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226 Int. J. Product Development, Vol. 18, Nos. 3/4, 2013
Copyright © 2013 Inderscience Enterprises Ltd.
An explorative model of business model scalability
Georg Stampfl*, Reinhard Prügl and
Vincent Osterloh
Chair for Innovation, Technology, and Entrepreneurship (CITE),
Friedrichshafen Institute for Innovation and Family Entrepreneurship (FIF),
Zeppelin University,
Am Seemooser Horn 20, 88045 Friedrichshafen, Germany
Email: georg.stampfl@zu.de
Email: reinhard.pruegl@zu.de
Email: v.osterloh@zeppelin-university.net
*Corresponding author
Abstract: There is broad consensus that a scalable business model plays a key
role in new venture success. However, the factors that influence business
model scalability have received little attention thus far. This paper aims to shed
more light on this topic by introducing an explorative model of business model
scalability. The proposed model is based on an analysis of (a) a wide body
of literature, in particular including a broad range of studies from
entrepreneurship, strategy and business model research and (b) data from
qualitative research based on in-depth expert interviews with experienced
entrepreneurs and investors. The model provides a basis for understanding the
role of a scalable business model in new venture growth and identifies
mechanisms for successful web-based business model innovations. This study
serves as a starting point for further research on business model scalability and
provides guidance for executives in assessing the potential of new business
models.
Keywords: scalability; business model; business model innovation; internet;
new venture; start-up; venture capital; growth; e-business.
Reference to this paper should be made as follow: Stampfl, G., Prügl, R. and
Osterloh, V. (2013) ‘An explorative model of business model scalability’,
Int. J. Product Development, Vol. 18, Nos. 3/4, pp.226–248.
Biographical notes: Georg Stampfl is a PhD candidate at the Chair for
Innovation, Technology, and Entrepreneurship at Zeppelin University in
Friedrichshafen, Germany. He holds a master’s degree in Business
Administration and a bachelor’s degree in Business Law from Vienna
University of Economics and Business. His research focus lies in the field of
business models and business model innovation. He is particularly interested in
performance indicators for successful business model designs and in business
model adaptability and scalability. The focus of his doctoral thesis is on
business model innovation processes in incumbent firms.
Reinhard Prügl holds the Chair for Innovation, Technology, and
Entrepreneurship (CITE) and serves as Scientific Director of the
Friedrichshafen Institute for Innovation and Family Entrepreneurship (FIF) at
Zeppelin University at Lake Constance, Germany. In general his research deals
with questions on the intersection of innovation and entrepreneurship while
focusing more specifically on user and open innovation, technological
An explorative model of business model scalability 227
competence leveraging, family entrepreneurship and business model
innovation, with specific emphasis on aspects of search for innovation-related
knowledge. His research has been published in journals like Research Policy,
Journal of Product Innovation Management, Marketing Letters, and R&D
Management.
Vincent Osterloh holds a Bachelor of Arts in Corporate Management &
Economics from Zeppelin University in Friedrichshafen, Germany. He founded
his first company at the age of 17, worked for several start-ups in Silicon
Valley and Germany, and also invests in start-ups. He currently works on a
new Software-as-a-Service project in Berlin. His research interests include
business model innovation in start-ups and the scalability of internet-based
business models.
1 Introduction
Innovation pressure has increased steadily over the last few decades. Many companies
have attempted to rise to this challenge by aiming to develop breakthrough products and
services which promise higher returns on investment (e.g. Lilien et al., 2002; von Hippel,
2005). One alternative strategy which has recently attracted increasing attention in the
management field – both in the scholarly debate as well as business practice – is to come
up with innovative business model designs (e.g. Osterwalder et al., 2005; Markides,
2006; Teece, 2007; Johnson et al., 2008; Markides, 2008; Zott and Amit, 2008), i.e.
innovative and in some aspects game-changing approaches to value creation and the
related architecture of a company.
However, business model research has been criticised for its conceptual ambiguity,
resulting in “an invitation for faulty thinking and self-delusion” (Porter, 2001, p.77).
Chesbrough and Rosenbloom (2002) note that the concept “draws from and integrates a
variety of academic and functional disciplines, gaining prominence in none” (2002,
p.553). Nevertheless, it seems that scholars have managed to find common ground in this
regard. Zott et al. (2011) note that the business model has been established as a new unit
of analysis, providing the possibility of gaining a holistic view on how companies do
business. Now one of the next challenges is to explore which business model innovations
are likely to be successful.
Many of today’s most successful and fastest-growing companies (e.g. Facebook,
Groupon, Salesforce.com) are former internet start-ups that grew into multi-million
dollar businesses. The internet has probably been the most influential technological
phenomenon in the last 15 years and represents a fruitful arena for business model
innovation. Whereas the costs of starting a new business are relatively low, one resource
is very limited: attention. This has brought about a large number of young enterprises
with radically new business models. Due to the relatively low cost of experimentation
with different business models, these start-ups often revise their models several times
before they start making money. The internet therefore provides a very rich empirical
context to learn about successful business model innovations (Wirtz et al., 2010). Prior
research (e.g. Amit and Zott, 2001; Rappa, 2004; Bouwman and MacInnes, 2006)
identified the scalability of internet-based business models as a key factor in successful
business model innovation.
228 G. Stampfl, R. Prügl and V. Osterloh
However, there is hardly any research on factors that influence the scalability of these
business models. Therefore, this study uses qualitative interviews with entrepreneurs and
investors involved in internet start-ups in combination with a synthesis of the existing
literature to introduce an explorative model of business model scalability. While the
model is exploratory in nature, it provides an initial approach by identifying important
implications for business model design, implementation and operation in the context of
business model scalability. In addition, we briefly discuss what incumbents can learn
from web-based business model innovations and provide a short outlook on avenues for
future research.
2 Internet-based business models
There are three specific reasons why the internet is such an interesting playground for
and enabler of business model innovations: (a) new technologies have reduced the cost of
communication, (b) new ways of interaction between different parties have enabled
innovative transaction and exchange mechanisms (Amit and Zott, 2001) and (c) the high
‘clock speed’ (i.e. the high speed of change in the business environment) of information
technology fosters the development of new organisational forms (Mendelson, 2000).
