An Empirical Analysis of Performance Implications of Different Models

Student: Zarnishan Mansimova – 11386533

MSc. in Business Administration - Track

Supervisor: Dr. S. Von Delft

Final version: 23.06.2017

1 Statement of Originality This document is written by student Zarnishan Mansimova, who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is 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.

2

ABSTRACT

The implications of choice for firm performance have received significant attention from the scholars. However, the studies do not fully address these implications since many of them along with employing different definitions of the business model concept, have

focused on entrepreneurial firms or specific industries. Therefore, there is a shortcoming with

regards to generalizable understanding of the performance effects of business models and the

business model performance relationship of incumbent firms. We tried to address this

shortcoming by analyzing a set of publicly traded incumbent companies operating in different

industries. Morris, Allen, Schindehutte framework was used to identify different generic business

models employed by the firms in our sample. The target sample consisted of 142 publicly listed

on Euronext Amsterdam and Brussels. This study did not find a statistically significant

relationship between a business model and a firm performance. Implications are far reaching,

since a particular business model does not yield a superior financial performance, future research

should shift its focus in a number of different directions such as implementation of business

model, interaction of business model with environmental factors, life-cycle of a business model,

etc. From a managerial perspective, this study implies that in order to improve the firm

performance, management should not solely focus on a change of business model or should not

directly relate underperformance to a poor business model choice.

3 Table of Contents

1. Introduction ...... 5

2. Literature Review ...... 8

2.1. Business model definition ...... 8

2.1.1. MSA framework ...... 10

2.2. The business model and performance relationship ...... 15

3. Research Design ...... 22

2.3. Independent Variable & Control Variables ...... 23

2.4. Business model clusters ...... 25

2.4.1. Cluster definition and the case study ...... 28

4. Parametrization & Results ...... 32

4.1. Parametrization ...... 32

4.2. Results ...... 33

5. Discussion and Conclusion...... 36

5.1. Discussion ...... 36

5.2. Conclusion ...... 39

References ...... 40

4 1. Introduction

Business model is generally defined as the logic of how firms create and capture value from

products and services they offer (Teece, 2010, Casadesus-Masanell and Ricart, 2010, Johnson et

al., 2008). The concept has received significant attention not only in practice, but also in

literature (Wirtz et al., 2016). The surge of studies on the business model concept, corresponding

with the emergence of internet firms, is not a coincidence. Advances in technology have changed

the balance of the customer-supplier relationship, as these developments have provided

customers with more choices and transparency about the availability of these choices. These

changes have led managers to rethink not only how they address different customer needs, but

also how they appropriate value (Teece, 2010). As a result, the business model concept has

become an appealing topic for strategy scholars, as it offers new options for creating and

capturing value in dynamic and uncertain environments (Mc Grath, 2010).

Business models play a crucial impact on a firm performance (Zott et al., 2011). If well designed, a business model provides a firm a (Afuah and Tucci, 2001)

(Casadesus- Masanell, and Ricart, 2010). Wal-Mart, Dell, and Apple are well-known examples of companies that achieved superior performance and outperformed their rivals by employing more effective business models (Johnson et al., 2008). For example, when Apple started selling the iPod, bundling it with the iTunes music store, it disrupted the market. Soon this combination accounted for almost 50% of Apple’s revenue, even though the company was not the originator of digital music players.

Although these examples provide anecdotal evidence, and observations by scholars generally agree on importance of the business model for firm performance, the direct link between business models and firm performance has not been fully addressed by researchers. To date, no

5 extensive empirical analysis on such a link has been performed (Zott and Amit, 2007). The understanding of the business model to performance relationship remains highly context specific

(Teece, 2010). This can be related to several reasons, such as the relative novelty of the concept

in the strategy literature, the multi-theoretical nature of the concept, or the lack of agreed

definition and components (Wirtz et al., 2016, Morris et al., 2006), all of which hinder our ability to measure the concept in a unified way (Morris et al., 2006).

Many studies have focused on certain types of business models, especially on e-commerce firms and entrepreneurial firms (e.g. Afuah and Tucci 2001, Zott and Amit, 2007). Therefore, to further expand the field of study, we look into the relationship between the business model of incumbent firms and their financial performance. This is an important shortcoming in the

literature, as incumbent firms play a critical role in every economy and industry. Established

companies dominate the global economy, with 10% of the world’s established public companies

delivering 80% of all profits. Companies with more than $1 billion in annual revenue generate

nearly 60% of global revenues, and comprise 65% of market capitalization (Economist, 2016,

September). Analyzing the set of incumbent firms would bring valuable insight to how business

models are defined for a large portion of the economy, and whether the choice of business model

results in a superior financial performance. Moreover, some previous studies (e.g. Suarez,

Cusumano & Kahl, 2013; Patzelt, Knyphausen-Aufseb & Nikol, 2008) have focused on certain

type of firms that specialize in one area and employ a firm-specific definition of business

models. This, we believe, further hinders ability of these studies to shed light on business model

performance relationship in more generalizable sense. In order to address this shortcoming, in

our study, we focus on generic business models employed by incumbent firms that operate in

different industries. Thus, this thesis aims to address the following research question:

6 What are the performance implications of different business models?

In order to define business models utilized by incumbent firms, we adopt the Morris,

Allen, and Schindehutte (MSA) framework introduced by Morris et al. (2005). The MSA framework comprehensively defines business models as a set of six decision areas, including value proposition, market, internal capabilities, competitive strategy, economic factors, and personal/ investor factors. This framework is then applied to a set of companies (142 in total) listed on the Euronext Amsterdam and Brussels exchanges. In total, four generic business models were defined. These business models are then matched with the financial data in order to address the research question.

After running a series of regressions, while at the same checking the robustness of the results, we did not find any statistically significant relationships between business models and firm performance. Regardless, this study makes several contributions to the literature. Firstly, by

focusing on the performance effects of business models of incumbent firms, this paper provides

insights on what type of business models these firms utilize. As was previously mentioned,

incumbents are important part of global industries, therefore these insights are valuable for the

business model, strategy, and business literature in general. Secondly, this paper further

contributes to business model literature by conducting empirical research that focuses on

different types of firms operating in various industries. This is a valuable contribution, since

previous studies have mainly focused on e-commerce firms or on certain types of firms, which

have provided more context specific understanding of the topic and have hindered

generalizability of the study results for different type of firms and business models. Finally, the

result of this study implies that future research should switch its focus in a number of different

directions, such as the implementation of business models, the interaction of business models

7 with environmental factors, the life-cycle of a business model, etc.

The rest of the paper is divided into four sections. Firstly, we review the relevant literature on business models, and the business model-performance relationship. Secondly, we discuss our

research design, sample, measurement, and methods. Thirdly we present the results of the

analysis. In the final section, we discuss the implications of the results, explain limitations of the

study, provide directions for future research and conclude with some final thoughts.

2. Literature Review

2.1. Business model definition

All firms explicitly or implicitly operate a business model (Teece, 2010), however business models have only recently become the focus of both researchers and practitioners (Zott et al.,

2011). Despite a fair number of studies, the literature on business models lacks an agreed definition of the concept by scholars (Zott et al., 2011; Teece, 2010). A uniform approach to the concept is still developing (Wirtz et al., 2016). However, generally, a business model is defined as the logic based on which a firm creates and captures value from the products and services they offer (Teece, 2010, Casadesus-Masanell and Ricart, 2010, Johnson et al., 2008). This is in contrast to a body of literature which takes a rather pragmatic approach to business models.

