Implementing the SIV model on an intensively innovation-oriented firm: the case of Autoadapt AB

Abstract

Purpose – Small to medium-sized enterprise (SME) evaluation models lack a clear coupling to innovation and its impact on firm performance. A model which can achieve this is the Survival Index Value (SIV) model. The purpose of this paper is to demonstrate the ability of the SIV model to indicate and predict the performance of a company. The firm, Autoadapt AB, is an innovation-oriented enterprise, adapting personal cars to be driven by handicapped people. The authors knew in advance about the good performance of the firm and its high efficiency in conducting its operations and expected the SIV model to reflect correctly on Autoadapt's performance. Because the handicap degree of each of the individuals who benefit from the firm activities differs from one person to another, product solutions have to be individually designed. Therefore the firm has had to pursue a high level of innovativeness and it had to abide with this policy right from the start. The product development processes in the firm needed to adapt to such strategies. Design/methodology/approach – To be able to demonstrate the ability of the SIV model to indicate a positive performance due to the intensive innovation activities of Autoadapt AB, a case study approach was used. Case studies are very suited for in-depth analysis of an object under a longer period of time. It is a widely-used research method in firm performance studies. Findings – The results of the SIV analysis indicated that the model is able to project correctly the performance of the object firm. At all the four levels of analysis, i.e. SI values, the SPI slope, the survival factors, and the survivability coefficients, the SIV analysis performance indicated a stable positive development of the firm through the life time of the enterprise. Originality/value – Measuring performance of SMEs is an important issue. There are couple of models stemming from the traditional accountancy disciplines in use; however these models suffer from clear disadvantages. Recently a new model, the SIV model, was introduced and has shown the ability of being a better candidate for performance analysis. The paper demonstrates the ability of the SIV model to judge correctly the performance of an innovative firm.

Full text

Implementing the SIV model on an
intensively innovation-oriented
firm: the case of Autoadapt AB
Adli Abouzeedan
Department of Innovation and Entrepreneurship, Sahlgrenska Academy,
University of Gothenburg, Gotheburg, Sweden
Magnus Klofsten
IEI/HELIX Excellence Center, Linko
¨ping University, Linko
¨ping, Sweden, and
Thomas Hedner
Department of Innovation and Entrepreneurship, Sahlgrenska Academy,
University of Gothenburg, Gotheburg, Sweden
Abstract
Purpose – Small to medium-sized enterprise (SME) evaluation models lack a clear coupling to
innovation and its impact on firm performance. A model which can achieve this is the Survival Index
Value (SIV) model. The purpose of this paper is to demonstrate the ability of the SIV model to indicate
and predict the performance of a company. The firm, Autoadapt AB, is an innovation-oriented
enterprise, adapting personal cars to be driven by handicapped people. The authors knew in advance
about the good performance of the firm and its high efficiency in conducting its operations and
expected the SIV model to reflect correctly on Autoadapt’s performance. Because the handicap degree
of each of the individuals who benefit from the firm activities differs from one person to another,
product solutions have to be individually designed. Therefore the firm has had to pursue a high level of
innovativeness and it had to abide with this policy right from the start. The product development
processes in the firm needed to adapt to such strategies.
Design/methodology/approach – To be able to demonstrate the ability of the SIV model to
indicate a positive performance due to the intensive innovation activities of Autoadapt AB,
a case study approach was used. Case studies are very suited for in-depth analysis of an
object under a longer period of time. It is a widely-used research method in firm performance
studies.
Findings – The results of the SIV analysis indicated that the model is able to project correctly the
performance of the object firm. At all the four levels of analysis, i.e. SI values, the SPI slope, the
survival factors, and the survivability coefficients, the SIV analysis performance indicated a stable
positive development of the firm through the life time of the enterprise.
Originality/value – Measuring performance of SMEs is an important issue. There are couple of
models stemming from the traditional accountancy disciplines in use; however these models suffer
from clear disadvantages. Recently a new model, the SIV model, was introduced and has shown the
ability of being a better candidate for performance analysis. The paper demonstrates the ability of the
SIV model to judge correctly the performance of an innovative firm.
Keywords Sweden, Small and medium-sized enterprises, Organizational innovation,
Organizational performance, Performance evaluation models, SIV model, Firm efficiency,
Business platform model, Financial parameters, Non-financial parameters
Paper type Case study
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/2042-5961.htm
World Journal of Entrepreneurship,
Management and Sustainable
Development
Vol. 8 No. 2/3, 2012
pp. 122-145
rEmerald Group Publishing Limited
2042-5961
DOI 10.1108/20425961211247743
The working paper upon which this article is based has been presented at the 56th Annual ICSB
Conference, Stockholm, Sweden, June 15-18, 2011.
122
WJEMSD
8,2/3

Introduction
The factors contributing to a better performance of small to medium-sized enterprise
(SMEs) are in need of more analysis and understanding (Acs, 1999). Coupled to that is
the necessity of understanding the environment in which the firm is residing and
relating that to its performance (Gnyawali and Park, 2009). The survival analysis is the
best technique for firm failure prediction (Keasey et al., 1990). Models of firm
performance prediction focussed on failure and neglected other more positive scenarios
of firm life cycle. The research which directly examined factors influencing survival of
SMEs and incorporated them in model structures is limited (Castrogiovanni, 1996).
The variations in the nature of the organizational structure of firms demanded a
dichotomy in the way one should looks at firms. The two dominant approaches
being the open- vs the closed-system approach (Scott, 2003). There is a clear
connection between networking and performance (Wincent and Westerberg,
2005; Wincent et al., 2009). The established strategy of building performance
evaluation models is based on computing a large initial set of ratios and then
letting statistical methods reduce them into limited group (Keasey and Watson, 1991).
Because that approach of the usage of business ratios in developing SME have
been criticized (Klofsten, 2010). The purpose of the traditional financial ratio
analysis is to detect the company operating and financial difficulties (Altman, 1968).
Yazdipour and Constand (2010) criticized the negligence of the financial distress
researchers to the managerial decision-making aspects in their approach to firm
failure analysis.
Innovation in firms comes in different forms (Trott, 2008) and the impact of the
innovation activities vary depending on the structure of organizations (Damanpour
et al., 1989; Daft, 1982; Damanpour and Evan, 1984). Damanpour and Gopalakrishnan
(1988) looked at the relationship between organizational structure and innovation.
To deal with the different types of innovation, a number of approaches were
proposed using a spectrum of predictive variables (Downs and Mohr, 1976). Firms in
the modern economy obtained new probabilities due to ITCs. The new tools resulted
in a more connectivity between firms. As a result, the open innovation paradigm is
propagated for by Chesbrough (2001, 2003). Open innovation demands the inventing
of new business models which are more open in their nature (Chesbrough et al.,
2006). The term “open innovation” was first proposed by Chesbrough (2001) to describe
how useful knowledge and technology is becoming increasingly widespread
when newly developed technologies and products are benefiting from the
integration of knowledge and expertise from multiple sources. Using external
knowledge relations influences the way firms are organizing and managing their
innovation activities (Teirlinck and Spithoven, 2008). In open innovation, external
knowledge relations are considered vital elements and being complementary to the
internal research (Cohen and Levinthal, 1990; Veugelers, 1997; Chesbrough et al., 2006).
Hedner et al. (2011) argued that in their early days the industrial progress was
lead through open innovation approach as in case of the pharma industry. It is only
later that pharma industry shifted into a more closed system of innovation (Hedner
et al., 2011).
Innovation and performance
Innovation is necessary to economic progress (Schumpeter, 1934). The innovativeness
of an economy can be achieved by other dynamics where the larger or more mature
firms acquire innovative and successful smaller firms (Lindholm, 1994). Studies of
123
Implementing
the SIV model