Therefore, we have observed many new designs to create and capture value, especially
among young, flexible organisations. Although new ventures face many disadvantages
compared to incumbent firms due to their newness and smallness (Dean and Meyer,
1996), this very characteristic results in an agility that enables entrepreneurial firms to
respond to changing competitive conditions quickly by introducing new business models.
A start-up usually revises its business model four times or more before it becomes
profitable (Johnson et al., 2008). In the field of e-business, the internet often allows
companies to experiment with the business model at relatively low cost. For instance,
internet start-ups can switch from a pay-per-use model to a subscription model by
making just a few modifications to the website. They can even experiment with the
simultaneous use of different business models to see how customers react to different
offers. Entrepreneurial firms are accustomed to dealing with uncertainty regarding their
understanding of causes and effects in the environment and regarding outcomes
(McMullen and Shepherd, 2006) – a typical challenge in every business model
innovation project (Markides, 2008). With these facts in mind, it does not come as a
surprise that many industry disruptions have been triggered by successful internet start-
ups and that the majority of disruptive business model innovations are introduced by
industry newcomers (Markides, 2008). Hence, the context of new internet-based ventures
represents a highly suitable field for understanding and exploring business model
innovations. In the next section, we discuss which e-business model innovations are
likely to succeed and bring about exceptional company growth.
3 Scalability of business models
In order to gain an overview of prior research on the topic of scalability in the context of
business models, we searched the EBSCO Business Source Premier database for
academic articles containing the term scalability either in the abstract or title and the term
business model in text.1 We thus obtained 201 articles. An analysis of these papers
An explorative model of business model scalability 229
revealed that around 83% of the studies focused on scalability in information technology,
while only 9% focused on business and 8% were devoted to other subject areas (e.g.
psychology, nuclear science). Scalability is very often used in a technological context to
describe a system’s ability to accommodate an increased workload (Hill, 1990). In the
business context, there is no generally accepted definition of scalability. However, the
primary understanding seems to be strongly related to the growth potential of a company,
which is often understood as its ability to exploit economies of scale (i.e. a rise in
production capacity leads to a decrease in the unit costs of production) (Rappa, 2004).
For the purposes of our research, we focus on the economic understanding of scalability,
the growth of a company and the underlying function of the business model.
In their comprehensive review of the literature on new venture growth, Gilbert et al.
(2006) conclude that “the most important predictors of new venture growth include the
entrepreneur characteristics, resources, strategy, industry, and organisation structure and
systems:” (p.928). As an issue closely tied to strategy,2 the business model has been
identified as another important factor influencing company growth. One particular
characteristic of the business model – its scalability – is identified as a key factor for
successful business model innovation (e.g. Amit and Zott, 2001; Rappa, 2004; Bouwman
and MacInnes, 2006). Business model scalability can be defined as a business model’s
ability to increase revenues faster than the corresponding cost base (Hallowell, 2001).
Companies such as Dropbox, Zynga or Salesforce.com share a common
characteristic: their business models were able to scale quickly and made them important
contenders on the web. Today, it is no longer a matter of big companies outperforming
small start-ups; rather, those firms which grow fast and are responsive to change end up
winning the game. Consequently, investors often look for ‘infinite scalability’: an early
investment during a phase of low profitability might yield high returns as the company
becomes highly profitable after amortising fixed costs by serving a large customer base
(Hallowell, 2001).
Of course, the effective and efficient use of technology plays an important role in
internet-based business models, but it appears that success in e-business requires more, as
stated by Carr (2000), former executive editor of the Harvard Business Review:
“On the internet, if you can’t scale – if you can’t get really big really fast –
you’re nowhere. And it’s not enough for just your technology to be scalable.
Your entire business model has to have scalability as well. You need to be able
to quickly extend your business into new markets, either horizontally or
vertically. Will it scale? Is one of the first questions venture capitalists ask”
Airbnb, a web start-up founded in August 2008 and based in San Francisco, California,
provides an online marketplace for people to list and book unique spaces to rent around
the world. Castles, cabins, sailboats, luxury villas or just regular apartments are listed for
rent online. Airbnb is a community which brings tenants and landlords together,
facilitates the booking process and handles the financial transactions. The site takes a
commission of 10% of the booking price. Even though the factors that led to Airbnb’s
enormous growth (the company reported a growth rate of 800% in 2010)3 are manifold,
one key factor was the scalability of its business model. Listings are available in
170 countries, and the costs of additional transactions are close to zero (except for
additional server capacity). Investors recognised that potential, and Airbnb just recently
received funding of more than 100 million dollars.
230 G. Stampfl, R. Prügl and V. Osterloh
Groupon has a rather different history. The website was launched in November 2008
and offers discount gift certificates which can be used at local companies. Groupon is
one of the fastest-growing internet companies and recently spurned a $5.3 billion
takeover offer from Google. The service is available in cities in North America, Europe,
Asia and South America. The company has profited from viral growth, as users
recommend deals they like to their peers in order to ensure that the required minimum
number of people sign up for an offer (if the minimum is not reached, the deal is
cancelled). However, Groupon faces a major disadvantage in its business model:
currently the company has to rely on a great number of employees to acquire and manage
new deals. This severely inhibits the scalability of its business model. The huge
workforce required to execute the business model substantially increases the costs of new
deals. In order to accelerate growth, Groupon could consider switching to self-service
technology, where businesses interested in offering a deal could sign up directly on the
website and the company would only have to decide which deals to accept.
In light of the two examples above and Google’s advertising-based business model,
which requires a high volume of user traffic, it becomes obvious that scalability is
particularly important in e-business. In this field, many business models are designed for
mass markets, meaning that they are based on the realisation of low marginal costs
(i.e. the costs of serving additional customers) and low customer acquisition costs.
