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Returns to scale of Business Administration research
in Germany
Marcel Clermont •Alexander Dirksen •Harald Dyckhoff
Received: 11 September 2014 / Published online: 17 March 2015
Akade
´miai Kiado
´, Budapest, Hungary 2015
Abstract In order to efficiently allocate academic resources, an awareness of the properties
of the underlying production function’s returns to scale is of crucial importance. For instance,
the question arises as to what extent an expansion of a university department’s academic staff
would be advisable in order to utilize increasing marginal gains of research production. On
the other hand, it is disputable whether an optimal university department size exists. Em-
pirical studies covering these questions render various answers. In this paper, we analyse
which properties of returns to scale the Business Administration research of universities in
Germany exhibits. On the basis of research data from 2001 until 2009 provided by the Centre
for Higher Education, and using Data Envelopment Analysis, we demonstrate that typically
sized business schools show nearly constant returns to scale. Furthermore, we observe ten-
dencies of decreasing returns to scale for large-sized business schools. Diverse robustness
and sensitivity analyses confirm the validity of our inferred empirical findings.
Keywords Business Administration research Centre for Higher Education Data
Envelopment Analysis Empirical production function Returns to scale Scale efficiency
JEL Classification C61 D24 L25
Introduction
An answer to the question of which properties of returns to scale the research and/or
teaching technology of universities or university departments feature, is essential for
M. Clermont (&)A. Dirksen H. Dyckhoff
Chair of Business Theory: Sustainable Production and Industrial Control, School of Business and
Economics, RWTH Aachen University, Templergraben 64, 52056 Aachen, Germany
e-mail: clermont@lut.rwth-aachen.de
A. Dirksen
e-mail: dirksen@lut.rwth-aachen.de
H. Dyckhoff
e-mail: dyckhoff@lut.rwth-aachen.de
123
Scientometrics (2015) 103:583–614
DOI 10.1007/s11192-015-1561-2
determining an optimal institution size. Such an optimum exists, for example, when returns
to scale increase with a growing decision making unit (DMU) size at first and then decrease
later. On the minimum efficient scale, the average cost per unit of scientific benefit is
minimal (Gutenberg 1983). However, an unambiguous optimum does not always exist,
because for example, all DMU sizes are optimal in the event of constant returns to scale. In
such cases, the average cost of scientific benefit with constant factor-input ratios and prices
is independent of the size of a DMU. That means, for example, that the choice of the
particular departmental size is irrelevant to research efficiency in this case, which is
different from the case of an unambiguous minimum efficient scale.
Basically, there are plausible arguments for any type of returns to scale in universities or
university departments (e.g. Cohen 1980; Kyvik 1995; Fandel 2007). Reasons for in-
creasing returns to scale are, for example, synergy effects induced by economies of scale
and scope. Such effects can be explained by potential cooperation and joint research
projects as well as more evenly allocated teaching assignments and administrative tasks.
Furthermore, there might be a minimum level of successful university research that con-
sists of an adequate number of university departments or corresponding professorships
accompanied by a particular infrastructure of personnel and equipment as well as a certain
number of students. In contrast, decreasing returns to scale are also conceivable and, for
instance, justifiable due to innovation-adverse regulations and routines as well as increased
coordination requirements.
Findings of empirical studies that examine correlations between the size of universities
or departments on the one hand and research productivity on the other hand do not render
unambiguous results with regard to different countries and disciplines (e.g. Johnston 1994;
von Tunzelmann et al. 2003). Rather, the authors observe increasing, decreasing, and
constant returns to scale. In this respect, Bonaccorsi et al. (2006, p. 393) state that ‘‘there is
lack of consensus on the existence of economies of scale in scientific production and higher
education’’. Because of the heterogeneity of individual scientific fields, investigations of
returns to scale are supposed to be carried out merely for specific scientific disciplines
(Abramo et al. 2012). Due to varying organizational structures and research objectives of
universities in different countries, such analyses can still only be properly interpreted for
institutions within individual countries.
From the point of view of a German Business Administration department or rather of a
German business school (hereafter: BuS), Dyckhoff et al. (2009) examine the relation
between the size of a BuS and its research output. They show that a BuS comprised of
between 8 and 16 professors exhibits approximately constant returns to scale with regard to
its research production. Due to the limited number of BuSs with more than 16 profes-
sorships, however, the authors are not able to derive results for that respective cluster. They
base their study on research data originating from the Centre for Higher Education (CHE)
acquired in 2004; i.e. the respective CHE output and input indicators refer to the years
2001 up until 2003.
The non-profit organization CHE conducts comprehensive evaluations of academic
research in Germany that differentiates between specific academic disciplines. It acquires
and evaluates comprehensive data on German universities’ research and teaching activities.
Almost every academic discipline is analysed in a 3-year-cycle, in order to create trans-
parency for interested stakeholders. Evaluation results are published on the CHE’s website
as well as through its media partner, the German magazine DIE ZEIT. CHE data acquisition
and evaluation of business research attract great attention in the German business academic
community. Especially German governmental bodies and university managements utilize
the CHE data and results in order to evaluate research between different BuSs in a
584 Scientometrics (2015) 103:583–614
123
comparative manner. Based upon these assessments, several governmental institutions and
universities derive objectives and performance agreements, thus co-opting the results of the
CHE research ranking (Clermont and Dirksen 2015).
In the 2004 CHE datasets used by Dyckhoff et al. (2009), research effectiveness of a
BuS is represented by three output criteria: the publication points of nationally visible
publications, the number of PhD dissertations, and the amount of expended third-party
funds. The research efficiency of a BuS is measured by the CHE insofar as each of these
output criteria is proportioned to a staff assignment quantity. Since 2004, the CHE data
acquisition for BuS research evaluation has been conducted two more times (2007 and
2010), leading to datasets covering the (consecutive) period from 2001 up until 2009.
However, the CHE has enhanced the design and data acquisition of several research
indicators over the course of time, in order to account more strongly for qualitative aspects
of research performance. For instance, the CHE has integrated the number of international
publications as an additional output indicator.
Accordingly, the CHE provides an extensive, regularly acquired and enhanced database
that is also recognized by the relevant scientific community. As a result, empirical analyses
based on these datasets are appropriate. With respect to the analysis of returns to scale,
better and more extensive datasets in relation to the research activities of BuSs in Germany
are now available through the 2007 and 2010 data acquisitions. This means that analyses
regarding properties of returns to scale of German BuSs are now feasible over a con-
secutive period of 9 years. Thus, from an empirical perspective, the question arises as to
whether Dyckhoff et al.’s results of constant returns to scale of German BuS research
production represent a robust observation with reference to alternative time periods and
evaluation criteria; otherwise it might actually illustrate a one-time result, incurred by the
specific design and procedure of data acquisition according to the chosen temporary ref-
erence period. Hence, the objective of this paper is to analyse the returns to scale of BuS
research production at universities in Germany covering the time period from 2001 up until
2009 and to derive respective implications.
Our paper is structured as follows: In the next section, we provide a brief overview of
the state of the art regarding empirical analyses of the properties of research production’s
returns to scale. Subsequently, we introduce the conceptual design of our examination
referring to both the used datasets and the applied methodology. Based upon the described
method, we then present and interpret the resulting findings. In addition, we conduct
comprehensive robustness and sensitivity analyses to ensure the validity of the inferred
results. We conclude our paper by addressing implications and limitations as well as
providing future prospects on further research questions.
State of the art
A first overview covering the results of international empirical studies that examine returns
to scale in research is provided by the reviews of Cohen (1991), Johnston (1994) and von
Tunzelmann et al. (2003). Cohen unconditionally perceives proportional relations between
research group size and respective performance, while the other two reviews give more
versatile, and even inconsistent, insights. In general, Johnston likewise observes a linear
relationship between the size of scientific institutions and corresponding research perfor-
mance. However, he gives substantial evidence of an optimal institution size existing
Scientometrics (2015) 103:583–614 585
123
(Johnston 1994, p. 32). In contrast, von Tunzelmann et al. (2003, p. 8ff) consider the
existence of an optimal institution size in research for groups or teams, but not for (or-
ganizationally superior) departments or whole universities.
More current studies disclose heterogeneous results as well, even though most of them
constitute a constant ratio between the used input and the produced output (as in Abbot and
Doucouliagos 2003; Laband and Lentz 2003; Bonaccorsi and Daraio 2005; Bonaccorsi
et al. 2006; Abramo et al. 2012,2014). Compared to this, Longlong et al. (2009) derive
different findings for returns to scale for Chinese universities. They observe either in-
creasing or decreasing returns to scale between a university’s size and its research and
teaching performances. This, in turn, implies that an optimal institution size of the
evaluated universities exists. Van der Wal et al. (2009) and Brandt and Schubert (2013),
however, only show decreasing returns to scale with reference to research groups.
With regard to the relation of BuS size in Germany and generated research productivity,
so far there mainly exists the study by Dyckhoff et al. (2009). Using Data Envelopment
Analysis (DEA), they construct and analyse the empirical production function—generated
by the CHE research data of German BuSs between 2001 and 2003—especially with
regard to the properties of returns to scale. In view of multi-dimensional total productivity
measurements, their examinations demonstrate that BuSs employing between 8 and 16
professors produce research outputs with nearly constant returns to scale. A similar result
had already been detected by Ahn et al. (2007), as a minor conclusion while conducting
efficiency analyses of the prior CHE research data from 1998 up until 2000.
Altogether, the studies mentioned above mainly differ with respect to the way in which
the evaluated organizational units are defined (individual scientists, research groups, de-
partments or universities) and in the choice of indicators. Since we focus our examinations
on the relation between the research productivity and the size of German BuSs, we
evaluate and extend the results achieved by Dyckhoff et al. (2009). Due to changes in the
German university system, it is questionable as to whether their results are valid for
subsequent years, too. Since the CHE data acquisition in 2004, the Business Administra-
tion research landscape in Germany has changed decisively with regard to numerous
influencing factors. First of all, owing to diverse funding programs originating from the
federal government and state administrations—for instance, the so called ‘‘Excellence
Initiative’’—many BuSs have grown in size (measured by the number of employed sci-
entists). Moreover, for young scientists, the raising of third-party funds and in particular
the placing of articles in international top journals have become an increasingly necessary
condition in order for them to be merely considered when they apply for vacant profes-
sorships (Schrader and Hennig-Thurau 2009, p. 202). Because of performance-based salary
reforms in Germany and performance-based management by objectives, even for already
appointed scientists, the pressure to raise funds and to publish research results is increasing
(Bort and Schiller-Merkens 2010, p. 340). As the CHE has incorporated the developments
outlined above regarding the design and data acquisition of research indicators, we have
taken CHE data from 2004 up until 2009 as the basis for our study. In addition, we also use
the (already analysed) data from 2001 up until 2003, in order to achieve robust findings
with respect to a more comprehensive and consecutive time period.
