Diversification and Performance in Dutch Health Care
An Empirical Analysis
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: S148680
: Gijs Gunterman
: dr. A.A.C.J. van Oijen
: dr. ir. B.R. Meijboom
: Tilburg University, Faculty of Economics and Business Administration
: Master Strategic Management
: January 12, 2012
: 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
Methodology ................................................................................................................................... 16
Chapter 4
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
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 highquality 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
1
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.
2
1.3
Research questions
In order to answer the former problem statement, the following research questions are constructed:
1.4
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?
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.
3
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).
4
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.
5
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.
6
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.
7
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 crosssubsidizing (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 wellestablished 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.
8
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 productcount 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.
9
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.
10
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.
11
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 marketoriented 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
12
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.
13
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).
14
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.
15
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.
16
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 selfselection 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.
17
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 noninvoicable 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.
18
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.
19
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.
! " 4
$
Hypothesis 2a: Unrelated diversification is positively related to administrative expenses.
%
! " 4
$
Hypothesis 2b: Unrelated diversification is negatively related to performance.
! " 4
$
Hypothesis 3: The number of hospital locations affects the performance of hospitals.
/ '
! " 4
$
20
Table 4.1
Variable definitions
Variable
Dependent Variables
ROR
ROA
MEDELS
MEDAD
MEDCOMB
EFF
PROD
ADEXP
Variables of Interest
BSD
NSD
LOGMNSD
HINT
Control Variables
SIZE
TOPCLINIC
ACADEMIC
BSEGMENT
VOLATILITY
PROVINCE
CR4
Definition
Financial performance measured by Return on Revenue. Profit divided by total revenue for 2010.
Financial performance measured by Return on Assets. Profit divided by total assets for 2010.
Medical performance based on the Elsevier study for 2010. A combination of service speed,
service quality, and patient orientation.
Medical performance based on the Algemeen Dagblad ranking for 2010. The percentage score
of maximum score.
Medical performance constructed from both Elsevier and AD.
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.
Organizational performance measured by total revenues divided by average FTE over 2010.
Administrative expenses. Overhead, information technology costs, maintenance costs, and
energy costs for 2010, divided by total revenue.
Broad Spectrum Diversity. Measures unrelated diversification by the number of two-digit SIC
categories in which the firm operates in 2009.
Narrow Spectrum Diversity. Measures related (within-industry) diversification by the number of
eight-digit SIC categories in which the firm operates in 2009.
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.
Horizontal integration. Number of hospital locations in 2009.
Logarithm of total assets for 2010.
Dummy variable for hospital type. Value is one if top clinical, zero if otherwise.
Dummy variable for hospital type. Value is one if academic, zero if otherwise.
Capital reimbursement in the B-segment divided by total reimbursement for 2010, controlling
for privatization.
Four-year period (2007 to 2010) volatility of hospital earnings. Standard deviation of difference
in annual profit divided by mean value of total assets.
Measures the amount of residents in each province per hospital in that province, controlling for
industry concentration.
Concentration ratio. Sum of the revenues of the four largest hospitals in a province divided by
total revenue in that province.
21
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
ROA
MEDELS
MEDAD
MEDCOMB
88
88
88
88
88
0.0192
0.0181
2.5161
76.5512
0.4826
0.0195
0.0191
0.8543
7.4992
0.1726
-0.0282
-0.0448
1
53.74
0.1710
0.1011
0.0848
4
90.47
0.8260
EFF
PROD11
ADEXP
BSD
NSD
88
88
88
88
88
0.1241
100859.1
0.2731
3.9091
30.3636
0.0401
10592.16
0.0336
1.4905
5.9484
0.0460
66200
0.1789
1
17
0.2424
130000
0.3829
9
45
LOGMNSD12
HINT
SIZE
TOPCLINIC
ACADEMIC
88
88
88
88
88
8.5524
2.4545
5.2023
0.2841
0.0909
2.6463
1.3123
0.7002
0.4536
0.2891
3.89
1
4.0035
0
0
20
6
6.9752
1
1
BSEGMENT
VOLATILITY
PROVINCE
CR4
88
88
88
88
0.2576
0.0155
189948.8
0.7079
0.0823
0.0206
28694.89
0.1904
0.0399
.0011
129539
.5205
0.3934
.1896
246948
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.
