A Resource-Based Valuation Framework for Small and Young IT Businesses

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Master Thesis Business Administration - Small Business & Entrepreneurship:

A Resource-Based Valuation Framework for Small and Young IT Businesses

By: Christian Wiebe Cnossen July 6th, 2011

University of Groningen EBM712A20 Supervisor: Dr. C.H.M. Lutz Second Supervisor: Dr. C.K. Streb

Abstract

Traditional models of firm valuation have long been criticised to be inapplicable to small and young firms. For this reason, research on the Resource Based View (RBV) of the firm is employed to

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Acknowledgements

The author would like to express his thanks to Marcel Kalsbeek, Gideon de Kok, Eric Korver, Sietse de Glee, and Jobert Abma, who all have cooperated in the interviews for this research. Extra gratitude is expressed towards Gideon de Kok, for aiding in a time of need. Also, the author would

like to express his thankfulness to his supervisor, Dr. Clemens Lutz, who constantly provided valuable feedback.

Furthermore, a large amount of appreciation goes out to my mother and father, who, in their own words, provided a ‘fertile soil for ideas’ to work with. Finally, an incredible amount of thankfulness goes out to the author’s partner, Anne de Jong, who provided continuous advice and ideas while

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List of Figures

Figure 1: Conceptual Model ... 25

List of Tables

(excluding appendices) Table 1: Comparative statistics Industry Variables - Profit per employee 2010/2009/2008... 33

Table 2: Descriptive statistics EMU Industry Variables... 34

Table 3: Result Summary... 41

Table 4: Validity & Reliability... 42

List of Formulae

(excluding appendices) Formula 1: Resource - capability combination worth... 18

Formula 2: Strategic Deviation Potential... 21

Formula 3: Performance calculation year t... 23

Formula 4: Constant growth perpetuity - asset valuation... 24

Formula 5: New venture valuation... 24

Formula 6: Adapted PPE performance calculation in year t... 29

Formula 7: Delaney & Huselid (1996) Standardization formula (Z-value)... 30

Formula 8: Capital Asset Pricing Model (CAPM) ... 31

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1. Introduction

Originally Launched in 2003 (LinkedIn, 2011), the professional social networking website LinkedIn offered an initial public offering (IPO) on May 19th 2011. During this first day of share-trading, LinkedIn's share price climbed with a total of 109 percent at day's end. Valued at an original US$ 3 billion, LinkedIn saw its company value climb to a peak of US$ 9 billion in a single day (Baldwin & Selyukh, 2011). Clearly, the market established LinkedIn's corporate value at a considerably higher level than what business analysts had considered it to be. While IPOs are generally undervalued by 15 percent, LinkedIn was 11,4 times more undervalued than usual, as its share price peaked at 171 percent growth in its first day (Baldwin & Selyukh, 2011). Evidently, placing a value on a technology company, even a relatively mature one, is very problematic for business analysts.

The example of LinkedIn’s recent IPO illustrates the difficulty business analysts face in placing a value on a firm. Yet, LinkedIn’s case was simply the first in what is to be a large number of initial public offerings of internet software firms. The tumultuous experiences from LinkedIn’s IPO are likely to be reflected in these further internet software firm IPOs. Consider for example, the case of Groupon. Groupon is an internet business which seeks to find purchase deals for consumers by buying products in bulk orders. Users register with Groupon, and Groupon matches the demand of local consumers, and thus negotiates high rates of discount with local businesses for its users (Groupon, 2011a). Communication to its users is done through several social networks, as well as through e-mail advertisements. Groupon was established in November 2008 (Groupon 2011b). Recently, Groupon voiced its intention to make its business public by filing for an IPO. This has resulted in Groupon having to disclose its financial statements, which undisputedly state the business has never managed to make a profit through organic growth (Bar, 2011). Be that as it may, Groupon is currently valued at a market worth of roughly US$15 to US$20 billion (Baldwin et al., 2011). This IPO is underwritten by respectable auditing firms, such as JP Morgan Chase & Co. Yet, financial valuation of Groupon through realized historical performance only allows the firm to be valued at a maximum of roughly US$2 billion (Bar, 2011). It remains a mystery where financial analysts at Groupon, JP Morgan Chase & Co, and other underwriters of the IPO managed to find the US$13 to US$18 billion in surplus business value to make the firm worth roughly US$15 to US$20 billion. Certainly, the gap between possible financial potential of Groupon of roughly 1000 percent, and LinkedIn’s value increase of 171 percent in one day, are large enough to question the methods by which these new information technology ventures are being valued.

Both LinkedIn’s and Groupon’s examples highlight the difficulty in formalizing profit expectations of a new venture, and by extension, the value of the business as a whole. However, corporate valuation is more difficult in some industries than in others (Berk et al, 2004). One mayor issue with financial valuation of firms is that it requires knowledge about future earnings. While the future cannot be predicted with certainty, financial analysts generally busy themselves with making predictions as accurate as possible. In general, their assumptions and predictions mostly hold for traditional business models and established industries (Sliwoski, 1999). However, application of traditional financial evaluation models to small and young firms seems to fall short. Due to their small size, small and young firms generally cannot cushion financial setbacks, resulting in higher income volatility levels than established firms (Bernstein, 1953).

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information is usually not present for new ventures, and only partly indicative of future performance for small firms. As a result, small and young business valuation relies solely on projected profits to establish firm value. However, a small and young firm’s capabilities and future earnings are usually hard to estimate, if not extremely vague in nature (Alvarez & Busenitz, 2001). For instance, R&D firms will only discover the extent of the opportunity they are trying to uncover with further investment. However, for this investment to occur the opportunity must be valued, the magnitude of which cannot be uncovered without the investment; a circular problem becomes apparent (Berk et al 2004). Thus, financial valuation methods which rely on the estimation of future performance of a small or young firm generally fall short.

There are however, internal analysis tools in business management which can be employed in judging a business’s strengths, weaknesses and potential. One of these tools stems from strategy research, and is called the resource-based view (RBV) of the firm. In general, RBV theory poses that it is a firm’s resources and capabilities which allow the firm to attain a competitive advantage.

Competitive advantage, in turn, is generally believed to lead to a sustainable higher performance than rival firms (see also Andersén, 2011). Resource-based analysis has been around in strategic management literature for quite some time, stemming from the early works of Wernerfelt (1984) and Rumelt (1984). It was these authors which set the stage for one of the popular literature fields in strategy research. However, most Scholars agree that it was Barney (1991), who with his ground breaking article on the resource-based view of the firm, laid the fundamental assumptions and prerequisites necessary in advancing the theory. Since then, the RBV of the firm has become one of the dominant subjects in strategic management research.

