazon cloud in Europe. However ninety percent of all the files that Dropbox stores are now stored in their own servers. Although Dropbox switched on a company level to use their on premise servers they still are a firm using cloud computing technology to server their customers. The new system that is part of Dropbox’s key resources is called “Magic Pocket” (Metz, 2016a). However, not only the software component needed an adjustment for this data migration. Dropbox stores enormous amounts of data, so it needed machines suited to that task that fit its unique needs. The engineers of Dropbox achieved to build this and named it “Diskotech” (Metz, 2016a). Measuring only one-and-half-feet by three-and-half-feet by six inches, each Diskotech box holds as much as a petabyte of data, or a million gigabytes. Just 50 of these machines could store everything human beings have ever written (Metz, 2016a). Th combination of maintaining its own data centres and still using Amazon Web services is reflected in the cost structure. However Dropbox has it reasons for creating its own cloud. The first is size and growth. Dropbox has 500 million users and is storing 500 petabytes of data. The second reason is that Dropbox wanted to have end-to-end control of the infrastructure so that they could control the performance, reliability and overall user experience (Butler, 2016) Cloud computing is also intensely entwined with the value proposition Dropbox offers: Online storage available everywhere at any time on any device and Automatic synchronization. Without the usage of cloud computing technology this would not be possible


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5.4.6 Does it meet the disruptive innovation criteria? In order to be able to say whether or whether not Dropbox can be categorized as a firm using disruptive innovation the framework of Hang, Chen and Yu (2011) needs to be applied. Hereafter the framework is completed based on the previously gathered and discussed information about Dropbox.

Question Yes No




Are incumbents in the main-stream market willing to run away or ignore the initial disruptors? x

B. Technology


Adequate for a foothold in the low-end market x Adequate for a foothold in a new, niche market x




A simple proposition x

Table 11: Assessment framework of Hang, Chen and Yu (2011) applied to Dropbox

From the above shown table can be derived that only two times “no” has been ticked. In this case for the existing performance overshoot in mainstream market and are incumbents in the main-stream market willing to run away or ignore the initial disruptors. These two can both be easily declared because Dropbox solved in fact a problem customers did not know they had and by doing so they created a market for online file sharing, syncing and collaboration. Two times a “no” ticked means that the frame work indicates that in this case a new market disruption is on its way (Hang, Chen, & Yu, 2011). The reason why low-end market is still ticked however is because Dropbox first reached a huge foothold in the personal market and thus only on a later stage began to invade the business market from below. Some of the questions ticked “yes” are also interesting to add some context. For example Dropbox in fact offers a simple proposition: everyone who enters a username and a password got two gigabytes of cloud-based storage free (Wagner, Benlian, & Hess, 2014). If that isn’t enough a payment is required to get more storage. Dropbox also benefited from tremendously clever software engineering and product design (Newton, 2015). That bought the company years to explore new products and businesses in order to further improve their performance.


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As mentioned in the methodology chapter a cross validation of the results of the application of the framework of Hang, Chen and Yu simultaneously (2011) is also executed by using the criteria sheets of Keller and Hüsig (2009):

Entrant Dropbox








Phase total 3 2 0

Main market entry





Phase total 4 0 0





Phase total 2 0 1

Total 9 2 1 Table 12: Assessment framework for entrant of Keller and Hüsig (2009) applied to Dropbox

The application of the framework developed by Keller and Hüsig (2009) confirms the result that was found by applying the framework by Hang, Chen and Yu (2011). Dropbox can be seen as firm using Cloud computing as a disruptive innovation. Only three criterions are not fulfilled. Being the products perform worse based on established attributes, Value network has a low overlap and profitable business model targeting over-satisfied customers. The fact that these are not fulfilled can be declared by the fact that Dropbox in fact created a new market and there was no market with other products to which it could be compared on the level of attributes and thus also no customers that were over-satisfied. The second framework used here focusses not entirely on the same factors that can enable disruption so this double check serves as an worthy addition. To avoid too much duplication of the same arguments I mainly focus on these extra attributes: The main difference with the


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model of Hang, Chen and Yu (2011) that can be noted is the fact that also business model, processes and value network are also taken into account. The business model of Dropbox is very straightforward: it charges customers directly. No advertising or cookie gimmicks. Just good old fashioned “you want our service? Pay for it”-logic (Merino, 2014). To be clear, the company utilizes a freemium model: customers can get the basic service for free and they can upgrade for a fee and receive a more extended product (Merino, 2014). For the discussion of the framework used for cross validation of our initial result that Dropbox can be seen as firm using disruptive innovation only the first criteria sheet of Keller and Hüsig (2009) is used. The reason for this is that when Dropbox started providing their service there was no market for offering storage and syncing data via the internet.


