“Simplified Model of Business Model Scalability”
Name author: Jemaira van der Velden Student number: 0529915
Date Submission: 31-3-‘18 Version: Final Name Institution: UvA
Master MSc EPMS, Strategy Name first supervisor: Jeroen Kraaienbrink Name second supervisor: Hans Strikwerda
Statement of Originality
This document is written by Student [Jemaira van der Velden] who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in
creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Main goal of this study is to identify factors positively increasing the scalability of a business model. The majority of research involving scalability has been done on e-commerce business models. We aim to improve general understanding of mechanisms involved in business model scalability for both e-commerce business models as non-e-commerce business models.
Based on thirteen case studies, including an equal number of in-depth interviews with founders or senior executives we propose a model, which helps practitioners focus their scaling efforts. Our study points out that scalability efforts should be directed towards one (or more) of the four scalability areas: scalability in adaptability; Scalability in
production; scalability in distribution; and scalability in infrastructure. Which area or areas should be chosen heavily depends on the context. Nine scalability factors: Standardization of processes; automation of processes; Cost and Revenue Structure; Adaptability to
Different Legal Regimes; Network Effects; User Orientation; Partnering; Ability to Adapt; and Professional Network, provide more detail helping the entrepreneur during the ideation phase of their scaling project.
Abbreviations
BM Business model
B2C Business to consumer
CBO Auteursrechtenorganisatie van muziekauteurs en muziekuitgevers EMBMS Explorative model of business model scalability
IFA Independent financial advisor IP Interview Partner
2 Theoretical Review ... 2
2.1 Conceptual Development Business Model ... 2
2.2 BM design and value creation ... 4
2.3 Scalability ... 5
2.4 (Re-) Designing for scalable business models... 6
2.5.1 Intro ... 6
2.5.2 Technology ... 7
2.5.3 Cost & Revenue streams ... 7
2.5.4 Adaptability to different regimes ... 8
2.5.5 Network effects ... 9
2.5.6 User Orientation... 10
2.6 Conclusion ... 11
3 Methodology ... 13
3.1 Research Philosophy and approach ... 13
3.3 Data collection ... 15
3.4 Analytical procedures ... 17
3.5 Validity and reliability ... 19
4 Within case analysis ... 20
4.1 Introduction ... 20
4.2. Exploring practitioner views about business models and scalability ... 20
4.2.1 Business models ... 20
4.2.2 Scalability... 21
4.2.3 Link between business model scalability and growth ... 22
4.3.Case descriptions and scalability scores ... 23
4.3.1 Case In001 ... 23 4.3.2 Case In002 ... 26 4.3.3 Case In003 ... 29 4.3.4 Case In004 ... 32 4.3.5 Case In005 ... 37 4.3.6 Case In006 ... 39 4.3.7 Case In007 ... 42 4.3.8 Case In008 ... 45 4.3.9 Case In009 ... 48 4.3.10 Case In010 ... 50 4.3.11 Case In011 ... 53 4.3.12 Case In012 ... 53 4.3.13 Case In013 ... 57 4.4 Conclusions... 59 5 Across case analysis ... 61 5.1 Introduction ... 61
5.2 Factors model Stampfl ... 61
5.2.1 Technology ... 61
5.2.2 Cost and Revenue structure ... 65
5.2.3 Adaptability to different Legal Regimes ... 67
5.2.4 Network Effects ... 68 5.2.5 User Orientation... 70 5.3 Other factors ... 72 5.3.1 Focus... 72 5.3.2 Standardization of processes ... 74 5.3.3 Network entrepreneur ... 75 5.3.4 Ability to Adapt ... 77 5.3.7 Partnerships ... 78 6 Discussion ... 81 6.1 Introduction ... 81
6.2 Implications research findings for academics ... 82
6.2.1 Rethinking the data ... 82
6.2.2 A simplified model ... 85
6.2.3 The four scalability areas explained ... 87
6.3 Implications for practice ... 93
6.4 Limitations and suggestions for future research ... 93
6.4.1 Limitations ... 93
6.4.2 Suggestions for future research ... 94
6.1 Conclusion ... 95 Appendices ... 97 Appendix A ... 97 Appendix B1 ... 100 Appendix B2 ... 102 Appendix C ... 105 Appendix C continues ... 107 Appendix D ... 109
1 Introduction
Environmental turbulence, such as technological progress, competitive changes, or governmental and regulatory alterations are affecting the rules of the ‘create and capture value’ game (Johnson Mark W, Christiensen C M, 2008). In the light of change, established business models have to be developed and adapted to stay effective and capture value. The introduction of Internet has been one of the most influential environmental changes within the last two decades (Wirtz, Schilke, & Ullrich, 2010). An innovative business model can either create a new market or allow a company to create and exploit new opportunities in existing markets. With the advent of the Internet the search for a scalable business model became an important driver of business model (BM) innovations (Amit & Zott, 2001; Rappa, 2004; Stampfl, Prügl, & Osterloh, 2013). It is scalability that has drawn our attention. A scalable BM seems to offer more profitability and huge growth opportunities. Also in the light of raising venture capital it is very important business model is capable of scaling. A lot of information on scalability in a business context or scalable business models can be found on the Internet, however academics have paid less attention to the subject. Little is known about the factors relevant to the scalability of a business model. Which is striking
considering that scalability has been identified as a primary driver of business growth (Berry, Shankar, Parish, Cadwallader, & Dotzel, 2006; Miller, 2001). The question now is, what makes a business model scalable and is it possible to design for scalability? Academic articles do not provide a lot of insights. To our knowledge, there is only one article proposing a model describing factors.
This study builds on and extends the explorative model of business model scalability by Stampfl, Prügl, & Osterloh (2013). This model has been chosen because it is to our
knowledge, the only model describing factors that influence scalability. The model proposes five factors (technology; Costs and revenue structure; Adaptability to different Legal
Regimes; Network effect; User Orientation), which influence the scalability of a BM. Presence of these factors, are possible indicators of company growth and Investor attractiveness. The aim of this study is answering the following question: Which factors influence a business models ability to increase revenues faster than the corresponding cost base? The consequences of business model scalability “Company growth” and “Investor
attractiveness” are not part of this study. Using qualitative interviews with thirteen experts a simplified model of business model scalability will be introduced. Although exploratory, we propose a simplified model of business model scalability. The model consist of 4 scaling areas: (1) Scalability in Production; (2) Scalability in Distribution; (3) Scalability in
Infrastructure; and (4) scalability in adaptability of service/product. And 9 scaling factors, which should help executives focus their scaling efforts.