Being one of the primary frameworks in use in strategic management literature, employing the RBV in construction of a performance evaluation framework seems almost self-explanatory. Be that as it may, there is a large literature gap in RBV theory when it comes to ex-ante performance accreditation to a firm based on its resources or capabilities (Priem & Butler, 2001b; Nevo & Wade, 2010). Indeed, one of the most frequently voiced critiques on the RBV relates to the inability of RBV literature in the prediction of performance, as more focus is placed on ex-post performance

accreditation to certain resources and capabilities. This research will seek to address this literature gap.

Indeed, judging a small and young business on its projected abilities would be far more useful than looking at historical performance figures, if these are even available at all. This is where the RBV, as an alternative method of business evaluation, could easily show its value. However, considering the literature gap in ex-ante performance prediction with regards to the RBV (Priem & Butler, 2001b), further inquiry should make clear to what extent this is actually feasible. This is where the research question of this study stems from:

How can a resource-based framework be constructed for evaluating the expected future performance of a small and young firm in the IT industry?

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this reason, the available literature base on the RBV will need to be investigated thoroughly, as there are a number of problems to resolve before one can answer the research question posed above.

This study will continue by featuring a thorough review of the established firm valuation methods. This is followed by a discussion of the resource based view’s basic foundations and further theoretical background. Then, a series of hypotheses will be constructed with regards to the main question of this research. Consequently, the proposed methodology of this research will be conveyed. Fourth, the results from the different required analyses conducted will be elaborated upon. Fifth, the retrieved results will be discussed, and implications are derived from their

combinations. The research is then rounded off by providing a general conclusion, and answer to the research question stated above.

2. Theory

The purpose of this section is fourfold. The first objective of this chapter is to convey the theoretical underpinnings by which young firms are generally valued, since this constitutes a major topic of interest to this study’s research questions. Second, this chapter will explain the major points about the resource based view (RBV) and its historical development. Second, as there are still unresolved critiques related to the RBV, all these critiques will be discussed in this section as well, along with their solutions, when these are available. Fourth, with these solutions and problems, a resource-based new venture evaluation framework will be constructed.

2.1 Financial evaluation of a new venture

It is very interesting to see that investors generally look at very different things while employing the same methods of evaluation. For instance, in their study on the comparability of investment

decisions by investors in the US, Hong Kong, India, and Singapore, Locket et al. (2002) found very diverse valuation criteria which venture capitalists generally employ. This dispersion in valuation was found to not only relate to financial characteristics of a small firms, but also to other factors, such as importance of a business plan and the management team in charge of the business (Locket et al., 2002). Other authors have found similar results in terms of financial valuation of businesses. For instance, Waldron & Hubbard (1991) find that in their sample of professionals which they presented with a case study firm, investors valued the case firm at US$8.85 million on average, while

consultants valued the case firm on an average US$13.17 million1. Clearly, this difference was found to be highly significant. Coupled with other very divergent results from their tests of investment criteria, it is no surprise the authors conclude:

“From these results it is easy to see why so many consider the valuation of a closely held business akin to alchemy.” (Waldron & Hubbard 1991: 49).

There is thus, in terms of financial valuation a large difference between groups of professionals on the variables employed to evaluate a small business. And even when equal variables are used, results

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will still vary. The reason for the large variation in financial value placed upon a small firm are ambiguous, since many professionals use very equal toolsets.

As can be inferred from the above, there is quite some debate on what the appropriate measure would be to value a venture, and what investors should look at when deciding to invest in a business or not. There are a number of commonly used financial indicators which investors and consultants are interested in when they evaluate a business. The generally used methods are the multiples-methods (Liu et al., 2002), valuing the saleable assets a business has (Carland & White, 1980; Locket et al., 2002), using discounted cash flow methods and Net Present Value (NPV) (Carland & White, 1980; Firer et al., 2008), and option based valuation methods (Locket et al., 2002). First, a concise review of each of these methods follows. Second, results on the application of different valuation methods from the available body of literature are discussed.

Valuation of a business using multiples boils down to using different financial ratios in comparing a firm’s financial attractiveness. Using multiples, a firm’s equity value is calculated based on its comparative performance to its pears. In essence, business valuation employing multiples is simply the comparison of financial ratios between firms in a comparable industry. For instance, the ratio of range of firms in terms of earnings to company value is calculated. This ratio is subsequently applied to the firm which is to be valued. The ratio is multiplied by the firm’s earnings, and therefore the value of the business is at a certain level. There are a number of valuation methods which fall under the heading of valuation using multiples. The most commonly used method however, is called the price-earnings (PE) ratio (Eberhart, 2004). This method entails the dividing of the current share price by the earnings per share of a certain firm. A higher PE ratio then signals either an overpriced share, or a business with a large future profit. However, PE ratios can only be compared within the same industry, as a PE ratio disregards the differences in industry profitability specifics. Clearly, a benefit of using multiples in business valuation is its ease of use, and the availability of the required data. However, this study regards the usage of multiples in business valuation to be overly simplistic. While some previous research has found business valuation using multiples to be quite effective (Liu et al 2002), they themselves admit to the non-representativeness of their conclusions.

The second method of small business valuation which is generally employed by venture capitalists is the asset valuation method. This method is even simpler than the valuation of a business using multiples, as it simply entails the valuing of the saleable assets of a business (Carland & White, 1980; Locket et al., 2002). Similar to the multiples method, this method is quite easy to use. A drawback again, is the simplicity of this valuation method. Furthermore, many small and young firms do not keep accurate track of their balance sheet, which is required for this method to be effective (Carland & White, 1980). Another drawback of this method is that it simply sees a business as a sum of its parts, rather than an income-generating entity (Carland & White, 1980). Thus, it foregoes the value of income to be made in the future.

Third, a much more generally accepted method of firm valuation is the valuation using the discounted cash flow (DCF) method, or alternatively the net present value (NPV) method. These methods are generally used interchangeably in that NPV calculation is an extension of DCF analysis. The NPV method extends the DCF method in that it provides a rule of thumb: if an investment’s NPV is higher than zero, the investment is profitable and should be conducted (Firer et al., 2008).

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assumptions need to be made about the future cash inflows of a business (Berk et al., 2004). While this can be quite accurately done based on historical performance for established firms, most young firms have very little historical figures to base such assumptions on (Carland & White, 1980), while small firm income volatility is considerably than for a larger firm (MacLeod & Sloan, 2007). A second assumption which must be made relates to the employed discount rate of the cash flows. Without accurate historical performance data to calculate an expected rate of return on, this proves to be a tedious and subjective task. Third, it must be established whether or not the firm will have a finite or infinite life span, which is rather problematical to judge beforehand. Furthermore, two issues in valuation using NPV are further important: Information asymmetry and the agency cost problem. Information asymmetry relates to a difference in the available information of different valuators of a business. When different parties valuing a business have different information, the calculated value of the business will differ. When it comes to new ventures, it is often the case that an entrepreneur has more information than an outside party, leading to an information asymmetry problem (Chua & Woodward, 1993). Second, expected returns for a business might drop significantly if an

entrepreneur leaves a business or sees his ownership share in a business decrease. This notion is termed the agency cost problem (Chua & Woodward, 1993). While these drawbacks in NPV valuation of small and young firms are substantial, in general it can be said to be a very solid valuation method. The necessary assumptions however, will result in very large ranges of values placed upon a single company by different business analysts.

