Diversification and Performance in Dutch Health Care

An Empirical Analysis

ANR : S148680

Author : Gijs Gunterman

Supervisor : dr. A.A.C.J. van Oijen

Second reader : dr. ir. B.R. Meijboom

Organization : Tilburg University, Faculty of Economics and Business Administration

Program : Master Strategic Management

Date : January 12, 2012

Word count : 14.068

Abstract

The Dutch health care industry has a very particular business environment in relation to other industries. As

from 2006, more market forces are introduced into the health care industry which increased the need for

flexibility, quality, and efficiency. This made it inevitable for hospitals to be more dynamic and market

oriented, as privatization puts pressure on certainty and security by increasing own responsibility. One

popular way for hospitals to cope with this pressure, is to develop new revenue sources through

diversification. International research claims that diversification can generate increased performance.

However, large differences exist between related and unrelated diversification and empirical research in

the health care industry is lacking. Therefore, the question is what the effect of diversification is on

performance in the Dutch health care industry, either related or unrelated. Performance is analyzed

empirically using a cross-section of data primarily from the annual reports of all Dutch hospitals. Moreover,

performance is measured in terms of financial, medical, and organizational performance. This research

shows that related diversification does not by definition outperform unrelated diversification. Hospitals

that hold many clinics of specialism, and hence can be considered as related diversified hospitals, on

average outperform hospitals that have fewer clinics of specialism. Unrelated diversification proved to

have a positive effect on financial, medical, and organizational performance. Horizontal integration,

although it is an important trend in the Dutch health care industry, did not bring many significant findings.

Only the negative effect of horizontal integration on one of the financial performance indicators is useful.

This finding indicates that the number of hospital locations negatively affects financial performance.

Additionally, a two-way Analysis of Variances (ANOVA) was conducted, which gave a clear overview of the

average performance of four different diversification groups. Although clear differences can be identified

when observing the group means, pair wise comparison of group means hardly showed significance with

respect to the performance indicators. Only the unrelated diversification groups proved to be significantly

different on financial performance. This proves that unrelated diversification for Dutch hospitals can be

profitable. Overall, it can be concluded that both related and unrelated diversification affect the

performance of Dutch hospitals in different ways. However, firm characteristics like size, hospital type, or

privatization grade often dominate the results. Hence, the level of market forces and diversification in the

Dutch health care industry may not be as far as people nowadays expect and may not be that decisive for

performance yet. Nevertheless, in 2012 the privatized part of revenues will grow from 34% to 70%, which is

expected to have a far greater impact on performance. The final part therefore deliberately discusses the

future development of hospitals

Keywords: diversification, performance, privatization, and hospitals.

Table of contents

Chapter 1 Introduction ................................................................................................................................... 1

1.1 Problem indication ................................................................................................................................................ 1

1.2 Problem statement ............................................................................................................................................... 2

1.3 Research questions ............................................................................................................................................... 3

1.4 Research plan ........................................................................................................................................................ 3

1.5 Validity and Reliability ........................................................................................................................................... 4

Chapter 2 Diversification ............................................................................................................................... 5

2.1 Diversification: a definition ................................................................................................................................... 5

2.2 Related and unrelated diversification .................................................................................................................. 6

2.3 Horizontal integration ........................................................................................................................................... 7

2.4 Advantages and disadvantages of diversification ............................................................................................... 8

2.5 Diversification and financial performance ........................................................................................................... 9

2.6 Conclusions ........................................................................................................................................................... 11

Chapter 3 Dutch health care ......................................................................................................................... 12

3.1 Changes in the environment: privatization and deregulation ............................................................................ 12

3.2 Hospital strategy ..................................................................................................................................................13

3.3 Hospital arrangement ......................................................................................................................................... 14

3.4 Consequences for Dutch hospitals ..................................................................................................................... 14

3.5 Conclusions ...........................................................................................................................................................15

Chapter 4 Methodology ................................................................................................................................... 16

4.1 Research design and operationalization ............................................................................................................ 16

4.2 Data collection ...................................................................................................................................................... 17

4.2 Variable specification .......................................................................................................................................... 18

4.3 Empirical models ................................................................................................................................................. 20

Chapter 5 Results ............................................................................................................................................ 22

Chapter 6 Conclusion and discussion ................................................................................................................ 35

6.1 Conclusion ........................................................................................................................................................... 35

6.2 Limitations ........................................................................................................................................................... 37

6.3 Implications for academic literature ................................................................................................................... 38

6.4 Implications for hospitals .................................................................................................................................... 38

References ........................................................................................................................................................ 40

Appendix A Hospital overview ............................................................................................................................ 43

Appendix B Medical performance methodology .................................................................................................. 45

Appendix C Background information on privatization .......................................................................................... 47

Appendix D STATA/SPSS output .......................................................................................................................... 48

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Chapter 1 Introduction

In the nineties telecom providers, post offices, and other public service corporations had to undergo

privatization. As from 2006 time has come for Dutch hospitals.

In public health care there is a tendency towards a lack of resources and a continuous increase in demand

(Prior, 1996). For that reason it is necessary to implement decisions which promote efficiency and

alignment with demand. The next few years will bring a lot of changes in Dutch health care: privatization,

an aging society, and other social developments will have an effect on the set-up of hospital offerings and

especially its market operation (Blank and Wats, 2009). On the one hand it could be possible that in a

couple of years the Netherlands has an inconvenient and badly accessible health care sector. On the other

hand it could happen that this sector will flourish and will offer an extensive supply of convenient and high-

quality medical health care.

1.1 Problem indication

Privatization of health care services will improve the ability to anticipate to changes of demand as well as

the need for efficiency, but could also cause problems. That is why changes in the supply of treatments and

medical care are expected in the near future. Now hospitals are becoming private, costs have to be

controlled by the hospitals themselves which could affect the continuity as well as the medical quality.

Also, rational and economic motives become more important and do not always match with public

interest. The changes in market structure and regulation have affected the relative costs of providing

different medical services (Snail and Robinson, 1998). The first signs of these changes are already

noticeable: the number of hospital locations declined from 231 hospitals in 1980 to 127 in 2009 by horizontal

mergers (Nivel, 2000; RIVM, 2009a). Additionally, dozens of hospitals are on the edge of bankruptcy,

others suffer under quality issues while high-quality specialized hospitals enter the market (Laan, 2010).

Introducing more market forces into the health care environment increased the need for flexibility, quality,

and efficiency. This makes it inevitable for hospitals to be more dynamic and market oriented, as

privatization will put pressure on certainty and security by self-regulation. One popular way for hospitals to

cope with this pressure is to develop new revenue sources through diversification. The result is that

nowadays several huge general hospitals exist that resemble academic hospitals. This expansion goes hand

in hand with diversification through which it is not needed to refer patients to another hospital.

Diversification is achieved by expanding product lines or acquiring related companies (Chandler, 1962, 1973;

Ansoff, 1965; Rumelt, 1986), such as medical research, drug production, pharmacies, health care

management services, or expanding into unrelated product lines. By investing in such projects, it is claimed

that hospitals can increase their profitability and reduce their risk (Clement, 1987). The increased profits

can be a source of added equity for expansion, renovation, or simply financial stability. Hereby, hospitals

can ensure continuity to provide health services in the future.

The question is, however, whether hospitals should expand in a limited number of services (related

diversification) or engage in a broader package (unrelated diversification). In literature little is known

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about this specific matter (Blank and Wats, 2009). From a political view, diversification is not beneficial

since all hospitals should be of similar quality as well as efficient and accessible for any citizen. Whereas

specialized health care is now accessible in every hospital, it is not efficient neither of similar quality (RIVM,

Zorgbalans, 2010). Improved transparency, coordination, and distribution of specialisms between hospitals

should increase overall medical quality (RIVM, Zorgbalans 2010). That is why several initiatives were

developed that provide quality indicators (Inspectie voor de Gezondheidszorg, Nederlandse

Zorgautoriteit), performance rankings (Elsevier, Algemeen Dagblad, MediQuest, Roland Berger

Consultancy), and comparison databases (Zorgkiezer, Kiesbeter, Independer).

All these developments in hospital organizations are taking place without rigorous empirical study. The gap

between governmental policies and economic theory is still present in health care literature. Hence, a

compelling need exists for research into the causes and effects of hospital diversification (Snail and

Robinson, 1998). The biggest problem in the Netherlands is not low quality or performance in health care,

but great differences between the performance of hospitals. This research will contribute to the

understanding of these quality differences by investigating whether financial, medical, and organizational

performance of Dutch hospitals is affected by the trend of diversification.

1.2 Problem statement

How does diversification affect the performance of Dutch hospitals in a changing environment?

� Diversification implies being active in different industries or market segments. This research will

investigate (1) related diversification, which for Dutch hospitals specifically concerns diversification

in different medical specializations (within-industry diversification): cardiology, neurology,

orthopaedics, plastic surgery, internal medicine, dermatology, urology, etc. Additionally this

research will investigate (2) unrelated diversification, which specifically concerns diversification in

different and less-related businesses like separate clinics, parking lots, food services, etc.

� Performance in this particular case implies overall performance of hospitals (i.e. financial, medical,

and organizational performance).

� The perspective of this study will be all 88 hospital organizations in the Netherlands (Nationale

Atlas Volksgezondheid, 2010; Nederlandse Zorgautoriteit (NZa), 2010) consisting of 55 general

hospitals, 25 top clinical hospitals, and 8 academic hospitals (including a total of 139 locations).

Categorical hospitals (specialist clinics) are left out of consideration due to lack of consistent

registers and rankings, which could negatively influence the results of this study.

� The environment of Dutch hospitals is changing due to deregulation concerning privatization, an

aging society, and other social developments. Although the changing environment itself is not part

of the analysis, it is the setting of this research and it holds important background information.

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1.3 Research questions

In order to answer the former problem statement, the following research questions are constructed:

� How could diversification be defined?

� How does diversification affect performance?

� How could the changing Dutch health care environment be described?

� Why do Dutch hospitals engage in diversification?

� How does diversification affect the performance of Dutch hospitals?

1.4 Research plan

To create a clear and explicit view on recent developments, several theoretical arguments regarding

diversification in general as well as healthcare-specific are explained. A solid theoretical foundation is

achieved by specifically consulting top journals like the Strategic Management Journal (Impact Factor:

3.344). Additionally, governmental authorities and supervisory NGOs (NZa, Wfz) are consulted. The

following databases are assessed: JSTOR, Web of Science, Web of Knowledge, Google Scholar, and Tilburg

University Library (ABI/INFORM Global). These sources are widely used and contain a broad and clear base

of scientific information. Next, the Dutch health care system and its developments are described. In order

to answer the practical research questions, several hypotheses are constructed. A hypothesis overview can

be found in chapter four, page 20. Data is gathered from multiple-source secondary data, predominantly

annual reports from Dutch hospitals in 2009 and 2010. Furthermore, performance rankings and comparison

databases are used. Financial data is systematically, though manually, extracted from the reports. A large

part of the financial data is gathered in cooperation with Deloitte, established in Rotterdam. Deloitte

annually publishes a financial benchmark for hospitals. This benchmark reports on financial developments

and key figures. The primary purpose is to give hospitals insight in their financial performance relative to

peers. I took part in this party and contributed to the Cure Benchmark 2011.

The main concepts of this study are diversification and performance. The analysis explains what part of the

variation in financial, medical, and organizational performance in 2010 is explained by the different

diversification strategies of Dutch hospitals in 2009. Mainly one-sided OLS regressions are used for the

empirical analysis in STATA. Furthermore, a two-way Analysis of Variance (ANOVA) is conducted in SPSS.

This research is relevant since the privatization developments in the Dutch health care environment have

made hospitals more aware of the possibilities of diversification. Literature has shown that diversification

can have great consequences for (financial) performance. Hospitals are the most important component of

the health care sector. Besides, the developments will also affect investors (financial institutions) and

supervisory NGOs. Indeed, the hospital sector is so large that it is important and interesting in its own right.

This research contributes to academic literature by mapping diversification and economic performance for

the Dutch health care industry. Additionally, empirical research in Dutch health care is desirable now this

industry is being deregulated and privatized.

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1.5 Validity and Reliability

Validity refers to whether the findings are really about what they appear to be about (Saunders et al., 2009,

p. 157). If hospitals believe that results may disadvantage their organization in some way, they can

manipulate the results. To this potential validity problem, different performance rankings are compared

(predictive validity) to get valid results. External validity means that findings may be equally applicable to

other research settings, such as other organizations. Since great differences exist in national healthcare

between countries, this research is not generalizable to other countries. Nevertheless, findings may be

equally applicable in other pre-private organizations like other public services.

Reliability refers to the extent to which the data collection techniques or analysis procedures will yield

consistent findings (Saunders et al., 2009, p. 156). Data is gathered from annual reports and governmental

authorities, which are most likely to be reliable and trustworthy (O’Dochartaigh, 2002). The continued

existence of such organizations is dependent on the credibility of their data. Consequently, their

procedures for collecting and compiling data are likely to be well thought out and accurate. In addition to

this assumption, data collection methods and sampling techniques are examined carefully. Finally, the two

medical performance rankings used in this research, Elsevier and Algemeen Dagblad, use different

methodologies and are not related with each other (Appendix B).

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Chapter 2 Diversification

This first chapter will serve as theoretical framework and therefore examines the first two research

questions. In sections 2.1 to 2.3 general theoretical concepts of diversification will be discussed, answering

the first research question: How could diversification be defined? Furthermore, consequences of

diversification will be considered in sections 2.4 and 2.5. Hereby the second research question will be

answered: How does diversification affect performance? Section 2.6 will conclude this chapter.

2.1 Diversification: a definition

Diversification can be defined as a strategy that involves the entry into new markets with new products or

services (Chandler, 1962, 1973; Ansoff, 1965; Rumelt, 1986), or in other words the firm’s degree of market

involvement (Kamien and Schwartz, 1975). Diversification, which involves the entry of new markets, is not

mutually exclusive with other forms of expansion. Snail and Robinson (1998) identified three commonly

recognized forms of organizational expansion: diversification, horizontal integration, and vertical

integration.

� Diversification is the entering of new markets with new products. For example a hospital

diversifying into an ambulance service provider, pharmacies or medicine production.

� Horizontal integration is the combining of several organizations that have substitute outputs

(Conrad, Mick, Watts, and Hoare, 1988). For example the merger of two regional hospitals.

� Vertical integration involves the integration of two successive stages in the production chain. For

example a firm that integrates with its supplier. Therefore, the hospital does not have to purchase

its products from suppliers but produces those products itself.