innovation in SMEs are still limited as compared to larger firm (Vermeulen et al., 2005).
Innovation in small firms tends to follow one of the three themes: how the research and
development (R&D) or new product R&D processes are managed; how to best measure
innovation and technology management; and how small firms secure competitive
advantage of using innovation (Motwani et al., 1999). Innovative SMEs which engage
in innovation activities are better performers (Geroski and Machin, 1992; Soni et al.,
1993; Freel, 2000). SMEs are more flexible, agile and innovative than larger firms (Qian
and Li, 2003; Acs and Yeung, 1999; Vermeulen et al., 2005; Wolff and Pett, 2006).
Damanpour et al. (1989) studied the impact of adapting administrative and technical
innovation on performance of organizations. The first type, the administrative
innovation is more coupled to the management innovation, while the technical
innovation is more related to product and/or process innovations. Acs and Yeung
(1999) indicated that product and process improvements in SMEs can be directly
attributed to increased creativity and innovativeness in the firm. Other researchers
suggested alternative components other than the ones named by Abouzeedan
and Busler (2005) to define the innovative capacity of society. Corley et al. (2002)
discussed the issue of physical, R&D and human capital. The authors argued
that differences across countries and industries in the rate of investment in different
types of capital would reflect itself in productivity levels across European Union
(EU) and US industries. The success of SMEs innovation activities shown to be related
to the owner-manager of the enterprises (Hadjimanolis, 2000). Entrepreneurial
managers are key sources of innovative ideas and innovation in the small firms
(Vossen, 1998).
SME performance models
Traditional approach to SME performance
SME performance evaluation models have a significant set of deficiencies.
Historically, SME performance studies focussed strongly on statistical analysis
and modeling. They often neglected non-financial indicators (Houghton, 1984;
Libby and Lewis, 1982). SME performance models relied heavily on business
ratios and financial parameters. This approach has been criticized (Klofsten, 2010;
Keasey and Watson, 1993). The models incorporate the non-financial parameters
through their indirect effect on the financial ones (see Altman, 1983, 1968; Altman
et al., 1977). SME performance models lack the coupling of firm innovation activities
to their performance (cs Mazzarol and Rebound, 2008; Vermeulen et al.,2005;
Wolff and Pett, 2006). The models are focussed on failure and bankruptcy
analysis and neglected the other aspects of firm performance (Keasey et al.,1990).
They neglected the networking impact on firm performance (Wincent and
Westerberg, 2005; Wincent et al., 2009). The existing models deal with firms as
closed systems in contrast to the open-system approach (Scott, 2003). There are
two deficiencies in the existing SME performance models in relation to studying
younger firms. First, the models are non-holistic and tend to be more qualitative
in their nature. Second, they can not be used for managerial purposes (Davidson
and Klofsten, 2003).
The soft models of SMEs performance
Klofsten (1992a, b) looked at the way technology-based enterprises progress in their
development. His focus was on the earlier stages of their lives. The majority of the SME
performance models in this group are based on firm life cycle approach. Klofsten
124
WJEMSD
8,2/3

(1992a, b) described couple of models. These models belong to the life cycle approach to
firms. The life cycle models postulate a stage-wise progress of the firm. The first group
is closed-ending type. The concept of birth and death model proposed by Abouzeedan
and Busler (2005) uses both the closed- and open-ending approach. As thus it
resembles both the life cycle classical model as well as the evolutionary model
(Abouzeedan and Busler, 2005). Evolutionary models, on the other hand, such as the
ones proposed by Helms and Renfrow (1994) and Nelson and Winter (1978), postulate
the open-end nature of firm progression in its life. The business platform model
belongs to this softer type of models (Klofsten, 2010). Klofsten (1992b) has theorized
that eight cornerstones need to be in place supporting this business platform. These
are idea, product, market, organizational development, core group expertise, prime
mover and commitment, customer relations and other firm relations (Klofsten, 1992b).
The business platform is belonging to the third group of SME models which are
strategy focussed.
The hard models of SMEs performance
This group has been developed based on the accountancy traditions. Historically there
are six models in this group of model. These are stochastic models, learning models,
hazard rate models, Z-scores, Zeta-scores and neural networks (NN). The stochastic
theories take the stands that firm growth and firm size are independent variables. The
basis of such theorization is the Gibrat Law. Gibrat Law is a theoretical extrapolation
stemming out of the frequent observation of the way stochastic models behave
(Hart and Paris, 1956). Gibrat’s Law postulates that if growth rates of firms in a
fixed population are independent of their initial sizes, the growth rates shows no
variance with size (Caves, 1998). This means that firms which differ in their size
are able to grow at the same rate. The learning model theory of firms assumes that firm
do posses a cost parameter ( Jovanovic, 1982). Although each firm knows the
distribution of this parameter for all firms the true cost is unknown to the enterprise
( Jovanovic, 1982). As each period passes the firm revises its beliefs about its true
managerial ability based on the previous period’s profits and costs. Inefficient
unlearning firms decline and exit while efficient learning firms survive and grow
( Jovanovic, 1982).
Duration or hazard modeling is used to analyze performance of firms and to
examine their survival. The model anticipates a hazard rate which is the probability
that a firm closes given that it was alive at the beginning of the analysis period
(McPherson, 1995). Hazard models can be in discrete or continuous time, and
parametric or non-parametric (McPherson, 1995). Z-scores combine traditional
financial ratio analysis with discriminant analysis. Z-score procedure, combines five
financial measures to arrive at an overall credit score (Z) (Altman, 1983). The
discriminant analysis classifies a company into one of two groups (failed/non-failed) on
the basis of a statistic (Z-score) (Altman, 1983). The Z-score is derived by assigning
weights to the variables, such that the variance between the groups is maximized
relative to the within group-variance (Keasey and Watson, 1991). In 1977, and using
the same technique Altman and his colleagues developed a second-generation failure
prediction model, which became known as “Zeta” (Altman et al., 1977). Argenti (1976)
expressed doubts whether this method is able to work well for very large or very
small firms.
Neural networks analysis is another important decision-making tool in various
types of two-group classification problems, such as bankruptcy prediction and new
125
Implementing
the SIV model