The final valuation method of interest to a small and young firm is a relatively new one, namely the real options valuation method. In essence, an option constitutes the right to invest or partake (rather than an obligation) in a certain asset, at a predetermined price and time (Burger-Helmchen, 2007). Generally speaking, a firm can be said to have many different real options in deciding its course. Examples of such would be: the option to defer investment, the option to abandon investment, the option to switch investment, the option to contract or expand the

investment, the option to grow the investment, and the option to break up or merge the investment (Burger-Helmchen, 2007: 390). Thus, the valuation of real options busies itself with predicting the attractiveness of each real option. That being the case, real option analysis is a complicated analysis of different future states of a firm (Ferreira et al., 2010), the respective likelihoods of which

ultimately influence the value of a real option. Real option analysis shares many similarities to DCF investment valuation (Arnold & Shockley, 2010). Hence, it also shares the previously presented drawbacks of the method in its application to small and young firms. While real option analysis can certainly be stated to be the most inclusive method of firm valuation, its application to a small or young firm is still subject to assumptions which have detrimental impact on the usability of the method.

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2.2 Resource-based theory

As mentioned in this papers introduction, Wernerfelt (1984) was one of the major first players in the field of RBV literature. Wernerfelt’s 1984 article was the first to mention an actual resource based view of the firm. However, ideas about resources being crucial to the performance of firms have been around for a much longer time in business and economics research. One can trace the earliest mentioning of firm specific resources as a determinant of firm profitability back to authors such as Chamberlin (1933) and Robinson (1933). These economics researchers posed that firm specific assets and capabilities were a determinant of what made imperfect competition possible, and thus also what could lead to higher than market average returns (Fahy, 2000). Years later, it was Penrose (1959) who developed the ideas of Chamberlin (1933) and Robinson (1933) further, laying the foundations for what would become the resource-based view of the firm.

However, it was Wernerfelt (1984) who combined all the previous ideas of other authors into a new theory: the resource-based view of the firm. Interestingly enough however, it was not until Barney’s (1991) article that the RBV research took off as a theory which had many academics pursuing it. Indeed, today Barney is hailed as one of the most influential authors in RBV research, and certainly one of its most heavyweight proponents. Barney proposed two assumptions which lay at the basis of the RBV: namely that resources are spread heterogeneously among firms, and that these resources cannot be transferred between firms without costs (Barney, 1991). As opposed to neoclassical economics, RBV thus assumes competitive heterogeneity rather than homogeneity, and rather than perfect mobility of factors and resources, it assumes imperfect mobility of these. While the former draws upon monopolistic competition models (Fahy, 2000), the latter can be said to be in line with transaction cost economics and industrial organization economics (Priem & Butler, 2001a). Based on his two major assumptions, Barney (1991) posed that certain resource combinations can lead to a sustainable competitive advantage, which in turn could lead to a favourable performance. For this to be possible, resources would need to be valuable and rare. This would lead to a

competitive advantage. Then, if such resources were also found to be inimitable, non-transferable and hard to substitute, the resource would be a source of sustainable competitive advantage. If not, the competitive advantage would only be short lived, which seems plausible when competitors can easily acquire similar resources. Competitive advantage in turn, leads to a higher performance and more value to the firm.

Nevertheless, simply obtaining resources and using a resource in isolation is not the main value of a resource. It is said that resources become far more valuable to a firm when they are combined with other resources into resource bundles (Locket et al., 2009; Newbert, 2008). Such a resource bundle is called a capability. There is a whole literature stream in strategic management dedicated to research on capabilities (Wickham, 2006), however, others claim capability research and RBV research are quite interchangeable (Hoopes et al., 2003).

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theorists to date still have not been able to refute the criticisms presented by less enthusiastic authors about the RBV. This is the subject of the next paragraph.

2.3 Resource-based view’s limitations

In the early years of the previous decade, a series of articles was published with criticism on the resource-based view of the firm. Most notably, Priem & Butler’s 2001(a,b) articles, in which they entered a discussion with Barney (2001) on the usefulness of the RBV as a theory, are famous cases of theoretical discussions. Still, the criticisms on the RBV Priem & Butler (2001a,b) echoed were picked up by subsequent authors. In 2003, Hoopes et al. (2003) Expanded the criticism by Priem & Butler (2001a,b), and added several new insights from economic theory. Other criticasters such as Miller (2003) came up with entirely new views for RBV theory, dubbing it the asymmetry based view. The criticisms highlighted by the authors mentioned above remain in place today, as Nevo & Wade (2010) find somewhat comparable criticisms on the RBV as a whole.

The authors mentioned in the previous paragraph sport a number of different main criticisms on the RBV. First, the tautological nature in definitions employed by Barney (1991) is the main scorn echoed by critics of the RBV. Second, the lack of environmental influences taken into account by the RBV is highlighted. Third, the ambiguity in empirical applications of RBV is a topic of discussion. Fourth, the lack of predictive merit in the RBV when it comes to ex-ante performance prediction. All other criticisms of the RBV are either equal to the four presented above, or share very common similarities and argumentation. What follows is a brief discussion of these four points.

Definitely the most heard criticism on the RBV is the tautological nature in definitions applied in the field of study. A tautology is a statement which is true by default. An easy example of a

tautological statement would be: “the weather outside is nice because it is nice”. While very simplistic, it does get the message across that this statement is true because the same thing is said twice. The RBV suffers from similar statements, albeit more sophisticated than the example above. In this context, the tautologies are problematic as they refer to empirically testable phenomena (Priem & Butler, 2001b), which should be falsifiable rather than necessarily true. The problem arises mainly due to the definitions placed on value and competitive advantage by Barney (1991). As was said before, a resource, in Barney’s (1991) theory needs to be valuable, rare, inimitable,

non-substitutable, and non-transferable. Value here is a main indicator of resource usefulness. Barney defines value in resources as:

"[...]Resources are valuable when they enable a firm to conceive of or implement strategies that improve its efficiency and effectiveness." (Barney, 1991: 106)

While competitive advantage is defined as:

"[...]a firm is said to have a competitive advantage when it is implementing a value creating strategy not simultaneously being implemented by any current or potential competitors." (Barney, 1991: 102)

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effectiveness, which lead to competitive advantage, which is defined as implementing strategies which lead to higher efficiency and effectiveness (Priem & Butler, 2001b). This statement is thus not falsifiable and necessarily true, and therefore holds no value in terms of empirics. The same

reasoning relates to the inimitability which needs to be present in a resource, according to Barney (1991). This inimitability in turn will lead to sustainable competitive advantage which in turn, is a competitive advantage which is hard to imitate by competitors. Clearly, here too a non-falsifiable statement is made. Many researchers take issue with this phenomenon in RBV conceptualization. As of yet, there are no clear universally accepted solutions to these problems (Kraaijenbrink et al., 2010), since it does not seem to be a priority for RBV theorists.