From a methodological point of view, the previously outlined studies of the state of the
art discuss and apply different approaches to examine the properties of returns to scale.
What most studies have in common is that they utilize stochastic methods in order to
deduce functional relationships between in- and output values of the underlying data
(points)—usually by executing one-dimensional and/or multi-dimensional regression
analyses. These approaches construe a function that is supposed to reflect the average
586 Scientometrics (2015) 103:583–614
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transformation of inputs into outputs. Such methods, though, assume no inefficient uni-
versities to be existent within the dataset. However, it does not seem plausible that each
(considered) university is producing in an efficient manner.
Assuming the generated data to be deterministic as well as comparable to each other, it
is possible to analyse dominance relations between the inputs and outputs of the considered
universities. This implies inefficient universities to be potentially existent in such a dataset.
By definition, a production function only describes the efficient frontier of the underlying
production technology (Farrell 1957). Because we focus on properties of returns to scale
(with respect to the research production of German BuSs), the efficient frontier (of pro-
duction)—or more precisely the production function—is our main subject matter. The
DEA methodology identifies such a (best-practice) production function by generating an
efficient frontier using the underlying input and output data.
In this regard, there are numerous articles in the scientific literature on efficiency
analyses of universities that use DEA. A database query on the Web of Science produces
1674 articles in scientific journals based only on the search terms ‘‘Data Envelopment
Analysis’’ or ‘‘DEA’’ in connection with ‘‘research efficiency’’ or ‘‘teaching efficiency’’ (as
of November 25, 2014). In principle, such articles can be classified based on whether
aspects or models of DEA are developed further, whereupon they are validated on the basis
of application examples (e.g., Cook and Zhu 2007), or whether the empirical research
question regarding the efficiency of universities is emphasized, which is determined on the
basis of previously established DEA models (e.g., Fandel 2007). In regard to the empirical
applications, the fundamental DEA models are frequently used (e.g., Ahn et al. 1988);
however new models, such as DEA applications in hierarchical output structures, or
Balanced DEA, are also used for analysing university performance (see Meng et al. 2008;
Dyckhoff et al. 2013). Apart from model selection, the applications are also distinguished
according to whether teaching (e.g., Johnes 2006; Ray and Jeon 2008), research (e.g.,
Johnes and Johnes 1993), or both components [integratively (e.g., Tomkins and Green
1988) as well as separately (e.g., Beasley 1995)] are taken into consideration. These
applications are further distinguished with respect to the demarcation of the production
system, which is to say whether the focal point is that of entire universities (e.g., Breu and
Raab 1994) or departments (e.g., Madden et al. 1997; Kao and Hung 2008) or individual
professorships or researchers (e.g., Gutierrez 2007). The input and output factors that are
used in these articles also vary. In the context of research efficiency analysis, frequently
publication and third-party factors are employed (e.g., Johnes and Johnes 1993; Abramo
et al. 2011). What all these approaches have in common is that they determine and analyse
efficiency scores of the university organizational units that they are investigating.
Analyses of properties of returns to scale with respect to research productivity using
DEA, by contrast, is a divergent area of research that, to our knowledge, has only been
addressed in a few articles. Given the fact that DEA has been established in efficiency
analyses of universities, and Dyckhoff et al. (2009) demonstrated the expedience of DEA
with respect to the analyses of properties of returns to scale of German BuSs, we have
chosen to utilize this method in our paper. As the description of the methodological
approach in the next section will make clear, although our research design differs from that
of pure efficiency analyses, the analysis of properties of returns to scale may be helpful in
conceptionally designing efficiency analyses using DEA. This is because in most DEA
applications, the user determines ex-ante which properties of returns to scale the pro-
duction system under consideration is featuring. An analysis of returns to scale done in
advance could therefore deliver essential informational support for selecting relevant DEA
models in following efficiency analyses.
Scientometrics (2015) 103:583–614 587
123
Research design
Datasets: CHE research data on German BuSs
Before analysing the relation between the size of a BuS and its research productivity, first
of all we imperatively have to define how the research production of a BuS is actually to be
construed and operationalized. In general, quantitatively measurable indicators are used to
describe research activities, especially bibliometric measures, such as the number of
publications and citations.
1
As already introduced before, a comprehensive evaluation of
BuS research production in Germany, and especially the associated acquisition of an
approved and accepted database of the research production of German BuSs, is carried out
by the non-profit organization CHE. To date, the CHE research data have been available
for acquisition years 2001, 2004, 2007 and 2010. The respective data referring to a year of
acquisition always correspond to the used inputs and achieved outputs of the preceding
3 years. This means that, e.g., the 2004 dataset represents research production for the years
2001 up until 2003.
Since the data acquisition in 2007, the CHE has operationalized research outputs by
using the following four absolute indicators
2
:
•Publication points of nationally visible publications (PP-nat),
•Number of internationally visible publications (Pub-int),
•Number of PhD dissertations (PhD),
•Amount of expended third-party funds (TPF).
The value of the indicator PP-nat is determined via publications of post-doctoral sci-
entists (including professors) of each BuS. For each acquisition period, the BuSs forward
lists of post-doctoral scientists’ names to the CHE. Thereupon, the CHE identifies the
respective publications by querying the literature database WISO, which covers English as
well as German literature of Economics and Business Administration. The identified
publications are then converted into publication points by incorporating the number of
authors, quantity of pages, and, in the case of journal articles, the perceived quality of the
publication media—in reference to the journal-ranking JOURQUAL2 of the German
Academic Association for Business Research (Schrader and Hennig-Thurau 2009).
3
Since
the relevance of journal articles is steadily increasing in the Business Administration
discipline in Germany, in 2007 the CHE integrated a new indicator, namely internationally
visible publications. The value of this indicator is identified by means of a query on the
literature database Web of Science (WoS), which covers scientific literature with a focus
on English-language papers. The resulting number of publications, which is acquired by
1
Nevertheless, it has to be considered that depending on the examined academic discipline, varying partial
activities, i.e. varying partial indicators, are subsumed under the respective total research production.
Research production, in turn, certainly only constitutes one partial aspect of a department’s (total) pro-
duction in favour of a university; especially academic teaching activities remain unconsidered.
2
On the basis of these absolute indicators, the CHE determines corresponding relative indicators. However,
since the exact CHE approach is irrelevant for the subject matter of the present paper, we refrain from an
explicit illustration and refer to the appropriate explanations of the CHE (e.g. Berghoff et al. 2011).
3
However, the quality-weighting for journal articles—in line with the calculation of publication points—
had not yet been incorporated into the data acquisition in 2004. Moreover, in 2004 only those publications
were included which had at least one professor as (co)author—identified by a BuS-submitted list of all
respective professors’ names. From 2007 on, however, all publications featuring at least one post-doctoral
scientist as (co)author, have been taken into account.
588 Scientometrics (2015) 103:583–614
123
the bibliometric team at Forschungszentrum Ju
¨lich, is not converted into weighted pub-
lication points, though.
4
The amount of expended third-party funds during an evaluation
period and the number of PhD dissertations per semester—covering the six semesters
within the 3-year survey period—are acquired by surveying the BuS.
The validity of these preselected CHE output indicators depends on the primary ob-
jective of scientific research. This consists in the production of new, usually publicly
available, knowledge about the world (Chalmers 1990, p. 23). The dissemination and
discussion of this knowledge mainly takes place via written articles. Therefore, indicators
based on these publications are generally accepted and well-established in research and
practice, in order to evaluate the research of BuSs. By establishing a quality-weighting for
journal articles depending on the reputation of the publication medium and by introducing
an additional indicator Pub-int (from 2007 onwards), the CHE has furthermore taken
additional qualitative aspects into account. Accordingly, publications (in terms of PP-nat
and Pub-int) represent appropriate output measures of BuS research.
PhDs per se comply with the objective of educating and training young scientists.
Hence, corresponding indicators are initially applicable in order to measure a desired
secondary objective of scientific research. Since any PhD degree is inevitably intercon-
nected with the generation and publication of new scientific findings through a published
PhD dissertation, the number of PhD dissertations can indeed indicate certain achieve-
ments of objectives within the research activities of a BuS.
Compared to the already outlined indicators, the classification of expended third-party
funds as a benefit category is less clear-cut, though. Whereas governmental research policy
increasingly employs third-party funds as a performance indicator, the scientific literature
controversially discusses their applicability for measuring research. This heterogeneous
view results from the fact that third-party funds either constitute a resource effort and
therefore an input, or they may be interpreted as a desired research achievement—repre-
senting a proxy indicator associated with the raising of funds or predictions of their use—
and thus an output.
5
It is, then, subject to the respective decision-making context or the
underlying objectives as to whether third-party funds are to be regarded as inputs or
outputs in research productivity analyses.
6
However, due to their positive perception by
governmental research policy in general, as well as by university administrations in par-
ticular, the consideration of TPF as a desired output indicator appears to be an acceptable
research indicator.
Depending on the examined academic discipline, varying resources constitute the de-
cisive inputs of research activities. But while for several disciplines, production factors
such as machinery, laboratories etc. build crucial research inputs, Business Administration
research only requires accordant facilities to some extent. Therefore, such factors probably
have only little impact on the specific progression of the BuS research production’s
4
Relating to Pub-int, there is a difference from the dataset of 2004, too, since this indicator was not part of
the CHE indicator set and was not acquired accordingly. Furthermore, due to the lack of Pub-int weighting,
there is a certain inhomogeneity between the two publication indicators in both the indicator design and the
indicator acquisition. However, because we would like to base our analyses on the original CHE data, we
initially forego a conceivable Pub-int weighting analogously to the PP-nat procedure. We address the extent
to which such weighting has an impact on the resulting returns to scale in the sensitivity analyses.
5
A detailed examination concerning the relevance of TPF in research productivity and performance ana-
lyses is provided by Rassenho
¨vel (2010, p. 85ff).