venture success ( Jain and Nag, 1997). NN’s strongest feature is its ability to learn
relationships from data (Gritta et al., 2000). NN do not require a prior specification of
the functional relationship between variables. This makes them one of the best when it
comes to prediction models based on two-group classification ( Jain and Nag, 1997).
NN are not a clearly dominant mathematical technique compared to traditional
statistical techniques such as discriminant analysis (Altman et al., 1994).
The SIV model, a background
Although the SIV model can be confused to be one of the hard type models, it differs
from the rest members of the group in significant aspects. The model incorporate
innovation in the form of technology intake and it has a balanced qualitative/
quantitative nature. Although the SIV model uses business ratios, it incorporates them
in a more simplified way to suit the situation for SMEs. The selection of the parameters
in the SIV model is theory driven. The model can be used as a managerial tool. The SIV
model takes in account the variations between business sectors and regional contexts.
It does relate the performance of the firm to the general situation of the sector within
which the firm is active. The SIV model considers the whole spectrum of firm
performance including survival and growth. In the work which introduced the SIV
model, Abouzeedan (2011) proposed a parameter to evaluate SME performance using
an indicator. Known as the survival index (SI) the indicator measures firm
performance. The input parameters, which are incorporated in the SIV model, can be
divided into “internal” and “external” parameters in accordance with the SPF
Classification System (Abouzeedan, 2002). Each of the two groups is divided into
subgroups. One of the subgroups of the “internal” group of the parameters is the
“structural” parameters subgroup. It consists of the parameters, which are
characteristic of the physical structure of the firm (Abouzeedan, 2002). Based on the
SPF Classification System, the two most important structural parameters incorporated
in the SIV model are firm age and firm size (Abouzeedan, 2011; Abouzeedan and
Busler, 2002; Abouzeedan, 2002). The firm age concepts presented in the SIV
encompass a new approach, as the enterprise age is related to the youthfulness of the
business sector in which the firm is active. To account for the age perspective of
the business sector within which the firm is active, Abouzeedan (2011) and Abouzeedan
and Busler (2002) introduced a new conceptual parameter of age which they called the
average life span. The average life span, which was designated with the symbol (L
j
),
indicates the youthfulness of the sector in average term. Relating the individual
firm age to the age of the whole sector gave better sense of the youthfulness of
the firm (Abouzeedan and Busler, 2002). Abouzeedan and Busler (2003)
developed further the original SIV model and introduced the concept of “survivability
coefficient.”
Methodology
Autoadapt AB, the company
To demonstrate the ability of the SIV model to reflect on the role of innovation in
enhancing firm performance, we chose to run the SIV analysis on a Swedish SME,
which is an active company in the field of health care. The firm, Autoadapt AB is an
SME that works with the adaption of personal cars to the usage of the handicapped
and disabled people. The SNI codes presenting the activities of the firm are 30,920,
29,200 and 45,310. The firm was selected due to its high level of product development
activities. The history of the Autoadapt AB is presented in Appendix 1.
126
WJEMSD
8,2/3

The survival index value (SIV) equation
When we calculate the SI of a firm, we use the SIV equation or SIE. The equation has
the following general structure.
The SIV equation:
SIij ¼SIoi þSIti
The SIV equation incorporates the variables, which determine SME performance,
where:
SIoi ¼Aa
Yi
Lj
!"Ei
Ex
!"Fi
C3i
!"
PiþAb
C1si
C1i
!" ð1aÞ
and
SIti ¼Ac
C2i
C3i
!" ð1bÞ
Such that SI
oi
is the operating conditions part of the SI, for the ith enterprise, called
hereby, operating conditions survival index and SI
ti
is the technology intake part
of the SI, for the ith, called hereby, technology intake survival index; E
i
the number
of employees of the ith enterprise; E
x
the maximum number of employees distinguishing
the different categories of enterprises (e.g. for Swedish SMEs, E
xs
¼250 employees or
E
xa
¼50 employees); Y
i
the firm age, since the ith enterprise has existed, called years of
operation; L
j
the average life span of the jth business sector; F
i
the annual sales (turn-
over), that the enterprise generates (in US dollar or other currency) per periodicity unit; C
2i
the intake and absorption of new technologies indicated by the annual investment (in US
dollar or other currency), per periodicity unit; C
3i
the total costs of production (US dollar or
other currency), per year; C
1i
the initial investment costs (US dollar or other currency);
C
1si
the self-financed initial capital of investment (US dollar or other currency); P
i
the
profit margin (a neutral percent figure); SI
ij
the SI for the ith enterprise in a jth business
sector.
In building up the SIV model the selection of the input parameter was both literature
driven (i.e. the most important factors, as the literature survey indicated, were used)
and a theory driven. In the theory-driven approach selected parameters were chosen
and placed in the model in relation to their ability to reflect a true understanding as
to how the different input parameters would contribute to the increasing the efficiency
of the firm (Abouzeedan, 2011).
The constants A
a
,A
b
and A
c
are the proportionality factors used to adjust segments
of the SIV equation so that the product will be of approximately in power order, to each
other. Because the model was constructed and being guided through only
mathematical modeling (and not statistical modeling) (see Abouzeedan, 2011), the
coefficients had to be chosen to give equal weight for the contributions from the three
components of the SI equation, namely the operation segment, the finance segment and
the technology-intake segment. The reader need to be reminded that the SIV model is a
logic-driven model, expressed mathematically, and not an arithmetically constructed
and derived equation.
127
Implementing
the SIV model