The value of a resource was already highlighted above, but there are more criticisms regarding the definition put on it. In the RBV, value is determined by forces exogenous to a firm (Kraaijenbrink et al., 2010). Thus, it is not the firm but its environment, through opportunities and threats, which determines which resource is valuable and which is not (Barney, 1991). This in turn implies that when an environment changes in nature, so must the value of a resource. RBV theory on the other hand is completely dependent on the assumption that an environment is stable (Sirmon et al., 2006; Priem & Butler, 2001a). Naturally, this assumption is unrealistic, as environments are generally attributed with all kinds of degrees of uncertainty. Thus, a common criticism of RBV is that it should incorporate the environment into its model as well (Fahy, 2000; Sirmon et al., 2006).

Ambiguity in empirical use of RBV is another main point of criticism directed at the RBV. While the debate on the tautology of the resource-based view’s employed definition is one aspect in this respect, it is not the only one. The assumption on firm heterogeneity implies that a sample of firms in a statistical research are all necessarily different in nature. If this is the case, one cannot make conclusions about firms as a group, and thus it makes it impossible to measure causes of competitive advantage in a sample (Locket et al., 2009). RBV theory in its form written up by Barney (1991) thus, does not allow for a comparison between firms, and only allows for single firm studies rather than statistical investigation. Kraaijenbrink et al. (2010) refuted this notion by claiming that firm comparison based on RBV is perfectly possible when one is not “overly academic”. Considering that this counter-argument entails taking the RBV less literal than possible, it does not hold up as a very sound reasoning.

Aside from firm heterogeneity, ex-ante resource value prediction is one of the major subjects of this particular study. As it stands, RBV researchers have overlooked the subject of a priori

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management, these will still be of value (Kraaijenbrink et al., 2010). The fact that scholars have overlooked or neglected the subject thus far only poses challenges to future researchers, not restraining problems.

Combined, these four points of critique on the RBV comprise the current challenges RBV theory faces. While the arguments brought forth may be convincing as a whole, they do not ensure that RBV literature holds no value. How the remaining limitations of RBV research, which have not been solved to date, can be overcome is the focus of the next paragraphs, where these will be employed to create new theory, and a new venture evaluation framework. For this reason, even though each of the next paragraphs relates to a concept which will be employed in the conceptual model of this study, each paragraph also addresses a specific problem in RBV literature. The reader will observe the duality in objectives of each paragraph in their respective titles.

2.4 The nature of Competitive Advantage – The tautology difficulty

If one is to establish a new venture evaluation framework using resource based theory, the first aspect which will need to be defined is what, in the end, the result of the framework will be. In case of most, if not all, resource-based theory the end result of what a resource may accomplish is the aiding in sustainable competitive advantage. Barney’s (1991) definition is generally used in defining what this particular theoretical construct means, and was already mentioned in the literature review of this study. Considering the many critiques that have been placed upon the definitional works of Barney (1991) however, this definition may not be the most suitable one to employ.

As explained in the literature review section of this study, the influence of the environment is grossly overlooked when it comes to RBV literature. Like most other economical work, RBV theory seems to be dependent on the ceteris paribus condition of ‘all other influences being equal’. However, if RBV was to hold any predictive value in a business environment, one cannot simply regard the environment as being static over a longer period of time. One major implication that this notion has, is related to sustainable competitive advantage. The question becomes, how can a competitive advantage be sustainable in an environment which is constantly changing? Naturally, the answer is that this is impossible for a very long period of time (D’Aveni et al., 2010). Especially in the context of small and young firms, which tend to be entrepreneurial, the environment is quite volatile. As exposed by Schumpeter (1934) and Kirzner (1972), two of the most reputable Austrian-school economists, economies are constantly shifting between equilibrium and disequilibrium due to entrepreneurial activity. This entails the nature of an industry is constantly shifting.

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copyrighted material. Take note that Youtube was not even in the same industry as the music businesses, and became a competitor by chance rather than intent.

Clearly, especially in terms of industries characterized by high innovation and fierce competition, the notion that any firm can sustain a competitive advantage over a very long time period without constantly changing facets of its business is unrealistic (D’Aveni et al., 2010).

Therefore, this study assumes that the end result of an RBV framework should not be a competitive advantage which is sustained, but rather one that is temporary.

Thus, the conclusion from what is presented above should be that competitive advantage in a dynamic environment is unlikely to be sustainable in the long run, and must be characterized by a temporary nature. By acknowledging competitive advantage is of a temporary nature, part of the issues regarding RBV and the environment are resolved. As was stated in previous paragraphs, the RBV assumes that value is determined outside the firm, but does not take into account that these exogenous value appropriation variables might change over time (Fahy, 2000; Sirmon et al., 2007). By acknowledging the end result of an RBV model will not be perpetual competitive advantage but temporary competitive advantage, the changing nature of the environment is recognized.

Now that the nature of a competitive advantage is clear, the origin of the competitive advantage needs to be established. As stated before, this research makes a point of avoiding the definitional works of Barney (1991), due to their inherent criticisms. Competitive advantage is believed to be of a temporary nature due to industry specific changes. In that respect, it should come as no surprise that the degree of competitive advantage is also dictated by the industry a firm finds itself in. This notion is nothing new, for instance it was highlighted by Porter (1980) in his seminal work on the competitive forces that shape the industry. Porter (1980) posed that a firm could position itself in an industry in such a way that it could attain a competitive advantage. Thus, competitive advantage is a function of the firm’s environment. If a definition were to be placed on the degree of competitive advantage then, its origin in the industrial context should be clear. Besanko (2005: 345) provides such a definition of competitive advantage:

“When a firm earns a higher rate of economic profit than the average rate of economic profit of other firms competing within the same market”

Competitive advantage is thus about economic profit, meaning a firm makes a higher-than-average return compared to companies in the same industry. While the difference between this particular definition on competitive advantage and Barney’s (1991) definition is subtle, it has a mayor implication in its natural assumption that competitive advantage is industry defined. This line of reasoning is in accordance with statements by Priem & Butler (2001a,b), who highlight the importance of accounting for the influence of the environment in the RBV.