While some scholars view business models in terms of different processes within a firm or as a business architecture (e.g. Amit and Zott, 2001), the majority of authors (e.g. Casadesus-

Masanell and Ricart, 2010, Johnson et al., 2008, Morris et al., 2005, Osterwalder, Pigneur, 2010)

emphasize the structure of business models and outline certain components of the concept (Wirzt

et al., 2016).

Amit and Zott (2001) define business model as “the content, structure, and governance

of transactions designed so as to create value through the exploitation of business opportunities”

8 (p. 22). This definition mainly focuses on the value creation part of the business model, however

value capture, i.e. making money from the value created, is an inseparable part of the concept

(Teece, 2010).

The studies which focus on structure, and explain the components of business models,

vary in their approach to the concept and in the content of the components (Wirtz et al., 2016).

Johnson et al., (2008), Casadesus-Masanel & Ricart (2010), and Baden-Fuller, & Mangematin

(2013) define business models as consisting of only a few components. Conversely, Osterwalder

& Pigneur (2010) and Morris et al. (2005) define the concept more comprehensively as a set of

different components.

Johnson et al. (2008) argues that a business model consists of four elements: the customer

value proposition, the profit formula, key resources, and key processes. These elements together

comprise a business model, and subsequently combine to create and capture value. Baden-Fuller

& Mangematin (2013) also offers a typology of the concept that consist of four elements, with

two of them (customer value proposition and profit formula) being similar to those in the

Johnson et al. (2008) definition. Specifically, the Baden-Fuller & Mangematin (2013) typology of business models includes customers, customer engagement, monetization of customer value,

the , and linking mechanisms of actors in the value chain. The authors argue that each

of these elements are linked to either value creation, value capture, or both. Although these definitions are valuable, especially in terms of providing a general picture of the concept, a more detailed explanation what a business model should include seems more useful, since it will not neglect critical dimensions of the concept (Wirtz et al., 2016) This kind of definition will also enable us to establish dynamic links between the components of concept, as well as with other elements of the firm, such as competitive advantage and firm performance. Therefore, building

9 on this argument, for the purpose of our study we adopt the MSA framework introduced by

Morris et al. (2005). The authors conceptualize business models as a set of six decision areas, which include the value proposition, market, internal capabilities, competitive strategy, economic factors, and personal/investor factors. The MSA framework builds upon the previous literature, culminating in a set of questions to characterize each element of the company as described above. The framework provides a comprehensive and measurable set of components of the business model concept, which also enables this framework to be applicable to a diverse group of industries and firms. The MSA framework has also been adapted and empirically applied by

Morris et al. (2006) in generating generic business models. The next section explains the framework and its applicability in detail.

2.1.1. MSA framework

As already was mentioned, the MSA framework was first introduced by Morris et. al (2005), building on a number of different elements in the literature. Figure 1 depicts the overall framework, and Table 1 describes the components of each decision area, while also providing instructions for the application of the framework. The six decision areas included in the model are represented in the following questions:

1. How does the firm create value?

This question focuses on the value proposition of the firm. The decisions here concerns selection of the product or service mix, and how the value proposition is delivered to customers.

In order to create value, a firm needs to design its value proposition in a way that helps its customers get an important job done (Johnson et al., 2008). A “Job” here refers to an important problem in a given situation, that requires a solution. The better you are at getting the “job” done, the greater your customer value proposition is.

10 1. For whom does the firm create value?

This question concerns the type and scope of the market in which the firm operates. Who is

the customer and where in the value chain does the firm position itself? Customers are at the

heart of any business model, and without profitable customers, no firm can be successful

(Osterwalder &Yves Pigneu, 2010).

2. What is the firm’s internal source of advantage?

Here, the focus is on the internal capabilities of the firm that underlie its competitive

advantage. Capabilities refer to the firm’s ability to perform certain set of activities. Hamel

(2001) defines core competencies as an internal capability, or set of skills that the firm performs

better than its competitors. Therefore, these competencies are integral part of any business model

and crucial for firm success (Applegate, 2001).

3. How does the firm differentiate itself?

This question is concerned with how the firm positions itself in the marketplace. Specifically,

the question is focused on the choice of the basis of differentiation that determine the way in

which firm competes (Casadesus-Masanel, 2010). The MSA framework includes 5 bases of

differentiation: operational excellence, product capabilities (e.g. quality, availability

characteristics), innovation leadership, low cost, and intimate customer relationships.

4. How does the firm make money?

This question directly addresses the “value capture” part of business models. To put it

differently, it defines how the firm creates value for itself, while delivering value to the customer

(Johnson et al., 2008). The MSA framework defines the value capture mechanism of a business

model in four dimensions: operating leverage (the extent to which the cost structure is dominated

by fixed versus variable costs), the firm’s emphasis on higher or lower volumes in terms of

11 market opportunity and internal capacity, the firm’s ability to achieve relatively higher or lower margins, and the firm’s revenue model, including the flexibility of revenue sources and prices.

5. What are the firm’s time, scope, and size ambitions?

Differences among firms are reflected in their competitive strategy, resources and competences, profit model, and economic performance. Therefore, authors also include the entrepreneur’s time, scope, and size ambitions, or what they also termed as the firm’s

“investment model”. These models are subsistence, income, growth, and speculation. A firm employing the subsistence model focus only on its survival, and its ability to meet basic financial requirements. The income model involves making investments to the extent that they generate an ongoing and stable income stream for the principals. With the growth model, in order to grow its value, along with significant initial investment, the firm also makes substantial reinvestments. In firms employing a speculative model, the entrepreneur’s main focus is to show the firm’s profit potential with the aim to be able to sell it afterwards.

12 Value proposition

Personal/ Market investor factors factors

Business Model

Internal Economic capability factors factors

Competitiv e strategy factors

Figure 1. The MSA framework: Defining business model (Morris et al., 2005)

Table 1. Application of the MSA framework. Adopted from Morris et al., 2005

1. Value Proposition (VP): (select one from each set) How does the firm create

value? VP: primarily products/primarily services/heavy mix

VP: standardized/some customization/high customization

VP: broad line/medium breadth/narrow line

VP: access to product/ product itself/product bundled with other firm's product/service

VP: internal product or service delivery/outsourcing/licensing/reselling/value added selling

VP: direct distribution/indirect distribution

13 2. Market Factors: For (select one from each row) whom does the firm create • type of organization: B2B/B2C/both/other value? • market scope: local/regional/national/international

• value chain: upstream supplier/downstream supplier/ /wholesaler/ retailer/service provider

• exchange type: transactional/relational

3. Internal capability (select those that apply) factors:

What is the firm’s internal • production/operating systems source of advantage? • selling/marketing

• information management

• technology/R&D/creative or innovative capability

• financial transactions

• supply chain management

• networking/resource leveraging

4. Competitive strategy (select those that apply) factors:

How does the firm • image of operational excellence/consistency/dependability differentiate itself? • product or service quality/selection/features/availability

• innovation leadership

• low cost/efficiency

• intimate customer relationship/experience

14 5. Economic factors: (select one from each row) How does the firm make

money? • pricing & revenue sources: fixed/mixed/flexible

• operating leverage: high/med/low

• volumes: high/medium/low

• margins: high/medium/low

6. Personal/Investor (select one) factors: • income model/growth model/speculative model/subsistence

model What are the firm’s time, scope, and size ambitions?