4 Research method
The current stock of knowledge on factors influencing the scalability of internet-based
business models is quite limited. Consequently, in order to develop initial theoretical
insights into business model scalability, this research follows Strauss and Corbin’s
(1990) explorative and cumulative approach of grounded theory, a qualitative method
which aims to generate an inductively derived theoretical model by systematically
applying a certain set of procedures. It must be noted, however, that we also follow the
notion put forth by Vandenbosch et al. (2006) that it is unrealistic to demand that the
researcher should be completely free of any theoretical expectations in the process of
data collection and analysis – a postulate often attributed to this approach. “All
knowledge is ultimately a function of antecedent interests and passions” (Vandenbosch
et al., 2006, p.264). Hence, a priori knowledge does not stand in the way of the chosen
approach in deriving an initial model of variables driving business model scalability.
4.1 Research design
The design of this research effort follows an established procedure widely used in
innovation studies in order to develop an explorative model in a rather new field of
research (e.g. Khilji et al., 2006; Vandenbosch et al., 2006; Brockman et al., 2010). The
development of the model began with a synthesis of the relevant literature on the
scalability of business models. As discussed above, most of the studies focused on
technical scalability, while only few discuss the scalability of business models.
Consequently, our analysis includes a wider body of literature, in particular studies from
entrepreneurship, strategy and business model research.
An explorative model of business model scalability 231
In our synthesis of the extant literature, we identified initial factors that might
influence scalability prior to conducting in-depth interviews. During and after the
interviews, we uncovered several new factors that influence the scalability of the business
model as well as factors that can be seen as a consequence of business model scalability.
In line with the goals of qualitative research (Glaser and Strauss, 1965; Vandenbosch
et al., 2006), the interviews were used to identify and define aspects of business model
scalability instead of testing a preconceived model.
4.2 Data collection
Besides the literature analysis, we used in-depth, semi-structured expert interviews to
collect data. Expert interviews are an appropriate means of accessing specialised expert
knowledge in order to gain initial empirical insights into fields which have seen only
little attention in research, as is the case in our study (Silverman, 2005). Expert status is
attributed to people who have deep and accessible knowledge on a specific topic (Bogner
and Menz, 2009). For the purpose of this study, a high level of expertise regarding the
design and operation of business models was used as the primary selection criterion.
Furthermore, interviewees had to have experience in working with business models on an
analytical and conceptual level. In addition, the semi-structured format allowed us to
keep our interviews focused on the topic of this study and to maintain consistency by
asking prepared questions (Spiggle, 1994). At the same time, open-ended questions and a
non-judgemental approach ensured openness to new input from respondents. Through in-
depth interviews, the research team was able to identify and highlight issues which
otherwise may have remained undiscovered. We conducted 12 in-depth interviews with
two different groups of experts (entrepreneurs and investors) and aimed to ensure a
satisfactory level of data saturation (Guest et al., 2006).
In our sample, half of the experts are entrepreneurs who have founded at least one
internet-based company (most of them have founded more than one internet start-up; the
average is 2.4 per interviewee), while the other half are investors who have made
investments in at least two start-ups of this kind (see Table 1 for detailed information).
All of the experts were familiar with the particularities of e-business, as they have track
records proving their experience in that field. We selected a mix of entrepreneurs and
investors in order to gain insights from people with an ‘inside view’ as well those with an
‘outside view’ of a company. In particular, entrepreneurs provide operational insights,
while investors see many different start-ups and various business model designs. The
data were collected between 15 February and 22 March 2011.
The interviewers started conversations by asking respondents about their current
business activities and their understanding of the term business model. The interviews
continued with questions regarding preferences for specific types of business models,
factors inhibiting and supporting business model scalability and the role of business
model scalability for new ventures and investment decisions based on the respondents’
prior experience. Furthermore, experts were asked to give examples of web-based
companies that exhibit perfect scalability (or the opposite) in their business models.
When new topics arose during the conversations, researchers asked for further details in
order to explore emergent themes. All interviews were recorded and carefully
transcribed. The interviewers took notes during all of the interview sessions. Finally, the
resulting transcripts were reviewed for accuracy.
232 G. Stampfl, R. Prügl and V. Osterloh
Table 1 Expert sample
Interviewee Profession Level Focus
1 Entrepreneur CEO Internet/Mobile
2 Entrepreneur CEO Agency/Mobile
3 Investor Partner Incubator
4 Investor Partner Venture Capital
5 Entrepreneur CEO Internet/TV
6 Investor Partner Venture Capital
7 Entrepreneur CEO Internet/Gaming
8 Entrepreneur CEO Internet
9 Entrepreneur CEO Internet
10 Investor Partner Venture Capital
11 Investor Partner Incubator
12 Investor CEO Internet
4.3 Data analysis and model development
For the purposes of data analysis and model development, we adopted Strauss and
Corbin’s (1990) approach to qualitative data analysis, which is based on coding raw data
and grouping it according to related concepts (Brockman et al., 2010). To this end, we
adhered closely to the three primary coding procedures suggested by Strauss and Corbin
(1990): (a) open coding, (b) axial coding and (c) selective coding. During open coding,
researchers identify emergent issues and subsequently categorise them. This procedure
facilitates the typological evaluation required in order to categorise subjects in the study
(Vandenbosch et al., 2006). In this study, open coding served to identify a multitude of
factors which potentially influence business model scalability. Axial coding makes it
possible to establish connections between constructs by grouping themes based on
theoretical considerations, thus enhancing conceptual coherence and internal validity. In
this study, axial coding was used to group similar factors identified during open coding.
Selective coding entails the selection of the core category, relating it to other categories
and validating the emergent relationships. Thus, this step serves to refine and integrate
the emerging model. In this study, selective coding was used to distinguish relationships
between business model scalability and other model variables.