Combining the notions of temporary competitive advantage with industry dependent degrees of competitive advantage, a new type of competitive advantage is created. Definitions of sustainable competitive advantage in the RBV literature have not made the theory more valid to date. Indeed, it is one of the mayor points of critique which reviewers of RBV theory highlight. By defining competitive advantage as both temporary and industry defined, one resolves the

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employed definition is an explanation in the relationship between competitive advantage and performance levels. When competitive advantage relates to degrees of economic profit levels, performance needs not necessarily be disappointing. Even without competitive advantage, firms could still make an accounting profit, which could be satisfactory in many cases. Competitive advantage is thus not necessary in gaining performance (Durand, 2002), yet is an indicator of higher than normal performance.

2.5 Determinants of a resource – the definitional and empirical criticisms

Switching over to the particular topic of resources, it should first be accurately established what exactly a resource constitutes. Barney (1991: 101) defines a resource as:

"*…+firm resources include all assets, capabilities, organizational processes, firm attributes, information, knowledge, etc. controlled by a firm that enable the firm to conceive of and implement strategies that improve its efficiency and effectiveness"

As stated before, as Barney’s (1991) definitional works have come under a rather large amount of criticism over the years, Barney’s (1991) definition may not be the most appropriate definition in this context. Indeed, this particular definition has hailed quite a large share of criticism due to the

inclusion of the words ‘efficiency and effectiveness’, which are mirrored in the definitions of value and competitive advantage. For this reason, a simpler definition of resources is employed in this research. According to Newbert (2008: 766) resources are:

“The tangible or intangible assets a firm possesses or has access to”.

This particular definition circumvents the criticism placed upon the earlier definitions of a resource, as it makes the resource definition much less all-inclusive (Kraaijenbrink et al., 2010). However, the RBV is not simply about resources alone, but how such resources interplay to generate value for a business. The combinations of resources, and the abilities to exploit these are termed capabilities. According to RBV logic, a resource is only valuable once a firm exploits it in combination with other resources so as to attain a certain capability (Penrose, 1959; Barney, 1991; Locket et al. 2009). According to Eisenhardt & Martin (2000), capabilities are dynamic in nature however. In essence, this means that capabilities are not static entities which will function in the same way indefinitely. The definition of a dynamic capability is the following:

“Dynamic capabilities thus are the organizational and strategic routines by which firms achieve new resource configurations as markets emerge, collide, split, evolve, and die” (Eisenhardt & Martin, 2000: 1107)

Thus, the value and lifetime of a capability is, once again, subject to the tumult of the industry a firm finds itself in. Be that as it may, the main concepts in the RBV, resources, are not very interesting unless they are combined with other resources so as to create a value generating capability. For that reason, one must wonder why one would even measure resources and capabilities separately. Following the logic of Newbert (2008) and Locket et al. (2009), it is the resource-capability

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general. Considering that resource-based theory indeed does not argue that having resources results in a competitive advantage, Newbert’s (2008) argumentation here is convincing: resources are only able to contribute to a firm’s performance when they are combined and utilized. Furthermore, from a new venture valuation framework perspective, it would not be interesting to measure resources and capabilities separately, as this would only unnecessarily complicate the framework. For these reasons, this study will employ resource-capability combinations as the firm’s primary mode of gaining temporary competitive advantage.

In RBV research, a large debate continues to take place on what makes a resource or

capability useful. This particular discussion is one of the fundamental debates of the RBV, as it lies at the very basis of useful resource and capability identification. There has been a multitude of research on what makes a particular resource or capability valuable to an organization (e.g. Barney, 1991; Prahalad & Hamel, 1990), as this lies at the heart of RBV research. Barney’s (1991) framework on what makes a resource valuable has been discussed in the literature review, and is generally referred to as the VRIN characteristics, after: value, rareness, imitability, and non-substitutability. There are however, other views on what characterizes a useful resource or capability for an organization (e.g. Prahalad & Hamel, 1990), although there are also generally returning characteristics of resources in these views (Rangone, 1998). When summarizing the most important resource characteristics, Rangone (1998) found that the five tests of: competitive superiority, imitability, duration, appropriability, and substitutability are generally echoed by researchers of RBV theory. Unfortunately, Rangone’s (1998) model’s empirical validity is not assessed. More recently,

researchers have returned to works by Barney (1991), adapting his VRIN resource characteristics into a new framework. This new framework substitutes the non-substitutability characteristic with an organizational possibility to exploit the resource –characteristic, which was successfully employed in empirical research by several authors (e.g. Terziovski, 2010). Thus, the new characteristics are dubbed VRIO. The conceptual definitions of the VRIO characteristics are the following: Value: the degree to which a resource-capability combination allows a firm to reduce costs and/or respond to environmental cues (Barney, 1991); Rareness: the degree to which a resource-capability combination is not widespread enough to allow perfect competition for it (Newbert, 2008); Imitability: the degree to which a resource-capability combination is costly for competing firms to imitate (Terziovski, 2010); Organization: whether the firm is organized so as to exploit the resource-capability combination (Terziovski, 2010).

In this research, the VRIO framework is chosen as the characteristics to which a resource-capability combination must conform to if it is to have any worth for a new venture. The primary reason for this is the available empirical and conceptual evidence on the validity of this framework, which competing frameworks do not possess. Newbert (2008) researched the influence of resource-capability combination value and rareness on competitive advantage, and by extension,

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resource-capability worth (Andersén, 2011), the logical choice is not to deviate from this normative model. For these reasons, the new VRIO framework is employed in judging resource worth, rather than any other framework.

Extracting from the literature then, a resource or capability is subject to the four tests of worth, after which the general worth of a resource to an organization can be assessed. However, since there are four tests involved, the question is to how many of the four tests a capability set must conform if it is to be precious. Generally speaking, all four tests for resource-capability worth are deemed equally important in RBV theory, as RBV literature thus far has not investigated the possibility of a higher degree of importance of any of the criteria compared to the remaining criteria. Moreover, Andersén (2011) argues all these characteristics must at least be met to an extent by a resource-capability combination in order for it to be useful. Naturally, this results in the conclusion that the tests are of equal importance, as none of the tests can be omitted. For this reason, an aggregated score can be constructed from testing the degree of success of a resource-capability combination on each of the VRIO scales. Rangone (1998) took a similar approach in his work. However, since his work was based around different tests of resource worth, and has not been empirically tested to be valid, this framework becomes non applicable. For this reason, a new general framework for assessing the worth of a resource should be constructed employing the VRIO scale.

The remaining question is on what scale the VRIO characteristics should be scored.