6
Beasley (1990,1995) and Fandel (2007), for instance, use third-party funds both as an input and an output
in their empirical studies.
Scientometrics (2015) 103:583–614 589
123
efficient frontier.
7
In contrast, measures of staff assignment in terms of professors and
research assistants and their intrinsic human resources represent the fundamental research
inputs. As input factors (and in order to define the size of a BuS), we therefore use the
number of (staffed) positions of BuS professors and research assistants. This information is
obtained by the CHE directly from the BuSs. According to the CHE surveys, the BuSs
were explicitly requested to enter such positions that were financed with state money and
not with third party funding.
Consequently, it appears that the CHE indicator design appears quite reasonable from
the perspective of the field of German Business Administration. That is also substantiated
in the formation and consultation of a scientific advisory board made up of well-known
expert German scientists in that particular field. The relevance and acceptance of the CHE
data is also apparent in the appreciation and application of this data by German govern-
mental bodies and university managements. The CHE methodology has also been given a
positive assessment in the scientific literature (e.g. Tavenas 2004; Usher and Savino 2006;
Marginson and van der Welde 2007). For instance, in an analysis by Stolz et al. (2010),
examining 25 European data acquisition and subsequent appraisal methods for university
evaluation, the CHE is rated best.
To summarize, the CHE has generated core competencies in the acquisition and
evaluation of university research indicators since the early 2000s. With respect to the
indicator Pub-int, with the bibliometric team at the Forschungszentrum Ju
¨lich, the CHE
also has expert cooperation partners for matters pertaining to publication indicator design
and acquisition from a scientometric perspective. Not least of all, the CHE data acquisi-
tions are carried out continuously and will be continued into the future; this secures the
maintenance of current datasets in order to ensure further empirical analyses in the future
as well. Even though, there is criticism regarding the CHE indicator design or acquisition
in the literature (in general, see, e.g., Frey 2007; Jarwal et al. 2009; Kieser 2012; for a
specific focus on the CHE research ranking, see Clermont and Dirksen 2015), we initially
accept these disadvantages and analyse which properties of returns to scale German
Business Administration research production exhibits when using these preselected re-
search indicators as inputs and outputs. The effects of expedient and feasible modifications
to the included research indicators on the validity of our results are investigated within the
framework of robustness and sensitivity analyses.
However, the dataset of the first data acquisition in 2001 (inputs and outputs of 1998 up
until 2000) can only be considered as a kind of pioneer dataset, used as a foundation for
successive enhancements of indicator design and acquisition method in the following
years. Due to the acquisition procedure, as well as for validity and consistency reasons, this
dataset is applicable for empirical analyses just limited (Gilles 2005, p. 130ff; Dyckhoff
et al. 2009, p. 27f). Therefore, considering the years of data acquisition from 2004, 2007
and 2010, research data from 2001 up until 2009 are taken as a basis.
These datasets were provided to us by the CHE and—in terms of internationally visible
publications—by the bibliometric team of Forschungszentrum Ju
¨lich. The resulting data-
sets are given in the Appendix, namely Tables 3,4and 5. Thereby, only those BuSs are
presented that feature complete input and output data. All values embody the total sum of
respective absolute indicator values referring to the incorporated 3 years of the survey
7
At the very least, this conclusion is valid for the considered periods of our analyses. Meanwhile however,
methodological approaches in Business Administration studies are increasing as well, requiring considerably
more physical resources. For example, this applies to neuropsychological and experimental research
approaches.
590 Scientometrics (2015) 103:583–614
123
periods. That is, the annualized input and output values are ascertainable by dividing the
total values by three. In addition, the top 10 most research-reputable BuSs for each ac-
quisition year are highlighted in bold. The classification of the research reputation of a BuS
has been determined on the basis of a supplemental CHE survey. Accordingly, the CHE
requested all professors of Business Administration in Germany in each data acquisition
year to state up to 5 BuSs that they perceive as being notably strong in research.
Methodological approach: DEA and scale efficiencies
From the perspective of DEA, the BuSs represent DMUs, and the previously discussed
input and output indicators constitute their research technology. In analogy to Dyckhoff
et al. (2009), we make the following assumptions:
•The considered BuSs are comparable among each other in terms of the respective
timeframes investigated, i.e. an absence of untypical BuSs.
•The data are deterministic and unbiased.
•All (conceivable) convex combinations of data points form new fictive, but realizable,
activities of virtual BuSs that also belong to the technology of German Business
Administration research.
The third above-mentioned assumption corresponds to a data envelopment of all ob-
served data points. The efficient frontier of this five-dimensional (dataset of 2004) or six-
dimensional (datasets of 2007 and 2010) convex polyhedron configures the best practice
empirical production frontier. In order to determine properties of returns to scale, DEA is
an approved instrument, since it has proven to be exceptionally appropriate with reference
to multidimensional analyses of the teaching and research efficiency of scientific institu-
tions (see the state of the art). In general, multidimensional productivities are usually
subsumed into total productivities; by application of specific weighting factors for all
inputs and outputs, aggregation to one-dimensional inputs and outputs and subsequent
following quotient creation. DEA enables us—by using the underlying research indica-
tors—to determine the multidimensional relative (empirical) efficient frontier of the re-
search technology as well as to identify its properties. Apart from the multidimensional
perspective of DEA, this method’s decisive advantage consists of the fact that ex ante no
weighting decision of the diverse (research) objectives has to be made, because they are set
endogenously. Furthermore, within the scope of DEA, no parametrical production function
type has to be presumed a priori, unlike the case of Stochastic Frontier Analysis (SFA).
8
DEA spans a multidimensional polyhedron, based on the input and output indicators of
all analysed BuSs. The efficient frontier of this polyhedron is determined by the group of
efficient BuSs, while all remaining (inefficient) BuSs are located inside the polyhedron.
Referring to fundamental DEA models,
9
we can generally distinguish between four model
types, resulting from two ex-ante user-based basic assumptions. On the one hand, we can
postulate either constant or variable returns to scale (CCR vs BCC models). On the other
hand, we have to differentiate between input and output orientation. In the case of input-
oriented models, the respective efficiency scores are determined by calculating the max-
imal possible proportional (or radial) input reduction without decreasing any output; and
8
Compared to deterministic DEA, SFA is more applicable in allowing for randomly scattered data.
However, we account for this sensitivity problem of DEA with different robustness and sensitivity analyses.
9
For the basics of fundamental DEA models, cf. in particular the explanations in the pioneer works of
Charnes et al. (1978) and Banker et al. (1984).
Scientometrics (2015) 103:583–614 591
123
vice versa with reference to output-oriented models, by computing the maximal achievable
output expansion without increasing any input. The method works via equiproportional
projection of each considered BuS onto a frontier’s facet of the predefined multidimen-
sional polyhedron, which is spanned by linear combinations (CCR model) or convex
combinations (BCC model) of efficient BuSs. The larger the distance of a BuS to the
efficient frontier, the lower its respective efficiency score is. In the case of an output (input)
orientation, for example, an equiproportional increase of each output by 100 % (decrease
of each input by 50 %) without increasing any input (decreasing any output) amounts to an
efficiency score of 50 %.
10
In order to obtain insights into properties of returns to scale with reference to the
underlying empirically observed production technology, we adopt the concept of scale
efficiency. It is defined as the quotient of the CCR-efficiency and the BCC-efficiency score
(Banker et al. 1984, p. 1088f). If in a specific size range, CCR-efficiency scores with
constant returns to scale equal the respective BCC-efficiency scores with variable returns
to scale, the slopes of the empirically determined CCR and BCC production functions are
identical. This, in turn, entails that in this size range (of staff assignment), locally constant
returns to scale must exist. Thereby, the convex BCC production technology always
constitutes a subset of the linear CCR production technology. As a result, the BCC-
efficiency scores are at all times higher or equal to the efficiency scores under CCR
models. In addition, CCR-efficient BuSs are always BCC-efficient. Therefore, scale effi-
ciencies mandatorily feature values of between 0 and 100 %.
Returns to scale of Business Administration research over time
Analysis of all BuSs
In the context of the following examinations, we conduct separate scale efficiency analyses
for the three CHE datasets of the acquisition years 2004, 2007 and 2010. In Fig. 1, the
resulting output-oriented scale efficiencies of all BuSs are visualized in dependency on the
annualized number of professorships.
11
The data points of 2004 are illustrated as rhom-
buses, data points of 2007 as triangles and those of 2010 as circles.
12
The 10 BuSs
10
For further basic information on DEA and corresponding mathematical model descriptions, cf. e.g.,
Cooper et al. (2007).
11
First, all deliberations refer to results based on output-oriented DEA models. From a short-term and
middle-term point of view, this proves to be plausible, since scientific staff can generally be considered as
(almost) fixed in the short run and rather variable in the long run. Accordingly, input-oriented models only
make sense for long-term considerations—if at all. Within the scope of the sensitivity analyses later on, we
will also address results originating from input-oriented models.
12
Due to the predefined CHE indicator set, the presented scale efficiencies of 2004 are still based on three
outputs (excluding Pub-int). While the calculated efficiency scores in the context of DEA can only increase
with the addition of supplementary indicators, scale efficiencies as a quotient of BCC and CCR efficiency
score can either increase or decrease. This may basically limit the intertemporal comparability of our scale
efficiency analyses. However, our sensitivity analyses indicate that the results and the statements are robust
or valid with respect to modifications to the preference relation, which is to say that the exclusion of the Pub-
int indicator from the 2007 and 2010 datasets does not produce any notable changes in our results.
592 Scientometrics (2015) 103:583–614
123
featuring the highest research reputation for the respective acquisition year are highlighted
in black.
It becomes evident that for all three periods, only a few BuSs employ under 8 or more
than 16.5 professors. But whereas the number of small BuSs (under 8 professors) remains
constantly low over time, the quantity of larger BuSs (more than 16.5 professors) rises in
the course of the periods, and a large number of reputable BuSs are among this group.
Based on the dataset of 2004, Dyckhoff et al. (2009) discovered approximately constant
returns to scale for a size range of 8–16.5 professors. In this range, all BuSs are charac-
terized by output-oriented scale efficiencies of at least 90 %, most even up to nearly
100 %. To facilitate a comparison to these findings, in Fig. 1two dotted lines divide the
diagram into four sectors (I–IV). Hence, one line indicates a scale efficiency of 90 % and
the other marks the (previously) identified number of 16.5 employed professors. When
considering all three periods, it becomes apparent that some BuSs in the size range of 8 up
to 16.5 professors are located below scale efficiencies of 90 %. Therefore, these BuSs
prevent an analogous (unrestricted) transfer of the previously discovered empirical con-
clusion (of constant returns to scale) onto the data of 2007 and 2010.
