the financial structure of innovative smes in germany
TRANSCRIPT
ORI GIN AL PA PER
The financial structure of innovative SMEs in Germany
Detlev Hummel • Boris Karcher • Christian Schultz
Published online: 22 March 2013
� Springer-Verlag Berlin Heidelberg 2013
Abstract In politics and business the special role of innovative businesses whose
research and development activities expedite technological progress has received
steady attention. Especially small and medium sized businesses (SMEs) have ini-
tiated promising innovation projects. However, when analysing these projects our
research must take into account that SMEs cannot be viewed as a homogeneous
business category. Moreover, financing their innovations, SMEs are subject to
unique issues. To shed light on these problems, this study will develop an index
measuring degrees of innovation. It allows the 171 sample companies to be cate-
gorised into three groups: non-innovative, moderately innovative or highly inno-
vative. A multinomial logistic regression is used to examine the quality of this
typology. In addition, group-specific differences in the financing mix are demon-
strated. Finally, from a theoretical point of view, the implications of the pecking
order theory are basically validated. On the other hand, the concept of the financial
growth cycle does not deliver satisfactory results.
Keywords Small and medium-sized business financing � SMEs �Degree of innovation � Innovation financing � Financing of innovative businesses
JEL Classification G32 � O31 � O39
D. Hummel (&) � B. Karcher
Lehrstuhl fur Betriebswirtschaftslehre mit Schwerpunkt Finanzierung und Banken,
Universitat Potsdam, August-Bebel-Straße 89, 14482 Potsdam, Germany
e-mail: [email protected]
B. Karcher
e-mail: [email protected]
C. Schultz
Lehrstuhl fur Innovationsmanagement und Entrepreneurship,
Universitat Potsdam, August-Bebel-Straße 89, 14482 Potsdam, Germany
e-mail: [email protected]
123
J Bus Econ (2013) 83:471–503
DOI 10.1007/s11573-013-0662-8
1 Introduction
As early as 1934 Schumpeter’s work emphasised the special role of innovative
businesses that triggered macroeconomic changes through creative destruction.
Innovative companies develop market innovations, shatter existing market struc-
tures and destroy the previous innovators’ sources of income. The surmounting of
prevailing technologies promotes technological change, creates new jobs and
generates economic growth (see Audretsch 1995; Bartelsman et al. 2004). The
speed and direction of the technological change are significantly determined by the
allocation and distribution of the scarce resources. In this context the access to
capital plays a decisive role in enabling innovation efforts to occur in the first place
(see Dosi 1990). The effectiveness of financial intermediaries in ensuring the
optimal supply of capital is, of course, limited due to the various characteristics of
innovative companies. High entry barriers for investors can arise due to significant
sunk costs and an extended time span between research and development efforts and
actual commercialisation. A possible consequence is that innovative and promising
companies fail as a result of encountering financial bottlenecks.
Therefore, in this study two crucial research questions will be examined: ‘What is
the source of the required capital that innovative SMEs use for their cost intensive
innovation activities?’, and ‘Does the capital structure of businesses differ in
relation to the degree of innovation?’
In order to take into adequate account the heterogeneity within the group of
innovative companies, as a first step an innovation index will be designed to enable
the classification of the sample used later. Next, the hypotheses derived from
existing theories and models concerning capital structure will be scrutinised.
Moreover, the current analysis is based upon data compiled for the specific purpose
of this study. Finally, based on the original data collection, the innovation index
developed by the authors for the classification of innovative businesses is employed.
Therefore, the current study possesses a decidedly explorative character.
2 Theoretical background
Various studies demonstrate the importance of the optimal financial structure in the
success of a business (Nelson 1959; Arrow 1962). The neoclassical assumption of a
perfect capital market and thus the irrelevance of the capital structure do not actually
comply with reality. Imperfect capital markets and asymmetrical distribution of
information influence the supply of capital as well as the investment decisions made
in the real economy, since the costs of capital vary according to the source of
financing and the investment project (Meyer and Kuh 1957; Myers and Majluf 1984).
In the present essay the significance of various financial instruments for differing
innovative small and medium sized enterprises (SMEs) is examined. A broad
literature base has long existed that explains the financial structure and coherent
theories and concepts have been developed. Especially useful in the context of the
financing of SMEs are the pecking order theory (Myers 1984; Myers and Majluf
1984) and the financial growth cycle (Berger and Udell 1995, 1998). In these
472 D. Hummel et al.
123
theoretical approaches that form part of the new institutional economics, the capital
structure is determined primarily by means of available information and the
information exchange between businesses and investors (principal-agent theory).
On the basis of information asymmetry between lenders and borrowers as well as
a necessary costly analysis of the condition of any given company, various
disadvantageous effects can result, like moral hazard and adverse selection. In
particular due to the share of fixed costs for transactions, small businesses find it
disproportionately expensive to overcome informational asymmetries. An additional
factor is the lower quality of information provided by small companies, causing an
increase in the monitoring costs for the lenders. Thus debt capital appears optimal
for transparent companies after their internal resources are exhausted. However, the
quality of information provided by some companies could lead directly to the use of
external equity capital, as debt capital providers might not be willing to accept a
potentially higher risk (Jensen and Meckling 1976; Berger and Udell 1998).
Below, the main points of the pecking order theory as well as financial growth
cycle will be briefly summarised and analysed as to the extent to which they offer
implications for the financial strategies for innovative SMEs.
The pecking order theory postulates that the company management makes use of
different financing instruments according to a hierarchical order of precedence (a
‘pecking order’). Consequently internal funds would be first means of financing;
followed by debt. The least desirable financing tool would be external equity
capital. This hierarchy derives from the relative costs of the respective sources of
financing, the potentially obscure information available about the equity increase
could be interpreted by outsiders as a signal of an overvaluation of the company, as
well as a general aversion on the part of the management towards external investors
who could claim monitoring and co-management rights within the company (Myers
1984; Myers and Majluf 1984).
Berger and Udell’s financial growth cycle is based on an analogy between
evolutionary biology and the business world according to which businesses, similar
to living beings, may experience a growth cycle ranging from birth (start-up) to
death (insolvency). Therefore, the need for and access to capital is dependent upon
the particular stage of company development at any given time and thus upon its
size, age and availability of information for potential financiers. One side of this
size/age/information continuum is represented by small, new and informationally
opaque companies. They must finance themselves at first through diverse internal
financial resources (such as those deriving from family, friends and fools), trade
credit and/or business angels. In the course of the company growth, the access to
venture capital as well as to medium-term loan improves. In the later phase of the
company’s existence, given a comprehensive company history, increasing experi-
ence and more transparency, public equity and long-term loan capital are finally
available. Accordingly, the use of various financial instruments depends upon the
actual situation of a company, chiefly judged by age, size and availability of
information. At companies with differing profiles the financial mix is hence
composed in differing proportions (Berger and Udell 1995, 1998).
While it is true that both approaches were developed based on the assumption of
differently distributed information between principals and agents (Jensen and
The financial structure of innovative SMEs in Germany 473
123
Meckling 1976), financial growth cycle was explicitly modelled for SMEs, whereas
the basic model of the pecking order theory developed by Myers and Majluf (1984)
had as its subject an account of the management behaviour of companies listed on
the stock exchange. In this connection Norton (1990) calls into question the
appropriateness of applying to SMEs those theories developed to describe large-
scale enterprises. In relation to fiscal factors major enterprises and SMEs differ
greatly (Walker and Petty 1978). While this question has not been definitively
answered, the pecking order theory has nevertheless been applied to SME‘s and
their implications were empirically examined (Berger and Udell 1998; Berggren
et al. 2000). Asymmetric information between companies and investors is in this
connection one of the most decisive factors in SME financing. In the case of
innovative companies is this asymmetry especially pronounced, so that its negative
influences on the supply of capital is to be assumed (Chittenden et al. 1996), while
the capital structure can also be explained with reference to demand. There is
general consensus in the literature that SMEs attempt to preserve their independence
and to choose their financial instruments accordingly. This behaviour explains why
SMEs favour internal funding over debt and why debt is preferred over (new)
equity. Innovative SMEs also give priority to internal rather than external capital,
but diverse studies demonstrate that they prefer equity to loaned capital (Oakey
1984; Hyytinen and Pajarinen 2002). These findings appear plausible, since
providers of private equity, like venture capitalists or business angels, are more
capable of overcoming information opacity and thus as capital providers are more
willing to make equity available (Gompers and Lerner 2003). Accordingly,
empirical studies demonstrate that companies with higher research and development
intensity, more patents, a smaller proportion of tangible assets and a higher
proportion of highly qualified employees are confronted with larger problems
accessing external debt capital. At the same time, the dependence of innovative
companies on internal sources of finance, for example retained earnings, increases
(Freel 2007).