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6 Part VI: Results 6.1 Comparing business models In the previous chapter an in depth discussion of each of the selected cases of this case study was provided. Interesting as these discussions of each consecutive case are, their main course is to help and answer the three questions that together form the main research question of this thesis. In this chapter I suggest an answer to each of these questions based on the discussed cases.

6.1.1 How can Enterprise modelling help in analysing the impact of cloud computing The first question that needs to be answered is in what way enterprise modelling can help in this analysis of the impact of cloud computing. In this thesis the business model canvas of Osterwalder and Pigneur (2010) was chosen as a tool to do this. The usage of the business model canvas offered several advantages. To start with, the drafting of these different business model canvasses made it possible to compare the different cases that were studied on the same components. It offered a common language and reference for all the information I had collected about the different cases. By comparing the different canvasses, the differences and similarities between the different cases became immediately clear. Because initially all the cases that I looked into, looked dissimilar. The application of the business model canvas made clear that the value proposition for all the cases had something specific in common: offer something that should be accessible anywhere at any time on any device. Further the left side of the canvasses, or the internal business, showed several other similarities. The biggest differences were found on the right side of the business model canvas where the customer-related aspects are described. A downside of using the business model canvas is that it does not offer a valuation of what are the most crucial or important components driving the business model. It just provides an overview of what is done in relation to each component. Another downside is that in the business model canvas no external forces such as e.g. competition are included.

6.1.2 The impact of cloud computing on the business models This question relates to the impact of cloud computing on the business models. In the previous chapter this impact, as shown in the business model canvas of each of the cases, was already illustrated. The impact of cloud computing on the four firms could be seen for all four the cases in the same parts of their business model canvas. More specific in the Key partners, the key resources, the cost structure and the value proposition. In some of the cases it could also be noted in the customers’ relationship; however this was not the case in all cases. That the impact of cloud computing appears in these components should be no surprise. The key partners are in fact the network on which the firm relies externally. The form of this partnership could take on any form. In the discussed cases here, mostly an external third party cloud provider could be found. The key resources are the internal drivers of the value proposition. Each of the four cases also has key resources that use cloud computing in order to drive the value proposition. Underneath this all the cost structure is located. This is where the company can list all the costs of the above mentioned internal company side of the


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business model. In this component of the business model canvas cloud comes to the forefront as a cost for maintaining its own key resources or as cost that needs to be paid to a third party provider. For the value proposition of each of the four cases the resemblance was the most in this order that all four firms offer something that should be accessible anywhere at any time on any device and this is realized by utilizing cloud computing technology. This fact that the impact of cloud computing appeared for all cases in the same components of the business model canvas is a logical consequence of the fact that cloud computing as a technology helps the firms that were discussed in offering their value proposition to the customer. Merely saying that cloud technology helps would be cutting it short. In all the cases discussed, in fact cloud computing is the main enabler to deliver their respective value proposition. The impact of cloud can be best experienced on the internal side of the business model and thus the left side of the business model canvas. The differences among the cases are best notable on the right side of the business model. How each of the firms reaches their customers, what segments they address, how they organise their customer relationships or on how their revenue model is built up.

6.1.3 Is cloud computing a disruptive or a sustaining innovation? In chapter five of this thesis, four cases were discussed. The firms in question were all active in a different kind of market and were all offering a different product. The firms all had at least one thing in common. They were using cloud computing technology to provide their product or service to their customers. The impact of this technology on how these firms run their business is reflected in each of their business model canvasses and was already discussed in the previous section. As mentioned in the previous chapter, cloud computing technology was the main enabler to deliver their respective value proposition. The value proposition in the business model canvas does not only fulfill the customers demand but also represents the reason why a customer prefers one firm over another (Voigt, Bulinga, & Michl, 2017). This last is very important to reconsider because this means that a value proposition driven by cloud computing technology is preferred above another offer. This must mean that the performance attributes offered by cloud computing as a technology are appreciated by customers in the market. Possible attributes offered by cloud computing are: on-demand IT service provisioning, self-service and elasticity. These are different than what the main values of the mainstream IT sector were when cloud computing first came to the fore front: improved computing capacity, network capacity, reliable, secured agile resources at reduced cost (Foster, Zhao, Raicu, & Lu, 2008) (Buyya R. , Yeo, Venugopal, Broberg, & Brandic, 2009) (Lyer & Henderson, 2010). Because of this I can say that cloud computing is not a sustaining technology as it does not offer an improved version of something that is attributes already valued by the mainstream (Kaltenecker, Hüsig, Hess, & Dowling, 2013). A part of this discussion in chapter five consisted of checking whether or not these firms would fit the criteria to be catalogued as disruptive innovation by using the framework of Hang, Chen and Yu (2011). By doing so for each consecutive case I found that each of the cases passed the test and could be seen as disruptive. To verify this result a second framework was used. The framework of Keller and Hüsig (2009) was used to cross validate the results by the previously mentioned framework. For all the cases this framework confirmed the previously found results. The main denominator for all these cases was the application of cloud computing