The following section contains the theoretical framework, followed by methodology and within case analysis. Next an across case analysis follows and finally a discussion. For a more detailed outline of the paper see table of contents.
2 Theoretical Review
2.1 Conceptual Development Business Model
Since mid-‘90’s –with the arrival of the internet- the business model is gaining increasingly more attention. The number of (peer-reviewed) publications in which the BM is the subject of analysis has rapidly grown between 1995 and now (Zott, Amit, & Massa, 2011; Wirtz 2016). The term business model has gained widespread use in the practice community. Entrepreneurs largely adopted the business model Canvas as a tool to help understand, describe, design and challenge the core aspects of a business and how it creates value (Osterwalder, 2004). In contrast, the academic literature is a bit more fragmented and has provided very divers interpretations and definitions of the business model concept. One possible explanation could be, the various fields (e.g., strategy, entrepreneurship, organization theory) of research participating in the scientific discourse about the topic. In addition, the term business model is not always used in the right context. It is often used interchangeably with the de terms, business plan, revenue model and economic model (Magretta, 2002).
Despite the wide variety of available definitions, academics generally agree with each other. the business model concept developed along three views starting with an emphasis on economics, followed by operational and strategic perspectives. Depending on the level of analysis and emphasise of the definition the business model is characterized by different key elements (M. H. Morris, Schindehutte, Richardson, & Allen, 2006; Wirtz, Pistoia, Ullrich, & Göttel, 2016). With the advent of the Internet e-business model research dominated the
field and until approximately 2002, this highly influenced the view on business models. Researchers were mainly concerned with economics – the logic of profit generation-, driven by technology (in this case the internet). Key elements of such models include revenue streams, pricing methodologies and cost structures. The defeniction that Amit & Zott, (2001) provide is a good example of this economic view: “A depiction of the content, structure, and governance of transactions designed so as to create value through the exploitation of business opportunities”. Some other researchers, sharing the same principal emphasis in developing their definitions, are Slywotzky, Morrison, & Andelman (2007) and Stewart & Zhao, (1999).
Subsequently, other researchers saw the business models as a representation of an architectural configuration. The business model as a blueprint for a (profitable) company. This view primarily focusses on a firm’s operational level, its internal processes and its infrastructure to create a unique value proposition. Key elements of such models include key resources, distribution channels and revenue model (M. H. Morris et al., 2006; Wirtz et al., 2016). The business model is perceived as a model capturing value creation and the coordination of resources (Amit & Zott, 2001). This view is consistent with how
entrepreneurs view the BM concept and the practical implication is huge.
Practitioners describe the business model as a representation - at organization-level - of a company. “A design of organizational structures to enact a commercial opportunity” (George & Bock, 2011, p.99). Entrepreneurs particularly focus on the question how to commercialize an opportunity? The business model seem to be the output of the ideation phase, it translates an idea into a design – “how resource structure and trans active structure interact to make value” - and makes the opportunity actionable (George & Bock, 2011, p.99). The business model is used to communicate the main activities and underlying assumption to stakeholders, to investors for example. Any flaws in the design are more easily to detect.
Simultaneously another view emerged, emphasizing on the strategic positioning of the firm. (Wirtz et al., 2010). Key elements of such models include, Value proposition, competitive strategy, target markets, value network and customer interface.
Recently the three basic views are developing into the direction of a converged business model view. In which the strategic positioning of the firm and internal processes are included (Wirtz et al., 2010). Morris, Schindehutte, & Allen (2005) provide a definition,
containing elements of strategy (competitive advantage), how the firm is operated and value created. “A business model is a concise representation of how an interrelated set of decisions variables in the areas of venture strategy, architecture, and economics are addressed to create sustainable competitive advantage in define market”.
In summary, it may be clear that the BM concept has gained a lot of attention from academics. Although literature cannot provide an unambiguous definition of the concept, there appears to be a kind of core. Key activities, value proposition, channels and
competitive advantage are elements often addressed. Furthermore three perspectives can be distinguished based on level of analyses. At the lowest level, the emphasis is mainly on the transaction, closely linked to technology. The majority of the research is based on e-commerce BM’s, emphasizing elements such as revenue model, pricing and transaction structure. One level up the focuses on the organization and its internal processes and infrastructure. At the highest level, strategy is also part of the BM concept
For the purpose of this study we analyse the business model because it provides an overarching framework, making it easier to compare businesses with each other. It is also said that business models help entrepreneurs making more informed decisions (Trimi & Berbegal-Mirabent, 2012). We expect that by using this concept the research findings will be adopted by practitioners more easily.
On a final note, we see the business model as a blueprint of a company. It makes clear how the company works and where it can be improved. The business model canvas developed by Osterwalder (2004) is a good translation of how we see the business model. See Appendix D for a template of the business model Canvas.
2.2 BM design and value creation
When starting a business the boundaries of the firm need to be defined. A totally new design can be created and / or an old design can be copied. Either way an entrepreneur has to decide What he is going to sell; who his customer are; via which channel(s) he is going to sell his product / service; and how he is going to organize his business.
Every entrepreneur wants his business to be profitable. The question rises whether there is a relationship between business model design and firm performance? Delmar and Shane (2003) and Locke and Lathan (2002) failed to prove such a relationship. However Amit & Zott (2007) found a positive correlation between business model design in
Entrepreneurial firms and profitability. Their dataset contained 190 of U.S. and European pubic exchanges listed entrepreneurial firms. In 2011 HBR publicised an article named “how to design a winning business model”, implies it is possible to design for profitability.
As described earlier, business models describe how economic exchanges take place, how value is created. If a business model design resembles a new way of conducting an economic exchange, one can say the business model has been innovated (Zott & Amit, 2007).
The emergence of the Internet had a great influence on BMs and BM innovation. Electronic-business (e-business) models emerged. New ways of creating and delivering value arose, for example, through cost savings and customization (Dubosson-Torbay, Osterwalder, & Pigneur, 2002). Scalability of those e-business models is frequently appointed as a key success factor of web-based firms. Scalability seems to be an important factor contributing to company growth and business model innovation (Amit & Zott, 2001; Rappa, 2004; Stampfl, Prügl, & Osterloh, 2013). Nowadays scalability is often mentioned in the same breath as start-ups. Start-ups being companies with huge growth ambitions. Both examples still signal the origin of the term scalability coming from a technical context. Scalability being closely related should impose more than only technology, right?