13
But remarkably, the
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
05101520253035
Professorships
Output-oriented
scale efficiency
2004 2007 2010
16.5
II
III IV
I
To
p
10 Re
p
utation
Fig. 1 Output-oriented scale efficiencies according to the number of professorships over the three time
periods
13
If, however, one generously operationalizes locally constant returns to scale already from a scale effi-
ciency of C80 %, a size range results from 8 up to 18.6 professors, in which over all timeframes investigated
Scientometrics (2015) 103:583–614 593
123
majority of all BuSs are located within the sectors I and III, while sector II only contains
two BuSs (2010) and sector IV merely a few, especially smaller BuSs (of 2007 and 2010).
When further taking the respective ten most reputable BuSs of each acquisition year as a
basis—thus accounting for the expertise of the evaluated professors with regard to the
research-strongest BuSs in Germany—then these are almost entirely located within the
sectors I and III.
Generally, most BuSs lie within sector I. All inefficient BuSs in this sector are radially
projected onto edges of the BCC-efficient frontier. Thereby, the BuS-specific output mix
determines the concrete direction of the projection. The (virtual) outputs of those edges, in
turn, must match the outputs of the corresponding reference point on the CCR-efficient
frontier by at least 90 %. This also means that the distance between the BCC-production
and CCR-production function in this region merely amounts to 10 % of the outputs at
maximum (in the case of an equiproportional projection). Accordingly, these BuSs could
raise their efficiency score—presuming constant returns to scale—only from 90 to 100 %
at most, hence by roughly 11 %. The assumption of (approximately) constant returns to
scale in this size range seems plausible at this point.
In contrast, the larger sized BuSs in sector III exhibit smaller scale efficiencies. BCC-
frontiers and CCR-frontiers differ quite substantially from one another, which exhibits an
indication of decreasing returns to scale for larger sized BuSs. Indeed, such conclusions
must generally be stated conditionally, because there are only a few BuSs located in sector
III. But certainly a high fraction of these consists of highly research-reputable BuSs;
precisely those that are considered to be ‘‘strong in research’’ by the surveyed experts. This
is why this observation has potentially higher validity.
In the considerations and results presented above, only the input factor professorships
has been used so far to define the size of a BuS. However, research assistants also make a
crucial contribution to the research production of a BuS, which is why the CHE acquires
them as well and also why they are integrated into our DEA analyses as a second input
factor. In order to analyse what influence the number of research assistants has on the
respective scale efficiency and on the resulting returns to scale, these two input factors are
compared in Fig. 2. The number of professorships is still on the abscissa and the number of
research assistants is now on the ordinate. In Fig. 2, we abstract from the precise degree of
the scale efficiency by only distinguishing between scale efficiencies that are greater than
or equal to 90 % (grey) and scale efficiencies that are less than 90 % (white).
An apparently linear relationship between Business Administration professorships and
research assistants becomes clear, which is reflected in relatively high correlation coeffi-
cients (between 0.62 and 0.75, depending on the acquisition year, with a significance level
of 1 %). Although these two inputs do not have a perfect linear relationship, they rather
correspond to each other in a certain range. We can infer from Fig. 2that the assertion of
constant returns to scale with respect to professors is independent of the number of re-
search assistants assigned to them. By contrast, we cannot derive clear assertions about
properties of returns to scale only with respect to the number of research assistants.
14
On
that basis, the number of research assistants is not included in the definition of the size of a
BuS in the following analyses.
Footnote 13 continued
approximately constant returns to scale can be discovered. For the following analyses, though, we will
define such a size range of constant returns to scale more closely, in order to assure a higher robustness of
our results.
14
Note that the number of research assistants is, however, always part of our scale efficiency analyses.
594 Scientometrics (2015) 103:583–614
123
In order to analyse the aforementioned conclusions of constant returns to scale for
medium-sized BuSs and decreasing returns to scale for larger-sized BuSs in more detail,
Fig. 3illustrates the output-oriented scale efficiencies, dependent on the number of pro-
fessors for each of the acquisition years separately. Due to their limited number, conclu-
sions with respect to smaller BuSs are necessarily problematic. In contrast, deriving
findings with reference to the group of larger BuSs is rather reasonable, because of this
group’s quantitative increase over time as well as the major presence of reputable BuSs.
Based on these deliberations, we subsequently take a closer look at the respective size
range of between 8 and 28.7
15
professors (reputable BuSs are again highlighted in black).
Next, in order to mitigate the impact of single data outliers on an overall conclusion, we
approximate the calculated data points with a linear regression assuming a quadratic
function.
16
In Fig. 3, the resulting functional relations are illustrated for each of the three
timeframes.
If we now operationalize approximately constant returns to scale by means of scale
efficiencies of more than 90 %, then the point of intersection between regression and 90 %
scale efficiency determines the crucial transition region. It marks the annualized number of
≥ 90% scaleefficiency
0
10
20
30
40
50
60
70
80
90
100
05101520253035
Professorships
Research Assistants
2004 2007 2010
16.5
Fig. 2 Relationship between professorships, research assistants and scale efficiency
15
This number (of professors) corresponds to the largest of all top 10 reputable BuSs over the three
consecutive timeframes investigated.
16
We applied the respective R
2
as the quality criterion for the function approximations. With values of
0.753 (2004), 0.632 (2007) and 0.545 (2010), they all exhibit high levels.
Scientometrics (2015) 103:583–614 595
123
employed professors up to which the assumption of constant returns to scale can be
confirmed in terms of the respective timeframe. Supporting the results of Dyckhoff et al.
(2009) for 2004, we identify a size range from 8 up to 16.7 professors of nearly constant
returns to scale. This size range slightly decreases for the timeframe of 2007 (from 8 up to
15.2 professors), whereas it increases for 2010 (from 8 up to 18.9 professors).
17
Because of the aforementioned presumption of convexity with regard to the data en-
velopment (in the context of BCC-production functions), approximately locally constant
returns to scale in the respective size ranges ceteris paribus imply that smaller BuSs
consisting of less than 8 professors must exhibit increasing, and larger BuSs with more
than 16.7 (2004), 15.2 (2007) or 18.9 (2010) decreasing, returns to scale. With reference to
practical applications, such observed size ranges of constant or approximately constant
returns to scale are typically rather small; in consideration of many medium-sized, but only
comparably few small and large DMUs. Hence, the relatively large size ranges of ap-
proximately constant returns to scale in the course of the consecutive timeframes inves-
tigated turn out to be remarkable.
0,5
0,6
0,7
0,8
0,9
1
51015202530
0,5
0,6
0,7
0,8
0,9
1
51015202530
0,5
0,6
0,7
0,8
0,9
1
51015202530
2004
2007
2010
Professorships
16.7
15.2
18.9
Output-oriented
scale efficiency
2004 2007 2010
To
p
10 Re
p
utation
Fig. 3 Regressions of the output-oriented scale efficiencies according to the number of professorships over
the three time periods
17
When utilizing all data points for each of the timeframes investigated instead of the interval from 8 up to
28.7 professors focused on here, we observe comparable results. However, the approximation of data points
by regression falls off in quality, as measured and revealed by lower R
2
values.
596 Scientometrics (2015) 103:583–614
123
As mentioned before, drawing conclusions about the properties of returns to scale with
regard to larger BuSs for the acquisition year 2004 is inevitably problematic because of
their limited number. In view of 2007 and 2010, however, particularly the number of larger
BuSs is increasing substantially, which enables us to derive statements in favour of returns
to scale. Due to decreasing marginal scale efficiencies of the regressions in Fig. 2, i.e.
lower scale efficiencies of larger BuSs, the tendency of decreasing returns to scale with
regard to (too) large BuSs appears to be confirmed. Furthermore, an optimal BuS size with
respect to research productivity seems to exist; from this size onwards, marginal gains of
additional professorships are diminishing. In order to examine the last-mentioned con-
clusions more precisely within the scope of an individual analysis of particular BuSs, in the
next section we examine the temporal development of research-reputable BuSs’ returns to
scale. A more detailed analysis of reputable BuSs based on expert judgments by the
scientific community for Business Administration in Germany on their own peer-group
should reveal interesting and significant results.
Analysis of research-reputable BuSs
Figure 4illustrates an extract of Fig. 1, focusing on intertemporal movement patterns of
scale efficiencies with reference to seven reputable BuSs. In particular, all those BuSs are
considered that are part of the top 10 most research-reputable BuSs in each of the three
consecutive acquisition years. The depicted paths indicate the temporal development of
these BuSs with respect to both their size and (resulting) scale efficiency over the three
acquisition years.
The movement patterns confirm the results previously discussed. On the one hand, some
reputable BuSs are located within sector I of constant returns to scale over all time periods;
undertaken moderate expansions or reductions of employed professors do not implicate
their scale efficiencies to be decreasing to such an extent that they would move outside of
this sector. On the other hand, for other reputable BuSs, more considerable expansions are
accompanied by a reduction of the respective scale efficiencies, especially when exceeding
16.5 professors. For instance, BuS 55 (Mu¨nster) is steadily increasing in size, which is
accompanied by a slight decrease in scale efficiency from 100 (2004) to 93 % (2007) and
finally to 89 % (2010). A similar result can be deduced by observing BuS 70 (Vallendar).
While this BuS is still scale-efficient in 2004 and 2007, after a considerable increase in the
number of professors, the scale efficiency is reduced to a mere 87 % in 2010. This trend
becomes particularly evident for BuS 50 (Mannheim), which is the most research-reputable
BuS in Germany over all three surveys. Still scale-efficient in 2004, its continuous en-
largement is also accompanied by a decrease in scale efficiency to 81 % (2007) and
ultimately 69 % (2010). In contrast, referring to BuS 52 (LMU Mu¨nchen)—which is the
second most reputable BuS in Germany in all three surveys—we can observe an opposite
movement pattern. Starting from low scale efficiencies in 2004 and 2007, it achieves a
scale efficiency of 100 % in 2010, while simultaneously reducing its professorships.