In this research, diversification and horizontal integration are the concepts of interest. Diversification

strategies, in many cases, are selected because markets have been identified outside of the organization’s

core business that offers potential for substantial growth (Swayne, Duncan, and Ginter, 2006) or because

present markets constrain growth or profitability (Christensen and Montgomery, 1981). But there are also

other reasons why organizations diversify: distribution of risk, utilization excess product capacity,

compensation for technological obsolescence, reinvestment of earnings, and obtainment of top

management (Ansoff, 1958). Following Ginter et al. (2002), the two primary reasons for hospitals to

engage in diversification are to reduce hospital costs or to offer a wider range of services. Additionally,

health care organizations may identify opportunities for growth in less-regulated markets such as specialty

hospitals, long-term care facilities, or managed care (Ginter, Swayne, and Duncan, 2002). Diversification is

generally seen as a risky strategy since the organization is entering a relatively unfamiliar market or

offering a product that is different from its current products or services (Ginter et al., 2002). Although

hospitals are multiproduct firms, not all engage in the same activities to the same degree (Snail and

Robinson, 1998). Two types of diversification can be identified: related and unrelated diversification. The

next section will further elaborate those two different forms of diversification.

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2.2 Related and unrelated diversification

In related diversification, an organization chooses to enter a market that is similar or related to its present

operations (Wrigley, 1970; Rumelt, 1974). In this research, related diversification is defined as being active

in more than one market niche within the industry, also named within-industry diversification (Li and

Greenwood, 2004; Stern and Henderson, 2004; Tanriverdi and Lee, 2008). For hospitals, an example of

related diversification could be the set up of a new medical specialism.

Organizations have found that the risk of diversification can be reduced if markets and products are

selected that complement another and thereby will create synergy (Ginter et al., 2002). Synergy is a

complementary relationship where the combined entity outperforms the sum of its parts. Literature

indicates that related diversification strategies within an industry can influence firm survival (Stern and

Henderson, 2004) and profitability (Li and Greenwood, 2004).

The second diversification strategy, unrelated diversification, can be defined as entering new or unrelated

product lines or markets where no physical or knowledge resources are shared other than financial

(Wrigley, 1970; Rumelt, 1974). Examples of unrelated diversification for hospitals are diversifying into

operation of a parking lot or in household textiles. Unrelated diversification brings some unique

advantages primarily gained from financial synergies. Palich, Cardinal, and Miller (2000) and Barney (1997)

suggest that unrelated diversification is a suitable strategy to reduce risk between multiple industries.

Consequently, reduced riskiness can increase the debt capacity (Seth, 1990). Concurrently, unrelated

diversification can make it difficult to share activities and transfer competencies between units, and also

diseconomies of scope can become a problem (Palich et al., 2000), especially for an innovative industry like

the health care industry. At this point, the costs of extended diversification outweigh the benefits of

diversification. Unfortunately, this “break-even” point is hard to designate as it differs with industry and

firm characteristics. Hence, the discussion on related versus unrelated diversification remains.

Figure 2.1 on the next page shows the two forms of diversification for hospitals following Ginter et al.

(2002). The upper part of this figure displays several related activities of hospital care. The lower part

shows unrelated diversification, either within or outside the health care industry. Note that unrelated

diversification within the health care industry seems odd, indicating that it is difficult to strictly distinguish

between related and unrelated diversification. This research will overcome the problem by using Standard

Industrial Classification (SIC) codes, which is an objective system for classifying industries. Hence, this

research partly deviates from the classification in Figure 2.1. This study tries to create more insight in the

effect of the number of medical specialisms on performance. Therefore, this study considers the upper

part of Figure 2.1 as non-diversified activities instead of related diversification. Instead, related

diversification is measured by the number of medical specialisms. The lower part is, identical to Ginter et al.

(2002), considered as unrelated diversification. Next, section 2.3 will briefly discuss horizontal integration.

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Figure 2.1

Hospital diversification strategies

Source: Ginter et al. (2002, p. 221), adjusted.

2.3 Horizontal integration

Horizontal integration is the combining of several organizations that have substitute outputs (Conrad et

al., 1988) and can lead to specialization in a particular stage of the production chain. A merger of two firms

producing the same product or service in the same market is a good example of horizontal integration.

These mergers can achieve economies of scale in purchasing1 or production and higher capacity utilization

(Snail and Robinson, 1998). Historically, mergers occur in waves and often with different purposes

(Harford, 2005; Qiu and Zhou, 2007). Sudarsanam (2003, pp. 13–18) states that in the early 1900, mergers

occurred with the purpose of monopoly. The following wave, almost 10 years later, was due to change in

regulations and particularly generated oligopolies. Waves in de 60s, 70s, and 80s were characterized by

growth, conglomeration, and acquisitions to combine and divest respectively. The mother of all waves so

far -90s- focused on core competences as the source of competitive advantage. Mergers and acquisitions

do not occur in waves by coincidence, they are often the result of changes in economic environment or

changes in regulation (Sudarsanam, 2003). As privatization and deregulation of the Dutch health care

industry incorporates changes in regulation and economic environment, this could indicate a new merger

1 For hospitals, purchasing improvements could imply a better negotiation position with health insurers. Although a health insurer may be seen as a customer of the hospital, the negotiation of treatment prices is an important issue for both parties.

8

wave in the Netherlands or at least a lot of dynamics in the health care industry. The current period is

therefore a very interesting time to investigate horizontal integration for Dutch hospitals. In fact, the

number of general hospitals in the Netherlands has declined from 231 hospitals in 1980 to 127 in 2009 by

horizontal mergers (Nivel, 2000; Rijksinstituut voor Volksgezondheid en Milieu (RIVM), 2009a). Chapter

three will come back on the changing environment and horizontal integration in particular. Next, section

2.4 continues with advantages and disadvantages of diversification.

2.4 Advantages and disadvantages of diversification

As suggested in previous sections, diversification can have a broad impact on strategy and business in

general. Starting with Ansoff’s work in 1958, the diversification phenomenon is still widely open for

discussion. Nevertheless, research in the last decades yielded many consequences of diversification. The

main advantages and drawbacks of diversification are summarized below:

Advantages and opportunities

� Increased efficiency and productivity of operations. When a firm expands operations

opportunities for economies of scale and economies of scope (synergies) arise (Ginter er al.,

2002). This means that operational and financial resources can be shared between operations.

� Knowledge transfer. When a firm diversifies in different operations, knowledge, skills, and R&D

can be transferred or combined between the operations (Li and Greenwood, 2004).

� Risk reduction. By diversifying, a firm distributes its financial risk: when a business unit

underperforms this can be compensated with another well-performing unit. This is called cross-

subsidizing (Palepu, 1985; Montgomery, 1994; Palich et al., 2000).

� Increase of market power. Again the possibility of cross-subsidizing can be an advantage which

non-diversified competitors lack (Palich et al., 2000).

� Market involvement. By being present in different facets of the value chain, a firm can use its well-

established brand name or corporate identity to gain benefits (Montgomery, 1994).

� Li and Greenwood (2004) added maybe the most challenging opportunity: coordination of all

former factors. They found that it is not sufficient to adopt diversification by positioning a firm in

favourable market niches and pursue economies of scope: the firm must also interact strategically

with other parties located in the same market niches. This means that firms need to expand their

view and to adapt to different environments, which leads to another opportunity:

� Mutual forbearance. Diversification creates multi-market contact between firms (Edwards, 1955).

Multi-market contact is a situation where firms meet the same rival firms in more than one market.

This contact creates an opportunity for firms to strengthen their negotiation position, thereby

weakening competition; firms are vulnerable in several markets and will be more cautious.

Weakened competition could strengthen strategic behaviour of firms (Scherer, 1980; Hughes and

Oughton, 1993). Baum and Greve (2001) found that such collusion enables the capture of high

returns.

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Drawbacks

� Administrative needs. In 1962, Chandler already observed this potential drawback. When a firm

grows bigger due to diversification, administration costs increase and bureaucratic issues arise.

� Inflexibility. A larger and cumbersome company may be less capable of adapting to market

changes, either it will take more time or be more costly (Grant, Jammine, and Thomas, 1988).

� Other inefficiency. When overextending diversification, several costs can lead to an inefficient

organization. Examples are management costs and control costs. Additionally, a diversified firm

could create conflicts and loss of direction (negative synergies, mismatches) which again increases

complexity and inefficiency (Hoskisson, 1987).

In sum, diversification strategy holds a vast range of advantages and opportunities which can be, if

combined in the right way, very profitable. The next section will therefore discuss the present progress on

empirical evidence of diversification and performance.

2.5 Diversification and financial performance

The work of Rumelt (1974) was pioneering among the strategic management studies that examined the

profit impact of diversification. Rumelt used a categorical measure of diversification instead of product-

count measures. He examined the financial performance of 246 diversified firms and concluded that firms

that diversified in related industries outperform other types of firms. Datta, Rajagopalan, and Rasheed

(1991) intensively investigated literature on the effects of diversification on economic performance.

Economic performance, in their work, arises from greater market power, economies of scale and scope,

synergies, and risk reduction, as discussed in section 2.4. Hughes and Oughton (1993) state that

diversification can increase profitability by increased efficiency through greater asset exploitation,

reduction in transaction costs, economies of scope, and recognition of interdependence. Palich et al.

(2000) synthesized findings from three decades of research on the diversification-performance relationship

and concluded that moderate levels of diversification yield higher levels of performance than either limited

or extensive diversification. It seems that literature on diversification has found important effects of

diversification on performance. Nevertheless, it should be taken into account that benefits of related

diversification and unrelated diversification could occur to a different degree. Li and Greenwood (2004) are

the first who specify their investigation onto within-industry diversification. They suggest that

diversification creates advantages by synergy, mutual forbearance, and market structuration. Market

structure variables are for example market concentration and firm size.2 Nonetheless, research from

Christensen and Montgomery (1981) already suggested, in conjunction with economic theory, that market

structure variables influence performance and diversification strategy. However, literature on

diversification of hospitals still has only weak ties to economic theory (Clement, 1987). This seems odd,

2 This research will control for the market structure variables suggested by Christensen and Montgomery (1981). Chapter four will discuss this matter in more detail.

10

because for hospitals, just like other business, diversification could also create synergies. For instance

greater market power, reduced transaction costs, economies of scope, and risk reduction can be achieved

by entering new markets or simply by being active in more market niches. A more practical example is a

hospital that has many clinics of specialism, its own pharmacy, etc. This hospital has a whole

(complementary) medical process under its roof and creates a one-stop-shopping atmosphere3: the patient

does not have to be sent to another hospital or organization to complement its needs. This could create

more revenues and improved efficiency due to better alignment and knowledge transfer between clinics of

specialism (and other complementary divisions), hereby improving medical performance. These

expectations are in line with the positive effect of related diversification on performance found by Snail

and Robinson (1998) in their research on American hospitals. Hence, hypothesis 1 investigates whether the

common effects of diversification on performance are also valid for hospitals in the Netherlands.

Performance is measured in terms of financial, medical, and organizational performance to create a more

comprehensive view.

Hypothesis 1: Related diversification is positively related to performance.

On the other hand, it must be recognized that diversification is not a costless process: diversification can

impose significant costs in terms of bureaucratic and control costs (Porter, 1985; Jones and Hill, 1988). As

mentioned in section 2.4, Chandler observed increased administrative expenses and bureaucratic issues in

1962, caused by overextended diversification. Hypothesis 2a, which is an introductory hypothesis,

considers whether this is also the case for Dutch hospitals that engage in this overextended, unrelated

diversification.

Hypothesis 2a: Unrelated diversification is positively related to administrative expenses.

Other inefficiencies of diversification are inflexibility and considerable strains on top management

(McDougall and Round, 1984). These problems often occur as firms overextend diversification. As it

becomes harder to share activities and transfer competencies between units, the above mentioned costs

could outweigh the potential benefits of diversification. This suggests that firms diversifying outside of

their core business (unrelated diversification) have more difficulty sharing activities and competences, and

consequently are less profitable than firms that pursue related diversification. Christensen and

Montgomery (1981) found, next to their suggestions about the market structure variables above, unrelated

diversifiers to be underperforming in contrast to related diversifiers. In fact, Lamont and Polk (2002) found

unrelated diversified firms even value destroying. In sum, unrelated diversification has shown to be less

profitable than related diversification; opportunities for economies of scope (by sharing resources)

3 On August 30, 2010, Frits Baltesen published an article in the NRC about a one-stop-shopping experiment in Schiedam: all medical care under one roof. This concept seems to be more cost effective and to provide high quality care. Still, despite these preliminary findings, more sophisticated research on this subject is desirable in the near future.

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decrease, while decision making, control, and governance become more and more difficult to manage.

Regarding medical or organizational performance, it is suggested that unrelated diversification does not

produce significant operating synergies (Michel and Shaked, 1984). Furthermore, it can be expected that

hospitals engaging in business unrelated to health care could lose their focus which does not contribute to

medical quality. Hence, hypothesis 2b will test whether hospitals that are more engaged in unrelated

businesses underperform relative to other hospitals.

Hypothesis 2b: Unrelated diversification is negatively related to performance.

Nevertheless, firms that are able to create an efficient way to transfer competencies between units could

benefit from unrelated diversification and gain higher equity returns, when controlled for variables such as

risk and industry effects (Luffman and Reed, 1984; Michel and Shaked, 1984; Dolan, 1985; Dubofsky and

Varadarajan, 1987). This is also in line with the suggestion of Li and Greenwood (2004) that the biggest

challenge of diversification is to coordinate all the different factors and opportunities. These empirical

findings indicate that the relation between diversification and firm performance is not perfectly clear.

In short, much of the strategic management research has been devoted to the relation between

diversification and performance. Many results have been published although they are not consistent and

straightforward, which means this relation remains open for debate. For that reason it is important to

continue empirical research in the field of diversification. Nearly all studies conceptualized performance in

terms of economic measures of return or risk, as accounting-based and market-based measures of

performance. However, those economic measures of performance are not the only legitimate outcomes

for industries such as the health care industry. This research will take on a much broader view of

performance. Chapter three will come back on this issue.

2.6 Conclusions

Diversification can be defined as a strategy that involves the entry of an organization into new markets

with new products or services. Diversification strategies are selected because markets have been identified

outside of the organization’s core business that offer potential for growth, synergies, or risk reduction. For

healthcare organizations this could infer opportunities for growth in less-regulated markets such as

specialty hospitals, long-term care facilities, or managed care. Diversification is also seen as a risky strategy

since the organization is entering a relatively unfamiliar market or offering a product or service that is

different from its current products or services. The same applies for performance, where many research

has generated results in different directions. Nevertheless it can be said that related diversification is

generally more profitable than unrelated diversification. Hence, many factors play a role in the

determination of performance.