2.3 Scalability
Showing from the 5.320.000 results (within 0,79 seconds) of a Google search, “scalable business models” is a hot topic. But what is scalability or a scalable business model? Depending on the source of discipline the definition of ‘scalability’ varies. The term is primarily used in the context of technological abilities for increased workload and flexibility in additional client accommodation (Mark D. Hill, 1990; Nussbaum & Agarwal, 1991). Both social and commercial entrepreneurs have borrowed the term “scalability”. In the context of social entrepreneurship Desa & Koch (2014) provide the following definition: “Scaling social impact is the process of expanding or adapting an organization’s output to better match the magnitude of the social need or problem being tackled”. Scale is described as increasing impact of non-profit organizations. In a more commercial context the emphasises lies more on the cost instead of impact. Stampfl, Prügl, & Osterloh (2013) define scalability as, the ability to expand and grow revenue without an equivalent increase in operational costs. The ability to expand meaning, being able to grow while maintaining (or even increase)
performance or efficiency. A system can be complex, but a company is complex in a
different way. Besides the technology (operating systems, applications and such) a company consist amongst other things of people, values, customer/ clients, products and services. Since we are talking about scalability in a business context, should this not be part of the definition? Mathaisel (2015) provides the following definition: “Scalability refers to the ability of the enterprise to grow without losing customer, diminishing quality, or changing the core value proposition of the organization”. There is not one conclusive definition. What is included in the definition and what is not depends on the context. However, the base concept is consistent – the ability for a business or technology to accept increased volume without giving in on performance.
To conclude, In theory a scalable business model has a positive effect on
performance, since a scalable business model is able to grow the business without losing on performance and proportionally increase its cost base. With this in mind, should not every company design for scalability?
2.4 (Re-) Designing for scalable business models
2.5.1 Intro
A scalable BM could potentially lead to exponential revenue growth. Therefore it’s an important investment criterion for a venture capitalist (Zott, Amit, & Massa, 2011). The question arises, how do we design to grow larger? Which factors are influencing the scalability of a business model. To our knowledge, there is only one article describing the factors influencing the scalability of a business model, the Explorative model of business model Scalability proposed by (Stampfl et al., 2013). Their study describes five factors: (1) Technology; (2) cost & revenue streams; (3) adaptability to different legal regimes; (4) network effects; and (5) User Orientation, which positively influence the scalability of a BM and consequently leading to increased revenues and Investor attractiveness.
In our aim to gain a deeper understanding of business model scalability in a broader context than e-business or merely technology driven business models, we build on the EMBMS. Since the EMBMS was the only model we could find on business model scalability we further draw on literature focussing on, growth strategies and scaling social impact.
2.5.2 Technology
Starting with “ Technology” which is probably most associated with scalability. As described earlier, technological developments often lead to cost savings and efficiency gains. The arrival of the Internet created the possibility of very scalable business models, the
e-business model. Sharing. The advent of Cloud computing enabled companies to handle rapid increases in customers without a noticeable decline in performance or increase in costs (Bochmann, et al., 2003). E-business models are often objects of study, leading to insights is growth and scalability.
Nowadays companies are implementing technologies like, Robotic Process Automation or Artificial Intelligence to automate and digitalize their workforce. All
increasing the scalability of their business models, since robots don’t go on holiday, make less errors and are able to handle a certain task faster than their human counterparts. Implementing those technologies equals a decrease in dependency of human capital. In most cases this lowers the fixed cost (if not, the business case in negative, one of the reasons can be because volumes are to low). Implementing those technologies are beneficial for the scalability of the BM, but is it always beneficial for the value of the company. Some researcher argue it comes at a cost, because people add value. So what happens when customers don’t value the service anymore because they have the feeling a company treats them with a “one size fits all” mentality?
The applicability of technology in industries enhances the development of scalable business models. However one should always take into account the effect of perceived added value of the customers when implementing a new technology.
2.5.3 Cost & Revenue streams
The second factor of influence is “cost & revenue streams”. In which the revenue stream refers specifically to the individual methods by which money comes into a company and cost structure refers to the expenses that a firm must take into account when manufacturing a product or providing a service (Dubosson-Torbay, Osterwalder, & Pigneur, 2002). The cost structure contains all costs incurred to keep a business running. Shifts in volume (scaling up and down) impact this cost structure and might impact scalability.
Fixed and Variable costs are parts of the cost structure. The fixed to variable ratio is an indicator of the scalability of a business model. Examples of fixed cost are, rents, salaries, depreciation, insurance etc. Those cost are not affected by changes in volume. Variable
Costs do vary in proportion to changes in volume or services. One can think of, production supplies, direct materials, billable staff wages etc. A business model is scalable if variable cost per unit are low and fixed cost do not exceed revenues at times volumes are high. The cd business or pharmaceutical industry are examples. They are characterised by high development / R&D cost, but the cost to reproduce are very low (low incremental cost due to low variable costs).
Take “economies of scale” for example. In theory when a company grows, cost per unit decrease, because fixed cost can be divided over a larger number of produced units, with other words margins increase. The company obtains a cost advantage through growing. However one has to keep in mind capacity constraints. One machine can only produce [X] units, when [X] + 1 units have to be produced, the company has to make an capital
investment (buy an extra machine), and will not benefit from economies of scale anymore, until a certain point is reached. Thus a company can benefit from growth but economy of scale does not equal scalability, since operating margins do not automatically increase as revenues increase.
Economies of scope enables a company to leverage their infrastructure / operations to offer its customer other product or services than the original offer. One can think of Amazon, their infrastructure was built for selling books via the Internet and now they are offering other goods and cloud computing services via the same infrastructure. Whether the company is scalable or not, it is always important to understand the dynamics between the cost, earnings and business growth.
2.5.4 Adaptability to different regimes
The third factor suggested by Stampfl, Prügl, & Osterloh (2013) is “adaptability to different legal regimes”. This is particular of importance when a company has the ambition to expand internationally. Uber is a good example of a company who is experiencing a lot of difficulties rolling out their service / platform worldwide due to rules and legislation. AirBnB also
encounters legal troubles (Tun, 2015) . Impact of the legal regimes on the firm seems to be related to firm size. The smaller the firm, the greater the impact of legal boundaries (Beck, Demirguc-Kunt, & Maksimovic, 2005).
2.5.5 Network effects
“Network effects” is the third factor proposed by the EMBMS. If a consumer values a product more because, other consumers use it, one can speak of a positive network effect. The most used example to explain “network effects” is the telephone. When only one person has a telephone, the phone itself has no use, who are you going to call if you are the only one with a phone? But when more people poses a phone the value of the product increases. This is an example of a direct network effect (Katz & Shapiro, 1985; Liebowitz & Margolis, 1994).