A
a
is used to adjust the left-side value of SI
oi
namely:
Yi
Lj
!"Ei
Ex
!"Fi
C3i
!"
Pi
So that it attains an order of power of magnitude close to the right side of that index,
namely:
C1si
C1i
!"
The profit margin for SIV model is defined as: [(result after depreciation þfinancial
returns)/annual turnover] (cs Abouzeedan, 2001, p. 75).
This can be expressed also as: [result after financial returns and costs/annual
turnover].
The production cost, C
3i
, includes costs of raw material and accessories, additional
external costs and costs of labor and human resources.
The proportionality factor, A
b
, is used to adjust the value (C
1si
/C
1i
). The
proportionality factor, A
c
, is used to adjust the value of the technology intake survival
index, SI
ij
to achieve the same purpose.
Having emphasized the deductive logic in choosing the coefficients’ values, it is
important to emphasize that one should use the same coefficients values for firms
within the same sector and regional location. This is important for the internal validity
of the SIV measurements and for the internal harmonizing of the SIV of the sample
firms.
The technology intake segment:
SIti ¼Ac
C2i
C3i
!"
The level of innovation activities of the firm is expressed in the SIV model as the
annual investments in the intake and absorption of new technologies. The investments
in innovation activities of the firm (being products, process and management
innovation) are split in the SIV equation in two types. The first type of investments are
coupled to the development of own innovations with the firm (so called outward-
focussed technology intake). The investments for incorporating and absorbing
innovation brought to the company from outside sources (so called inward-focussed
technology intake).
For our analysis, the value of E
xs
was taken to be 250 employees in accordance
with the Swedish standard, which is the same definition used for SMEs in the EU
definition of small firms. The proportionally factor A
a
was taken to be 10,000. The
proportionality factor, A
b
, was taken to be 10 while the proportionality factor, A
c
, was
taken to be 1,000. We used the profit margin as a fraction value and not as a
non-fraction number (e.g. for 18 percent, we used P
i
¼0.18). (C
1si
/C
1i
) ratio was taken to
be 1.0, which is the unity value. This is because and according to the company owner,
Ha
˚kan Sanberg, all the capital for the establishment of Autoadapt was paid through
own-financing. This value will be used as a base-value for the subsequent years even if
it was paid at the first years only. This is because self-financing approach to firms has
128
WJEMSD
8,2/3

a positive effect through the whole of the life of the enterprise. However, we are aware
that such positive impact would propagate for the initial of years but then and as the
company will need growth capital its importance will decline.
Thus it may be wise to the limit the number of years of using the initial self-fining
ratio. In this paper, we kept the ratio as equal to unit, through the whole period of
analysis. A suggestion for the future can be a period of five years as this is the period
after which the firm clears the critical stage of their development according to the
literature.
Different from standard SMEs models, the SIV does not recognize clear
threshold cut value between bankrupt and non-bankrupt enterprises (as does the
Z-scores and Zeta-scores models) because it see the transition area as region
between good performers and bad performers were firms can proceed into worse
or better performance. That gives flexibility for the model. It reduces the risk of
judging wrongly the future of the firms on the slightest trouble as do models
which have cut threshold values such as the Z-score and Zeta-scores (Altman, 1968;
Altman et al., 1977). Altman (1968) and Altman et al. (1977) as well as others
have recognized the problem and warned for placing firms in the bankrupt group
and such inducing an artificial bankruptcy by preventing the enterprise from
getting financial resources a critical stage of their development. Altman (1968)
and Altman et al. (1977) even introduced an error, which occurs by judging
a firm which is having a good potential to be a bad performer with bankrupt
possibility.
Selecting the members of the representative sample
We had two sources of information about firms in the sector related to adjusting cars to
handicapped people. The first list was provided by Timo Einari Toivonen from Turku
School of Economics, Finland, using the ORBIS database. The second list came from
Affa
¨rdata (a Swedish commercial database). To select the firms, we looked at the codes
used to describe the activities of Autoadapt AB. There were basically three of them:
30,920, 29,200 and 45,310 (Appendix 2). We combined two lists, the first list with 48
firms and the second list with 18 firms to produce a new combined list. After
eliminating all the firms that we do not have information about their activities and the
ones which reported no turn over in the last four years, a primary sample of total of
34 firms was created. Autoadapt AB was designated the firm number (34) for that list,
as it is the firm which will be analyzed it stood as the last firm on the list. We also
needed to eliminate all the firms which has registration date after April 7, 2000,
because the aim of using the sample is to compare it with the activities of Autoadapt
AB. This meant that the firm in the sample has to be starting its activities before
or at that date. It is clear that as we started to analyze the performance of Autoadapt
some firms were registered and we could have included them in the sample, but
that would have disturbed the homogeneity of the sample because these firm would
have been very early start-ups and that would have caused problems in reliability
of the results. That is why we neglected the firms which were established later.
Naturally, if we would to study the performance of Autoadapt AB in the future
where the reference date have been moved forward, due to new take over or a new
merger, these firms need to accounted for. That reduced the list further to its
final setting (Appendix 2). The total number of firms in the sample, N
s
, was 21
enterprises.
129
Implementing
the SIV model

Calculations of different firm age concepts
In this part of the paper, we are reintroducing the different life span concept
which was first displayed in Abouzeedan and Busler (2004). We are indicating
how they could be calculated for Autoadapt AB. The average firm life of the
sector (L
j
), as expressed in the SIV equation, is the theoretically optimum value of
youthfulness of the sector. Initially, the parameter L
j
can obtain a variety of value
levels for each specific sector. The investigator need to start with selected
sample and would use the sample to make a first guess of the youthfulness
of the business sector. That is why such average is called the sample average value
and is designated by the symbol (L
j
s
). L
j
value can be expressed in the following
equation:
Ls
j¼PNs
j¼1Yi
Nsð2Þ
where Y
i
is the years of operation of the ith enterprise; N
s
the number of firms in the
sample.
L
j
s
is usually calculated as the first time run when one wants to apply the SIV
model analysis on a number of firms selected from a specific business sector. The (L
j
s
)
figure will be improving all the time as one adds move firms to the sample. L
j
s
was
calculated such that, N
a
¼N
s
þN
d
where N
d
the additional number of firms added to
the original sample.
The subsequent life span parameter is called then the accumulated average life span
(L
j
a
) and can be calculated using the following equation:
La
j¼PNs
i¼1YiþNs%tsþPNd
i¼1Yi
Nað3Þ
where Y
i
is the years of operation of the ith enterprise; N
a
the accumulative number of
firms working within the jth sector; t
s
the age increment of the samples’ firms relative
to the reference date when, the SIV analysis was performed.
As the number of firms is increased in the sub-population, we would be getting close
to the actual age of the sector. If the accumulated number of firms became large enough
and almost equal the total population of the selected business sector (N
t
), within
specific geographical area, the average life span calculated in this case will be the real
age of the sector within that specific geographical area. That figure is the ultimate
average life span and is designated as (L
j
u
). Such condition can be expressed as
following: if N
a
EN
t
, then L
j
EL
j
u
.
The exact definitions of these life span forms are displayed in Table I (from
Abouzeedan and Busler, 2004). Applying Equation (3), we needed only to account
for the time change from the reference date and the date we are making our
new evaluation (called evaluation date and designated as, D
e
). So in our case D
e
are
the dates December 31, 2000, December 31, 2001 and so on until we reach December 31,
2010 (cs Table II). In continuation we will write D
e
(December 31, 2000) and
D
e
(December 31, 2001). In all calculations we used the registration date, designated as
D
r
, as the starting point for the firm age calculation of the sample.
130
WJEMSD
8,2/3