18
18
It should be pointed out here that the meaning of a scale efficiency is not comparable with the efficiency
score of an original productivity analysis using DEA. A high scale efficiency indicates that the DMU is
located in a size range in which the efficient frontiers of the production functions, that are empirically
created using DEA by assuming constant and variable returns to scale, are close together. This value does
not allow for the derivation of any assertions about the research efficiency of an individual BuS, which is
why no concrete efficiency improvement recommendations can be made. Such recommendations rather
depend on the specific system of objectives of a BuS and are not part of our paper.
Scientometrics (2015) 103:583–614 597
123
Thus, altogether we can claim that the considered reputable BuSs, by trend, grow in size
over time, accompanied by decreasing scale efficiencies. Moreover, it should be noted in
particular that in this section we have exclusively investigated movement patterns of
research-reputable BuSs. Because of the high research reputation assigned to each of them,
we obviously cannot imply that they are particularly unproductive; this, in turn, gives
greater weight to our observation. Accordingly, both previously derived main findings—
regarding the size ranges of constant returns to scale and decreasing returns to scale
afterwards—can still be confirmed subsequent to this detailed investigation of reputable
BuS-specific temporal movement patterns.
Robustness and sensitivity analyses
The observed empirical findings are valid in line with the ex-ante chosen framework of
production theory, and with the underlying datasets and allowing for the assumptions set.
In particular, the present examinations of returns to scale are theoretically founded on
production theory and based on the fundamental causality between production function and
the concept of efficiency, and subsequently scale efficiency. Efficiency is always relative in
a twofold sense: First, it depends on the underlying preference relation—i.e. the choice of
considered performance indicators and their attributed values—as well as the existing
spanned production possibility set—i.e. potentially realizable activities in the form of real
or virtual BuSs. Hence, evaluation modifications in these two criteria have an impact on the
derived production function and the corresponding properties of returns to scale. In the
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
100%
51015202530
Professorships
Output-oriented
scale efficiency
16.5
BuS70 BuS55
BuS50
BuS6
BuS7 BuS43
BuS52
II I
III IV
2004 2007 2010
Fig. 4 Movement patterns of reputable BuSs according to output-oriented scale efficiencies and number of
professorships over the three time periods
598 Scientometrics (2015) 103:583–614
123
following, we therefore analyse the robustness of the obtained results using sensitivity
analyses with respect to these two criteria.
Sensitivity analyses with respect to the production possibility set
When selecting DMUs in the context of productivity and efficiency analyses, their com-
parability constitutes an elementary prerequisite. Only under the assumption that pro-
duction technologies as well as considered inputs and outputs of the respected DMUs are
homogeneous—i.e. they pursue the same objectives—do productivity analyses within this
peer group make sense at all. Since we base our analyses on departments of the same
academic discipline, a certain degree of homogeneity appears to be ensured. But because
each BuS is integrated into a university-specific environment that is characterized by
heterogeneous external parameters and differing stakeholder interests, resulting in indi-
vidual department structures and possibly diverse objectives, the existence of deviations is
quite plausible.
19
In order to reveal such characteristics, we now abstract from BuSs
turning out to be untypical and subsequently analyse the resulting modified efficiency
scores.
When selectively analysing certain partial productivities—meaning exclusively focus-
ing on each input/output combination—the respective efficient frontiers of the determined
partial production functions are usually substantially characterized by just a few BuSs
featuring exceptionally high outputs or low inputs. Single erroneous or untypical data
points on the efficient frontier exert a potentially strong influence on (in-) efficiency scores
of other BuSs and thus also on the results regarding properties of returns to scale. In
contrast, inefficient BuSs—having no impact on the efficient frontier—are only responsible
for their own efficiency scores. Therefore, we focus on efficient outliers. When closely
observing (all) partial productivities for each acquisition year, several BuSs are con-
spicuous, outperforming the others considerably with reference to their total value. Hence,
they are taken out of the datasets in the scope of the following sensitivity analyses.
The process is exemplified in Fig. 5. This figure illustrates the partial productivities of
the BuSs with respect to the internationally visible publications (ordinate) and the number
of professorships (abscissa) for 2010. It becomes apparent that BuS 52 is clearly distinct
from the other BuSs with regard to this partial productivity and especially with respect to
the absolute number. Hence, this BuS substantially determines the respective partial effi-
cient BCC-frontier of the empirically identified partial production function and thus may
have an influence on multidimensional efficiency analyses using DEA. Hence, BuS 52 is
identified as an outlier and is excluded in the following.
Similar arguments apply to BuS 70 in 2004 concerning the indicator PP-nat, to BuSs 53,
56 and 71 in 2007 with reference to TPF as well as to BuS 56 in 2010 regarding PhD
dissertations. All of these six BuSs are BCC-efficient; except for BuSs 53 and 56 in 2007,
they are also CCR-efficient
20
; beyond that, all six are benchmarking-partner for other BuSs
multiple times. So, they might have (considerably) influenced the previously determined
19
Utilizing DEA certainly involves the advantage that respective weightings of objectives are determined
(endogenously) for each BuS individually and most favourably. In this way, taking into account BuS-
specific profiles is per se inherent in DEA analyses. Thus, the problem of deviating BuS objectives is already
mitigated ex-ante by the method itself.
20
These two BuSs are not CCR-efficient, merely because BuS 71 exhibits the highest partial efficiency with
regard to TPF in 2007; BuS 71 therefore obviously has to represent one benchmarking partner for both of
them. However, this does not change the fact that all three BuSs feature untypically high TPF values and
thus have to be considered in the scope of sensitivity analyses.
Scientometrics (2015) 103:583–614 599
123
properties of returns to scale. If we now classify these six BuSs as untypical and exclude
them from our DEA analyses, in fact the single efficiency scores increase, while the
corresponding output-oriented scale efficiencies still remain on a very high level (see
Table 1).
When comparing the resulting (modified) scale efficiencies with the original calcula-
tions and especially the identified range of constant returns to scale, then the scale effi-
ciencies of 2004 and 2007 barely differ from one another. Only in 2010 do they decline
more (by 5 %), but still exhibit 91.2 % on average. Therefore, the conclusion of constant
returns to scale for medium-sized BuSs seems to be still valid. The differences in scale
efficiency levels of all BuSs in comparison to those from the interval of constant returns to
scale primarily result from (the described) strongly decreasing scale efficiencies of larger
BuSs; thus, our previously deduced conclusion of decreasing returns to scale for large
BuSs can still be verified.
Sensitivity analyses with respect to the preference relation
In order to assess the influence of individual research outputs on the properties of returns to
scale, in the following we vary the comprehensive set of partial output components. For
this purpose, in each case the indicator Pub-int together with one further output indicator is
excluded from the respective DEA analysis.
21
As can be seen in Table 2, excluding certain
0
10
20
30
40
50
60
70
80
90
100
010203040
Professorships
Pub-int
BuS 52
Fig. 5 Two-dimensional partial
productivity referring to the
indicator Pub-int 2010
21
We intentionally decided to exclude the indicator Pub-int from all sensitivity analyses with respect to the
preference relation, since this indicator was not raised in 2004. However, for the datasets of 2007 and 2010,
we calculated the accordant scale efficiencies both including and excluding Pub-int, respectively. When
comparing the resulting scale efficiencies of the cases a)—excluding just one indicator—and b)—excluding
this indicator and additionally Pub-int—, then the resulting regression line adds up to y=0.9469x?0.0405
(R
2
=0.8181) in the event of additionally excluding PP-nat, y=0.9889x?0.004 (R
2
=0.9166) in the
case of additionally excluding TPF, and y=1.0133x-0.0196 (R
2
=0.9126) if additionally excluding
PhD dissertations. According to this, the obtained results are nearly identical, which is why we only
illustrate case a) in our sensitivity analysis of this subsection.
600 Scientometrics (2015) 103:583–614
123
output indicators only exerts minor influence on our observed empirical results. In the
previously identified size intervals of constant returns to scale, the corresponding averaged
four-dimensional output-oriented scale efficiencies are—over all sensitivity analyses and
timeframes investigated—considerably higher than 90 %. The low scale efficiency values
considering all BuSs are caused by (partly strongly) decreasing scale efficiencies of larger
BuSs as well. However, it is quite obvious that the averaged scale efficiencies in the
respective size range of constant returns to scale are decreasing distinctly in the course of
the investigated periods when both indicators addressing publication activities (Pub-int and
PP-nat) are excluded simultaneously. As a consequence, with 91.9 % in 2010, they fea-
ture—compared to all other partial considerations—the lowest values in the ‘constant’
interval. Hence, the results tend to react more sensitively to a simultaneous exclusion of
both publication indicators.
As noted in the description of the indicator design and acquisition before, the nationally
visible publications are weighted based on the number of authors and the perceived quality of
the publication medium. By contrast, such weighting is not done for the internationally visible
publications by the CHE. For the benefit of a symmetrical procedure, we therefore also
investigate whether differences result concerning the properties of returns to scale when the
internationally visible publications are weighted analogously to the nationally visible
Table 1 Averaged output-oriented scale efficiencies in the case of untypical BuSs being excluded
Averaged output-oriented scale efficiency
2004 2007 2010
All
BuSs
(%)
8–16.7
professors
(%)
All
BuSs
(%)
8–15.2
professors
(%)
All
BuSs
(%)
8–18.9
professors
(%)
Considering all BuSs in
DEA
92.9 96.9 91.1 95.1 93.4 96.2
Exclusion of untypical
BuSs in DEA
91.9 95.6 91.1 94.7 88.2 91.2
Table 2 Averaged output-oriented scale efficiencies in the case of the indicator mix being varied
Averaged output-oriented scale efficiency
2004 2007 2010
All
BuSs
(%)
8–16.7
professors (%)
All
BuSs
(%)
8–15.2
professors (%)
All
BuSs
(%)
8–18.9
professors (%)
Original indicator set 92.9 96.9 91.1 95.1 93.4 96.2
Exclusion of Pub-int – – 90.2 94.0 92.9 95.9
Exclusion of Pub-int
and PP-nat
92.8 96.3 87.4 94.7 87.4 91.9
Exclusion of Pub-int
and TPF
92.1 96.2 89.6 93.7 93.9 96.5
Exclusion of Pub-int
and PhDs
89.9 95.3 89.7 93.8 93.4 96.5
Pub-int weighted – – 90.6 94.6 92.1 95.2
Scientometrics (2015) 103:583–614 601
123
publications. Thereby, we weight the number of authors in accordance with the process used
in the CHE’s PP-nat. With respect to journals’ quality weighting, we do not refer to the
JOURQUAL2 but rather use the citations of the respective articles. In doing so, we take the
divergent acquisition methodology of the Pub-int indicator based on a WoS query into
consideration. In general, the impact on scientific advancements and the dissemination of
knowledge is operationalized based on citations to create conditions for knowledge spillover
benefits. Citations thus represent a proxy measure of the value of output and are therefore
predestined for a corresponding weighting. However, the citations of the articles compiled in
the Pub-int indicator follow a strongly skewed distribution, and numerous articles remain
uncited at the moment of inquiry. For that reason, the actual citations are not used for
weighting here. The WoS journal impact factor offers an alternative weighting option, par-
ticularly with respect to journal quality. We therefore weight each article in the interna-
tionally visible publications with the WoS journal impact factor at the respective moment of
inquiry.