12

Chapter 3 Dutch health care

Chapter three will first explain the Dutch health care environment. Subsequently, changes in this

environment are examined, answering the third research question: How could the changing Dutch health

care environment be described? Next, this chapter will elaborate the recent tendency in hospital strategy

and thereby answer the fourth research question: Why do Dutch hospitals engage in diversification? Some

general concepts of privatization are discussed and consequences for Dutch hospitals are elaborated.

3.1 Changes in the environment: privatization and deregulation

Over the years, the Dutch government has responded to several developments in the health care industry

by implementing a variety of regulations: regulation concerning the terms under which insurers reimburse

the hospitals, control over entry of new hospitals, investment and expansion, but also on the quantity and

quality of medical care. Despite these efforts, the biggest problem in Dutch health care was the passive

public environment versus the active private environment. In 1962 already, Averch and Johnson found that

the traditional method of production-based reimbursement by the government could result in a

misallocation of economic resources. Under this system, higher hospital costs directly generated additional

revenues (Sloan, 1982). Hereby hospitals have an incentive to work in an uneconomic fashion that is

difficult for the government to monitor and control (Liston, 1993; Raad voor Volksgezondheid en Zorg

(RVZ), 2006). Considering both the rapid rise in health care expenditures and the problems mentioned

above, it is important that hospitals have a strong incentive to reduce costs (Vickers and Yarrow, 1988;

Armstrong, Cowan, and Vickers, 1994). Former regulations did not provide meaningful financial incentives

to reduce costs. Hence, the government decided to restructure the health care system around market-

oriented principles: costs and prices had to be transparent and hospitals had to be more responsible for

their own management. It had become clear that certain functions need not be performed by the

government and may safely be left to markets (Crew and Kleindorfer, 2002). Privatization was the answer.

In March 2005 it was presented to the parliament that hospitals had to be more efficient and aligned with

health care needs. Hospitals were to be gradually privatized and several regulations had to be abolished to

gradually increase market forces: deregulation. Another reason for the privatization were the increasing

costs of the Dutch health care, but also an aging society and other social developments were affecting the

environment and the set-up of hospitals (Blank and Wats, 2009). Social developments include patients

becoming more articulated customers who demand a higher quality, are more informed and thus can make

a better choice.

Privatization is implemented as follows: with the introduction of a basic insurance in 2006, WTZi (Wet

Toelating Zorginstellingen) and WTG (Wet Tarieven Gezondheidszorg), the government started with the

privatization of the health care sector. The most extensive change comes from the use of DBC (Diagnose

Behandel Combinatie) which registers and finances provided care. DBC is split into the A-segment and the

B-segment. The B-segment is at most 34 percent as from 2009. There were no further changes in 2010 due

to the fall of the Dutch government, but as from 2012 the B-segment will increase to 70 percent of total

13

hospital production, which will be its final value. The reason that the B-segment is not allowed to grow to

100 percent is that highly specialized care is relatively unprofitable and could therefore disappear.

Treatments in the A-segment have fixed prices, but the prices of treatments in the B-segment must be

negotiated between hospitals and health insurers (DBC Onderhoud, 2011). In short, DBC is a very important

instrument for safe and affordable health care, but also for transparency and competition: on the one hand

the B-segment provides transparent prices and efficient production, while on the other hand A-segment

insures against dangers of full privatization. Extra information on privatization is found in Appendix C.

3.2 Hospital strategy

Due to changes in the health care environment, financial institutions have set new and higher requirements

for hospitals (Kriek and Dooyeweerd, 2009). The formation of investment arrangements is now dependent

on negotiations between hospitals and investors. With the new regulations, not only the sort and size of

the investment is important, but especially how the hospital is planning to repay their investment. After all,

investments are no longer in accordance with statutory regulation but need to be earned by supplying

health care products or other activities (RVZ, 2006). Investments should be aligned with strategic

objectives. Also effects on market position should be taken into account, not to forget stakeholder interest

like impact on health care quality, patient satisfaction, and support of employees and professionals.

In addition to the alignment of strategy and finance discussed above, Putters (2003) investigated the

strategic behavior of Dutch hospitals as a response to privatization. The research distinguishes between

two kinds of behavior as a response to privatization:

� Offensive behavior; focused on reinforcement and increase of market position by expansion, for

example by diversification (e.g. expanding research activities or entering of new markets);

� Defensive behavior; focused on reinforcement and control of market position by preservation, for

example by uncertainty avoidance (e.g. protection of budgets, creation of entry barriers, and

collaboration with other hospitals or insurance companies).

The offensive market strategy includes mergers, networks, or coalitions to incorporate expansion,

renovation, and innovation in their own organization. The defensive market strategy strives to strengthen

the organization and to modernize the internal relationships by focusing on their core-business (Putters,

2003). Both strategies are aimed at reducing market uncertainty such as uncertainties about customer

relationship, competitive position, and even the core-business. Jeurissen, Brummelman, and Heurck (2003)

extended this view and state that horizontal integration is mainly a defensive reaction to the arrival of

privatization. They also point out that bed reduction and empire building have played an important role.

Vertical mergers are thus far limited, although some general hospitals have joined forces with nurse- and

rest homes or primary health care. Academic hospitals have not merged, although in the 90s a merging

trend with medical faculties (medical schools) occurred. A relevant example of an offensive hospital

strategy can be found nearby, namely in the St. Elisabeth Hospital in Tilburg: this hospital tries to expand its

educational division by bringing in top specialists and researchers, for example around the intervention

technology. By profiling itself as educational specialist they try to meet the regional health care demand.

14

3.3 Hospital arrangement

In 2010 the Netherlands contained 88 hospital organizations (Nationale Atlas Volksgezondheid, 2011;

Nederlandse Zorgautoriteit (NZa), 2011) consisting of 55 general hospitals, 25 top clinical hospitals, and 8

academic hospitals (including a total of 137 locations). A general hospital is a concentration of facilities for

research and treatment (Rivm, 2011), where also doctors and nurses are educated. Top clinical hospitals are

facilities of highly specialized care, e.g. neurosurgery or transplantations. Academic hospitals are

comparable to general hospitals regarding care and education, although academic hospitals extend this

educational environment to top reference research and development as they are connected to a University

or medical school.

3.4 Consequences for Dutch hospitals

The developments and changes in the environment mentioned in section 3.1 and 3.2 have great impact on

hospitals in terms of strategy and performance. Large general hospitals with top clinical features as well as

academic hospitals have increased their market share at the cost of small and medium hospitals (Den

Hartog and Janssen, 2002). This development was due to mergers and acquisitions. The resulting

economies of scale enable further specialization which diminishes the role of academic hospitals. To

improve the division of tasks and capacity, (regional) cooperation is important (Vereniging Academische

Ziekenhuizen (VAZ), 2000). These developments improve the competitive advantage of top-clinical

hospitals in relation to academic hospitals as top clinical hospitals are now able to focus on education and

research as well. Eventually boundaries between hospitals will fade as hospitals proceed to a continuum.

Note that this does not mean that different hospital categories will coincide to a single profile. Another

consequence is that basic care could disappear in less crowded areas. The government could compensate

diseconomies of scope in these sparsely populated areas. Market operation of hospitals is expected to be

more threatening in small and sparsely populated areas than in high-end hospitals in densely populated

areas. This empirical analysis of this study will control for these factors.

Section 3.2 discussed the new responsibility of hospitals for their investment policy due to privatization. To

guarantee continuity in the health care industry, the Waarborgfonds voor de Zorgsector (WfZ) has

increased the solvability threshold from 8 to 15 percent, which indicates that hospitals are obligated to

finance 15 percent of total revenues with equity. Most of the hospitals do not satisfy this demand yet,

resulting directly in financial pressure and indirectly in pressure on medical performance. Another factor is

the increased importance of insurance companies, and especially the negotiation position towards the

insurance companies. As a consequence of mergers of insurance companies, hospitals will also merger to

compensate for their negotiation position (Nivel, 2002b). Nevertheless, merged hospitals often choose to

concentrate specialized functions on one location and will be more careful about their treatment selection

(Nationaal Kompas Volksgezondheid, 2011). This defensive behavior could lead to less basic care offerings

by academic hospitals and more cuttings on complex treatments by general hospitals. The reason for these

cuttings is an increased understanding of treatment costs (due to DBC) whereby hospitals will reconsider

whether treatments are cost-effective and relevant (C. Vos, personal communication, April 18, 2008).

15

In contrast, RIVM (2010) states that the supply of health care is becoming more diversified as a response to

changes in demand. RIVM expects the number of hospitals to decrease to between 40 and 70 in 2014 due

to horizontal integration. As discussed in section 3.2, Jeurissen et al. (2003) state that horizontal

integration is mainly a defensive reaction to the arrival of privatization. Den Hartog and Janssen (2002)

found that mergers between general hospitals have consequently caused that beds inside those hospitals

have increased with 61 percent between 1984 and 2000, at the expense of small and mid-sized hospitals.

This suggests that mergers and acquisitions should not specifically be due to privatization but could be a

trend. The decrease in number of hospitals does not lead to an equal decline in locations, which indicates

that one hospital holds several locations. Different hospital locations stand for distribution of care which is

a good thing for accessibility. Nevertheless, hospitals often choose to concentrate specific care in one

location. This results in the fact that some locations will have more extended facilities than others. A

hospital that is active in several medical specializations in different locations can be seen as a horizontally

integrated hospital as it contains different market segments distributed over several locations. It is

important to watch this defensive development carefully as it can induce impoverishment of care, as well

as changes in operations and financial performance. Hospitals gain different appearances due to a

changing environment. Hence, it is interesting to know whether hospitals that have more locations actually

perform significantly different than other hospitals. Although little research has been done on effects of

horizontal integration on performance, Snail and Robinson (1998) expected that local hospital mergers

could realize improvements of economies of scale in purchasing and production, and improvements of

capacity utilization and efficiency. However, diseconomies in coordination activities and performance

incentives could arise. Ermann and Gabel (1984) found that horizontal integrated hospitals had higher costs

than single hospitals. Due to few and inconsistent findings in previous research, hypothesis 3 will test the

effect of horizontal integration on financial, medical, and organizational performance without a

predetermined direction.

Hypothesis 3: The number of hospital locations affects the performance of hospitals.

3.5 Conclusions

From 2006 onwards the Dutch health care sector is gradually privatized. Privatization is a major operation

and brings a lot of dynamics. Hospitals have to face more competition and are forced to increase efficiency

and transparency. Some hospitals engage in a more offensive strategy, while others choose for a more

defensive strategy. Nevertheless, both strategies are aimed at reducing market uncertainty. The Dutch

health care sector is subject to much horizontal integration (mergers) and also the supply of health care

becomes more diversified. Additionally, hospitals enter different markets as deregulation and privatization

allows more market oriented strategies and opportunities. Chapters two and three have discussed the

concept of diversification and the Dutch health care environment respectively and also defined several

hypotheses. Now, chapter four will explain the methodology for the empirical part of this thesis to test

these hypotheses.

16

Chapter 4 Methodology

Methodology is an important part of an empirical analysis. This chapter will give a clear explanation of how

this research is undertaken to answer the problem statement. As formulated in the introduction, this

research question is:

How does diversification affect the performance of Dutch hospitals in a changing environment?

The empirical methodology of this research addresses this question and is designed to find evidence of

effects of diversification on financial, medical, and operational performance. Several variables of interest

are used to test the expected effects and magnitude. The first two sections explain the data collection and

variable specification, where the final section describes the empirical models used for this study.

4.1 Research design and operationalization

Many studies based their empirical design on a two-dimensional, categorical measure of diversification,

building on the work of Wood (1971), Palepu (1985), and Varadarajan and Ramanujam (1987). A desirable

feature of this measure is that it does not require data on revenues of business segments, while still

providing insight in the degree of both related and unrelated diversification. Moreover, the detailed

business information, which is required for entropy measures of diversification, is generally unavailable and

when it is available likely to be of untested validity. However, a significant drawback of the categorical

measure is that the data loses a large part of its value when constructing the categories. Therefore, this

study will primarily use a one-sided4 Ordinary Least Squares (OLS) model, thereby retaining the continuous

value of the data. To provide a complete empirical analysis, this research will also supply a two-way

Analysis of Variances (ANOVA), based on the methodology of Varadarajan and Ramanujam (1987). This

research uses the diversification variables designed by the above mentioned authors: Wood (1971) created

two distinct patterns of diversification, Narrow Spectrum Diversification (NSD) and Broad Spectrum

Diversification (BSD). NSD represents related (or within-industry) diversification and BSD represents

unrelated diversification. Varadarajan and Ramanujam (1987) refined this method by modifying the NSD

measure to an average measure, MNSD (Mean Narrow Spectrum Diversification). Hereby it becomes

visible whether a firm is active in many or few related businesses. This research specifically refines the

categorical measure of diversification for the healthcare sector. As the purpose of this study is to create a

thorough insight into the effects of hospital diversification (as in the scope of specialism), 8-digit SIC

categories are acknowledged instead of the usual 4-digit SIC categories. In this way clinics of specialism can

be identified and hence the scope of operations diversity within Dutch hospitals. Figure 4.1 shows a visual

representation of a firm’s diversity. This four-cell matrix has similarities to other seminal conceptualizations

of diversification, like Rumelt’s (1974) diversification categories and Palepu’s (1985) four-cell

categorization, using Jacquemin and Berry’s (1979) weighted entropy measures. For this analysis the BSD,

NSD, and MNSD have a mean of 3.91, 30.36, and 8.55 respectively (see descriptive statistics in Table 5.1).

4 Because most hypotheses formulate a specific direction, a one-sided OLS is a good fit and also increases the range of acceptance. For hypothesis 3, a two-sided OLS is used, because this hypothesis does not formulate a specific direction.

17

Figure 4.1 A two-dimensional conceptualization of diversity in firms

Source: Varadarajan and Ramanujam (1987), adjusted.

* Broad Spectrum Diversity (BSD) is the number of two-digit SIC categories in which a firm concurrently operates.

** Mean Narrow Spectrum Diversity (MNSD) is the number of eight-digit SIC categories in which a firm operates divided by the

number of two-digit SIC categories in which it operates.