Calculations of the slope of the survival progression indicator (SPI)
Using the survival index curves (SIC) segments slopes it is possible to calculate the
slope of the SPI line (Abouzeedan and Busler, 2003). The SPI line slope is designated as
(F), while the slope of the individual SIC segments is designated as (u) (Abouzeedan
and Busler, 2003) The SPI line slope is also called “survivability coefficient.” The two
equations used in the calculations of uand Fare as following:
uk¼ðSIiÞno&ðSIiÞno&1
ðYiÞno&ðYiÞno&1
ð4Þ
F¼Pno
s
k¼1uk
ns
ð5Þ
where SI
i
is the survival index values for the ith; Fthe survivability coefficient; uthe
slope of the SIC segment, called survival factor; n
s
the number of segments in the SIC;
n
s
o
the segment number; nthe number of points of data making the SIC, such that:
ns¼n&1and no
s¼no&1
where n
o
is the data is point number and nis the number of data points.
Designation Name Member group Designation When used
L
j
Average life span Any number of firms NNot specific
L
j
s
Sample average life
span
Selected sample N
s
Alone or combined
with SIV
application
L
j
a
Accumulated
average life span
Both sample and
additional firms
N
a
¼N
s
þN
d
SIV application
L
j
u
Ultimate average life
span
Total population N
t
Ultimately
Table I.
Usage of average of life
span concepts within
the SIV model
Period number D
e
t
s
Y
22
L
j
a
1 December 31, 2000 0.773 0.733 21.503
2 December 31, 2001 1.733 1.733 22.503
3 December 31, 2002 2.733 2.733 23.503
4 December 31, 2003 3.733 3.733 24.503
5 December 31, 2004 4.733 4.733 25.503
6 December 31, 2005 5.733 5.733 26.503
7 December 31, 2006 6.733 6.733 27.503
8 December 31, 2007 7.733 7.733 28.503
9 December 31, 2008 8.733 8.733 29.503
10 December 31, 2009 9.733 9.733 30.503
11 December 31, 2010 10.733 10.733 31.503
Table II.
Calculating L
j
a
values for
the new group based on
reference date D
0
(April 7,
2000) and annual
periodicity
131
Implementing
the SIV model

The SPI angle of inclination (y), called survivability angle, is defined as:
Tan&1ðFÞ¼yð6Þ
The values of (y) gives indication to the general direction of the SPI line and help
visualizing the performance of the enterprise. The last values of (F) and (y) at the end
of the analysis period is called, respectively, the true survivability coefficient, F
>
, and
the true survivability angle, y
>
.
Structuring the survival index diagrams (SIDs)
To be able to draw the SPI line, we are using the following equation:
ðSIiÞ'
no¼FiðYiÞno&ðYiÞ1
#$
þðSIiÞ1ð7Þ
where n
o
is the data-point number.
Calculating of the prediction power of the SIV model
To evaluate the ability of the SIV to reflect better on the survivability of SMEs, we have
to consider the level of analysis done. Since the survivability coefficient, Fis calculated
as the average of the slopes of the SIC segments, it is expected that the larger the
number of segments in the SID the more power of analysis has our investigation. This
is also clear from comparing the results obtained for SIV analysis of the Swedish firms
using different periodicity units (Abouzeedan and Busler, 2004). As one goes from two-
monthly, quarterly and down to tertiary periodicity, the analysis becomes less stable.
When the binominal periodicity analysis is used it produced a contradictory result
with a negative Fvalue. That gives an evidence of the existence of a breaking point at
which the dynamics of the SIV model analysis is not stable. To evaluate that limit or
breaking point, Abouzeedan and Busler (2004) introduced a new parameter called,
prediction power of the SIV model designated C
i
. The new parameter can be defined
according to the following:
Ci¼O%Tið8Þ
where Ois the periodicity coefficient of the analysis method defined as the number of
period-units per year. For example when a quarterly analysis is used then O¼4; T
i
the
actual age of the firm at the time of the analysis such that,
Ti¼YiðDeÞ&YiðDrÞð9Þ
where Y
i
(D
e
) is the years of operation of the ith firm at the evaluation date, shall be
called initial value of year of operation; Y
i
(D
r
) the years of operation of the ith firm at
the registration date, shall be called final value of years of operation.
Usually the registration date is the same date when firms are considered to start
activity. In that case the value of Y
i
(D
r
) will be equal to 0. However, we may have a
situation when the firm has been in the hand of other owners and thus the firm is older
because the initial value of the years of operation will be mathematically negative,
inducing a positive increment to the actual age of the firm. On the other hand, may
be the firm will start its activity at a later date after its registration. Then the initial
value for the years of operation will be positive, reducing the actual age of the firm.
132
WJEMSD
8,2/3

For our current case study the Y
i
(D
r
) value was taken to be 0. If there was interest in
studying the performance of that company during all period since the enterprise was
established then we would considered the number of years of operation before the
take over as the value for Y
i
(D
r
), giving it a negative sign. From Equation (8) and
Equation (9), the prediction power equation can be rewritten as:
Ci¼O%½YiðDeÞ&YiðDrÞ) ð10Þ
We can use the periodicity coefficient to calculate number of periods, with any years
of operations interval by the equation:
np¼O%DYið11Þ
where n
p
is the number of periods; DY
i
the interval of years of operation within
which the SIV analysis is performed.
We also define a new parameter called periodicity compression coefficient, Z, which
is expressed mathematically as follows:
Z¼O
np
ð12Þ
The new coefficient gives indication of the length of the periodicity unit used in the
analysis compared to the total number of periods. A lower, Z, value, given same
periodicity coefficient, indicates a higher compression and higher evaluations
frequencies when the whole period of evaluation is considered. That reflects
stronger analysis with a larger number of points. A higher value of Z, indicate a weaker
analysis with wider time range between analysis points. The reason to do this analysis
is be sure that the periodicity used is adequate to run the analysis and give a stable
true survivability coefficient. In the case of Autoadapt AB and because we are use
extend period of time over the years of operation the compression coefficient is stable at
0.1, regardless of the periodicity used. That is why in the analysis of this enterprise we
did not need to calculate the true suitability coefficient for more than the annual
periodicity case. In general it is recommended that when the final evaluation period
expressed as number of operating years, is short to use more frequent analysis
(i.e. high-periodicity coefficient) while when the period is longer to use a longer periods
of analysis (i.e. lower periodicity coefficient).
Results and analysis
The accumulative average life span for the total sample
To establish an initial value for the L
j
, we used a sample of Swedish SMEs with number
of employees below 250. We used the registration date of Autoadapt-Bev AB as the
reference date. This is expressed as D
0
(April 7, 2000). Ages of individual firms were
calculated starting from the registration date, D
r
, and using a reference date, D
0
(April
7, 2000), which is the registration date of the new emerged Autoadapt-Bev AB. Later
on the firm returned the original name, Autoadapt AB. The Average Life Span for the
original sample was calculated using Equation (2). To calculate the new average life
span value after adding the new member to the previous sample, in our case Autoadapt
AB, the so-called accumulative average life, L
j
a
, we used Equation (3). The average
133
Implementing
the SIV model