Note that the descriptive statistics are not based on this transformed variable LOGMNSD, but on the original variable,
since this is easier to interpret.
12
22
Table 5.2
Two-tailed Pearson correlations
BSD
NSD
LOGMNSD
HINT
SIZE
TOPCLINIC
ACADEMIC
BSEGMENT
VOLATILITY
PROVINCE
CR4
BSD
1
0.736
0.843
0.057
0.464
0.056
0.420
0.542
0.069
0.068
0.046
NSD
LOGMNSD
HINT
SIZE
TOPCLINIC
ACADEMIC
BSEGMENT
VOLATILITY
PROVINCE
CR4
1
0.349
0.051
0.728
0.217
0.569
0.684
0.057
0.173
0.039
1
0.126
0.145
0.023
0.162
0.227
0.053
0.045
0.083
1
0.035
0.263
0.262
0.103
0.094
0.365
0.111
1
0.337
0.653
0.751
0.226
0.181
0.014
1
0.199
0.081
0.130
0.173
0.012
1
0.747
0.088
0.007
0.020
1
0.001
0.011
0.068
1
0.036
0.172
1
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.
23
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.
24
Table 5.3
Estimation results for ROR, using OLS model
CONSTANT
(1)
(2)
(3)
(4)
0.005
-0.064*
-0.065*
-0.066*
(0.016)
(0.038)
(0.038)
(0.038)
BSD
0.002
0.001
(0.002)
(0.002)
NSD
SIZE
0.001*
0.001
(0.001)
(0.001)
0.000
0.007
0.004
0.005
(0.000)
(0.006)
(0.006)
(0.006)
0.008
0.005
0.004
0.005
(0.006)
(0.006)
(0.006)
(0.006)
-0.005
-0.003
-0.005
-0.005
(0.017)
(0.013)
(0.013)
(0.013)
0.000
0.110**
0.108 **
0.113**
(0.000)
(0.048)
(0.047)
(0.048)
0.054
0.129
0.105
0.113
(0.105)
(0.106)
(0.106)
(0.108)
0.000
0.000
0.000
0.000
(0.000)
(0.000)
(0.000)
(0.000)
0.013
0.014
0.014
0.014
(0.011)
(0.011)
(0.011)
(0.011)
N
88
88
88
88
Adjusted ()
0.003
0.058
0.066
0.057
F-test
(0.408)
(0.121)
(0.096)
(0.135)
TOPCLINIC
ACADEMIC
BSEGMENT
VOLATILITY
PROVINCE
CR4
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.
25
Table 5.4
Estimation results for ROA, using OLS model
CONSTANT
(5)
(6)
(7)
(8)
-0.004
-0.023
-0.027
-0.028
(0.015)
(0.037)
(0.036)
(0.036)
BSD
0.003**
0.001
(0.002)
(0.002)
NSD
SIZE
0.002***
0.001**
(0.001)
(0.001)
-0.000
-0.001
-0.006
-0.005
(0.000)
(0.006)
(0.006)
(0.006)
0.008
0.008
0.007
0.007
(0.006)
(0.006)
(0.006)
(0.006)
0.007
-0.004
-0.007
-0.007
(0.016)
(0.013)
(0.012)
(0.012)
0.000
0.045
0.048
0.051
(0.000)
(0.047)
(0.045)
(0.046)
0.106
0.127
0.090
0.095
(0.101)
(0.104)
(0.101)
(0.103)
0.000
0.000
0.000
0.000
(0.000)
(0.000)
(0.000)
(0.000)
0.015
0.015
0.015
0.015
(0.011)
(0.011)
(0.010)
(0.010)
N
88
88
88
88
Adjusted ()
0.039
0.066
0.115
0.105
F-test
(0.176)
(0.097)
(0.022)
(0.036)
TOPCLINIC
ACADEMIC
BSEGMENT
VOLATILITY
PROVINCE
CR4
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.
