The Effects of Integration Strategies on Firm Performance

MSc. in Finance and International Business Department of Business Administration Author: Selen Gül Advisor: Valerie Smeets An Empirical Study on Danish Manufacturing Firms Abstract: The firms diversification strategy choices and their impact on corporate performance have been the center of attention both empirically and theoretically in the fields of strategy and finance for more than 30 years. However in general, previous studies have analyzed the integrationperformance relationship without differentiating the industries that the firms were operating in, but rather the samples were pooled across industries. The aim of this paper is to investigate the performance effects of vertical, horizontal, unrelated integration and un-diversification strategies, by using a sample of 147 Danish manufacturing companies distinguished among 5 large industries, through the years 2009 to 2005. Empirical evidence shows that horizontal (related) integrated companies are outperforming the corporate performance of unrelated diversified firms, and the structure of the market, the level of concentration have varying effects on performance for each type of industry. Out of 5 industries, the manufacture of food products has the highest average performance measure, and the empirical results underline the significant and positive effect of the horizontal integration strategy for the manufacture of food products and manufacture of machinery and equipment industries that were subject to be tested. August 2011 Aarhus School of Business, Aarhus University

Table of Contents 1. Introduction... 3 1.1.Research Questions... 4 1.2.Structure of the Thesis... 5 2. Literature Review... 6 2.1.Theories of Vertical Integration... 6 2.1.1. Make or Buy Decision... 6 2.1.2. The Transaction Cost Theory... 7 2.1.3. The Property Rights Theory... 8 2.1.3.1.Benefits and Costs of Contracts... 9 2.1.4. The Theory of Relational Contracts... 10 2.1.5. Is Vertical Integration Beneficial for the Firm?... 10 2.1.6. Empirical Evidence on Vertical Mergers... 11 2.2.Horizontal Integration... 12 2.2.1. Economies of Scale and Scope... 13 2.2.2. The Learning Economy... 14 2.2.3. Empirical Evidence on Horizontal Mergers... 15 2.3.Diversification... 16 2.3.1. Product Diversification... 17 2.3.2. Geographic Diversification... 17 2.3.3. The Determinants and Motives for Diversification... 18 2.3.4. The Resource-Based View... 19 2.3.5. Diversification and Firm Performance... 20 2.3.6. Empirical Evidence on Diversification and Firm Performance... 22 3. Development of Hypotheses... 24 4. Methodology... 26 5. Data Construction... 28 5.1.Sample Selection... 28 5.2.Variables Measurement... 30 5.2.1. Performance Measures (Dependent Variables)... 30 5.2.2. Independent Variables... 31 5.2.3. Control Variables... 32 1

5.3.Limitations... 36 6. General Descriptive Analysis of Each Industry... 37 6.1.Manufacture of Basic Pharmaceuticals and Pharmaceutical Preparations... 37 6.2.Manufacture of Food Products... 41 6.3.Manufacture of Chemicals and Chemical Products... 44 6.4.Manufacture of Furniture... 46 6.5.Manufacture of Machinery and Equipment... 48 7. Industry Comparisons... 51 8. Empirical Findings and Discussion of Results... 53 8.1.Manufacture of Food Industry... 53 8.2.Manufacture of Machinery and Equipment Industry... 57 8.3.Discussion of Results... 60 9. Conclusion... 63 References... 65 Appendices... 72 2

1. INTRODUCTION In this new era, where technological innovations are growing at a fast pace leading to a more globalized world, corporations are facing a change in their form, structure and scope. These new technologies engendered goods to be produced at lower costs, compared to what organizations could achieve using older technologies. In order to benefit from these production opportunities, firms require reliable supplies of inputs, access to widespread distribution and retail outlets. Based on these necessities, the relationships among manufacturers, their suppliers, and their distributors have been affected by this product line and volume expansion. In relation to this phenomenon, the question of the diversification-performance relation, has been generally the most studied in the literature. The scholars main focus has been on the value enhancing or destructive effects of diversification, and the conclusions vary based on the perspectives of the studies that are conducted. Santalo & Becerra (2008) underline that, while several authors have found strong evidence of trading at a discount for diversified firms, supporters indicate that diversified firms are more productive compared to stand-alone businesses. Moreover, the early contributions of Rumelt (1974) and Penrose (1995) indicate that, as firms diversify into more unrelated areas, a lower performance outcome is more likely. Besides the effects of unrelated diversification and firm value, the companies may initially choose to either vertically or horizontally integrate. Manufacturing firms increasingly choose to vertically integrate; meaning that, rather than relying on independent suppliers, factors and agents, they choose to produce the raw materials themselves and even distribute finished goods. Moreover, new production technologies have given firms the opportunity to exert scope economies by producing a wider range of products at a lower cost, compared to be produced separately, leading them to horizontally integrate. (Besanko et. al, 2007) Through diversification within their areas of business, the companies desire to reduce costs and improve market effectiveness by utilizing economies of scale and scope. Besides these integration strategies, geographic diversification plays a key role in the strategic behavior of the large companies and their corporate performance. The company s expansion to different geographic locations as to different global regions and countries would define international diversification (Hitt et. al, 1997) Its importance comes from the utilization of the foreign market opportunities. The research on diversification and firm value has focused primarily on US and European based companies, without taking the performance effects of vertical and horizontal 3

integration into consideration. In addition, there are few studies that have focused on a single country, as Kahloul & Hallara (2010) evaluated the performance effects of the French firms. This paper will evaluate the performance measures by combining the impacts of unrelated diversification and as well as vertical, horizontal integration strategies and remaining undiversified. Moreover, in order to specify the results and overcome the socio-cultural differences among countries, the main focus will be on Danish manufacturing companies and the outcomes are to be evaluated based on five different industries. 1.1. Research Questions Based on the definitions mentioned above, it is crucial to highlight the relationship between firm performance and its level of integration strategies. By extending the study of diversification-firm performance analyzers (Penrose, 1995; Rumelt, 1974; Bettis, 1981), the aim of this paper is to question whether firms with an unrelated diversification, horizontal integration, vertical integration or un-diversification strategy perform better or worse compared to each other, and how these choices affect the firm performance. Prior studies generally have taken the effect of integration strategies homogenous across the industries, whereas this study investigates the effect of the strategies on performance by differentiating the industries. This homogenous approach is neglected since different industries bear different structural characteristics, which will lead to various average profits in each industry (Bettis & Hall, 1982), and the type of concentration and competition within an industry are the leading factors that orientate the companies to integrate or not (Penrose, 1995). The questions to be addressed are as follows: What is the dominant integration strategy that each industry embraces and which one has the highest affect on performance? How does the level of concentration change among the industries and does it have a relation with the strategies chosen? Does the integration strategies have an impact on corporate performance and do these effects differ based on the industries? Does the number of countries the firm is operating in, have an impact on firm operating performance? 4

1.2.Structure of the Thesis The next section will highlight the theoretical and empirical findings on the topic. Section 3 develops the hypothesis based on the theoretical and empirical arguments mentioned in the literature review. Section 4 gives in depth information of the methodology used, and Section 5 describes the data collection procedure. Section 6 presents the summary statistics for the industries involved in the study. Section 7 illustrates the comparisons among these industries based on their summary statistics. Section 8 presents the empirical findings and the discussion of the results, and finally, Section 9 makes concluding remarks regarding the study. 5

2. LITERATURE REVIEW 2.1.Theories of Vertical Integration Coase (1937) suggests that the introduction of the firm is initially based on the existence of the marketing costs. The number of transactions or the activities of the firm within its boundaries is the determining component in assessing the size of the firm, rather than its output. These boundaries are defined as the vertical boundaries since these activities are related at the various levels of the supply chain. Sudarsanam (2010) defines vertical integration as the combination of successive activities in a vertical chain under common coordination and control of a single firm. (p. 153) Vertical integration defines the activities that the company performs within its boundaries, compared to the purchases from independent firms in the market (Besanko et.al, 2007). In other words, vertical merger replaces two or more independent firms with a single firm, and rather than relying on arm s length market-based transactions or contractual dealings, it internalizes the coordination of the successive activities of the firm. Fan & Goyal (2006) indicate that vertical mergers procure acquiring companies with ownership and control over contiguous stages of production. These mergers allow firms to substitute internal exchanges within the boundaries of the firm for contractual or market exchanges. Although vast amounts of theoretical studies on vertical integration exist, there is inadequate number of empirical work on vertical mergers, and the ones conducted are based on small samples. 2.1.1. Make-or-Buy Decision Make-or-Buy decisions address the questions of: Why do some firms prefer a vertically integrated structure, while others specialize in one stage of production and outsource the remaining stages to other companies? In other words, should a firm produce its own inputs, buy them in the spot market or preserve the relationship with a specific supplier. This decision determines the firm's level of vertical integration, since every decision identifies which operations the firm will engage in and which it will outsource from the suppliers (Walker & Weber, 1984). This notion is concerned with the decision whether to integrate backwards, which is to internalize production of an input rather than source it from an external supplier. (Sudarsanam, 2010, p. 158) Therefore the make part of the decision emphasizes that ownership is joint and control rights are integrated, whereas under the latter, they are separate. Moreover, the costs and benefits of either alternative have to be taken into consideration. For instance, this choice may depend on a range of factors such as; the current and future availability of spot markets for arm s length transactions, the cost of sourcing from 6

the spot market, the direct and indirect costs of contracts and informal arrangements, uncertainty and information asymmetry between buyer and seller and indirect costs of internalizing production. (p.158) Based on these factors, the company can choose to perform the activities in-house or buy them from the specialists in the market that are called market firms (Besanko et. al, 2007). There are many advantages and disadvantages of using the market firms to source the upstream activities in the vertical change. The benefits would be achieving scale and learning economies, as well as efficient division of labor and specialization from the supplier s side. On the other hand, the downsides would be the issue in coordinating the production process, the leak of private information, agency and influence costs, moral hazard and disincentives for innovation. 2.1.2. The Transaction-Cost Theory The transaction costs theory (TC) can be traced back to Coase (1937) who indicated that the production will take place within the firm when the cost of organizing the production through the market exchange is larger than within the firm. In other words, the firms may avoid the costs of transacting with the market firms by carrying out the activity in-house. This cost of transacting with independent market firms is defined by Coase (1937) as the cost of using the price mechanism. The size of the firm will be based on the cost of using the price mechanism, in which a firm will tend to expand until the costs of organizing an extra transaction within the firm become equal to the costs of carrying out the same transaction by means of exchange on the open market or the costs of organizing in another firm. (p. 395) Leiblein & Miller (2003) argue that, although the applicants of the theory generally assume that markets ensure a more efficient mechanism for exchange compared to the hierarchy, in certain situations the costs of the market exchange may be too high and surpass these efficiencies procured by the market. Therefore, the theory focuses on determining the features of exchanges that are best suited to the firms and the market. Williamson (1975) indicates that these inefficiencies originate from small numbers of bargaining situations. Due to the bounded rationality of decision-makers, the asymmetric distribution of relevant information, and the inability to completely specify behavior in the presence of multiple contingencies, the theory maintains that all contracts are incomplete and therefore subject to renegotiation and the possibility of opportunistic behavior. (Leiblein & Miller, 2003, p. 842) Opportunistic behavior is more apparent, when an exchange demands one or more parties to get involved in 7

significant transaction-specific investments, which in turn create quasi-rents 1 that, may lead to hold-up 2. Such relation-specific investment creates difficulty in switching to a new customer due to the increases in costs, thus locking the supplier into that relationship (Sudarsanam, 2010). Besanko et al. (2007) and Sudarsanam (2010) are underlining the types of specificities as; site, physical characteristics, dedicated assets and human assets specific. Therefore, based on these downsides of contracts, vertical integration is thought to be beneficial, where hold-up concerns are severe. Firms are expected to depend on in-house production when the transactions are complex, specific investments are included, those specific assets are unceasing, the quality of those assets are hard to be verified, the environment is uncertain and when the quasi-rents based on the relationship are large. 2.1.3. The Property-Rights Theory The property-rights theory, which has been developed by Grossman & Hart (1986), emphasizes how asset ownership can change investment incentives. They propose two types of contractual rights as; the specific rights and residual rights of control. When it is too costly for one party to specify a long list of the particular rights it desires over another s party s assets, it may be optimal for that party to purchase all the rights except for those specified in the contract. (p. 692) The purchase of the residual rights of control is called ownership. All the residual control rights of the physical assets in question are held by the entity under integration, whereas under non-integration, the assets are owned individually (Hubbard, 2008). Moreover, Grossman & Hart (1986) present that the allocation of residual control rights to one party strengthens the investment incentives of that party, while weakening the counter party s investment incentives. Integration shifts the incentives for opportunistic and distortionary behavior, but it does not remove these incentives. (p. 716) Therefore, both costs and benefits from integration will exist. One of the concluding remarks of Grossman & Hart (1986) is that, integration is suggested when one party s investment incentives is relatively more important to the other firm s incentives. On the other hand, when both investment decisions are equally and somewhat crucial, non-integration is preferable. Compared to the TC literature, the PR literature does not underline the ex post haggling, renegotiation and opportunistic behavior. Instead it stresses contractual incompleteness and develops formal models that show how ex post bargaining affects ex ante investment in non-contractible assets. (Lafontaine & Slade, 2007, p. 650) Kim & Mahoney 1 Quasi-rent would be the extra profit that you get if the deal goes ahead as planned, versus the profit you would get if you had to turn to your next-best alternative. (Besanko et. Al, 2007, p. 126) 2 The term hold-up will be explained more in detail under section 2.1.3.1. 8

(2005) further indicate the importance of property rights theory, as that various specifications of property rights arise in response to the economic problem of allocating scarce resources, and how it affects the economic behavior and economic outcomes in return. 2.1.3.1. Benefits and Costs of Contracts According to the theories mentioned above, the existence of market failures may lead the firms to source its inputs from suppliers by negotiating contracts. The duration of these contracts may be short or long-term in nature. Williamson (1971) introduces three alternatives to be considered: a life time contract, a series of short-term contracts, and vertical integration. The once-for-all type of contracts are facing the dilemma of the redesign issues due to changing technology, in which sequential decision process is needed. If, however, contractual revisions or amendments are regarded as an occasion to bargain opportunistically, which predictably they will be, the purchaser will defer and accumulate adaptations, if by packaging them in complex combinations their true value can better be disguised; some adaptations may be forgone altogether. (Williamson, 1971, p. 116) Therefore, short-term contracts may be more preferable due to sequential decision making and adaptation. However, the downsides would be the necessity of relation-specific investments and the existence of a first-mover advantage for one of the parties (Williamson, 1971). These downsides would generate the hold-up problem or behaving opportunistically, in which it occurs when one of the parties would attempt to renegotiate the terms of the contract. The party that has been held-up could be either the buyer or the supplier, but most likely the one that has engaged in a relation-specific investment (Besanko et. al, 2007). In order to eliminate this hold-up problem, Williamson (1971) suggests the firms to vertically integrate, in which the disadvantages of long and short term contracts would be avoided. Sequential adaptations become an occasion for cooperative adjustment rather than opportunistic bargaining; risks may be attenuated; differences between successive stages can be resolved more easily by the internal control machinery. (Williamson, 1971, p. 116) Besides the solution of vertical integration, only a complete contract can eliminate opportunistic behavior. Besanko et al. (2007) argue the applicability of complete contracts, and underline that this type of contracts would be feasible only if the parties are able to specify each contingency to be occurred and the set of actions to be taken. Therefore, contracts in the real-world are incomplete, which involve some degree of open-endedness or ambiguity. The literature on transactions costs highlights that incomplete contracts can cause a non-integrated relationship to yield outcomes that is inferior compared to complete 9

contracts. The three fundamental factors preventing to achieve complete contracting are; bounded rationality, difficulties specifying or measuring performance and asymmetric information. 2.1.4. The Theory of Relational Contracts In relation to this phenomenon of contracts, the third insight is formed by Baker et al. (2002) indicating that relational contracts are informal agreements and unwritten codes of conduct that powerfully affect the behaviors of individuals within firms. (p. 39) These relational contracts affect the behaviors of firms in their business relations with other firms, whether vertical or horizontal. Baker et al. (2002) underline in their study the ease of relational contracts between and within the firms, compared to the difficulties encountered in formal contracting. For example, a formal contract must be specified ex ante in terms that can be verified ex post by the third party, whereas a relational contract can be based on outcomes that are observed by only the contracting parties ex post, and also on outcomes that are prohibitively costly to specify ex ante. (p. 40) Therefore, a relational contract empowers the parties to exploit their detailed knowledge to their particular situation and to adapt this situation to new information as it becomes available. Based on these advantages of relational contracts, the authors are adding dynamics to the previous models and illustrate how these dynamics will affect the vertical integration decisions by introducing game theory models such as; trust games, repeated trust games and trigger strategies. 2.1.5. Is Vertical Integration Beneficial for the Firm? According to Sudarsanam (2010), vertical integration increases technical efficiencies in some ways; however arises inefficiencies in some other ways. The author describes these technical efficiencies as coordinating, monitoring, and enforcement in the process of production. On the other hand, interdivisional rivalry may lead to opportunism and an increase in influence costs. Moreover, information asymmetry in integrated firms may exist between various levels of management and divisions. In particular, a firm that purchases its supplier, thereby removing residual rights of control from the manager of the supplying company, can distort the manager's incentives sufficiently to make common ownership harmful. (Grossman & Hart, 1986, p. 692) When the residual rights are captured by one party, they are lost for the contrary party that may lead to distortions. On the other side, by vertically integrating no alternative use of the good will exists, leading to a value of zero quasi-rent and no hold-up problems (Williamson, 1971). 10

2.1.6. Empirical Evidence on Vertical Mergers The efficiencies of vertical integration have been subject to be tested by several scholars in order to illustrate why firms take parts production in-house and what types of specificities are affecting vertical integration (Monteverde & Teece, 1982; Masten, 1984), and how the duration of the contracts are affecting the choice to vertically integrate (Joskow, 1985). Monteverde & Teece (1982) have explained vertical integration by examining the U.S. automobile industry for the two firms, GM and Ford. The study observed a significant and a positive effect on the engineering effort and specificity coefficients, meaning that a high level of engineering effort and the specificity of the component will more likely lead the component to be produced in-house. GM and Ford are more likely to bring component design and manufacturing in-house if relying on suppliers for preproduction development service will provide suppliers with an exploitable first-mover advantage. (p. 212) Moreover Masten (1984) has followed a similar approach by analyzing the variables on vertical integration by using a sample from the U.S. aerospace industry of 1,887 aerospace components. The author has found a significant positive effect for specialization and complexity coefficients, in which the higher the complexity and specialization of the inputs, the higher the probability to vertically integrate. In addition, Joskow (1985) has conducted a study by examining the U.S. coal-burning electric generating plants in order to identify the role of contract duration on vertical integration decisions. The author points out that the variation in the contract duration is based on the level of relation-specific investments, in which longer commitments are engaged where relation-specific investments are more important. Moreover, in the studies of Fan & Goyal (2006), the authors give the basic idea of a vertical merger as, the two industries are vertically related if one of the firms uses the other s output for its own production or if the firm can supply its product or services as the other s input. This measure can be captured by Input-Output tables and is applicable to measure the vertical relations in large samples. Therefore, where merging firms are from the same Input-Output industries, the merger is categorized as vertical. Moreover Sudarsanam (2010) specifies that the empirical evidence on vertical mergers and their value effects is rare, compared to the ones that have analyzed horizontal and diversifying mergers. Colangelo (1995) has studied the effect of pre-emptive merging for vertical vs. horizontal integration and underlined that the overall gain from a vertical integration is generally greater than that from a horizontal integration. In our context vertical integration gives rise to three different gains: (a) it eliminates double marginalization; (b) it 11

enables price discrimination against non-integrated rivals; (c) it avoids the loss coming from being non-integrated after a horizontal merger. (p. 324) In addition, the findings of Leiblein & Miller (2003) regarding the semiconductor industry point out that, the vertical boundary choices are affected significantly depending on the firm-level competences and strategies. For instance, the companies with greater experience in a specific type of technology have the tendency to internalize the manufacturing activities than firms without such production knowhow. Similarly, firms with high levels of sourcing experience are more likely to outsource their production than firms that do not have such experience. (p. 854) To sum up, firms internalize transactions when it is expected that they will need to renegotiate supplier contracts due to high asset specificity. 2.2.Horizontal Integration Besanko et al. (2007) indicate that a firm s horizontal boundaries determine the quantities and varieties of products and services that it produces. It refers to a merger of two or more firms producing the same good under one consolidated firm (Chakravarty, 1998). Horizontal boundaries vary obviously across industries and across the firms within them. The optimal horizontal boundaries of the firms are appertaining crucially to economies of scale and scope. Economies of scale and scope exist whenever large-scale production, distribution, or retail operations have a cost advantage over smaller operations. Economies of scale and scope not only affect the sizes of the firms and the structure of markets, but they are also central to many issues in business strategy. (Besanko et al., 2007, p. 75) Economies of scale and scope are the essence for merger and diversification strategies. They have an effect on entry and exit, pricing, and the capability of the firm to protect its long-term sustainable advantage. Sudarsanam (2010) underlines that, a number of firms in wide-ranging sectors such as utility, electricity, banking, pharmaceuticals, insurance, oil and gas, automobiles, food and drinks, steel and healthcare have merged with one another, in the recent years. Such mergers are defined as horizontally related mergers. Where the firms selling the identical product merge, it is described as a pure horizontal merger. Where firms selling products that are not identical in terms of end use but nevertheless share certain commonalities, such as technology, markets, marketing channels, branding or knowledge base, merge, we refer to such mergers as related mergers. (p.123) For simplicity, Sudarsanam (2010) refers to the 12