2.2. The business model and performance relationship

The business model and performance relationship is one of the areas of research in the business

model literature that has received a fair amount of attention by scholars (Wirtz et al., 2016).

Business models can be crucial in explaining firm performance (Zott et al., 2011). The survival

and prosperity of firms is directly linked to their ability to both create and capture value (Shafer

et. al. 2004). As an architecture of value creation, delivery and capture, business models are critical to a firm’s success (Teece, 2010). The same idea or technology taken to market through two different business models will generate different outcomes (Chesbrough, 2009).

Firm performance itself is a result of a two-stage process: value creation, and value capture (Coff, 1999, Mizik & Jacobson, 2003). While value created refers to the amount the customer is willing to pay for the firm’s offering, value is captured when the firm earns more than the costs of creating this value (Porter, 1985). For example, IBM previously created superior value by bringing the most innovative computers to market, however Intel and

15 Microsoft were ultimately able to capture much of the profit from this innovation (Coff, 1999).

Therefore, it comes as no surprise that the performance implications of business models are

linked to the value created and captured through it (Zott et al., 2011). By taking this link into

consideration, and integrating MSA framework, Figure 2 illustrates the business model- performance relationship.

Business Model

Value Creation Value Capture - Value proposition Firm - Market factors Economic factors Performance - Capability factors - Competitive advantage factors - Personal/Investor factors

Figure 2. The business model performance relationship

Amit & Zott (2001) argue that no single theory is able to completely explain the value

creating potential of a firm. Therefore, they conclude that the business model concept, drawing

on and integrating different theoretical perspectives, explains the sources of value creation in a

firm. The authors draw on theoretical perspectives such as the resource-based view (RBV),

transaction cost theory, the value chain framework (Porter, 1985), Schumpeterian innovation

16 (Schumpeter, 1934), transaction cost economics (Williamson, 1975) and strategic network theory

(Jarillo, 1995). Morris et al. (2005) argue that the business model concept is grounded on principal underpinnings of business strategy, and they have built the MSA framework on these theoretical perspectives.

Porter’s (1985) value chain framework identifies activities that firm performs, and then studies the performance effects of those activities. The framework helps to determine the activities a firm should perform, and the required configuration of these activities that enable the firm to compete in the industry. The value chain explores the firm’s primary activities, which have a direct impact on value creation, as well as support activities, which affect value only through their impact on the performance of the primary activities. According to this framework, value can be created through differentiating along every activity or set of activities, resulting in products or services that either lower costs or improve performance of offerings for customers.

Business model concepts builds upon this framework, since it comprises configuration of activities firms perform, and also includes choices of activities through which firms differentiate themselves (internal capability factors and competitive strategy factors; Morris et al., 2005).

According to the RBV, creating configurations of resources and capabilities that are valuable, rare, inimitable, and firm specific may facilitate value creation (Peteraf, 1993, Barney,

1991). The theory argues that firms differ in terms of the resources and capabilities they possess, therefore unique combinations of these resources and capabilities can lead to superior value creation and subsequently competitive advantage. In this sense, business models embody the competencies that contributes to a firm’s competitive advantage, and are consistent with the value creation mechanism identified by the RBV (Morris et al., 2005).

In Schumpeter’s theory (1934), new combinations (innovation) are the only source of

17 value creation. New combinations can represent the introduction of new products or services, new methods of production, new markets, new sources of supply, or new ways of industry organization. Consistent with this theory, a business model that embodies unique combinations can create a superior value (Morris et al., 2005). In literature on the business model concept, it has been linked to innovations in two ways (Zott et al, 2001). Firstly, it is viewed as a model through which firms commercialize innovation. Secondly, business models themselves can be a source of innovation by delivering existing products and services in new ways.

In transaction costs economics (Williamson, 1975), reduced transaction costs represent the major source of value creation. The theory generally concerns itself with the choice of an efficient governance form by the firm that minimizes its transaction costs. Therefore, the theory strives to explain the conditions under which a firm should internalize or outsource certain activities. In this sense, through the choices involved in its business model, a firm decides how to set its boundaries efficiently and create more value.

The strategic network theory also sheds some light on the value created through business models. Effective positioning, and trying to create valuable relationships within the larger value network of customers, suppliers, and partners can be an important factor for value creation

(Morris et al, 2005).

A firm’s ability to capture the value created can be explained by bargaining power theory and game theory. According to game theory, bargaining power between different actors

(employees, suppliers, customers) determines the division of the value created (Brandenburger,

2002). Bargaining between the firm and the supplier explains the level of the cost to the firm for purchasing those resources, or the price of the resources the supplier gets. Bargaining between the firm and the customer defines the price the firm receives for selling its products and services,

18 or the cost the buyer incurs by these offerings. Business models, providing a structure of costs

and revenues, delineate the firm’s mechanism of value capture (Teece, 2010). In the MSA framework, this ability of business models is captured in the economic factors section (Figure 2,

Table 2).

The empirical and conceptual studies completed to date have examined the relationship between business models and performance by focusing on certain types of business models or firms. Some studies such as Afuah & Tucci (2001) and Zott & Amit (2007) have mainly focused on the business models of e-commerce firms. While Afuah and Tucci (2001) conducted a conceptual study, Zott and Amit (2007) empirically examined the relationship between business models and firm performance. In their study, Afuah & Tucci (2001) explore how the advent of internet has led to the surge of internet business models, and how this affected firm performance.

The authors define a business model as one of the determinants of the firm performance, basing

this argument on the function of the components of a business model and the linkages between

these components. In particular, they argue that business models are critical to firm performance,

due to them defining the type of value firm offers, the type of customers to who they offer this

value, the scope of products and services that carry this value, the pricing of the value, the type

of activities and capabilities that create the value, and the that firms can use to

maintain a competitive advantage.

Zott and Amit (2007) have examined the performance effects of two different business

model design themes - novelty-centered and efficiency-centered design themes of entrepreneurial

firms that generate their revenues fully or partly from online transactions. A novelty-centered business model is defined as creating and capturing value in new ways, by employing new ways of conducting economic transactions. An efficiency-centered business model, in contrast, is

19 focused on imitating existing economic transactions by conducting them in a more efficient way.

The authors argue that the performance of new entrants is highly dependent on the design of

boundary-spanning transactions with suppliers, customers, and partners. In order to prove this

relationship, they tested how business model design themes, “as the context, content, and

structure of boundary spanning transactions” (Zott and Amit, 2007, p. 183), influence the

performance of these firms. More specifically, the authors have taken the chosen business model

as the independent variable, and linked it to the stock performance of entrepreneurial firms,

moderated by the environment. The authors found a significant positive relationship between

novelty-centered business models and firm performance. As Afuah and Tucci (2001) note, they

also relate this relationship to the value creation potential of the business model design and the

firm’s ability to appropriate that value.