Measuring the VRIO characteristics should be done based on a scale ranging from low to high, as all characteristics in the VRIO framework can be relatively low, medium, or high for a certain resource-capability combination. In this research, it was chosen to measure the VRIO characteristics on a scale ranging from 0 to 10. The reason for this is that it allows for more nuanced differences in judgement of each of the VRIO characteristics than other scales. The alternative would be a likert-scale ranging from 1 to 5, where 1 constitutes ‘very low’, and 5 is ‘very high’. However, the differences between these groups are twice as large as when a scale from 0 to 10 is used. In order to be able to define the worth of a resource-capability combination as accurately as possible, a scale ranging from 0 to 10 is therefore used. Thus, if a business owner scores the aspects of Value (V), Rareness (R), Imitability (I), and Organizational possibility to exploit the resource (O) on a scale of 0 through 10, 10 being the perfect score, then a resource’s worth could be measured by employing formula 1:

𝑅𝑒𝑠𝑜𝑢𝑟𝑐𝑒 − 𝑐𝑎𝑝𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑐𝑜𝑚𝑏𝑖𝑛𝑎𝑡𝑖𝑜𝑛 𝑤𝑜𝑟𝑡𝑕_𝑖 = (𝑉𝑖, 𝑅𝑖, 𝐼𝑖, 𝑂𝑖) 4

𝑖=1

4 (1)

Where i stands for resource i. All values for the scores on the four characteristics are summated, and consequently divided by four. This results in an average score for resource worth based on the scores given for the VRIO characteristics. As mentioned before, research on the RBV has not established the higher importance of one characteristic over other characteristics. For this reason, all four

characteristics will need to be equally treated, resulting in the average of these four characteristics being the correct measure to employ.

2.6 Resources and capabilities in categories – the heterogeneity solution

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methodology, but rather to the identification of resources. The problem here lies in the problematic identification routines on what exactly constitutes a resource and what does not. Identification of capabilities is no different in this respect, as capabilities are often defined as being intangible, and intangible objects are hard to identify by nature. This is where the general criticism on the RBV’s over-inclusive definitions of resources and capabilities comes from (Kraaijenbrink et al., 2010). Furthermore, under the notion of bounded rationality, it would be difficult for managers to identify and name each single resource or capability an organization possesses (Andersén, 2011; Newbert, 2008; Kunc & Morecroft, 2010). What’s more, many resources are quite comparable in nature (Peteraf & Bergen, 2003). For these reasons, Barney (1997) posed that resources could be framed into several different categories. Barney (1997) employed the categories of financial resources and capabilities, human resources and capabilities, organizational resources and capabilities, and physical resources and capabilities. Supplementing this, Newbert (2008) posed that for high tech industries, intellectual resources and capabilities would be considerably different enough from the other categories to warrant its own class of resources-capability combination. Thus, the total number of resource-capabilities combination categories in the IT industry should be five, namely: financial, human, organizational, physical, and intellectual. Newbert (2008) found these five categories of resources and capabilities to be significantly different in nature, considering the maximum Pearson correlation coefficient (r) between these categories was 0,358 in a total of 55 correlations tests. Furthermore, a pilot study by Newbert (2008) resulted in the conclusion that these resources and capabilities groups were representative of the bulk of a technology firm’s resources and capabilities. As Newbert’s (2008) sample of technology firms is highly similar to this study’s target population of information technology firms, the financial (F), human (H), organizational (O), physical (P), and intellectual (I) resource-capability categories will be applied here as well.

Aside from these five categories, one could argue that a new venture features another key resource-capability category: the entrepreneur. As is evidenced by many empirical results on what venture capitalists look at when they decide which firm to invest in, the entrepreneur (or

entrepreneurial team) is a key characteristic of a small and young business (Tyebjee & Bruno, 1984). However, considering that human resource and capabilities are already included in the typology of Barney (1997), it would be difficult to argue that a new venture RBV evaluation framework should include a separate heading for the entrepreneur.

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was chosen not to list the entrepreneur, or the entrepreneurial team as a separate resource-capability category, but rather as being part of human resources and capabilities.

Following the logic presented above then, all firms consist of five groups of resource-capability categories, termed the F,H,O,P,I resource-resource-capability sets. A logical extension of this argumentation is the notion that, if all firms in a certain industry consist of the same categories of resources and capabilities, they can be compared. By identifying equal groups of resource-capability combinations for each firm in an industry, the building blocks of firms become homogeneous in nature. Naturally, this argument only holds for industries, or industry subsets, significantly comparable in terms of operations and conduct. Such comparable industries (or subsets of

industries) are termed strategic groups. A strategic group’s primary protection from market entrants are barriers to mobility (Short et al., 2007). In an industry characterized by immaterial products (Zahra & Bogner, 2000) which can be transferred without material complications through

communication channels such as the internet however, there are no barriers to mobility. Therefore, the information technology industry is such an industry without barriers to mobility, leading to the conclusion that the IT industry as a whole can be considered a strategic group. As a result, a single IT firm is expected to be a direct possible competitor to all IT firms in the industry.

While some firms may feature relatively large amounts of endowment in one resource-capability category, whereas other firms have higher endowments in other resource-capabilities categories, they remain comparable as the same categories of resource-capabilities are applied to all firms. This logic solves the problems of over-inclusive resource-definitions and non-comparability of firms due to their inherent heterogeneity. The former problem is addressed as resources and capabilities must fall into one of the five resource-capability groups. The latter problem is dealt with because all firms are believed to feature equal resource-capability categories. Thus, in this particular new venture resource-based valuation framework, five categories of resource-capability

combinations are identified, which are: financial (F), human (H), organizational (O), physical (P), and intellectual (I) resource-capability sets. If the assessment of these combinations is conducted in terms of the VRIO characteristics employed in formula 1, and each category of resource-capability

combination is scored, a general score for all resources and capabilities in an organization can be calculated. The combination of these five resource-capability groups, since that is all a firm consists of, should explain the extent of temporary competitive advantage.

The one question that remains however, is the weight of importance attached to each of the five categories used. As this research is set to be conducted in the context of the IT industry, it is not unfeasible that certain groups of resources and capabilities are more important than others. Rajala & Westerlund (2007) present evidence that software developing firms generally display skill in

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concluded there are certain resources or capabilities which are of higher or lesser value than other resources and capabilities in the software IT industry. The only logical conclusion here, is that there is no across the board argument for the IT industry as a whole to dictate the higher importance of one group of resources and capabilities over others. Thus, all groups of resources and capabilities in the IT industry hold their own value and necessity.

As an illustration of this, consider a software firm with a large amount of human resources and capabilities. It might be able to produce excellent products, yet without financial resources the firm will only have the option of being a service provider, rather than a business offering a single product, as its human resources will require payment while developing this single product before it can be marketed. Furthermore, without an excellent organizational resource and capability bundle, the firm will have no clients to sell to, or suppliers to outsource to. The same reasoning goes for the rest of the resource-capability combinations as well. All in all, arguments were presented indicating that firms in the IT industry are all made up of the five (F, H, O, P, I) resource-capability combination groups, all of which are assumed to be equally important to a firm’s operations. It is also the

endowment in these five resource-capability combinations which allows for the attainment of competitive advantage. Furthermore, the IT industry as a whole is comparable in nature, and does not feature outspoken strategic groups which are protected by entry barriers, ensuring there are no protected niches requiring specific resources and capabilities. Hence, formula 2 is the following:

𝑆𝑡𝑟𝑎𝑡𝑒𝑔𝑖𝑐 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑃𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙𝑖=

(𝐹_𝑖, 𝐻_𝑖, 𝑂_𝑖, 𝑃_𝑖, 𝐼_𝑖) 5

𝑖=1

81₃ − 3 (2)

Where i stands for company i. With this particular formula, the average worth of the five resource categories becomes the firm’s strategic deviation potential. Rather than employing a scale from 0 to 10 for this score however, a scale from -3 to 3 is the end result. The consequential scale results in a normal curve with the mean in the middle of its distribution. This scale is constructed rather than any other scale as it translates one-on-one into Z-scores which can be employed in statistical analysis. The reason for this will become clearer in the following paragraph.