4.2 Data collection

Snail and Robinson (1998) indicated that lots of empirical papers on hospital diversification suffer from self-

selection bias. However, the Netherlands provides a rather small set of hospitals which are all included in

this research. Hence, self-selection bias is not a problem in this study. The sample consists of 88 Dutch

hospitals (general, top clinical, academic). Categorical hospitals (specialist clinics) are left out of

consideration because of their different setting and lack of data. The primary sources of data for this

research are (1) the annual reports of all 88 Dutch hospitals from 2009 and 2010, obtained from the official

governmental health care website5, (2) REACH database6, (3) Elsevier’s “Beste Ziekenhuizen 2010”, and (4)

Algemeen Dagblad’s “Ziekenhuizen Top 100”. The annual reports contain most of the financial data as well

as the diversification data required for the empirical section. The investigations from the two magazines

contain medical performance data. Other data sources are the NGO websites and databases (RIVM, RVZ,

NVZ, and NZa) and Company.info, which is related to the Chamber of Commerce.7 Data concerning

diversification is collected for 2009 and financial data for 2010 to secure the lag in strategic effects. If

available, consolidated balance sheets and income statements have been used. The dataset does not

contain any missing values. Using this dataset, it is expected to measure effects of diversification in 2009 in

the following year’s performance. Appendix A presents an overview of all 88 Dutch hospitals used in this

research and all the relevant industries.

The overall reliability of the database is considered to be acceptable as the financial data was collected by

one person and double-checked afterwards by several other persons to minimize errors in the dataset.

5 Annual reports are available from www.jaarverslagenzorg.nl, including digiMV for social responsibility (digitale Maatschappelijke Verantwoording). 6 REACH (REview and Analysis of Companies in Holland) is also known as Orbis (Bureau van Dijk). REACH is an electronic data source that contains information of many Dutch enterprises. The database predominantly contains financial information and industry classification. 7 Dutch: Kamer van Koophandel.

18

Additionally, control calculations were used to clarify any error. The data concerning diversification was

collected from several sources and hereby double-checked. Regarding medical performance, both Elsevier

and AD improved their research methods every year from 2006 onward (Appendix B). Several

developments have been made on the provision of information and the transparency of hospital quality.

The IGZ has created several official quality indicators for hospital quality, which are taken into account by

AD. Roland Berger Consultancy (2009) stated that most of the indicators give a clear indication of quality.

4.2 Variable specification

Dependent variables. The dependent variable in this research is performance, but it is measured in three

different types of performance: (1) financial, (2) medical, and (3) organizational efficiency.

Financial performance is measured with two common measures, total margin (MARGIN) and return on

assets (ROA). MARGIN is an indicator of a firm’s short-term performance (Clement, d’Aunno, and Poyzer,

1993) and is defined as the total earnings divided by total revenues. ROA is a typical measure for financial

performance (Wheeler, Burkhardt, Alexander, and Magnus, 1999) and a better measure of long-term

viability (Clement et al., 1993). Apart from the fact that Dutch hospitals do not hold stock, Grant, Jammine,

and Thomas (1988) recommend the choice of accounting profit because it more directly reflects the impact

of corporate strategy on a firm’s performance than stock price, which measures investor’s expectations

about future profits.

Medical performance is measured by variables based on a study of Elsevier, a ranking of Algemeen Dagblad

and a combination of these two performance measures. The Elsevier measure (MEDELS) is based on speed

of service, quality of service, and patient orientation. The AD measure (MEDAD) is a ranking based on a

percentage of a maximum score. The third indicator for medical performance is constructed from both

Elsevier and Algemeen Dagblad (MEDCOMB). The Elsevier measure is a four point score and for this

combined indicator multiplied by 0.25 to make it comparable with the AD score, which lies between zero

and one (as a percentage). Next, the two measures are multiplied and thereby equally weighted in the

combined measure. It should be noted that this variable is used as an extension of the original two medical

performance variables and is only reported when it supports the results of medical performance.

Organizational performance is measured by two variables, efficiency (EFF) and productivity (PROD). EFF is

measured by the amount of gross work-in-progress. Less work-in-progress indicates a more efficient

hospital. This research will use gross (instead of net) work-in-progress since this variable is unaffected by

deposits and facilities. Net work-in-progress extracts health insurance deposits and facilities for non-

invoicable performance, which are important determinants of efficiency. Therefore, gross work-in-progress

is used instead. PROD is measured by total revenues divided by average FTE, hereby measuring the

productivity per Full-Time-Equivalent.

19

For one hypothesis the effect of unrelated diversification on administration expenses (ADEXP) is

considered. Since administrative expenses are not directly tied to a specific function, these expenses are

related to the organization as a whole as opposed to an individual department.

Variables of interest. Unrelated diversification is measured by Broad Spectrum Diversity (BSD), which

measures the number of two-digit SIC categories in which a hospital is active in 2009.

Related (within-industry) diversification is measured by Narrow Spectrum Diversity (NSD), which is

expansion into an industry with a different eight-digit industry code, but the same two-digits.

Since for a given two-digit SIC code a hospital may be active in many or few eight-digit SIC codes, the NSD

measure is supplemented by the Mean Narrow Spectrum Diversity (MNSD). This variable counts the

number of eight-digit SIC categories in which a firm operates and divides this by the number of two-digit

SIC categories in which it operates. The greater part of those eight-digit SIC categories include clinics of

specialism like neurology, cardiology, dermatology, etc. To improve the distributional characteristics of this

variable, the logarithm of MNSD is used in the analysis, obtaining the variable LOGMNSD.

Horizontal integration (HINT) is measured by the number of hospital locations that a hospital operates. A

wider range of hospital location indicates stronger horizontal integration.

Control variables. Obviously, many factors other than the variables of interest may influence our dependent

variables. The first one is SIZE (measured as the log of total assets).8 Hospital size is expected to increase

the ability to diversify business-wise but also to diversify risk, so positive signs are expected for SIZE.9

Hospital-type characteristics are neutralized by two dummy variables, TOPCLINIC and ACADEMIC. These

variables are one when the hospital is a top-clinical or academic hospital respectively, and zero otherwise.

BSEGMENT controls for the privatized share of production, because more privatized hospitals could

influence the results. To control for financial risk, the four-year period volatility of the hospital's cash flows

(VOLATILITY) is included.10 VOLATILITY is defined as the standard deviation of the hospital’s profits over a

four-year period divided by the mean value of total assets in the same four years. Hospitals with more

stable earnings are assumed to be less risky (Bradley et al., 1984; Valvona and Sloan, 1988). Two indicators

are used to control for industry concentration because when used in isolation, these indicators can be

misleading (Baye, 2005). Therefore, PROVINCE and CR4 are included to control for industry concentration

8 For SIZE, the number of beds (BED) and the number of full time equivalents (FTE) are also considered, but the log of assets (SIZE) showed to be the most reliable and consistent control variable: regressions including BED or FTE are comparable with regressions including SIZE, but reduce the significance (F-test) and the adjusted R2 of the models. 9 Cohodes (1983a) reinforces this argument for hospitals specifically, stating that large hospitals have better access to debt markets than smaller hospitals and hence have greater investment opportunities. 10 Also a second indicator for financial risk was considered, the interest coverage ratio (times interest earned), which measures the ability to pay interest on outstanding debt. The lower the ratio, the more burdened by debt and hence smaller opportunities for diversification. Unfortunately, this variable proved to negatively affect the models and results and was therefore omitted from the analysis.

20

and other geographic conditions (Capon, Farley, and Hoenig, 1990). PROVINCE controls for the number of

residents per hospital in every province in which the hospital is located. CR4, an abbreviation for

Concentration Ratio, measures the percentage of market share owned by the four largest hospitals in the

province in which the hospital is located. CR4 ranges from zero to one, where zero indicates high

competition with little market power and one indicates a monopolistic market with high market power.

Hence, the effects of different geographical or industry characteristics are incorporated.

Table 4.1 on the next page presents all variables used in the empirical part including their definition. As this

table shows, eight dependent variables, four independent variables, and up to seven control variables are

used in the analysis. The descriptive statistics for all variables can be found in chapter five.

4.3 Empirical models

Figures D.1 to D.7 in Appendix D show the distribution of the most important dependent variables. Next, to

maintain clarity, all hypotheses formulated in chapter two and three are presented, followed by the

corresponding regression equations of the general model as preparation for the statistical sequel. Of

course, several alternative regressions are run to compare results and to improve robustness.

Hypothesis 1: Related diversification is positively related to performance.

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21

Table 4.1

Variable definitions

Variable Definition

Dependent Variables

ROR Financial performance measured by Return on Revenue. Profit divided by total revenue for 2010.

ROA Financial performance measured by Return on Assets. Profit divided by total assets for 2010.

MEDELS Medical performance based on the Elsevier study for 2010. A combination of service speed,

service quality, and patient orientation.

MEDAD Medical performance based on the Algemeen Dagblad ranking for 2010. The percentage score

of maximum score.

MEDCOMB Medical performance constructed from both Elsevier and AD.

EFF Organizational performance measured by work-in-progress. Indication for DBC turnaround and

ability of adequate registration. Gross work in progress before subtracting health insurance

deposits and facilities for non-invoiceable performance divided by total revenue, for 2010.

PROD Organizational performance measured by total revenues divided by average FTE over 2010.

ADEXP Administrative expenses. Overhead, information technology costs, maintenance costs, and

energy costs for 2010, divided by total revenue.

Variables of Interest

BSD Broad Spectrum Diversity. Measures unrelated diversification by the number of two-digit SIC

categories in which the firm operates in 2009.

NSD Narrow Spectrum Diversity. Measures related (within-industry) diversification by the number of

eight-digit SIC categories in which the firm operates in 2009.

LOGMNSD Mean Narrow Spectrum Diversity. Measures the relation between related and unrelated

diversification by the logarithm of the number of four-digit SIC categories in which the firm

operates divided by the number of two-digit SIC categories in which it operates in 2009.

HINT Horizontal integration. Number of hospital locations in 2009.

Control Variables

SIZE Logarithm of total assets for 2010.

TOPCLINIC Dummy variable for hospital type. Value is one if top clinical, zero if otherwise.

ACADEMIC Dummy variable for hospital type. Value is one if academic, zero if otherwise.

BSEGMENT Capital reimbursement in the B-segment divided by total reimbursement for 2010, controlling

for privatization.

VOLATILITY Four-year period (2007 to 2010) volatility of hospital earnings. Standard deviation of difference

in annual profit divided by mean value of total assets.

PROVINCE Measures the amount of residents in each province per hospital in that province, controlling for

industry concentration.

CR4 Concentration ratio. Sum of the revenues of the four largest hospitals in a province divided by

total revenue in that province.

22

Chapter 5 Results

This chapter discusses the estimation results of the empirical models that are specified in chapter four and

modified models. At the same time, the final research question is answered: How does diversification

affect the performance of Dutch hospitals? First, descriptive statistics and correlations between the

independent variables are reported. Second, regression results are discussed. Due to the comprehensive

set of dependent variables, it is important to maintain clarity in the results. Therefore, headings will

indicate which hypothesis is discussed.

Descriptive statistics and correlations

Table 5.1 reports the descriptive statistics of all variables used in the empirical part. The table shows the

number of observations, mean, standard deviation, minimum and maximum values. Two-tailed Pearson

correlations among the variables of interest and control variables can be found in Table 5.2. Variables that

are highly correlated (0.80 or higher) will not be used jointly in a regression. These variables are shown in

bold. However, most variables can be included in the model without much fear for multicollinearity.

Table 5.1 Descriptive statistics of all variables used

Observations Mean Standard Deviation Min Max

ROR 88 0.0192 0.0195 -0.0282 0.1011

ROA 88 0.0181 0.0191 -0.0448 0.0848

MEDELS 88 2.5161 0.8543 1 4

MEDAD 88 76.5512 7.4992 53.74 90.47

MEDCOMB 88 0.4826 0.1726 0.1710 0.8260

EFF 88 0.1241 0.0401 0.0460 0.2424

PROD11 88 100859.1 10592.16 66200 130000

ADEXP 88 0.2731 0.0336 0.1789 0.3829

BSD 88 3.9091 1.4905 1 9

NSD 88 30.3636 5.9484 17 45

LOGMNSD12 88 8.5524 2.6463 3.89 20

HINT 88 2.4545 1.3123 1 6

SIZE 88 5.2023 0.7002 4.0035 6.9752

TOPCLINIC 88 0.2841 0.4536 0 1

ACADEMIC 88 0.0909 0.2891 0 1

BSEGMENT 88 0.2576 0.0823 0.0399 0.3934

VOLATILITY 88 0.0155 0.0206 .0011 .1896

PROVINCE 88 189948.8 28694.89 129539 246948

CR4 88 0.7079 0.1904 .5205 1

Note: variable definitions are presented in Table 4.1

11 In the analysis, PROD is divided by 100.000 so it is easier to interpret. Descriptives are based on the original variable. 12 Note that the descriptive statistics are not based on this transformed variable LOGMNSD, but on the original variable, since this is easier to interpret.

23

Table 5.2 Two-tailed Pearson correlations

BSD NSD LOGMNSD HINT SIZE TOPCLINIC ACADEMIC BSEGMENT VOLATILITY PROVINCE CR4

BSD 1

NSD 0.736 1

LOGMNSD 0.843 0.349 1

HINT 0.057 0.051 0.126 1

SIZE 0.464 0.728 0.145 0.035 1

TOPCLINIC 0.056 0.217 0.023 0.263 0.337 1

ACADEMIC 0.420 0.569 0.162 0.262 0.653 0.199 1

BSEGMENT 0.542 0.684 0.227 0.103 0.751 0.081 0.747 1

VOLATILITY 0.069 0.057 0.053 0.094 0.226 0.130 0.088 0.001 1

PROVINCE 0.068 0.173 0.045 0.365 0.181 0.173 0.007 0.011 0.036 1

CR4 0.046 0.039 0.083 0.111 0.014 0.012 0.020 0.068 0.172 0.030 1

Note: variable definitions are presented in Table 4.1. Correlations are based on 88 observations. Underlined correlations represent negative correlations, bold correlations are above 0.80. Corresponding variables will therefore not be used jointly in the same regression.

24

Hypothesis 1

Hypothesis 1 states that related diversification is positively related to performance (measured by financial,

medical, and organizational performance). Consequently, this section discusses the results of related

diversification (NSD) on all dependent variables. The dependent variables are divided per performance

type over tables 5.3 – 5.6.

Financial performance. Table 5.3 on the next page shows the estimation results of specification (1) – (4),

where ROR is the dependent variable. Specification (1) includes only control variables. Surprisingly, the

model is insignificant (p-value of the F-test is 0.408) and has a relatively low adjusted R2. Specification (3)

adds the variable for related diversification (NSD) to the regression. NSD is positively significant at the 10%

level. LOGMNSD is not reported as it did not add any new insights to specification (3). Finally, specification

(4) includes both BSD and NSD. Both variables of interest are not significant. The control variable

BSEGMENT is significant at the 5% level in three out of four specifications, indicating that more privatized

hospitals on average generate higher ROR. Removing control variables did not bring any notable

improvements. Another effort to improve the model, including an interaction variable SIZE*BSEGMENT

that measures the effect of hospitals that are relatively large and privatized, was not significant and is not

reported. In short, NSD shows a positive significant effect on ROR when tested individually. When BSD is

included, this effect fades away. Additionally, the models of Table 5.3 are hardly significant (F-values) and

the adjusted R2 of these specifications are relatively low.