Another stream of study on the business model-performance relationship has focused on very specific types of firms and business models (Suarez, Cusumano & Kahl, 2013; Patzelt,

Knyphausen-Aufseb & Nikol, 2008). Although these studies frame the business model concept

differently by employing different definitions or focusing on certain components, they have

found that in certain circumstances, some business models are superior to others. Suarez,

Cusumano & Kahl (2013) have examined the relationship between business model choice and the performance of prepackaged software product firms, and found that it depends on the extent to which the business model emphasizes services as a source of revenue. The empirical examination found that software product firms that generate a high majority of their revenue from product sales are the most profitable, and firms with some emphasis on services see declining profit. The study has also found that services start having positive effect on firm profits

only when the sale of services accounts for the majority of revenues. The authors relate this

20 result to the accumulation of experience when a firm is focused on certain activities. Patzelt,

Knyphausen-Aufseb & Nikol (2008) have explored the moderating effect of business models on top management team composition and organizational performance relationship. The authors focused on biotechnology companies, and examined two types of business models that biotechnology firms might adopt: platform and therapeutics. The study included 226 biotechnology firms based in Germany. The authors found that the top management team’s industry experience has more positive performance effect for firms operating under the therapeutics model than for those operating under the platform model. The authors link this relationship to the managerial competences required for each business model to achieve high performance.

A further stream of studies has conceptually studied the link between business models, technology, and firm performance (Baden-Fuller & Haefliger, 2013; Boons & Ludeke-Freund,

2013). Baden-Fuller & Haefliger (2013) have conceptualized a framework that views business models as a mediator between technology and firm profitability. According to the framework of business models, the questions of who is the customer, how to engage with customer needs, and how to deliver and monetize value defines the link between technology and profitability. Thus, a poor choice can lead to low performance, and a good choice can lead to superior performance.

Boons & Ludeke-Freund (2013) view the business model concept as an important tool that links sustainable innovation with firm performance. The authors relate profitability of sustainable technology to its embeddedness in a superior business model that successfully links the value proposition with the supply chain, customer interface and revenue model.

The reviewed literature seems to have a consensus on the fact that there is a ositive relationship between financial performance and business models. However, there are a number

21 of shortcomings. The definition of business model is not aligned across the studies, thus running

a risk that each study defines the business model differently to fit the industry or type of

company (i.e. entrepreneurial, e-commerce, etc.), hence losing the ability to generalize the results. Based on the above literature review, it can be predicted that there is a direct relationship between business model and firm performance, which implies that the firms employing different business models will vary in their performance. To examine this relationship, as was already mentioned, we will focus on the business models employed by incumbent firms operating in different industries, and compare them against the firm performance.

3. Research design

As noted earlier, this research concentrates on the empirical relationship between business

model choice and firm performance. Business models are defined using the MSA framework,

while firm performance is captured via return on assets. Furthermore, a number of control variables are used in order to account for other factors that may drive firm performance.

Sample

To conduct this research, we studied the business models of incumbent firms operating in different industries. We considered public companies only, to ensure data availability. Therefore,

we examined the business models of firms that are listed on the Euronext Amsterdam and

Euronext Brussels exchanges. This is in order to ensure that the information is available in

English, and to have the population limited in size to avoid using sampling and performing

analysis on a sample set. The data was extracted directly from Euronext, and contained an initial

set of 271 companies. The data related to business models and firm performance was collected

from the websites and annual reports of the companies. In order to keep only incumbent firms in

the sample, we eliminated four firms that have been in operation for less than 10 years

22 (Hellmann & Puri, 2002). Moreover, during the data collection, 125 firms were removed from

the list due to a lack of sufficient information, and no availability of company websites and

annual reports in English. Therefore, the final sample consists of 142 firms.

2.3. Independent Variable & Control Variables

To determine firm performance, we used the return on assets ratio (ROA). ROA is one of the

most commonly used measures of firm performance for incumbent firms, as can be seen from the

reviewed literature. The choice of the firm performance proxy varies, with choices ranging from

Tobin’s Q, to return on equity and return on assets. ROE was ruled out due to it being more

sensitive to capital structures (Hitt et al., 1997). Tobin’s Q is defined approximately as the ratio

of market value to book value of the firm. There are well-known measure issues associated with

Tobin’s Q, one being the volatility behind the market value of the firm’s equity can potentially distort the measurement (Shane & Spicer, 1983). Given that we concentrated only on a cross- section of companies, instead of also looking at a time dimension for each company, for our sample Tobin’s Q can potentially have significant noise due to equity performance of the firm in that given year. ROA, which is defined as the ratio of net income to total assets, is book value based. The required data to calculate ROA was derived from the annual reports of the sampled firms. To ensure time alignment between the independent variable (current business models) and dependent variable (performance), we focused on financial results and annual reports from 2016.

For a number of firms, however, the 2015 fiscal year was used due to unavailability of 2016 results.1

The financial performance of the company is not solely driven by the business model it

operates. In order to isolate the impact of various other potential variables, the analysis will

1 For some companies, the accounting year end is March. Given that these companies are public, it can take up to 180 days to provide audited financial statements.

23 include a number of controls. For this purpose, we will control for firm size (in terms of number

of employees), industry, firm age, and capital structure (using a ratio of equity to liabilities).

These variables are the most commonly used in the business literature to establish common

ground for financial performance comparisons (Murphy, Trailer & Hill, 1996).

Regarding the impact of age, one stream of research argues that since older firms have

more experience and have generated learning over time, they are not challenged by the liabilities

of newness and therefore they can generate superior performance. However, another stream of

research, argues that these firms tend to inert and be bureaucratized. This implies that, they are

unlikely to be flexible to adapt to changing conditions, and are likely to be outperformed by

newer and more flexible firms (Majumdar, 1997) Age will be measured by the number of years

from the founding of the company

Size of the firm expected to have a positive impact on firm performance, since larger

firms are likely to employ better technology, be more diversified, and better managed. Moreover,

larger firms may also benefit from economies of scale (Himmelberg et al., 1999). However,

larger firms may experience inefficiencies related to bureaucracy, and larger monitoring costs

(Margaritis & Psillaki, 2009). In Zott & Amit (2007) the company size, proxied by the number

of employees, was used as one of the factors.

Profitability of the industry can affect the performance of the firms operating within it

(Dess, 1979, 1981). Therefore, we also control for industry effects. In order to differentiate the

industry effect, we utilize the European standard for industry classification NACE codes. We used one-digit level NACE codes. The industry codes of firms were collected from the Amadeus

database, and 11 different industries were identified. Table 2 provides the descriptive statistics

for the independent and control variables.

24

Table 2 Descriptive statistics

Standard N Minimum Maximum Mean Deviation

Return on 142 -0.835 0.26 0.030 0.123 Assets

Liability to 142 -35.332 18.96 1.703 5.242 Equity Size 142 20 73,5725 17010 71665 (Number of employees) Age of the 142 1575.0 2007.0 1944.507 71.8631 company

2.4. Business model clusters

For each company in the sample, it was rated along the 6 decision areas. The data

required to identify the business models was derived from company websites and annual reports.