number of days in a month is always taken as 30 days in our calculations. The size of
the accumulative number of firms (N
a
) is 22. The calculations were used only for one
periodicity alternative and that annually. The results of these calculations are shown
in Table II.
The SIV
Using the SIV equation, the SIV were calculated for the object firm at two stages. The
first stage was to calculate the operational SI. In Table III we calculated the production
cost. In Table IV, the profit margin is calculated. In Table V, the operational SI is
calculated. From Table V, one can see there are two main jumps in the operational SIV.
The first one is between 2000 and 2001, were the SIV increased from &0.64732 to
9.69824, an increment of 15 folds. Another jump occurred between 2002 and 2005
when the operational SI increased 13 folds from þ4.43902 and þ57.60587. To
calculate the technology intake, the development costs were calculated and the results
are displayed in Table VI. From the table the development costs range from 4.95 to
10.41 percent, which is a high figure. In Table VII, the SIV are calculated.
Period
number
Result after
depreciation
Result after financial
returns and costs
Annual
turnover
Profit
margin
1 806,481 &553,628 55,882,367 &0.00991
2 6,012,998 4,531,859 73,174,881 0.06193
3 3,412,697 1,982,825 80,112,860 0.02475
4 8,143,419 6,636,913 95,240,316 0.06986
5 8,272,767 7,824,921 96,808,756 0.08083
6 8,055,580 8,576,798 96,473,603 0.08890
7&1,863,786 &108,910 95,855,376 &0.00114
8 6,321,035 6,079,736 110,413,905 0.05506
9 8,974,724 8,868,232 117,997,357 0.07516
10 9,007,922 8,921,048 126,706,544 0.07041
11 9,554,297 9,314,450 133,973,961 0.06952
Table IV.
Calculating the
profit margin
C
3(22)
(divided)
Period
number F
22
Costs for raw material
and accessories
Additional
external costs
Costs of labor and
human resources C
3(22)
1 55,882,367 29,570,341 7,193,492 17,882,339 54,646,183
2 73,174,881 33,967,355 8,544,199 21,626,655 64,138,209
3 80,112,860 33,792,522 12,859,122 27,565,851 44,131,503
4 95,240,316 38,118,680 12,822,685 34,131,106 85,072,471
5 96,808,756 38,412,019 14,830,638 34,561,080 87,803,737
6 96,473,603 37,457,893 13,770,468 36,312,853 87,541,214
7 95,855,376 40,160,543 15,868,788 39,541,854 95,571,185
8 110,413,905 44,445,625 17,144,831 40,731,520 102,321,976
9 117,997,357 43,747,958,12 22,906,294 40,300,538 106,954,790
10 126,706,544 50,550,636 24,230,335 42,524,767 117,305,738
11 133,973,961 50,950,000 26,502,764 47,310,383 124,763,147
Table III.
Calculating the
production costs
134
WJEMSD
8,2/3

The survival factors
Using Equation (4), the survival factor values were calculated for Autoadapt AB using
the annual periodicity. The results of these calculations are shown in Table VIII.
The slope of the survival progression indicator (SPI)
Using Equation (5), the survivability coefficient values were calculated for Autoadapt AB
using the annual periodicity. The results of these calculations are shown in Table IX.
Annual period number Year Y
22
/L
j
a
E
22
E
22
/E
xa
F
22
/C
3(22)
P
22
SI
22o
a
1 2000 0.03408 49 0.196 0.97789 &0.00991 &0.64732
2 2001 0.07701 58 0.232 0.87651 0.06193 þ9.69824
3 2002 0.11628 70 0.280 0.55087 0.02475 þ4.43902
4 2003 0.15235 83 0.332 0.89324 0.06986 þ31.56293
5 2004 0.18559 87 0.348 0.90698 0.08083 þ47.34827
6 2005 0.21632 82 0.328 0.91326 0.08890 þ57.60587
7 2006 0.24481 85 0.340 0.99704 &0.00114 &0.94607
8 2007 0.27130 84 0.336 0.92671 0.05506 þ46.51244
9 2008 0.29600 79 0.316 0.90642 0.07516 þ63.72283
10 2009 0.31908 82 0.328 0.92581 0.07041 þ68.22282
11 2010 0.34070 90 0.360 0.93125 0.06952 þ79.40552
Table V.
Calculations of the
annual-based operational
survival index (SI
io
a
)
Period number Year Development costs, C
2(22)
Turn over, F
22
Percent of turn over
1 2000 2,760,018 55,882,367 4.94
2 2001 3,447,795 73,174,881 4.71
3 2002 4,209,846 80,112,860 5.26
4 2003 4,875,198 95,240,316 5.11
5 2004 6,635,323 96,808,756 6.85
6 2005 8,502,855 96,473,601 8.81
7 2006 8,515,044 95,855,376 8.88
8 2007 9,219,604 110,413,905 8.35
9 2008 11,143,266 117,997,357 9.44
10 2009 13,185,500 126,706,544 10.41
11 2010 13,199,898 133,973,961 9.85
Table VI.
The calculating of the
percent of development
costs to the annual
turn over
Period number Year C
2(22)
/C
3(22)
SI
it
a
SI
22
a
1 2000 0.05051 50.51 59.86268
2 2001 0.05376 53.76 73.45824
3 2002 0.09539 95.39 109.82902
4 2003 0.05731 57.31 98.87293
5 2004 0.07557 75.57 132.91827
6 2005 0.09713 97.13 164.73587
7 2006 0.08910 89.10 98.15393
8 2007 0.09010 90.10 146.61244
9 2008 0.10417 104.17 177.89283
10 2009 0.11240 112.40 190.62282
11 2010 0.10580 105.80 195.20552
Table VII.
Calculations of the
survival index
(annually based) (SI
22
a
)
135
Implementing
the SIV model