Table 5.4 on page 26 presents the estimation results of specification (5) – (8), where the dependent

variable is ROA, the second indicator for financial performance. Specification (5) includes only control

variables and shows no significance. Nevertheless, specification (7) shows a positive effect of NSD on ROA,

significant at the 10% level. Specification (8) improves the robustness of specification (7), because NSD is

also positive and significantly related to ROA when both NSD and BSD are included in the model. This

means that related diversification is on average positively related to ROA. Replacing NSD with quadratic

terms (NSD^2), following Palich, Cardinal, and Miller’s (2000) quadratic relationship between

diversification and performance, did not bring new insights. A significant quadratic coefficient would

indicate that the relation between diversification and performance may be other than linear. Results prove

that this is not the case for this sample. Removing the control variable PROVINCE, which is insignificant and

has little magnitude, did improve the adjusted R2 to 12.5% and the p-value of the F-test to 0.012, thus

improving the model. However, the coefficients did not make any relevant changes and therefore it is not

reported.

25

Table 5.3

Estimation results for ROR, using OLS model

(1) (2) (3) (4)

CONSTANT 0.005

(0.016)

-0.064*

(0.038)

-0.065*

(0.038)

-0.066*

(0.038)

BSD 0.002

(0.002)

0.001

(0.002)

NSD 0.001*

(0.001)

0.001

(0.001)

SIZE 0.000

(0.000)

0.007

(0.006)

0.004

(0.006)

0.005

(0.006)

TOPCLINIC 0.008

(0.006)

0.005

(0.006)

0.004

(0.006)

0.005

(0.006)

ACADEMIC -0.005

(0.017)

-0.003

(0.013)

-0.005

(0.013)

-0.005

(0.013)

BSEGMENT 0.000

(0.000)

0.110**

(0.048)

0.108 **

(0.047)

0.113**

(0.048)

VOLATILITY 0.054

(0.105)

0.129

(0.106)

0.105

(0.106)

0.113

(0.108)

PROVINCE 0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

CR4 0.013

(0.011)

0.014

(0.011)

0.014

(0.011)

0.014

(0.011)

N

Adjusted ()

F-test

88

0.003

(0.408)

88

0.058

(0.121)

88

0.066

(0.096)

88

0.057

(0.135)

Note: Variable definitions can be found in Table 4.1. Values in parentheses represent standard errors for the estimates and p-values for the F-test. The asterisks ** and * denote significance at the 5% and 10% level respectively.

26

Table 5.4 Estimation results for ROA, using OLS model

(5) (6) (7) (8)

CONSTANT -0.004

(0.015)

-0.023

(0.037)

-0.027

(0.036)

-0.028

(0.036)

BSD 0.003**

(0.002)

0.001

(0.002)

NSD 0.002***

(0.001)

0.001**

(0.001)

SIZE -0.000

(0.000)

-0.001

(0.006)

-0.006

(0.006)

-0.005

(0.006)

TOPCLINIC 0.008

(0.006)

0.008

(0.006)

0.007

(0.006)

0.007

(0.006)

ACADEMIC 0.007

(0.016)

-0.004

(0.013)

-0.007

(0.012)

-0.007

(0.012)

BSEGMENT 0.000

(0.000)

0.045

(0.047)

0.048

(0.045)

0.051

(0.046)

VOLATILITY 0.106

(0.101)

0.127

(0.104)

0.090

(0.101)

0.095

(0.103)

PROVINCE 0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

CR4 0.015

(0.011)

0.015

(0.011)

0.015

(0.010)

0.015

(0.010)

N

Adjusted ()

F-test

88

0.039

(0.176)

88

0.066

(0.097)

88

0.115

(0.022)

88

0.105

(0.036)

Note: Variable definitions can be found in Table 4.1. Values in parentheses represent standard errors for the estimates and p-values for the F-test. The asterisks ***, **, * denote significance at the 1%, 5%, and 10% level respectively.

Medical performance. Table 5.5 on the next page shows the estimation results on both dependent variables

that represent medical performance (MEDELS and MEDAD). Specification (9) and (11) only include control

variables, where the other specifications gradually include the variables of interest. Neither specification

(10) or (13) shows a significant effect of related diversification (NSD) on medical performance. However,

the control variable ACADEMIC is negatively significant at the 1% and 5% level in all specifications for

MEDELS and MEDAD respectively, indicating that academic hospitals on average achieve lower scores on

medical performance than other hospitals, following Elsevier and Algemeen Dagblad’s methodologies.

Regressing NSD separately did not affect the results and is not reported. Furthermore, omitting the

insignificant control variables BSEGMENT, VOLATILITY, PROVINCE, and/or CR4 in specification (10) did not

change the direction or significance of any remaining variable.

27

For MEDAD, also SIZE and CR4 are significant at the 1% and 10% level, respectively. The coefficient for SIZE is

positive, indicating that large hospitals on average have higher scores on the AD performance ranking. CR4

shows negative signs which indicates that hospitals located in a province with less competition and higher

market power on average earn a lower score on AD’s performance indication. The marginal effect of CR4

on MEDAD in specification (13) is 0.389. This indicates that a one standard deviation increase in CR4 on

average increases the score on AD’s ranking with 0.389. Note that the coefficients of MEDAD-regressions

are much higher than others because the MEDAD variable is measured in percentage numbers ranging

from 53.74 to 90.47 (see descriptive statistics). Results on MEDCOMB are not reported as it did not add any

interesting findings.

Table 5.5 Estimation results for medical performance, using OLS model

(9) (10) (11) (12) (13)

Dependent variable MEDELS MEDELS MEDAD MEDAD MEDAD

CONSTANT 1.619

(1.491)

1.880

(1.530)

48.896***

(13.869)

44.543***

(13.957)

44.249***

(14.077)

BSD 0.006

(0.085)

1.013*

(0.603)

0.873

(0.780)

NSD -0.023

(0.028)

0.072

(0.253)

SIZE 0.077

(0.231)

0.160

(0.249)

5.580***

(2.153)

5.335***

(2.134)

5.106**

(2.292)

TOPCLINIC -0.284

(0.236)

-0.265

(0.239)

-0.330

(2.193)

-0.188

(2.170)

-0.263

(2.199)

ACADEMIC -1.492***

(0.515)

-1.449***

(0.522)

-10.361**

(4.794)

-10.230**

(4.741)

-10.379**

(4.798)

BSEGMENT 0.604

(1.842)

0.168

(1.943)

7.725

(17.141)

16.491

(17.736)

16.804

(17.874)

VOLATILITY 3.832

(4.267)

4.240

(4.348)

16.878

(39.694)

20.684

(39.316)

18.972

(40.001)

PROVINCE 0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

CR4 -0.180

(0.436)

-0.173

(0.439)

-7.215*

(4.055)

-7.388*

(4.011)

-7.390*

(4.034)

N

Adjusted ()

F-test

88

0.212

(0.000)

88

0.202

(0.001)

88

0.115

(0.018)

88

0.134

(0.012)

88

0.124

(0.020)

Note: Variable definitions can be found in Table 4.1. Values in parentheses represent standard errors for the estimates and p-values for the F-test. The asterisks ***, **, * denote significance at the 1%, 5%, and 10% level respectively.

28

Table 5.6

Estimation results for organizational performance, using OLS model

(14) (15) (16) (17) (18)

Dependent variable EFF EFF EFF EFF PROD

CONSTANT 0.056

(0.070)

0.090

(0.069)

0.080

(0.070)

0.003

(0.073)

0.841***

(0.201)

BSD -0.008***

(0.003)

-0.013

(0.011)

NSD -0.002**

(0.001)

0.001

(0.004)

LOGMNSD 0.030**

(0.014)

SIZE -0.004

(0.011)

-0.002

(0.011)

0.003

(0.011)

-0.005

(0.011)

0.033

(0.033)

TOPCLINIC 0.031***

(0.011)

0.030***

(0.011)

0.033***

(0.011)

0.030***

(0.011)

0.023

(0.031)

ACADEMIC 0.093***

(0.024)

0.092***

(0.023)

0.096***

(0.024)

0.091***

(0.024)

0.173***

(0.068)

BSEGMENT 0.352***

(0.087)

0.282***

(0.088)

0.310***

(0.088)

0.322***

(0.086)

0.365

(0.255)

VOLATILITY 0.187

(0.202)

0.157

(0.194)

0.220

(0.199)

0.151

(0.197)

1.198**

(0.570)

PROVINCE 0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

CR4 -0.033

(0.021)

-0.031

(0.020)

-0.032

(0.020)

-0.029

(0.020)

-0.056

(0.058)

N

Adjusted ()

F-test

88

0.200

(0.001)

88

0.258

(0.000)

88

0.229

(0.000)

88

0.237

(0.000)

88

0.107

(0.034)

Note: Variable definitions can be found in Table 4.1. Values in parentheses represent standard errors for the estimates and p-values for the F-test. The asterisks ***, **, * denote significance at the 1%, 5%, and 10% level respectively.

29

Organizational performance. Table 5.6 on the previous page presents the estimation results for the effects

of diversification on efficiency (EFF) and productivity (PROD). The coefficient of NSD is negatively

significant on EFF, meaning that related diversification is positively related to efficiency.13 The control

variables TOPCLINIC, ACADEMIC, and BSEGMENT are positively significant at the 1% level, indicating that

top-clinical, academic, and more privatized hospitals are on average less efficient than other hospitals,

ceteris paribus. By removing several insignificant control variables (VOLATILITY and PROVINCE) in

specification (15), only the constant term becomes significant at the 10% level. The changes of remaining

estimation results are minimal and therefore not reported. By including both BSD and NSD in the same

model, NSD becomes insignificant. For EFF, also a specification including LOGMNSD is reported. The

coefficient of LOGMNSD is positively significant at the 5% level, indicating that hospitals that have more

related businesses (NSD) within its broader businesses (BSD), on average are less efficient. For PROD, none

of the variables of interest show significant results, separate or combined. Hence, only the complete model

(BSD and NSD) is reported. Note that VOLATILITY becomes significant at the 5% level in this specification.

This finding indicates that hospitals that have larger fluctuations in earnings, making them more risky, are

on average more productive in terms of revenue per FTE.

To conclude on hypothesis 1: related diversification (NSD) has a positive effect on financial performance,

both ROR and ROA. However, the magnitude is rather small. For medical performance, no significant

effects of related diversification are found. Finally, regarding organizational performance, related

diversification shows a weak but positive effect on efficiency. For this reason, hypothesis 1 is accepted. An

important note here is that medical performance is inconclusive, but that the effects on financial and

organizational performance are in line with the expectations, albeit small. Overall, judging from the p-

values of the F-tests, the hypothesis that all coefficients equal zero can be rejected for all specifications,

meaning that the models are useful.14

13 Note that a negative effect on EFF represents a lower amount of work-in-progress, thus indicating higher efficiency. 14 The models in specification (1), (2), (4), and (5) are not significant. The results from these specifications are therefore not considered when making conclusions.

30

Hypothesis 2a

Hypothesis 2a is constructed to test the effect of unrelated diversification on administration expenditures.

Table 5.7 shows all relevant estimation results. Specification (19) only includes control variables,

specification (20) adds unrelated diversification (BSD), and specification (21) improves the model by

omitting insignificant control variables. BSD does not show any significant effect in specification (20) nor in

(21). The controls TOPCLINIC, ACADEMIC, and BSEGMENT are significant. Despite the improvements of the

estimation significance as well as the model (adjusted R2, F-test) in specification (21), BSD remains

insignificant. Hence, hypothesis 2a is rejected.

Table 5.7

Estimation results for administration expenditures (ADEXP), using OLS model

(19) (20) (21)

CONSTANT 0.198***

(0.062)

0.209***

(0.063)

0.212***

(0.025)

BSD -0.003

(0.003)

-0.002

(0.003)

SIZE -0.002

(0.010)

-0.001

(0.010)

TOPCLINIC 0.022**

(0.010)

0.022**

(0.010)

0.023***

(0.008)

ACADEMIC 0.046**

(0.021)

0.046**

(0.021)

0.047***

(0.019)

BSEGMENT 0.243***

(0.077)

0.221***

(0.080)

0.232***

(0.069)

VOLATILITY -0.053

(0.177)

-0.063

(0.178)

PROVINCE 0.000

(0.000)

0.000

(0.000)

CR4 0.000

(0.018)

0.001

(0.018)

N

Adjusted ()

F-test

88

0.121

(0.014)

88

0.119

(0.019)

88

0.158

(0.001)

Note: Variable definitions can be found in Table 4.1. Values in parentheses represent standard errors for the estimates and p-values for the F-test. The asterisks ***, **, * denote significance at the 1%, 5%, and 10% level respectively.

31

Hypothesis 2b

This hypothesis states that unrelated diversification is negatively related to performance. Results for

financial, medical, and organization performance can be found in the same tables as for hypothesis 1, only

this time BSD is the variable of interest.

Financial performance. In specification (2) from Table 5.3, only the control variable BSEGMENT shows a

significant effect on ROR. Specification (4), which includes both BSD and NSD, reports the same findings.

However, as discussed earlier, the models in these regressions are not significant. Therefore, no

conclusions can be made based on these findings. Table 5.4 shows the effect of BSD on ROA. The findings

in specification (6) show that BSD is positive significant at the 5% level, which does not match with the

expectations. Additionally, when both BSD and NSD are included in the regression, the significance of BSD

fades. Replacing BSD with a BSD variable in quadratic terms (BSD^2) did not bring new insights in any

specification. When the insignificant and low magnitude control variable PROVINCE is removed from

specification (8), the t-values of the remaining variables slightly increase, as well as the adjusted R2 and the

p-value of the F-test. However, it does not bring new insights or higher significance. Therefore PROVINCE is

kept in the regression. In an attempt to increase robustness of BSD, a regression was also run with the

most primitive dummy variable for diversification from Clement, D’Aunnu, and Poyzer (1993), which simply

measures whether firms are diversified (one) or not (zero). For both ROR and ROA this did not result in any

significant findings. These regressions are not reported.

Medical performance. The effect of BSD on the medical performance indicator of MEDELS is not significant,

combined nor separate. Regarding MEDAD however, BSD is positively significant at the 10% level. The

marginal effect of BSD in specification (12) is 0.611, meaning that a one standard deviation increase in BSD

on average increases the score on AD’s ranking with 0.611 (AD is measured in percentages). Unfortunately,

BSD and NSD in a combined model (specification (13)) do not result in a significant effect. Again,

regressions on MEDCOMB are not reported because no new insights were found.