26
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
1.880
48.896***
44.543***
44.249***
(1.491)
(1.530)
(13.869)
(13.957)
(14.077)
0.006
1.013*
0.873
(0.085)
(0.603)
(0.780)
BSD
NSD
SIZE
-0.023
0.072
(0.028)
(0.253)
0.077
0.160
5.580***
5.335***
5.106**
(0.231)
(0.249)
(2.153)
(2.134)
(2.292)
-0.284
-0.265
-0.330
-0.188
-0.263
(0.236)
(0.239)
(2.193)
(2.170)
(2.199)
-1.492***
-1.449***
-10.361**
-10.230**
-10.379**
(0.515)
(0.522)
(4.794)
(4.741)
(4.798)
0.604
0.168
7.725
16.491
16.804
(1.842)
(1.943)
(17.141)
(17.736)
(17.874)
3.832
4.240
16.878
20.684
18.972
(4.267)
(4.348)
(39.694)
(39.316)
(40.001)
0.000
0.000
0.000
0.000
0.000
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-0.180
-0.173
-7.215*
-7.388*
-7.390*
(0.436)
(0.439)
(4.055)
(4.011)
(4.034)
N
88
88
88
88
88
Adjusted ()
0.212
0.202
0.115
0.134
0.124
F-test
(0.000)
(0.001)
(0.018)
(0.012)
(0.020)
TOPCLINIC
ACADEMIC
BSEGMENT
VOLATILITY
PROVINCE
CR4
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.
27
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
0.090
0.080
0.003
0.841***
(0.070)
(0.069)
(0.070)
(0.073)
(0.201)
BSD
-0.008***
-0.013
(0.003)
(0.011)
NSD
-0.002**
0.001
(0.001)
(0.004)
LOGMNSD
0.030**
(0.014)
SIZE
-0.004
-0.002
0.003
-0.005
0.033
(0.011)
(0.011)
(0.011)
(0.011)
(0.033)
0.031***
0.030***
0.033***
0.030***
0.023
(0.011)
(0.011)
(0.011)
(0.011)
(0.031)
0.093***
0.092***
0.096***
0.091***
0.173***
(0.024)
(0.023)
(0.024)
(0.024)
(0.068)
0.352***
0.282***
0.310***
0.322***
0.365
(0.087)
(0.088)
(0.088)
(0.086)
(0.255)
0.187
0.157
0.220
0.151
1.198**
(0.202)
(0.194)
(0.199)
(0.197)
(0.570)
0.000
0.000
0.000
0.000
0.000
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-0.033
-0.031
-0.032
-0.029
-0.056
(0.021)
(0.020)
(0.020)
(0.020)
(0.058)
88
88
88
88
88
Adjusted (
0.200
0.258
0.229
0.237
0.107
F-test
(0.001)
(0.000)
(0.000)
(0.000)
(0.034)
TOPCLINIC
ACADEMIC
BSEGMENT
VOLATILITY
PROVINCE
CR4
N
)
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
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 pvalues 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.
The models in specification (1), (2), (4), and (5) are not significant. The results from these specifications are therefore
not considered when making conclusions.
14
29
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
CONSTANT
(19)
(20)
(21)
0.198***
0.209***
0.212***
(0.062)
(0.063)
(0.025)
-0.003
-0.002
(0.003)
(0.003)
BSD
SIZE
-0.002
-0.001
(0.010)
(0.010)
0.022**
0.022**
0.023***
(0.010)
(0.010)
(0.008)
0.046**
0.046**
0.047***
(0.021)
(0.021)
(0.019)
0.243***
0.221***
0.232***
(0.077)
(0.080)
(0.069)
-0.053
-0.063
(0.177)
(0.178)
0.000
0.000
(0.000)
(0.000)
0.000
0.001
(0.018)
(0.018)
N
88
88
88
Adjusted ()
0.121
0.119
0.158
F-test
(0.014)
(0.019)
(0.001)
TOPCLINIC
ACADEMIC
BSEGMENT
VOLATILITY
PROVINCE
CR4
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.
30
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
31
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.