Constructing the SIDs
The values obtained from the calculations of the SIV (via the SIV equation) and the
survivability coefficients obtained from Equation (5), together with Equation (7) where
used to construct the SIDs for the firm for the annual periodicity. The calculations for
the SPI line are shown in Tables VII and X. The data from that table is used to
construct the figure (SID). From Figure 1, one can see that there were two years were
the SIV dropped, one at 2003 and a deeper drop in 2007. The data from Table VIII are
Segment number (n
s
)D(SI
22
a
)D(Y
22
) Survival factor (u)
1 13.59556 1 13.59556
2 36.37078 1 36.37078
3&10.95609 1 &10.95609
4 34.04534 1 34.04534
5 31.81760 1 31.81760
6&66.58194 1 &66.58194
7 48.45851 1 48.45851
8 31.28039 1 31.28039
9 12.72499 1 12.72499
10 4.58270 1 4.58270
Table VIII.
Calculations of the
survival factor (u) values
for annual periodicity
Accumulated segment
number (n
s
a
)Pns
k¼1uk
Survivability
coefficient (F)
Survivability
angle (y)
1 13.59556 13.59556 85.774
2 49.96634 24.98317 87.708
3 39.01025 13.00342 85.602
4 73.05559 18.26390 86.866
5 104.87319 20.97464 87.270
6 38.29125 6.38188 81.095
7 86.74976 12.39282 85.387
8 118.03015 14.75377 86.122
9 130.76014 14.52890 86.061
10 135.34284 13.53428 85.774
Table IX.
Calculations for the
survivability coefficient
(F) and survivability
angle (y) based on
annual periodicity
Period number (Y
22
)
n
(DY
22
)
n
(SI
22
a
)
n
*
1 0.773 0 59.86268
2 1.733 1 73.39696
3 2.733 2 86.93125
4 3.733 3 100.46553
5 4.733 4 113.99982
6 5.733 5 127.53410
7 6.733 6 141.06838
8 7.733 7 154.60267
9 8.733 8 168.13695
10 9.733 9 181.67124
11 10.733 10 195.20552
Table X.
Calculations for
the SPI line based
on annual periodicity
136
WJEMSD
8,2/3

used to construct Figure 2. From Figure 2, which indicates the value of the survival
factor, one can see a clear drop in two points where the performance has worsened. The
first one is in 2002-2003 and the more serious one occurred on 2005-2006. The data from
Table IX is used to construct Figure 3. In Figure 3, one see that the accumulated value of
performance, expressed as survivability coefficient, has been positive all the way. The
first years up to the period 2005-2006 were characterized by fluctuation in the
performance between improving and worsening conditions. Starting from 2006 to 2007,
the performance stabilized at almost at a constant positive average rate of improvement.
Discussion and analysis
The SIV model has the advantage of doing the analysis at four levels thus increasing
the internal reliability of the calculations.
250
200
150
100
50
0
0 2 4 6 8 10 12
Serie 1
Serie 2
Notes: x-axis, years of operation; y-axis, survival index; series 2 is the SPI line
Figure 1.
Survival index diagram
based on annual
periodicity
0 2 4 6 8 10 12
Serie 1
Notes: x-axis, segment number; y-axis, survival factor
60
40
20
0
–20
–40
–60
–80
Figure 2.
Survival factor diagram,
based on annual
periodicity
137
Implementing
the SIV model

The first level: the SIV. As Figure 1, shows the SIV were all positive which an indication
of a good performance is in itself. There are fluctuations between the different periods,
but this is natural as firms may do less well in a period than other. However, one should
be careful in distinguishing between doing less worse in single periods. Two such
worsening periods appear (periods (4) and (7)) (Table VII). Such worsening is not clear
indication of bankruptcy. Actually a company may deliberately in single years, spend
money, by expanding its production capacity, or investment in a new facility, and thus
reducing SIV. This is a strategic decision and not an effect induced by bankruptcy.
Bankruptcy requires successive worsening in consecutive periods after a period until the
SIV are transferred across the zero line to being negative. That is not enough in itself to
judge the firm to be in bankruptcy conditions there must be a continuous increasing in
these negative values and not fluctuation across the zero line.
The second level: the SPI line. One can see from Figure 1, that SPI line had a positive
slope, above 0.6, indicating a firm that belongs to the category of firms with positive
slope (so called: SPI( þ)) (see Abouzeedan, 2011).
The third level: survival factors. From Figure 2, one can see that the change in the SIV
between the periods were for the most positive except in two occasions. The first is the
shift in SIV between periods (3) and (4) and second is the shift between (6) and (7) (Table
VIII). That produced two occasions were the survival factors had negative values.
The fourth level: survivability coefficient. In Figure 3, one can see the average value of
the accumulated survival factors (so called survivability coefficient) is positive always.
It also fluctuates in the beginning (up to the seventh period), and then settle around an
average survivability coefficient value of 149, presenting a growth at a fixed rate. This
coincides with the literature which indicates that the survivability coefficient within
the first five years to be most important in its development. The additional years may
be due to the high technology activities of Autoadapt AB.
Conclusions
Measuring performance of SMEs is an important issue. There are couple of models
stemming from the traditional accountancy disciplines, used. These models suffer from
30
25
20
15
10
5
0
02
Notes: x-axis, accumulated segment number; y-axis, survivability coefficien
t
468
10 12
Serie 1
Figure 3.
The survivability
coefficient diagram, based
on annual periodicity
138
WJEMSD
8,2/3

clear disadvantages. However, recently a new model, the SIV model was introduced
(Abouzeedan, 2011). The model has shown the ability of being a better candidate of
performance analysis. The purpose of this case study was to demonstrate the ability
of the SIV model to judge correctly the performance of an innovative firm. To do that
we ran the SIV analysis on a Swedish SME that is active in the health care sector. We
knew, beforehand, the situation for the firm, Autoadapt AB as an innovative firm and
we wanted to show that the SIV model can capture that impact on the performance.
The firm adapt personal cars for the usage of handicapped people.
The SIV model has four level of analysis giving it very high level of internal
reliability. At the four levels, the firm showed a positive indication of its performance.
By having mostly positive survival factor values, which are single data points, during
years of operation, and also having mostly positive survivability coefficient values,
which are agglomerate data points, the SIV model proved its functionality. The
survivability coefficient curve showed a steady improvement of firm performance due
to extensive investment in product development. Clearly, the model has a good
potential to be developed and fine-tuned even more.
Future research
Traditional models of SME performance have focussed mostly on financial indicators.
Non-financial parameters are, for the most part, excluded from these models. Only
minor efforts are recorded in that direction. The SIV model incorporated two
non-financial parameters in its structure: firm age and firm size. However, there
remains a need to study the ability of non-financial parameters other than age and size
to impact the performance of SMEs. There is also a need to further develop the
SIV model to study specific cases, such as firm birth at the project stage prior to
pre-launching, when the firm is only in the business idea phase. Studying how
variations in the business sector and location of firm impact SME performance can be
of value to the goal of projecting and investigating firm development.
References
Abouzeedan, A. (2001), Factors Affecting the Performance of SMEs and the Survival Index
Equation: A Quantitative Evaluation (ISBN 91-628-6140-9), Washington International
University, King of Prussia, PA.
Abouzeedan, A. (2002), “Performance factors of small and medium-size enterprises: a new
classification system”, in Johansson, I. (Ed.), Uddevalla Symposium 2002 Anthology
(Research Reports 03:1), Innovation, Entrepreneurship, Regional Development and Public
Policy in the Emerging Digital Economy, University of Trollha
¨ttan/Uddevalla, Uddevalla,
pp. 7-19.
Abouzeedan, A. (2011), “SME performance and its relationship to innovation”, Dissertation
No. 1364, PhD thesis, Linko
¨ping Studies in Science and Technology, Linko
¨ping University,
Linko
¨ping.
Abouzeedan, A. and Busler, M. (2002), “Small and medium-size enterprises performance: an
evaluation using the survival index value (SIV) model”, Paper No.15, paper presented at
the International Conference on Medium Enterprise Development, Collingwood College,
University of Durham, Durham, July 14-16 (CD).
Abouzeedan, A. and Busler, M. (2003), “Survivability coefficient; new measurement of SMEs
performance”, paper presented at ISBA 26th National Small Firms Policy and Research
Conference, November 12-14, University of Surrey, Guildford, UK.
139
Implementing
the SIV model