term horizontal merger as to both pure horizontal mergers and related mergers 3 of firms selling a range of similar products. Horizontal mergers often qualify industries and markets whose products are generally in the mature or declining stages of the production life cycle. These markets have a low overall growth rate, and firms have accumulated production capacity that far exceeds the demand. This combination of low market growth and excess capacity engenders difficulties on firms to attain cost efficiencies through consolidating mergers. Such efficiencies may be achieved from scale, scope and learning economies. 2.2.1. Economies of Scale and Scope The origin of costs may have crucial inferences for industry structure and the behavior of the companies. Besanko et al. (2007) denote that the production process for specific good or service exhibits economies of scale over a range of output when average cost declines over that range. (p.75) Moreover, economies of scale exist if the firm attains unit-cost savings as it raises the production of a given good or service. In order to achieve these scale economies, the associated costs, risks and the extent of cost savings have to be taken into notice (Sudarsanam, 2010). Therefore, firms should be conscious about diseconomies of scale, which arise from complexities of monitoring, diffusion of control, ineffectiveness of communication, and numerous layers of management. In addition to these disadvantages, Besanko et al. (2007) also underline the limits to economies of scale, in which beyond a certain size, bigger is no longer better and may even lead to worse outcomes. The most important reasons for these limits are; labor cost and firm size, conflicting out, spreading specialized resources too thin, and incentive and bureaucracy effects. Moreover, economies of scale may be more crucial for the manufacturing organizations, since the high capital costs of plant need to be recovered over a high volume of output. (Johnson et al. 2008, p. 99) The manufacturing sectors that have been generally important have been motor vehicles, chemicals and metals. In terms of distribution and marketing other industries such as drinks, tobacco and food, the scale economies would be crucial (Johnson et al. 2008). Economies of scope exist, if an increase of production in the variety of goods and services saves the firm from the costs it bears. Whereas economies of scale are usually defined in terms of declining average cost functions, economies of scope are usually defined in terms of the relative total cost of producing a variety of goods and services together in one firm versus separately in two or more firms. (Besanko et al., 2007, p. 76) In other words, Panzar & Willig (1981) point out to the existence of economies of scope where it is less 3 This paper will handle related diversification under the term horizontal integration. 13

costly to merge two or more product lines in one firm compared to supplying them separately. Based on the definitions above, scope economies are available only for multi-product firms. Certainly, both economies may be recognized by the increase of the output of individual products as well as the total output of all the firm s products. The research on the extent of scope economies is scarce, in contrast to the literature on scale economies. One possible explanation is that until recently product costing did not allocate costs to the different products correctly, based on the related activities. Activity-based costing (ABC) mitigates this issue; however the problem of how to compare these product costs in the merged firm with the costs on the similar products produced separately by different companies still exists (Sudarsanam, 2010). 2.2.2. The Learning Economy Experience is an important determinant to fulfill the tasks faster and attain the output. The magnitude lies under the idea of the learning curve. Besanko et al. (2007) determine that economies of scale points out to the advantages that flow from increase in production to a larger output at a given point in time. The learning curve refers to advantages that flow from accumulating experience and know-how. (p. 94) Sudarsanam (2010) specifies that the economy of learning comes to light when workers and managers become more experienced and effective over time in using the available resources of the firm, and help decrease the cost of production. The time required to do a job will decrease each time the job is done, that the time per unit will decrease at a decreasing rate, and that the time reduction will be predictable. (Lindsey & Neeley, 2010, p. 73) It is a function of cumulative output over several periods, and increasing cumulative output raises the motivation to learn more efficient and effective ways of producing each unit of the output for the managers and workers. Employees learn not only from their personal experience but also from that of their colleagues. The limit to learning and its affect on cost reduction is designated by the minimum efficient learning scale (MELS). At this level, maximum learning has been procured (Besanko et al., 2007). Based on the studies conducted, the semiconductors and aircraft production are some of the industries that the learning economy may be more crucial. The learning rates averaged about 20 to 40 percent respectively. Learning curve efficiency entails that the firms have a large sales quantity and therefore a relatively large market share. Therefore, the cost of acquisition of the increased market share needs to be balanced against the subsequent cost savings from increased learning efficiency. (Sudarsanam, 2010, p. 138) Moreover, Besanko 14

et al. (2007) emphasize that learning occurs at different rates for different organizations and processes, according to the variation in slopes across firms and products. Although organizational learning is highlighted as the essence of the process, primarily it is individuals who learn. While individuals do the learning, the firms can take the steps to enhance learning and the maintenance of knowledge in the organization. Horizontal mergers lead to the consequence of a sudden increase in the quantity of output when the output of each merging firm is combined. While each firm has the opportunity to learn from the experience of the other firm, this learning may not engender the cumulative output of the merged entity to increase more. In the period subsequent the merger this output may increase, hence creating opportunity for further learning. However, if the output of the merged company is already large, it is expected to have passed the minimum efficient learning scale (MELS) of cumulative output (Sudarsanam, 2010). For instance, mergers involving complex technological processes such as drug discovery may yield potentially valuable learning opportunities, but they are also problematic because of the coordination and management problems. (p. 139) 2.2.3. Empirical Evidence on Horizontal Mergers Lipczynski et al. (2005) signify that the empirical evidence on the increased profitability through increased market power or cost savings of horizontal mergers is rather conflicting and inconclusive. For instance, Cosh et al. (1980) examine 211 mergers in the UK between the years 1967 and 1969, comparing profitability during a five-year period before the merger, with profitability during the five years subsequent the merger. The merged firms are observed to have experienced an increase in average profitability. On the contrary, Meeks (1977) detects a fall on average profitability during the seven-year period following the merger in a study of mergers in the UK between 1964 and 1972. In addition to these studies, Ravenscraft & Scherer (1987) examine the pre-merger profitability of 634 US target firms in the late 1960s and early 1970s. The target firms profitability (the ratio of operating income to assets) was observed to be 20 percent, which is much greater than the average profitability of all firms of 11 percent. Moreover, Weiss (1965) inspects the impacts of horizontal mergers on seller concentration for six manufacturing industries for the period 1926-1959. Changes in concentration ratios over approximate 10-year intervals are decomposed into effects arising from the internal growth of firms, the exit of incumbent firms, mergers, and turnover or changes in the identity of the largest firms in each industry. (Lipczynski et al., 2005, p. 263) 15

Therefore, internal growth and exit seem to have a more crucial role than mergers in affecting the changes in concentration. Finally, Colangelo (1995) underlines the gains from horizontal integration as: it leads to an increase in the market power due to the internalization of the cross-price effect on demand, and it prevents the loss coming from being non-integrated after a vertical integration. 2.3.Diversification The incentive and consequences of diversification on firms has been committed to a vast amount of studies by both economists and business researchers. However, these two groups approached the phenomenon from different perspectives. Economists have treated the extent of a firm s diversification as determined by structural variables in the industries in which the firm operated and the economics of the organization of activity within the firm operated and the economics of the organization of activity within the firm compared to via the market. (Lecraw, 1984, p. 179) On the other hand, business researchers have paid attention on the human and physical assets of the firm, by taking its internal strengths and weaknesses into consideration in determining its diversification strategy. This paper will have the focus of the economists perspective in identifying the companies diversification strategies, in which the structural variables of the industry and the activity of the firm within this industry will be highlighted. Lipczynski et al. (2005) define a diversified firm or a conglomerate as; to being involved in the production of a number of various goods and services, making it a multiproduct firm. According to the authors, the types of diversification can take the forms as product extension, market extension and pure diversification. Product extension would be achieved if a firm can diversify by producing a new product that is strongly related to its existing products. Market extension involves diversifying into a new geographic market with the same line of products, and a pure diversification strategy involves a transition into unrelated areas of business activity. Rumelt (1982) depicts the first and the last components of the strategies respectively as related 4 and unrelated business companies. Lipczynski et al. (2005) further indicate two ways in which a diversification strategy can be performed; either through internally generated expansion, or through mergers and acquisitions. Conglomerate merger involves the integration of firms that operate in different product markets, or in the same product market but in different geographic markets, whereas internally generated expansion is likely to require the simultaneous extension of the firm s 4 Recall that this paper takes related diversification strategy under the name of horizontal integration. 16

plant and equipment, workforce and skills base, supplies and raw materials, and the technical and managerial expertise of its staff. (p. 593) Diversifying through a conglomerate merger may be less demanding in this matter. 2.3.1. Product Diversification As indicated above, the strategies of related and unrelated integration are defined under product diversification. Although this paper will refer to the concepts as horizontal integration and unrelated integration strategies, it is worth mentioning this broad definition and its performance effects. Ravichandran et al. (2009) notes that, product diversification which illustrates the scope of the multiple and distinct product markets that the firm is operating in, has been lately under the focus of strategic management researchers. Geringer et al. (2000) indicate the relationship of performance and the product mode of diversity is well established by studies in two related directions type of diversification and degree of diversity. (p. 54) Rumelt (1974) found differences across his relatedness categories, in the seminal study of qualitative types of diversification. The author divided the integration strategies into 7 categories; which were single business, dominant vertical, dominant constrained, dominant linked-unrelated, related constrained, related linked and unrelated business. In order to specify the strategy that a company possesses, Rumelt (1974) constructed intervals of ratio specification. Based on these intervals of ratios (specialization ratio, related-core ratio, related ratio and vertical ratio) the companies strategies were specified. Following studies using his methodology have generally underlined that related diversification generated higher performance levels than unrelated diversification, although industry effects and other firm-level variables tend to eliminate much of the effect of the diversification type. Therefore, the general outcome of the studies is that related diversification is associated with a profitability advantage (Geringer et al., 2000). 2.3.2. Geographic Diversification Geographic diversification is identified as the firm s expansion into various geographic locations or markets across the borders of regions and countries (Hitt et al., 1997). Thus, a firm's level of international diversification is reflected by the number of different markets in which it operates and their importance to the firm (as measured, for instance, by the percentage of total sales represented by each market). (p. 767) This type of diversification strategy has its motivations as well as downsides. Denis et al. (2002) identify several motivations as; global diversification is a mechanism that combines the information- 17

based assets of buyers and sellers within the same firm. It generates value by creating flexibility within the firm, by giving the ability to respond to changes in relative prices. In addition, investors diversification choices can result as the benefit of geographic diversification. Ravichandran et al. (2009) adds the scope and scale economies, enhanced market power, and the ability to supply lower-cost factor inputs to the benefits of global diversification. Moreover, increased operational flexibility by global diversification reduces the risks across the markets. (Kim & Mathur, 2008, p. 749) However as from the downside perspective, a globally diversified entity is more complex compared to a purely domestic firm. The costs of information asymmetry between corporate headquarters and the difficulty of monitoring managerial decision-making may give rise (Denis et al., 2002). Based on the empirical studies conducted, Ravichandran (2009) and his colleagues specify that, multinational corporations (MNCs) experienced a positive valuation effect relative to purely domestic firms because of their role as financial intermediaries. (p. 210) Moreover, Lepetit et al. (2004) illustrate that the announcements of the mergers and acquisitions beyond regions and countries have a positive effect on the market. On the other hand, the effect on firm performance may be negative due to high transaction costs and managerial-information processing demands. Moreover, Delios & Beamish (1999) have found a positive relationship between the geographic scope and firm s performance by collecting a data of 399 Japanese manufacturing firms. Their findings illustrate that expanding into new geographic markets is an effective strategy for developing the performance of Japanese companies. However, in the study of Kim & Mathur (2008) where a sample of 28,050 firm year observations from 1990 to 1998 was used, a firm value decrease was associated for both industrial and geographic diversification. We find that geographically diversified firms have higher R&D expenditures, advertising expenses, operating income, ROE and ROA than those of industrially diversified firms. (p. 764) 2.3.3. The Determinants and Motives for Diversification In exploring the determinants of diversification, Rondi et al. (1996) focuses on three theories of diversification. The first, attributed to Marris (1964) and Penrose (1995), propose that the managers seek to maximize the growth of the firm. The operation of specific assets such as marketing skills and technical enterprise in other industries offers a convenient vehicle in order to achieve the growth objective. The second theory attributed to Bain (1959), puts emphasis on the conditions that yield entry possible or attractive. These incorporate industry-level characteristics such as growth and concentration, average profitability, as well 18

as barriers to entry. The third theory, attributed to Rumelt (1974) and Williamson (1975), focuses on relatedness between industries that makes diversification attractive, in which relatedness refers to the similarities between markets, technologies, and organizational structures (Lipczynski et al., 2005). The scope of this paper generally refers to the third theory where relatedness is the underlying concept. As mentioned above under the heading of horizontal boundaries, related diversification represents the horizontally integrated mergers. Therefore, this part will consider the value creation for the acquisitions of unrelated businesses. Sudarsanam (2010) underlines the motives of value creation as having an increased market power or operating an efficient internal capital market. Market power is the ability of a firm in a market to pursue anticompetitive behavior against its current rivals or potential entrants. (p. 184) This power is not obtained from the monopoly position in that market, but from the range of the firm s activities and its size. Based on this market power, the conglomerates assign investment funds to a wide range of individual entities. If these entities were stand-alone, independent firms, their funds would be supplied directly from the capital markets. Thus, the conglomerate firm serves the role of capital markets. The firm will create value, in case it possesses an effective performance compared to the external capital market. Moreover, Lipczynski et al. (2005) add more motives such as; saving costs, reduction of transaction costs and the managerial motives for diversification. 2.3.4. The Resource-Based View A vast amount of the management literature on diversification follows the resourcebased view of the firm. The resource view argues that rent-seeking firms diversify in response to excess capacity in productive factors, here called resources. (Montgomery, 1994, p. 167) Under this perspective, firms acquire companies to keep the balance among the required competitive profile and competences, and their current endowments of resources. However, the amount of resources available are limited, therefore firms are not limitless in their ability to pursue new investment opportunities (Wiersema & Bowen, 2008). Apart from this limitation, conglomerate acquisition may be undertaken by the same motives for acquiring competitive profile and competences. Other reasons may be the need for growth, and to utilize the excess capacity the firm possesses. These idle resources should be reused in more productive and profitable areas. Therefore the question to be answered is, how best the firm can exploit these resources outside of its current operations. In the book of Silverman (2002), three sets of factors are pointed out as the firm s diversification behavior. Initially is 19

the specific range of applications to which the firm s current resources may be useful. These depict the possible set of businesses in which the firm s resource base will provide competitive advantage. The second is the scope of transaction costs in the relevant markets for the firms existing resources. These determine the firm s ability to exploit its resources through contractual arrangements, which can prevent the need for expansion of the firm s boundaries. The third set of factors deal with the sustainability of the competitive advantage furnished by the firm s resources. For the reason of prioritization, a firm will decide on to focus first on the exploitation of those resources that offer the most sustainable competitive position, since it cannot use all of its resources at once. Finally, in order to generate sustainable competitive advantage, it has been argued that firms resources and capabilities should be rare, valuable, difficult to imitate, non-substitutable and non-transferable in that they cannot be easily purchased in resource markets. (Matraves & Rondi, 2007, p. 38) 2.3.5. Diversification and Firm Performance Firm diversification has been extensively researched both empirically and theoretically in the fields of strategy and finance for more than 30 years. The literature on diversification generally focuses on the economic rationale behind the diversificationperformance relationship, and the main common objective of this work has been to verify the effect of diversification on the creation or destruction of firm value. Thus, the researches center of attention has been on the performance of the diversified firms compared to specialized firms (Santalo & Becerra, 2008). Many researchers have studied the effects of operating performance on diversified firms compared to undiversified, which is measured by accounting profits or productivity. They have found the relationship between performance and corporate diversity to be ambiguous. Profits were more likely to be determined by industry profitability, coupled with how the firm related new businesses to old ones, rather than diversification per se. (Besanko et al., 2007, p. 180) Ravichandran et al. (2009) specify that firms may choose to diversify into related or unrelated markets, based on the similarity or relatedness of the new business. Related diversification is believed to lead to better performance than unrelated diversification because the former leverages significant business synergies while the latter suffers from agency costs and inefficient resource allocation. (p. 206) This belief has been widely studied by many scholars. Prahalad & Bettis (1986) explain this logic more in depth, by indicating the four major and nine minor categories that Rumelt (1982) has used to identify the diversification 20

strategies of the firms. The major categories are to be single business, dominant business, related business and unrelated business. Rumelt (1982) has used statistical models to relate diversification strategy to performance and pointed out that capital productivity is greater in moderately diversified firms. However the firms in between moderate and high levels of unrelated diversification acquired moderate or poor productivity. In other words, on the average related diversification strategies outperformed the other diversification strategies. On the other side, the unrelated business strategy was observed to be the lowest performing (Prahalad & Bettis, 1986, p. 486). Moreover, Noel Capon (1988) and his colleagues found that firms that restricted their diversification to narrow markets performed better than did broader firms, presumably because of their learning particular market demands. (Besanko et al., 2007, p. 180) Although the theoretical and empirical findings on the area of diversification are quite rich, the results have not been consistent. Despite the inconclusive results, diversification has been an effective firm strategy for growth (Ravichandran et al., 2009). Lately, Nathanson & Cassano (1982) conducted a statistical study of diversity and performance with a sample of 206 firms through years 1973 and 1978. They utilized two factors which are market and product diversity to distinguish the diversification strategy that improves Rumelt s categories. The findings illustrated that, an increase in product diversity decreased the average returns, whereas the returns remained rather stable for an increase in market diversity. Also, they discovered that size plays a crucial role on the relationships. For both the market and product diversity, smaller firms did well relative to larger firms in categories marked by no diversification and in categories of extremely high diversification, and larger firms did significantly better than smaller firms in the in-between categories those characterized by intermediate levels of diversification. (Prahalad & Bettis, 1986, p. 486) In both these studies of Rumelt (1982) and Nathanson & Cassano (1982), the key point is to decide on the generic strategy of diversification (the level of relatedness) in order to achieve performance (Prahalad & Bettis, 1986). According to this phenomenon Kiker & Banning (2008) support that, diversification is an issue of creating fit with the most significant contingencies and an effective fit will improve the overall performance of the firm. According to this view, diversification does not necessarily lead to increased overall firm performance; rather it relies upon how effective the diversification fits the particular contingencies of the firm. Research from this perspective has generally found that this occurs to the extent that firms diversify only in a direction which is related to their core competencies. (Kiker & Banning, 2008, p. 20) 21

2.3.6. Empirical Evidence on Diversification and Firm Performance A vast number of researches have been conducted in order to examine the relationship between diversification and firm performance, by utilizing industry structure variables like concentration, scale, industry growth, etc. In these studies, accounting indices, such as return on equity or return on invested capital have been generally used to measure performance. The common measure for diversification has been the Herfindahl index; for instance, one minus the sum of the squared percentages of a firm's total revenues in each of its markets. These studies nearly always find a neutral or negative, not a positive, relationship between diversification and firm performance. (Montgomery, 1994, p. 169) Montgomery & Wernerfelt (1988) and Lang & Stulz (1994) presented a similar analysis using Tobin's q, from the perspective of the stock price performance (the capital market value of the firm divided by the replacement value of its assets) to measure performance. Their findings illustrated a reduction on the firms profitability as the level of diversification increased (Montgomery, 1994). In other words, highly diversified firms are consistently valued less than specialized firms. (Lang & Stulz, 1994, p. 1278) Schoar (2002) has examined the effect of productivity on firm performance and found that diversification has caused to a destructive new toy effect. While the newly acquired plants increase their productivity by three percent, incumbent plants show productivity declines of almost two percent. (p. 2380) This study is also supported by Lichtenberg (1992) who underlines the fall of the productivity of plants as the level of diversification increases (Schoar, 2002). Ravichandran et al. (2009) focused on the effects of IT technology spending to product and geographic diversification and firm performance. They have defined the firm profitability by using the accounting-based measure of return on assets (ROA) and the measure of Tobin s q for firm valuation. The authors concluding remarks were; IT needs to be viewed as a valuable asset by the managers in highly diversified firms, based on the performance critical role when implemented with diversification. However, they must be attentive that the impacts on performance are dependent on types of diversification. In firms with unrelated product diversification or with high geographic diversification, IT may not contribute to performance as much as in related diversifiers and in low geographic diversification contexts. (p. 233) These findings are also supporting the work of Miller (2006), which specifies the greater value creation from technological diversity of the multibusiness firms compared to single-segment firms, and the greater performance of diversified as technological diversity increases. 22