Organizational performance. Table 5.6 on page 28 provides an overview of the effects of diversification on

efficiency and productivity. In specification (15), BSD is negatively related to EFF at the 1% level. This

indicates that hospitals that are more unrelated diversified on average have less work-in-progress than

other hospitals, indicating them to be more efficient. This result is not in line with the expectations of

unrelated diversification. Also in this specification, the control variables TOPCLINIC, ACADEMIC, and

BSEGMENT prove to be important estimators of efficiency. Effects on productivity (PROD) are not

significant.

To conclude on hypothesis 2b: unrelated diversification shows to be positively related to performance. The

effects on ROR are inconclusive due to insignificant models, but the findings for ROA show a positive

relationship between unrelated diversification and financial performance. Despite only one significant

32

result of BSD on medical performance, all specifications report positive coefficients. Together with the

positive effect on organizational performance, the overall effect seems to be small but positive. Hence,

hypothesis 2b is rejected.

Hypothesis 3

To test the effect of horizontal integration on performance, this hypothesis does not use the constructed

variables of related and unrelated diversification used in all previous models, but simply the number of

hospital locations (HINT). Additionally, the quadratic term HINT (HINT^2) is tested for a potential quadratic

relation between the number of locations and performance. However, this effort did not bring any

significant results and is not reported in the tables. Table 5.8 on the next page reports the estimation

results for horizontal integration (HINT). Note that this table does not report 10% level significance because

for this hypothesis a two-sided OLS is used. For two-sided regressions, restrictions regarding significance

are more severe and therefore 10% significance is not relevant. The reason for the different methodology is

that hypothesis 3 does not specify a direction but simply whether there is an effect of HINT, either positive

or negative. Each specification represents a regression of one of the six performance indicators.

Financial performance. As specification (22) shows, HINT is negatively related to financial performance.

Namely, the coefficient of HINT on ROR is negatively significant at the 5% level. Note that the ROA-model in

this case is not very useful as the p-value of the F-test is too high (0.129).

Medical performance. For both MEDELS and MEDAD no significant results of HINT can be found. The

combined variable MEDCOMB, constructed from both Elsevier and MEDAD, is not reported as this

regression did not bring anything to the other two regressions. No new findings can be added to those

from Table 5.5.

Organizational performance. Also for the two indicators of organizational performance EFF and PROD, no

significant findings appear.

It seems that the number of hospital locations only slightly (negatively) affects performance on a financial

basis. Results on medical and organizational performance remain absent. Additionally, when considering

the insignificant coefficients, no pattern in direction can be found making it impossible to conclude in favor

of the hypothesis. For these reasons, hypothesis 3 is rejected.

33

Table 5.8

Estimation results for horizontal integration (HINT), using OLS model

(22) (23) (24) (25) (26) (27)

Dependent variable ROR ROA MEDELS MEDAD EFF PROD

CONSTANT -0.066

(0.037)

-0.018

(0.037)

1.819

(1.508)

47.877***

(14.084)

0.055

(0.072)

0.751***

(0.198)

HINT -0.004**

(0.002)

-0.003

(0.002)

0.066

(0.072)

-0.336

(0.675)

-0.001

(0.003)

-0.013

(0.009)

SIZE 0.009

(0.006)

0.001

(0.006)

0.046

(0.234)

5.733***

(2.185)

-0.004

(0.011)

0.038

(0.031)

TOPCLINIC 0.006

(0.006)

0.009

(0.006)

-0.296

(0.236)

-0.268

(2.207)

0.031***

(0.011)

0.028

(0.031)

ACADEMIC -0.011

(0.013)

-0.010

(0.013)

-1.372***

(0.532)

-10.971**

(4.971)

0.092***

(0.025)

0.151**

(0.070)

BSEGMENT 0.089**

(0.045)

0.016

(0.045)

0.609

(1.844)

7.699

(17.222)

0.352***

(0.088)

0.465

(0.242)

VOLATILITY 0.150

(0.105)

0.139

(0.105)

3.343

(4.305)

19.375

(40.197)

0.191

(0.204)

1.356**

(0.564)

PROVINCE 0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

0.000

(0.000)

CR4 0.017

(0.011)

0.017

(0.011)

-0.223

(0.439)

-6.999

(4.097)

-0.032

(0.021)

-0.049

(0.057)

N

Adjusted ()

F-test

88

0.093

(0.044)

88

0.055

(0.129)

88

0.210

(0.001)

88

0.106

(0.029)

88

0.190

(0.001)

88

0.118

(0.020)

Note: Variable definitions can be found in Table 4.1. Values in parentheses represent standard errors for the estimates and p-values for the F-test. The asterisks *** and ** denote significance at the 1% and 5% level respectively.

34

Since not all expected results are found, an additional analysis is performed. Table 5.9 summarizes the

results of a two-way Analysis of Variance (ANOVA). The table summarizes the group means of four

diversification categories explained in chapter four (Figure 4.1). The cut-off points for the categories (low-

high) are the sample means of BSD (3.91) and MNSD (8.55). Note that the groups are not equally divided,

but that especially group B and C are densely populated. Fortunately, those two groups are the most

interesting as they correspond with Rumelt’s (1974) and Palepu’s (1985) related and unrelated categories.

The ANOVA is repeated for all seven performance indicators. “F-ratios” present the F-values of a test

whether group means of cells significantly differ from each other. Although the group means are different

between the cells, only differences between the BSD groups for ROR are significant. The remaining ones

do not show significant differences across categories. Similarly, the interaction term is uniformly

insignificant for all performance measures. Concluding from Levene’s Test of Equality of Error Variances, all

dependent variables have homogeneity of variances. In other words, the variance across groups is

significantly constant. Considering the estimation results on performance in the first part of chapter five,

these insignificant findings do not add new insights. The main message here is that differences do exist

between diversification categories of hospitals, although not (yet) clear enough and statistically relevant.

Table 5.9

Estimation results of two-way Analysis of Variance (ANOVA) of Performance by Diversification Category

Note: Variable definitions can be found in Table 4.1. Under “diversification categories”, values denote group means per cell for each performance indicator. Under “F-ratios”, BSD, MNSD, and Interactions denote the F-ratios regarding differences between high-low BSD/MNSD groups, and all four groups within the matrix for each performance indicator respectively. For total population, N=88. The asterisks ***, **, * denote significance at the 1%, 5%, and 10% level respectively.

Diversification categories F-ratios

Performance measures Cell A:

Low MNSD –

Low BSD

Cell B:

High MNSD –

Low BSD

Cell C:

Low MNSD –

High BSD

Cell D:

High MNSD –

High BSD

BSD MNSD Interactions

Financial performance

ROR 0.016 0.021 0.019 0.018 0.005* 0.104 0.222

ROA 0.014 0.018 0.019 0.018 0.135 0.065 0.176

Medical performance

MEDICALELS 2.926 2.622 2.379 2.333 2.644 0.461 0.252

MEDICALAD 76.346 74.916 77.264 80.460 2.027 0.151 1.039

MEDICALCOMB 0.563 0.492 0.461 0.472 1.340 0.324 0.435

Organizational performance

EFF 0.142 0.134 0.113 0.131 1.831 0.152 1.190

PROD 0.993 1.018 1.007 0.993 0.023 0.026 0.350

N per cell 9 30 44 5

Cell label Little

diversified

Related-

diversified

Unrelated-

diversified

Highly

diversified

35

Chapter 6 Conclusion and discussion

This study addresses the question of how diversification affects the performance of Dutch hospitals in a

changing environment. The deregulation and privatization process in the health care industry, which

started in 2006 and has not finished at the time of writing, is a unique and interesting period to study

strategic developments for hospitals. Chapter two explained classic and recent diversification theories.

Chapter three described the Dutch health care industry and its developments regarding (de)regulation,

privatization, and strategy. Finally, chapter four and five served for the empirical part of this study. Chapter

six will conclude this study and extend the findings with a discussion about the future of Dutch health care.

6.1 Conclusion

Chapter five explained the empirical results and assessed the hypotheses formed in the literature section.

Hypothesis 1 was accepted. Hypotheses 2a, 2b, and 3 were rejected. Although a reasonable part of the

hypotheses were rejected, it does not mean that no interesting conclusions can be made. The empirical

results provide evidence on general hospital economics as well as on the effects of changes in the industry.

Moreover, this research has distinguished itself by a unique health care environment in the Netherlands.

Additionally, by using several performance perspectives, this research gives a refreshing view on the

performance of hospitals. In fact, some of the results brought unexpected results for this industry. First of

all, the economic factors for Dutch hospitals do not always behave as expected considering international

research on hospital performance. Effects on financial performance which seemed clear, like the effect of

unrelated diversification (Rumelt, 1974; McDougall and Round, 1984), administration costs (Porter, 1985;

Jones and Hill, 1988) or efficiency (Hughes and Oughton, 1993), do not apply to Dutch hospitals. The results

from chapter five highlight the dangers of generalizations regarding the nature of diversification and

performance. As an illustration, firms engaging in related diversification are often expected to outperform

firms that engage unrelated diversification segment (Varadarajan and Ramanujam, 1987). This research

shows that related diversification does not by definition outperform unrelated diversification.

Furthermore, regressions concerning effects on performance often generated insignificant estimates and

also several models proved to be insignificant. Hence, the level of privatization and therewith

diversification in the Netherlands may not be as far as people nowadays expect and consequently may not

be that decisive for performance yet. The industry may still be too much controlled by the government. On

the other hand, several interesting significant relationships are found. The key findings are as follows:

Hospitals that hold many clinics of specialism, and hence can be considered as within-industry diversified

hospitals, on average outperform hospitals that have fewer clinics of specialism. Although the result only

holds for financial and organizational performance, it is an interesting finding. Namely, the RIVM (2010)

stated that the supply of health care is becoming more diversified due to mergers. Now we know that in

2010 the average effect of diversification on performance is at least not negative. Unfortunately, this

research cannot conclude about any effect of related diversification on medical performance.

36

Nevertheless, the results clearly show that academic hospitals on average underperform on medical

quality. This effect is evident for both Elsevier and AD methodologies. This could be the case because

academic hospitals do not carry unambiguous priorities: due to their educating role much effort is put in

research, but convenient and high-quality medical care is important as well. Also, regarding the AD medical

performance indication, larger hospitals and hospitals located in a province with tougher competition and

less market power on average outperform others.

Furthermore, the effects of unrelated diversification proved to be different than expected. Where

hypothesis 2a stated that unrelated diversification is positively related to administrative expenses, this

research cannot accept this hypothesis: the indicator for unrelated diversification remained insignificant,

despite several attempts to improve the model. Nevertheless, it was found that several other variables are

important in explaining administration expenses. Top clinical hospitals have on average higher

administration expenses and academic hospitals even more. Also, hospitals that earn a larger share of their

revenue from the privatized part (B-segment), on average have higher administration costs. In short,

hospital characteristics are important factors in explaining administration expenses.

Unrelated diversification proved to have a positive effect on financial, medical, and organizational

performance. Unrelated diversification is positively related to efficiency. As noted before, this finding is not

in line with literature (Rumelt, 1974; McDougall and Round, 1984; Hughes and Oughton, 1993), but not less

interesting. It could indicate that hospitals engaging in unrelated diversification work in a more business-

wise fashion and can therefore better handle problems of efficiency. Other, less business oriented

hospitals may still be used to work as a governmental institution and lack the motivating environment of

free markets. Furthermore, it could be the case that work-in-progress is simply not relevant in the

unrelated activities (e.g. a parking lot does not hold work-in-progress). Of course these assumptions have

to be treated with care, as the results are not extraordinarily strong.

Horizontal integration, although it is an important trend in the Dutch health care industry, did not bring

many significant findings. Only the negative effect of horizontal integration on one of the financial

performance indicators is useful. This finding indicates that the number of hospital locations negatively

affect financial performance. Although the other indicator of financial performance may not be used to

make conclusions, it does report the same direction. Unfortunately, the specifications reporting on medical

and organizational performance are insignificant. For Dutch hospitals, the number of hospital locations

only slightly affects the (financial) performance of hospitals.

Finally, the Analysis of Variances (ANOVA) following Varadarajan and Ramanujam (1987) gave a clear

overview of the average performance of the four different diversification groups. Although clear

differences can be identified when observing the group means, pairwise comparison of group means

hardly shows any significance with respect to any of the performance indicators. Only the unrelated

diversification groups prove to be slightly significantly different on financial performance (ROR). This

proves that unrelated diversification for Dutch hospitals can be profitable.

37

Overall, it can be concluded that both related and unrelated diversification affect the performance of

Dutch hospitals in different ways. However, firm characteristics like size, hospital type, or privatization

grade often dominate the results. Hence, the level of market forces and diversification in the Dutch health

care industry may not be as far as people nowadays expect and may not be that decisive for performance

yet. Nevertheless, regarding an increase of the B-segment towards 70 percent in 2012, it is important to

continue research on new developments in order to continuously improve the understanding of market

forces in health care. There is no doubt that health care costs are rising and that even more interesting

market circumstances lie ahead, but the question remains what this new health care system will bring the

Dutch citizen.

6.2 Limitations

Much research has been conducted on the effects of diversification. However, for the health care industry

this type of research remains scarce, especially for the Dutch health care industry. This is due because

deregulation and privatization are a recent trend, and before these developments, market forces as well as

data were limited. For instance, data of divisional financial performance was unavailable. Hence, the only

possibility for this research was analyses on the aggregate level. Furthermore, despite the fact that

transparency is one of the prominent improvements the government wants to achieve, sophisticated data

on strategic subjects like diversification is still hard to find. Therefore, the indicators for diversification are

constructed from data of annual reports and other secondary data. Apparently, these constructed

variables lack explanatory power and require more sophisticated data. Similarly, the available indicators for

medical performance are insufficient to create an objective representation of care quality. Because there is

no alternative we are forced to use the medical indicators available and more importantly, hospitals lack an

essential incentive to supply maximal quality (Roland Berger, 2009). Nevertheless, improvements in the

measurement of quality can be identified over the years. Another limitation is that administration costs

were not reported as such and had to be calculated otherwise. This could have affected the results. Time

constraints could also be considered as a limitation: diversification is probably subject to latent effects,

meaning that the effect is not always notable in the short term. Although this research used a one year lag,

this period of observation may still be too short to measure the emergence of the effects of diversification.

Finally, concluding from the different results, other factors that are not included in this research might be

relevant: with adjusted R2 values ranging from 0.3% to 26%, it seems that a large part of the variation in

performance is explained by other factors. Other factors could be quality of management, degrees of

medical specialists, lobbying power (considering the large power of the government still), research and

development, real estate policies, etcetera.