Website and annual report sections such as company profile, strategy, products and services,

brands, history, and financial statements were used to collect data for each of 6 areas noted above. Next, each component of the question was treated as dichotomous variable and binary

coded where a positive response is 1, or else is 0. To give an example, assume company named

XYZ, which is active in the onshore wind industry in the region of Wallonia, Belgium. The firm

supplies rotor bearings to onshore wind developers, which appreciate XYZ’s service and use it as a preferred bidder. On the basis of this example, this company, under questions 2, will be (i) assigned 1 to B2B, while 0 B2C, both and other, (ii) assigned 1 to regional while 0 to local and international (iii) assigned 1 to upstream supplier while 0 to the remaining within this group and finally (iv) assigned 0 to relational while 0 to transactional.

25 Upon collecting the data for all firms, and classifying the firm characteristics into an

appropriate dichotomous group, we derive the business models by applying cluster analysis to

the collected data. Cluster analysis is generally applied to classify a sample of objects based on

studied characteristics of interest when there is little information about the population (Punji &

Stewart, 1983). Given that the lack of characteristic should not represent a common

characteristic, from the 0 and 1 classification we derived a similarity matrix by using the Jaccard

Index. This matrix, consisting of coefficients between 0 and 1, representing the similarity

between different companies, were clustered together using hierarchical clustering, between

group linkages, and clustering on the back of squared Euclidean distance. The initial range was

set between the 2-cluster option to the 9-cluster option. Each cluster-option was then analyzed

using dendograms, agglomeration schedules and the distribution of companies in each cluster. As a result of the analysis, the 4-cluster option was deemed appropriate since it provided an even distribution of companies in each group, while at the same time providing a reasonable level of differentiation between the groups based on the dendogram and the amalgamation schedule. In order to validate our clustering methodology, we also run clustering using K-mean classification

methodology. The 4-cluster approach resulting from the K-means classification had c.60% rank

correlation with the hierarchical methodology, with a number of companies in each group being

similar.

The following table provides the decomposition of the dichotomous decision element of the

MSA frameworks per 4-clusters defined above.

Table 3. The Result of Cluster Analysis

Classification Cluster 1 Cluster 2 Cluster 3 Cluster 4 Basic Offering Primarily Products 28 38 2 0

26 Primarily services 5 0 23 24 Heavy mix 15 5 0 3 Customization Standardized 4 34 14 5 Some Customization 11 8 7 8 High Customization 32 2 4 16 Breadth of the Broad Line 2 14 1 5 product line Medium Breadth 36 17 6 5 Narrow Line 9 13 18 18 Depth of the Deep line 22 20 4 6 product line Medium Breadth 15 9 9 9 Shallow Line 10 14 12 13 Product Forms Access to Product 4 13 5 5 Product Itself 30 25 17 20 Bundled with Other Firms' Products 13 5 3 2 Distribution Direct 43 14 23 27 Indirect 4 29 2 0 Source of Internal Production/ Service Delivery 43 31 23 27 Production Outsourcing 0 1 1 0 Licensing 3 1 1 0 Reseller 0 4 0 0 Value-added seller 1 6 0 0 Organization B2B Organization 41 17 5 21 Type B2C Organization 0 11 2 0 Government/Other 0 0 0 0 Combination 6 15 18 6 Market Scope Local 0 0 0 0 Regional 0 0 1 1 National 2 0 5 2 International 43 43 19 24 Value Chain Wholesale 1 12 0 0 Retail 1 6 1 0 Up-stream supplier 26 11 1 2 Down-stream supplier 10 13 1 0 Service provider 11 1 22 25 Customers 0 0 0 0 Exchange Type Transactional 7 40 20 2 Relational 39 3 4 25 Core Product/Operating Systems 41 35 20 13 Competences Selling and marketing 3 18 7 7 Information Management 11 2 6 6 Tech. / R&D Innovative capability 42 22 10 2

27 Networking/Resource Leveraging 22 11 7 19 Supply chain management 11 17 2 1 Financial transactions 1 2 10 15 Source of Image of Operational Excellence 20 9 10 6 Differentiation Product/Service Quality 28 37 19 19 Innovation Leadership 34 19 9 2 Low Cost/Efficiency 5 10 2 1 Intimate Customer Relationship 5 3 7 22 Pricing and Fixed 6 30 16 4 Revenue Source Mixed 27 12 9 6 Flexible 14 1 0 17 Operating High 15 9 17 23 Leverage Medium 20 21 5 2 Low 13 13 3 2 Volume High 22 39 20 4 Medium 17 4 4 16 Low 8 0 2 7 Margins High 8 5 4 13 Medium 16 14 12 4 Low 24 24 9 10 Time-scope and Growth Model 18 20 5 6 size ambitions Income Model 29 23 20 21

2.4.1. Cluster definition and the case study

The clustering analysis generated four clusters. The clusters differ significantly from eah other. However, some factors are substantially homogeneously distributed across all clusters.

These factors include source of production and market scope. Since our sample includes publicly listed incumbent firms, the majority of the companies have a market scope that exceeds the boundaries of their home countries. Therefore, most of the companies have an international market scope. Even though there have been some cases in which the source of production is outsourcing, or the firms is a reseller, in the majority of companies the main source of production is internal service or product delivery. Moreover, none of the firms in our sample pursue subsistence or speculative investment models. Since all of them are established publicly listed

28 firms, they tend to pursue growth or income models. Below we summarize significant

characteristics of each cluster and provide one example for each cluster.

Cluster 1: Customized product firm- 33%

The majority of the firms in this cluster offer highly customized products in the B2B segment, with some of them offer mix of products and services. Most of the firms are upstream suppliers with medium breadth and a deep line of products. However, there are also firms that position themselves as downstream suppliers or service providers. These firms have core

capabilities in networking and resource leveraging, R&D, and production, and differentiate

themselves with innovation leadership and product quality. The majority of exchanges are

relational and distribution is direct. The source of revenue and pricing is somewhat mixed.

Operating leverage and volume of production is mostly medium to high, while margins are low

to medium.

An example of a firm in this cluster is Abylynx, a Belgian biopharmaceutical company.

The company specializes in proprietary product development. The main research focus of the

company is Nanobodies – a new class of therapeutic proteins. It has 45 proprietary programs and

partnerships in 5 disease areas. This means that the company has medium and deep line of

offerings. The company has both its own and collaborative development programs. Abylynx has

partnerships with large pharmaceutical companies, and generates revenue from licensing of

proprietary products and from R&D activities. The offerings are not standardized, and the

volume is small, with only 8 products in clinical development. The company’s core competences

lie in its R&D capabilities and somewhat in its partnerships. Based on partnerships and long-

terms relations, the exchanges are relational and the distribution of offering is direct. Abylynx

pursues development of differentiated and innovative products.

29 Cluster 2: Standardized product firm – 30%

This cluster includes firms that mainly offer standardized products in B2B and B2C

segments. There are both upstream and downstream suppliers in this cluster. Most of the

exchanges are transactional, distribution is direct and the sources of revenue and pricing are

fixed. The majority of firms have core competences in selling and marketing, supply chain

management, production, and technology, with the source of differentiation mainly being

product quality, with moderate to low emphasis on innovation leadership and low cost. The

firms have a high volume of production, with low to medium operating leverage and margins.