Indirect network effects take place when the use of one product increases the value of complementary products. Multi-sided platform business models leverage this effect (Evans, 2003). Think of credit card companies, who need card holders and merchants to succeed. Or Microsoft for example, who needs application developers and users. Compatibility plays a big role in the size of the effect (Matutes & Regibeau, 1992). For example, if a certain CPM operating system can run on different kinds of computer brands, the effect will be bigger, because the size of the network consist of the set of brands that are compatible with that operating system (Katz & Shapiro, 1985). Apple is a good example of a company who cleverly uses the consumers desire for compatibility and
interconnectivity and fully leveraging the network effects via their platform. They create direct network effects by developing applications like IMassage and Face-Time. So everyone who owns an Apple device is able to communicate with each other without extra costs. Next they create a lock-in when people are buying compatible products, like a Mac Book or an IPad. Switching to competitors will become difficult since it will entail costs (Wirtz et al., 2010). So for example, someone has an IPhone and a Mac, it’s unlikely he will switch to Samsung. All his photos are in the Cloud, he is used to IOS and maybe half of his app will not work on Samsung operating system. They also benefit from indirect network effects via the app. There is a large number of people owning an Apple device, therefore the financial opportunity for app developers is also large. As a consequence the variety of available app is also large. This is kind of a vicious circle where an increase in users attracts developers and vice versa. However, negative network effects can also arise when the network reaches is total capacity (Liebowitz & Margolis, 1994). This can for example be the case when there are too many apps in the App store, and the financial opportunity for the developers decreases.
Network effects can even have such an effect on (perceived) value that under certain circumstances an inferior product can be chosen over a superior product (Katz & Shapiro, 1986). Shapiro & Varian (1999), provide a framework on how to win a battle and become the dominant technology, in a market with strong network effects. As shown from the provided examples network effects apply to both commodities as technology driven markets (Farrell & Saloner, 1992; Liebowitz & Margolis, 1994).
To be able to utilize network effects a multi-sided business model needs users on both the supply and the demand side. A strategy often used is to gain a critical mass on one side of the platform, by for example giving away free memberships, investing in one side of the market or creating viral content (Stampfl et al., 2013). For an overview of example see Evans (2003). It also works the other way around, if for some reason either on the supply or demands side of a platform the number of users decreases, this will negatively affect the number of users on the opposing side.
2.5.6 User Orientation
The fifth and last factor that is described is “User Orientation”. Stampfl et al., (2013) divides user orientation into three into three sub-factors: (1) problem-solving; (2) previous user knowledge; (3) simplicity. Problem-solving refers to the target group. Does the service truly solve a problem? With customer centricity as a new topic. Is there an actual demand for the service/product? This is related to the size of the market segment, a company who develops services or products nobody wants cannot scale. It also goes the other way around. If the total addressable market is small a business model cannot scale either. For example, a pharmaceutical company developing a medicine for a life threatening decease only 1 out of every 10M people has, wont scale.
As described earlier, the use of certain technologies like RPA or AI improve scalability of a BM, because in most cases those technologies replace human capital. This does not have to be a problem. However it does, when it negatively affects customer satisfaction. Caused by poor customer service or impersonal approach. Because nowadays competition might be one click away. The massages here is, solve their problem in the best way possible. This will be beneficial in a later stage. Happy customers seem to be more loyal, showing from higher retention rates (Anderson, Fornell, & Lehmann, 1994; Rust & Zahorik, 1993).
Customer satisfaction seem to have a positive association with “word of mouth (WoM)” (Söderlund, 1992)A lot of happy customers might decrease “customer acquisition costs”, due to the positive WoM. It is also said that when customers are organically acquired they add almost twice as much long-term value to the firm (Villanueva, Yoo, Hanssens, & Yoo, 2008). Mainly because those customers are more satisfied than customers acquired via marketing campaigns, showing form a higher NVP, lower churn and higher conversion rate. Keep in mind that the positive association also has been found with negative customers (Söderlund, 1992). Keeping track of customer acquisition can provide useful insights, because one might acquire customers, which never materialize in revenue. According to Villanueva et al., (2008) this can be a consequence of a not compelling enough value proposition.
Previous user knowledge refers to the knowledge of the target group regarding the product or service. The idea is that if the target group has skills in relation to, and prior knowledge of, a particular product or service, adaptability of the product will increase (Stampfl et al., 2013). However this might work the other way around. Does adaptability of the product increase when value of the product can be clearly and easily communicated to the customers.
Looking at social entrepreneurship. Clearly defining vision and signalling purpose is essential to their success in scaling social impact. People need to pick-up on
social/educational programs with the use of as less as possible resources. Social entrepreneurs need to be capable of communicating the core elements of their social innovation in a clear and easy way (Dees, Anderson, & Wei-Skillern, 2004). Does this also apply for commercial entrepreneurs? Instead of scaling social impact, customers might pick up op the product more easily.
The first two factors primarily refer to the target group. Simplicity refers to the product or the service itself. A product must be easy to understand and/or simple to use. Nowadays it is getting more and more important to think from a customer’s perspective. 2.6 Conclusion
The prevailing trend now is that only tech companies are scalable, but every company lies somewhere on the continuum of “not scalable at all” to “very scalable”. In our opinion it is
possible for every company to improve scalability or to design for scalability, because scalability lies not merely in technology (Hindi en Blumberg).
The choice of a business model seems to be key to the success of a business. When the choice is made, how a company earns money and for whom they create value. Can a company also make deliberate choices regarding scalability? Probably, if the dynamics between scalability, business model design and profit are clear.
To get a better understanding of the factors positively or negatively impacting the scalability of a business model, we conducted 13 in-depth interviews. Figure 1 illustrates the EMBMS, which is to our knowledge the only model describing factors positively influencing the scalability of a business model. The model distinguishes two phases, business model conceptualization and business model realization. The factors mentioned under business model (circled with red) conceptualization provide the back ground of the analysis that follows. However it is possible that Interview partners discuss items mentioned under business model realization. And most likely they will mention totally new items.
Figure 1
Explorative model of business model scalability
3 Methodology
3.1 Research Philosophy and approach
Important assumptions about how the world is interpreted by the researcher – how data about a phenomenon will be gathered, analysed and used - are grounded in the research philosophy. There are four main philosophical positions: (1) positivism; (2) realism; (3) interpretivism; (4) pragmatism. The approach of this study is from a interpretivist view (Saunders & Lewis, 2012)
Researchers who believe that only knowledge, which is gained through study observable and measureable variables in controllable conditions is trustworthy adheres to the positivism view. “Factual” knowledge is being collected and there is only one objective truth, resulting in law-like generations. Researchers who adopted the interpretivism view believe there is no other reality other than what individuals create in their context. In other words knowledge is subjective and constructed by each actor (Saunders & Lewis, 2012).