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technology. Therefor cloud computing can be seen as a disruptive innovation. This is in line with what previous researchers have found before me (Slater & Mohr, 2006) (Krikos, 2011) (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011).


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7 Part VII: Conclusion 7.1 Conclusion The purpose of the thesis research was to gain additional insights in the role of Cloud Computing as a disruptive technology. The process of putting together the extensive literature overview can be seen in the second chapter contributed for a great part to my understanding of the subject. In this thesis a total number of four case-studies were investigated in order to determine whether cloud computing presents a sustaining or disruptive technology. The cases that were studied are: Netflix, Salesforce, Spotify and Dropbox. All these firms are applying cloud computing in their business model and all appeared to possess the necessary characteristics to be seen as disruptive in their respective domain according to two applied frameworks to test this. The impact of Cloud Computing as a disruptive technology was modelled and analysed using the business model canvas. The business model allowed a better understanding of how the four cases could be compared. The central research question was “How can Enterprise Modelling help in analysing the impact of Cloud Computing on business and operating models?” The usage of the business model canvas helped in seeing on which parts of the business model cloud computing technology had influence. This appeared to be the case specifically on the internal business model side of the canvas. This means that the cloud computing technology had almost no applications on the right side of the business canvas except for customer relationships. The business model canvas presents a total view of the business model used by a certain firm. The fact that cloud computing as disruptive innovation only has an impact on approximately half of the canvas, primarily the left side, gives way to confirm that a disruptive innovation does not come from technology alone also the applied business model is important (Crockett, McGee, & Payne, 2013) (Markides, 2006). This logic can in fact already be derived from Christensen initial framework (1997). He explained that a technological shift could offer different attributes to customers. These customers could be part of the given market and be price sensitive (low-end) or these could be non-consumers and thus no part of the market. The Shift in fact can be caused by a couple of things: first of all a better technology that makes offering a service or product cheaper combined. Secondly an old tech with a new business model that addresses customers need better (DaSilva, Trkman, Desouza, & Lindic, 2013).


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7.2 Limitations of the research To this research also some limitations apply. The cases discussed in this thesis all were selected based on presumption that they would be disruptive. This was done because the main focus of this thesis was not to only check if this was the case but to compare the business models of the firms at hand. Some scholars might comment to this, as Danneels (2004)did back then, on the methods used by Christensen (1997) that the fact that Christensen only withheld cases where potentially disruptive technology did succeed, presents in fact an analytical problem. In this thesis it does not form an analytical problem as such however it should be noted. Another limitation of tis thesis is the construct validity: Yin (2014) sums up some actions like the use of several sources of evidence, the use of a member check or the use of key informants who read the case study report, formulate comments and focus especially on the chain of evidence to ascertain a decent construct validity. In this thesis only the use of several source of evidence and a special focus on the chain of evidence was used. This thesis was written by me alone so I had no sparring partner to double check the work. An option to resolve this was to let the thesis be read by an information technology professional to review the case studies however due to time issues this was not done.

7.3 Further research From studying literature I came across to several interesting avenues for further revenues. The first one would be to go further on the work that has been done in the line of business model innovation (Chesbrough H. , 2007a) (Baden-Fuller & Haefliger, 2013) (Rayna & Striukova, 2016). I believe it would make an interesting study to determine the true value of this type of innovation compared to a more technological innovation as studied in this thesis. A second line of research that seems interesting to pursue could go further on the work by Iacob, et al. (2014). In their paper they provided a way to link business models and enterprise architecture by researching a link between the BMO framework, or the business model canvas and Archimate. The practical value of this relation is that cost analysis techniques can be composed so that the output of one can be used as the input for the other which can lead to more realistic calculations (Iacob, et al., 2014). Applying the links and findings of theses researchers to the cases of disruptive innovation and perhaps even more interesting to some cases of incumbents facing these disruptions can serve as an interesting avenue for further research.