Another perspective in assessing the performance-diversification linkage is highlighted in the study of Santalo & Becerra (2008), in which this linkage is examined by differentiating the industries. Their evidence illustrates that the effect of diversification on performance is not homogenous across industries. Diversified firms observe a diversification discount if and only if they compete in industries with a large number of single-segment companies or, equivalently, when specialists hold a considerable market share. (p. 851) On the other hand, industries that bear only a few non-diversified firms competing, leads the diversified firms enjoy a premium in those industries in which specialists acquire a small market share. In addition, Bettis (1981) has conducted a study using a sample of 80 firms, in order to investigate the performance differences between related and unrelated diversified firms. By regressing the return on assets to advertising, R&D, plant investment, size, risk and diversification strategy, the author concluded that, on average related diversified firms perform better than unrelated diversified firms by about one to three percentage points of return on assets. Moreover, Denis (2002) and his colleagues examined the effects of global and industrial diversification on the firm value, by using a sample of 44,288 firms through years 1984 and 1997. The findings highlight an increase in global diversification over time, whereas a reduction for industrial diversification. However both global and industrial diversification is associated with valuation discounts, which are statistically significant compared to purelydomestic firms. Moreover, the authors have found no evidence of tendency to replace the global diversification strategy for industrial diversification by the individual firms. Finally, Capar (2009) examined a sample of 196 firms through years 1995-2000, based on the effects of international and product diversification on innovation assets and firm performance. The results are found to be significant for the effects of international diversification on innovation assets and a negative effect for an increase of product diversification. Therefore, the present study provided strong evidence that innovation assets lead to higher performance. (p. 6) 23

3. DEVELOPMENT OF HYPOTHESES Based on the prior research on the effects of the integration strategies on firm s performance, this section outlines the hypotheses that are subject to be tested. This paper will focus on the two industries (manufacture of food products and manufacture of machinery and equipment) out of the 5 industries in computing the regression models, due to the adequateness of the amount of data. In order to examine if the results obtained will be related to the previous studies, two separate hypotheses have been developed for manufacture of food products and manufacture of machinery and equipment industries respectively. Bettis & Hall (1982) underline the importance of the differences among industries. Since this paper is analyzing the effects of the integration strategies by differentiating the industries that the firms compete in, it is crucial to note that the different industries have different structural characteristics (in the industrial organization economics sense), and these different structural characteristics result in different average (and potential) profits in each industry. (Bettis & Hall, 1982, p. 255) In relation to this phenomenon, it is expected that the effects of the integration strategies on firm s profitability may vary among the industries. Moreover, Besanko et al. (2007) ask the question of whether they will encounter considerable differences in profitability of business units within industries and a modest variation in profitability across the industries. If so, the effect of the market environment on profitability is unimportant, but the effect of a firm s competitive position in the industry is important. (p. 349) The question can be asked vice versa, and the authors conclude that both the market and the firm s competitive position in the industry can explain profitability. McGahan & Porter (1997) indicate that the industry is responsible for about 19 percent of the variation in profit across industries, whereas the percentage is 32 for the business-specific effects. In relation to these differences, the effects of the integration strategies can be tested based on the theories presented above. Prior research indicates that, in order to prevent the hold-up problem, firms tend to vertically integrate when their investment incentives are more crucial compared to the counter party s incentives (Grossman & Hart, 1986). Since the firms tend to internalize their transactions in order to avoid the renegotiations of contracts and investing huge amounts on the relation-specific assets, the following hypothesis is formed: H 1 : Vertical integration strategies have a positive effect on firm s performance. The findings of the early studies of Rumelt (1982) designate that firms were different not only in terms of their product diversity but also in the patterns of relationships they created among various lines of businesses. Moreover, the different types of strategies of 24

diversification were associated with differing corporate profitability based on the strategy chosen. The highest levels of profitability were exhibited by those having a strategy of diversifying primarily into those areas that drew on some common core skill or resource. (p. 359) On the other hand, the lowest levels were those of vertically integrated businesses and firms following strategies of diversification into unrelated businesses (Rumelt, 1982). Besides the findings of Rumelt, the general empirical evidence has a strong support in highlighting the related diversified firms are outperforming the unrelated diversified companies (Montgomery, 1994; Lang & Stulz, 1994; Bettis, 1981). For instance, Chang & Wang (2007) have examined a sample of 2,402 U.S. firms through years 1996 to 2002, and found strong support that related product diversification leads to positive performance effects. Conversely, unrelated product diversification not only has a weaker influence than related product diversification, it actually damages the performance of multinational firms (p. 77) Since this paper takes the related diversification strategy under the definition of horizontal integration the second hypothesis will be: H 2 : Horizontal integration strategies outperform unrelated diversification strategies. Based on the value-reducing and enhancing effects of global diversification, the prior studies indicate conflicting evidence of geographic diversification on the firm s value. Researchers found that wide-ranging multinational operations were associated with higher performance (Delios & Beamish, 1999; Hitt et al., 1997) and lower levels of risk. However, given that international operations encumber a firm because of the increased difficulty and costs found in operating in foreign markets, it remained a question whether the higher performance of multinational firms was attributable to a firm s possession of superior resources (i.e. proprietary assets 5 ), or to other benefits of international operations. (Delios & Beamish, 1999, p. 723) The third hypothesis will test the positive aspect of geographic diversification, taking into account that the geographically diversified firms have higher values of performance measures such as operating income and ROA, compared to industrially diversified firms ( Kim & Mathur, 2007). H 3 : Geographic diversification has positive effects on firm s performance. 5 Proprietary asset, usually, is any asset that is considered in the realm of intellectual property that should not be disclosed. These assets may include trade secrets and undisclosed inventions (VentureLine). 25

4. METHODOLOGY The study involves the analysis of 147 Danish firms between the years 2005-2009, based on the 5-year average values. The recent empirical studies mostly focus on market share prices and event studies in analyzing the diversification-performance linkage, whereas this paper will focus on the operating performance perspective. Performance is measured as operating revenue per employee (ORPE) and net income per employee (NIPE), which are used as the dependent variables. The independent variables would be horizontal integration (HI), vertical integration (VI), unrelated diversification (UR) and un-diversification (UD) strategies which were explained with binary (dummy) variables that take on the values 1 and 0 depending on the type of strategy. Moreover, the analysis will examine the effects of global diversification (COUNTRY) by focusing on the number of subsidiaries. Control variables involve the firm specific characteristics such as: risk (RISK), size (SIZE), capital intensity (CINT), market share (MARS), cost per employee (CPE) and the ratio of the cost of employee to operating revenue (RATIO). Apart from these measures, this study has conducted the Herfindahl index, entropy measure, concentration ratio and the relative measure for the four largest firms in order to illustrate how concentrated and diversified the industries are. These measures will not be included in the regression analysis, since the concentration indices are calculated for all the years (2009-2005) rather than computing averages. The analysis will begin by distinguishing each of the 5 industries and presenting their descriptive statistics. This separation is crucial, since a computation of the summary statistics of the whole sample would be misleading based on the differences among the industries. In addition to these summary statistics, the study will present two regression models with the inclusion and exclusion of the interaction effects (Bettis, 1981). The data for the regression analysis will be conducted for only two industries separately, due to having sufficient number of companies. These industries would be the manufacture of food and the manufacture of machinery and equipment industries, with 54 and 48 companies respectively. It will be designed to explore the performance differences between vertically integrated, horizontal integrated, unrelated diversified and un-diversified firms. The models will be estimated with the simple OLS regression, by conducting for ORPE and NIPE performance measures separately. Below the models are briefly identified: Model without the interaction effects: ORPE = β 0 + β 1 (SIZE) + β 2 (RISK) + β 3 (CINT) + β 4 (MARS) + β 5 (CPE) + β 6 ( RATIO) + β 7 (COUNTRY) + β 8 (VI) + β 9 (HI) + β 10 (UR) + e 26

NIPE = β 0 + β 1 (SIZE) + β 2 (RISK) + β 3 (CINT) + β 4 (MARS) + β 5 (CPE) + β 6 ( RATIO) + β 7 (COUNTRY) + β 8 (VI) + β 9 (HI) + β 10 (UR) + e In this model VI, HI and UR are dummy variables, in which the un-diversification variable is excluded from the model. "This was done since if all the binary variables are included, the normal regression equations are not independent and thus not have a unique solution." (Bettis, 1981, p. 384) Therefore, β 0 embraces the effects of the un-diversification strategy (Bettis, 1981). The second model includes the interactive terms in order to explore more the reasons for differences in performance effects between different diversification strategies. In this type of model the forward stepwise regression procedure was used (Bettis, 1981), in order to include every potentially useful predictor in the model and then delete those terms not making significant partial contributions at some pre-assigned significance level. (Agresti & Finlay, 1997, p. 528) The forward selection begins with none of the variables and adds one variable at a time to the model until it reaches a point where an inclusion of the remaining variable does not make a significant contribution in predicting Y. In order to further modify the forward selection, stepwise regression leaves the variables out from the model, in case they lose their significance as other variables are added. Therefore, a variable previously entered into the model at some point may be eliminated due to the overlap with other variables that have entered at later stages (Agresti & Finlay, 1997). The interactive regression model to be tested under this forward stepwise procedure is constructed as follows 6 : Model with the interaction effects: ORPE = β 0 + β 1 (SIZE) + β 2 (RISK) + β 3 (CINT) + β 4 (MARS) + β 5 (CPE) + β 6 (RATIO) + β 7 (COUNTRY) + β 8 (VI) + β 9 (HI) + β 10 (UR) + β 11 (SIZE) (VI) + β 12 (RISK) (VI) + β 13 (CINT) (VI) + β 14 (MARS) (VI) + β 15 (CPE) (VI) + β 16 (RATIO) (VI) + β 17 (COUNTRY) (VI) + β 18 (SIZE) (HI) + β 19 (RISK) (HI) + β 20 (CINT) (HI) + β 21 (MARS) (HI) + β 22 (CPE) (HI) + β 23 (RATIO) (HI) + β 24 (COUNTRY) (HI) + β 25 (SIZE) (UR) + β 26 (RISK) (UR) + β 27 (CINT) (UR) + β 28 (MARS) (UR) + β 29 (CPE) (UR) + β 30 (RATIO) (UR) + β 31 (COUNTRY) (UR) + e The same model will be conducted for net income per employee performance measure (NIPE). Here, the inclusion of the interaction terms between diversification strategy and the other variables were of major interest. These terms would strongly suggest reasons for performance differences. For instance, the inclusion of β 21 would suggest that one reason for the high performance of horizontally integrated firms was the market share (Bettis, 1981) 6 Forward stepwise regression procedure is conducted using the Stata11 Statistics software program. The forward stepwise regression model is used by Bettis (1982) as well, in which the study involved a sample of 58 companies to identify the performance differences among related and unrelated firms. 27

5. DATA CONSTRUCTION 5.1. Sample Selection This paper investigates the performance effects chosen among vertical, horizontal, unrelated and undiversified strategies, using a sample that embraces the operating performance measures of the Danish companies that are distinguished among 5 large industries, through years 2009-2005. The decision to use a 5-year time period was based on the motivation of having a long time period for the study, as well as preventing excessive missing data by keeping the time frame limited (Capar, 2009). 7 The company selection process has been conducted in the Orbis Database, which covers 80 million companies around the world, including 395,183 companies operating in Denmark (Appendix 1). Moreover, some companies have been double checked in the WebDirect Database. The selection criterion was restricted to Danish companies at the industry-level data that are operating in the manufacture of food products (NACE Rev. 2, core code 10), manufacture of chemicals and chemical products (NACE Rev. 2, core code 20), manufacture of basic pharmaceutical products and pharmaceutical preparations (NACE Rev. 2, core code 21), manufacture of machinery and equipment (NACE Rev. 2, core code 28) and manufacture of furniture (NACE Rev. 2, core code 31) industries 8. The service firms are excluded in order to diminish the confusing effects of the differences between manufacturing and service firms (Ravichandran et al. 2009). Moreover, there are significant differences between manufacturing and service firms in their disaggregation of financial data by business activities. (Ravichandran et al. 2009, p. 218) Therefore only manufacturing firms are to be chosen from the 2-digit NACE industry classification, and meeting the following criteria: (1) years with available accounts: 2009-2005. (2) Number of employees having a minimum value of 10, for all the years. (3) Operating revenue (turnover) with a known value for all the years. According to the sampling criteria defined above, an initial sample of 158 companies was obtained. Out of these firms, 11 of them are eliminated due to being holding companies. These holding companies generally had more than one primary NACE code, which could not be distinguished among the industries the company is operating in. Based on the industries; 1 firm from the manufacture of food products, 3 firms from the manufacture of chemical, 3 firms from the manufacture of pharmaceuticals, 1 firm from the manufacture of furniture, and 7 The diversification strategies (VI, HI, Unrelated and Undiversified) are stable and the same over the defined time. 8 Industries are chosen based on the research of the most crucial industries in Denmark. 28

finally 3 firms from the manufacture of machinery industries are eliminated. The final sample, which is a balanced panel, is composed of 147 companies with 1911 firm average year observations (Appendix 2). Table 1: Final number of companies by industry Industries Number of Companies Pharmaceutical Industry 11 Food Industry 54 Chemical Industry 19 Furniture Industry 15 Machinery Industry 48 Total 147 Although the analysis will be based upon 5-year average values of the variables, the sample is under the category of a panel data, since it contains time series observations of a number of individuals (Hsiao, 2005). This type is combining both the time-series and crosssectional data analysis and looks at multiple subjects and how they change over the course of time. (Wikipedia) Several advantages that the panel data has over cross-sectional and timeseries data could be classified as: (1) More correct assumptions of model parameters. (2) Greater capacity for confining the complexity of human behavior compared to a single crosssection or time-series data. (3) Simplifying computation and statistical analysis (Hsiao, 2005). Besides using the Orbis Database in collecting the data, the Input-Output tables are gathered in order to analyze the presence of vertical integration. With the IO data, we can capture the vertical relationship between a pair of merging firms from the dollar amount of input transfer between the industries in which the merging firms operate. (Fan & Goyal, 2006, p. 878) If a company uses the other s products or services as input or vice versa, the two industries are categorized to be vertically related. The IO tables are obtained from statbank.dk, where detailed statistical information on the Danish society exists (Appendix 3). The majority of the studies that have been conducted in the field of diversification are classifying the firm s integration strategies with the use of the SIC codes (Santalo & Becerra, 2008; Miller, 2006; Ravichandran et al. 2009). Primarily in this study, the firms integration strategies are classified according to the first 2-digit NACE Rev. 2 codes. Different from the world level SIC codes, NACE codes are on the EU level. In addition, NACE is derived from ISIC, in the sense that it is more detailed than ISIC. (NACE Rev. 2 Guide, p. 14) They have exactly the same items at the highest levels, where NACE is more detailed at lower levels. Based on the primary and secondary 2-digit NACE codes, the procedure used to categorize the companies according to their choice of integration strategy is as follows: 29

Firms with one NACE code (primary only): Undiversified firm 9 Firms with the same primary and secondary 2-digit NACE codes: Horizontally integrated Firms with different primary and secondary 2-digit NACE codes: o Checked the IO table to trace vertical integration, if the supplying industry exceeds the 1% average threshold 10, the company is vertically integrated. o If the supplying industry falls below the 1% threshold, the company is unrelated diversified. Firms with more than one secondary 2-digit NACE codes are classified based on the importance level of the NACE codes. The first code to be reported has been regarded as the most crucial. (Appendix 4) Table 2 illustrates the number of companies that fall under each type of integration strategy out of the 147 companies. Table 2: Number of companies based on integration strategies Strategies Number of Companies Vertical Integration 27 Horizontal Integration 35 Unrelated Diversification 31 Undiversified 54 Total 147 5.2.Variables Measurement 5.2.1. Performance Measures (Dependent Variables) Prior studies have put a large emphasis on return on assets (ROA) and return on sales (ROS) in taking these variables as performance measures (Kahloul & Hallara, 2010; Capar, 2009; Ravichandran, 2009; Bettis, 1981). This study will analyze the management effectiveness of the Danish companies with operating revenue per employee and net income per employee measures (Appendix 5). 11 The operating revenue per employee simply measures the amount of the currency sales, or revenue, generated per employee and high levels of this 9 Double checked the company products and activities from their websites; based on the activities 6 of the companies have been changed from undiversified to either HI or unrelated. 10 1% index has been computed looking at the first 10-15 supplying industries average percentages through years 2005-2007. 11 In Denmark, the companies assets may be misrepresentative. This conclusion is reached by examining the ROA ratios of Novo Nordisk A/S (See Appendix 5). 30

indicator is preferable. However compared to a high-tech industry, the labor-intensive industries may be less productive and generate low levels of the indicator (Investopedia). Operating revenue per employee (ORPE) = Operating Revenue (Turnover) / Number of employees The other performance measure, net income per employee is taken as an indicator of management efficiency. Net income per employee measures the ratio of operating income to the number of employees that is required to produce that level of income. Net income per employee (NIPE) = Net Income /Number of employees Therefore, net income per employee determines the management's ability to utilize their employee resources effectively in order to generate profits for the company. Comparisons of income per employee should only be made between companies in similar industries. When comparing two companies, the company with a higher value for income per employee is to be more efficient (Money-Zine) 5.2.2. Independent Variables The integration strategies of vertical, horizontal, unrelated and undiversified strategies are defined as mentioned above. These variables will be binary (dummy) variables that take on the values 1 and 0 depending on the integration strategy of the firm (Bettis, 1981). A value of 1 will represent the existence of the strategy, whereas a value of 0 indicates that the firm has not undertaken that particular integration strategy. For simplicity, it is crucial to note that the choices of strategies are assumed to be mutually exclusive, in which a company cannot undertake more than one integration strategy. 12 In the real world, it is most likely to have a company with more than one integration strategy; however this assumption will help to assign the effects of a specific integration choice on performance measures more explicitly. Apart from the strategies defined above, geographic diversification has been taken into consideration in the past research. Several measures for this type of diversification that have been used would be, (1) the measure of international sales as a percentage of total sales, (2) the number of overseas subsidiaries, (3) the Herfindahl index, (4) the entropy measure, (5) and the number of countries in which a firm has overseas subsidiaries (Ravichandran et al. 2009). Ravichandran et al. (2009) underlines that, each measure has its own advantages and 12 One particular strategy among the four choices has to be identified for each firm. 31

limitations. This study will use the number of countries in which a firm has foreign subsidiaries, in order to reflect the dispersal of the company s functions across countries. Moreover, this paper will present some measures of the concentration indices, in order to give an understanding on how concentrated and diversified the industries are. The measure of the diversification of the firm has been valued from two continuous measures which are the Herfindahl index and the entropy measure (Kahloul & Hallara, 2010). The Herfindahl index is a measure of market concentration, where the ratio of the firm s sales within the firm s primary industry to the firm s total sales (Jacquemin & Berry, 1979, p. 359) is computed. In the formula, n is the number of firm s activities and P i is the relative weight of each activity evaluated as the proportion of the sale x i of the activity i of a firm. (Kahloul & Hallara, 2010, p. 152) A rise in the Herfindahl index usually depicts a decrease in competition and an increase of market power, whereas reductions indicate the opposite. The higher the value of the index, the less likely a given industry will reveal competitive behavior (Lipczynski & Wilson, 2001). Moreover, the Horizontal Merger Guidelines of U.S. Federal Trade Commission has presented ranges in specifying three types of concentration: Un-concentrated Markets: HHI below 0.15 Moderately Concentrated Markets: HHI between 0.15 and 0.25 Highly Concentrated Markets: HHI above 0.25 (part 5.3) On the other side, the entropy measure is the inverse of the Herfindahl index that weighs each market share (P i ) by the logarithm of P i. It is a measure that enumerates the degree of uncertainty in a given industry, and the lower value of the index would expose greater certainty of the established firms future relationships with the buyers in the market. The entropy measure is also more sensitive than the Herfindahl index to very small firms. (Jacquemin & Berry, 1979, p. 360) Since E is an inverse concentration measure, the value is small for highly concentrated industries, whereas large for a low concentrated industry (Lipczynski et al. 2005). Moreover, by dividing the entropy measure by the number of companies, relative measure (RE) could be obtained, which provides convenience in making comparisons among industries. The minimum 32

possible value is RE=0 for a monopoly, and the maximum possible value is RE=1 for an industry comprising N equal-sized firms. (Lipczynski et al. 2005, p. 203) In addition to these indices, concentration ratio (CR4) for the four largest companies can be calculated, in order to illustrate the scope of market control of the largest firms in the industry and to present the degree to which an industry is oligopolistic (Wikipedia). However, the concentration ratio measure suffers from the problem that it only focuses on the top firms in the industry and takes no account of the distribution of remaining firms. (Lipczynski & Wilson, 2001, p. 109) CR m = Σ m i=1 s i Where, m is the number of firms taken into account (which is 4 in this study) and s i is the market share of the firm i. Based on these measurements, the market shares in this study are computed by summing the operating revenues of all the firms in one particular industry for that year, and dividing each company s individual operating revenues to this total industry turnover. This industry turnover is taken to be a representative value for the whole industry, since the Orbis Database could not identify applicable operating revenue values for all the companies. In addition; the measures that are obtained from Orbis were not adequate enough to distinguish the firm s activities within its operating sales. Moreover, the analysis for the concentration indices is conducted for 4 years, due to the missing values of 2005 for the pharmaceutical company Novo Nordisk A/S. 13 5.2.3. Control Variables The study involves several control variables in order to determine the effect of integration strategies on the firm performance by eliminating the other affects on firm variables. Based on the theories developed to enlighten the integration strategies, empirical studies have commonly used the factors of size, risk, and capital intensity as control variables (Bettis, 1981; Ravichandran et al. 2009) In addition to these variables average market share, average cost per employee and the ratio of average cost per employee to average operating revenue per employee will be included in the analysis 14. 13 Novo Nordisk A/S did not have an applicable operating revenue value for the year 2005. In order to have an accurate representation of the indices the analysis of concentration indices is limited through years 2009-2006. The analysis of average market shares is to be taken for 5 years, besides Novo Nordisk A/S. 14 Other common control variables such as R&D expenditure and Added Value were not applicable in Orbis Database. 33