ABInBev is a global beer brewer with Belgian roots. The company specializes in production and sales of different beer brands. ABInBev operates in 50 markets, selling its products in over 150 markets, and has 35 brands of beer. This means that the company has a narrow and deep line of offerings, and a high volume. Having deep historic roots in brewing, the

company has core competences in development and production of different types of beer. Being

committed to building great brands, and investing continuously in marketing, the company has core competences in marketing. The company’s Budweiser and Stella Artois brands are the number one and number four beer brands in the world, respectively. The company differentiates

itself by quality of the products, as the company positions itself as a traditional brewer of beer

that produces the best quality beers by using only finest natural ingredients. Pricing and source of

revenue is mostly fixed, since the firm generates revenue mainly from the sales of beer. The

company sells its products through indirect distribution channels, and the exchanges are

transactional.

Cluster 3: Standardized service provider – 18%

30 The firms in this group mostly offer standardized services to both B2B and B2C

customers. They offer a narrow and shallow line of services. The exchanges are transactional, the

firms have core capabilities in financial transactions and technology, and they differentiate themselves through service quality and operational excellence. Pricing and revenue sources are mainly fixed, operating leverage is high, volume of sales is high, and the margins are somewhat medium. The majority of firms pursue an income model.

Aegon is a global financial services company. The company offers life insurance, pensions, and asset management services, and has global as well as local brands in markets it operates in. Therefore, the company has a medium and deep line of offerings. The services are relatively standardized and are offered to both B2B and B2C segments. The core capabilities of the company are mainly in financial transactions, and the firm pursues operational excellence and service quality to differentiate itself. The volume of services is high, the company operates in more than 20 markets and serves 30 million customers, and exchanges are mainly transactional due to the large proportion of its B2C segment.

Cluster 4: Customized B2B service firm- 19%

This cluster includes the firms that offer highly customized B2B services. The offerings are narrow and shallow to medium line. Exchanges are relational, the firms have core competences in financial transactions, networking, and resource leveraging, and differentiate themselves with service quality and intimate customer relations. Pricing and revenue sources are flexible, operating leverage is high, margins are high to low, volume of the services is medium.

The majority of firms in this cluster pursue an income model.

Sofina is an investment company that specializes in three areas: long-term minority investments, investments in venture capital and private equity funds, and investments in fast

31 growing . This means that the company has a medium and relatively shallow line of

offerings. Each investment is unique; therefore, the value proposition is highly customized. With

23 employees and 23 investments in its portfolio, Sofina has medium volume. As an investment

company, Sofina’s core competences lie in financial transactions. The company relates its

uniqueness to strong emphasis on long term and close relationships with its partners, decades of

expertise and experience, being pragmatic and solution oriented that makes it a reliable partner.

4. Parametrization & Results

4.1. Parametrization

In order to run the regression between the independent variable and the dependent variables, the

following parameterization was used:

{ , … } = + + + + { , … ) + 1 2 3 𝑡𝑡= 1 2 11 𝑖𝑖 0 1 𝑖𝑖 2 𝑖𝑖 3 𝑖𝑖 𝑡𝑡= 1 2 11 𝑖𝑖 12 𝑖𝑖 𝑅𝑅𝑅𝑅𝐴𝐴 𝛽𝛽 𝛽𝛽+𝐵𝐵𝐵𝐵𝐶𝐶 𝛽𝛽+𝐵𝐵𝐵𝐵𝐶𝐶 𝛽𝛽 𝐵𝐵𝐵𝐵𝑀𝑀+ 𝐵𝐵 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇 𝛽𝛽 𝐴𝐴𝐴𝐴𝐸𝐸

13 𝑖𝑖 14 𝑖𝑖 𝑖𝑖 Where, 𝛽𝛽 𝑆𝑆𝑆𝑆𝑆𝑆𝐸𝐸 𝛽𝛽 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑄𝑄 𝜀𝜀

= Observation in our dataset

𝑖𝑖 = Return on asset

𝑖𝑖 𝑅𝑅𝑅𝑅𝐴𝐴 = Business model cluster 1 dummy variable 1 𝑖𝑖 𝐵𝐵𝐵𝐵𝐶𝐶 = Business model cluster 2 dummy variable 2 𝑖𝑖 𝐵𝐵𝐵𝐵𝐶𝐶 = Business model cluster 3 dummy variable 3 𝑖𝑖 { , … } 𝐵𝐵𝐵𝐵𝐶𝐶 = Industry dummy variable for 11 industries denoted using 𝑡𝑡= 1 2 11 𝑖𝑖 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑇𝑇= Year of incorporation 𝑡𝑡

𝑖𝑖 𝐴𝐴𝐴𝐴𝐸𝐸 = Size of the company

𝑖𝑖 𝑆𝑆𝑆𝑆𝑆𝑆𝐸𝐸 = The ratio of liability to equity.

𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑄𝑄𝑖𝑖

32 = error term

𝑖𝑖 Initial𝜀𝜀 check of a scatter plot between the independent variable and the dependent variable did not reveal any non-linearity. In order to check for multicollinearity, we analyzed the correlation matrix of the variables, revealing no significant correlations between the variables.

4.2. Results

A top to bottom regression approach was used, where for the first regression all of the variables were included. At every iteration, one of the controls was dropped, until the regression was run using solely the business model classifications. These results are presented in the first table below. In order to check the robustness of our regression against outliers, both independent and control variables were winsorized at 95%. The results utilizing the winsorized data are presented in the second table below. For every regression, checks were completed to monitor for the

homogeneity of outcomes, autocorrelation, and the normal distribution of residuals.

The significance of the variables is tested using T-statics. In order to test the join effect of

the dummy variables, an F-test for joint significance was performed.

From the tables, we can observe that there is no direct link business classification via the MSA framework and firm performance. Even when controlling for industry, age, size, and capital structure of the company, the analysis does not yield any significant impact of the selected business model on firm performance. These results hold even after the same regression analysis was performed on the winsorized data, implying a minor effect of outliers on the outcome of this analysis. The lack of relationship is not only considering the combined effect, but also the intra

business model classification. None of the separate business models has significantly better

performance compared to other business model classification.

33 One of the surprising outcomes was the lack industry effect on asset return. Given that different industries following different economic cycles, one would expect that different industries would be a significant effect, given this study’s use of a single financial year of data.

The only variable that has a significant impact, at 10% significance, is the ratio of liabilities to equity, and that only becomes weakly significant based on the winsorized data. This ratio has a negative impact, implying that additional leverage (more payables, short and long-term debt, and the financial market) to lower returns on asset.

Table 4. Regression results based on the actual data. BMC stands for the business model cluster. Superscript above BMC denotes the appropriate cluster. Age is proxied by the year of incorporation of the company. Size is proxied by the number of employees. LIBTEQ stands for the ratio of liabilities to equity. Linear regression was used. The number of observations used for the regression is 142, concentrating over public companies listed on the Euronext Amsterdam and Brussels exchanges.