Capabilities, as defined by Eisenhardt & Martin (2000: 1107) entail the combinations of resources which allow a firm to undertake actions. Indeed, the method described above does not allow for infinite resource combinations to be measured separately, since this is virtually impossible in a practical sense. For this reason, and reasons of firm comparability, resources and capabilities were grouped into five categories. Formula 2 therefore measures resources and capabilities in these five groups. Interaction between these groups is not measured separately, but rather inferred from the mathematical combination of resources and capabilities. The result of interaction between the five resources and capabilities categories is reflected in the retrieved strategic deviation potential of a firm. The alternative, measuring all possible combinations of resource-capability categories, results in 5! combinations2, which equals 120. Note that all these 120 hypothetical combinations would need to be measured using the VRIO characteristics discussed earlier, resulting in a total of 480 variables used in this new venture evaluation framework. Not only is this extremely impractical, it defeats the purpose of formula 2, which by mathematical combination estimates resource-capability potential in generating competitive advantage. Since competitive advantage is derived from combining different

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resources and capabilities, the value of the building blocks of such capabilities should be indicative of the potential competitive advantage which can be derived from their combinations. The result of formula 2 is therefore a conservative estimate of competitive advantage potential. Thus, while not all possible capability combinations are measured, the mathematical combinations of these groups allow for the estimation of the extent of interaction effects between the groups. Indeed, this conceptualization of the resource-interaction concept requires careful operationalization, which will be done in the methodology chapter.

2.7 Determinants of Venture performance – the performance attribution issue

One of the mayor problems in young firm performance prediction relates to the unavailability of historical financial performance. According to traditional firm valuation frameworks, the best prediction of future firm performance would be historical performance (Carland & White, 1980). However, young firms in general do not have extensive historic financial results. Furthermore, estimation of financial performance of a small firm is hampered by the increased income volatility of small firms compared to larger firms (Bernstein, 1953). This presents a particular challenge for financial performance prediction of a new venture using financial methods. However, using RBV logic, future performance should be predictable based on the resource-capability combinations a firm possesses.

As was established in paragraph 2.4, having a temporary competitive advantage means that a firm can outperform its industry for a certain period of time. Thus, a firm would be able to earn higher than average profits in a certain market. If the specific, unique combination of resources in a firm allows it to attain such an advantage, then the degree to which the resource-capability

combinations a firm possesses should result in the degree to which it can outperform the industry. In that sense, all one would need to know from a particular industry would be the average

performance, and the standard deviation in that performance. A firm sporting average resource-capability combinations should not be able to attain a temporary competitive advantage. However, a firm able to deploy very unique and valuable resource-capability combinations should be able to achieve a proportionately higher than average financial performance. Applying statistical logic, and assuming the earnings in an industry to be normally distributed, 99,8% of all observations should fall within three standard deviations from the mean of the distribution (Hair et al., 2006). If the average earnings in an industry then relate to having zero competitive advantage, a firm could have either a positive or negative competitive position.

This is where the logic of the employed scale in formula 2 comes in. This formula results in a scale ranging from -3 to 3. If a firm would have only averagely valuable, rare, imitable, and

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performance higher or lower than average can be calculated by multiplying the Z-score with the standard deviation of earnings in an industry. This result is added with the average industry earnings, as having no competitive advantage does not mean there is no accounting performance, only an average performance. This reasoning leads to the establishment of formula 3:

𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑡 = 𝑆𝑡𝑟𝑎𝑡𝑒𝑔𝑖𝑐 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑃𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙𝑖 × 𝜎𝜋𝑖𝑛𝑑 + 𝜇𝜋𝑖𝑛𝑑 (3) Where 𝑆𝑡𝑟𝑎𝑡𝑒𝑔𝑖𝑐 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑃𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙𝑖 relates to the result of formula 2, 𝜎𝜋𝑖𝑛𝑑 is the standard deviation in profitability in the firm’s industry, and 𝜇𝜋𝑖𝑛𝑑 equals the mean profitability in the firm’s industry. The resulting value would be the maximum performance a firm should be able to achieve in year t. Competitive advantage and, by extension performance, in this way are completely industry dictated, which conforms to RBV logic. Naturally, a relative performance indicator should be used in formula 3, which takes into account the size of a new venture. Examples of such relative measures are return on assets, return on equity, or return per employee.

2.8 The value of a new venture – the managerial implication

Establishing the value of a new venture using the logic employed thus far would be subject to two particular issues. First, applying NPV method logic, it is assumed that the value of an investment is simply the sum of its expected earnings, reduced by its initial investment. Naturally, during all these steps, the DCF method is applied, meaning the time-value of currency will need to be taken into account. Second, considering that this study assumes competitive advantage to be of a temporary nature, the positive or negative competitive position of a firm will not be of a sustained nature. Therefore, the potential duration of the competitive advantage will need to be estimated.

Moore’s law is one of the oldest prophecies when it comes to computer technology, and states that the number of transistors that can be placed on an integrated circuit doubles roughly every two years (Intel, 2005). Thus far, Intel’s co-founders’ law has held up rather well, despite it dating back to 1965. What this serves to illustrate, is the extreme speed by which the industry develops. In essence, doubling the amount of transistors on an integrated circuit implies doubling the processing speed this hardware features. Thus, the information technology sector develops with an extreme speed (Zahra & Bogner, 2000). On the other hand, one needs to make a clear distinction between hard- and software in this respect. A circuit board consisting of transistors cannot be upgraded or expanded by an end-user. However, a software developer should be more than able to adapt his product to the requirements of new hardware. Today, software products are characterized by a long life cycle, in which the original product will continuously be improved (Aramand, 2008). However, should a software product not be continuously improved and expanded, it will ultimately be competed out of the market (Aramand, 2008). The effect thus is clear, an IT firm’s competitive advantage in terms of its products is of a temporary nature. However, if a new venture is able to continuously improve its products, it should be able to stay ahead of competition.