There is a great diversity of companies and a wide array of different business
models. How the business is run depends on the entrepreneur and how he or she interprets reality. For example, two CEO’s would run the same company in two different way, because what they perceive what would be beneficial for the company could be totally different. This study assumes that, the entrepreneur gains knowledge through experience, which is based on a context that was interpreted by him or her, while running their business. In this case the knowledge of the CEO is not a measurable variable and he or she did not gain this knowledge in a controllable condition.
Furthermore, the phenomenon that is being studied is the business model in relation to scalability hereafter called business model scalability. There are many interpretations of what a business model is and in practise there are different outcomes to the realisation of the same business model. To be able to get a deeper understanding of scalability in a business context, one has to view the world as having multiple contextualized realities, in this stage of research.
There is only one study which proposes a model which provides a basis for
understanding business model scalability. The proposed model and also other studies do not justify conducting research of quantitative nature. A deeper understanding is necessary to
be able to make generalizable claims, which will not be possible based on the current study. Given the explorative nature of this research the inductive approach has been adopted to see whether new themes arise and the proposed model needs to be modified (Saunders & Lewis, 2012)3.2 Research design
This study was designed to answer the following question: Which factors influence the scalability of a business model. Scalability refers to the ability to increase revenues faster than the corresponding cost base. The current study examined whether the model proposed by Stampfl, Prügl, & Osterloh (2013) is complete or need to be adjusted. As shown in Figure 1, the model decribes the factors that influence the scalability of a business model and provided the basis for this study. Their research data is based on in-depth expert interviews with experienced entrepreneurs and investors of web-based companies. It is very likley that not al the important variables of business model scalability are known, since they only selected e-business for their study. Besides the work of Stampfl et al. (2013) not a lot of academic literature can be found on scalability in a business context. To create a richer understanding a diversity of business models and the perspectives of different experts have been studied. Thirteen companies were selected of those companies the scalability of their business model was scored and thirteen semi structured in-depth interviews were
conducted. The first part of the questionnaire consisted of opened ended questions the second part of the questionnaire consisted both of open ended and closed ended questions. When an answer needed clarification, the researcher was able to ask additional questions. Both inductive and deductive approaches have been used to analyse the data.
The within case study provides general views of the experts regarding business models and scalability. During this phase all the possible scaling factors have been identified as a preparation for the across case analysis. At last a scalability score between 1-5 (five very scalable and 1 not at all scalable has been assigned to each business model based on the interviewee his answer and a business model analysis of the researcher. The across case analysis has been used to see if the data supports the EMBMS and whether new themes emerged. Starting with comparing the experts views on the factors proposed by the EMBMS, followed by factors not proposed by the EMBMS but mentioned by the experts. When fifty or more percent of the interview partners expressed similar views either the
scalability factor proposed by the EMBMS got adopted or provided suggestions for additional items.
The reasons presented to support the selection of these methods are found in the need of using exploratory inquiries with the purpose of developing themes for the data for an issue that has not been explored before (Creswell, 2003, p. 18, 22). In preparation of the interviews McNamara’s (2008) principles were applied. The cases for this study are selected based on the diversity of their business models. Sampling is not aimed at empirical
generalization, however the goal is to propose a model applicable for a wide arrange of companies.
3.3 Data collection
Thirteen Interview Partners were recruited using LinkedIn and email. All interviewees were founders or senior executives within the company and perceived as subject matter experts. For the purpose of this study the interviewee had to have “experience in working with business models on an analytical and conceptual level” (Stampfl, Prugl, & Osterloh, An explorative model of business model scalability, 2013). By only interviewing the founder or persons involved in the strategy of the company, it was ensured the interviewee met this criterion. This was confirmed by the (audit) question: What is a business model according to you? Eighty-five percent of the entrepreneurs in this sample founded two or more
companies, this might indicate that the interviewees in this sample are experts. In a sample of Baron & Ensley (2006), entrepreneurs labelled as “expert” founded on average 2.6 companies. For a detailed description of the sample see Table 1.
Table
1
Sample description
Case Focus Product / Service Age Company (y) Nr. of employees Level > 2 company founded In001 Music
Industry Service 10 14 Founder Yes
In002 Events Application 1.5 26 Founder Yes
In003 Internet
In004 Retail Product 11 - Founder Yes
In005 Finance Service 15 225 CFO Yes
In006 Home
appliances Application 2 4 Founder Yes
In007 Sun
energy Product 5.5 55 Founder Yes
In008 Legal Service 8 7 Founder Yes
In009 Horeca Service /
Product 2 - Owner Yes
In010 Online
platform Product 5.5 22 Founder Yes
In011 Lifestyle Service 3 7 Founder No
In012 Lifestyle Service 1.5 ~12 Founder Yes
In013 Winter
Sports Product 1.5 1 Founder No
Note: No data available when
table contains [-]
In preparation of the interview two pilot tests were conducted. This resulted in a revision and refinement of some of the questions. The sequence of the questions was also modified. The interview was semi-structured and divided in two parts. To make sure the participants were not uncomfortable to share information, the interview took place in a neutral
environment (not at their office).The first part consisted primarily of open-ended questions regarding scalability and business models in general. The open-ended question allowed interviewees to talk freely about the subject, which ensured openness to new input. The second part of the interview consisted of both open- and closed-ended questions regarding scalability of the business model of the company in question. In the second part of the interview questions were specifically formulated to get information about the company itself and the expert view on the factors proposed by the EMBMS.
Before the interview started the interviewer shortly introduced herself and gave a clear explanation of the purpose of the study. Before the start the respondent had to sign an informed consent and permission for recording the interview was asked. After that, the format of the interview was explained and then interview started. The interviewer made clear that in the first part of the interview the interviewee is not supposed to relate their answers to their own company. The interview started with the question what the
interviewee considered to be a business model. The reason to ask this question is twofold: (i) because in the academic literature there is not one clear definition of business model; (ii) it serves as an audit question to check whether the Interview Partner knows what he is talking about. To get the focus of the interviewee away from their company the interviewer asked if the interviewee could give an example of a perfect scalable business model, and explain why he choose this example. The interview continued with questions regarding factors inhibiting and supporting business model scalability. When new topics arose the interviewer asked for further details. In the second part of the interview participants were asked to assess the scalability of their business model using questions like: Indicate on a scale of 1-5 how scalable the infrastructure of your company is, whereas 1 is not scalable at all and 5 is very scalable; Whether they find this an important factor or not; How has the profit developed over the last 5 year. See Appendix A for the complete questionnaire. During the interviews notes were taken when factors were discussed that are already part of the model, but also when new topics emerged. In this way the data interpretation already started at the beginning of the interview. The interviews were recorded on a digital audio recorder and transcribed using a transcription program called “F5” version 2.5.