SIZE Kumar et al. (1999) has conducted a study of cross-country analysis in which they have found that institutional factors such as the efficiency of the judicial system and the development of financial markets as well as technological factors such as capital intensity and market size seem to influence the size of firms. (p. 30) The managerial literature has covered a number of variables to measure firm size including number of employees, average assets, and average sales (Leiblein & Miller, 2003). The firm size in this study is measured as the average natural logarithm of total employees over the past 5-year period, 2009-2005. RISK Bettis (1981) points out to the limitation of the empirical work conducted on the relationship between profits and risk. However, the importance of risk as an economic variable has been accepted for many years. The term risk is commonly used to define some degree of hazard, which could be financial as bankruptcy or solvency. Such risk can result from a variety of sources such as short-term fluctuations in profits, changes in consumer tastes, changes in technology, changes in government policy and strategic moves of competitors. (Bettis, 1981, p. 383) For instance, in the studies of Fisher & Hall (1969) that included 11 different industries, observed a positive relationship among risk and profit within the industries. Moreover Bettis & Hall (1982) observed in a study of 80 large diversified firms through years 1973-1977 that unrelated diversified firms illustrated a positive relationship between return on assets and the standard deviation of the return on assets, whereas no relationship or negative one was detected for related diversified companies. Although most studies of risk has been conducted at the degree of the securities markets, this paper will include the variable of risk as the measure of standard deviation of return on assets over the average period of 2009 to 2005. CINT A company would be capital intensive if a business process demands large amounts of money and other financial resources to produce a good or service. Capital intensity will be based on the ratio of the capital required to the number of labor that is required. Oil production and refining, telecommunications and transports such as railways and airlines industries could be given as examples of having high capital intensity. Companies in capital-intensive industries are thus often marked by high levels of depreciation and fixed assets on the balance sheet. (Investopedia) Therefore, it could be underlined that the capital intensity of industries varies widely and some industries could be more capital intensive based on the nature of the technology (Bettis, 1981). Moreover Porter (1976) has indicated that capital intensity may act as a barrier to exit if taken as an industry specific asset. In general, capital intensity imposes a greater degree of risk because assets are frozen in long-lived forms that may not be easy to sell. (Bettis, 1981, p. 382) In this study, capital intensity is measured 34

by taking the ratio of average fixed assets to the average number of employees over the years 2009-2005. MARS The companies market shares are computed by dividing the company s sales in a particular period by the total sales of the industry at that same period. This variable will be giving a general idea of the size of a company regarding its markets and competitors. The rise and fall of the market share would be an indicator of the relative competitiveness of the company's products or services. Therefore, a company that is increasing its market share will observe a growth in its revenues, which would be faster compared to its competitors. Economies of scale and improvement in profitability could be achieved based on the increases of market share (Investopedia). Based on this phenomenon, the average value of the 5-year market shares for each firm is computed, and taken as a control variable in analyzing the effects on performance. CPE Average cost per employee is used in this study, in order to take the effect of how much each employee would cost based on the total costs of the firm. The total costs would be the sum of fixed costs, variable costs and semi-variable costs (InvestorWords) The Orbis database had this measure calculated. RATIO The average of cost of employee to operating revenue ratio will represent if the costs of the employees are exceeding the company s operating revenue. In other words, it is to observe how many times the costs are exceeding the revenues. This control variable will help to examine the effect of this ratio on the firm s performance measurement. Based on these definitions, Table 3 summarizes the calculations of the variables presented above. Table 3: Variable Definitions Variable Definitions Formulas Average Operating Revenue per Employee Avg. Operating Revenue / Avg. Number of Employees Average Net Income per Employee Avg. Net Income / Avg. Number of Employees Size SIZE=1/ln(Number of Employees) Risk RISK= Standard deviation of ROA for 2009-2005 Capital Intensity CINT=Avg. Fixed Assets / Avg. Number of Employees Average Market Shares MARS=Total Market Share (for 5 years) /5 Average Cost per Employee CPE=Total Cost per Employee (for 5 years) /5 Ratio of Cost & Revenue RATIO=(Average) Cost of Employee / Operating Revenue 35

Although the study s aim is to differentiate the values among the industries, it is worth to summarize the whole data as illustrated in Table 4. The sample of the 147 firms has average operating revenue per employee (ORPE) of 2,622 and a net income per employee (NIPE) of 116. Due to the differences in industries; the performance measures, capital intensity, and market share variables have high volatility, standard deviation. This volatility could also be observed by the huge differences of the minimum and maximum values presented. Therefore, the separate analysis of the industries aims to reduce this volatility and attain more accurate representations of how the integration choices affect firm performance. Table 4: Summary statistics Variable Observations Mean Std. Dev. Min. Max. ORPE 147 2,621.8 3,753.5 403.3 40,836 NIPE 147 116.2 299.9-514.9 2,114.1 RISK 147 7.80 7.06 0.18 43.37 SIZE 147 0.21 0.07 0.10 0.40 CINT 147 951.5 1,713 14.5 18,134 MARS 147 0.03 0.09 0.0001.65 CPE 147 406.28 101.86 110.56 919.3 RATIO 147 24.41 13.57 1.27 93.65 COUNTRY 147 4.36 9.20 0 64 5.3.Limitations Due to the scope of this study, limited number of industries and firms may not represent the whole Danish economy. The number of industries is to be taken as the 5 biggest industries, based on the highest number of firms involved in those industries. Restricting the number of industries leads to the restriction of the sample size as well. A longer time period would be recommended to more effectively capture the effects of the sample. The data had to be restricted to include the firms with available sales figures and the other variable measurements, therefore the descriptive statistics had to be computed with small number of firms within each industry. Moreover, only manufacturing firms are taken into consideration, which may confine the generalizability of the findings. In addition, a crucial limitation would be the lack of identifying the relative shares of the various activities within the firm-level. 36

6. GENERAL DESCRIPTIVE ANALYSIS OF EACH INDUSTRY As mentioned above, it is crucial to differentiate the industries in order to investigate how the effect of the integration strategies on performance vary based on the industry. Santalo & Becerra (2008) are avoiding taking the effect of diversification on performance as homogenous across industries, but rather illustrating that diversified firms observe a diversification discount if and only if they compete in industries with a large number of single-segment companies, or enjoy a premium in those industries in which only a few nondiversified firms compete. (p. 851) Moreover, Montgomery & Christensen (1981) have examined significant performance differences among Rumelt s categories of diversification and the market structure variables (market share, market concentration, market growth and firm size). The market structure-performance linkage has suggested that firms located in markets which constrain their growth or profitability is the most likely candidates for diversification. (p. 338) Therefore, firms in low opportunity markets have the tendency to pursue unrelated diversification. This section will illustrate the summary statistics output 15 for the industries; manufacture of basic pharmaceutical products and pharmaceutical preparations (NACE 21), manufacture of food products (NACE 10), manufacture of chemicals and chemical products (NACE 20), manufacture of furniture (NACE 31), and finally manufacture of machinery and equipment respectively (NACE 28). Out of these 5 industries, the manufacture of food products and the manufacture of machinery and equipment industries will be subject to be tested under the OLS regression. The remaining industries will be out of the analysis due to having insufficient number of companies. Moreover, it is crucial to note that the data consists of firms only having applicable values for the variables, therefore taken to be as the representatives of the whole industry. 6.1. Manufacture of Basic Pharmaceutical Products and Pharmaceutical Preparations The data for the pharmaceutical industry that has been obtained from the Orbis database comprised of an initial sample of 14 companies, which had applicable values for the variables. 3 of the companies (Origio A/S, Exiqon A/S & Lifecycle Pharma A/S) have been eliminated due to being holding companies, leaving a sample of 11 companies. Based on the comparisons of the primary and secondary NACE codes of the firms, the mutually exclusive integration strategies are differentiated. Out of these 11 companies, 4 are 15 Obtained from the statistics program Stata11. 37

vertically integrated, 1 is horizontally integrated, 3 are unrelated diversified and 3 of them are undiversified (Appendix 6, T. 11) 16. Table 5: Number of companies based on integration strategies Strategy VI HI UnRe. UnDiv. Total Num. of Firms 4 1 3 3 11 Although the sample embraces only one horizontally integrated company, it is worth illustrating for each integration strategy the averages of the profitability measures, market shares and the numbers of countries the companies are operating in. Table 6: General analysis based on integration strategies Analysis VI Std. Dev. HI Std. Dev. UnRe. Std. Dev. UnDiv. Std. Dev. Avg. 1,482.9 573.33 2,247.5-2,754 1,262.69 1,662.7 972.97 ORPE 17 Avg. NIPE 669 1,100.8 1,053.2-450.92 481.66 125.34 442.18 Avg. MARS 0.08 0.17 0.02-0.22 0.24 0.04 0.06 Avg. COUNTRY 16 19.24 4-31 31.66 9 15.01 Since the VI, unrelated and undiversified strategies have more or less the same number of firms; among them the unrelated integration strategy has the highest average operating revenue per employee measure (2,754), whereas the vertically integrated firms have the lowest (1482.9), as shown in Table 6. Moreover, the companies with an unrelated diversification strategy are enjoying larger market shares on the average (22%) and they are more dispersed in foreign countries (31). As it will be mentioned below, this could be due to the low competition within the industry, where the companies observe an advantage in seeking other profitable industries in which to participate. These seek will in return permit a wider range of areas to work in for the companies (Bettis & Hall, 1982). From the net income per employee (NIPE) point of view, it is observed that vertically integrated companies tend to have the highest value on the average (without taking into account the HI strategy). However it is important to note that, there is high volatility in the value of NIPE for all the integration strategies, where some companies have reported negative values of NIPE whereas others reported positive. In order to have an understanding of the competition within the industry, and the big players market shares, it is crucial to examine the concentration indices of the industry. The 16 T.11 stands for Table 11 under Appendix 6. 17 The variables are defined in detail under subsection 5.2 Variables Measurement. 38

indices have been conducted for a 4 year period (2009-2006) instead of a 5 year for all the industries, due to the missing values for the year 2005 for Novo Nordisk /AS. Table 7: Concentration indices Concentration 2009 2008 2007 2006 Indices Entropy Measure 1.505 1.518 1.526 1.406 Herfindahl Index 0.287 0.290 0.294 0.333 Relative Measure 0.137 0.138 0.139 0.128 CR 4 0.916 0.905 0.906 0.939 According to Table 7, it is observed that the Herfindahl index has been slightly decreasing through the years, indicating an increase of competition and a decrease in market power. The average HHI would be 0.301 which is above the 0.25 threshold (Horizontal Merger Guidelines) therefore highlighting high concentration, meaning that this industry is not competitive and has dominant players. Moreover, the concentration ratio (CR 4 ), which is the sum of the 4 biggest players in the industry, is illustrating a slight decrease in their market shares due to this increasing competition. However, the overall level of competition is low in the industry and CR 4 is supporting this with the high level of market shares. The entropy measure is varying oppositely to the Herfindahl index, since the sum of the products of market shares to its natural logarithm are taken. The relative measure is the value of the entropy measure divided by the number of firms, in order to be able to make comparisons among the industries 18. Moreover, the descriptive statistics below indicates a high standard deviation for the average market shares (MARS), which is greater than the mean. This is specifying a wide range of market shares, and if examined individually it is observed that for the year 2009 Novo Nordisk A/S has 22.97% more market share than H. Lundbeck A/S, the company with the second highest market share. Table 8: Descriptive statistics of pharmaceutical products and pharmaceutical preparations industry Variable Obs. Mean Std. Dev. Min. Max. ORPE 11 1,948.1 960.02 636.68 4,110.3 NIPE 11 496.13 730.66-514.92 2114.07 RISK 11 9.60 8.62 0.58 29.47 SIZE 11 0.17 0.07 0.11 0.33 CINT 11 3,465.02 5,158.11 577.56 18,133.7 MARS 11 0.10 0.15 0.0005 0.48 CPE 11 562.42 129.47 447.33 919.30 RATIO 11 38.49 21.95 15.37 93.65 COUNTRY 11 16.91 21.23 0 64 18 RE will be analyzed further when comparing the industries. 39

Table 8 contains the summary statistics for the manufacture of pharmaceutical products industry by taking the average values for the period 2009-2005 (Appendix 6, Output 1). The firms have average operating revenue per employee of 1,948 and an average net income per employee of 496. This difference could be due to the two companies (Bavarian Nordic A/S and Mekos Laboratories ApS) that have reported negative average values of net income through the 5 year period. The effect of these negative values could be observed by the high standard deviation which is 730.66. Apart from this, high volatility could be detected for the average capital intensity, market share and the number of countries, which could be due to the small number of sample size. Moreover, examining the values of kurtosis and skewness for the variables would indicate whether the data is peaked or flat respectively compared to the normal distribution and if the data is lack of symmetry (Engineering Statistics Handbook). A distribution that is not symmetric, but rather has most of its values either to the right or to the left of the mode, is said to be skewed. (Harnett & Soni, 1991, p. 34) The value of kurtosis being near the value of 3 and 0 for skewness would indicate a normal distribution. Based on these definitions, the values for capital intensity (CINT), market share (MARS), cost per employee (CPE) and the ratio of cost of employee to operating revenue (RATIO) measures have moderate and positive kurtosis and skewness (Appendix 6, Output 2). For these data sets, there is a peaked and right skewed distribution meaning that few companies exist with a value greater than the mean of the measurement (Appendix 6, Output 4). Table 9: Correlations Correlation ORPE NIPE ORPE 1.00 NIPE 0.17 1.00 RISK -0.30-0.13 SIZE 0.04-0.43 CINT -0.05 0.78 MARS 0.16 0.20 CPE 0.38-0.04 RATIO -0.74-0.45 VI -0.38 0.19 HI 0.10 0.25 UR 0.54-0.04 UD -0.19-0.33 COUNTRY 0.10 0.20 The correlations presented at Table 9, are indicating a positive correlation for operating revenue per employee with the variables net income per employee, size, market share, cost per employee, horizontal integration, unrelated diversified and the number of countries. Moreover, the net income per employee measure is positively correlated with capital intensity, market share, vertical integration, horizontal integration and the number of countries. Therefore, for both of the performance measures the risk, ratio (cost of employee to operating revenue) and un-diversification strategies are not favorable in which negative correlation exists. Among the integration strategies, unrelated diversification strategy 40

has the highest correlation with ORPE (0.539) which supports the high average value of the companies under this category. In addition, this industry favors geographic diversification by indicating a positive correlation with the performance measures and the unrelated integration strategy (Appendix 6, Output 3). 6.2.Manufacture of Food Products Following the manufacture of pharmaceuticals industry, data for the food industry has been obtained, with an initial sample of 55 companies. East Asiatic Co. LTD A/S has been eliminated due to being a holding company, leaving a sample of 54 companies. Out of the 54 firms, it is identified that 6 of them are vertically integrated, 16 are horizontally integrated, 9 are unrelated diversified and 23 companies are undiversified. By looking at these numbers, one could say that this industry dominates the un-diversification strategy (Appendix 7, T. 12). Table 10: Number of companies based on integration strategies Strategy VI HI UnRe. UnDiv. Total Num. of Firms 6 16 9 23 54 According to the analysis presented below in Table 11, the profitability measure of operating revenue per employee (ORPE) is the highest for horizontally integrated companies (5,016) followed by vertical integration (4,182.7), unrelated diversified (3,540) and undiversified strategies (2,724). Although the unrelated companies are not observed to be the lowest performing, the horizontally integrated companies are outperforming the unrelated diversified companies based on the average ORPE performance measure, as this outperformance has been supported by previous studies (Bettis, 1981; Miller, 2006 & Rumelt, 1974). From the NIPE point of view, the unrelated diversified companies tend to have the highest on average, however representing high volatility due to the number of employees that each company embraces and positive or negative values of net income announced. Table 11: General analysis based on integration strategies Analysis VI Std. Dev. HI Std. Dev. UnRe. Std. Dev. UnDiv. Std. Dev. Avg. ORPE 4,182.7 3,117.4 5,016 9,679.9 3,540 3,552.30 2,724 2,096.7 Avg. NIPE 22.63 159.93 78.82 121.95 194.05 329.07 36.55 82.23 Avg.MARS 0.01 0.007 0.04 0.12 0.03 0.06 0.005 0.005 Avg.COUNTRY 3 3.50 2 3.24 7 12.83 2 4.58 41

By having a high number of undiversified companies, which represents 42% of the whole industry, it is crucial to examine the level of concentration within the industry. Table 12 illustrates the indices computed through the years 2009 to 2006. Table 12: Concentration indices Concentration Indices 2009 2008 2007 2006 Entropy Measure 2.120 2.153 2.125 2.017 Herfindahl Index 0.252 0.243 0.248 0.278 Relative Measure 0.040 0.041 0.040 0.038 CR 4 0.659 0.684 0.700 0.731 Until the year 2009, a slight decrease in Herfindahl index is observed, indicating an increase in competition. According to the Horizontal Merger Guidelines, in the years 2006 and 2009 the industry was highly concentrated (0.278 and 0.252 respectively) since the HH index is exceeding the 0.25 threshold. Between the years 2008 and 2007, moderate concentration existed, due to having a value in between 0.15 to 0.25 thresholds. Moreover a slight decrease of the concentration ratio of the 4 biggest players is observed. The increase in competition could be due effect of the dominant undiversified companies, since they are aiming to protect and increase the market shares within the industry, without integrating. Moreover, as Jacquemin & Berry (1979) has highlighted, the entropy measure reveals the degree of uncertainty in a given industry and the lower values would indicate greater certainty of the firms relationships with the buyers in the market. Therefore, compared to the pharmaceutical industry, the food industry reveals greater uncertainty. The summary statistics for the manufacture of food industry, presented in Table 13, depicts average operating revenue per employee of 3701.21 and a net income per employee measure of 73.78. Both of these measures present high volatility (5668.6, and 170.69 respectively). The other variables having high variability are the risk (6.59), market share (0.06), and the number of countries the firm is operating in (6.35). The variability in market share could be due to the low competition with dominant players in the industry, in which the Leverandorselskabet Danish Crown Amba Company has a market share of 47.54% (more than 30% of the second company with the highest market share). The volatility of the number of countries is also understandable, since there are 9 companies that are unrelated diversified in an industry where 23 undiversified firms are operating. Moreover, apart from the variables size and cost per employee, the remaining values are positively skewed with high kurtosis (Appendix 7, Output 5-6). 42

Table 13: Descriptive statistics of food industry Variable Obs. Mean Std. Dev. Min. Max. ORPE 54 3,701.2 5,668.6 783.47 40,836 NIPE 54 73.78 170.69-385.58 820.71 RISK 54 5.99 6.60 0.18 33.81 SIZE 54 0.21 0.06 0.10 0.37 CINT 54 1,055.7 939.09 79.44 4,692.9 MARS 54 0.02 0.07 0.0002 0.48 CPE 54 392.79 95.34 188.35 707.62 RATIO 54 17.86 10.50 1.27 58.58 COUNTRY 54 3.28 6.36 0 40 Besides the analysis presented above, the coefficient correlations are demonstrated in Table 14. The results show that risk, market share, cost of employee to operating revenue ratio, unrelated integration, un-diversification strategy, and the number of countries are negatively correlated with the operating revenue per employee measure. The positive correlation of horizontal and vertical integration strategies are reasonable, taking into account the highest values of ORPE to be presented above. The other performance measure, net income per employee, is negatively correlated with market share, vertical integration, undiversification strategy and the number of countries. This would be explained as; any rise in of these coefficients or attempt to undertake the integration strategies would result in a decrease in the performance measures. Correlation ORPE NIPE ORPE 1.00 Moreover a positive correlation exists with NIPE 0.25 1.00 risk, size, capital intensity, cost per employee, RISK -0.17 0.22 SIZE 0.47 0.41 and cost of employee to operating revenue CINT 0.63 0.21 ratio, horizontal and unrelated integration; MARS -0.05-0.03 CPE 0.36 0.47 meaning that a rise or decrease in one of the RATIO -0.45 0.18 variables will affect the performance measure VI 0.03-0.11 HI 0.15 0.02 of NIPE in the same direction. The food UR -0.01 0.32 industry is not favoring geographic UD -0.14-0.19 COUNTRY -0.08-0.09 diversification, due to the negative correlation Table 14: Correlations of both performance measures with the number of countries (Appendix 7, Output 7). 43