32
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
-0.018
1.819
47.877***
0.055
0.751***
(0.037)
(0.037)
(1.508)
(14.084)
(0.072)
(0.198)
-0.004**
-0.003
0.066
-0.336
-0.001
-0.013
(0.002)
(0.002)
(0.072)
(0.675)
(0.003)
(0.009)
0.009
0.001
0.046
5.733***
-0.004
0.038
(0.006)
(0.006)
(0.234)
(2.185)
(0.011)
(0.031)
0.006
0.009
-0.296
-0.268
0.031***
0.028
(0.006)
(0.006)
(0.236)
(2.207)
(0.011)
(0.031)
-0.011
-0.010
-1.372***
-10.971**
0.092***
0.151**
(0.013)
(0.013)
(0.532)
(4.971)
(0.025)
(0.070)
0.089**
0.016
0.609
7.699
0.352***
0.465
(0.045)
(0.045)
(1.844)
(17.222)
(0.088)
(0.242)
0.150
0.139
3.343
19.375
0.191
1.356**
(0.105)
(0.105)
(4.305)
(40.197)
(0.204)
(0.564)
0.000
0.000
0.000
0.000
0.000
0.000
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
0.017
0.017
-0.223
-6.999
-0.032
-0.049
(0.011)
(0.011)
(0.439)
(4.097)
(0.021)
(0.057)
N
88
88
88
88
88
88
Adjusted ()
0.093
0.055
0.210
0.106
0.190
0.118
F-test
(0.044)
(0.129)
(0.001)
(0.029)
(0.001)
(0.020)
HINT
SIZE
TOPCLINIC
ACADEMIC
BSEGMENT
VOLATILITY
PROVINCE
CR4
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.
33
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 (lowhigh) 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
Diversification categories
Performance measures
F-ratios
Cell A:
Cell B:
Cell C:
Cell D:
BSD
MNSD
Interactions
Low MNSD –
High MNSD –
Low MNSD –
High MNSD –
Low BSD
Low BSD
High BSD
High BSD
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
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
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
Related-
Unrelated-
Highly
diversified
diversified
diversified
diversified
Financial performance
Medical performance
Organizational performance
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.
34
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.
35
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 businesswise 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.
36
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 Bsegment 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
37
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.
38
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 reevaluate 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.
39
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Appendix A
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52
Hospital overview
Name
Location
Alkmaar
Twente
Flevoziekenhuis
Meander
Amstelland
AMC
BovenIJ
OLV Gasthuis
Slotervaart
Sint Lucas Andreas
VU
Gelre
Alysis
Wilhelmina
Lievensberg
Rode Kruis
Tergooi
Maasziekenhuis
Amphia
IJsselland
Reinier de Graaf
Ommelander
Jeroen Bosch
Haaglanden
Haga
Bronovo
Gemini
Deventer
Cura Mare
Slingeland
Pasana
Albert Schweitzer
Nij Smellinghe
Gelderse Vallei
Catharina
Maxima
Leveste
MS Twente
Sint Anna
Rivas
Groene Hart
Martini
UMC Groningen
Kennemer Gasthuis
Saxenburgh
Sint Jansdal
Tjongerschans
Atrium
Elkerliek
Spaarne
Westfries Gasthuis
Leeuwarden
Alkmaar
Almelo
Almere
Amersfoort
Amstelveen
Amsterdam
Amsterdam
Amsterdam
Amsterdam
Amsterdam
Amsterdam
Apeldoorn
Arnhem
Assen
Bergen op Zoom
Beverwijk
Blaricum
Boxmeer
Breda
Capelle a/d IJssel
Delft
Delfzijl
Den Bosch
Den Haag
Den Haag
Den Haag
Den Helder
Deventer
Dirksland
Doetinchem
Dokkum
Dordrecht
Drachten
Ede
Eindhoven
Eindhoven
Emmen
Enschede
Geldrop
Gorinchem
Gouda
Groningen
Groningen
Haarlem
Hardenberg
Harderwijk
Heerenveen