A study performed by Bozkaya and De La Potterie (2008) confirms the
implications of the pecking order theory as well as those of the financial growth
cycle model. On the other hand, critics remark that the financial growth cycle
paradigm has methodological weaknesses, since the determinants it uses are not
selective. Thus size and age necessarily correlate very positively but not completely
with the available information. Berger and Udell (1998, p. 622) recognise these
weaknesses and counter this criticism by conceding that the financial growth cycle
represents a recognised descriptive concept or a rule of thumb rather than a theory
with a claim to general applicability. Consequently, the financial growth cycle
concept should be interpreted as a model useful for its high prognosis potential
rather than as a theory in its own right.
It should be clear that in the case of innovative as well as new companies a high
sense of uncertainty exists as to whether sufficient cash flow can be generated to
cover costs and produce a profit. The uncertainties are caused by concerns about
market success and the spill-over effects through which portions of the monetary
return flow following the market launch of innovative products or services drain off
to competing businesses (Arrow 1962). These factors additionally increase the level
474 D. Hummel et al.
123
of risk for financiers. Based on banks’ aversion to risk it suggests that the more
innovative companies are, the more they must increasingly fall back on short-term
and expensive overdrafts and trade credit as well as on special forms of financing,
such as leasing, as well as on external equity. In addition, innovative companies are
also more prepared to relinquish control rights and thus are more receptive to
venture capital (Berggren et al. 2000).
Based on these findings, it is determined that due to various financial restrictions,
a special financial situation exists for innovative SMEs (Czarnitzki and Hottenrott
2010; Hall 2002; Myers and Majluf 1984; Arrow 1962). In this context, a financial
bottleneck during the innovation process is especially problematic for innovative
SMEs as it can lead to the failure of the whole enterprise (Oakey 1995). Thus the
insolvency rate of innovative SMEs is clearly higher than the average of all business
segments. The company’s financial value declines more drastically than in the case
of conventional SMEs when actual insolvency occurs (Grabherr 2001, p. 40). The
value of the company primarily consists of its growth potential and the specialised
as well as immaterial assets, so that exploitable insolvency assets turn out to be
comparatively smaller (Myers 1977). Hence the question concerning the financing
of innovation activities being examined here appears especially relevant, particu-
larly since to date only very few empirical studies on the financing behaviour of
innovative SMEs in Germany exist.
3 Hypotheses of the empirical examination
The brief analysis above of selected theories on capital structure suggests that
innovative SMEs are faced with special financial obstacles. In Table 1, the
hypotheses will be linked in summary fashion with the strands of the literature
presented in the previous chapter. Then the hypotheses will be discussed in
detail.
A higher degree of innovation is manifested chiefly by higher research and
development and innovation activity, resulting correspondingly in more strongly
Table 1 Theoretical basis and derived hypotheses
No. Theoretical basis Derived hypothesis
1 Pecking order
theory
The financial mix of SMEs differs basically in relation to the company’s degree
of innovation
2 Pecking order
theory
With respect to the hierarchy of the financial instruments in innovative
companies, internal funds have the highest importance, followed by short-
term, middle-term and long-term debt and finally external equity
3 Financial growth
cycle
With an increasing degree of innovation, there is a corresponding gain in
importance of internal funding as well as typical risk capital (PE/VC,
business angels, mezzanine), overdrafts, credit substitutes and public
funding. At the same time medium- to long-term bank financing loses its
importance
The financial structure of innovative SMEs in Germany 475
123
pronounced information asymmetries and consequently in higher transaction costs
than those of non-innovative companies. As an answer to the basic question whether
the financial mix between innovative and non-innovative companies differs, the first
hypothesis can be posited.
H1: The financing mix of SMEs differs basically in relation to the company’s
degree of innovation
More recent studies even suggest that innovative companies could enjoy
advantages in the raising of external equity, since investors could be attracted by the
existing growth potential (Audretsch and Lehmann 2004). Moreover, private equity
providers have a greater ability to overcome information opacity than other
investors. Consequently, external equity is preferable to external debt. On the basis
of the second hypothesis it should now be investigated whether the pecking order
theory is valid in innovative companies, whether innovative companies have an
advantage in raising external capital or whether a higher acceptance of external
equity prevails in contrast to that of conventional companies.
H2: With respect to the hierarchy of financing instruments in innovative
companies, internal funds have the highest importance, followed by short-
term, middle-term and long-term debt and finally external equity
As already mentioned, innovative companies could acquire more external equity
than non-innovative companies are capable of due to their growth potential.
However, regarding long-term debt, companies at an increasing degree of
innovation are likely to be disadvantaged because of their inherent risk.
Consequently, the third hypothesis, based on the financial growth cycle, states:
H3: With an increasing degree of innovation, there is a corresponding gain in
importance of internal funding as well as typical risk capital (PE/VC, business
angels, mezzanine), overdrafts, credit substitutes and public funding. At the
same time medium- to long-term bank financing loses its importance
As a first step to be able to examine the established hypotheses, SMEs with a
differing innovation degree will be identified in order to prepare the second step, a
comparison of the financing mix.
4 Measurement of the company’s degree of innovation
Despite extensive attempts to characterise innovative companies on the basis of
their degree of innovation or to make the degree of innovation on the company level
measurable, to date no standardised methodology has been generally accepted
(compare different definitions of innovation, among others, at Schumpeter 1934;
Totterdell et al. 2002; Oslo Manual 2005; on various indicators for measuring
innovations, see, among others, Hagedoorn and Cloodt 2003; Kleinknecht et al.
2002; Kleinknecht 2000). Chiefly, the extremely large spectrum of activities that
can be subsumed under the term ‘innovation’ presents a problem here. Thus the
validity of assessing a company’s degree of innovation on the basis of its branch
476 D. Hummel et al.
123
membership has received widespread acceptance (see, for example, Metzger et al.
2008; Cuervo-Cazurra and Un 2010; Spithoven et al. 2009; Heidenreich 2009; Hall
et al. 2009). Typically, by means of research and development intensity, a
distinction is made in branches of advanced and high technology. Applying rather
general characteristics (such as, for example, the particular economic sector) as an
indicator of individual innovation intensity, hide advantages as well as disadvan-
tages. On the one hand, such procedure is relatively inexpensive and leaves no room
for interpretation. On the other hand, this procedure also hides a not to be
underestimated potential for error, since the individual innovation activity can
strongly deviate positively as well as negatively from the average innovation
activity in the respective industry segment.
In order to carry out a detailed analysis on company level, it is thus sensible to
ascertain the innovation degree of a company by means of several suitable
indicators. These innovation indicators can, to simplify matters, be categorised as
input- and output-factors. Input factors comprise, for example, the R&D expendi-
tures or the percentage of the R&D employees in relation to all other employees.
The indicators of output are mostly the number of patents as well as the number of
product and process innovations. The measurement of the indicators takes place in
turn with the aid of two alternative approaches. The ‘objective’ procedure measures
directly measurable indicators, such as the number of patents a company registers.