6.3.Manufacture of Chemicals and Chemical Products The manufacture of chemicals and chemical products industry comprised of 22 companies initially, in which 3 of them (Auriga Industries A/S, Flugger A/S and SP Group A/S) have been eliminated due to being holding companies, leaving a sample of 19 firms. These 19 companies are distinguished in Table 15 as 2 vertically integrated, 6 horizontally integrated, 5 unrelated diversified and 6 of them are undiversified. This distribution does not highlight a specific dominant strategy that is undertaken within this industry (Appendix 8, T. 13). Table 15: Number of companies based on integration strategies Strategy VI HI UnRe. UnDiv. Total Num. of Firms 2 6 5 6 19 According to the integration strategies, it is illustrated in Table 16 that horizontally integrated companies have been outperforming the remaining strategies with an average value of 4132.93 operating revenue per employee and an average market share of 13%. It is followed by undiversified companies (3046.18), vertically integrated firms (2432.6) and the lowest being the unrelated diversified companies (2174.22). These values are supporting the findings of Rumelt (1982) where on the average related diversification strategies outperformed the other integration strategies, whereas the unrelated business strategy was the lowest performing. From the perspective of net operating income per employee, high volatility exists for the companies that have undertaken horizontal integration, unrelated diversification and un-diversification strategies. Moreover, the unrelated diversified companies have the highest amount of countries that are operating in. Table 16: General analysis based on integration strategies Analysis VI Std. Dev. HI Std. Dev. UnRe. Std.Dev. UnDiv. Std. Dev. Avg. ORPE 2,432 317.62 4,132.9 2,636.8 2,174.2 822.27 3,046.2 3,254.5 Avg. NIPE 297.63 188.76 222.02 290.20 87.89 138.32 3.47 87.65 Avg. MARS 0.005 0.001 0.13 0.26 0.02 0.03 0.005 0.005 Avg. COUNTRY 2 0.71 6 8.94 11 21.74 4 8.09 The concentration indices presented below in Table 17 are indicating high values of concentration and low competition, since the values for the Herfindahl index is above the 0.25 threshold. Moreover through the years 2009 to 2006 first an increase and then a fall of the HH 44

index is observed, specifying an increase in competition. Due to this increase, the first 4 biggest players in the market have experienced a slight fall in their market shares. In addition, the entropy measure reveals greater certainty about the future relationships of the companies due to its lower value compared to the previous industries. Table 17: Concentration indices Concentration 2009 2008 2007 2006 Indices Entropy Measure 1.222 1.085 1.066 1.098 Herfindahl Index 0.368 0.452 0.479 0.451 Relative Measure 0.064 0.057 0.056 0.058 CR 4 0.792 0.815 0.823 0.801 The summary statistics for the manufacture of chemicals industry is presented in Table 18, highlighting average operating revenue per employee to be 3095.31 and net income per employee 125.67. The values with high volatilities would be the net income per employee, market share and the number of country measures. The effects of these high deviations are observed from the values of skewness and kurtosis as well. For instance, the values for market share are demonstrated as 3.89 and 16.43 respectively, which are beyond the values of 0 and 3 for a normal distribution. Since there is low competition with dominant players in the market, it is reasonable to observe a massive difference between minimum and maximum values of market shares (0.0001 and 0.65 respectively) (Appendix 8, Output 8-9). Table 18: Descriptive statistics of chemicals industry Variable Obs. Mean Std. Dev. Min. Max. ORPE 19 3,095.3 2,381.9 601.7 9,517.2 NIPE 19 125.67 208.60-144.19 708.35 RISK 19 7.24 6.93 1.12 24.24 SIZE 19 0.21 0.07 0.11 0.40 CINT 19 952.8 908.43 14.52 4,292.2 MARS 19 0.05 0.15 0.0001 0.65 CPE 19 442.5 93.13 293.88 640.90 RATIO 19 20.47 11.89 5.47 51.26 COUNTRY 19 5.95 12.55 0 50 Correlation ORPE NIPE ORPE 1.00 NIPE 0.45 1.00 RISK -0.09-0.06 SIZE 0.13-0.02 CINT 0.55 0.46 MARS 0.53 0.32 CPE 0.47 0.43 RATIO -0.69-0.35 45

VI -0.10 0.29 HI 0.30 0.32 UR -0.24-0.11 UD -0.01-0.41 COUNTRY -0.11-0.07 46 In order to give a deeper understanding of the values presented above, Table 19 illustrates the correlation coefficients for the chemicals industry. Based on the table, the Table 19: Correlations operating revenue per employee performance measure is negatively correlated with risk, cost of employee to operating revenue ratio, vertical integration, unrelated diversification, un-diversification strategies and the number of countries. A rise in one of these values will lead a fall in the value of ORPE. On the other side, net income per employee performance measure is negatively correlated with risk, size, ratio, unrelated diversification, un-diversification strategies and the number of countries. Therefore, it could be said that either the performance measures or the strategies/control variables are not favoring each other, in terms of being risky, unrelated, undiversified, geographically diversified, and having high cost of employee to operating revenue ratio (Appendix 8, Output 10). 6.4.Manufacture of Furniture The third industry, the manufacture of furniture, involved 16 companies with one holding company (Boconcept Holding A/S). After the elimination the sample is left with 15 companies, with 4 vertically integrated, 2 horizontally integrated 2 unrelated diversified and 7 undiversified firms. This sample is dominated by undiversified companies by 47% of the industry (Appendix 9, T. 14). Table 20: Number of companies based on integration strategies Strategy VI HI UnRe. UnDiv. Total Num. of Firms 4 2 2 7 15 As it has been generally analyzed, Table 21 indicates the highest value of the operating revenue per employee as 1603.82 and the market share as 19%, being under the horizontal integration strategy. This is followed by the unrelated diversification, vertical integration and un-diversification strategies. On the other side, the average net operating income values are highest for the undiversified companies, followed by horizontal integration, vertical integration and a negative value for unrelated diversification. It is again crucial to note that the sample size is small in order to be able to demonstrate a very accurate representation for the whole industry. However, these values can indicate that within this industry, the average operating revenue per employee figures are not volatile among the strategies to be chosen. The differentiation is more explicit with the net income per employee

performance measurement, where the undiversified companies are benefiting most and the unrelated diversifiers the least. Table 21: General analysis based on integration strategies Analysis VI Std. Dev. HI Std. Dev. UnRe. Std. Dev. UnDiv. Std. Dev. Avg. ORPE 1,372.4 134.89 1,603.8 49.11 1,537.2 317.13 1,362.9 1136.7 Avg. NIPE 32.41 28.31 60.02 33.83-124.85 116.64 258.41 460.07 Avg. MARS 0.04 0.02 0.19 0.24 0.05 0.007 0.03 0.06 Avg. COUNTRY 3 2.38 1 0 2 0 3 6.69 Apart from the previous industries that are mentioned above, Table 22 illustrates the Herfindahl indices to be measured as moderately concentrated through the years 2009 to 2006. This reasoning is due to the threshold of having a value in between 0.15 to 0.25 (Horizontal Merger Guidelines), meaning that competition would not be as low as in the industries mentioned above. The manufacture of furniture industry is more competitive with less dominant players. This could also be observed from the value of the concentration index, which on average is 63.67% and is less than the average of the previous industries where dominant players existed. Moreover apart from the manufacture of food products industry, the entropy index is higher compared to other industries, which reveals greater uncertainty within the industry. Table 22: Concentration indices Concentration 2009 2008 2007 2006 Indices Entropy Measure 1.702 1.755 1.729 1.736 Herfindahl Index 0.156 0.148 0.173 0.180 Relative Measure 0.114 0.117 0.115 0.116 CR 4 0.623 0.616 0.658 0.649 The descriptive statistics for the manufacture of furniture industry in Table 23 depicts the average operating revenue per employee to be 1,420.78 and the net income per employee 120.59. High standard deviation is present for the variables net income per employee, risk, capital intensity, market share and the number of countries. According to the industry average of operating revenue per employee, the horizontal and unrelated companies are above this value although each strategy embraces only 2 companies (Appendix 9, Output 11-12). Table 23: Descriptive statistics of furniture industry Variable Obs. Mean Std. Dev. Min. Max. ORPE 15 1,420.78 757.68 403.27 3,536.95 47

NIPE 15 120.59 335.84-207.32 1,240.93 RISK 15 9.49 10.05 0.62 43.37 SIZE 15 0.24 0.08 0.14 0.38 CINT 15 427.72 460.77 40.65 2,020.22 MARS 15 0.06 0.94 0.001 0.36 CPE 15 353.03 41.02 252.12 417.85 RATIO 15 30.99 14.14 10.50 63.67 COUNTRY 15 2.4 4.56 0 18 Table 24: Correlations According to Table 24, operating Correlation ORPE NIPE ORPE 1.00 revenue per employee is negatively correlated NIPE 0.83 1.00 with size, ratio, vertical integration and undiversification strategies. On the other side, RISK 0.28-0.08 SIZE -0.53-0.14 CINT 0.73 0.85 net income per employee performance MARS 0.42 0.27 CPE 0.35 0.05 measure is negatively correlated with risk, RATIO -0.85-0.49 size, ratio, vertical integration, horizontal VI -0.04-0.16 HI 0.10-0.07 integration, unrelated diversified strategies. UR 0.06-0.30 These correlations are consistent with the UD -0.07 0.40 COUNTRY 0.75 0.84 analysis of Table 21 above, in which ORPE is positively correlated with HI strategy, therefore having the highest value for this type of strategy. NIPE is positively correlated with UD strategy and therefore has the highest value under this strategy. Moreover for both of the performance measures the firm s size, ratio, and the vertical integration strategies are negatively correlated (Appendix 9, Output 13). 6.5.Manufacture of Machinery and Equipment Finally, the manufacture of machinery and equipment industry has initially a sample of 51 companies, in which 3 of them (Skako A/S, Svejsemaskinefabrikken Migatronic A/S and Roblon A/S) have been eliminated due to being holding companies. The final sample embraces 48 companies, with 11 vertically integrated, 10 horizontally integrated, 12 unrelated diversified and 15 undiversified firms. It is observed that this industry involves the integration strategies more evenly dispersed (Appendix 10, T. 15). Table 25: Number of companies based on integration strategies Strategy VI HI UnRe. UnDiv. Total Num. of Firms 11 10 12 15 48 Based on the average analysis computed for the each integration strategy in Table 26, the undiversified companies indicate the highest operating revenue per employee (2,619) and 48

the market share (0.047) on average; followed by unrelated diversified, vertically integrated and horizontally integrated. The average ORPE for the whole sample is 1,749 and based on this the only strategy above this mean is the un-diversification strategy. However, in terms of net income per employee the unrelated companies are outperforming, and still the undiversified companies are above the average NIPE of the whole sample (71.62). Moreover the average countries that the companies are operating in is the same and highest for the horizontally integrated and un-diversified companies (4). In general it could be underlined that this industry is favoring the undiversified companies more in terms of operating revenue per employee, compared to the other industries. Table 26: General analysis based on integration strategies Analysis VI Std. Dev. HI Std. Dev. UnRe. Std. Dev. UnDiv. Std. Dev. Avg. ORPE 1,489.9 777.5 1,002.55 147.9 1,527.3 376.11 2,619. 2363.46 Avg. NIPE 20.22 56.84-43.26 99.54 157.97 371.71 116.8 111.51 Avg. MARS 0.01 0.02 0.006 0.006 0.009 0.009 0.05 0.07 Avg. COUNTRY 1 1.30 4 3.76 2 1.30 4 4.67 The concentration indices presented in Table 27, indicate an un-concentrated industry since the values for the Herfindahl index are generally below the threshold of 0.15. Therefore, the industry embraces high competition with no dominant players. However, by observing the trend of the HH index throughout the years, an increase would be noticed. Although the index is still low and moderate concentration exists for the year 2009, competition has slightly declined over time. Moreover, the concentration ratios for the 4 biggest companies indicate a rise throughout the years, which could be due to the fall of competition. As mentioned before, since the industry is favoring the undiversified companies and dominating the industry with the highest number of firms (15) this high competition maybe the presence of these undiversified companies. Table 27: Concentration indices Concentration 2009 2008 2007 2006 Indices Entropy Measure 2.472 2.752 2.950 2.944 Herfindahl Index 0.174 0.115 0.084 0.083 Relative Measure 0.053 0.059 0.063 0.063 CR 4 0.662 0.551 0.458 0.464 In Table 28, the summary statistics of the machinery industry is presented with an average operating revenue per employee of 1749.7 and net income per employee of 71.62. 49

The variables that have high volatility are the measures of net income per employee, capital intensity, market share and the number of countries. According to the previous industry descriptive statistics, all of the industries presented high volatility for the average net income per employee, market share and number of countries. Since the operating revenue per employee illustrated high volatility only in the manufacture of food industry, this performance measure could be considered to be more representative and reliable compared to NIPE (Appendix 10, Output 14-15). Table 28: Descriptive statistics of machinery and equipment industry Variable Obs. Mean Std. Dev. Min. Max. ORPE 48 1749.71 1489.56 774.10 10297.62 NIPE 48 71.62 211.21-243.94 1254.11 RISK 48 9.13 5.83 1.88 29.13 SIZE 48 0.20 0.05 0.12 0.39 CINT 48 421.30 475.98 50.79 2833.54 MARS 48 0.02 0.04 0.0002 0.26 CPE 48 387.96 83.05 110.56 617.50 RATIO 48 28.07 10.09 4.27 54.10 COUNTRY 48 2.69 3.40 0 18 As mentioned above, this industry has the highest performance measurement values for the undiversified companies on average. This is supported by the correlation outputs illustrated in Table 29. The operating revenue per employee value is positively correlated with net income per employee, capital intensity, Correlation ORPE NIPE ORPE 1.00 market share, cost per employee, undiversification strategy and the number of NIPE 0.32 1.00 RISK -0.08-0.16 SIZE -0.23-0.07 countries. The only difference for the net CINT 0.32 0.82 operating per employee value is that; it is MARS 0.82 0.19 CPE 0.25 0.04 positively correlated with the unrelated RATIO -0.65-0.44 diversification strategy as well. Since the level VI -0.10-0.13 HI -0.26-0.28 of correlation with the unrelated UR -0.09 0.24 diversification (0.2385) is greater than the undiversification strategy (0.1458), the average UD 0.40 0.15 COUNTRY 0.10 0.13 Table 29: Correlations net income per employee value is greater for the unrelated diversification strategy (157.97) (Appendix 10, Output 16). 50

7. INDUSTRY COMPARISONS The analysis of the summary statistics of the 5 industries has given an insight on how the industries could differ in terms of the strategies undertaken and their effects on corporate performance. This section will aggregate the findings above, in order to designate the differences by conducting a comparison among the industries (Appendix 11). According to the number of firms in each industry, the dominant industries in the sample are the manufacture of food products (37%) and the manufacture of machinery and equipment industries (33%). Among the 5 industries, the food industry has the highest average value of operating revenue per employee (3,701), which is followed by the chemicals industry (3,095), pharmaceutical industry (1,948), machinery industry (1,749) and the furniture industry (1,421). On the other hand, the pharmaceutical industry has the highest net income per employee (496.13) on the average, which has a huge difference from the other industries. However, recall that for all the industries the values of NIPE are highly volatile. Moreover, the pharmaceutical industry preserves its leadership in having the highest average values of number of countries the firms are operating in (17) and the market share (9.04%) (Appendix 11, Graphs 1-5). In addition, the Appendix 11-Table 8 illustrates a summary of the signs of positive and negative correlations between the performance measures and the variables. According to this, almost all the industry average performance measures have a positive correlation with the capital intensity, market share, cost per employee and horizontal integration variables. Since this study is differentiating the integration strategies for each of the industries and analyzing the effects on performance measures, it is crucial to indicate which industry is outperforming the others based on each strategy. Initially, the manufacture of food industry preserves its highest value of operating revenue per employee in the vertical, horizontal and unrelated integration strategies. This industry maybe more efficient in the sense of coordinating, monitoring and enforcing the process of production more effectively and has greater achievements from the scale, scope and learning economies from the perspectives of vertical and horizontal integration (Sudarsanam, 2010). Moreover, the unrelated diversified firms could be benefiting more from the reductions in transaction costs and the efficient use of internal capital markets. On the other hand, the leader for the undiversified companies is the highly concentrated manufacture of chemicals and chemical products industry. In addition, from the perspective of net income per employee measure the manufacture of 51

pharmaceutical industry is having the highest values for vertical, horizontal, and unrelated integration strategies, which indicates that compared to other firms under each type of strategy the pharmaceutical industry can more efficiently utilize their employee resources in order to generate profits for the company. However, recall that effective comparison of NIPE should only be made between companies in similar industries (Money-Zine) and this analysis with 11 companies may not be an effective representation for the whole pharmaceuticals industry (Appendix 12). In addition to these comparisons, it is worth to underline the differences of the average concentration indices in order to have an overall understanding of the industries competition. Although there is a huge difference of total average market shares between the pharmaceutical industry and the remaining industries, the average concentration ratio of the 4 biggest players in the markets do not present high variability among them. The highest CR 4 is held by the pharmaceutical industry (92%) and is followed by the chemicals industry (81%) the food industry (69%), the furniture industry (64%) and the machinery industry (54%). This values indicate the low competition in pharmaceuticals and chemicals industries were dominant players have high market shares, whereas the decrease in this ratio indicates an increase in competition and a reduction in market shares are observed. Moreover, based on the Herfindahl index the chemicals industry is highly concentrated (0.4373), which indicates low competition involving dominant players. This indication is also supported by the entropy measure, which has the lowest value for the chemicals industry (1.1176). Since the entropy measure is the inverse measure of HH index, it depicts that the lower values of this index will reveal more certainty in the relationships of the firms with the buyers in the market (due to lower competition). On the other side, the machinery industry s HH index indicates a competitive industry relative to the other industries, since it has the lowest value (0.1141). Therefore, this industry has less dominant players compared to the others and the total market shares of the first 4 companies (CR 4 ) are the lowest. Finally, in order to make comparisons among the industries, the entropy can be divided by the number of firms in the industry. In that case, the food industry has the lowest value of RE (0.0397) and the pharmaceutical industry has the highest (0.1353). This highlights that the firms in the food industry are exposed to low competition (on a per company basis), while the pharmaceutical companies are exposed to a high competitive environment (Appendix 13). Finally, a general analysis of the whole sample can be demonstrated in order to differentiate the highest performing strategies without taking the differences of industries into account. Out of the 4 strategies, the horizontally integrated firms are attaining the highest 52

value for operating revenue per employee (3,444) and the unrelated companies with the net income per employee (167) figures. Therefore the choice of integration strategy may be a trade-off for the companies in the sense that different strategies may favor different effects on performance outcomes. The highest market share is preserved by horizontally integrated firms, and the number of countries that the firms operate in is greatly undertaken by the unrelated diversifiers (Appendix 14). 8. EMPIRICAL FINDINGS AND THE DISCUSSION OF RESULTS This section will introduce the findings of the regression models conducted separately for the manufacture of food products and the manufacture of machinery and equipment industries. The remaining industries do not have adequate number of companies to perform a statistical analysis. The regression analysis will be based on the OLS regression model with inclusion and exclusion of the interaction effects 19. For each of the two industries the dependent variables of operating revenue per employee (ORPE) and the net income per employee (NIPE) performance measures will be used. 8.1.Manufacture of Food Industry Table 30 summarizes the estimation of the non-interactive regression model for the food products industry. Initially, the performance measure of operating revenue per employee (ORPE) is taken as the dependent variable. For the hypotheses H 1 and H 3 (positive moderating effect), the coefficients of VI and COUNTRY should be positive and significant, and by H 2 the coefficient of HI should be greater than UR s and significant. This model indicates that the vertical integration strategy has a negative effect on ORPE (-510.7), and is not statistically significant at the 0.10 significance level. Therefore the hypothesis H 1 is rejected, in which the positive effect of vertical integration was to be tested. The dummy variables of horizontal integration (HI) and unrelated diversification (UR) are statistically significant at the 0.10 level, where the HI strategy is outperforming the UR diversification strategy by 6408.24 units on the ORPE. Based on this analysis, the second hypothesis H 2 is not rejected. However, it is crucial to note that it cannot be determined whether this significant and positive effect on performance leads firms to horizontally integrate or if horizontal integration causes this high operating revenue per employee. Finally, the third hypothesis H 3 is tested if geographic diversification had a positive effect on firm performance. The model indicates a positive coefficient for the variable COUNTRY, however statistically 19 Detailed explanation under section 4, Methodology. 53