The effects of the notion presented above are of a crucial nature to the evaluation

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share, maintaining customer loyalty, gaining access to distribution channels, and ensuring future profits in software firms (Zahra & Bogner, 2000: 139). As these four aspects are extremely crucial in doing business in the IT industry, it should be safe to believe continuous technology innovation is crucial in the IT industry as well. The question simply becomes whether or not this new venture evaluation framework will assume the venture to continuously improve its products. Assuming that it is the venture’s goal to continue its operations, one would argue that the firm would indeed

continuously improve its products and business in order to stay ahead of competition. In terms of an RBV based evaluation framework, this entails an interesting facet. As soon as a business changes its conduct or products, it changes its resources and capabilities. In essence, the firm will have

completely changed its resource and capability configuration, both of which lie at the basis of the firm’s performance prediction. Consequently, one would need to make a prediction on the value of a hypothetical future temporary competitive advantage created by hypothetical future resource-capability combinations to find out the performance after the primary temporary competitive advantage has eroded. Naturally, such a prediction would be completely unrealistic.

The solution to this problem this research provides is the following. While competitive advantage is of a temporary nature, the innovation speed in the IT industry allows temporary

competitive advantages to be stacked into a competitive advantage which might be sustained for the single firm. However, to achieve this stacked sustained competitive advantage, the firm must

continuously improve itself, which entails reconfiguring its resource-capability combinations. Be that as it may, a valuation framework will need to assume that a firms strategic deviation potential (formula 2) is an indicator for its future temporary competitive advantage possibility.

Armed with these assumptions, it should be entirely possible to calculate the value of a new venture employing the logic presented in the DCF and NPV valuation methods. Considering however that the firm’s earnings potential would be stable for the amount of years in the valuation model, the calculation of firm value would equal the calculation of the present value of a perpetuity. This

conclusion logically follows from the argumentation presented before: if the only way for a firm to survive and maintain a competitive advantage is to continuously improve itself, and the degree to which it holds a competitive advantage at present is indicative to the future competitive advantage potential, a venture’s returns over time would be relatively stable compared to its industry. There is one note to be made here however: industry earning are not equal in absolute terms over a large number of years. This will need to be reflected in the expected earnings of a firm. Thus, one needs to account for the average change in profitability in the industry. The formula which is applicable in this setting would be the formula of a constant growth perpetuity, which is formula 4 (Chambers & Lacey, 2004: 117):

𝐴𝑠𝑠𝑒𝑡 𝑉𝑎𝑙𝑢𝑒 = 𝐶𝐹𝑡

𝑟 − 𝑔 (4)

Here, 𝐶𝐹𝑡 refers to the cash flow which is generated at time t, r entails the discount rate of the asset, while g is the growth rate of the asset. Adapting the calculation of a constant growth perpetuity (Chambers & Lacey, 2004: 117), the calculation for firm value of a new venture in the IT industry becomes equation 5:

𝑁𝑒𝑤 𝑉𝑒𝑛𝑡𝑢𝑟𝑒 𝑣𝑎𝑙𝑢𝑒 =𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑡

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Where 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑡 relates to the result of formula 3, 𝑟 stand for the appropriate discount rate of the asset, and 𝑔𝜋 𝑖𝑛𝑑 relates to the average growth in industry profitability rate. Inherent to the logic of formula 4 is the notion that the higher the performance of a new venture, the higher the value of the firm will be.

All in all, the reasoning from this entire chapter of the research leads to a conceptual model of sorts. There are five main factors involved in this research, namely resources, capabilities, and their combinations; temporary competitive advantage, firm performance, and firm value. This being the case, the general conceptual model can only feature these five concepts as well. The result is a deceptively simple conceptual model, which can be found in figure 1.

Figure 1: Conceptual model

Note: a positive sign indicates a positive relationship.

The conceptual model features three relationships which are assumed to be valid. First, the ways in which a firm combines its resources and capabilities into resource-capability groups allows a firm to obtain a temporary competitive advantage. Second, temporary competitive advantage will lead to a higher firm performance. Third and last, a higher firm performance will, naturally, lead to a higher value of the firm. These relationships echo the constructed hypotheses in this chapter. Next, this study will continue with a methodology section.

3. Methodology

The methodology of this research consists of two distinct chapters. First, the methods by which data was gathered during this study will be conveyed in paragraph 3.1. Second, a number of definitions have not been provided as of yet. The missing definitions will be supplied in paragraphs 3.2 through 3.6.

3.1 Data Gathering

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this is the case, this research consists of a number of case-studies which have been described in the results section of this research. The case study method is the most applicable considering the nature of this paper’s research question and the conceptual character of many of its key ideas. Thus, the framework was tested in the business setting employing a case-study approach. A total of four firms had been found willing to cooperate intensively with the researcher, which allowed for very detailed information gathering. The results of these analyses will be conveyed on a case-by-case basis. The interviews with the subject firms were based around a structured questionnaire, a copy of which can be retrieved in Appendix A. Extensive notes were made during the interview sessions, as this was believed to be of help in later stages of the analysis of findings.

Second, information about the industry of subject needed to be retrieved. Information on the global IT industry was retrieved using the ORBIS database which is available from Bureau van Dijk (Bureau van Dijk, 2011). This database features financial information on a total of 73.259.998 firms from all over the world, from different industries. For this research, a completely random sample of Information Technology related firms was drawn from the available population of 1.162.782 total IT firms which were registered in the database. For specific information on the characteristics and results of this dataset, consult chapter 4.1. The employed dataset in this study was retrieved may 31st 2011, and contains financial information on a total of 21.700 firms for the years 2008, 2009, and 2010. This final number of firms is much smaller than the initial population, due to the absence of crucial data for many firms in the database. The total sample of 21.700 firms were found to possess most of the required data for this research. Moreover, the technical restriction on the number of rows (65.536) in Microsoft Excel 2003 (installed on University of Groningen Computers) ensured 21.700 was the maximum amount of firms available for extraction in a single .xls document. All in all, the sample of 21.700 firms was drawn from the total population of IT firms featuring information on profit levels, and employee numbers, in a completely random manner. This ensures there is little to no selection bias in this particular sample. For further information on the definitions employed to construct this sample, consult chapter 3.4.

Third, a credible value for the discount rate would need to be retrieved in order to be able to employ formula four. Even though the industry growth rate can be calculated using the values retrieved in the ORBIS database, a discount rate could not. For this reason, different sources needed to be consulted for this value.

3.2 Conceptual operationalizations – VRIO characteristics

Aside from the methodological definitions of what exactly constitutes the IT industry and what is a new venture, there are a number of theoretical definitions which have not been provided yet. These theoretical definitions will be presented first. The first of these theoretical definitions relates to the series of constructs in the VRIO characteristics, which have been presented in section 2.5. Recall that VRIO stood for: Value, Rareness, Imitability, and Organizational possibility to exploit a resource-capability combination. While the theoretical meaning of these terms has been provided, the measurement by which these constructs were measured in this research were not. Hence, the operational definitions of these variables are the following:

A Resource-Based Valuation Framework for Small and Young IT Businesses