3.4 Analytical procedures
This study largely adopted O’Dwyer’s (2004) approach of qualitative data analysis. The first two parts of the analysis consisted primarily of preparing for the actual analysis. The researchers first started with coding. A pre-set of codes (see Appendix B1) were derived based on the theoretical model of Stampfl, Prugl, & Osterloh (2013) and on the notes taken during the interview. The codes were descriptive and gave an overview of topics. Although starter codes were used, openness to new and emerging themes and codes was very important. Next, parts of the transcripts were highlighted based on several observations and/or notes were taken when: (i) themes were repeatedly described; (ii) new concepts
(codes) seem to emerge; (iii) the Interview Partner gave the impression it was really important; (iV) parts of the transcript can be categorized under the pre-set of codes. After this the actual coding of the transcripts using NVIVO started. Words and phrases are used to classify the information. During all the coding sessions notes were taken when ideas, issues or anything that needed to be written down emerged.
The first cycle of coding served to identify multiple factors which potentially influence business model scalability, a breakdown of the concepts both old and new. The notes on the transcripts and the highlighted parts were also coded. A second round of coding consisted of refining the codes. In some cases codes were deleted and in other cases codes were broken down in subcodes or categories were made by bringing several codes together. Quotes from the transcripts were selected - and coded - to support decisions regarding construct
development. After two rounds of coding mind maps were created to structure thoughts and ermerging concepts. A few Queries were run using Nvivo to further explore the data.
The third cycle of coding consisted of coding the anserwers of the questions of the second part of the interview. This made it easier - in a later stage of analysis - to compare the answers of the different Interview Partners on a paticular question.
The second part of the analysis was the scalability assessment. The business model of each case has been quantified by assigning a score (grade) between 1-5 (1 “not scalable at all” and 5 “very scalable”) to each scalability item and sub-item described by the EMBMS. The first idea was to let the Interview Partner score their business model. However, with selfscoring we could not guarantee consistantcy in scoring. Therefor the researcher scored the business models as well. The scores are assigned based on the total interview and knowledge of the company. In several cases the Interview Partner was recontacted to very cetain thoughts. The total score per item has been calculated by taking the average of the subitems. For example: Case In001 scores a 3 for Automation of Processes and a 4.5 for technical Infrastructure the average score for Technology is 3.8. Network effects has been graded slightly different. Normaly the sub-items are also graded, but in this case two
questions were answered with yes or no and a total score was assigned for Network Effects. Question one: Are they using viral marketing. Question two: Does the company need to reach a critical mass before they are able to scale?
The third part of the analysis consisted of a within case and across case analysis. The within case analysis was conducted to identify statements which support and / or oppose
the EMBMS and to identify new construcs. To Identify themes common to all participants or a sub-set of participants a cross case analysis has performed.
Per case the scalability items described by EMBMS were analyzed. Does the data suggest that the corresponding Interview Partner mentions the item as scalability factor. And does the Interview Partner mentions factors not described by the model.
Cross case analysis has been used to analyse whether new knowledge could be produced and additions to the model can be proposed. When in 50% or more cases the data
suggested a new item was emerging , the item was taken into concederation. The next step was to check if the companies could be classified based on the factors not proposed by the EMBMS and whether the scores of the companies improved if the factors were taken into concideration.
During the analysis the researchers came to the conslusion that the current way of analysis did not led to the right conclusions. They reanalyzed the data using a method comparable to Grouded Theory. See chapter 6 paragraph 6.2.1 and 6.2.2 for a detailed description.
3.5 Validity and reliability
The nature of qualitative research itself poses risk on the validity and reliability of the findings, therefore several tactics have been implemented to counter them. To reduce measurement error and improve scoring consistency, scoring the business model on scalability has been done by the researcher. The self-scores of the interviewees have not been used. This study has enhanced generalizability (Yin, 1994) by including multiple in-depth cases in the study that represent various types of business models beside the internet model from different industries.
To increase validity of responses, participants were very well informed beforehand. They were aware of the nature of the project, how data got collected and what the researcher was going to do with it. There was a trust relationship with the interviewees
As with any type of questioning, one never knows whether the interviewee is lying or not. However, there was a trust relationship with the interviewees and we have the idea the interview partners answered the questions honestly. Recordings were used to enhance the accuracy of the transcript
4 Within case analysis
4.1 Introduction
First the overal perception of the interviewees regarding business models, the influence on scalability and the link with company growth will be described. Next a summary of the analysis of each case will be provided. The researchers first started with an overal analysis to identify possible scaling factors and after that proceeded with a scalability evaluation. Hower the results of both analysis are combined and shown per case herafter. Starting with a short company overview followed by a busines model scalability evaluation. Each
scalability evaluation proceeds acoording to a predefined order of scalability factors, starting with “Technology”, followed by, “Cost and Revenue structure”, “Adaptability to Diffirent Legal Regimes”, “Network Effects” and User Orientation” (all the factors proposed by the EMBMS in the conceptualization phase). Items that seem to be relevant according to the interview partners but were not described by the explorative model of Stampfl, Prugl, & Osterloh, (2013) will be described in the section “other scalability items”. Table 3 shown at the end of this chapter provides an overview of the scalability scores per case and Appendix C provides an overview of all the scalability factors mentioned per case. This chapter ends with a short conclusion.
4.2. Exploring practitioner views about business models and scalability
4.2.1 Business models
All interviewees have a clear representation on what a business model entails. Although a variety of components have been mentioned, their view on the concept “business model” is quite similar. Value creation was frequently mentioned. Examples of statements made in the interview include: “A model which describes how you are going to make money” (Interview Partner 1), “A model that describes how an organization value creates for its customers”(Interview Partner 4). In general the Interview Partners view a BM as a plan of execution, which explains how to create value and which resources are needed. This plan consists of several components, which can be arranged in different ways. Interview Partner 6 said: “success is a combination between the best elements of all building blocks of a business model”. Interview Partner 8 metaphorically describes a business model as a
“Blueprint of a commercial idea, whereby the blueprint shows how to turn an idea into a viable business”.
To conclude, the overall view regarding the concept business model is similar among the interview partners. The most frequently mentioned elements of a business model, were: value proposition, cost structure, revenue model, resources needed and target group.