insignificant. Therefore, H 3 is rejected. The constant having a high significance is difficult to interpret but can be viewed roughly as a pure competitive equilibrium rate of return that would be earned in the economist s model of pure competition. (Bettis, 1981, p. 388) In a pure competitive industry the economic profit in equilibrium would be zero, therefore here the economic profit can be taken as the minimum return necessary for a company to stay in the business. Hence, the constant can be taken as the equilibrium profit for a purely competitive firm, in an accounting sense. The returns above this embodied in the other coefficients indicate some degree of monopoly power. (Bettis, 1981, p. 388). Moreover, the other statistically significant variables are the size and the capital intensity (CINT). The size, which is the natural logarithm of the average number of employees, has a significant and positive effect on ORPE, meaning that a one unit increase in the natural logarithm of number of employees will increase the operating revenue per employee by 31,113 DKK. The capital intensity, which is the ratio of fixed assets to number of employees, depicts a significant effect, in which a 10% increase of this ratio may increase the ORPE by 249. Finally, the entire regression was highly significant based on the F-statistics, and R 2 is 61% in which illustrates the total variation of the sample Y-values that has been explained by the linear relationship with the independent variables X (Appendix 15, Output 17). Table 30: Manufacture of food products industry regression model (ORPE-dependent variable) ORPE Coefficient Std. Err. T-statistic Significance (Constant) -9,084.94 3,480.5-2.61 0.012* RISK -68.54 95.98-0.71 0.479 SIZE 31,113.2 12,066.6 2.58 0.013* CINT 2.49 0.82 3.03 0.004* MARS -704.4 9796.3-0.07 0.943 CPE 12.12 8.13 1.49 0.143 RATIO -83.71 64.60-1.30 0.202 VI -510.8 1,867.3-0.27 0.786 HI 3,025.9 1,372.9 2.20 0.033* UR -3,382.3 1,881.8-1.80 0.079* COUNTRY 128.71 124.55 1.03 0.307 Number of Obs.= 54, F-statistics= 6.82 (significance= 0.000) R 2 = 0.61, adjusted R 2 = 0.52 *Significant at the 0.10 level. Moreover, the model has been tested with the inclusion of the interaction terms, in which it was designed to investigate more fully the reasons for differences in performance among different diversification strategies. (Bettis, 1981, p. 384) This model is constructed with the forward stepwise procedure, in which suitable subsets of independent variables are 54

chosen from the total regression model. In addition to this, the Appendix 16-Output 20 encloses the simple OLS regression model with the interaction effects; however since the sample size is small with 48 companies, having 33 regression variables will be misrepresentative of the estimations. Table 31 presents the significant interaction terms of the final model. These interaction terms can be interpreted as; the reasons of high performance for horizontally integrated firms were the size of the firm (SIZE), and their capital intensity (CINT). The low performance of the HI companies would be due to the cost per employee, which an increase of this average would lead to a decline in operating revenue per employee. This negative correlation is observed in the correlation matrix of the food industry in Appendix 7, where all the integration strategies apart from the unrelated diversification have negative correlations with the cost per employee measure. Therefore the significant and positive effect of CPE (14.42) could be due to this correlation with the unrelated diversification strategy, although this model does not represent UR as significant. Moreover, the cost of employee to operating revenue ratio (RATIO) has a negative significant effect on performance. This could be expected since the correlation matrix is depicting a negative relationship between the RATIO and ORPE, and an increase in RATIO will indicate that the costs are greater than the revenues. The F-statistic for the entire regression is highly significant, with a R 2 value of 90% (Appendix 16, Output 19). Table 31: Interactive regression model (ORPE) for manufacture of food products industry ORPE Coefficient Std. Err. T-statistic Significance (Constant) 189.74 1,209.9 0.16 0.876 (CINT)(HI) 7.85 0.75 10.42 0.000* (CPE)(HI) -36.54 7.22-5.06 0.000* (SIZE)(HI) 44,390.4 15,424.4 2.88 0.006* RATIO -151.05 25.68-5.88 0.000* CPE 14.42 2.81 5.12 0.000* Number of Obs.= 54, F-statistics= 88.34 (significance= 0.000) R 2 = 0.90, adjusted R 2 = 0.89 *Significant at the 0.10 level. In terms of average net operating income per employee (NIPE) as the dependent variable, Table 32 presents the non-interactive regression model. Here, the only significant values to be observed are the constant and the size. The decrease in the number of significant variables could be due to the reason that operating revenue per employee is more reliable and explanatory as a performance measurement compared to the net income per employee value. 55

Therefore, all the three hypotheses are rejected since the variables are not statistically significant, although the coefficients have a positive value for VI, HI, UR and COUNTRY. Moreover the horizontal integration strategy has a higher coefficient compared to the unrelated diversification strategy. The entire regression is significant based on the F- statistics, however the R 2 is lower compared to the previous model (35%) (Appendix 15, Output 18). Table 32: Manufacture of food products industry regression model (NIPE-dependent variable) NIPE Coefficient Std. Err. T-statistic Significance (Constant) -390.66 135.38-2.89 0.006* RISK 2.82 3.73 0.76 0.454 SIZE 819.82 469.36 1.75 0.088* CINT 0.02 0.03 0.73 0.472 MARS 217.24 381.05 0.57 0.572 CPE 0.38 0.32 1.19 0.239 RATIO 3.59 2.51 1.43 0.160 VI 9.42 72.63 0.13 0.897 HI 72.07 53.40 1.35 0.184 UR 53.50 73.20 0.73 0.469 COUNTRY 0.31 4.84 0.06 0.949 Number of Obs.= 54, F-statistics= 2.36 (significance= 0.0250) R 2 = 0.35, adjusted R 2 = 0.20 *Significant at the 0.10 level. Table 33 demonstrates the forward stepwise interactive regression model. Compared to the simple OLS regression presented above, this model has specified more significant variables by adding and removing the variables based on their significance level. Here, the high performance of the unrelated diversified firms is dependent upon their capital intensity (CINT), cost of employee to operating revenue ratio (RATIO) and their market share (MARS). The firms may be keen on diversifying into unrelated areas when their fixed assets, costs and market shares are high or initially being UR diversified may be the outcome of these positive interaction affects. The causality of the impacts cannot be determined strictly. For horizontally integrated companies, their low performance will be due to an increase in the variable RATIO. Moreover, the low performance of an increase in the number of countries (increasing geographic diversification) may depend on the effect of capital intensity (CINT). Compared to other industries such as oil production, telecommunications etc., the food industry could be considered as having a low capital intensity. Based on this determination, an increase in the number of countries may increase the number of employees being hired more than the need of capital, which overall decreases capital intensity. Besides the effects of the 56

interactive variables, this model underlines the significance of unrelated and horizontally diversified variables. Here, horizontal integration is outperforming the unrelated diversified companies by having a large and positive effect on the net income per employee performance measure (287.11> (-702.44)). This significance is supporting the hypothesis H 2. The model is significant with a high value of F-statistics and an R 2 of 69%, lower than the interactive model for ORPE (Appendix 16, Output 21). Table 33: Interactive regression model (NIPE) for manufacture of food products industry NIPE Coefficient Std. Err. T-statistic Significance (Constant) 56.03 21.16 2.65 0.011* UR -702.44 120.82-5.81 0.000* HI 287.11 69.66 4.12 0.000* (CINT)(UR) 0.20 0.04 5.75 0.000* (RATIO)(HI) -16.37 4.03-4.07 0.000* (CINT)(COUNTRY) -0.007 0.003-2.36 0.023* (RATIO)(UR) 24.79 3.13 7.92 0.000* (MARS)(UR) 2,585.81 1,490.91 1.73 0.090* Number of Obs.= 54, F-statistics= 14.69 (significance= 0.0250) R 2 = 0.69, adjusted R 2 = 0.64 *Significant at the 0.10 level. 8.2.Manufacture of Machinery and Equipment Industry The second industry, the manufacture of machinery and equipment is illustrated in the non-interactive regression model in Table 34. The average operating revenue per employee (ORPE) is taken as the dependent variable initially, and based on the output the size (SIZE), market share (MARS), cost per employee (CPE), the ratio of cost of employee to operating revenue (RATIO) and the horizontal integration (HI) variables are statistically significant at the 0.10 level. The variables of size have been significant and positive for both the food and the machinery industry. Among the significant variables, the market share (MARS) stands out with its high positive significance (t-value, 10.84) and this is supported by the high correlation with the dependent variable ORPE (0.53). The reason of this significance could be due to the high competition within the industry (average HH index, 0.1141), where increasing a company s share in the market may result in an effective increase in performance. The model statistically indicates that a 1% increase in market share will lead an increase of 25,497 DKK in operating revenue per employee. In addition, H 1 and H 2 are rejected, since the vertical integration (VI) and the COUNTRY variables are not statistically significant, although they embrace a positive coefficient. On the other side, the horizontal integration strategy is statistically significant at the 0.10 level with a greater coefficient than the unrelated 57

diversification strategy. However, the UR variable is statistically insignificant. To sum up, this model is highly significant with a high value of F-statistics and with a 90% value of R 2 (Appendix 17, Output 22). Table 34: Manufacture of machinery and equipment industry regression model (ORPE dependent variable) ORPE Coefficient Std. Err. T-statistic Significance (Constant) -181.74 538.06-0.34 0.737 RISK -3.45 16.83-0.21 0.839 SIZE 6,992.5 1,949.1 3.59 0.001* CINT -0.27 0.21-1.31 0.195 MARS 25,497.6 2,353.2 10.84 0.000* CPE 5.85 1.07 5.49 0.000* RATIO -82.14 11.90-6.90 0.000* VI 190.43 252.60 0.75 0.456 HI 510.76 291.89 1.75 0.088* UR 216.24 254.75 0.85 0.401 COUNTRY 9.91 31.66 0.31 0.756 Number of Obs.= 48, F-statistics= 32.07 (significance= 0.000) R 2 = 0.90, adjusted R 2 = 0.87 *Significant at the 0.10 level. The same model is tested with the inclusion of the interaction terms and the significant variables are presented in Table 35, which are the outputs of the forward stepwise regression model. The cost per employee (CPE), the cost of employee to operating revenue ratio (RATIO), and the size (SIZE) are statistically significant and positive as the previous model presented above. The number of countries has an interactive positive effect with the market share and a negative effect with the capital intensity. It can be interpreted as the high performance of geographically diversified companies can be due to high market shares and the low performance would be attributable to capital intensity. And finally, the horizontal integration strategy s high performance depends on the cost per employee, however with a relatively lower significance (0.087) compared to other variables significance levels (0.000). This model is highly significant with an F-statistics of 75.13 and a R 2 of 92% (Appendix 18, Output 24). Table 35: Interactive regression model (ORPE) for manufacture of machinery and equipment industry regression model ORPE Coefficient Std. Err. T-statistic Significance (Constant) 713.72 419.07 1.70 0.096* (MARS)(COUNTRY) 4,437.4 338.46 13.11 0.000* RATIO 82.99 9.68-8.58 0.000* CPE 5.84 0.87 6.73 0.000* SIZE 4,153.1 1,435.4 2.89 0.006* 58

(CPE)(HI) 0.94 0.54 1.75 0.087* (CINT)(COUNTRY) -0.09 0.02 4.89 0.000* Number of Obs.= 48, F-statistics= 75.13 (significance= 0.000) R 2 = 0.92, adjusted R 2 = 0.90 *Significant at the 0.10 level. Finally, the average net income per employee (NIPE) performance measure is taken as the dependent variable in estimating the regression model of the machinery industry. In Table 36, the significant values that are observed to be are the capital intensity (CINT) and the vertical integration strategy (VI). However the vertical integration strategy (-89.9) has a negative impact on the firms performance in terms of NIPE, which is not supporting the first hypothesis H 1. This negative impact could be due to the decreases of the net income figures of the companies over the 5 year period 20. Moreover, the downsides of vertical integration could be another reason for the negative effect which are the opportunism due to interdivisional rivalry and the increase in influence costs (Sudarsanam, 2010), whereas the significance could be due to having greater experience in a specific type of technology (Leiblein & Miller, 2003) since machinery and equipments industry is based on more technological know-how compared to the food industry. The remaining hypotheses are not supported as well, since the HI, UR and COUNTRY variables are not significant and apart from the unrelated diversified strategy, their coefficients are negative. And, this last non-interactive regression model has high significance in terms of its F-statistics and a high value of R 2 which is 76% (Appendix 17, Output 23). Table 36: Manufacture of machinery and equipment industry regression model (NIPEdependent variable) NIPE Coefficient Std. Err. T-statistic Significance (Constant) 73.15 112.42 0.65 0.519 RISK -2.93 3.52-0.83 0.410 SIZE 468.45 407.24 1.15 0.257 CINT 0.37 0.04 8.59 0.000* MARS -305.96 491.66-0.62 0.538 CPE -0.24 0.22-1.08 0.288 RATIO -2.55 2.49-1.03 0.311 VI -89.90 52.78-1.70 0.097* HI -50.38 60.99-0.83 0.414 UR 18.19 53.23 0.34 0.734 COUNTRY -9.55 6.61-1.44 0.157 Number of Obs.= 48, F-statistics= 12.78 (significance= 0.000) R 2 = 0.76, adjusted R 2 = 0.71 *Significant at the 0.10 level. 20 In general vertically integrated companies have reported negative net income values in the last years, while number of employees were not highly volatile and somewhat stable. 59

The last interactive regression model is illustrated in Table 37, where the output is obtained by regressing the net income per employee performance measure to its individual variables and interactive terms. Based on the outcome, the capital intensity, ratio, and the constant are statistically significant at the 0.10 level. The reasons of the high performance of the unrelated diversified firms are the capital intensity and the market share. Moreover, the performance of the horizontally integrated companies is dependent significantly upon the market share that the firm holds. Therefore, this model is consistent with the ORPE regression models presented above, in the sense that the machinery industry gives more emphasis on the market share due to being a competitive industry. As in the previous interactive regression models, this model has a significant value of F-statistic, with an R2 of 84% (Appendix 18, Output 26). Table 37: Interactive regression model (NIPE) for manufacture of machinery and equipment industry NIPE Coefficient Std. Err. T-statistic Significance (Constant) 145.22 52.82 2.75 0.009* CINT 0.13 0.05 2.71 0.010* (CINT)(UR) 0.29 0.05 5.50 0.000* RATIO -5.04 1.46-3.45 0.001* (MARS)(UR) -7,295.8 2,730.5-2.67 0.011* (MARS)(HI) -7,234 3,690.6-1.96 0.057* Number of Obs.= 48, F-statistics= 42.99 (significance= 0.000) R 2 = 0.84, adjusted R 2 = 0.82 *Significant at the 0.10 level. 8.3.Discussion of Results The empirical outcomes illustrate that the effects of the variables on the performance measurements are varying based on the type of industry. These differences are related to the firm s environment, and specifically to the characteristics of the markets in which they participate. (Montgomery & Christensen, 1981, p. 328) Initially, in order to see the similarities of the regression models outcomes between the two industries, the same significant non-interactive and interactive terms are highlighted. Non-interactive model o ORPE as dependent variable: Horizontal integration (HI) and the firm size (SIZE) o NIPE as dependent variable: None Interactive model 60

o ORPE as dependent variable: (Cost per employee)(horizontal integration), cost per employee and cost of employee to operating revenue ratio o NIPE as dependent variable: (Capital intensity)(unrelated diversification) and (Market share)(unrelated diversification) From the perspective of the operating revenue per employee, the positive and significant integration strategy for the two industries is horizontal integration, in which it is outperforming the unrelated diversification strategy. This result is consistent with the findings of Rumelt (1982), Bettis (1981) who indicate that the related diversified companies are outperforming the unrelated diversified firms in terms of corporate performance. In the noninteractive model, the two industries did not have a common variable that has been significant at the 0.10 level for the net income per employee performance measure. This could highlight that operating revenue per employee is more explanatory and relevant in explaining the relations with integration strategies and the control variables. In addition, in the interactive regression models, the interactions with the horizontal and un-diversification strategies were capturing more significance for the two industries and in general the control variables of cost per employee, capital intensity, and the firms size were of major interest in explaining the corporate performance. Besides these significant variables, it is crucial to note that the findings do not underline significant effects for the vertical integration (VI) and geographical diversification (COUNTRY) strategies. Previous findings indicated that vertical integration occurred when the investment involved high specificity in knowledge, assets and know-how (Monteverde & Teece, 1982). A rise of complexity and specialization of the inputs would increase the probability to vertically integrate (Masten, 1984). According to these classifications, the inputs that the firms are internalizing may not be specific and critical enough to capture a significant impact on the performance measures. The only significant and negative effect of vertical integration has been observed on the net income per employee measure, which was for the manufacture of machinery and equipment industry. On the other side, the reason of the inability to capture a significant effect could be due to the small sample size. For instance, the number of vertically integrated companies is 6 and 11 for manufacture of food and machinery industries respectively. 61

Based on the summary statistics presented above, these two industries average performances could be analyzed more in detail, in accordance with the empirical results. 21 The manufacture of machinery and equipment industry has the highest average operating revenue per employee figure for undiversified companies (2,619). Since it is an unconcentrated industry with no or few dominant players, the presence of many undiversified companies is reasonable. Moreover, when the size (natural log of number of employees) of the undiversified firms was compared to the remaining companies within the industry, they were smaller. This may support the findings of Nathanson & Cassano (1982) which indicated that smaller firms were performing better compared to larger firms in the categories of no diversification or extremely high diversification. Moreover, the food industry has the highest operating revenue per employee (5,016) and market share (0.04) for its horizontally integrated companies, within a highly concentrated market. The related diversifiers appear to be more profitable in part because they operated in very profitable, highly concentrated markets, and were able to acquire large shares in those markets. (Montgomery & Christensen, 1981, p. 339) The ownership of sufficient level of skills and resources are crucial in these high opportunity markets in explaining the companies above-average market shares, due to expanding into related areas. Therefore, the combination of the market opportunity and the ability to take advantage of that opportunity leads to successful performance outcomes (Montgomery & Christensen, 1981). In addition to operating revenue per employee measures, the net income per employee values indicate the highest values to be attributed to the unrelated diversified firms for both of the industries. Since net income (in its general form) is the revenues minus expenses, the unrelated firms may have the ability to cover their costs more efficiently, by creating value by maintaining an effective performance compared to the external capital markets. This efficiency can lead the diversified companies to realize economies of scope, reduce risks and uncertainty, and reduce transaction costs with the means of internal capital markets. However, the profitability of the primary business has an important role on the decision to diversify (Lipczynski et al., 2005). A conglomerate that reallocates capital from a less profitable core activity to a more profitable non-core activity contributes to an improvement in the efficiency of capital allocation. (p. 577) 21 Recall that these two industries have a sample size greater than the remaining industries, indicating a more accurate comparison. 62

9. CONCLUSION Both the summary statistics and the empirical results tried to underline the differences between the 5 industries, in terms of the companies choices of integration strategies and the effects on their corporate performance. According to the descriptive statistics, the choice and the dominance of strategies are varying based on the industry that the firms are operating in. The manufacture of food products industry is favoring the vertical, horizontal and unrelated integration strategies in terms of average operating revenue per employee performance measure. This outperformance could suggest that the food industry is encouraging the vertically integrated firms in having the efficiencies of coordinating, monitoring and the enforcement in the process of production (Sudarsanam, 2010). Moreover, the companies in this sector could be reaching the efficiencies of scale, scope and learning economies more favorably for horizontally integrating. Within this industry, the horizontally integrated firms are outperforming the unrelated diversified companies; however the reason of having the highest ORPE out of the remaining industries for this type of diversification could be the effective reductions of transaction costs and making efficient use of internal capital markets. On the other hand, the undiversified companies in the highly concentrated manufacture of chemicals industry have on average highest value of ORPE performance. Apart from the chemicals, food and pharmaceuticals industry; the manufacture of machinery and furniture industries are subject to higher competition and lower values of performance measures are observed for the integration strategies chosen. The general empirical evidence suggested that high levels of asset specificity and know-how may lead the firms to vertically integrate (Monteverde & Teece, 1982) in order to prevent the hold-up problem and extensive quasi-rents (Grossman & Hart, 1986; Williamson, 1971). However, the manufacture of food and the manufacture of machinery and equipment industries did not underline a significant positive effect of vertical integration strategy, which could be due to not having critical relation-specific assets that would significantly affect the performance of the companies. On the other side, both of the industries have illustrated high significant positive effect for the horizontal integration strategy, which has been consistent with the findings of Rumelt (1982), Bettis (1981), and Montgomery (1994). However, based on the differences of market structures the effects of horizontal integration is not the same, in which higher significance is observed for the food industry. Finally, the empirical evidence on the effect of geographic diversification generally indicated a positive relationship between the 63

geographic scope and firm s performance (Delios & Beamish, 1999; Lepetit et al., 2004; Ravichandran, 2009). This study could not observe any significant outcomes for the number of countries that the firms are operating in, although the coefficients were positive for the ORPE measure. Therefore, the effects of the strategy of vertical integration and geographic diversification could not reach a specific conclusion, which may be attributed to the limited observation sample used in the study. The main reason has been the difficulty of finding applicable values for the variables used in the analysis. Future studies could increase the number of industries with different measures of profitability and diversification strategies and the inclusion of more control variables such as R&D intensity and advertising. As the separate research traditions that study corporate economic performance become integrated, both research and managerial practice will be enriched. (Montgomery & Christensen, 1981, p. 340) 64

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List of Appendices Appendix 1. Orbis snapshot and the search strategy... 72 Appendix 2. List of companies based on industries... 73 Appendix 3. The IO table from statbank.dk for the food industry... 76 Appendix 4. A representation on how the industries have been identified... 81 Appendix 5. The ROA value for Novo Nordisk A/S through 2005-2009... 82 Appendix 6. The summary statistics for the manufacture of basic pharmaceuticals and pharmaceutical preparations industry... 83 Appendix 7. The summary statistics for the manufacture of food products ind.... 87 Appendix 8. The summary statistics of the manufacture of chemicals and chemical products industry... 91 Appendix 9. The summary statistics for the manufacture of furniture industry... 95 Appendix 10. The summary statistics of the manufacture of machinery and equipment industry... 98 Appendix 11. Industry comparisons of the 5 industries... 102 Appendix 12. Differentiating the integration strategies for the whole sample... 107 Appendix 13. Concentration indices... 109 Appendix 14. Integration strategy comparison for the whole data... 111 Appendix 15. Stata outputs for the manufacture of food industry by simple OLS... 113 Appendix 16. Stata output for the manufacture of food products industry by forward stepwise regression with interactive terms... 114 Appendix 17. Stata outputs for the manufacture of machinery and equipment industry by simple OLS... 118 Appendix 18. Stata output for the manufacture of machinery and equipment industry by forward stepwise regression with interactive terms... 119 72