{ , … } Adj Regression (Joint F-𝒕𝒕=statistic)𝟏𝟏 𝟐𝟐 𝟏𝟏𝟏𝟏 𝟏𝟏 𝟐𝟐 𝟑𝟑 𝒊𝒊 𝟐𝟐 𝟎𝟎 𝑰𝑰𝑰𝑰𝑰𝑰𝑰𝑰𝑻𝑻 𝟐𝟐 𝜷𝜷 𝑩𝑩𝑩𝑩𝑪𝑪 𝑩𝑩𝑩𝑩𝑪𝑪 𝑩𝑩𝑩𝑩𝑪𝑪 𝑨𝑨𝑨𝑨𝑨𝑨 𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳 𝑹𝑹 𝑹𝑹

1 Coefficient 33.46 0.03 (0.05) (0.13) (0.0) 0.01 0.03 6% -7%

P-value 26% 87% 78% 32% 33% 90% 74%

F-statistic of joint significance 0.73 0.44

2 Coefficient 40.26 0.04 (0.03) (0.12) (0.1) 0.01 6% -6%

P-value 25% 79% 84% 35% 31% 91%

F-statistic of joint significance 0.74 0.44

3 Coefficient 40.30 0.04 (0.03) (0.12) (0.1) 6% -5%

P-value 24% 79% 84% 35% 31%

F-statistic of joint significance 0.63 0.45

4 Coefficient 7.67 0.06 (0.00) (0.12) 5% -5%

P-value 56% 70% 99% 35%

F-statistic of joint significance 0.66 0.49

5 Coefficient 4.27 (0.01) (0.06) (0.11) 1% -1%

P-value 7% 93% 58% 29%

F-statistic of joint significance 0.53

34

Table 5: Regression results based on the winsorized data. BMC stands for the business model cluster. Superscript above BMC denotes the appropriate cluster. Age is proxied by the year of incorporation of the company. Size is proxied by the number of employees. LIBTEQ stands for the ratio of liabilities to equity. Linear regression was used. The number of observations used for the regression is 142, concentrating over public companies listed on the Euronext Amsterdam and Brussels exchanges.

{ , …

Regression (Joint F𝒕𝒕-= 𝟏𝟏 𝟐𝟐 𝟏𝟏𝟏𝟏 Adj 𝒊𝒊 𝟏𝟏 𝟐𝟐 𝟑𝟑 statistic)𝑰𝑰𝑰𝑰𝑰𝑰𝑰𝑰𝑻𝑻 𝟐𝟐 𝟐𝟐 𝜷𝜷𝟎𝟎 𝑩𝑩𝑩𝑩𝑪𝑪 𝑩𝑩𝑩𝑩𝑪𝑪 𝑩𝑩𝑩𝑩𝑪𝑪 𝑨𝑨𝑨𝑨𝑨𝑨 𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳𝑳 𝑹𝑹 𝑹𝑹 1 Coefficient 19.4 0.16 0.03 (0.00) (0.08) (0.03) (0.19) 11.3% -0.8%

P-value 29% 23% 91% 63% 41% 79% 7.1%

F-statistic of joint significance 0.74 0.51

2 Coefficient 16.1 0.10 (0.02) (0.07) (0.06) (0.05) 9.0% -2.7%

P-value 39% 54% 91% 60% 51% 57%

F-statistic of joint significance 0.65 0.87

3 Coefficient 14.3 0.10 (0.02) (0.07) (0.05) 8.7% -2.1%

P-value 44% 52% 90% 56% 56%

F-statistic of joint significance 0.76 0.86

4 Coefficient 3.56 0.12 (0.00) (0.07) 8.5% -1.6%

P-value 6% 46% 98% 57%

F-statistic of joint significance 0.80 0.88

5 Coefficient 3.80 0.10 (0.00) (0.05) 1.5% -0.6%

P-value 7% 39% 99% 67%

F-statistic of joint significance 0.70

35 5. Discussion and Conclusion.

5.1. Discussion

This study endeavored to investigate the relationship between business models and firm performance. Various authors have argued that there is a direct relationship between these two.

Moreover, some empirical studies have found significant relationships between business model and firm performance (e. g. Zott & Amit, 2007; Suarez, Cusumano & Kahl, 2013). These studies, along with the general definition of business model, have employed different definitions of the concept, and have been narrow studies examining specific business models or firms. By employing the MSA framework to define different generic business models, we examined the relationship between business models and performance for incumbent firms operating in different industries. We predicted that the choice of business model will affect firm performance, therefore there will be significant performance differences between firms employing different business models. However, our study did not prove this effect. The possible theoretical explanations for the results of this study may guide future research in shifting its focus to new directions of study. The result of the study can be explained by several reasons.

Firstly, firms employing similar business models can implement it differently. Some firms may fail to apply the business model properly, and subsequently fail to fully use the value creating and value capturing potential of the business model. Brea-Solis, Casadesus-Masanell &

Grifell-Tatje (2015), in their study of Walmart’s business model evaluation and the company’s sources of advantage, have found a similar result. The study showed that even though Walmart’s core business model didn’t change over time, the company’s performance varied significantly depending on the differences in implementation by each new CEO. Moreover, the choice of business model may not demonstrate performance differences in a one year period. Firms

36 operating similar business models that are at different stages of the lifecycle and implementation of the model may yield different performance results. For example, firms at later stages of the implementation of a certain business model, may perform better than newly adopters, since experience and learning generated over time enable them to perform similar activities better and more efficiently. Therefore, these variances, may erode the differences in performance implications of different business models.

Secondly, the results can also be related to strategy and environmental factors. Our definition of business model also included the competitive strategy factors of the firm. Firms operating similar business models and competitive strategy combination in different environments may have delivered different performance results. For example, Miller (1988) found that in uncertain (unpredictable and dynamic) environments, while innovation leadership strategy is positively associated with firm performance, this relationship is negative for firms pursuing a cost-leadership strategy. A further study by McArthur and Nystrom (1999) has found an interaction between environmental factors, firm strategy, and firm performance. Even though in our study we controlled for industry effects, it may not isolate different dimensions such as dynamism, complexity, hostility, or competitiveness of the environment within which the firm operates.

We cannot prove contribution of these possible reasons to the result of our study, since neither implementation and lifecycle of business models, nor moderating effect of environmental factors, were within the scope of our analysis. We only examined the direct relationship between different business models and firm performance. However, these two possible explanations can inspire future research on the topic.

37 From a managerial perspective, this study implies that in order to improve firm performance, management should not solely focus on business model change or should not directly relate underperformance to a poor business model choice. This may help them avoid the temptation of altering the firm’s business models when trying to further improve the firm performance or dealing with underperformance. By doing this, managers will be able to switch their attention to more important issues, at the same time saving on resources, time and effort.

This study has several limitations which can also provide directions for future research.

First, although the use of the MSA framework to define types of business models enables measuring and quantifying business models of different firms, to a certain extent it relies on the subjective interpretation of the information by the researcher. Even though we tried to minimize this effect by removing firms that do not provide clear information about the business model factors in their websites and annual reports, future research may benefit from matching this method with other, less subjective ways of data collection such as surveying and interviewing.