4.2.2 Scalability
The interview partners have a general understanding of the concept scalability. The analogy with scalability in IT has been made very often. It seems company dependent what kind of examples the interview partners use when explaining scalability and which parts of the business model they find important to scale.
Wikipedia divines scalability as the capability of a system, network, or process to handle a growing amount of work, or its potential to be enlarged to accommodate that growth. This definition roughly consists of two parts, the first part relates to performance and costs. The second part relates to being able to grow without deviating from current ways of doing business. The interviewees explain scalability in line with the definition provided by
Wikipedia. On the one hand they provide answers referring to the first part of the definition, answers contain comments like: “not equally increasing overhead when turnover increases” (Interview Partner 1); “being able to downsize and scale the business without turning in on quality or increasing costs” (Interview Partner 12). On the other hand they provide answers referring to the second part of the definition, answers contain comments like: “Being able to add more units without losing identity and brand values” (Interview Partner 007).
Particularly the Interview Partners (IP’s) owning a IT related company or platform really focus on the cost side of scaling a business model. IP’s owning non-IT related companies focus on being able to scale their business without losing the current business culture, vision, identity and core values, also keeping cost in mind, but the focus is different.
To conclude, Interview Partners understand the concept “scalability”. Definitions provided by them often included being able to grow without giving in on performance and adding extra costs. However their perception of what is important to scale differs per company.
4.2.3 Link between business model scalability and growth
Majority of the Interview Partners indicate they are taking scalability into account when (re)-defining their business model. It seems they are aware of the link between scalability and business growth, showing from the business model changes they initiated. Seventy-five percent of the Interviewees adjusted their business model and they all indicated the
changes positively impacted the scalability of their company. This was backed up by the positive development of their revenues. Three of the four companies, which did not alter their business model saw their revenues decline and are operating at a loss. However, no actual financial data has been analysed. See table 2 for an overview of business model adjustments per case.
Not surprisingly, staff and automation are often mentioned as factors impacting the scalability of a business model respectively negatively and positively. Both factors directly impact the cost structure of a business model and consequently impacting margins. Interview Partner 008 explains: “The services I deliver are limited scalable, it’s because our model consist of billable hours. A human on average works 8 hours a day and that limits this business model”. The potential revenue is capped by time, the only way for him to increase revenue is to increase billable time, in other word hire more lawyers which increases costs as well. He further explains that by automating several services his business model becomes more scalable, for example automating the process of drafting certain standard contracts. To summarize, according to the Interview Partners a scalable business model - when
executed well - has an advantage over a non-or lesser scalable model and consequently will drive profit growth.
4.3.Case descriptions and scalability scores
4.3.1 Case In001 Company Overview
Company 1 is founded in 2005 (see Table 1), they are a globally operating music identification and rights monitoring company. They install “monitor boxes” at clubs or events. These boxes monitor and identify all the music that has been played. The company offers a broad range of solutions, including but not limited to: rights monitoring; dB control; live streaming and audio recording.
Scalability evaluation items EMBMS
Technology: Automation of processes currently scores a 3. The company is continuously improving their processes and it is expected that automation of processes will score a 4 later this year. Interview partner 1 explains that scalability of the technical infrastructure is very important for IT related businesses. Their infrastructure is very scalable, they’ve made
Table 2
Answers Interview Partners Business Model Adjustments
Y / N If yes what? Positive Influence on scalability? In002 4.1 1 Y fiscal structure to protect
IP internationally Yes Loss 6 to 20 In010 4.0 2 Y from local to national
(platform thinking) Yes Increased enormously 40 to 20 In012 3.7 3 Y added new value
proposition / new revenue Yes Increased 2 to 12 In006 3.6 4 Y distribution channel Yes Increased 1 to 4 In001 3.2 5 Y added revenue model Yes Increased enormously due
to cost savings and 1 to 13 In009 3.1 6 Y Value proposition Yes from loss to profit decreaxsd slightly In005 3.1 6 N - Increasing losses 60 to 225
In003 3.0 7 N - Loss
-In004 2.7 8 Y Cost structure / Flexibility Yes steadily increased
-In007 2.3 9 N - linear growth 3 to 25
In013 2.1 10 Y Value Proposition Yes slightly increased 1
In008 2.0 11 N - Decreased 2 to 6 development employees Case Average scalability score Order of scalability*
Business model adjustment
advantageous agreements with server providers compared to their competitors. Technical infrastructure scores a 4.5. Their average technology score 3.8.
Cost & Revenue structure: They started with one offs at festivals, meaning monitoring the music at a certain event for one time. For this service they have to set up and demolish their equipment at every stage per event. Margins are low and personnel costs are relatively high. Recently they added a recurring revenue model, which is now generating 30 /40 % of their income. They only have to install the monitors once and from then on the client pays a monthly fee for monitoring the music that is being played at the venue. This slightly
improved their revenue structure, but the personnel cost - mainly software developers - are still high. They are planning to add extra services for example, selling their data, which will improve their cost and revenue structure since data services in general are scalable. In the current situation they only benefit from economies of scale if they on-board new clients (developing cost can be divided). This item scores a 2.
Adaptability to different legal regimes: Interview Partner 1 indicates that this is quite an important item, he states: “Against all odds laws and regulations regarding copyright
between countries are quite different. At first we expected that our business model would be easily transferable to other countries”. Although it was harder than expected, company 1 managed to adapt to different legal regimes very well. This items scores a 4.
Network effects: According to interview partner 1, clients (club owners or organisers of an event) don’t value the service more, the more other clients use it. This is not entirely the case. If the body that represents the interest of music authors (CBO) acknowledges the companies’ database, the value of the service delivered by IP 1 his company will increase. The more clients use the service, the more it becomes the standard, consequently creating a pull effect for non-users. In the Netherlands, the company has an agreement with the Dutch CBO (Buma Stemra), which obligates certain events to purchase several services from the company to ensure the events pay for the copyrights. Showing from the examples, the network does positively affects the service’s economic value. The score assigned to this item by the interview partner is 2. The network effect is being underestimated has a greater impact on the scalability. The service they provide is becoming the industry standard when a certain mass is reached. After that they can increase the price of the service. This item scores a 3.