Appendix 1. Orbis snapshot and the search strategy Data update 8619 Username Aarhus Business School-3474 Export date 08/04/2011 1. World region/country/region in country: Denmark 395,183 2. NACE Rev. 2 (Primary codes only): 10 - Manufacture of food 1,617,959 products, 20 - Manufacture of chemicals and chemical products, 21 - Manufacture of basic pharmaceutical products and pharmaceutical preparations, 28 - Manufacture of machinery and equipment nec, 31 - Manufacture of furniture 3. Years with available accounts: 2009, 2008, 2007, 2006, 2005 5,642,512 4. Number of employees: 2009, 2008, 2007, 2006, 2005, min=10, for all the selected periods 628,275 5. Operating revenue (Turnover): All companies with a known value, 2009, 2008, 2007, 2006, 2005, for all the selected periods Boolean Search: 1 And 2 And 3 And 4 And 5 TOTAL 158 3,473,365 73

Appendix 2. List of companies based on the industries. Table 1: Manufacture of Basic Pharmaceuticals and Pharmaceutical Preparations Pharmaceutical Industry- Companies 1. Novo Nordisk A/S 7. Xelia Pharmaceuticals ApS 2. H. Lundbeck A/S 8. Basf A/S 3. Novozymes A/S 9. Bavarian Nordic A/S 4. Leo Pharma A/S 10. Contura International A/S 5. Alk Abello A/S 11. Mekos Laboratories ApS 6. Nycomed Danmark ApS Table 2: Manufacture of Chemicals and Chemical Products Chemical Industry-Companies 1. Borealis Group 11. Aga A/S 2. Cheminova A/S 12. Trevira Neckelman ApS 3. Hempel A/S 13. Sun Chemical A/S 4. Dako Denmark A/S 14. Yara Praxair A/S 5. FiberVisions A/S 15. Flint Group Denmark A/S 6. Brenntag Nordic A/S 16. Syntese A/S 7. Koppers Denmark A/S 17. Basf Construction Chemicals Denmark A/S 8. Teknos A/S 18. Nordalim A/S 9. Danlind A/S 19. GK Pharma ApS 10. Air Liquide Danmark A/S Table 3: Manufacture of Furniture Furniture Industry-Companies 1. Tvilum ApS 9. Fredericia Furniture A/S 2. Dan-Foam ApS 10. Ropox A/S 3. Expedit A/S 11. Kvik Production A/S 4. Invita Kokkener A/S 12. P.P. Mobler ApS 5. Dansani A/S 13. Lystrup Rustfri Stal ApS 6. Labflex A/S 14. Solrod Mobel A/S 7. Duba-B8 A/S 15. Aktielskabet J.L. Mollers Mobelfabrik 8. JKE Design A/S 74

Table 4: Manufacture of Machinery and Equipment Machinery Industry-Companies 1. Vestas Nacelles A/S 27. Tetra Pak Hoyer A/S 2. Vestas Blades A/S 28. Glunz & Jensen A/S 3. Vestas Towers A/S 29. CFS Slagelse A/S 4. Grundfos A/S 30. Epoke A/S 5. Vestas Control Systems A/S 31. HOH Water Technology A/S 6. LM Wind Power A/S 32. Kroll Cranes A/S 7. Sauer-Danfoss ApS 33. Dantherm Filtration AS 8. Gea Process Engineering A/S 34. Westrup A/S 9. Alfa Laval Copenhagen A/S 35. Vola A/S 10. Alfa Laval Kolding A/S 36. Soco System A/S 11. SPX Flow Technology Denmark A/S 37. Egholm Maskiner A/S 12. Kongskilde Industries A/S 38. Alfa Laval Nakskov A/S 13. Desmi A/S 39. Scanomat A/S 14. Sondex A/S 40. KJ Industries A/S 15. Hojbjerg Maskinfabrik A/S 41. Skako Lift A/S 16. Andritz Feed & Biofuel A/S 42. Serman & Tipsmark A/S 17. Disa Industries A/S 43. KSM Kragelund ApS 18. Wittenborg ApS 44. Heta A/S 19. Kverneland Group Kerteminde A/S 45. Acta A/S 20. Struers A/S 46. Boe-Therm A/S 21. Caljan Rite-Hite ApS 47. Abeto-Teknik A/S 22. SFK Systems A/S 48. Magnus Jensen A/S 23. Jensen Denmark A/S 24. Exhausto A/S 25. Gram Commercial A/S 26. Haas-Meincke A/S Table 5: Manufacture of Food Products Food Industry- Companies 1. Leverandorselskabet Danish Crown 28. Dan Cake A/S Amba 2. Danisco A/S 29. Pharma Nord ApS 3. Royal Greenland Seafood A/S 30. Thorfisk A/S 4. Aarhuskarlshamn Denmark A/S 31. Valsemollen af 1899 A/S 5. Arovit Petfood A/S 32. Rahbekfisk A/S 6. Lantmannen Danpo A/S 33. Aktieselskabet Saby Fiske Industri 7. Toms Gruppen A/S 34. Cremo Ingredients A/S 8. Fiskernes Fiskeindustri Amba 35. Daloon A/S Skagen 9. Ferrosan A/S 36. Odense Marcipan A/S 10. Lantmannen Schulstad A/S 37. Hanstholm Fiskemelsfabrik A/S 11. Rynkeby Foods A/S 38. Hjalmar Nielsen A/S 12. Lantmannen Cerealia A/S 39. Hamlet Protein A/S 13. Kohberg Brod A/S 40. Sydvestjydsk Pelsdyrfoder Amba 14. Kelsen Group A/S 41. Norager Mejeri A/S 75

15. Dragsbak A/S 42. Fodercentralen for Holstebro og Omegn Amba 16. Aktieselskabet Beauvais 43. Easyfood A/S 17. Bisca A/S 44. Credin A/S 18. Palsgaard A/S 45. Dangront Products A/S 19. CO-RO Food A/S 46. Agrana Juice Denmark A/S 20. Protein og Oliefabrikken Scanola 47. P/F Fiskavirkid A/S 21. Haribo Lakrids, Aktieselskab 48. PK Chemicals A/S 22. Scandic Food A/S 49. Samso Konservesfabrik A/S 23. Rieber & Son Danmark A/S 50. European Freeze Dry ApS 24. Stryhns A/S 51. Sjallands Pelsdyrfoder Amba 25. Vital Petfood Group A/S 52. Aarhus Slagtehus A/S 26. Gumlink A/S 53. CP Kelco Services ApS 27. P/F Havsbrun 54. P/F Kosin 76

Appendix 3. The Input-Output table from statbank.dk for the manufacture of food products industry Agriculture-(Supplying) 3454 6 2005 2006 2007 2005 (perc.) 2006 (perc.) 2007 (perc.) 0,47 0,47 0,48 0,47 Mfr. of dairy products-(supplying) 3908 4098 3803 0,05 0,05 0,05 0,05 Wholesale except of motor vehicles-(supplying) 3200 3749 4004 0,04 0,05 0,05 0,05 Production etc. of meat and meat products-(supplying) 3378 3207 2947 0,05 0,04 0,04 0,04 Freight transport by road and via pipelines-(supplying) 2443 2450 2312 0,03 0,03 0,03 0,03 Advertising-(Supplying) 1796 1692 2048 0,02 0,02 0,03 0,02 Mfr. of starch, chocolate and sugar products-(supplying) 1524 1673 1947 0,02 0,02 0,03 0,02 Other business activities-(supplying) 1568 1552 1439 0,02 0,02 0,02 0,02 Mfr. of vegetable and animal oils and fats-(supplying) 1037 1025 1320 0,01 0,01 0,02 0,01 Fishing-(Supplying) 1061 1097 1149 0,01 0,01 0,02 0,01 Production and distribution of electricity-(supplying) 936 1078 1109 0,01 0,01 0,01 0,01 Processing and preserving of fish and fish products-(supplying) 951 1078 989 0,01 0,01 0,01 0,01 Mfr. of pulp, paper and paper products-(supplying) 1038 959 897 0,01 0,01 0,01 0,01 Consulting engineers, architects -(Supplying) 1016 958 905 0,01 0,01 0,01 0,01 Manufacture of sugar-(supplying) 1021 864 888 0,01 0,01 0,01 0,01 Mfr. of rubber products and plastic packing goods etc.-(supplying) 826 858 886 0,01 0,01 0,01 0,01 Financial institutions-(supplying) 899 816 831 0,01 0,01 0,01 0,01 Manufacture and distribution of gas-(supplying) 757 846 742 0,01 0,01 0,01 0,01 Processing and preserving of fruit and vegetables-(supplying) 746 733 643 0,01 0,01 0,01 0,01 Building-cleaning activities-(supplying) 646 553 586 0,01 0,01 0,01 0,01 Letting of non-residential buildings-(supplying) 560 580 613 0,01 0,01 0,01 0,01 Post and telecommunications-(supplying) 580 606 531 0,01 0,01 0,01 0,01 Mfr. of various metal products-(supplying) 77 582 542 549 0,01 0,01 0,01 0,01 Manufacture of beverages-(supplying) 508 576 524 0,01 0,01 0,01 0,01 3522 1 3630 6 Avera ge

Activities of other transport agencies-(supplying) 657 525 409 0,01 0,01 0,01 0,01 Mfr. of pharmaceuticals etc.-(supplying) 348 328 380 0,00 0,00 0,00 0,00 Sewage removal and purifying plants-(supplying) 373 337 340 0,01 0,00 0,00 0,00 Mfr. of building materials of metal-(supplying) 322 312 344 0,00 0,00 0,00 0,00 Repair and maintenance of buildings-(supplying) 324 273 309 0,00 0,00 0,00 0,00 Mfr. of detergents and other chemical products-(supplying) 224 296 368 0,00 0,00 0,00 0,00 Software consultancy and supply-(supplying) 327 268 257 0,00 0,00 0,00 0,00 Recreational, cultural, sporting activities (market)-(supplying) 272 302 244 0,00 0,00 0,00 0,00 Non-life insurance-(supplying) 269 274 201 0,00 0,00 0,00 0,00 Mfr. of ovens and cold-storage plants-(supplying) 232 247 265 0,00 0,00 0,00 0,00 Manufacture of tobacco products-(supplying) 243 258 225 0,00 0,00 0,00 0,00 Mfr. of other electrical machinery and apparatus-(supplying) 232 215 218 0,00 0,00 0,00 0,00 Computer activities exc. software consultancy and supply-(supplying) 229 208 216 0,00 0,00 0,00 0,00 Mortgage credit institutions-(supplying) 283 192 173 0,00 0,00 0,00 0,00 Horticulture, orchards etc.-(supplying) 180 210 228 0,00 0,00 0,00 0,00 Construction materials for own-account repair-(supplying) 239 180 187 0,00 0,00 0,00 0,00 Activities of membership organizations-(supplying) 215 209 175 0,00 0,00 0,00 0,00 Renting of transport equipment and machinery-(supplying) 218 202 174 0,00 0,00 0,00 0,00 Mfr. of bread, cakes and biscuits-(supplying) 163 215 201 0,00 0,00 0,00 0,00 Mfr. of machinery for industries-(supplying) 164 168 204 0,00 0,00 0,00 0,00 Restaurants -(Supplying) 172 181 172 0,00 0,00 0,00 0,00 Refuse collection and sanitation-(supplying) 173 181 137 0,00 0,00 0,00 0,00 Mfr. of refined petroleum products etc.-(supplying) 103 116 183 0,00 0,00 0,00 0,00 Collection and distribution of water-(supplying) 127 124 137 0,00 0,00 0,00 0,00 Accounting, book-keeping, auditing -(Supplying) 129 127 127 0,00 0,00 0,00 0,00 Mfr. of agricultural machinery-(supplying) 137 116 122 0,00 0,00 0,00 0,00 Cargo handling, harbours etc., travel agencies-(supplying) 130 115 107 0,00 0,00 0,00 0,00 Mfr. of transport equipment excl. ships, motor vehicles etc.-(supplying) 103 128 118 0,00 0,00 0,00 0,00 Civil engineering-(supplying) 117 88 99 0,00 0,00 0,00 0,00 78

Transport via railways-(supplying) 131 93 79 0,00 0,00 0,00 0,00 Hotels-(Supplying) 101 103 87 0,00 0,00 0,00 0,00 Maintenance and repair of motor vehicles-(supplying) 92 97 100 0,00 0,00 0,00 0,00 Real estate agents etc.-(supplying) 89 84 76 0,00 0,00 0,00 0,00 Publishing activities, excluding newspapers-(supplying) 94 78 65 0,00 0,00 0,00 0,00 Steam and hot water supply-(supplying) 92 93 49 0,00 0,00 0,00 0,00 Manufacture of other plastic products n.e.c.-(supplying) 77 75 80 0,00 0,00 0,00 0,00 Other service activities-(supplying) 77 71 80 0,00 0,00 0,00 0,00 Printing activities-(supplying) 76 68 78 0,00 0,00 0,00 0,00 Refuse dumps and refuse disposal plants-(supplying) 82 78 60 0,00 0,00 0,00 0,00 Other retail sale, repair work-(supplying) 66 67 74 0,00 0,00 0,00 0,00 Mfr. of wood and wood products-(supplying) 59 61 65 0,00 0,00 0,00 0,00 Retail trade of food -(Supplying) 57 62 64 0,00 0,00 0,00 0,00 Legal activities-(supplying) 62 65 54 0,00 0,00 0,00 0,00 Mfr. of basic non-ferrous metals-(supplying) 59 59 54 0,00 0,00 0,00 0,00 Publishing of newspapers-(supplying) 47 57 57 0,00 0,00 0,00 0,00 Mfr. of radio and communication equipment-(supplying) 40 35 69 0,00 0,00 0,00 0,00 General (overall) public service activities-(supplying) 56 42 35 0,00 0,00 0,00 0,00 Mfr. of industrial gases and inorganic bacis chemicals-(supplying) 42 44 40 0,00 0,00 0,00 0,00 Mfr. of office machinery and computers-(supplying) 55 36 35 0,00 0,00 0,00 0,00 Activities auxiliary to finance-(supplying) 40 46 39 0,00 0,00 0,00 0,00 Mfr. of marine engines and compressors -(Supplying) 33 41 30 0,00 0,00 0,00 0,00 Extr. of gravel and clay etc.-(supplying) 31 41 24 0,00 0,00 0,00 0,00 Taxi operation and coach services-(supplying) 34 31 29 0,00 0,00 0,00 0,00 Administration of public sectors exc. for bisiness-(supplying) 31 34 26 0,00 0,00 0,00 0,00 Mfr. of medical and optical instruments-(supplying) 28 33 28 0,00 0,00 0,00 0,00 Recycling of waste and scrap-(supplying) 24 31 25 0,00 0,00 0,00 0,00 Defence, police and administration of justice-(supplying) 17 18 44 0,00 0,00 0,00 0,00 Air transport-(supplying) 24 21 26 0,00 0,00 0,00 0,00 79

Manufacture of motor vehicles etc.-(supplying) 18 23 27 0,00 0,00 0,00 0,00 Sale of motor vehicles and motorcycles-(supplying) 19 22 26 0,00 0,00 0,00 0,00 Retail sale of automotive fuel-(supplying) 16 20 19 0,00 0,00 0,00 0,00 Department stores-(supplying) 15 16 18 0,00 0,00 0,00 0,00 Other scheduled passenger land transport-(supplying) 17 16 15 0,00 0,00 0,00 0,00 Mfr. of textiles -(Supplying) 18 13 15 0,00 0,00 0,00 0,00 Manufacture of pesticides and other agro-chemical products-(supplying) 13 16 16 0,00 0,00 0,00 0,00 Mfr. of concrete, cement, asphalt and rockwool products-(supplying) 14 14 17 0,00 0,00 0,00 0,00 Mfr. of dyes, pigments and organic bacis chemicals-(supplying) 23 14 6 0,00 0,00 0,00 0,00 Mfr. of glass and ceramic goods etc.-(supplying) 8 9 25 0,00 0,00 0,00 0,00 Water transport-(supplying) 12 13 14 0,00 0,00 0,00 0,00 Mfr. of cement, bricks, tiles, flags etc.-(supplying) 14 15 7 0,00 0,00 0,00 0,00 Adult and other education (market)-(supplying) 14 13 10 0,00 0,00 0,00 0,00 Regulation of and contribution to more efficient operation of business- 15 11 11 0,00 0,00 0,00 0,00 (Supplying) Mfr. of toys, gold and silver articles etc.-(supplying) 12 13 10 0,00 0,00 0,00 0,00 Adult and other education (other non-market)-(supplying) 13 13 7 0,00 0,00 0,00 0,00 Mfr. of builders ware of plastic-(supplying) 10 8 10 0,00 0,00 0,00 0,00 Re. sale of phar. goods, cosmetic art.-(supplying) 10 8 8 0,00 0,00 0,00 0,00 Mfr. of furniture-(supplying) 8 10 7 0,00 0,00 0,00 0,00 Research and development (market)-(supplying) 8 8 9 0,00 0,00 0,00 0,00 Mfr. of paints, varnishes and similar coatings, printing ink and mastics- 8 5 6 0,00 0,00 0,00 0,00 (Supplying) Mfr. of domestic appliances-(supplying) 6 6 5 0,00 0,00 0,00 0,00 First processing of iron and steel-(supplying) 3 5 8 0,00 0,00 0,00 0,00 Mfr. of plastics and synthetic rubber-(supplying) 10 4 3 0,00 0,00 0,00 0,00 Mfr. of wearing apparel-(supplying) 5 5 3 0,00 0,00 0,00 0,00 Casting of metal products-(supplying) 2 2 4 0,00 0,00 0,00 0,00 Higher education-(supplying) 2 2 1 0,00 0,00 0,00 0,00 80

Manufacture of fertilizers-(supplying) 2 1 2 0,00 0,00 0,00 0,00 Bakers shops-(supplying) 1 1 1 0,00 0,00 0,00 0,00 Building and repairing of ships and boats-(supplying) 1 2 1 0,00 0,00 0,00 0,00 Research and development (other non-market)-(supplying) 1 1 1 0,00 0,00 0,00 0,00 Mfr. of basic iron and steel and of ferro alloys-(supplying) 1 1 2 0,00 0,00 0,00 0,00 Medical, dental and veterinary activities-(supplying) 1 1 0 0,00 0,00 0,00 0,00 Forestry-(Supplying) 1 1 1 0,00 0,00 0,00 0,00 Agricultural services; landscape gardeners etc.-(supplying) 0 0 0 0,00 0,00 0,00 0,00 Mfr. of leather and footwear-(supplying) 0 0 0 0,00 0,00 0,00 0,00 Re. sale of clothing and footwear-(supplying) 0 0 0 0,00 0,00 0,00 0,00 Extr. of oil and natural gas-(supplying) 0 0 0 0,00 0,00 0,00 0,00 Construction of new buildings-(supplying) 0 0 0 0,00 0,00 0,00 0,00 Life insurance and pension funding-(supplying) 0 0 0 0,00 0,00 0,00 0,00 Dwellings-(Supplying) 0 0 0 0,00 0,00 0,00 0,00 Primary education-(supplying) 0 0 0 0,00 0,00 0,00 0,00 Secondary education-(supplying) 0 0 0 0,00 0,00 0,00 0,00 Hospital activities-(supplying) 0 0 0 0,00 0,00 0,00 0,00 Social institutions etc. for children-(supplying) 0 0 0 0,00 0,00 0,00 0,00 Social institutions etc. for adults-(supplying) 0 0 0 0,00 0,00 0,00 0,00 Recreational, cultural, sporting activities (other non-market)-(supplying) 0 0 0 0,00 0,00 0,00 0,00 Private households with employed persons-(supplying) 0 0 0 0,00 0,00 0,00 0,00 TOTAL SUPPLY (industry 10) 7426 8 *When the secondary NACE Rev. code was different than the primary code of the firm, vertical integration has been traced from this IO matrix. Based on the definition of the secondary NACE code, the supplying industry was searched from the list. If the average percentage exceeded 1% threshold, the company is considered to be vertically integrated. If not, the company is unrelated diversified. 7518 7 7618 0 81