The research team met regularly to discuss the interview process, to reflect on themes
received a priori from literature analysis and emergent issues and to discuss the
development of the model. Thus, the data interpretation process already began during the
interview stage. In order to improve data quality and reliability, two researchers were
used for data analysis, i.e. the coding procedure (Krippendorff, 1980). They read
transcripts several times and tabulated raw data independently in order to ensure effective
analysis (Perreault and Leigh, 1989). The authors started by noting statements from the
transcripts to recognise emerging constructs (open coding). Quotes from the transcripts
were selected to support decisions regarding construct development (some of those
quotes are included in the findings section below). Once open coding had been
completed, the data were re-analysed in order to establish more accurate connections
An explorative model of business model scalability 233
between categories and sub-categories, i.e. to group constructs in a theoretically
meaningful manner and at a higher level of abstraction (axial coding). In line with the
chosen approach, an analysis of the primary data allowed the authors to identify
emergent patterns regarding business model scalability. The emerging model was also
constantly reviewed in light of the issues derived from our analysis of prior literature.
The next step in the model’s development was the identification of relationships between
business model scalability and its antecedents and consequences (selective coding).
Finally, the model was shared with experts who match our sample characteristics. This
stage of our research served to further refine and finally confirm the accuracy of our
model (Charmaz, 2000).
5 Findings
Figure 1 presents our exploratory model for the scalability of internet-based business
models. The model consists of (a) antecedents and (b) consequences of business model
scalability as well as (c) moderators between the variables. In line with the literature, our
data enabled us to confirm that it is important to differentiate conceptually between
business model design and business model implementation (e.g. Mitchel and Coles,
2004; Pateli and Giaglis, 2004; Osterwalder et al., 2005; Shi, 2009; Teece, 2010). “A
business model cannot be successful per se. [...] A business model can be more or less
sound and coherent but then it still must be implemented. A “strong” business model can
be managed badly and fail, just as much as a “weak” business model may succeed
because of strong management and implementation skills” (Osterwalder et al., 2005,
p.13). In that sense, a business model design that fosters scalability represents growth
potential which eventually needs to be realised in the phase of business model
implementation (George and Bock, 2011). At this point, it is important to note that in
business model innovation practice, the design and implementation phases do not
necessarily follow a strict sequential procedure. This is especially true of internet-based
business model innovations, where new ventures constantly experiment with new
business models (Johnson et al., 2008). Thus, business model (re-)design and
implementation are rather parallel than strictly chronological processes (Sosna et al.,
2010). On a conceptual level, however, chronological separation is a useful way to
clearly identify the antecedents and consequences of business model scalability. This
separation was also reflected in our data, as interviewees differentiated between the
business model concept and its realisation in their statements regarding business model
scalability: “You can design a game-changing business model – it won’t scale if you do
not have what it takes to operate it” (Interview Partner 2). Consequently, the model
distinguishes between ‘business model conceptualisation’, i.e. the design of a business
model, and ‘business model realisation’, i.e. the implementation and operation of the
business model.
5.1 Business model conceptualisation: antecedents to business model
scalability
In developing our explorative model of business model scalability, we applied Baden-
Fuller and Morgan’s idea of business models as recipes (e.g. Baden-Fuller and Morgan,
2010; Sabatier et al., 2010). In this sense, business models are comparable to an
234 G. Stampfl, R. Prügl and V. Osterloh
architect’ s model, which is “used to demonstrate or give advice about how to do
something so that the results will come out right” (Baden-Fuller and Morgan, 2010,
p.166). A successful business model, of course, needs various ‘ingredients’. In our
model, we focused on the ingredients which influence scalability in the phase of business
model conceptualisation. The identified causes of business model scalability were
categorised as five mutually exclusive factors (Chrisman et al., 1988): (a) technology,
(b) cost and revenue structure, (c) adaptability to different legal regimes, (d) network
effects and (e) user orientation.
Figure 1 Explorative model of business model scalability
Technology: The effective and efficient use of technology was found to play a significant
role in business model scalability for web start-ups. For instance, Interview Partner 5
made the following comment regarding technology: “Scalability is one of the top criteria
regarding an investment decision. The business model has to be scalable. That’s why we
often invest in technology, because this is actually the part which enables scalability”.
As mentioned above, the issue of scalability is closely linked to technological scaling
(e.g. Colajanni et al., 1998; Bochmann et al., 2003). Our data suggest that in designing e-
business models, entrepreneurs try to reduce dependence on human resources by
automating processes (e.g. user registration or customer service).
Therefore, the automation of processes helps to keep personnel intensity low, which
in turn reduces fixed costs. Salesforce.com (a cloud computing company that distributes
business software on a subscription basis), for instance, uses self-service technology for
the entire process of registering new customers. Salesforce.com’s distribution is handled
completely digitally because there is no need to send out software physically. The
company merely provides access to its software-as-a-service (SaaS) offers.
However, considering Hallowell’s framework (2001) on the paradox of human
resources in e-commerce, business model design must exercise caution with automation:
“[B]ecause the nature and quantity of physical service necessary to deliver value to
An explorative model of business model scalability 235
customers influences the quantity of human intervention required, it also influences a
firm’s ratio of variable to fixed costs, which alters its ‘scalability’” (p.4). While investors
may see reduced scalability as a negative aspect, increased human intervention (e.g.
increased personal service) may enhance the customer experience, which in turn might be
a source of competitive advantage.
Another issue regarding technology in business model design is the scalability of
technical infrastructure. Technical scalability comprises techniques to improve a
system’s capacity to support more users without suffering a noticeable decline in
performance (Bochmann et al., 2003). This includes the development of the software and
server structure and plays a substantial role as soon as the start-up grows rapidly and user
numbers increase substantially. At this point, it becomes very important to be prepared
for exponential user growth in order to keep the technical infrastructure working and the
services running. If a start-up is not prepared for the growth challenge, the young
company is likely to “become surpassed by its own scaling efforts” (Interview
Partner 1).