22
As Table 2shows (‘‘Pub-int weighted’’ row), however, these modifications have
no decisive impact on the revealed properties of returns to scale, which is why our pri-
or assertions also apply to a corresponding weighting of internationally visible publications.
In addition, we analyse the effect on the properties of returns to scale, if we regard third-
party funds as a research input instead of an output. In this case, the averaged output-
oriented scale efficiencies still exhibit high values and show similar characteristics to those
already identified in the scope of the previous sensitivity analyses.
The respective progressions of the function approximations—calculated analogously as
before—also confirm this observation. Hence, similar intersections are to be seen com-
pared to Fig. 2, thereby the size range considered to be constant ceteris paribus often
diminishes slightly. Only an exclusion of both publication indicators leads to considerable
shifts of intersections. Accordingly, the previously observed plausible sensitivity and
relevance of the two publication indicators are confirmed once again.
One further (possible) modification addresses the orientation of the employed DEA
model. Presuming an input orientation in the context of scale efficiency measurements via
DEA leads to a less distinct revelation of the empirical findings presented and discussed in
the previous sections. Due to the conspicuously weak output performance of many BuSs
regarding the CHE postulated inputs, this is no effect, however, that is empirically rich in
content. Numerous BuSs feature relatively low output indicator values in this case. Re-
ducing personnel inputs, equiproportionally, while maintaining their low levels of output
performance, BuSs are projected onto straight-line segments of the BCC-efficient frontier
consisting of less than 8 professors and thus feature, in some cases strongly, increasing
returns to scale. This, however, does not enable any implications concerning other seg-
ments of the BCC production function consisting of more than 8 professors.
Implications, limitations and future prospects
On the basis of the non-parametric method of DEA, we analyse which properties of returns
to scale German BuSs exhibit with reference to research production for the nine-year time
22
The adequacy of using journal impact factors to quantify the outcome of research production is certainly
subject to the specific context of application. In this respect, the use of impact factors instead of citations can
be accepted for the specific subject matter of Business Administration research, where actually numerous
articles are uncited. It is, of course, not valid for a general theoretical perspective in terms of a generally
accepted approach for all academic disciplines.
602 Scientometrics (2015) 103:583–614
123
period from 2001 until 2009, thus evaluating and extending the analyses by Dyckhoff et al.
(2009) for the period 2001 to 2003. We employ the CHE research data to generate valid
datasets of relevant input and output criteria. Despite high dynamics in terms of the number
of included BuSs, their sizes as well as the design and acquisition of evaluation criteria, we
can determine clear and robust results. We discover that over all considered consecutive
periods, medium-sized BuSs from 8 up to 16.7 (2004), up to 15.2 (2007) and up to 18.9
(2010) averaged annualized professorships feature approximately constant returns to scale
in research. For larger BuSs above these size ranges, returns to scale decrease (quite
strongly in some cases), indicating that BuSs should not increase their academic staff in
excess of a certain (critical) level—at least from a research productivity point of view.
This, in turn, implies an optimal (productivity-) size of BuSs to exist in a medium-sized
range. By means of diverse sensitivity analyses, we illustrate that our findings are also
valid for different modifications of influencing parameters.
Certainly, our study is subject to some limitations, which should be reflected on. They
are either of an empirical nature, due to utilization of the CHE data, or of a methodical
kind, due to application of the DEA methodology. Apart from the discussed general
deliberations regarding input and output indicator characteristics and adequacy, the CHE
design and acquisition should be scrutinized. For instance, biases of data may occur,
because the BuSs classify their Business Administration professorships by themselves.
This is due to the fact that the definition and the differentiation of Business Administration
professorships from their academic environment are not distinct. The intertemporally
varying numbers of professors reported by accordant BuSs, suggest that the differentiation
from related and overlapping academic disciplines, e.g. Business Informatics, might not
always have been executed in a consistent manner (for a detailed analysis from an ac-
counting perspective, cf. Clermont and Dirksen 2015).
For output indicators, data biases might also occur. This especially applies to the
acquisition of publication data using queries in literature databases. In their studies,
Clermont and Dyckhoff (2012a,b) reveal that none of the common literature databases
feature a complete coverage of all relevant business and economic journals for German
Business Administration researchers. But altogether, against the background of practical
implementation, the considered indicators seem acceptable under the given conditions; in
particular by reasons of diverse and continuously improved procedures of quality assurance
by the CHE. In this way, the distinctiveness and robustness of our empirical results
becomes particularly evident (especially over the different consecutive timeframes).
Moreover, the research situations of BuSs are characterized by diverging environmental
surroundings and workloads in teaching (for instance, public universities versus private
universities), which are not considered by the underlying datasets. Therefore, we ultimately
conduct partial productivity analyses of the comprehensive academic productivity of BuSs
per se, even in the present multidimensional case. Interdependencies between teaching and
research activities are neither taken into account. Thereby, basically both positive and
negative effects of teaching activities on research productivity are plausible. In this sense,
teaching can be considered to be a time-consuming endeavour on the one hand; but on the
other hand, discussions with students might also promote research activities.
Further possible limitations result from the applied analysis method of DEA. By reasons
of the postulated property of convexity regarding the datasets (within the limits of gen-
erating the BCC production functions), the empirical production functions are inevitably
concave, so that returns to scale can only be increasing for low input quantities and
decreasing for large quantities. Hence, for a significant interval of constant returns to scale
to be deducible at all, datasets particularly have to include small and large BuSs amongst
Scientometrics (2015) 103:583–614 603
123
the BCC-efficient ones, adequately positioned in multidimensional data envelopment. For
this reason, detected size ranges of constant or nearly constant returns to scale are typically
less extensive in practical applications if there are many medium-sized but comparatively
few small and large DMUs. Therefore, referring to our empirical findings, the relative
extensive size range of (approximately) constant returns to scale over a period of 9 years is
exceptionally remarkable. For this interval as well as the observation of decreasing returns
to scale for larger BuSs, our results are quite reliable, while statements concerning the
properties of returns to scale for smaller BuSs are problematic due to their limited number.
Conditioned by the defined production possibility set and the chosen preference relation,
both of which are dependent on the available (and used) data, as well as the diverging
interests of different stakeholders (or decision-makers), the informative value of this study
is limited with regard to concrete recommendations for policy decisions. An unreflected
adoption of our findings in order to justify, for example funding decisions for universities
in Germany might then result in incorrect or misguided incentives. Hence, our study
represents a partial (specific) perspective of just one area of a university’s tasks, that is to
say research. Furthermore, not all thinkable and/or possible facets of research production
are covered. Therefore, our study should certainly not serve as an exclusive basis for
decision-making.
Since there is a lack of specific theoretically founded explanations, we can merely
speculate about the reasons for our empirical findings. Accordingly, substantial research
synergies in the form of economies of scale and scope between German Business Ad-
ministration professorships within the same BuS appear to be hardly existent. On the
contrary, administrative and coordination efforts seem to accumulate over-proportionally
(from a certain number of employed professors onwards). In the range of medium-sized
BuSs, the research outputs of individual BuS professorships (including respective research
assistants) rather add up. This conclusion is plausible in view of the fact that in the German
academic discipline of Business Administration, professors usually work individually.
Even though some projects are in fact undertaken in cooperation, often this is the case
within (their) professorships or between professorships of different universities. Coop-
eration between different professorships of the same BuS or university are less frequently
observed.
Cooperations within a BuS of one university could be promoted, for instance, in terms
of joint supervisions of PhD students by multiple professors in postgraduate programs, a
concept launched by some universities several years ago. A crucial prerequisite to foster
joint research projects between professors within the same BuS is a thematically close
profile and focus of a BuS in corresponding research clusters; as initialized by the German
federal government within the scope of the ‘‘Excellence Initiative’’. Such clusters could
facilitate similarities in content and/or methodology of research. Likewise, this trend to-
wards specialized BuSs and closely cooperating research clusters (or research areas) has
been initiated by several universities—in many in conjunction with the introduction of
Bachelor and Master programs in Germany. In BuSs, such programmes and convergences
of research topics could produce synergistic effects among the participating professorships.
Of course, higher coordination requirements arising from administrative processes, for
instance in the form of meetings and workshops, could have a negative impact. But the
nature and extent of the de facto effects still require additional research. Likewise, the
impact of such developments on corresponding properties of returns to scale therefore
cannot be assessed yet, posing an interesting starting point for future research.
Further research perspectives arise from the previously discussed limitations of our
study. In this paper, we have therefore assumed the data to be deterministic, which
604 Scientometrics (2015) 103:583–614
123
represents a necessary presumption in the application of DEA. A modification could now
be made to the effect that SFA, which allows stochastically distributed datasets to be
analysed, is used in lieu of DEA. The results of a SFA analysis can then be used to check
our findings and, if necessary, to further validate them. The fact that a concrete production
function must be assumed ex ante, however, is a disadvantage of using SFA. When DEA is
used, by contrast, the orientation of the applied DEA models can be changed. In this paper,
we have assumed that only the output can be changed (in the short- to middle-term); for the
sensitivity analyses, we also investigated which assertions result from exclusively varying
the inputs. Research into scale efficiencies in the event of simultaneous changes to the
input and the output, on the other hand, remains to be conducted. In that case, unoriented
DEA models must be used to calculate scale efficiencies.
As previously described in the discussion of limitations, the progression of the research
production function, and therefore the resulting returns to scale, depend on the input and
output factors that are used in the corresponding analyses. For example, qualitative aspects of
research could be more closely taken into consideration in future and continuative analyses.