The ‘subjective’ approach is based on the self-assessment of the company as to its
own innovation activities. This approach, among others, is applied in the European
Community Innovation Survey (CIS), such as the European Innovation Scoreboard
(EIS) for the investigation of the innovation behaviour on the company level
(Hughes 2002, p. 158). Referring to the investigation of innovation activities of
smaller and medium companies, studies indicate that, when applying the objective
approach, the innovation activities of smaller companies are systematically
underestimated (Oslo Manual 2005: because of a more limited inclination to patent
innovations). To counter this problem the innovation activity in the present
investigation will be determined by the indicator system developed by the authors
that consists of objectively measurable indicators as well as subjective indicators
derived from assessments by the companies examined.
The starting point of the innovations index is a number of examinations in order
to characterise innovative technological companies. On the basis of different
definitions, approaches and interviews, qualities have been identified as important
characteristics of this class of companies (Grinstein and Goldman 2006): The
implementation of research and development activities, the capacity for innovation,
a capital intensive product portfolio and a higher risk represent the decisive
characteristics of an innovative company. Based on such defining approach, it can
be assumed that a company is considered to be more innovative:
• the more intensively research and development activities are undertaken (input
indicator),
• the higher the proportion of research and development costs in relation to
volume of sales (input indicator) is,
• when innovation projects are carried out (input indicator),
The financial structure of innovative SMEs in Germany 477
123
• the higher the proportion of innovative products to the total volume of sales is
(output indicator)
• the higher the savings are through process innovations (output indicator).
To date, appropriate studies have seldom distinguished between intensive,
continual and sporadic or intermittent research and development activities (Rammer
et al. 2009; Huang et al. 2010). Companies intensively active in research and
development carry out continual research and development with their own R&D
personnel over many years. Other companies conduct R&D but only sporadically,
either once or simply at irregular intervals. The distinction appears extremely
important, since latter companies could be classified at the time of measurement as a
non-research company, although they possess decisive R&D expertise, routines and
resources. These companies with sporadic R&D activities that are aimed at specific
needs could show another innovation behaviour than companies that never conduct
any research at all. The index at hand has been dispensed with the separate
incorporation of the employees engaged in R&D, since SMEs often conduct R&D
without employing explicitly designated R&D employees (Rammer et al. 2011,
p. 46).
Moreover, research results confirm the assumption made that R&D activities
have a decisive influence on the capabilities of companies to successfully develop
new and innovative products (Huang et al. 2010; Kirner et al. 2009; Heidenreich
2009). Accordingly, companies that conduct research intensively introduce products
and market innovations more frequently than companies without an intensive
research programme. In this context, companies are considered non-research
intensive when they do not conduct their own R&D, i.e. display no expenditures for
their own research and development activities (Huang et al. 2010; Rammer et al.
2009). Companies are considered insignificantly research intensive if they spend
less that 2.5 % of their sales volume on R&D; moderately research intensive are
those that spend 2.5–7 %; and research intensive are those spending over 7 % on
R&D (Kirner et al. 2007, 2009; Rammer 2011).
The third indicator measures whether innovation projects have been carried out
in the last 3 years. This measurement provides information on the innovative
activity of a company, even if an innovation is not preceded by the company’s R&D
activity. Despite the above-mentioned positive connection between R&D activities
and the introduction of product and market innovations, a not insignificant
proportion of the innovative companies in Germany launch innovations without
having carried out parallel R&D activities (Huang et al. 2010; Kirner et al. 2009;
Heidenreich 2009). Since a large proportion of these innovative companies are
small companies focused upon in this study that do not feature their own R&D
(Rammer et al. 2011, p. 75), the inclusion of this indicator is nevertheless justified.
The last two indicators provide information on the output of R&D activities
already performed in the form of product and process innovations. Their weight for
the individual companies can be measured on the share of turnover of innovative
products or the reductions in costs resulting from process innovations (Mohnen and
Mairesse 2002, p. 228). The definition of innovative products as well as the
measurement of savings through process innovations are especially difficult to
478 D. Hummel et al.
123
judge. The same applies to the innovations output even in highly innovative
companies due to the generally speaking time delay between R&D or innovation
efforts and going to market., Therefore, these factors are given less weight in the
innovation index.
The listed factors pertaining to innovation activity in total now permit a grouping
of the companies according to their degrees of innovation. In this context various
input and output indicators of the innovation activities on company level are taken
into account, since the isolated consideration of a single indicator can lead to false
conclusions. Hence there is only a small connection between R&D percentage and
the innovation degree of the new product portfolio (see Salomo et al. 2008). For this
reason, a multi-dimensional instrument will be set against such a one-dimensional
procedure. This way, the companies surveyed in this investigation can be
subdivided on the basis of their degree of innovation. Depending on the forms of
and the emphasis upon the respective indicators, points will be allocated. Then the
total scores, in accordance with predefined limits, permit a company’s placement in
one of three groups (highly innovative, moderately innovative, non-innovative). The
allocation of the particular scores as well as the group assignments were conducted
on the basis of the relevant literature and were derived, among other methods, with
the help of benchmarks of the OECD, the NIW, the Fraunhofer ISI and the ZEW
(see NIW/ISI 2006; NIW/ISI/ZEW 2010; OECD 2002). The objective of the point
system is to distinguish in the clearest fashion the individual groups from each
other. Thus the intervals in the scores between non-innovative and highly innovative
SMEs are intentionally gauged very large (see Fig. 1).
Fig. 1 Composite of the innovation index
The financial structure of innovative SMEs in Germany 479
123
5 Analysis of financing preferences depending on a company’s degreeof innovation
The well-known lean state of information characterising smaller companies
determines on the one hand the need for research in this branch, and on the other
hand it makes the conducting of empirical research decidedly more difficult (see
Berger and Udell 1998, p. 617). Hence in the framework of this research project a
self-conducted company interview was implemented. A random sample of 25,000
SMEs out of a total of approximately 3.5 million SMEs in Germany (according to
the authors’ own calculations based on the Company Register of 2009) was used
requesting the respective managing CEOs per email to respond to an extensive
online questionnaire (including 5,000 emails that produced a Non-Delivery
Notification (NDN)). All together 339 SMEs took part in the survey between the
1st of June and the 1st of August in 2010. Since in total 168 companies did not
respond to the questionnaire completely, the sample used was reduced to the
complete data records of 171 SMEs. This number corresponds to a response rate of
about 0.7 % and thus remains within the normal parameters of comparable surveys
conducted online.
Due to the limited number of responses in comparison to the estimated total of
3.5 million SMEs in Germany, statistically verifiable conclusions can only be drawn
conditionally. Other quantitative studies with a focus on SMEs and innovation used
comparable small samples (see Autio 1997; Nassimbeni 2001; Fontes and Coombs
2001).
5.1 Descriptive data analysis
To begin with, the features of the companies surveyed will be depicted with the aid
of descriptive statistics. The companies classification here used as micro-, small-
and medium-sized enterprises mirror EU standards. It takes into account the number
of employees subject to compulsory social security insurance (s.s.i. employees) and
the volume of turnover, but not the balance sheet totals. Accordingly, the following
size groups are defined:
• micro-enterprises: up to 9 employees, up to 2 million euros in sales,
• small enterprises: 10–49 employees, [2 to 10 million euros in sales,
• medium-sized enterprises: 50–249 s.s.i.-employees, [10 to 50 million euros in
sales.
If a company exceeds the maximum number in one of the two criteria, it was
placed in the appropriate next higher category. As far as the available data permits,
the following descriptive data is compared to the companies’ generated data in the
sample survey and to the total of all SMEs in Germany. The basis for this comparison
is data provided by the Company Register of the Federal Statistical Office.