There are many famous examples of this scalability problem. A very recent example
is the young German start-up Phonedeck, which developed a web-based application and
service for users to control smartphones remotely. As the application was featured in the
media only a few days after the product launch, it gained international popularity
unpredictably quickly, resulting in a surge of new users. Phonedeck’s technological
infrastructure was not prepared for the run on its service, and the company consequently
had to apologise for the system overload: “Thanks for signing up for Phonedeck! Due to
the awesome response we’ve had from both press and users since launching yesterday we
are experiencing a heavy load at the moment, but please bear with us, there is plenty of
room for everyone!”4
Cost and revenue structure: Most scholars focusing on e-business as an area for
research on business models include financial aspects (e.g. revenue streams and cost
structure) in their business model conceptualisations (see Zott et al., 2011, for a detailed
account). The importance of pecuniary factors is self-evident; after all, growth needs to
be financed. For new ventures in particular, it is often impossible to finance fast and
powerful growth (exclusively) through cash flow (Storey, 1994), and these financial
shortcomings may limit business development (Hughes, 1996). Bootstrapping, i.e. “the
use of methods to meet the need for resources, without relying on long-term external
finance” (Winborg and Landstrom, 2000, p. 38), has limited scalability and thus hampers
growth (Patel et al., 2011).
As outlined above, Groupon has a huge sales force (high personnel intensity) and
therefore problems with scaling appropriately. Groupon has exhibited very impressive
growth over the last two years, but costs have increased as well because of the huge
workforce necessary to execute its business model. Groupon’s copycats, such as Living
Social, Daily Deal or the now-defunct City Deal, faced the same problem because they
simply replicated the Groupon’s model. This issue has raised doubts regarding the
viability of Groupon’s business model.
Our explorative study also indicates that financial aspects influence business model
scalability. The experts in our sample preferred business models, which generate
revenues early while keeping fixed costs low. Interview Partners 3, 4, and 6 expressed
similar views. Interview Partner 3, for instance, noted, “Investing in web-based business
models is so exciting, because, generally speaking, these business models usually imply
low initial costs. So, start fast and scale even faster!”
236 G. Stampfl, R. Prügl and V. Osterloh
Adaptability to different legal regimes: “You must not ignore legal aspects when
designing your business model, as there are always specific legal restrictions you have to
deal with” (Interview Partner 8; similar comments came from Interview Partner 1). As
expressed in the interviews, legal restrictions must be taken into account in the process of
designing a scalable business model. This issue was mentioned by 7 out of the 12
interviews conducted in this study. The fact that more than 50% of the interview partners
mentioned legal restrictions as a factor which can influence scalability illustrates the
importance of this issue. Interviewees underlined the fact that different legal restrictions
can severely inhibit scalability. These restrictions can arise in various forms. The
Swedish start-up Spotify (a music streaming service), for instance, is a famous example
for problems related to different intellectual property rights systems in different
countries. For a long time, Spotify was not available on the German market because its
system of digital music distribution did not comply with the relevant legal regulations in
Germany. The responsible local publishing rights society (GEMA) refused to permit
Spotify to introduce its service on the German market. The comparable German start-up
Simfyi, however, was more successful with its negotiations of licensing agreements and
received a licence to stream music legally in Germany. Regarding the relevance of legal
restrictions, Interview Partner 1 stated, “In many countries, services are not allowed or
blocked by the government. This can be a strong barrier in terms of scalability”. Besides
specific barriers due to the potential infringement of intellectual property rights,
interview partners identified the general need to accommodate specific countries’ legal
requirements as a crucial effort without which geographical expansion might be impeded.
As regards the effort involved in rolling out business models internationally, respondents
had highly differing opinions. Interview Partner 7, for example, explained:
“Internationalisation can be an inhibiting factor, because you need support
[...]. Independent from the way of how you internationalise, it always takes a
lot of work and effort. For example, are the texts translated? Do you know all
the legal restrictions? Which colours aren’t used in certain countries? All these
are factors which can threaten the scalability very strongly, constrain the
scalability and make it expensive”.
While Interview Partner 3 said: “It’s definitely a big asset if you have a business model
which can be easily transferred to different countries and rolled out worldwide”.
Internationalisation is particularly relevant and easier for internet-based business models
where content transactions are conducted completely via the internet and no physical
transactions are needed, e.g. online data storage and synchronisation services such as
Dropbox. However, conquering new markets is always associated with the need to
comply with local legal systems.
The indications of our explorative study are also supported by the literature, as recent
studies have identified the adverse impact of legal obstacles on corporate growth. For
instance, Beck et al. (2005) found that the smaller the firm, the more negatively it is
affected by legal barriers. Consequently, it is indicated that new ventures in particular
should deliberately design business models that are flexible with regard to different legal
regimes.
Network effects: The issue of ‘network externalities’ (Economides, 1996; Shapiro and
Varian, 1999) or ‘network effects5 has been discussed in numerous studies, providing
theoretical and empirical support for its strategic importance. The concept of positive
network effects refers to a situation in which “consumers value a product more, the more
An explorative model of business model scalability 237
other consumers use it” (Conner, 1995, p.210). When the network positively affects a
product’s economic value, “network effects are considered to be determinants of product
success” (Shim and Lee, 2012, p.310). Network externalities were first observed in
physical networks (e.g. in the context of telecommunications; Oren and Smith, 1987),
where the value of a network increases with its number of subscribers. The growing
network thus gains attractiveness for non-users. This logic can be transferred easily to the
context of internet-based business models, as social networks provide a textbook
example of this phenomenon. The value of Facebook as a social network platform
increases with every user who signs up and becomes active on the platform. People invite
their friends, tell them about the website, and so on. However, network effects are also a
scalability factor which can turn from a positive, supporting effect into a negative,
inhibiting effect or vice versa. In the negative case, the speed of user loss increases
exponentially with each user who cancels his/her account. The German Facebook clone
StudiVZ is probably the best recent example in terms of negative network effects. Before
Facebook became popular in Europe, StudiVZ was the leading social network for college
students in Germany, with very rapid user growth based on network effects. As soon as
Facebook became more common in Europe, the former positive network effect turned
into a disaster for StudiVZ in terms of user numbers. Facebook, which no longer limited
access to its platform to students and had already established itself as an international
platform, was more appealing to the typical social network user. As a consequence,
thousands of former StudiVZ users moved to Facebook and closed their StudiVZ
accounts, and the platform became less attractive as more and more users switched to
Facebook.