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8.2 Case study protocol

8.2.1 Overview of the case study project

8.2.1.1 Background information In order to convince the reader of the social relevance of this topic I would like to rely on a couple of bold statements. These will be used to clarify why this research is relevant and in what way this thesis can contribute to the already existing literature, which will off course be further discussed extensively afterwards. The main intention here is to briefly demonstrate the relevance of each of the building blocks that together form the basis for the research carried out in this thesis. The cloud is everywhere! If this bold statement would be spread in the case of a weather forecast this could be seen as a national disaster, which would require extra speed limits on the highway and what so on. However luckily this is not the case. The subject of this thesis has little to do with forecasting weather or what so ever. The subject will be cloud computing and this type of information technology appears to be everywhere. It is not only the proverbial reference of describing the technology, like a cloud, it is also literally everywhere and if not used or needed anymore it can disappear. Not that it actually disappears, it just seems like it does from a user’s perspective. When I look at my personal life, as a student, I can confirm that the number of applications using cloud computing is already quite large and it is still growing as applications using this information technology are emerging every day. Cloud computing only recently appeared in the Oxford English Dictionary. However its use is spreading rapidly because it captures a historic shift in the IT industry as more computer memory, processing power, and apps are hosted in remote data centres, or the “cloud.” (Regalado, 2011). There is some controversy about who coined the term cloud for the first time. It could be Eric Schmidt, a former CEO of Google, who introduced the term into common use by stating, at the 2006 search engine strategies conference, that Google’s services belong in a cloud somewhere (Fogarty, 2012). Others refer to the events that occurred inside the offices of Compaq Computer where a small group of technology executives was plotting the future of the Internet business and calling it “cloud computing” (Favaloro & O'Sullivan, 1996) (Regalado, 2011). Their vision was detailed and prescient. Not only would all business software move to the Web, but what they termed “cloud computing-enabled applications” like consumer file storage would become common in our nowadays (business) life (Favaloro & O'Sullivan, 1996) (Regalado, 2011). Also for the concept behind cloud computing there are quite some contenders who could be pointed out as “the first” or one of the first with the idea of what cloud computing is. In the early sixties, John McCarthy, the computer scientist who also coined the term “artificial intelligence,” came up with the theory of time-sharing, which is very similar to today’s cloud computing (McCarthy J. , 1962) (Pullen, Where Did Cloud Computing Come From, Anyway?, 2015a). Back then, computing time was extremely expensive and users wanted to make the greatest use out of a very precious asset. In addition, smaller companies who couldn’t afford


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a computer of their own also wanted to be able to do the type of automation that larger companies could do, but without making such an expensive investment. So, if users could find a way to “time-share” a computer, they could effectively rent its computational force without having to singularly pay the bill for its massive cost (Pullen, Where Did Cloud Computing Come From, Anyway?, 2015a). Another contender could be Western Union who dreamt in 1965 of a nationwide information utility (Harding & Oswald, 1965). It is true that several of the above ideas were indeed present for a long time. Nonetheless their confluence today in an environment where information can be accessed independent of device and location represents a major shift in computing as we know it (Marston, Li, Bandyopadhyay, Zhang, & Ghalsasi, 2011). It is perhaps not the best, however it seems like a very convenient way to illustrate that the usage of cloud computing is fully embedded in ‘our’ day to day life by referring to my own life as an example. I am off course aware that self-experimentation studies can raise questions about whether analyses of just a few individuals are scientifically valid. Self-monitoring experiments are not randomized or blinded like traditional human studies, and the experimenter’s personal involvement and motivations could make the research seem less objective (Landhuis, 2016). Despite these concerns and caveats, there are scenarios where self-experimentation may be not only acceptable but optimal (Landhuis, 2016). I am off course aware of the non-scientific value of the example, however I do believe it has value to show that cloud computing is almost everywhere and that it is available to use at any time by anyone. It is up to the person in question to decide whether or not he or she wishes to use it. The illustration of “the cloud is everywhere” statement comes down to the fact that this thesis was written while using a version of MS Word in a cloud environment, provided by the University of Ghent, called ‘Athena’. To avoid a thorny issue in case of a computer malfunction or crash as a safety measure I also made sure I always saved a copy of a recent version of this document in a Dropbox folder. Often if not always while typing or doing review work I would be listening to music on Spotify and on the rare moments while taking a break I would watch some series on Netflix to get my mind of the subject. As described, it is easy to see that this elementary process of writing a thesis already involved four applications of cloud computing technology. As mentioned before by Landhuis (2016) a single case does not always appears valid. The European Union however provides statistics about the use of cloud computing services by European firms. According to their research 21 % of the European firms are already using this technology in 2016 (European Commission, 2016). Significant differences can be observed across countries. In Finland, Sweden and Denmark, over 40 % of enterprises are using cloud computing. On the other hand, fewer than 10 % are using this technology in Greece, Latvia, Poland, Romania and Bulgaria. Belgium scores a little bit above the European average where 28% of the firms are using Cloud computing technology according to the European statistics (European Commission, 2016). Other research like the 2016 IDG Enterprise Cloud Computing Survey shows a higher figure of up to 70 % of organisations that have at least one application in the cloud (IDG enterprise, 2016). The discrepancy between the two results can be explained by the high degree of North American participants to the last mentioned survey and also that all the participants of this survey are IT professionals.