This research represents a preliminary study of the effects of health care diversification in the Netherlands.

Some of the important effects proved opposite to what was expected, which indicates that lots of

research opportunities regarding diversification exist. Also, regarding the further increases in the B-

segment towards 70 percent in the near future, it is important to continue research on new developments

in order to continuously improve the understanding of strategic developments in health care. Wider time

38

spans could generate more comprehensive understanding and may even generate more significant results.

For this reason the dataset of this research will be made available for future research. Other graduates

could perhaps supplement the database with data from future years and analyse developments over

longer time horizons. Finally, with the current debt crisis in mind, it might also be important to investigate

the financial aspects like debt capacities and solvency of Dutch hospitals.

6.3 Implications for academic literature

The results of this research indicate that literature on diversification, in specific industries like the health

care industry, is far from complete and that the quest for profitable diversification strategies continues.

More specifically, former results of diversification in other industries seem not to apply per se for (partly)

public or Dutch markets. The theories do not perfectly fit, so future research should try to close this gap.

For Dutch hospitals, related and unrelated diversification is positively related to performance. The effects

may be of less magnitude due to governmental interference, but soon hospitals will be more privatized.

Hence, research on diversification is expected to find stronger effects of diversification and other market

forces in the future. For now, firm characteristics often dominate the results. Furthermore, effects on

productivity are detected, however not with the same conviction as Schoar (2002) found in her research on

effects of diversification on productivity. Of course, she uses more sophisticated measures of productivity

on listed companies. Finally, because transparency is of great concern and is improved every year, medical

performance indicators are expected to become more objective and reliable and hence better suitable for

empirical research every year.

6.4 Implications for hospitals

To continue with medical performance indicators, this research shed some light on the practical use of the

current rankings. In this study, rankings of Elsevier and Algemeen Dagblad are used. What immediately

became clear is that in both measures academic hospitals on average score lower than other hospitals.

Academic hospitals have a reputation of highly specialized medical centers, in combination with a teaching

function. Therefore it seems odd that these “example” hospitals have disappointing medical performance

on both measures. A possible explanation is that academic hospitals do not carry unambiguous priorities:

due to their educating role much effort is put in research, but convenient and high-quality medical care is

important as well. Academic hospitals should be aware of this dilemma and make sure to not lose sight of

their medical performance. Furthermore, the medical score on AD is positively influenced by size and

negatively by concentration. Larger hospitals on average have higher scores on the AD measure and

hospitals that encounter less competition and more market power on average have lower scores. Hence,

introducing more competition in the health care industry could be a positive incentive to improve medical

quality. Although the recent trend of mergers and acquisitions counteracts competition, privatization of

hospitals could turn this effect around. These opposed effects are interesting for strategy, but the

government should be critical with regulations as these developments should never burden the citizens.

39

The healthcare industry has been subject of much discussion lately, especially in combination with

budgetary cuts due to unmanageable rising health care expenses and the economic recession. As

explained in the conclusion, the hospitals will have 70 percent of their revenues privatized in 2012. On the

one hand, this privatization brings more economic market circumstances, a greater need for efficiency and

better management. On the other hand, privatization brings new (and different) interests. Hospitals have

to control their costs, realize adequate revenues, create strategic plans, arrange sufficient investments,

and of course compete with other privatized hospitals within or even outside the Netherlands. All this

comes with a totally new environment. Most hospitals currently have the legal form of a foundation, which

restricts hospitals from distributing profits. However, when the hospitals would have the legal form of

BV/NV, and thus become for-profit, this profit restriction fades. In fact, Kerste and Kok (2010) state that

ownership, and therewith the freedom of distributing profits, is essential for the success of privatization in

the health care industry. Elsinga and Keuzekamp (2003) add that health care is a fair destination for private

investments and enhances social responsibility. Maybe medical specialists or hospital management are

interested in investing in their hospital or becoming co-owner, which on its turn could enforce alignment

and efficiency, which is in line with the current goals of privatization.

Transforming hospitals into for-profit (FP) institutions is a major alteration by all means and brings for one

thing opportunities as discussed above, but also problems that can have major consequences. The first

problem of hospitals becoming for-profit is loss of control. Maintaining control is one of the main reasons

private firms are private to begin with (Pagano and Roell, 1998; Brav, 2009). Second, conflicts of interest

between management and shareholders make agency costs more relevant and can be considered a waste.

Third, the economic rivalry between not-for-profit (NFP) and FP hospitals will cause investors to re-

evaluate the terms under which they are willing to invest in NFP hospitals (Wedig, Sloan, Hassan, and

Morrisey, 1998), which makes it even harder for NFP hospitals to survive. Fourth, assets should be sold at a

market-conform price to make sure no public resources are drained (Verweij and Bisschop, 2006).15

However, the current book values do not correctly represent the market values. Fifth, FP hospitals could

become so large that these conglomerates may dominate the health care industry (Moore, 1985) and will

not benefit the Dutch citizen. Staatsen (2001) defined this problem as monopoly development (see

Appendix C). Moreover, basic care could disappear in sparsely populated areas. The government could

compensate diseconomies of scope in these areas, but the question remains whether compensation is

plausible in a for-profit health care industry. Finally, when investors demand higher performance, hospitals

could improve financial performance at the cost of medical quality. The question remains whether the

benefits of privatization and the transformation of NFP hospitals into FP hospitals surpass the wide range

of drawbacks that causes public interest to fade. All in all, privatization demands a stronger vision from the

government and the board of hospitals; a vision on health care; a vision on finance; a vision on strategy.

15 Equity of Dutch hospitals is divided in collectively financed (bound) equity and non-collectively financed (unconfined) equity. Collective equity is created by governmental subsidies and care-related reimbursement and is obligated to remain within the organization. Non-collective equity is freely disposable.

40

References

Ansoff, H.I. (1958). A model for diversification. Management Science 4 (4): pp. 392 – 414.

Ansoff, H.I. (1965). Corporate Strategy. New York, NY: McGraw-Hill.

Almón, M.A., Domínguez, M.A.S.D., and Gómez, F.G. (2002). Privatization, deregulation and competition: evidence from Spain. Journal of Economics 6 (1): pp. 1 – 21.

Armstrong, M., Cowan, S., and Vickers, J. (1994). Regulatory reform: economic analysis and British experience. Cambridge, England: MIT Press.

Averch, H., and Johnson, L.L. (1962). Behaviour of firm under regulatory constraint. American Economic Review 52 (5): pp. 1052 – 1069.

Barney, J.B. (1997). Gaining and Sustaining Competitive Advantage. Addison-Wesley, Reading: MA. Baum, J.A.C., and Greve, H.R. (2001). Introduction: A multiunit, multimarket world. Oxford, England: Elsevier. Baye, M.R. (2005). Managerial Economics and Business Strategy. Fifth edition. Boston: McGraw-Hill.

Blank, J.L.T., Wats, M.G. (2009). Aanbod ziekenhuiszorg in 2020. Tijdschrift voor Openbare Financiën 41 (3): pp. 141 – 157.

Brav, O. (2009) Access to capital, capital structure, and the funding of the firm. Journal of Finance 64: pp. 263 – 268. Chandler Jr., A.D. (1962). Strategy and structure. Cambridge. Mass: The MIT Press.

Christensen, H.K., and Montgomery, C.A. (1981). Corporate economic performance: Diversification strategy versus

market structure. Strategic Management Journal 2: pp. 327 – 343.

Clement, J.P. (1987). Does hospital diversification improve financial outcomes? Medical Care 25: pp. 988 – 1001.

Clement, J.P., D'Aunno, T., and Poyzer, B.M. (1993). The financial performance of diversified hospital subsidiaries.

Health Services Research 27: pp. 741 – 764.

Conrad, D.A., Mick, S.S., Watts M.C., Hoare, G. (1988). Vertical structures and control in health care markets: a

conceptual framework and empirical review. Medical Care Review 45 (1): pp. 49 – 100.

Crew, M.A., and Kleindorfer, P.R. (2002). Regulatory Economics: Twenty years of progress? Journal of Regulatory Economics 1 (21): pp. 5 – 22.

Datta, D.K., Rajagopalan, N., and Rasheed, A.M.A. (1991). Diversification and performance: critical review and future

directions. Journal of Management Studies 28: pp. 529 – 558.

DBC Onderhoud (2011). http://www.dbconderhoud.nl/Over-de-DBC-systematiek. Retrieved on 09-02-2011.

Dolan, M. J. (1985). The case for the new conglomerate. Unpublished paper, Booz, Allen and Hamilton, New York.

Dubofsky, P., and Varadarajan, P.R. (1987). Diversification and measures of performance: Additional empirical evidence.

Academy of Management Journal 30: pp. 597 – 608.

Edwards, C. (1955). Conglomerate business as a source of power. Princeton, NJ: Princeton University Press.

Elsinga, E., and Keuzekamp, H. (2003). De zorg wordt niet beter zonder commercie. NRC Handelsblad

Ermann, D., and Gabel, J. (1984). Multihospital systems: issues and empirical findings. Health Affaires 3 (1): pp. 50 – 64.

Ginter, P.M., Swayne, L.E., and Duncan, W.J. (2002). Strategic Management of Health Care Organizations. Fourth edition.

Malden, MA: Blackwell.

Grant, R.M., Jammine, A.P., and Thomas, H. (1988). Diversity, diversification, and profitability among British

manufacturing companies, 1972 – 1984. Academy of Management Journal 31: pp. 771 – 801.

Harford, J. (2005) What drives merger waves? Journal of Financial Economics 77: pp. 529 – 560.

Hartog, den, M., and Janssen, R. (2002). Een ontwikkeling in kaart gebracht. ZM magazine 1: pp. 1 – 6.

Hoskisson, R.E. (1987). Multidivisional Structure and Performance: The Contingency of Diversification Strategy.

Academy of Management Journal 30: pp. 625 – 644.

Hughes, K., and Oughton, C. (1993). Diversification, multimarket contact and profitability. Economica, 60: pp. 203 – 224.

IGZ: Inspectie voor de Gezondheidszorg, Ministerie van Volksgezondheid, Welzijn en Sport. Internet: Last visited on

February 20, 2011. Utrecht.

Jacquemin, A.P., and Berry, C.H. (1979). Entropy measure of diversification and corporate growth. Journal of Industrial

Economics 27: pp. 359 – 369.

Jeurissen, P.P.T., Brummelman, E.G., and Van Heurck, M.C.E. (2003). Marktwerking in de medisch specialistische zorg:

achtergrondstudies 03/03. Raad voor de Volksgezondheid en Zorg (RVZ): Zoetermeer.

Jones, G.R., and Hill, C.W.L. (1988). Transaction cost analysis of strategy-structure choice. Strategic Management

Journal 9: pp. 159 – 172.

Kamien, M.I., and Schwartz, N.L. (1982). Market Structure and Innovation. New York: Cambridge University Press.

Kerste, M., and Kok, L. (2010) Winst in de eigendomsstructuur. Commissioned by NVZ, SEO Economisch Onderzoek 2010-2011. Amsterdam.

Kriek, R.J., and Dooyeweerd, R. (2009). Risicovol investeren in zorgvastgoed? Financieren van zorgvastgoed vanuit drie gezichtspunten. Real Estate: vastgoed en zorg 63: pp. 22 – 25.

Laan, M. (2010). Tien ziekenhuizen op rand bankroet. Het Parool, 19-03-10, Amsterdam.

41

Lamont, O., and Polk, C. (2002). Does Diversification Destroy Value? Evidence from the Industry Shocks. Journal of

Financial Economics 63 : pp. 51 – 77.

Li, S.X., and Greenwood, R. (2004). The effect of within-industry diversification on firm performance: Synergy creation,

multi-market contact and market structuration. Strategic Management Journal 25: pp. 1131 – 1153.

Loef, Van, G.J. (2000). Marktwerking bij ziekenhuizen? Publiek Management 6 (2): pp. 14 – 21.

Luffman, G. A., and Reed, R. (1984). The strategy and performance of British industry, 1970-80. London: MacMillan.

McDougall, F.M., and Round, D.K. (1984). A comparison of diversifying and non-diversifying Australian industrial firms.

Academy of Management Journal 27: pp. 384 – 398.

Michel, A., and Shaked, I. (1984). Does business diversification affect performance? Financial Management 13: pp. 18 – 24.

Milgrom, P., and Roberts, J. (1990). The Economics of Modem Manufacturing: Technology, Strategy and Organization.

American Economic Review 80 (3): pp. 511 – 528.

Montgomery, C.A. (1994). Corporate Diversification. Journal of Economic Perspectives 8 (3): pp. 163 – 178.

NIVEL: Nederlands Instituut voor onderzoek van de Gezondheidszorg. Internet: Last visited on August 2, 2010. Utrecht.

NVZ: Vereniging van Ziekenhuizen. Internet: Last visited on October 12, 2010. Utrecht.

NZa: Nederlandse Zorgautoriteit (2009). Monitor ziekenhuiszorg 2009: tijd voor reguleringszekerheid. Dutch Healthcare

Authority, Utrecht.

O’Dochartaigh, N. (2002). The internet research handbook. A practical guide for students and researchers in the social

sciences. London: Sage.

Pagano, M., and Roell, A. (1998). The choice of stock ownership structure: agency costs, monitoring, and the decision

to go public. Quarterly Journal of Economics 20: pp. 293 – 315.

Palepu, K. (1985). Diversification strategy, profit performance, and the entropy measure. Strategic Management Journal

6: pp. 239 – 255.

Palich, L.E., Cardinal, L.B., and Miller, C.C. (2000). Curvilinearity in the diversification-performance linkage: an

examination of over three decades of research. Strategic Management Journal 21: pp. 155 – 174.

Porter, M.E. (1985). Competitive advantage. New York: Free Press.

Prior, D. (1996). Technical efficiency and scope economies in hospitals. Applied Economics 28: pp. 1295 – 1301.

Putters, K. (2003). Anticiperen op marktwerking, achtergrondstudie 03/03. Raad voor de Volksgezondheid en Zorg (RVZ):

Zoetermeer.

Qiu, L.D., and Zhou, W. (2007). Merger waves: a model of endogenous mergers. Journal of Economics 38: pp. 214 – 226.

Ramanujam, V., and Varadarajan, P. (1989). Research on corporate diversification: a synthesis. Strategic Management

Journal 10: pp. 523 – 551.

RiVM: Rijksinstituut voor Volksgezondheid en Milieu (2006). Nationale Atlas Volksgezondheid. Internet: Last visited on

August 2, 2010. Bilthoven.

Rumelt, R.P. (1974). Strategy, structure and performance. Cambridge, Mass: Harvard University Press.