Heerlen
Helmond
Hoofddorp
Hoorn
Leeuwarden
43
Category
Top clinical
General
General
Top clinical
General
Academic
General
Top clinical
General
Top clinical
Academic
Top clinical
Top clinical
General
General
General
General
General
Top clinical
General
Top clinical
General
Top clinical
Top clinical
Top clinical
General
General
Top clinical
General
General
General
General
General
General
Top clinical
Top clinical
General
Top clinical
General
General
General
Top clinical
Academic
General
General
General
General
Top clinical
General
Top clinical
General
Top clinical
Province
Noord-Holland
Overijssel
Flevoland
Utrecht
Noord-Holland
Noord-Holland
Noord-Holland
Noord-Holland
Noord-Holland
Noord-Holland
Noord-Holland
Gelderland
Gelderland
Drenthe
Noord-Brabant
Noord-Holland
Noord-Holland
Noord-Brabant
Noord-Brabant
Zuid-Holland
Zuid-Holland
Groningen
Noord-Brabant
Zuid-Holland
Zuid-Holland
Zuid-Holland
Noord-Holland
Overijssel
Zuid-Holland
Gelderland
Friesland
Zuid-Holland
Friesland
Gelderland
Noord-Brabant
Noord-Brabant
Drenthe
Overijssel
Noord-Brabant
Zuid-Holland
Zuid-Holland
Groningen
Groningen
Noord-Holland
Overijssel
Gelderland
Friesland
Limburg
Noord-Brabant
Noord-Holland
Noord-Holland
Friesland
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
Diaconessenhuis
Leids UMC
Rijnland
IJsselmeer
AZM
Noorderboog
Sint Antonius
Canisius Wilhelmina
UMC Sint Radboud
Bernhoven
Waterland
Laurentius
Franciscus
Erasmus MC
Ikazia
Maasstad Ziekenhuis
Sint Franciscus
Vlietland
Orbis
Antonius Ziekenhuis
Ruwaard van Putten
Refaja
ZorgSaam
Rivierenland
Elisabeth
Tweesteden
Diakonessenhuis
UMC Utrecht
VieCuri
ADRZ
Sint Jans Gasthuis
Koningin Beatrix
Zuwe Hofpoort
Zaans MC
Lange Land
Isala
Leiden
Leiden
Leiderdorp
Lelystad
Maastricht
Meppel
Nieuwegein
Nijmegen
Nijmegen
Oss
Purmerend
Roermond
Roosendaal
Rotterdam
Rotterdam
Rotterdam
Rotterdam
Schiedam
Sittard
Sneek
Spijkenisse
Stadskanaal
Terneuzen
Tiel
Tilburg
Tilburg
Utrecht
Utrecht
Venlo
Vlissingen
Weert
Winterswijk
Woerden
Zaandam
Zoetermeer
Zwolle
44
General
Academic
General
General
Academic
General
Top clinical
Top clinical
Academic
General
General
General
General
Academic
General
General
Top clinical
General
General
General
General
General
General
General
Top clinical
General
General
Academic
Top clinical
General
General
General
General
General
General
Top clinical
Zuid-Holland
Zuid-Holland
Zuid-Holland
Flevoland
Limburg
Drenthe
Utrecht
Gelderland
Gelderland
Noord-Brabant
Noord-Holland
Limburg
Noord-Brabant
Zuid-Holland
Zuid-Holland
Zuid-Holland
Zuid-Holland
Zuid-Holland
Limburg
Friesland
Zuid-Holland
Groningen
Zeeland
Gelderland
Noord-Brabant
Noord-Brabant
Utrecht
Utrecht
Limburg
Zeeland
Limburg
Gelderland
Utrecht
Noord-Holland
Zuid-Holland
Overijssel
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
In Dutch: buikslagader
18
In Dutch: kindergeneeskunde en kraamzorg
19
In Dutch: patiëntkeurmerken
17
45
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).
46
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.
47
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
48
Figure D.3
Histogram of MEDELS, including normal distribution curve
Figure D.4
Histogram of MEDAD, including normal distribution curve
49
Figure D.5
Histogram of EFF, including normal distribution curve
Figure D.6
Histogram of PROD, including normal distribution curve
50
Figure D.7
Histogram of ADEXP, including normal distribution curve
51