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According to the Synergy Research Group 2015 was the year when cloud became mainstream and 2016 was the year that cloud started to dominate many IT market segments because major barriers to cloud adoption were almost a thing of the past (Synergy Research Group, 2017). Cloud technologies are now generating massive revenues for technology vendors and cloud service providers, yet there are still many years of strong growth ahead (Synergy Research Group, 2017). Gartner expects the cloud computing market in total to grow to about USD 247 billion worldwide in 2017 (Statista, n.d.) Innovation is participating and Disruptive innovation is Gold Just stating that innovation is important would be like kicking in an open door instead we used the reference to the Olympic catchphrase. The reason for this is quite straightforward: in order to maintain a certain market share a constant ‘innovation’ is needed, this can be linked to the product lifetime value or just as a way to keep customers close to the firm. Disruptive innovation is Gold. This is perhaps a bit too bold to say, however it is true that if a firm succeeds to disrupt a market they have a fair chance at becoming market leader for at least a certain period of time. When Christensen’s book “The innovator’s dilemma “was published in the late nineties, 1997 to be more precise it created a shockwave in the research field for innovation. Suddenly Christensen was seen as some kind of “guru” (Scherreik, 2000). His work was cited extensively by scholars working on diverse topics going from marketing to technology management. Before Danneels’ paper in 2004 there was not really a constructive criticism on the concepts described by Christensen. Until that publication nobody really had challenged the concepts in this top selling book. Discussion (Danneels, 2004) (Christensen C. M., 2006) still exists among both authors on the ex-ante applications of the disruptive innovation theory in predicting whether an early stage disruptive innovation case would succeed subsequently. Since the theory has been largely based on extensive study of empirical evidences of many successful cases in the past (ex post) (Hang, Chen, & Yu, 2011) instead trying to predict possible disruptions (ex ante). In line of this I will provide a contribution to the existing literature in trying to get an understanding of the business models of these firms who were successful in disrupting a market by using cloud computing technology. The goal is to investigate through a number of case studies whether cloud computing can be seen as disruptive innovation. A unique business model is needed to differentiate from competition One of the few ways left for companies to protect their margins is through business model differentiation (Plantes, 2017). An unique or distinctive business model has become the new basis of competition, replacing product features and benefits as the playing field on which companies emerge as dominant or laggards. This is happening because the traditional strategies, like branding and marketing communication, are less effective in maintaining margins. The main reason of their ineffectiveness is that they cannot cope with multiple forces that are accelerating market commoditization which leads to price wars. Some examples of these forces are globalization, copycat competition, price transparency and loss of messaging control. So to avoid this, leadership teams in organisations should be aware and keep their business model up to date to avoid sliding down in a commodity-like market. (Plantes, 2017)