In this regard, the third-party funds indicator could be differentiated such that the third-party
funds granted by different funders are separated and/or differently weighted because, for
example, the German Research Foundation (DFG) sets high standards for scientific rigour in
the context of assessing a third-party funding proposal (Joerk and Wambach 2013).
In principle, the CHE has used quantitative measures until now with respect to compiling
the indicator set and particularly regarding publication activities, which is to say that pub-
lished articles are counted (partially quality weighted). Including such indicators in the
performance assessment of BuSs could produce disincentives insofar as the research results
are divided and published in the smallest possible fragments. The number of publications
could therefore be increased correspondingly. This effect could be intensified in the under-
lying CHE 2007 and 2010 datasets, given that two quantitative indicators are used there to
determine publication activities. To reduce these effects, information about the impact or
outcome of the publications would be desirable. Referring to this, the citations that a research
article receives are a frequently discussed indicator in scientometrics (van Raan 1996). Nosek
et al. (2010), for instance, point out that citations represent an impact indicator that is ‘‘valid,
relatively objective, and, with existing databases and search tools, straightforward to com-
pute.’’ Therefore, it still has to be investigated which impact an inclusion of citations exerts on
the properties of returns to scale of BuSs’ research production.
As previously outlined, the analysed research productivity of a university generally
constitutes only one part of the comprehensive spectrum of academic activities, which
furthermore includes teaching and administration. In this sense, the used datasets could be
expanded by adding indicators that represent teaching and administration outputs in order
to analyse properties of returns to scale with reference to these additional outputs. With
respect to teaching, Johnes (2006), for example, uses two indicators consisting of the
alumni graduation marks in an offered course of study as output and the students’ school
marks when applying for a course of study as input. Other qualitative measures, such as
evaluation results, dropout rates, supervisory relationships or diversity of the teaching
portfolio, would also be conceivable in corresponding analyses. It should be noted, how-
ever, that to date there is no recognized set of productivity and performance indicators for
teaching, as opposed to research. This is primarily due to the fact that teaching and student
learning are tightly interwoven with one another in a co-productive process.
That our results are not random is shown by the inclusion of different consecutive
periods on the one hand and on the other hand by multiple robustness and sensitivity
analyses. As previously described in the introduction, analyses of returns to scale on
Scientometrics (2015) 103:583–614 605
123
research production processes are subject to particular country- and discipline-specific
environmental conditions. If these do not (significantly) deviate from each other, our
results can essentially be transferred to other objects of research, for instance other social
sciences in Germany, such as economics. However, this does not apply to equipment-
intensive disciplines, such as engineering, which, due to funding for machinery or ex-
perimental setups, are characterized by considerably higher third-party funding and
therefore, where applicable, induced economies of scale and scope. Regarding national
specifics, European university systems are comparable with the German one above all
others and should therefore exhibit similar returns to scale of research. To that effect,
Bonaccorsi and Daraio (2005) also found constant returns to scale for Business Admin-
istration research production in Italy. But it is questionable whether our results, for ex-
ample, are applicable to Business Administration research in the United States of America,
where the university system is characterized by a substantial degree of (highly competitive)
privatisation. Whether, in particular cases, similar results can be observed for other aca-
demic disciplines and/or countries, however, requires further research.
Acknowledgments The project underlying this article was funded by the Federal Ministry for Education
and Research (Germany) under Grant Number 01PW11014. The responsibility for the contents of the article
lies with the authors. In addition, the authors would like to thank two anonymous reviewers whose com-
ments helped crafting the article.
Appendix
See Tables 3,4and 5.
Table 3 CHE (2004) Business Administration research dataset for 2001 to 2003
Business School Professors
(staffed
positions)
Research
assistants (staffed
positions)
Nationally visible
publications
(points)
PhD
dissertations
(number)
Third-party
funds
(thousand €)
1 Aachen 24.0 71.00 187.83 29 1902.0
2 Augsburg 42.0 171.00 266.25 49 824.1
3 Bamberg 27.0 66.25 83.50 24 1035.0
4 Bayreuth 30.0 87.00 111.17 38 1535.1
6 Berlin FU 42.0 84.00 316.33 33 1307.7
7 Berlin HU 33.0 96.00 95.08 18 2510.7
8 Berlin TU 29.0 81.00 214.00 46 480.0
9 Bielefeld 18.0 48.00 106.00 10 58.2
12 Bruchsal 18.0 8.00 32.00 1 1595.1
13 Chemnitz 24.0 69.00 183.33 25 2651.1
16 Dortmund 24.0 78.00 85.92 27 686.1
17 Dresden 28.0 83.00 269.08 19 3846.9
18 Duisburg 61.0 143.00 317.75 46 1569.9
19 Du
¨sseldorf 31.0 68.00 153.33 10 177.9
20 Eichsta
¨tt-
Ingolstadt
50.0 96.00 290.08 44 1227.0
21 Erlangen-
Nu
¨rnberg
43.0 184.50 338.17 40 1811.1
606 Scientometrics (2015) 103:583–614
123
Table 3 continued
Business School Professors
(staffed
positions)
Research
assistants
(staffed
positions)
Nationally visible
publications
(points)
PhD
dissertations
(number)
Third-party
funds
(thousand €)
22 Essen 27.0 114.00 180.75 19 2658.9
23 Flensburg 18.0 15.00 31.83 9 1400.1
25 Frankfurt
Main Uni
82.0 198.00 276.00 52 7598.1
26 Frankfurt
Oder
27.0 67.00 81.50 24 3552.0
27 Freiberg 27.0 43.50 171.00 27 1016.1
28 Giessen 22.0 68.15 154.08 18 111.3
29 Go
¨ttingen 30.0 100.50 141.50 54 2161.8
30 Greifswald 29.5 41.25 107.17 14 2067.9
32 Halle-
Wittenberg
25.0 64.00 137.92 11 1062.6
34 Hamburg 40.0 177.49 261.00 41 1364.4
38 Hohenheim 35.0 73.00 202.58 39 4304.1
39 Ilmenau 42.0 123.00 110.08 17 2293.5
40 Jena 24.0 74.30 114.67 12 1622.1
43 Kiel 24.0 40.00 151.83 14 338.7
44 Ko
¨ln 51.0 201.48 311.67 71 3232.2
46 Leipzig HH 23.5 63.50 91.50 15 2917.2
47 Leipzig Uni 34.0 53.00 56.00 29 1310.7
48 Magdeburg 29.0 96.00 92.25 14 1299.9
49 Mainz 24.0 69.00 131.08 36 863.1
50 Mannheim 48.0 254.50 476.58 75 6579.0
51 Marburg 31.0 61.01 144.25 29 1131.6
52 Mu
¨nchen
LMU
52.0 273.00 432.17 77 4090.2
53 Mu
¨nchen
TU
36.0 106.00 384.33 38 9108.3
55 Mu
¨nster 39.0 144.00 417.25 75 1928.1
56 Oestrich-
Winkel
51.0 142.00 351.92 59 2988.0
57 Oldenburg 43.0 64.00 62.83 37 7029.6
58 Osnabru
¨ck 24.0 45.92 78.92 8 1437.3
59 Paderborn 24.5 42.75 175.42 18 1421.1
60 Passau 32.0 106.10 99.67 22 2163.9
61 Potsdam 18.0 39.00 137.17 16 291.0
62 Regensburg 27.0 81.00 148.83 40 1002.9
63 Rostock 27.0 74.25 182.00 23 315.3
64 Saarbru
¨cken 51.5 183.50 372.75 50 6695.1
65 Siegen 49.0 44.75 191.00 9 913.5
66 Stuttgart 22.0 51.00 269.92 33 2182.2
67 Trier 27.0 76.50 151.00 24 4224.9
Scientometrics (2015) 103:583–614 607
123
Table 3 continued
Business
School
Professors
(staffed
positions)
Research
assistants (staffed
positions)
Nationally visible
publications
(points)
PhD
dissertations
(number)
Third-party
funds
(thousand €)
68 Tu
¨bingen 24.0 61.00 116.33 18 803.1
70 Vallendar 43.0 142.00 654.83 60 876.6
73 Wu
¨rzburg 18.0 55.50 96.83 24 1552.8
All (total) values embody the 3-year sum of respective absolute indicator values over the incorporated
periods of 2001–2003
Highlighted in bold: Top 10 most reputable BuSs
FU Free University, HH Commercial College, HU Humboldt University, LMU Ludwig Maximilian
University, TU University of Technology
Table 4 CHE (2007) Business Administration research dataset for 2004 to 2006
Business School Professors
(staffed
positions)
Research
assistants
(staffed
positions)
Nationally
visible
publications
(points)
Internationally
visible
publications
(number)
PhD
dissertations
(number)
Third-
party