The literature indicates that business models based on generating network effects
might have specific characteristics (Kauffman et al., 2000). Innovators may charge
higher prices if their offer (e.g. software products) becomes the (industry) standard
(Brynjolfsson and Kemerer, 1996; Gallaugher and Wang, 2002). According to Conner
and Rumelt (1991), the distribution of software without copy protection might result in a
competitive advantage for software companies, as they more rapidly increase their
number of users. Historical examples such as the PC platform competition between IBM
and Apple in the late 1970s indicate that business models which create ‘platforms’ can be
very successful. IBM’s open architecture approach had the effect that a majority of
personal computers were sold as ‘IBM-compatible’, and Apple lost market share to IBM
(Shim and Lee, 2012). Business models that generate network effects create a ‘lock-in’
phenomenon (Amit and Zott, 2001) which prevents migration to competitors as the costs
of switching increase. Extant research supports a positive relationship between the size of
internet communities and business model scalability. Rothaermel and Sugiyama (2001)
argue that larger communities allow more transactions to take place, resulting in greater
margins and more transaction-based revenues for the website operator. These authors
name the following revenue sources for such businesses: (a) subscription fees, (b) usage
fees, (c) member fees, (d) advertising commissions, and (e) transaction commissions. It is
argued that only advertising commissions and transaction commissions are business
models which create sustainable value, whereas the others constrain positive network
effects. However, it is also important to note that (formerly positive) network
externalities may turn negative as soon as the network exceeds a particular size. In their
empirical study on peer-to-peer music sharing networks, Asvanund et al. (2004) found
that as the network size increases, users contribute less and less additional value to the
network and user cost increases. Furthermore, users are less likely to contribute as the
238 G. Stampfl, R. Prügl and V. Osterloh
network grows. The authors’ findings underscore the idea that a larger network does not
necessarily provide more value for users. Consequently, business model designers should
carefully evaluate network effects and their impact on scalability.
With regard to designing business models where the size of the user base affects
consumer ‘tastes’ (Conner, 1995), the interviews revealed that two aspects must be
considered with regard to scalability: critical mass and going viral.
The theory of ‘critical mass’, developed in the seminal work of Markus (1987),
suggests that “once a number or proportion of users (critical mass) have been attracted,
use [of an interactive medium] should spread rapidly throughout the community”
(Markus, 1987, p.500). Critical mass is a double-edged sword in terms of scalability. On
the one hand, it might precipitate the venture’s failure in an early phase, but on the other
hand it might boost growth even further as soon as this milestone has been passed. This is
especially true of community-based business models. They “must have reached a critical
mass of membership for advertising and transaction commissions to be a viable revenue
source” (Rothaermel and Sugiyama, 2001, p.301). There are various other examples
where critical mass is the key point in business model scalability. Interview partners
primarily named advertising networks, gaming platforms, social networks and shopping
clubs. As Interview Partner 10 explains “For advertisement or link-based business
models, there is usually an extremely high critical mass to reach in order to develop a
certain market power to use the traffic in a reasonable way”.
Zynga’s social game Farmville represents an interesting case where reaching critical
mass quickly was vital for success. Because it was based on the fast-growing social
network Facebook, reaching critical mass was far easier than it would have been with a
stand-alone game. There are tools such as Facebook Connect which can help generate
more sign-ups for the offered service and thus reach critical mass faster and more easily.
Interviewees furthermore expressed the importance of ‘going viral’, i.e. the use of
viral marketing techniques, to reach critical mass and foster positive network
externalities. Marketing literature supports these findings by underlining the considerable
advantage of going viral with regard to the possibility of raising attention to new offers at
light speed (Ferguson, 2008). Going viral enables to “leverage brand evangelists to
encourage trial and activation” (2008, p.181). From our data, it became evident that viral
marketing swiftly integrated into the business model can help a company scale its model
quickly. In business model design, it is necessary to consider how many users will follow
one user who signs up for the service. Groupon is a great example because users are
motivated to invite friends and recommend desirable deals to their peers in order to reach
the necessary minimum number of people buying a deal. As Interview Partner 2 noted,
“Groupon scales because it has a kind of a pull effect”.
User orientation: Due to increasing competitive pressure, companies have recognised
the need to change their perspective on innovation processes. Research has demonstrated
that innovation should not represent a mere ‘technology push’ approach, but rather be
more user-driven (‘need pull’) (Rothwell, 1994). In the business model literature,
practitioners are advised to reconfigure established internet business models in order to
meet changing user needs (e.g. Gambardella and McGahan, 2010; Wirtz et al., 2010).
“Customer experience must be at the heart of any innovation and design process”
(Kaplan, 2012, p.108). In addition, our data suggest that user orientation is a relevant
factor in business model design: Simple business models that solve a real problem and
are built around existing user knowledge will scale more easily which is in line with
research on product and service design (Schreier and Prügl, 2008; Schreier et al., 2007;
An explorative model of business model scalability 239
Prügl and Schreier, 2006). The issue of problem solving examines whether the business
model addresses a pressing problem which is eventually solved by the service or product
offered. This point is closely related to the size of the market segment to be targeted. In
other words, the number of people who actually face the problem is essential. The aspect
of problem solving has to be considered in relation to the future potential of the idea.
Some ideas seem small at first glance, but later become a major new business. The
scalability of the business model is also influenced by the relevance and existence of
previous user knowledge. Many successful products build on existing skills within the
target group and therefore do not require users to develop new knowledge. On the
internet, companies that offer easy-to-understand services will have an easier time
scaling their models quickly. As people tend to dislike using complex products or
services, business models which are easy to understand and are built around simple offers
are more likely to succeed. “One of the most important factors [regarding scalability] is
simplicity. People will immediately switch to another offer and won’t tell anyone about it
if they don’t understand what you offer them” (Interview Partner 9).