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A business model should answer five interdependent core strategy questions. To start with “Who is our target market and how to reach and relate to its members?” The second question handles the scope of the offering “What is in the scope and what is out of scope?” the third question is “What promise of value leads customers to us?” The fourth question relates to how you are going to protect this value promise? The last one is about factors that ensure the firms profitability in delivering (Plantes, 2017). Business model innovation embraces many different types of changes to an existing business model. These can run the gamut from incremental to transformative. The changes can be proactive or forced by competitors. Keep in mind that in a free market economy, every industry has a Wal-Mart. In other words, a company with a business model that makes them the lowest cost competitor, profitable competes on price. If that’s not you, avoid becoming your industry’s Sears/K-Mart. Differentiate your business models before it is too late. And if you are the Wal-Mart of your industry, watch out for your industry’s version of Amazon, Tesco and Dollar Stores who are coming after Wal-Mart’s low cost position with advantages Wal-Mart can’t easily copy (Plantes, 2017). The previous sentence already combines innovation or even disruptive innovation with the business model concept as I will continue on doing throughout this thesis. The focus will be put on business models of those innovative firms that try to take over the market by offering a different set of values than incumbents are offering. A Merger of three statements In this thesis the goal is to investigate through a number of case-studies whether Cloud Computing presents a sustaining or disruptive technology and what the impact of this technology is on the used business model of the firms in scope. These two goals are further down reflected in the research question. The cases in scope are Netflix, Salesforce, Spotify and Dropbox. At first sight all of these can be seen as successful companies benefitting from cloud computing technology. However we will not blindly believe what is noticed at a first glance. In order to make sure the cases selected are indeed an example of a disruptive innovation they will first be tested by applying a framework developed in the paper by Hang et al (2011). The whole reason for the continuing interest in disruptive innovation is because the impact can be so extreme that virtually non-existing firms can rise to dominance while leading incumbents can cease to exist or largely diminish (Baiyere & Salmela, 2013). Once these cases passed the test the research will continue. If they do not meet the requirements in the framework we will elaborate more on the why not. However this is not what is expected. The impact of Cloud Computing as sustaining or disruptive technology will be modelled and analyzed using various Enterprise Modeling techniques and associated tools (e.g., the Business Model Canvas). The purpose of this thesis research is to gain additional insights into the role of Cloud Computing as disruptive or sustaining technology and its impact on an organization’s business model or business processes. The central research question is: “How Enterprise Modeling can help organizations in dealing with the impact of Cloud Computing on their business?” Research activities included literature research and analysis of published case-studies and experience reports. The sources used are both academic as well as professional business- and IT literature.


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8.2.1.2 Short summary The thesis contains eight consecutive chapters and the structure is as follows: Introduction, Literature review, Research question, Methodology, Discussion of different cases, Results, Conclusion and Appendices. A huge part of the time invested in this thesis went to research and gathering the well needed data. As always data and the gathering of data is the cornerstone of a decent scientific research. Therefor I believed that I was only appropriate to invest a huge part of the time in this part. This is also reflected by the voluminous literature study that is provided in chapter two. Literature is only literature and does not always correspond to the real world. This real world is presented in chapter five by offering four well described cases that are studied in the light of this thesis. These are: Netflix, Salesforce, Spotify and Dropbox. In chapter six the results and findings can be found and in chapter seven I wrote a conclusion based on the executed research.

8.2.2 Substantive issues that are investigated

8.2.2.1 Rationale for case selection The cases that are being researched in this thesis are all examples of cases that at first sight can be seen as being innovative. The goal of this research is to determine whether or whether not they can be seen as truly a disruptive innovation. Another goal is to get a closer view on their respective business models. The cases selected are in random order: Netflix, Salesforce, Spotify and Dropbox. The reason for selecting these cases in particular is that they have all succeeded in reaching a market that was previously not there or that was served by other incumbent firms. A second reason is that all these firms use cloud technology in order to serve their customers or deliver their product. A third reason is that these firms are all relatively young. This is important because in the disruptive innovation framework disruption is mostly expected from new entrants or existing firms who differentiate to a new market and can be seen as new for that specific market. As mentioned before the idea in this thesis is to extensively investigate the four cases as mentioned in order to draw conclusions on how cloud computing as a technology helped these firms in becoming a disruptive innovative firm. A first step that needs to be taken is to ascertain that the selected cases can be seen as disruptive firms. This evaluation is described at the end of respectively each discussion about the individual cases.

8.2.2.2 Relevant literature All the relevant literature can be found in the first appendices. However some authors deserve a special reference:

Christensen 1997

Danneels 2004

Osterwalder & Pigneur 2010

Kopetzky, et al., 2013


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8.2.2.3 A guide for a case study report In a case study normally one has more flexibility however I chose fort the classical layout:

1. Part I: Introduction

2. Part II: Literature review

3. Part III: Central research question

4. Part IV: Methodology

5. Part V: Discussion different cases

6. Part VI: Results

7. Part VII: Conclusion

8. Part VIII: Appendices

cloud computing as a sustaining or a disruptive … · 5.4 dropbox ... 8.1 works cited ... 8.2.1...

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