Second, the size of the dataset does not allow us to draw generalizable conclusions about the result of the study. Therefore, future research may also benefit from applying the analysis to a broader data set. Third, our analysis does not capture the lifecycle of business models, and variations in firm performance depending on the change in the business model. Hence, future research may consider taking a longitudinal approach to research of business models and firm performance. Fourth, researchers can also study how interaction of different/certain business model components or emphasis on certain components affects the firm performance. Finally, as mentioned above, incorporating factors related to the environment in which firms operates may provide further insights on the topic.

38

5.2. Conclusion

In this study, we tried to examine the performance implications of different business models. To provide a generalizable understanding of the business model-performance relationship, and to expand the field of study, we focused on the generic business models employed by incumbent firms operating in different industries. For this purpose, we examined the business model-performance relationship of 142 publicly listed firms. Applying the MSA framework (Morris et al., 2005) and cluster analysis to generate the business models operated by

these firms, we identified four generic business models. The analysis did not find any significant

relationship between different business models and firm performance, since none of the business

model clusters we identified was associated with superior performance. In the discussion section,

we outlined the possible explanations of the results, which also implies that future research of the

topic needs to switch its focus to the study of these possible explanations.

39

References

1. Afuah, A., & Tucci, C. L. (2001). Internet business models and strategies: Text and cases.

New York: McGraw-Hill

2. Amit, R., & Zott, C. (2001). Value creation in e‐business.

journal, 22(6‐7), 493-520.

3. Beard, D. W., & Dess, G. G. (1981). Corporate-level strategy, business-level strategy,

and firm performance. Academy of management Journal, 24(4), 663-688.

4. Brea-Solís, H., Casadesus-Masanell, R. and Grifell-Tatjé, E. (2015), Business Model

Evaluation: Quantifying Walmart's Sources of Advantage. Strategic Entrepreneurship

Journal, 9: 12–33.

5. Baden-Fuller, C., & Haefliger, S. (2013). Business models and technological

innovation. Long range planning, 46(6), 419-426.

6. Baden-Fuller, C., & Mangematin, V. (2013). Business models: A challenging

agenda. Strategic Organization, 11(4), 418-427.

7. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of

management, 17(1), 99-120.

8. Boons, F., & Lüdeke-Freund, F. (2013). Business models for sustainable innovation:

state-of-the-art and steps towards a research agenda. Journal of Cleaner Production, 45,

9-19.

9. Casadesus-Masanell, R., & Ricart, J. E. (2010). From Strategy to Business Models and

onto Tactics. Long Range Planning, 43(2-3), 195–215.

40 10. Chesbrough, H. (2010). Business Model Innovation: Opportunities and Barriers. Long

Range Planning, 43(2-3), 354–363.

11. Coff, R. W. (1999). When competitive advantage doesn't lead to performance: The

resource-based view and stakeholder bargaining power. Organization science, 10(2),

119-133.

12. Hamel, G. (2000). Leading the revolution. Boston, MA: Harvard Business School Press.

13. Hellmann, T., & Puri, M. (2002). Venture capital and the professionalization of start‐up

firms: Empirical evidence. The journal of finance, 57(1), 169-197.

14. Himmelberg, C. P., Hubbard, R. G., & Palia, D. (1999). Understanding the determinants

of managerial ownership and the link between ownership and performance. Journal of

financial economics, 53(3), 353-384.

15. Hitt, M. A., Hoskisson, R. E., & Kim, H. (1997). International diversification: Effects on

innovation and firm performance in product-diversified firms. Academy of Management

journal, 40(4), 767-798.

16. Jarillo, J. C. (1995). Strategic networks: creating the borderless organization. Routledge.

17. Johnson, M.W., Christensen, C.M. and Kagerman, H. 2008. Reinventing Your business

model. Harvard Business Review.

18. Majumdar, S. K. (1997). The impact of size and age on firm-level performance: some

evidence from India. Review of industrial organization, 12(2), 231-241.

19. Magretta, J., (2002). Why business model matter? Harvard Business Review.

20. Margaritis, D., Psillaki, M. (2010). Capital structure, equity ownership and firm

performance. Journal of Banking & Finance, 34(3), 621-632.

41 21. McGrath, R. G. (2010). Business models: A discovery driven approach. Long range

planning, 43(2), 247-261.

22. McArthur, A. W., & Nystrom, P. C. (1991). Environmental dynamism, complexity, and

munificence as moderators of strategy-performance relationships. Journal of Business

Research, 23(4), 349-361.

23. Miller, D. (1988). Relating Porter's business strategies to environment and structure:

Analysis and performance implications. Academy of management Journal, 31(2), 280-

308.

24. Mizik, N., & Jacobson, R. (2003). Trading off between value creation and value

appropriation: The financial implications of shifts in strategic emphasis. Journal of

marketing, 67(1), 63-76.

25. Morris, M., Schindehutte M., and Allen, J. (2005). The entrepreneur’s business model:

toward a unified perspective. Journal of Business Research, 58 (6), 726–735.

26. Morris, M., Schindehutte, M., Richardson, J., & Allen, J. (2006). Is the business model a

useful strategic concept? Conceptual, theoretical, and empirical insights. Journal of Small

Business Strategy, 17(4), 27-5

27. Osterwalder A., Pigneur Y. 2010. Business model generation: a handbook for visionaries,

game changers, and challengers. – John Wiley & Sons

28. Patzelt, H., Knyphausen‐Aufseß, Z., & Nikol, P. (2008). Top management teams,

business models, and performance of biotechnology ventures: An upper echelon

perspective. British Journal of Management, 19(3), 205-221.

29. Peteraf, M. A. (1993). The cornerstones of competitive advantage: a resource‐based

view. Strategic management journal, 14(3), 179-191.

42 30. Porter, M. E. (1985). Competitive advantage: creating and sustaining superior

performance. 1985. New York: FreePress.

31. Punj, G., & Stewart, D. W. (1983). Cluster analysis in marketing research: Review and

suggestions for application. Journal of marketing research, 134-148.

32. Schumpeter, J. A. (1934). The theory of economic development: An inquiry into profits,

capital, credit, interest, and the business cycle (Vol. 55). Transaction publishers.

33. Shafer, S. M., Smith, H. J., & Linder, J. C. (2005). The power of business

models. Business horizons, 48(3), 199-207.

34. Suarez, F. F., Cusumano, M. A., & Kahl, S. J. (2013). Services and the business models

of product firms: an empirical analysis of the software industry. Management

Science, 59(2), 420-435.

35. Teece, D. J. (2010). Business Models, Business Strategy and Innovation. Long Range

Planning, 43(2-3), 172–194.

36. The rise of the superstars, The Economist, September, 2016

37. Williamson, O. E. (1975). Markets and hierarchies. New York, 26-30.

38. Wirtz, B. W., Pistoia, A., Ullrich, S., & Göttel, V. (2016). Business models: Origin,

development and future research perspectives. Long Range Planning, 49(1), 36-54.

39. Zott, C., Amit, R., & Massa, L. (2011). The Business Model: Recent Developments and

Future Research. Journal of Management, 37(4), 1019–1042.

40. Zott, C., & Amit, R. (2007). Business Model Design and the Performance of

Entrepreneurial Firms. Organization Science, 18(2), 181–199.

41. Zott, C., & Amit, R. (2008). The Fit between Product Market Strategy and Business

Model: Implications for Firm Performance. Strategic Management Journal, 29 (1), 1-26

43

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