5.2 Business model realisation: consequences of business model scalability and
moderating factors
As outlined above, designing business models that scale quickly will result in a potential
for growth. However, this potential can only be realised if the business model is well
implemented and operated (Osterwalder et al., 2005). As the main focus of this study lies
on business model scalability (in the sense of scalable business model design) and
not business model implementation, we do not further elaborate on what makes
implementation processes successful in general. Nevertheless, our interview data show
that the experts closely link the issue of business model scalability to the implementation
and operation of a scalable model. Consequently, the explorative model presented here
proposes four resulting variables for the phase of ‘business model realisation’ strongly
emphasised in our data: the two variables representing consequences of business model
scalability, which were taken from business model and entrepreneurship theory and
supported by the interview data, include company growth and investor attractiveness.
Furthermore, our data suggest market and management as two important moderating
factors.
5.2.1 Consequences of business model scalability
Company growth: As discussed earlier, business model scalability appears to have a
positive influence on company growth. Prior research has identified business model
scalability as a primary driver of venture growth (e.g. Miller, 2001; Berry et al., 2006; Li,
2009). Statements from entrepreneurs as well as investors in our sample also pointed out
that the experts agree widely on the importance of a scalable business model for
company growth. Examples of statements made in the interviews include: “I think that it
is really important for company growth that a business model is able to scale from the
very beginning, because it decides about whom you can attract as partners or investors
and also which types of investors are interested in making an investment” (Interview
Partner 2). Interview Partner 4 states, “if a business model is not scalable, everything
else can be fine, but it still makes no sense to invest”.
240 G. Stampfl, R. Prügl and V. Osterloh
Investor attractiveness: In addition, the growth potential inherent in business model
scalability seems to have a positive impact on investor attractiveness. In the interviews,
all six investors in our sample named business model scalability as one of the most
important investment criteria. Only the management team of a new venture seems to be
more relevant in investment decisions: “scalability is one of the most important
investment criteria, but the team is even more important” (Interview Partner 3). This is
consistent with prior research on venture capitalists’ evaluations of ventures (e.g.
MacMillan et al., 1987; Franke et al., 2006; Franke et al., 2008). Nevertheless, if
investors cannot see scalability potential in the business model, this seems to be a knock-
out criterion for them to invest in new internet-based ventures: “We do not invest in
business models that show no potential to scale quickly” (Interview Partner 12).
Whereas a company’s growth positively influences investor attractiveness, the latter,
in turn, also has a positive impact on the former. New ventures with a scalable business
model will more easily attract external funding, which, according to entrepreneurship
theory, leads to faster company growth (e.g. Cooper et al., 1994; Lee et al., 2001).
Consequently, there is a bi-directional relationship between investor attractiveness and
company growth in our explorative model.
5.2.2 Moderating factors
Before the two moderators identified are explained in greater detail, the following
example illustrates the seemingly moderating effects of market and management on the
potential outcomes of business model scalability: The German Company Rocket internet
is well known for successfully replicating e-business models. Instead of developing new
models, the firm concentrates on identifying quickly scalable business models (mostly
originating from the USA). Once an interesting model has been identified, Rocket
internet implements exactly the same model in Europe (or Asia), thus simply taking it to
a different market. Building on excellence in execution and implementation, Rocket
internet is experienced in the management of the company-building process. Hence,
Rocket internet builds ventures which in many cases outperform those which originally
piloted the ‘copied’ business model.
This example shows that an identical business model (with a certain level of
scalability) can be implemented in completely different business environments which
moderate the outcomes of the implementation and execution process. Although we do not
have detailed data on this topic, our interview data suggest that there are at least two
important moderators in realising the potential of a scalable business model:
Market: Prior studies have frequently emphasised the fact that market characteristics
have a significant impact on new venture growth (e.g. Eisenhardt and Schoonhoven,
1990; McDougall et al., 1994; Park et al., 2002). Similar indications were found in the
interview data: market potential, market dynamics and market education were identified
as potential moderators of the relationship between business model scalability and
investor attractiveness as well as the relationship between business model scalability and
company growth.
Market potential refers to the current volume of the market or market segment
targeted by the start-up and the maximum potential size of the market within a certain
time period. Companies implementing business models in large markets will, of course,
be more likely to achieve high growth rates. In a high-velocity and fast-changing
An explorative model of business model scalability 241
environment such as the internet, there still seem to be many white spaces which hold
great potential for successful start-ups (e.g. social networks, virtual goods, or SaaS
offers). Market dynamics can influence a company’s degree of success and the time
frame in which it can be attained (Zahra and Bogner, 1999). If the market is very
dynamic, the probability that the business model will exploit its full potential in a
relatively short period of time is high. Market dynamics are very closely connected to
market potential: the faster the speed of transition from the current market volume to a
realised market potential, the higher the market dynamics. The ability to grow in line
with the dynamics of a market can be a huge advantage. Especially the investors in our
sample saw market dynamics as the most important point for scalability and high-speed
growth. When asked about the most important criterion regarding the scalability of
e-business models, Interview Partner 4 (a partner in one of Germany’s biggest venture
capital firms), for instance, answered: “Market dynamics, followed by ticket size and
sales cycle”.
This issue is closely connected to another sub-factor, market education. Market
education is a factor that can have an inhibiting and supporting effect at the same time.
As long as the market is not educated (i.e. prospective customers do not understand why
they might need the product or service and what it is used for), it will remain very
difficult to enter the market. The need to invest in customer education (i.e. explaining a
service or product before selling it) severely inhibits the scalability of the business
model. However, as soon as target customers are familiar with the offer, it becomes
easier to expand the business. The need for market education can be an advantage for fast
followers. The company that initially offers the product or service might pave the way for
similar entrants by familiarising the customers or users with a new business idea.
Groupon again provides an interesting example of this effect. The company’s business
model is fairly simple: it combines group buying with viral marketing, a strategy which is
nearly identical to what letsbuyit.com already attempted in 2001. It is no secret that
letsbuyit.com ran out of money spectacularly, whereas Groupon continued to grow at a
fast pace. The difference can be seen in the companies’ progress in terms of market
education in this segment. Another example is the German auctioning platform for
craftsmen MyHammer: “What MyHammer does, the auction of craftsmen services,
already existed in 2001 with Yellout – identical concept, but the time wasn’t ready back
then” (Interview Partner 10).