Appendix 4. A representation on how the integration strategies have been identified (Example from the chemicals industry) Table 6: Manufacture of Chemicals and Chemical Products Industry Company Core Code Secondary Code Hempel A/S* 2030 -- BOREALIS Group** 2016 2059 FiberVisions A/S*** 2060 2365 4676 *As seen in the table above, if the company has revealed only its primary code as in Hempel A/S, the firm is regarded as being un-diversified. **If the first two digit primary & secondary codes are the same, we will take them as horizontally integrated (HI=1). ***If not, as in the third case, we will investigate vertical integration with the use of the IO matrix based on the definition of the secondary NACE code (23). The percentage level where the two industries are intercepting will give an idea of VI. For this, a minimum percentage index has to identified and based on common sense and 10 first biggest suppliers, an index of 1% is to be chosen. If NACE 23 is not a supplying industry (below 1%) for the chemicals industry, then the company is regarded as unrelated diversified. Here, the analysis is based on the priority level of the first secondary NACE code that is presented; therefore the code 4676 is not taken into consideration when identifying FiberVision A/S s integration strategy. This assumption is to preserve the mutual exclusivity of the integration strategies. 81

Appendix 5: The return on assets value for Novo Nordisk A/S through the years 2005-2009. Table 7: The first 3 companies ROA values from the pharmaceutical industry Companies 2009 2008 2007 2006 2005 Novo Nordisk A/S* 7.03 9.42 4.63 6.05 n.a H. Lundbeck A/S 15.57 16.84 20.78 14.04 19.40 Novozymes A/S 14.88 14.30 15.61 15.29 15.73 *The ROA values for Novo Nordisk A/S are not representing the success and the profitability of the company, compared to the other following firms. When the operating revenue and net income per employee figures are used, it is observed that Novo Nordisk A/S has the highest measures as shown in Table7. Table 8: The first 3 companies average operating revenue per employee values from the pharmaceutical industry Companies 2009 2008 2007 2006 2005 Novo Nordisk A/S* 2,551.24 2,538.58 2,499.33 2,567.76 n.a H. Lundbeck A/S 2,397.87 2,126.27 2,162.72 1,788.26 1,803.33 Novozymes A/S 1,631.59 1,640.90 1,614.22 1,606.04 1,569.72 Table 9: The first 3 companies ROA values from the food industry Companies 2009 2008 2007 2006 2005 Danish Crown Amba* 5.93 5.12 6.62 6.36 6.00 Danisco A/S 1.79 4.90 4.93 2.77 5.16 Royal Greenland A/S -6.82-3.09 1.28-3.00 1.04 *The same reasoning can be used here, that the ROA values are very low which are far beyond the profitabilities and leadership positions of the companies. The average operating revenue per employee figures presented in Table 9 are more reasonable and reflecting the successes of the firms. Table 10: The first 3 companies average operating revenue per employee values from the food industry Companies 2009 2008 2007 2006 2005 Danish Crown Amba 1,844.17 1,762.42 1,822.38 1,801.69 1,702.04 Danisco A/S 1,865.41 2,059.66 1,994.7 2,058.93 1,688.36 Royal Greenland A/S* 12,874.28 9,189.45 8,555.43 8,210.50 5,342.57 *Royal Greenland Seafood A/S has high operating revenue per employee values due to having lower number of employees compared to the other firms. 82

Appendix 6. The summary statistics of the manufacture of basic pharmaceutical products and pharmaceutical preparations industry Table 11: The pharmaceutical companies based on integration strategies Pharmaceutical Industry- Companies Integration Strategies 1. Novo Nordisk A/S UR 7. Xelia Pharmaceuticals HI ApS 2. H. Lundbeck A/S VI 8. Basf A/S UR 3. Novozymes A/S UR 9. Bavarian Nordic A/S VI 4. Leo Pharma A/S UD 10. Contura International A/S UD 5. Alk Abello A/S VI 11. Mekos Laboratories ApS UD 6. Nycomed Danmark ApS VI UR= Unrelated Diversified VI= Vertical Integration HI= Horizontal Integration UD= Undiversified Output 1. Summary statistics 83

Output 2. Detailed summary statistics 84

85

Output 3. Correlations Output 4. Sample histograms for highly skewed values (pharmaceutical industry) Capital Intensity: Cost per Employee: Density 0 5.0e-05 1.0e-04 1.5e-04 0 5000 10000 15000 Capital Intensity (fixed assets/employees D ensity 0.001.002.003.004.005 400 500 600 700 800 Average Cost per empl. Market Share: Ratio: Density 0 1 2 3 4 5 0.1.2.3.4 Average Market Shares Density 0.01.02.03 20 40 60 80 Average Cost of empl./opr. Rev per empl. 86

Appendix 7. The summary statistics of the manufacture of food products industry Table 12: The manufacture of food industry companies based on integration strategies Food Industry- Companies Integration Strategies 1. Leverandorselskabet Danish HI 28. Dan Cake A/S HI Crown Amba 2. Danisco A/S UR 29. Pharma Nord ApS UD 3. Royal Greenland Seafood A/S VI 30. Thorfisk A/S UD 4. Aarhuskarlshamn Denmark A/S UR 31. Valsemollen af 1899 A/S UD 5. Arovit Petfood A/S UD 32. Rahbekfisk A/S HI 6. Lantmannen Danpo A/S HI 33. Aktieselskabet Saby Fiske UD Industri 7. Toms Gruppen A/S UD 34. Cremo Ingredients A/S UD 8. Fiskernes Fiskeindustri Amba UD 35. Daloon A/S UD Skagen 9. Ferrosan A/S UR 36. Odense Marcipan A/S UD 10. Lantmannen Schulstad A/S UD 37. Hanstholm Fiskemelsfabrik HI A/S 11. Rynkeby Foods A/S HI 38. Hjalmar Nielsen A/S VI 12. Lantmannen Cerealia A/S VI 39. Hamlet Protein A/S UD 13. Kohberg Brod A/S VI 40. Sydvestjydsk Pelsdyrfoder HI Amba 14. Kelsen Group A/S HI 41. Norager Mejeri A/S UD 15. Dragsbak A/S HI 42. Fodercentralen for Holstebro UD og Omegn Amba 16. Aktieselskabet Beauvais HI 43. Easyfood A/S UD 17. Bisca A/S HI 44. Credin A/S VI 18. Palsgaard A/S UD 45. Dangront Products A/S HI 19. CO-RO Food A/S UR 46. Agrana Juice Denmark A/S UD 20. Protein og Oliefabrikken Scanola HI 47. P/F Fiskavirkid UD A/S 21. Haribo Lakrids, Aktieselskab VI 48. PK Chemicals A/S HI 22. Scandic Food A/S HI 49. Samso Konservesfabrik A/S UD 23. Rieber & Son Danmark A/S HI 50. European Freeze Dry ApS UD 24. Stryhns A/S HI 51. Sjallands Pelsdyrfoder Amba UD 25. Vital Petfood Group A/S UD 52. Aarhus Slagtehus A/S UD 26. Gumlink A/S HI 53. CP Kelco Services ApS HI 27. P/F Havsbrun HI 54. P/F Kosin UD 87

Output 5. Summary statistics Output 6. Detailed summary statistics 88

89

Output 7. Correlations 90

Appendix 8: The summary statistics of the manufacture of chemicals and chemical products industry Table 13: The manufacture of chemicals and chemical products industry companies based on integration strategies Chemical Industry-Companies Integration Strategies 1. Borealis Group HI 11. Aga A/S VI 2. Cheminova A/S HI 12. Trevira Neckelman ApS UD 3. Hempel A/S UR 13. Sun Chemical A/S UR 4. Dako Denmark A/S UD 14. Yara Praxair A/S VI 5. FiberVisions A/S UR 15. Flint Group Denmark A/S UD 6. Brenntag Nordic A/S HI 16. Syntese A/S UD 7. Koppers Denmark A/S HI 17. Basf Construction Chemicals UR Denmark A/S 8. Teknos A/S HI 18. Nordalim A/S UD 9. Danlind A/S UR 19. GK Pharma ApS UD 10. Air Liquide Danmark A/S HI Output 8. Summary statistics 91

Output 9. Detailed summary statistics 92

93

Output 10. Correlations 94

Appendix 9. The summary statistics of the manufacture of furniture industry Table 14: The manufacture of furniture industry companies based on integration strategies Furniture Industry-Companies Integration Strategies 1. Tvilum ApS HI 9. Fredericia Furniture A/S VI 2. Dan-Foam ApS UD 10. Ropox A/S UD 3. Expedit A/S VI 11. Kvik Production A/S UD 4. Invita Kokkener A/S UR 12. P.P. Mobler ApS UD 5. Dansani A/S VI 13. Lystrup Rustfri Stal ApS UD 6. Labflex A/S UR 14. Solrod Mobel A/S UD 7. Duba-B8 A/S VI 15. Aktielskabet J.L. Mollers UD Mobelfabrik 8. JKE Design A/S HI UD Output 11. Summary statistics Output 12. Detailed summary statistics 95

96

Output 13. Correlations 97

Appendix 10: The summary statistics of the manufacture of machinery and equipment industry Table 15: The manufacture of machinery and equipment industry companies based on integration strategies Machinery Industry-Companies Integration Strategies 1. Vestas Nacelles A/S UD 27. Tetra Pak Hoyer A/S HI 2. Vestas Blades A/S UD 28. Glunz & Jensen A/S VI 3. Vestas Towers A/S UD 29. CFS Slagelse A/S UR 4. Grundfos A/S UD 30. Epoke A/S VI 5. Vestas Control Systems UD 31. HOH Water UR A/S Technology A/S 6. LM Wind Power A/S UD 32. Kroll Cranes A/S VI 7. Sauer-Danfoss ApS VI 33. Dantherm Filtration AS UR 8. Gea Process Engineering UD 34. Westrup A/S HI A/S 9. Alfa Laval Copenhagen UD 35. Vola A/S UR A/S 10. Alfa Laval Kolding A/S UR 36. Soco System A/S HI 11. SPX Flow Technology UR 37. Egholm Maskiner A/S UR Denmark A/S 12. Kongskilde Industries A/S HI 38. Alfa Laval Nakskov VI A/S 13. Desmi A/S HI 39. Scanomat A/S UR 14. Sondex A/S UD 40. KJ Industries A/S HI 15. Hojbjerg Maskinfabrik HI 41. Skako Lift A/S UR A/S 16. Andritz Feed & Biofuel UD 42. Serman & Tipsmark HI A/S A/S 17. Disa Industries A/S UR 43. KSM Kragelund ApS UR 18. Wittenborg ApS UD 44. Heta A/S VI 19. Kverneland Group UD 45. Acta A/S VI Kerteminde A/S 20. Struers A/S UR 46. Boe-Therm A/S HI 21. Caljan Rite-Hite ApS UD 47. Abeto-Teknik A/S HI 22. SFK Systems A/S VI 48. Magnus Jensen A/S VI 23. Jensen Denmark A/S VI 24. Exhausto A/S UD 25. Gram Commercial A/S UD 26. Haas-Meincke A/S VI 98

Output 14. Summary statistics Output 15. Detailed summary statistics 99

100

Output 16. Correlations 101

Appendix 11. Industry comparisons of the 5 industries Graph 1: Companies by industries Number of Companies by Industry Machinery Industry 33% Pharmaceutical Industry 7% Food Industry 37% Furniture Industry 10% Chemical Industry 13% Graph 2: Average operating revenue per employee by industries Operating Rev. per empl. 4,000.00 3,500.00 3,000.00 2,500.00 2,000.00 1,500.00 1,000.00 500.00 0.00 Average Operating Rev. per empl. Pharmace utical Industry Food Industry Chemical Industry Furniture Industry Machiner y Industry Industries 1,948.10 3,701.22 3,095.31 1,420.78 1,749.71 Graph 3: Average net income per employee by industries Net income per empl. Average Net Income per empl. 600.00 500.00 400.00 300.00 200.00 100.00 0.00 Pharma ceutical Industry Food Industry Chemic al Industry Furnitur e Industry Machin ery Industry Average N.I. per empl. 496.13 73.78 125.67 120.59 71.62 102

Graph 4: Average number of countries by industries Average Num. of Countries 20 Number of Countries 15 10 5 0 Pharmac eutical Industry Food Industry Chemical Industry Furnitur e Industry Machine ry Industry Average Num. Of Countries 17 3 6 2 3 Graph 5: Average market shares by industries Average Market Share Market Share 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Pharmac eutical Industry* (4 years) Food Industry Chemical Industry Furniture Industry Machiner y Industry Average Market Share 9.04% 1.77% 4.66% 5.66% 2.03% Table 8: The sign of correlations among the variables Corr. Pharma. Ind. Food Ind. Chemicals Ind. Furniture Ind. Machinery Ind. ORPE NIPE ORPE NIPE ORPE NIPE ORPE NIPE ORPE NIPE RISK - - - + - - + - - - SIZE + - + + + - - - - - CINT - + + + + + + + + + MARS + + - - + + + + + + CPE + - + + + + + + + + RATIO - - - + - - - - - - VI - + + - - + - - - - HI + + + + + + + - - - UR + - - + - - + - - + UD - - - - - - - + + + COUNTRY + + - - - - + + + + 103

Graph 6: The pharmaceutical industry- operating revenue per employee and net income per employee comparison Performance Values Pharmaceutical Industry 3000.00 2500.00 2000.00 1500.00 1000.00 500.00 0.00 VI HI UnRe. UnDiv. Average ORPE 1482.92 2247.45 2754.01 1662.66 Average NIPE 668.86 1053.20 450.92 125.34 Analysis VI HI UnRe. UnDiv. Average ORPE 1482,92 2247,45 2754,01 1662,66 Average NIPE 668,86 1053,20 450,92 125,34 Average MARS 0,08 0,02 0,22 0,04 Average COUNTRY 16 4 31 9 Graph 7: The food industry- operating revenue per employee and net income per employee comparison Performance Values Food Industry 6000.00 5000.00 4000.00 3000.00 2000.00 1000.00 0.00 VI HI UnRe. UnDiv. Average ORPE 4182.67 5016.02 3539.98 2724.06 Average NIPE 22.63 78.82 194.05 36.55 Analysis VI HI UnRe. UnDiv. Average ORPE 4182,67 5016,02 3539,98 2724,06 Average NIPE 22,63 78,82 194,05 36,55 Average MARS 0,01 0,04 0,03 0,005 Average COUNTRY 3 2 7 2 104

Graph 8: The chemicals industry- operating revenue per employee and net income per employee comparison Performance Values 4500.00 4000.00 3500.00 3000.00 2500.00 2000.00 1500.00 1000.00 500.00 0.00 Chemicals Industry VI HI UnRe. UnDiv. Average ORPE 2432.59 4132.93 2174.22 3046.18 Average NIPE 297.63 222.02 87.89 3.47 Analysis VI HI UnRe. UnDiv. Average ORPE 2432,59 4132,93 2174,22 3046,18 Average NIPE 297,63 222,02 87,89 3,47 Average MARS 0,005 0,13 0,02 0,005 Average COUNTRY 2 6 11 4 Graph 9: The furniture industry- operating revenue per employee and net income per employee comparison Performance Values 1800.00 1600.00 1400.00 1200.00 1000.00 800.00 600.00 400.00 200.00 0.00-200.00-400.00 Furniture Industry VI HI UnRe. UnDiv. Average ORPE 1372.38 1603.82 1537.20 1362.87 Average NIPE 32.41 60.02-124.85 258.41 105

Analysis VI HI UnRe. UnDiv. Average ORPE 1372,38 1603,82 1537,20 1362,87 Average NIPE 32,41 60,02-124,85 258,41 Average MARS 0,041 0,19 0,05 0,027 Average COUNTRY 3 1 2 3 Graph 10: The machinery and equipment industry- operating revenue per employee and net income per employee comparison Performance Values Machinery Industry 3000.00 2500.00 2000.00 1500.00 1000.00 500.00 0.00-500.00 VI HI UnRe. UnDiv. Average ORPE 1485.90 1002.55 1527.34 2619.16 Average NIPE 20.22-43.26 157.97 116.82 Analysis VI HI UnRe. UnDiv. Average ORPE 1485,90 1002,55 1527,34 2619,16 Average NIPE 20,22-43,26 157,97 116,82 Average MARS 0,010 0,006 0,009 0,047 Average COUNTRY 1 4 2 4 106

Appendix 12. Differentiating the integration strategies for the whole sample Graph 11: Vertical integration Preformance Values 4,500.00 4,000.00 3,500.00 3,000.00 2,500.00 2,000.00 1,500.00 1,000.00 500.00 0.00 Pharmaceu tical Industry VI by industry Food Industry Chemical Industry Furniture Industry Machinery Industry Average ORPE 1,482.92 4,182.67 2,432.59 1,372.38 1,485.90 Average NIPE 668.86 22.63 297.63 32.41 20.22 Graph 12: Horizontal integration Performance Values 6,000.00 5,000.00 4,000.00 3,000.00 2,000.00 1,000.00 0.00-1,000.00 Pharmaceu tical Industry HI by industry Food Industry Chemical Industry Furniture Industry Machinery Industry Average ORPE 2,247.45 5,016.02 4,132.93 1,603.82 1,002.55 Average NIPE 1,053.20 78.82 222.02 60.02-43.26 107

Graph 13: Unrelated diversification strategy UR by industry Performance Vlaues 4,000.00 3,500.00 3,000.00 2,500.00 2,000.00 1,500.00 1,000.00 500.00 0.00-500.00 Pharmaceu tical Industry Food Industry Chemical Industry Furniture Industry Machinery Industry Average ORPE 2,754.01 3,539.98 2,174.22 1,537.20 1,527.34 Average NIPE 450.92 194.05 87.89-124.85 157.97 Graph 14: Un-diversification strategy Performance Values 3,500.00 3,000.00 2,500.00 2,000.00 1,500.00 1,000.00 500.00 0.00 Pharmaceu tical Industry UD by industry Food Industry Chemical Industry Furniture Industry Machinery Industry Average ORPE 1,662.66 2,724.06 3,046.18 1,362.87 2,619.16 Average NIPE 125.34 36.55 3.47 258.41 116.82 108

Appendix 13. Concentration indices Graph 15: Herfindahl index HH Index 0.5000 0.4500 0.4000 0.3500 0.3000 0.2500 0.2000 0.1500 0.1000 0.0500 0.0000 Pharmace utical Industry Average HH index Food Industry Chemical Industry Furniture Industry Machinery Industry Average HH index 0.3011 0.2552 0.4373 0.1641 0.1141 Graph 16: Entropy measure E Measure 3.0000 2.5000 2.0000 1.5000 1.0000 0.5000 0.0000 Average Entropy Measure Pharmac eutical Industry Food Industry Chemical Industry Furniture Industry Machiner y Industry Average Entropy Measure 1.4888 2.1037 1.1176 1.7303 2.7795 109

Graph 17: Concentration ratio (CR 4 ) CR4 Ratio 1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 Pharmaceut ical Industry Average CR 4 Ratio Food Industry Chemical Industry Furniture Industry Machinery Industry CR4 Ratio 0.92 0.69 0.81 0.64 0.53 Graph 18: Relative measure RE Measure 0.1600 0.1400 0.1200 0.1000 0.0800 0.0600 0.0400 0.0200 0.0000 Average Relative Measure Pharmac eutical Industry Food Industry Chemica l Industry Furnitur e Industry Machine ry Industry Average Relative Measure 0.1353 0.0397 0.0588 0.1154 0.0591 110

Appendix 14. Integration strategy comparison for the whole data Graph 19: Average operating revenue per employee Operating Rev. per employee Average Operating Revenue per Employee 4,000.00 3,500.00 3,000.00 2,500.00 2,000.00 1,500.00 1,000.00 500.00 0.00 VI HI UR UD Average O.R. Per empl. 2,138.05 3,443.85 2,335.34 2,495.30 Graph 20: Average net income per employee Net Income per employee Average Net Income per Employee 180.00 160.00 140.00 120.00 100.00 80.00 60.00 40.00 20.00 0.00 VI HI UR UD Average N.I. Per empl. 139.21 95.26 167.25 88.86 111

Graph 21: Average market share 6.00% 5.00% Average Market Share Market Share 4.00% 3.00% 2.00% 1.00% 0.00% VI HI UR UD Average Market Share 2.33% 5.12% 3.82% 2.13% Graph 22: Average number of countries Number of Countires 8.00 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 Average Num. Of Countries Average Num. Of Countries VI HI UR UD 4.04 3.31 7.55 3.37 112

Appendix 15. Stata outputs for the manufacture of food products industry by simple OLS Output 17. Regression output for ORPE as the dependent variable Output 18. Regression output for NIPE as the dependent variable 113

Appendix 16. Stata output for the manufacture of food products industry by forward stepwise regression with interactive terms. Output 19. Regression output for ORPE as the dependent variable 114

Output 20. Regression output with interaction effects by simple OLS (ORPE as the dependent variable) 115

Output 21. Regression output for NIPE as the dependent variable 116

Output 22. Regression output with interaction effects by simple OLS (NIPE as the dependent variable) 117

Appendix 17: Stata output for the manufacture of machinery and equipment industry by simple OLS Output 22. Regression output for ORPE as the dependent variable Output 23. Regression output for NIPE as the dependent variable 118

Appendix 18: Stata output for the manufacture of machinery and equipment industry by forward stepwise regression with interactive terms. Output 24. Regression output for ORPE as the dependent variable 119

Output 25. Regression output with interaction effects by simple OLS (ORPE as the dependent variable) 120

Output 26. Regression output for NIPE as the dependent variable 121

Output 27. Regression output with interaction effects by simple OLS (ORPE as the dependent variable) 122