User Orientation: There are a few things to take into account scoring this item. The first question that needs to be answered is, does the service solve a real problem? The company provides transparency to the music industry. But transparency is a double-edged sword. On one hand artists receive the royalties to which they are entitled. On the other hand, the money has to come from somewhere / someone. There are a few stakeholders who are benefiting from an opaque market with regard to copyrights. According to interview partner 1, in many cases, it is the CBO who benefits from a less transparent market (especially in markets outside the Netherland). To answer the question, the service does solve a problem, but it is not in everybody’s favour that this problem got solved. This probably has limited scalability of their business model in the past. Interview Partner 1 indicates that the industry has changed a lot, and that CBO’s are becoming more and more willing to cooperate. The second item which has to be taken into account is previous user knowledge. When the company was founded user knowledge was relatively low and stayed low for several years. Probably because there were no comparable services in the market. Music identification and monitoring rights, were still in its infancy. They did not get the support from all parties in the music industry, this has negatively impacted scalability. The last item, which has to be taken into account, is simplicity of the service. The technique behind the service is very complicated, but the service itself is very simple, mainly because everything is being installed and checked by the company. Interview partner two assigns a four to User Orientation. He explains that the service is very user oriented, because they offer tailor made applications. Although this is user oriented, since the customized service meets the needs of a particular client in the best possible way. This does not entirely covers what is meant by user orientation according to Stampfl, Prügl, & Osterloh (2013), but only covers the “problem solving” part. Furthermore, there is a trade-off between customization and scalability, meaning high customization at the expense of low scalability. After analysis of the business and the market it is concluded that the score provided by interview partner 1 is too high and a 3 has been assigned to this item.
Other scalability items
Interview Partner names location as an important factor. A location has to match with the activities of the company. IP 1 explains that his company cannot be located in the city. His company must be easily attainable for trucks, which need to load and unload equipment.
Interview partner 1 also mentions, finding new ways to leverage your companies IP is a way to increase the scalability of your company. This basically comes down to adding new revenue models. He also explains that both professional and social network can have a positive influence on company growth.
Business model scalability score
Being scalable and improving scalability seem to be very important for the company. Their scalability score is 3.2. Scoring high on technology and adaptability to different legal regimes. They’re planning to further increase the level of automation within the company, which will positively affect scalability. The company could improve their score on the item cost and revenue structure. Currently they are financing growth through cash flow. This limits development. Besides looking for investors, they could lower costs or re-think their revenue model and find out if there are more ways to create revenue. In particular the last option could be beneficial for scalability. They possess a lot of data that has the potential to generate cash flows.
4.3.2 Case In002 Company overview
Company 2 has developed a popular app (1M users). With the app, users can win free admission and passes to clubs and events across the Netherlands and the UK. The company’s revenue model is based on business to business transactions. The number of transactions is dependent on the number of winners actually visiting an event. Clients use the app as a marketing tool / platform, which is most effective either at the beginning or at the end of the process of selling tickets. The company has been founded in 2014 and currently has 20 employees. The interview partner is one of the founders.
Scalability evaluation items EMBMS
Technology: According to Interview Partner 2, technology is one of the most important factors influencing the scalability of a business model. Both automation of processes and scalability of hardware. The company is organized in such a way that the majority of the processes are automated. However the primary processes (IT development, customer care
and content generation) mainly depend on a human factors. This trade-off between
automation and human factors will remain unchanged in the future. The human component is too important for those processes. The combined score for this item is 3.8
Cost & Revenue structure: Development costs are high and will stay high. Keeping in mind that the number of users is growing daily. Within a certain amount of time they are going to expand to other countries across Europe. Bugs need to be fixed and the app has to be further developed. It’s expected that they need to hire 6 extra developers within the coming months.
The company generates revenues in several ways. The events have to pay a fee for every ‘ticket winner’ who shows up at an event. Furthermore the company receives advertising money, which is vastly increasing due to the growing number of active users. Since day one they are collecting massive amounts of data, selling this data to third parties is another way of generating revenues. The founders are already planning to sell the company within 4 years, the database and the marketing platform are the most valuable assets of the company.
Since the company is founded they are financing growth from the initial investment made by the founders itself and through cash flow. The founders still own 100% of the shares of the Dutch subsidiary. In the UK they work with partners, which all had to invest in the U.K. subsidiary. All the U.K. partners together own 49% of the shares. This item scores a 4.
Adaptability to different legal regimes: The company has very clear goals of
expanding in Europe. They found trusted partners for their UK rollout and recently launched the app in the UK. Expansion to the UK had a major impact on their fiscal structure for two reasons. The tax system differs from the Dutch system. To prevent major tax bills the fiscal structure needed to be adjusted. Secondly, the intellectual property rights needed to be protected. England was kind of a guinea pig, to see if the company was able to go abroad. Although they successfully expanded to the UK, this does not guarantees a successful rollout in other countries, for example Spain. The local rules and regulations have had quite an impact on their business case. In preparation of a European rollout, KPMG is developing an “international model”, they are designing a fiscal structure which can be easily transferred to other countries. The company managed quite well to adjust to the local regime of the UK, without losing value in their company. The assigned score for this item is a 4.
Network effects: It seems that network effects play an important role in three different way: (1) The value of the company, in case it got sold, is heavily dependent on the number of active users. The more users the higher the value, because of the increasing revenues from advertising and the increasing amount of data they possess; (2) An increasing number of users attracts more clients, as a result the diversity of participating events and number of free ticket the company can raffle among players increases. This probably attracts new users. We have to note that the positive effect might reverses, when the number of active users is growing faster than the number of tickets that is being offered in the app. Because this will imply that the chance of winning free tickets decreases. This could negatively affect the number of active users; (3) the app is used as a marketing platform. The platform becomes more valuable for partners like Uber or Spotify, when the number of active users and clients increases. And becomes less valuable when the number of people declines.
Regarding item one and three a certain critical mass has to be reached in order to develop a certain market power. They have certainly reached that mass, since they are in the top five of most downloaded apps in the Netherlands and UK. People are motivated to sign in and play for free tickets. This items scores a 4.5.
User Orientation: From a client perspective the app can be deployed at different stages to engage the audience leading up to an event. The app acts like a communication platform between event organisers and the consumer audience. This platform can be used for pre-promotional goals. It can also be used to draw people to events that are not sold out. In that way the organisation can increase the number of visitors, which would otherwise not attend that particular event, consequently creating extra revenues (drinks, food and merchandise). The company would have no clients without end-users. The founders are well aware that their business model only works if they have a lot of users. Therefore, the service is completely designed to provide an optimal customer experience both online and offline. Customers can contact a contact centre 7 days a week, 24 hours a day. Established partnerships with for example Uber are beneficial for the customers. The company really understand the customers’ point of view. The game element creates a massive pull-effect, over 1 M people are using the app. Their target market is huge and is only increasing, due to the diversity of participating events (from house parties to Musicals).