At 60.8 % the micro-enterprises represent the largest group in the random
sample. However, this class in relation to its distribution in all of Germany is
underrepresented (see Table 2). The proportion of small enterprises and medium-
sized enterprises is at 25.1 and 14.0 %, respectively, higher than in the total.
480 D. Hummel et al.
123
Table 2 Comparison of the
sample companies with all
German SMEs according to
company size
Percent according to the Company
Register 2009 (Federal Statistical
Office)
Percent
in the
sample
Micro-enterprises 89.7 60.8
Small enterprises 8.3 25.1
Middle-sized
enterprises
2.0 14.0
Total 100 100
Table 3 Comparison of the
sample companies with all
German SMEs by regional
distribution
Percent according to the
Company Register 2009
(Federal Statistical Office)
Percent
in the sample
Baden-Wurttemberg 13.4 9.4
Bayern 17.7 22.8
Berlin 4.2 6.4
Brandenburg 2.8 4.1
Bremen 0.7 1.2
Hamburg 2.8 3.5
Hessen 7.5 4.7
Mecklenburg-Vorp 1.9 2.3
Niedersachsen 8.7 7.6
Nordrhein-Westfalen 20.8 21.6
Rheinland-Pfalz 5.0 3.5
Saarland 1.2 0.0
Sachsen 4.8 7.0
Sachsen-Anhalt 2.3 1.2
Schleswig–Holstein 3.7 2.3
Thuringen 2.5 2.3
Total 100 100
Table 4 Percent of the
companies in the sample by life
phase of the company
Percent in the sample
\5 years 1.2
5 to 10 years 11.7
10 to \ 20 years 38.0
[20 years 49.1
Total 100
The financial structure of innovative SMEs in Germany 481
123
The regional distribution of the sample companies nearly matches the distribu-
tion throughout Germany of small and middle-sized enterprises (see Table 3), so
that regional specialties of the SMEs should not lead to drastic distortions.
With respect to the company ‘age’ structure, there are very few companies in the
sample that have not been in existence for fewer than 5 years. The majority of the
companies have been in the market longer than 10 years (see Table 4).
Consequently, it can be assumed that the results of the analysis apply less to the
special features of new companies. As a consequence, this over-representation
prevents a distortion in favour of newer companies that tend to be considered
innovative.
Service industries and manufacturing represent industry concentrations in the
sample with a total of 73.7 %. The remaining 26.3 % are split among building, trade
and other economic sectors (see Table 5).
On the basis of the innovation index introduced in Chapter 4, fifty-one companies
were assigned to Group 1 (non-innovative), seventy-five companies to Group 2
(moderately innovative) and forty-five companies to Group 3 (highly innovative).
The group boundaries between the three innovation groups were drawn at 0 and one
hundred points. Only five companies achieved the maximum number of 160 points.
In the distribution of companies by total scores, no serious distortions were caused
by the accumulation of case numbers on the separation boundaries. With reference
to the category company size by innovation groups, it appears that the percentage of
larger SMEs increases with the increasing degree of innovation (see Table 6).
Table 5 Percent of the
companies in the questionnaire
by industry
Percent in the sample
Manufacturing 30.4
Service 43.3
Building 14.6
Trade 9.4
Other 2.3
Total 100
Table 6 Percent of SMEs by company size and degree of innovation
Micro-enterprise (%) Small enterprise (%) Medium-sized (%) Total (%)
Non-innovative 70.6 21.6 7.8 100
Moderately innovative 64.0 22.7 13.3 100
Highly innovative 44.4 33.3 22.2 100
Non-innovative 34.6 25.6 16.7
Moderately innovative 46.2 39.5 41.7
Highly innovative 19.2 34.9 41.7
Total 100 100 100
482 D. Hummel et al.
123
5.2 Multinomial logistic regression
In what follows, the question will be examined as to whether the financing
preferences for differing innovative SMEs differ significantly. As a process for the
analysis of the group differences on the basis of existing independent variables,
discriminant analysis and logistic regression as quantitative procedures are in
principle available. Logistic regression is generally regarded as a more robust
method, since it is subject to less restrictive conditions with respect to the data base.
Thus no normally distributed independent variables and identical variance/co-
variance matrices are assumed (see Backhaus et al. 2008, p. 450). The multinomial
logistic regression is a variant of logistic regression in which the dependent variable
may exhibit more than two discrete outcomes. Here two groups of the dependent
variables are opposed to each other in order to examine whether the independent
variables permit a distinct separation of the groups. The independent variables can
be, as in the case of binary logistic regression, categorical as well as continual (see
Backhaus et al. 2008, p. 453). For the examination, whether or not the SMEs can
clearly be assigned to one of the three groups (i.e., non-, moderately and highly
innovative enterprises) on the basis of the estimate of the importance of chosen
financing instruments, the multinomial logistic regression is thus very suitable.
To begin with, the dependent as well as the independent variables are defined
(see Table 7). The importance of the various financing instruments was ascertained
with the aid of a typical rating scale from 0 (no importance) to 5 (very high
importance). The companies were obliged to answer a questionnaire comprised of
close-ended questions as to how they assess the importance of the individual
financing instruments in comparison to the other financing instruments. The six-
degree scale is defined as an interval scale, which is not uncommon in statistics (see
Bortz 1989, p. 32; Baker et al. 1966). In the process, it is assumed that there is an
equally large interval (equidistance) between the assessment steps.
The quality of the group classification by degree of innovation of the companies
can be assessed by means of various procedures, such as the likelihood ratio test,
pseudo R2 statistics and classification tables (see Backhaus et al. 2008, p. 261).
These quality measurements test whether and to what degree the independent
variables have any explanatory power. The Pearson Chi squared test is not used in
the present case, since the number of the covariate patterns (n = 164) does not
clearly lie beneath the number of the observations (n = 171), a sine qua non for this
Table 7 Dependent and independent variables used in the scale
Dependent variables in the scale: 1 (non-innovative, 2 (innovative), 3 (highly innovative)
Degree of Innovation
Independent variables in the scale: 0 (no importance) to 5 (very high importance)
Retained earnings Trade credit Mezzanine capital Debt guarantee
Financing from depreciation Bonded loans Private equity (PE/VC) Public subsidies
Bank credit Leasing Business angels
Overdraft credit Factoring/forfaiting Other capital market financing
The financial structure of innovative SMEs in Germany 483
123
test. The b-coefficients or the odds ratios (see Table 10), moreover, specify how in
the case of a higher estimate of the importance of a financial instrument by the value
of 1, the possibility is altered that a company belongs to the observed innovations
group in comparison to the reference group.
With reference to the likelihood ratio test, in a reliable model a significantly
higher Chi squared value ought to arise in the case of a significance level below the
significance threshold. In the present model a Chi squared value of 67.695 results at
a significance level of 0.0 %. Thus a good separating force for the groups by means
of the importance of the financing instruments and, correspondingly, a high quality
of the model can be inferred.
Pseudo-R2 statistics provide a commensurate picture. They quantify the percent
of the variance of the independent variables explained by the model. For a
significant model the values should be greater than 0.2, since values above 0.2 are
considered in the relevant literature as ‘acceptable’ and values over 0.4 as ‘good’.
Thus the values 0.327 in Cox/Snell and 0.370 in Nagelkerke indicate an acceptable
to good model.
An additional quite descriptive method for the evaluation of the total quality of
the model is the classification matrix (see Tables 8, 9). Here by means of a nine-
field matrix the number of cases that, on the basis of the model, was allocated to the
proper innovations group is clearly demonstrated. All told, the model correctly
predicted in 63.7 % of the cases how innovative a company is by the specified
importance of its financing instruments. This value now can be compared with the
degree of probability of a purely random allocation. The proportional chance
probability of 34.87 % shows that the logistic regression of 63.7 % provides a hit
ratio almost double that of a purely random allocation of the companies in one of the
three innovation groups. The degree of probability through this model thus clearly
exceeds a pure random prediction.