Rumelt, R.P. (1986). Strategy, structure and economic performance. Boston, MA: Harvard Business School Press.

RVZ: Raad voor de Volksgezondheid en Zorg. Internet: Last visited on August 23, 2010. Den Haag.

Saunders, M.N.K., Thornhill, A., and Lewis, P. (2009). Research Methods for Business Students. Fifth edition. London, FT:

Prentice Hall.

Scherer, F. M. (1980). Industrial Market Structure and Economic Performance. Second edition. Chicago: Rand McNally.

Schoar, A. (1999). Effects of corporate diversification on productivity. Working paper, University of Chicago.

Seth, A. (1990). Value creation in acquisitions: A reexamination of performance issues. Strategic Management Journal 11

(2): pp. 99 – 115.

Sloan, F.A. (1982). Government and regulation of hospital care. The American Economic Review 72 (2): pp. 196 – 201. Snail, T.S., and Robinson, J.C. (1998). Organizational diversification in the American hospital. Annual Review of Public

Health 19: pp. 417–453.

Staatsen, A.A.M.F (2001). Zin en onzin van privatiseren. De Boer & Croon Strategy and Management Group, Public SPACE

(center on Strategies for Public And Civil Entrepreneurs). Consultancy report: Amsterdam.

Stern, I., and Henderson, A. (2004). Within-Business Diversification in Technology-Intensive Industries. Strategic

Management Journal 25: pp. 487 – 505.

Sudarsanam, S. (2003). Creating value from mergers and acquisitions, London: FT Prentice Hall.

Swayne, L.E., Duncan, W.J., and Ginter, P.M. (2006). Strategic Management of Health Care Organizations. Fifth edition.

Malden, MA: Blackwell

Tanriverdi, H., and Lee, C.H. (2008), Within-industry diversification and firm performance in the presence of network

externalities: Evidence from the software industry. Academy of Management Journal 51: 381 - 397.

42

Teece, D.J., Pisano, G., and Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management

Journal 18: pp. 509 – 533.

Thio, V., Hazebroek, E., Oortwijn, W., and Berden, K. (2007). Kostenontwikkeling ziekenhuiszorg: implicaties vanuit

consumentenperspectief. Rotterdam. Ecorys Research and Consulting.

Varadarajan, P., and Ramanujam, V. (1987). Diversification and performance: A reexamination using a new two-

dimensional conceptualization of diversity in firms. Academy of Management Journal 30: pp. 380 – 393.

Liston, C. (1993). Price-Cap versus Rate-of-Return Regulation. Journal of Regulatory Economics 5: pp. 25 – 48. Vickers, J., and Yarrow, G. (1988). Privatization: An economic analysis. Cambridge, Massachusetts and London, England:

MIT Press. Vos, C. (2008). Interview with Franck Asselman, policy advisor of Academic Medical Centre in Amsterdam. De Volkskrant,

April 9, 2008. Retrieved from http://www.volkskrant.nl/vk/nl/2844/Archief/archief/article/detail/905167/2008/04/09/

Ziekenhuis-gaat-zorg-selecteren.dhtml.

Wedig, G.J., Sloan, F.A., Hassan, M., Morrisey, M.A. (1988). Capital structure, ownership, and capital payment policy: The case of Hospitals. Journal of Finance 43: pp. 21 – 40.

Wheeler, J.R.C., Burkhardt, J., Alexander, J.A., and Magnus, S.A. (1999). Financial and organizational determinants of

hospital diversification into subacute care. Health Services Research 34 (1): pp. 61 – 82.

Wood, A. (1971). Diversification, merger, and research expenditures: a review of empirical studies. In R. Morris and A.

Wood (Eds.). The corporate economy: Growth, competition, and innovative potential: pp. 428 – 453. Boston: Harvard

University Press.

Wrigley, L. (1970). Divisional autonomy and diversification. PhD Dissertation, Harvard Business School USA.

Zorgbalans. Internet: Last visited on August 24, 2010. Haarlem.

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Appendix A Hospital overview

# Name Location Category Province

1 Alkmaar Alkmaar Top clinical Noord-Holland

2 Twente Almelo General Overijssel

3 Flevoziekenhuis Almere General Flevoland

4 Meander Amersfoort Top clinical Utrecht

5 Amstelland Amstelveen General Noord-Holland

6 AMC Amsterdam Academic Noord-Holland

7 BovenIJ Amsterdam General Noord-Holland

8 OLV Gasthuis Amsterdam Top clinical Noord-Holland

9 Slotervaart Amsterdam General Noord-Holland

10 Sint Lucas Andreas Amsterdam Top clinical Noord-Holland

11 VU Amsterdam Academic Noord-Holland

12 Gelre Apeldoorn Top clinical Gelderland

13 Alysis Arnhem Top clinical Gelderland

14 Wilhelmina Assen General Drenthe

15 Lievensberg Bergen op Zoom General Noord-Brabant

16 Rode Kruis Beverwijk General Noord-Holland

17 Tergooi Blaricum General Noord-Holland

18 Maasziekenhuis Boxmeer General Noord-Brabant

19 Amphia Breda Top clinical Noord-Brabant

20 IJsselland Capelle a/d IJssel General Zuid-Holland

21 Reinier de Graaf Delft Top clinical Zuid-Holland

22 Ommelander Delfzijl General Groningen

23 Jeroen Bosch Den Bosch Top clinical Noord-Brabant

24 Haaglanden Den Haag Top clinical Zuid-Holland

25 Haga Den Haag Top clinical Zuid-Holland

26 Bronovo Den Haag General Zuid-Holland

27 Gemini Den Helder General Noord-Holland

28 Deventer Deventer Top clinical Overijssel

29 Cura Mare Dirksland General Zuid-Holland

30 Slingeland Doetinchem General Gelderland

31 Pasana Dokkum General Friesland

32 Albert Schweitzer Dordrecht General Zuid-Holland

33 Nij Smellinghe Drachten General Friesland

34 Gelderse Vallei Ede General Gelderland

35 Catharina Eindhoven Top clinical Noord-Brabant

36 Maxima Eindhoven Top clinical Noord-Brabant

37 Leveste Emmen General Drenthe

38 MS Twente Enschede Top clinical Overijssel

39 Sint Anna Geldrop General Noord-Brabant

40 Rivas Gorinchem General Zuid-Holland

41 Groene Hart Gouda General Zuid-Holland

42 Martini Groningen Top clinical Groningen

43 UMC Groningen Groningen Academic Groningen

44 Kennemer Gasthuis Haarlem General Noord-Holland

45 Saxenburgh Hardenberg General Overijssel

46 Sint Jansdal Harderwijk General Gelderland

47 Tjongerschans Heerenveen General Friesland

48 Atrium Heerlen Top clinical Limburg

49 Elkerliek Helmond General Noord-Brabant

50 Spaarne Hoofddorp Top clinical Noord-Holland

51 Westfries Gasthuis Hoorn General Noord-Holland

52 Leeuwarden Leeuwarden Top clinical Friesland

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53 Diaconessenhuis Leiden General Zuid-Holland

54 Leids UMC Leiden Academic Zuid-Holland

55 Rijnland Leiderdorp General Zuid-Holland

56 IJsselmeer Lelystad General Flevoland

57 AZM Maastricht Academic Limburg

58 Noorderboog Meppel General Drenthe

59 Sint Antonius Nieuwegein Top clinical Utrecht

60 Canisius Wilhelmina Nijmegen Top clinical Gelderland

61 UMC Sint Radboud Nijmegen Academic Gelderland

62 Bernhoven Oss General Noord-Brabant

63 Waterland Purmerend General Noord-Holland

64 Laurentius Roermond General Limburg

65 Franciscus Roosendaal General Noord-Brabant

66 Erasmus MC Rotterdam Academic Zuid-Holland

67 Ikazia Rotterdam General Zuid-Holland

68 Maasstad Ziekenhuis Rotterdam General Zuid-Holland

69 Sint Franciscus Rotterdam Top clinical Zuid-Holland

70 Vlietland Schiedam General Zuid-Holland

71 Orbis Sittard General Limburg

72 Antonius Ziekenhuis Sneek General Friesland

73 Ruwaard van Putten Spijkenisse General Zuid-Holland

74 Refaja Stadskanaal General Groningen

75 ZorgSaam Terneuzen General Zeeland

76 Rivierenland Tiel General Gelderland

77 Elisabeth Tilburg Top clinical Noord-Brabant

78 Tweesteden Tilburg General Noord-Brabant

79 Diakonessenhuis Utrecht General Utrecht

80 UMC Utrecht Utrecht Academic Utrecht

81 VieCuri Venlo Top clinical Limburg

82 ADRZ Vlissingen General Zeeland

83 Sint Jans Gasthuis Weert General Limburg

84 Koningin Beatrix Winterswijk General Gelderland

85 Zuwe Hofpoort Woerden General Utrecht

86 Zaans MC Zaandam General Noord-Holland

87 Lange Land Zoetermeer General Zuid-Holland

88 Isala Zwolle Top clinical Overijssel

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Appendix B Medical performance methodology

Elsevier’s “Beste Ziekenhuizen 2010”

Elsevier’s medical performance is based on two main factors: (1) the conditions of safe and efficient

medical care, and (2) the operation of a patient oriented organization. Elsevier, in collaboration with SiRM,

determined organizational operation by two features: quality of service/information and patient

orientation. The knowledge on these features is from the Inspectie voor de Gezondheidszorg (IGZ), the

governmental authority that monitors medical care quality, and the governmental program Zichtbare Zorg.

The starting point here is treatment of diseases like bladder cancer, diabetes, hip and knee replacement,

hernia, and varicose veins16. Also, the supply of information, patient involvement, and cancellation of

surgery within 24 hours before the scheduled time (from the hospital) are valued.

The other factor is based on speed of service, measured by the monthly required waiting lists. Waiting lists

are based on the “Treeknorm”, an agreement between hospitals and insurers. Exceeding this standard by

three times or more automatically results in the lowest score “1”. In sum, infinite questions for hospitals

exist, but not every question can be included in this performance study. Nonetheless, Elsevier’s sample

reveals much about the hospital culture regarding patient service (Elsevier, October 2010, pp. 84–86).

Algemeen Dagblad’s “Top 100 Ziekenhuizen”

Algemeen Dagblad’s seventh annual “Ziekenhuis Top 100” reviews the medical performance of all hospitals

within the Netherlands in 2010. This ranking consists of 31 quality criteria, of which 23 are composed by the

Inspectie voor de Gezondheidszorg (IGZ), the governmental authority that monitors medical care quality.

The 23 criteria concern medical quality like the number of re-surgeries, approaches of malnutrition, the

number of risky surgeries, and the monitoring of the correct patient data and medication. The

methodology of the Top 100 is updated every year: some criteria are replaced by new or updated criteria.

The hospitals have made the information public by instruction of the IGZ. The IGZ itself monitors the

correctness of the data. AD has awarded scores to the criteria. The choice for certain criteria and scores are

made in consultation with medical and industry organizations: because (1) the criteria are an indication for

better hospital quality, (2) the criteria are distinctive between hospitals or (3) clear quality standards are

composed by the Inspection of Medical Specialists. The latter holds, for example, for the number of

allowed operations to the abdominal aorta17, or for the speed at which strokes have to be treated. The

other eight of the 31 criteria are focused on patient satisfaction and friendliness. Patient satisfaction is

measured by interviews from Independer.nl in collaboration with Mediquest, a research agency for health

care (22.000 interviews in total). Three criteria concern pediatrics and maternity care18. These data are

obtained from the foundation Kind en Ziekenhuis. The final four criteria concern medical labels19.

16 In Dutch: spataderen 17 In Dutch: buikslagader 18 In Dutch: kindergeneeskunde en kraamzorg 19 In Dutch: patiëntkeurmerken

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Not every hospital performs every treatment and hence cannot be reviewed on every criterion. Therefore,

the AD ranking is a relative score: how does a hospital score relative to its maximum score. Hereby

comparison is possible. Specialist clinics are excluded from the Top 100 because comparison is not

meaningful as most of the criteria are not relevant for these hospitals. Academic hospitals, with relatively

more complex treatments, are included in the ranking. The criteria are common and play a role in every

hospital, either academic or general.

If a hospital meets all criteria, it can achieve 64 points. The percentage of the maximum number of points

determines the ranking. From 2010, hospitals can not only score one, two, or three points on a criterion,

but also parts of a point. This results in a more accurate distinction between hospitals. Serious incidents

happen in every hospital and are therefore not considered in determining the score, which is based on

thousands of contacts with patients. The worst specialist can work in the best hospital, and also the other

way around (Algemeen Dagblad, September 2010, pp. 16–21).

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Appendix C Background information on privatization

Privatization is one of the main issues where interests of both government and citizen meet. After all,

health care is important for everyone one way or another. Staatsen (2001) shows that privatization of

health care brings important benefits but not without several drawbacks for both government and citizen.

The main benefits and drawbacks of privatization for hospitals are summarized below:

Benefits

� Spread of power

� Government flexibility

� Increase of market- and customer response

� Increase of self responsibility

� Increase of independency citizen

Drawbacks

� Public interest could fade

� Loss of continuity, effectiveness and quality of facilities

� Loss of democratic supervision

� Hard to protect weaker segments

� Risk of monopoly development

The benefits above are exactly what the government tries to achieve with health care privatization, while

the drawbacks describe public dangers. While the benefits may be clear and publicly known, less attention

has been paid to the negative consequences of health care privatization. Note that the drawbacks

mentioned above give a relatively broad representation, while this research will particularly focus on the

effects on performance. Almón, Domínguez, and Gómez (2002) describe three clear arguments in favor of

privatization: economic reasons concerning increased efficiency; financial reasons concerning reduced

public financing; and political reasons aimed at reducing government involvement. Moreover, drawbacks

could become more severe when hospitals are allowed to become ‘for-profit’. Some say privatization of

hospitals is impossible since competition in or between hospitals is incompatible with justice and reliability

of care, as well as the right of well-being (Van Loef, 2000). Of course this is an important statement, since

the primary goal of health care is the treatment and prevention of illness and not being efficient. That is

why this research will also consider medical performance and organizational performance (efficiency).

Section 6.4 will also discuss this subject.

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Appendix D STATA/SPSS output

Figure D.1 Histogram of ROR, including normal distribution curve

Figure D.2 Histogram of ROA, including normal distribution curve

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Figure D.3 Histogram of MEDELS, including normal distribution curve

Figure D.4

Histogram of MEDAD, including normal distribution curve

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Figure D.5 Histogram of EFF, including normal distribution curve

Figure D.6 Histogram of PROD, including normal distribution curve

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Figure D.7 Histogram of ADEXP, including normal distribution curve