funds
(thousand
€)
1 Aachen 36.00 110.00 241 16 49 3239.0
2 Augsburg 40.00 168.00 299 38 55 4048.4
3 Bamberg 26.00 62.50 145 7 25 487.7
4 Bayreuth 29.00 105.50 184 22 29 1139.5
6 Berlin FU 47.80 91.20 274 22 34 2299.6
7 Berlin HU 45.00 52.62 180 26 21 2363.5
8 Berlin TU 27.00 93.00 188 52 54 5685.0
10 Bochum 30.00 118.00 237 4 32 1508.0
11 Bremen 34.00 53.50 231 6 43 1901.0
13 Chemnitz 27.00 73.65 154 7 38 2815.1
14 Clausthal 20.00 33.00 67 4 5 283.5
16 Dortmund 24.00 78.00 259 4 39 1914.0
17 Dresden 33.00 83.75 162 5 24 2592.2
18 Duisburg 51.50 127.99 281 11 33 1962.5
19 Du
¨sseldorf 21.00 48.00 70 2 22 280.8
20 Eichsta
¨tt-
Ingolstadt
51.00 152.00 222 7 48 592.4
21 Erlangen-
Nu
¨rnberg
36.00 140.50 320 23 106 4095.6
22 Essen 27.00 97.75 122 42 25 9188.1
23 Flensburg 31.00 13.00 93 6 14 1669.4
25 Frankfurt
Main
Uni
80.00 181.00 391 49 86 8268.7
26 Frankfurt
Oder
28.00 71.00 105 10 35 2255.3
27 Freiberg 18.00 38.00 100 2 17 896.7
28 Gießen 26.00 65.83 192 3 22 418.8
608 Scientometrics (2015) 103:583–614
123
Table 4 continued
Business School Professors
(staffed
positions)
Research
assistants
(staffed
positions)
Nationally
visible
publications
(points)
Internationally
visible
publications
(number)
PhD
dissertations
(number)
Third-
party
funds
(thousand
€)
29 Go
¨ttingen 30.00 128.32 198 11 60 1955.4
30 Greifswald 28.00 43.00 101 14 9 1290.0
31 Hagen 38.00 166.15 307 21 20 539.1
32 Halle-
Wittenberg
25.00 70.00 79 3 27 197.5
33 Hamburg
UBW
90.00 150.00 78 2 45 837.7
35 Hamburg Uni
Wi/Pol
38.00 26.00 67 4 24 930.4
36 Hamburg Uni
WiWi
49.50 167.00 409 31 47 2400.9
38 Hohenheim 41.00 81.25 332 4 44 4142.0
39 Ilmenau 44.00 127.00 183 9 12 850.2
40 Jena 24.50 66.83 199 7 7 725.7
41 Kaiserslautern 30.00 100.00 182 10 21 3512.3
43 Kiel 28.50 62.25 202 19 20 850.0
44 Ko
¨ln 60.00 263.85 525 24 79 4078.0
46 Leipzig HH 32.00 64.00 140 1 27 316.4
47 Leipzig 42.00 58.50 142 6 24 627.5
48 Magdeburg 38.50 105.00 225 15 14 1510.0
49 Mainz 29.00 86.50 115 4 24 1067.9
50 Mannheim 67.00 237.00 681 38 88 6134.0
51 Marburg 35.00 51.00 161 2 20 610.0
52 Mu
¨nchen
LMU
56.00 247.50 425 20 105 3366.9
53 Mu
¨nchen TU 59.00 164.12 364 22 79 15,909.8
54 Mu
¨nchen
UBW
54.00 70.50 90 1 43 240.5
55 Mu
¨nster 47.00 148.50 482 21 98 4036.7
56 Oestrich-
Winkel
100.00 156.00 433 15 117 16,615.9
57 Oldenburg 19.00 39.75 198 5 33 2685.6
58 Osnabru
¨ck 30.00 41.95 31 3 9 923.0
59 Paderborn 43.00 72.25 128 9 47 1197.4
60 Passau 23.00 64.00 161 11 18 1513.3
61 Potsdam 20.00 35.60 108 8 38 1233.5
62 Regensburg 33.00 94.50 209 10 43 1231.8
63 Rostock 22.92 47.82 106 3 52 1223.4
64 Saarbru
¨cken 48.50 175.00 546 20 37 2847.5
65 Siegen 45.00 48.50 204 15 25 3731.0
66 Stuttgart 20.00 65.60 166 5 28 2023.4
67 Trier 27.00 72.50 94 2 19 1073.0
Scientometrics (2015) 103:583–614 609
123
Table 4 continued
Business School Professors
(staffed
positions)
Research
assistants
(staffed
positions)
Nationally
visible
publications
(points)
Internationally
visible
publications
(number)
PhD
dissertations
(number)
Third-
party
funds
(thousand
€)
68 Tu
¨bingen 24.00 56.00 113 5 19 586.8
70 Vallendar 44.50 164.84 615 32 100 2740.3
71 Witten-
Herdecke
29.25 71.71 158 12 57 13,291.0
72 Wuppertal 49.00 85.50 170 6 29 1054.6
73 Wu
¨rzburg 21.00 50.00 118 3 23 1261.2
All (total) values embody the 3-year sum of respective absolute indicator values over the incorporated
periods of 2004–2006. The internationally visible publications were provided by the bibliometric team at
Forschungszentrum Ju
¨lich
Highlighted in bold: Top 10 most reputable BuSs
FU Free University, HH Commercial College, HU Humboldt University, LMU Ludwig Maximilian
University, TU University of Technology, UBW University of the German Armed Forces, Uni University,
Wi/Pol Economics and Politics, WiWi Business Sciences
Table 5 CHE (2010) Business Administration research dataset for 2007 to 2009
Business School Professors
(staffed
positions)
Research
assistants
(staffed
positions)
Nationally
visible
publications
(points)
Internationally
visible
publications
(number)
PhD
dissertations
(number)
Third-
party
funds
(thousand
€)
1 Aachen 39.00 139.00 160.32 29 66 5765.4
2 Augsburg 40.00 193.00 166.08 36 60 6051.0
3 Bamberg 27.00 71.00 144.51 14 31 632.0
4 Bayreuth 37.00 104.00 92.16 23 41 4152.8
5 Berlin ESCP-
EAP
28.00 35.50 140.25 7 15 381.1
6 Berlin FU 48.00 79.57 212.43 28 52 3309.6
7 Berlin HU 38.20 60.10 107.34 18 28 4368.2
8 Berlin TU 28.00 104.00 180.75 46 106 11,738.0
9 Bielefeld 17.00 36.25 65.43 56 15 876.0
10 Bochum 32.25 111.00 111.93 5 39 2012.1
11 Bremen 49.00 61.50 190.74 32 87 5641.0
13 Chemnitz 27.00 94.65 145.68 19 55 4995.6
14 Clausthal 24.00 44.75 69.99 6 5 579.0
15 Cottbus 56.00 124.00 71.34 5 12 1680.0
16 Dortmund 40.00 98.60 144.93 15 39 2912.0
17 Dresden 42.00 110.50 224.25 24 55 5290.9
18 Duisburg 48.25 73.15 109.26 96 36 3052.0
19 Du
¨sseldorf 30.00 69.00 203.82 2 27 1145.8
21 Erlangen-
Nu
¨rnberg
41.00 178.28 277.41 24 96 7326.2
610 Scientometrics (2015) 103:583–614
123
Table 5 continued
Business School Professors
(staffed
positions)
Research
assistants
(staffed
positions)
Nationally
visible
publications
(points)
Internationally
visible
publications
(number)
PhD
dissertations
(number)
Third-
party
funds
(thousand
€)
23 Flensburg 11.00 7.50 64.74 7 15 1951.5
24 Frankfurt
Main SFM
87.00 122.00 272.25 21 12 7223.6
25 Frankfurt
Main Uni
91.50 162.25 319.92 86 86 10,847.5
26 Frankfurt
Oder
45.00 66.00 193.41 23 26 1989.0
27 Freiberg 30.00 48.00 100.17 7 19 1535.7
28 Gießen 27.00 72.24 99.51 12 21 917.0
29 Go
¨ttingen 34.00 117.05 114.18 11 26 1437.7
30 Greifswald 32.00 39.50 90.18 12 14 2249.1
31 Hagen 39.00 165.50 246.00 19 27 552.0
32 Halle-
Wittenberg
23.00 69.75 77.67 4 15 296.3
33 Hamburg
UBW
57.00 95.00 99.00 8 22 295.0
34 Hamburg
Uni
69.00 191.54 328.59 71 64 4266.7
37 Hannover 70.00 183.75 178.74 66 53 3896.3
38 Hohenheim 42.00 141.80 252.09 27 60 2294.0
39 Ilmenau 45.00 116.50 213.51 8 15 596.5
40 Jena 31.00 90.50 214.17 17 21 904.4
41 Kaiserslautern 36.00 88.00 124.50 12 20 4461.0
42 Kassel 48.00 83.38 137.49 10 50 2222.0
43 Kiel 28.50 74.50 109.41 17 21 713.9
45 Konstanz 20.00 17.25 54.18 1 11 786.0
46 Leipzig HH 30.00 96.75 122.67 2 18 2960.6
48 Magdeburg 45.50 106.00 146.49 20 19 2878.5
49 Mainz 58.25 134.08 71.25 16 38 866.3
50 Mannheim 86.00 245.00 536.43 55 123 9885.2
51 Marburg 30.00 52.50 53.49 12 40 663.9
52 Mu
¨nchen
LMU
49.00 251.50 479.82 269 67 6705.6
53 Mu
¨nchen TU 27.00 93.33 263.34 24 92 8545.7
54 Mu
¨nchen
UBW
33.00 60.00 138.18 7 23 898.2
55 Mu
¨nster 49.00 157.00 394.68 35 105 6168.1
56 Oestrich-
Winkel
54.51 54.51 380.76 28 157 9959.5
57 Oldenburg 57.00 67.45 143.67 21 38 4706.4
58 Osnabru
¨ck 27.00 60.00 88.17 5 15 1324.5
59 Paderborn 90.00 153.40 249.18 40 52 4275.0
Scientometrics (2015) 103:583–614 611
123
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Table 5 continued
Business School Professors
(staffed
positions)
Research
assistants
(staffed
positions)
Nationally
visible
publications
(points)
Internationally
visible
publications
(number)
PhD
dissertations
(number)
Third-
party
funds
(thousand
€)
60 Passau 39.00 80.00 72.66 1 20 760.0
61 Potsdam 22.00 50.85 65.82 18 28 4785.6
62 Regensburg 37.00 110.00 123.99 8 57 2059.8
63 Rostock 28.50 63.50 70.17 11 37 1436.2
64 Saarbru
¨cken 45.00 179.50 318.24 21 45 6003.0
65 Siegen 33.56 48.14 79.68 15 32 1971.0
66 Stuttgart 21.00 95.20 103.74 8 31 1179.5
67 Trier 28.00 73.00 71.34 9 39 1736.9
68 Tu
¨bingen 29.00 57.50 83.07 26 24 931.8
69 Ulm 22.00 36.00 100.41 14 20 646.7
70 Vallendar 78.00 113.00 510.99 30 92 6903.0
71 Witten-
Herdecke
29.85 50.98 82.17 13 49 2480.0
72 Wuppertal 45.00 53.50 158.16 16 23 1193.5
73 Wu
¨rzburg 19.00 69.88 122.58 10 20 1759.0
All (total) values embody the 3-year sum of respective absolute indicator values over the incorporated
periods of 2007–2009. The internationally visible publications were provided by the bibliometric team at
Forschungszentrum Ju
¨lich
Highlighted in bold: Top 10 most reputable BuSs
ESCP-EAP Ecole Superieure de Commerce de Paris-Ecole des Affaires de Paris, FU Free University, HH
Commercial College, HU Humboldt University, LMU Ludwig Maximilian University, SFM School of
Finance and Management, TU University of Technology, UBW University of the German Armed Forces,
Uni University
612 Scientometrics (2015) 103:583–614
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