The sample was examined for distortions with respect to region (east/west),
company age (up to 10 years/older than 10 years) and company size (micro- or
small enterprise/middle-sized enterprises) by carrying out a regression with these
influence factors as dummy variables. For these variables there were, however, at
the 10 % level, no significant values. Thus it can be assumed that no important
distortions were derived from these factors of influence.
In a comparison of the financial instruments among the groups, ‘highly,
moderately and non-innovative companies’, it can be ascertained that for diversely
innovative enterprises, differing assessments in each case were made.
Table 8 Classification matrix
Predicted Percent correct
Non-innovative Moderately innovative Highly innovative
Non-innovative 27 19 5 52.9
Moderatly innovative 9 56 10 74.7
Highly innovative 4 15 26 57.8
Total (%) 23.4 52.6 24.0 63.7
484 D. Hummel et al.
123
6 Presentation of the financing preferences of diversely innovative SMEs
Against the background that the classification of the companies by degree of
innovation described above separates the groups well, with the following remarks a
model of financing inspired by the financial growth cycle will be designed in
accordance with the degree of innovation. Contrary to the theoretical approaches
described at the beginning of the paper, the following model derives the financing
structure from the empirically determined degree of innovation and not from
additional criteria, such as region, business branch, age or company size.
6.1 Financing preferences according to a company’s degree of innovation
For the concrete analysis of different financing preferences the t test will be
employed, an instrument to research mean value differences. The t test is a
procedure that permits a difference between the empirically derived mean values of
two groups to be examined more closely. In the present case differences between the
financing instruments of the individual innovation groups as well as differences
within the innovation groups can be analysed.
In t tests the mean differences should be normally distributed or at least thirty
characteristics per group should be present in order to maintain the significance
level in the case of a missing normal distribution (see Bortz and Schuster 2010). A
sample of n [ 30 is in the present case available. Moreover, the interval scale of
measurement is assumed. Bortz and Doring (1995, p. 168) calls attention in this
context to simulation studies according to which the results of a t test carried out by
means of data from rating scales were not influenced by the quality of the scales.
Thus the interpretation of the rating scale as being interval scaled has no negative
influence on the results of the t test.
The verification of significant differences in mean values between the individual
financing instruments in each instance of an innovation group is affected by means of a
t test for paired samples (see Tables 11, 12 and 13). Here the mean values of two
different financing instruments of the same innovation group are compared. For the
Table 9 Hypotheses and Results
No. Theoretical
basis
Derived hypotheses Results
1 Pecking order
theory
The financial mix of SMEs differs basically in relation on the
company’s degree of innovation
Confirmed
2 Pecking order
theory
With respect to the hierarchy of the financial instruments in
innovative companies, internal funds have the highest
importance, followed by short-term, middle-term and long-term
debt and finally external equity
Basically
confirmed
3 Financial
growth
cycle
With an increasing degree of innovation, there is a corresponding
gain in importance of internal funding as well as typical risk
capital (PE/VC, business angels, mezzanine), overdrafts, credit
substitutes and public funding. At the same time medium- to
long-term bank financing loses its importance
Partially
confirmed
The financial structure of innovative SMEs in Germany 485
123
verification of differences in the mean values between the individual innovation groups,
a t test for independent samples was carried out in addition (see Table 14 of Appendix).
In this test two mean values in independent groups were compared to each other. Since
the three different innovation groups can be seen as samples that are independent of each
other, the test is appropriate for this comparison of the mean values.
The following graphic (Fig. 2) illustrates the preferences of the SMEs in relation
to the differing financing instruments. The dark grey bars represent a marked growth
in the direction of the broad end and light grey bars a modest growth in the direction
of the broad end. The thickness of the bar is not proportional to the difference in
points. The horizontal lines indicate that almost no difference exists. It is thus
apparent which financing instruments in the case of highly, moderately and non-
innovative enterprises are more likely to have a high importance and how these tend
to change depending on the degree of innovation of a company (the order of the
finacial instruments is illustrated in brackets).
6.2 Interpretation of the results
6.2.1 Self-financing
The importance of self-financing is for all SMEs in Germany very high, no matter
how innovative the companies are. Though it becomes evident that the dependence
Fig. 2 Comparative analysis of the financing instruments by innovation group
486 D. Hummel et al.
123
on this source of capital becomes more pronounced in proportion to the clearly
increasing degree of innovation. This result is in keeping with the theory. The
internal resources rank highest in importance and in a comparison of highly
innovative companies with moderately innovative and non-innovative significantly
higher importance is placed on them. Admittedly, in order to enable self-financing,
sufficient cash flows must be generated, which naturally can only be accomplished
through the marketing of the appropriate products. Especially innovative SMEs are
required that their R&D activities are channelled as quickly as possible into
innovative products in order to generate an adequate self-financing capacity.
6.2.2 Depreciation as internal financing source
The analysis of depreciation as internal financing source reveals that this financial
source ranks similarly high in importance to that of subvention, leasing and bank
credit. No statistically secure statement can be made on the differences according to
degree of innovation, since in this respect no significant differences in mean value
could be ascertained. A decreasing importance of this financing tool with the
increasing degree of innovation could, however, be explained by the fact that highly
innovative SMEs have comparatively fewer depreciable assets than the companies
in the other two groups (see KfW 2007, p. 80). In general, depreciation has a
financing effect if the calculable depreciation assets are actually earned; that is if the
depreciation is received as liquid funds through sales revenue. Moreover, a
financing effect also results in the case of pure balance sheet depreciations when an
otherwise existing profit is reduced and consequently income tax is also delayed and
dividend pay outs lessened.
6.2.3 Credit financing and credit substitutes
In case of debt financing overdraft credit, trade credit and middle- or long-term bank
credit have the greatest importance. The differences between these three instruments
are in the case of highly innovative enterprises not significant. The importance of
overdraft credit as well as that of trade credit, however, declines with an increasing
degree of innovation.
Here the generally increasing risk aversion on the part of banks must be taken into
consideration. Since the products and services offered by innovative SMEs are as a
rule novel and technically complex, their market success is uncertain (see Backes-
Gellner and Werner 2007). Moreover, they invest primarily in intangible assets that
usually will not be accepted as collateral. Even if investments are made in tangible
assets, such as special-purpose machines for a new, innovative product, they are
difficult to sell or only at large discounts to value in the case of insolvency. Because of
a high percentage of ‘investment-related expenditures’ in (innovative) service
branches, there is likewise a smaller inventory available as credit collateral. Thus
innovative companies’ risk-return spectrum is largely unattractive for banks. The
relevant literature on the financing of companies explains that because of limited
ability to pay the interest rates, debt capital is not a suitable financing source for
highly innovation companies (see Denis 2004). Nevertheless the high importance of
The financial structure of innovative SMEs in Germany 487
123
bank credit shows that these companies in Germany resort to this financing form to a
large extent. Of course, only as long as credit lines in sufficient amounts are made
available in the first place to this risky business class (see Gottschalk et al. 2007).
This practice can primarily be traced back to the traditional close relationship banking
in Germany. However, the decreasing importance of overdraft credit and trade credit
is surprising in the case of increasing degree of innovation. Usually companies that
receive an insufficient long-term credit line from their banks or capital from other
capital sources frequently switch to, or must switch to, short-term outside capital.
Nevertheless, because of a dearth of alternatives these expensive and short-term
external financing instruments are ranked very high in the financial hierarchy. Bonded
loans as an additional form of long-term external financing with a decidedly minimum
volume play no role for SMEs.
One possibility that apparently is not sufficiently exploited or offered and thus
merely exercises a minor importance is the use of guarantees as loan collateral.