Management: The second moderating effect was labelled ‘management’ and
comprises ‘team’, ‘location’ and ‘partnerships’ – all factors that become particularly
important in realising business models. These factors appear to exert a moderating effect
on the relationship between business model scalability and investor attractiveness as well
as business model scalability and company growth.
The importance of the management team for start-ups has already been discussed, as
it is an important issue regarding investor attractiveness. Team composition, experience
and communication are important for the sales growth of the firm (Eisenhardt und
Schoonhoven, 1990; Ensley et al., 2002). In particular, investors working for incubators
described this influence as follows: “The team is the basis for scalability. Even if the
business model looks great on paper – with the wrong team, it will end up nowhere!”
(Interview Partner 11). Similar views were expressed by Interview Partners 10 and 11.
The fact that most successful internet-based companies were founded in Silicon
Valley, London or Berlin show how important location is for new ventures. Geographical
242 G. Stampfl, R. Prügl and V. Osterloh
location was recognised as a factor responsible for differences in the survival of start-ups
and small companies (Lechner and Dowling, 2003). Even if the internet makes it possible
to build a company based on a large number of virtual elements, new ventures still
depend heavily on the local environment to acquire the resources needed (Gilbert et al.,
2006): “If one takes a closer look at the team and the availability of resources, it is
important to be in an environment which provides the adequate people and other
resources you need, because otherwise the whole model cannot be scaled” (Interview
Partner 1).
In managing the realisation of a (scalable) business model, it is also necessary to
establish partnerships. Lechner and Dowling (2003) investigated the role of external
relationships and found that they represent a source of competitiveness and growth for
new ventures. “Different personal networks lead to the use of different economic
relations that lead in turn to different strategic options” (2003, p.3). The potential for
business model innovation is even higher if firms include partners in their business model
options: partners can expand the output of innovation activities and generate access to
new markets that may have otherwise remained unattainable (Chesbrough and Schwartz,
2007; Keinz and Prügl, 2010; Poetz and Prügl, 2010). “I think that it is essential who you
partner with regarding investors, business angels or other business partners” (Interview
Partner 3). In order to build a successful internet-start-up, many other partners are
necessary, such as link-building companies, online marketing agencies, consultancy
firms. These partners can accelerate the development of the company and represent an
important factor in the realisation of a scalable business model.
6 Discussion
Existing research and practical examples such as Facebook, Groupon and Salesforce.com
suggest that scalability is an important issue for new business models on the web. We
draw upon a wide body of literature in entrepreneurship, strategic management and
business model research and use in-depth expert interviews to shed light on the issue of
scalability in the phase of business model conceptualisation and realisation. Our analysis
led to the development of an explorative model for business model scalability which
includes five factors in the phase of business model design: (a) technology, (b) cost and
revenue structure, (c) adaptability to different legal regimes, (d) network effects and
(e) user orientation. Our model suggests that business model scalability is likely to result
in greater attractiveness to investors and will eventually lead to company growth. These
positive relationships seem to be moderated by at least two important factors: market and
management.
The results of our study might also be interesting for incumbents operating in the
offline world. It seems that the scalability of business models generally follows certain
key rules. Our study highlights the idea that scalability is an issue that deserves attention
already in the phase of business model conceptualisation. It is striking that the software-
as-a-service business model (frequently also referred to as ‘digital licence sales’) was
named by more than 50% of interview partners as an internet-based business model that
shows perfect scalability. In fact, SaaS business models such as those deployed by
Salesforce.com, 37Signals or Dropbox exhibit many essential advantages. One very
An explorative model of business model scalability 243
important point is that SaaS businesses do not have to deliver physical goods. Interview
Partner 6 explains why SaaS is currently one of the leading business models when it
comes to scalability:
“SaaS is absolutely the best business model in terms of scalability. The reason
is that you can offer your software online, everyone can download it easily, you
don’t need to ship boxes, you can do maintenance online and can provide
updates via download – that means: there is nothing better when it comes down
to scalability. You don’t need sales people who acquire customers at different
locations and you don’t need to keep inventory”
In identifying factors relevant to scalability, our model provides a basis for future studies
on business model scalability and might serve as a useful tool for companies in
developing new, scalable business models. However, this study also has several
limitations. The qualitative nature of our exploratory work implies that the model can
only serve as a starting point for future research. In particular, the relationships identified
between the constructs require further qualitative and quantitative analysis. The emphasis
of the model lies on business model design factors which enhance business model
scalability. In addition, our data revealed market and management as important aspects in
the implementation of scalable business models (but not their potential role in the design
of scalable business models). Thus, further research would be required in order to shed
more light on the specific role of these issues (e.g. their influence on design aspects).
Moreover, the method of expert interviews is prone to generating false statements and
is criticised for its subjective nature. However, we cross-checked interview data with
existing literature in order to overcome this issue. As this study represents early-stage
research in order to gain an initial understanding of business model scalability, the
number of interviews is limited and therefore the factors identified might not be
exhaustive. Although this research effort is based on existing research and the experience
of carefully selected experts, we are likely to see completely new business models in the
future that give rise to new implications regarding scalability.
Further research should more thoroughly investigate whether investors and
entrepreneurs have different perceptions regarding business model scalability. In
addition, we need greater clarity on the other factors – besides scalability – which lead to
successful business model innovations in the online as well as the offline world, for
example the environmental conditions for business model innovation (Stampfl and Prügl,
2011). Despite the constantly increasing number of studies in the field of business model
research, a wide variety of topics related to the implementation of new business models
have remained widely unexplored thus far.
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Notes
1 For a similar approach, see Certo et al. (2009) or Zott et al. (2011).
2 For a framework to separate and relate business model and strategy, see Casadesus-Masanell
and Ricart (2010).
3 http://techcrunch.com/2011/02/24/airbnb-hits-1-million-nights-booked-as-european-clone-
emerges/ (accessed on 30 August 2011).
4 This statement was published on Phonedeck’s Facebook page, which serves as the company’s
primary means of communicating with its customers, on 4th April 2012.
5 In line with prior studies on network effects (e.g. Conner, 1995), both terms are used
interchangeably for the purposes of this study.