They replace the often non-existent (physical) collateral, reduce the risk for the
financier and open new sources of capital. Especially the changed framework
conditions of Basel II or the future Basel III requiring more and stronger collateral
gives this instrument a new importance. Debt guarantees issued by guarantee banks
represent full credit collateral that is counter guaranteed by the federal and state
governments. Therefore, they are also an instrument to subsidize SMEs. These
guarantees are granted less restrictively than objective criteria might warrant due to
promotional aspects. For moderately innovative SMEs, the importance of this
finance tool is even less than for non-innovative SMEs. This fact is surprising, since
more innovative companies should particularly benefit from this kind of guarantees
to stimulate innovations. It could indicate a lack of knowledge on the part of the
SMEs with respect to this instrument.
The importance of leasing as a credit substitute is relatively high and has been
steadily increasing in the case of highly innovative enterprises in comparison to
moderately innovative firms. Access to this financial instrument is available even at
higher risk since the leased object serves as collateral. However, the suitability of
specific assets as leasing objects is in practice very limited, due to their special
character. They can be liquidated only at high discounts. For this reason an increase
in the importance for highly innovative companies is surprising and could once
again be interpreted as an indication of a lack of financing alternatives.
In the case of factoring, there is no significant difference between the individual
innovation groups. Moreover, it merely plays a subordinate role for SMEs. An
explanation for this situation is that this financial instrument is chiefly suitable for
companies that dispose of a sufficiently large and diversified portfolio of
homogeneous receivables. In addition, a factoring institute focuses primarily on
secure yields and marketability of the goods and services. Also it usually expects a
yearly turnover of at least 2.5 million euros. Most often factoring is economically
practical only for companies with a sales volume of at least 7–8 million euros
annually. Consequently, because of insufficient volumes and very specific demands
on the receivables portfolios, this instrument is neither practical nor available to
many SMEs.
488 D. Hummel et al.
123
6.2.4 Equity financing and mezzanine financing
For SMEs in Germany, financing through private equity or venture capital, business
angels or the capital market plays a very small role, just as mezzanine financing
does. In contrast to our expectations here are no significant differences between
highly innovative and non-innovative companies. Important is that moderately
innovative SMEs, as expected, place a higher importance on private equity than do
non-innovative SMEs. In general can be observed that venture capital firms are
more frequently withdrawing from the high-risk area of highly innovative
companies. Nowadays they are more often making capital available for moderately
innovative companies with lower risk profiles. For non-innovative companies
private equity is not available because of smaller chances of turning a profit. An
additional barrier, aside from the underdeveloped venture capital market in
Germany, might well be the missing readiness on the part of SMEs to relinquish
shares in their companies and thus concede ownership rights.
Like venture capital companies, business angels are typical innovation financers
and provide start-up funding. Unfortunately, Germany does not have a sufficiently
large number of wealthy private persons interested in investing their money in very
risky company phases (see Fryges et al. 2007; Sohl 2008), and if so only small
amounts of capital anyway, whereas venture capital firms make available larger
volumes of financing (see KfW 2008, p. 6). The danger of a supply gap is here
identified, since the availability of risk capital between the maximum investment
sum business angels offer and the minimum entry sum that venture capital
companies provide is problematic (see Ehrhard and Muller 2007, p. 65; BMWi
2007).
6.2.5 Public subsidies
As might be expected, due to the higher risk and the resulting lack of financing
alternatives, the importance of public financial sponsorship significantly increases
with the rising degree of company innovation. Unlike highly innovative companies,
in the case of moderately and non-innovative companies public subsidies plays a
subservient role. With the exception of subsidies, retained earnings and leasing, the
other financing instruments lose importance with increasing degree of innovation or
show no differences. Because of the perceived company risk in the case of highly
innovative SMEs, private investors appear unwilling to provide capital, so that the
government attempts to fill the gap. Based on various studies an causality defined as
‘crowding out’ of private capital through public subsidies is to be considered very
unlikely (see Krohmer 2010).
In this context, it is striking that the importance of public subsidies in the case of
highly innovative SMEs in Germany is significantly larger than that of typical
innovation financiers, such as private equity, venture capital and business angels.
Because of the more favourable conditions, the plenty of subsidies, the retention of
unlimited company autonomy and the underdeveloped equity capital and business
angel market in Germany, this result appears understandable.
The financial structure of innovative SMEs in Germany 489
123
7 Conclusions
7.1 Results and implications
The current study throws light on the financing nuances of variously innovative
SMEs. Especially remarkable is the difference between highly innovative SMEs, on
the one hand, and moderately and non-innovative SMEs, on the other. The results of
the multinomial logistic regression demonstrate that the value of the financing
instruments available to SMEs differs according to the degree of innovation, and the
innovation index developed here indicates a highly developed discriminatory power
among the three groups mentioned above.
The results of the hypotheses formulated at the beginning of the study are
presented in summary fashion in the table below. Afterwards the results are
explained in detail.
In summary, it can be affirmed that the importance of particular financing
instruments correlates with the degree of SME innovation. Hypothesis H 1
postulated at the beginning of this paper, ‘The financial mix of SMEs differs
basically in relation to the company’s degree of innovation.’, can thus be regarded
as confirmed.
In addition, it has been demonstrated that hypothesis H 2, ‘With respect to the
hierarchy of the financial instruments in innovative companies, internal funds have
the highest value, followed by short-term, middle-term and long-term debt and
finally external equity.’ can, in principle, be confirmed. However, in this case a
differentiated consideration of the individual financing instruments is required. For
example, not all internal financing instruments have preference over debt capital.
Likewise, bonded loan as long-term external financing scarcely plays any role in
SMEs and thus occupies a lower rank in the hierarchy. Equity financing in the form
of private equity (PE/VC), mezzanine financing, business angels and other capital
market financing play in compliance with pecking order theory a rather subordinate
role in all SMEs including innovative companies. Therefore, the results of similar
analyses concluding that innovative companies enjoy certain advantages through the
acquisition of external equity and therefore prefer external equity over external debt
(see Audretsch and Lehmann 2004) cannot be substantiated. The results picture to a
very large degree consistency with the pecking order theory in reference to the basic
hierarchy of the financial instruments.
With respect to hypothesis H 3, ‘With an increasing degree of innovation, there is
a corresponding gain in importance of internal funding as well as typical risk capital
(PE/VC, business angels, mezzanine), overdrafts, credit substitutes and public
funding. At the same time medium- to long-term bank financing loses its
importance.’, the study results show a differentiated picture. The importance of
financing from retained earnings, as expected, increases with a growing degree of
innovation. Likewise as expected, the importance of public subsidies increases with
a growing degree of innovation. However, inconsistencies with financial growth
cycle can be observed when evaluating short-term debt. At an increasing degree of
innovation short-term credit financing is expected to be of lesser importance. Short-
term debt is less important at higher innovation levels. Expectation would have been
490 D. Hummel et al.
123
that the more innovative a company is, the more short-term instruments of financing
with outside capital would be done. The most remarkable deviations from
theoretical implications can be observed at the typical innovative financers. They
surprisingly do not significantly differentiate between innovative and non-innova-
tive companies. An increasing importance of a growing degree of innovation would
have been expected. The deviations from the financial growth cycle could be
explained by recognising that a very important factor in this theory, the age of the
company, actually plays no role at all in financing based on company’s degree of
innovation. It is proof that companies can be innovative even so they are not viewed
as young because they are already on the market for more than 5 years.
In principle, it can be inferred from the high importance of financing from
retained earnings, unavailability of additional internal financing and the very large
distance from alternative financing instruments that the dependency of highly
innovative companies upon the financing from retained earnings is much higher
than in the case of non-innovative companies. The very strong increase in
importance of governmental subsidies for highly innovative companies and the
decrease of importance for nearly all other financing instruments testify that the
access to the mostly private economic financing sources in case of a high degree of
innovation is strongly restricted (see in this context Harhoff 1998). These results
imply that the available internal financial resources limit the innovation activities
and thus the growth of innovative companies (see in this context Carpenter and
Petersen 2002). The typical innovative financiers, in the form of private equity
(PE/VC) or business angels, appear non-existent to close this capital gap. Primarily
public subsidies have partially ameliorated the lack of financing alternatives.
The management of highly innovative SMEs should basically concentrate on
embracing measures for the reduction of dependency on short-term financing
instruments and increasingly make use of long-term instruments. For example, this
could be accomplished by improving the quality of information and the use of
professional management tools. This way, investors could be encouraged to also
make long-term capital available. Likewise, the increased inclusion of guarantees
ought to be recommended. Consequently, the positive influence of an active finance
management would be established. At the same time, politicians should make an
effort to moderate the high dependency on self-financing and short-term credit of
highly innovative SMEs by initiating increased measures for the strengthening of
equity capital bases.
7.2 Limitations
Like all empirical studies, the present study is subject to various limitations. The
regrouping of companies according to respective degrees of innovation, companies
of various ages as well as differing branches and sizes are divided into highly,
moderately and non-innovative companies and within the groups regarded as
homogeneous, whereby potential differences are neglected. A very weak significant
correlation at a 5 % level between company size and degree of innovation was
ascertained that could possibly lead to distortions. Moreover, the majority of the
companies in the sample have been on the market longer than 10 years, which just
The financial structure of innovative SMEs in Germany 491
123
as the deviation in the size structure in comparison to the population in Germany,
could lead to a limited applicability of the results. A correlation of degree of
innovation and region as well as company age could, however, not be established. A
general problem for the data collection by means of the point scale is the ‘tendency
towards the middle’ effect on the part of the respondents, which increases as the
survey goes on. In addition, the differing range of the innovation groups can
potentially lead to distortions.
The problem of causality must likewise be mentioned. To date it has not been
definitively explained whether companies with a high cash flow are for this reason
exceptionally innovative or whether innovative companies generate a higher cash
flow (see Cainelli et al. 2006). This problem is especially relevant for the question
of internal financing options (see Elsas and Florysiak 2008). Finally, the temporal
context must be taken into consideration when interpreting the results. The
economic and financial crisis that erupted in 2008 had an important influence on the
business situation and thus on the results of the questionnaire.
Appendix
See Tables 10, 11, 12, 13, 14
492 D. Hummel et al.
123
Ta
ble
10
Par
amet
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ates
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The financial structure of innovative SMEs in Germany 493
123
Ta
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75
0.9
47
Tra
de
cred
it-
0.0
96
0.1
41
0.4
61
0.4
97
0.9
09
Ov
erd
raft
cred
it-
0.2
53
0.1
61
2.4
81
0.1
15
0.7
76
Ban
kk
red
it0
.098
0.1
73
0.3
19
0.5
72
1.1
03
Fin
anci
ng
from
dep
reci
atio
n-
0.0
72
0.1
56
0.2
13
0.6
45
0.9
31
Lea
sin
g-
0.0
41
0.1
48
0.0
78
0.7
80
0.9
60
Fac
tori
ng
/fo
rfai
tin
g-
0.2
07
0.2
03
1.0
35
0.3
09
0.8
13
Deb
tg
uar
ante
e-
0.1
67
0.1
60
1.0
91
0.2
96
0.8
46
Mez
zan
ine
cap
ital
0.2
36
0.4
07
0.3
37
0.5
61
1.2
66
Pri
vat
eeq
uit
y(P
E)*
0.5
55
0.2
94
3.5
70
0.0
59
1.7
43
Busi
nes
san
gel
s-
0.6
04
0.5
51
1.2
01
0.2
73
0.5
46
Bon
ded
loan
s-
0.1
29
0.7
14
0.0
33
0.8
57
0.8
79
Cap
ital
mar
ket
0.8
58
0.6
13
1.9
55
0.1
62
2.3
58
Pu
bli
csu
bsi
die
s0
.023
0.1
77
0.0
18
0.8
95
1.0
24
**
*a
B0
.01,
**
aB
0.0
5,
*a\
0.1
494 D. Hummel et al.
123
Ta
ble
11
tte
stin
pai
red
sam
ple
s:h
igh
lyin
no
vat
ive
ente
rpri
ses
Sig
.
(tw
o-t
aile
d)
Ret
ained
earn
ings
(4.7
3)
Tra
de
cred
it
(2.1
1)
Over
dra
ft
cred
it
(2.1
3)
Ban
k
cred
it
(2.0
0)
Fin
.
from
dep
r.
(1.6
0)
Lea
sing
(1.8
4)
Fac
tori
ng/
forf
aiti
ng
(0.5
1)
Deb
t
guar
ante
e
(0.8
7)
Mez
zanin
e
capit
al
(0.4
0)
Pri
vat
equit
y
(PE
/VC
)
(0.4
4)
Busi
nes
s
angel
s
(0.2
0)
Bonded
loan
s
(0.1
1)
Cap
ital
mar
ket
(0.1
8)
Publi
c
subsi
die
s
(1.8
4)
Ret
ained
earn
ings
(4.7
3)
x
Tra
de
cred
it
(2.1
1)
0.0
00
x
Over
dra
ft
cred
it(2
.13)
0.0
00
0.9
42
x
Ban
kcr
edit
(2.0
0)
0.0
00
0.7
54
0.5
86
x
Fin
anci
ng
from
dep
reci
atio
n
(1.6
0)
0.0
00
0.0
93
0.0
98
0.1
80
x
Lea
sing
(1.8
4)
0.0
00
0.3
90
0.3
72
0.6
32
0.3
89
x
Fac
tori
ng/
forf
aiti
ng
(0.5
1)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
x
Deb
tguar
ante
e
(0.8
7)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
04
0.0
01
0.0
70
x
Mez
zanin
e
capit
al
(0.4
0)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.4
81
0.0
59
x
Pri
vat
eeq
uit
y
(PE
/VC
)
(0.4
4)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.6
96
0.0
13
0.8
28
x
Busi
nes
s
angel
s(0
.20)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
29
0.0
01
0.2
37
0.0
40
x
The financial structure of innovative SMEs in Germany 495
123
Ta
ble
11
con
tin
ued
Sig
.
(tw
o-t
aile
d)
Ret
ained
earn
ings
(4.7
3)
Tra
de
cred
it
(2.1
1)
Over
dra
ft
cred
it
(2.1
3)
Ban
k
cred
it
(2.0
0)
Fin
.
from
dep
r.
(1.6
0)
Lea
sing
(1.8
4)
Fac
tori
ng/
forf
aiti
ng
(0.5
1)
Deb
t
guar
ante
e
(0.8
7)
Mez
zanin
e
capit
al
(0.4
0)
Pri
vat
equit
y
(PE
/VC
)
(0.4
4)
Busi
nes
s
angel
s
(0.2
0)
Bonded
loan
s
(0.1
1)
Cap
ital
mar
ket
(0.1
8)
Publi
c
subsi
die
s
(1.8
4)
Bonded
loan
s
(0.1
1)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
03
0.0
00
0.0
74
0.0
10
0.1
03
x
Cap
ital
mar
ket
(0.1
8)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
15
0.0
01
0.2
00
0.0
63
0.7
85
0.2
61
x
Publi
c
subsi
die
s
(1.8
4)
0.0
00
0.4
73
0.4
05
0.5
44
0.4
66
1.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
x
pval
ue;
inth
ebra
cket
sar
eth
em
ean
val
ues
of
the
finan
cing
inst
rum
ents
496 D. Hummel et al.
123
Ta
ble
12
tte
stin
pai
red
sam
ple
s:m
oder
atel
yin
novat
ive
ente
rpri
ses
Sig
.
(tw
o-t
aile
d)
Ret
ained
earn
ings
(3.9
9)
Tra
de
cred
it
(1.9
3)
Over
dra
ft
cred
it
(2.0
7)
Ban
k
cred
it
(1.9
5)
Fin
.
from
dep
r.
(1.6
7)
Lea
sing
(1.3
7)
Fac
tori
ng/
forf
aiti
ng
(0.5
5)
Deb
t
guar
ante
e
(0.8
0)
Mez
zanin
e
capit
al
(0.3
1)
Pri
vat
e
equit
y
(PE
/VC
)
(0.6
9)
Busi
nes
s
angel
s
(0.2
9)
Bonded
loan
s
(0.2
1)
Cap
ital
mar
ket
(0.2
7)
Publi
c
subsi
die
s
(0.8
4)
Ret
ained
earn
ings
(3.9
9)
x
Tra
de
cred
it
(1.9
3)
0.0
00
x
Over
dra
ft
cred
it(2
.07)
0.0
00
0.4
85
x
Ban
kcr
edit
(1.9
5)
0.0
00
0.9
53
0.4
71
x
Fin
anci
ng
from
dep
reci
atio
n
(1.6
7)
0.0
00
0.2
14
0.0
82
0.1
63
x
Lea
sing
(1.3
7)
0.0
00
0.0
15
0.0
03
0.0
13
0.1
44
x
Fac
tori
ng/
forf
aiti
ng
(0.5
5)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
x
Deb
tguar
ante
e
(0.8
0)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
03
0.1
47
x
Mez
zanin
e
capit
al
(0.3
1)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
23
0.0
00
x
Pri
vat
eeq
uit
y
(PE
/VC
)
(0.6
9)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
01
0.4
35
0.5
67
0.0
10
x
Busi
nes
s
angel
s(0
.29)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
53
0.0
01
0.8
49
0.0
04
x
The financial structure of innovative SMEs in Germany 497
123
Ta
ble
12
con
tin
ued
Sig
.
(tw
o-t
aile
d)
Ret
ained
earn
ings
(3.9
9)
Tra
de
cred
it
(1.9
3)
Over
dra
ft
cred
it
(2.0
7)
Ban
k
cred
it
(1.9
5)
Fin
.
from
dep
r.
(1.6
7)
Lea
sing
(1.3
7)
Fac
tori
ng/
forf
aiti
ng
(0.5
5)
Deb
t
guar
ante
e
(0.8
0)
Mez
zanin
e
capit
al
(0.3
1)
Pri
vat
e
equit
y
(PE
/VC
)
(0.6
9)
Busi
nes
s
angel
s
(0.2
9)
Bonded
loan
s
(0.2
1)
Cap
ital
mar
ket
(0.2
7)
Publi
c
subsi
die
s
(0.8
4)
Bonded
loan
s
(0.2
1)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
09
0.0
00
0.1
46
0.0
01
0.0
57
x
Cap
ital
mar
ket
(0.2
7)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
16
0.0
01
0.6
34
0.0
06
0.7
08
0.3
75
x
Publi
c
subsi
die
s
(0.8
4)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
18
0.1
21
0.8
02
0.0
00
0.4
32
0.0
01
0.0
00
0.0
00
x
pval
ue;
inth
ebra
cket
sar
eth
em
ean
val
ues
of
the
finan
cing
inst
rum
ents
498 D. Hummel et al.
123
Ta
ble
13
tte
stin
pai
red
sam
ple
s:n
on
-in
nov
ativ
een
terp
rise
s
Sig
.
(tw
o-t
aile
d)
Ret
ained
earn
ings
(3.9
0)
Tra
de
cred
it
(2.5
7)
Over
dra
ft
cred
it
(2.9
6)
Ban
k
cred
it
(2.2
9)
Fin
.
from
dep
r.
(1.9
6)
Lea
sing
(1.6
3)
Fac
tori
ng/
forf
aiti
ng
(0.7
3)
Deb
t
guar
ante
e
(1.2
0)
Mez
zanin
e
capit
al
(0.3
1)
(PE
/
VC
)
(0.3
5)
Busi
nes
s
angel
s
(0.3
1)
Bonded
loan
s
(0.1
8)
Cap
ital
mar
ket
(0.1
0)
Publi
c
subsi
die
s
(0.8
0)
Ret
ained
earn
ings
(3.9
0)
x
Tra
de
cred
it
(2.5
7)
0.0
01
x
Over
dra
ft
cred
it(2
.96)
0.0
06
0.0
86
x
Ban
kcr
edit
(2.2
9)
0.0
00
0.2
30
0.0
04
x
Fin
anci
ng
from
dep
reci
atio
n
(1.9
6)
0.0
00
0.0
20
0.0
00
0.1
42
x
Lea
sing
(1.6
3)
0.0
00
0.0
02
0.0
00
0.0
09
0.2
15
x
Fac
tori
ng/
forf
aiti
ng
(0.7
3)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
x
Deb
tguar
ante
e
(1.2
0)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
06
0.0
92
0.0
93
x
Mez
zanin
e
capit
al
(0.3
1)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
14
0.0
00
x
Pri
vat
eeq
uit
y
(PE
/VC
)
(0.3
5)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
50
0.0
01
0.0
67
x
The financial structure of innovative SMEs in Germany 499
123
Ta
ble
13
con
tin
ued
Sig
.
(tw
o-t
aile
d)
Ret
ained
earn
ings
(3.9
0)
Tra
de
cred
it
(2.5
7)
Over
dra
ft
cred
it
(2.9
6)
Ban
k
cred
it
(2.2
9)
Fin
.
from
dep
r.
(1.9
6)
Lea
sing
(1.6
3)
Fac
tori
ng/
forf
aiti
ng
(0.7
3)
Deb
t
guar
ante
e
(1.2
0)
Mez
zanin
e
capit
al
(0.3
1)
(PE
/
VC
)
(0.3
5)
Busi
nes
s
angel
s
(0.3
1)
Bonded
loan
s
(0.1
8)
Cap
ital
mar
ket
(0.1
0)
Publi
c
subsi
die
s
(0.8
0)
Busi
nes
s
angel
s(0
.31)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
15
0.0
01
1.0
00
0.6
87
x
Bonded
loan
s
(0.1
8)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
03
0.0
00
0.2
79
0.1
41
0.1
09
x
Cap
ital
mar
ket
(0.1
0)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
01
0.0
00
0.0
55
0.0
14
0.0
62
0.3
22
x
Publi
c
subsi
die
s
(0.8
0)
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.0
00
0.7
35
0.1
80
0.0
17
0.0
29
0.0
14
0.0
01
0.0
00
x
pval
ue;
inth
ebra
cket
sar
eth
em
ean
val
ues
of
the
finan
cing
inst
rum
ents
500 D. Hummel et al.
123
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Table 14 t test in independent samples
Sig. (one-tailed) Non- vs. highly
innovative
Moderately
vs. highly
innovative
Non- vs.
moderately
innovative
Retained earnings -4.294*** -4.391*** -0.372
Trade credit 1.353* (x) -0.568 2.040**
Overdraft credit 2.299** -0.195 2.834***
Bank credit 0.835 -0.162 1.105
Fin. from depreciation 1.156 0.246 1.135
Leasing -0.661 -1.563* 0.920
Factoring/forfaiting 0.883 0.177 0.783
Debt guarantee 1.060 -0.276 1.427* (x)
Mezzanine capital -0.437 -0.558 0.050
Private equity (PE/VC) -0.548 1.277 -1.877**
Business angels 0.801 0.679 0.133
Bonded loans 0.549 1.012 -0.283
Capital market -0.787 0.634 -1.445* (x)
Public subsidies -3.137*** -3.183*** -0.147
t values; * a B 0.01, ** a B 0.05, *** a B 0.1; in the case of inequality of the variances the t value as
well as the significance level is indicated on the basis of the Welch-Test
(x), very small difference in mean value
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