Abouzeedan, A. and Busler, M. (2004), “Analysis of Swedish fishery company using SIV model: a
case study”, Journal of Enterprising Culture, Vol. 12 No. 4, pp. 277-301.
Abouzeedan, A. and Busler, M. (2005), “The birth and death model: a new concept of firm life
cycle”, in Johansson, I. (Ed.), Uddevalla Symposium 2005 Anthology (Research Report
2006:1) Innovations and Entrepreneurship in Functional Regions, University West,
Uddevalla, pp. 9-25.
Acs, Z.J. (1999), “The new American evolution”, in Acs, Z.J. (Ed.), Are Small Firms Important?,
Kluwer Academic, Boston, MA, pp. 1-30.
Acs, Z.J. and Yeung, B. (1999), “Conclusion in small and medium-sized enterprises”, in Acs, Z.J.
and Yeung, B. (Eds), The Global Economy, University of Michigan Press, Ann Arbor, MI,
pp. 164-73.
Altman, E.I. (1968), “Financial ratios, discriminant analysis and the prediction of corporate
bankruptcy”, The Journal of Finance, Vol. 23 No. 4, pp. 589-609.
Altman, E.I. (1983), “Exploring the road to bankruptcy”, The Journal of Business Strategy, Vol. 4
No. 2, pp. 36-41.
Altman, E.I., Haldeman, R.G. and Narayanan, P. (1977), “Zeta* analysis: a new model to identify
bankruptcy risk of corporations”, Journal of Banking and Finance, Vol. 1 No. 1,
pp. 29-54.
Altman, E.I., Marco, G. and Varetto, F. (1994), “Corporate distress diagnosis: comparisons using
linear discriminant analysis and neural networks (the Italian experiment)”, Journal of
Banking and Finance, Vol. 18 No. 3, pp. 505-29.
Argenti, J. (1976), Corporate Collapse, the Causes and Symptoms, McGraw-Hill Book Company
(UK) Limited, Maidenhead.
Castrogiovanni, G.J. (1996), “Pre-startup planning and the survival of new small businesses:
theoretical linkages”, Journal of Management, Vol. 22 No. 6, pp. 801-23.
Caves, R.E. (1998), “Industrial organization and new findings on the turnover and mobility of
firm”, Journal of Economic Literature, Vol. 36 No. 4, pp. 1947-82.
Chesbrough, H.W. (2001), “Open innovation: a new paradigm for managing technology”, paper
presented at the OECD Conference on New Business Strategies for R&D, Paris, October 22.
Chesbrough, H.W. (2003), Open Innovation: the New Imperative for Creating and Profiting from
Technology, Harvard Business School Press, Boston, MA.
Chesbrough, H., Vanhaverbeke, W. and West, J. (2006), Open Innovation: Researching a New
Paradigm, Oxford University Press, Oxford.
Cohen, W.M. and Levinthal, D.A. (1990), “Absorptive capacity: a new perspective on learning and
innovation”, Administrative Science Quarterly, Vol. 35 No. 1, pp. 128-52.
Corley, M., Michie, J. and Oughton, C. (2002), “Technology, growth and employment”,
International Review of Applied Economics, Vol. 16 No. 3, pp. 265-76.
Daft, R.L. (1982), “Bureaucratic versus nonbureaucratic structure and the process of innovation
and change”, In Bacharach, S.B. (Ed.), Research in the Sociology of Organizations, JAI
Press, Greenwich, CT, pp. 129-66.
Damanpour, F. and Evan, W.M. (1984), “Organizational innovation and performance: the problem
of organizational lag”, Administrative Science Quarterly, Vol. 29 No. 3, pp. 392-409.
Damanpour, F. and Gopalakrishnan, S. (1988), “Theories of organizational structure and
innovation adoption: the role of environmental change”, Journal of Engineering and
Technology Management, Vol. 15 No. 1, pp. 1-24.
Damanpour, F., Szabat, K.A. and Evans, W.M. (1989), “The relationship between types of
innovation and organizational performance”, Journal of Management Studies, Vol. 26
No. 6, pp. 587-601.
140
WJEMSD
8,2/3

Davidson, P. and Klofsten, M. (2003), “The business platform: developing an instrument to gauge
and to assist the development of young firms”, Journal of Small Business Management,
Vol. 41 No. 1, pp. 1-26.
Downs, G.W. and Mohr, L.B. (1976), “Conceptual issues in the study of innovation”,
Administrative Science Quarterly, Vol. 21 No. 4, pp. 700-14.
Freel, M.S. (2000), “Do small innovating firms outperform non-innovators?”, Small Business
Economics, Vol. 14 No. 3, pp. 195-210.
Geroski, P.A. and Machin, S. (1992), “Do innovating firms outperform non-innovators?”, Business
Strategy Review, Vol. 3 No. 2, pp. 79-90.
Gnyawali, D.R. and Park, B-J. (2009), “Co-operation and technological innovation in small and
medium-sized enterprises: a multilevel conceptual model”, Journal of Small Business
Management, Vol. 47 No. 3, pp. 308-30.
Gritta, M.W., Davalos, S., Wang, M. and Chow, G. (2000), “Forecasting small air carrier
bankrupticies using a neural network apporach”, Journal of Financial Management &
Analysis, Vol. 13 No. 1, pp. 44-49.
Hadjimanolis, A. (2000), “A resource-based view of innovativeness in small firms”, Technology
Analysis and Stratgeic Management, Vol. 12 No. 2, pp. 263-81.
Hart, P.E. and Paris, S.J. (1956), “The analysis of business concentration: a statistical approach”,
Journal of the Royal Statistical Society, Vol. 119 No. 2, pp. 150-91.
Hedner, T., Cowlrick, I., Wolf, R., Olausson, M. and Klofsten, M. (2011), “The changing structure
of the pharmaceutical industry – perceptions on entrepreneurship and openness”, in
Cassia, L., Paleari, S. and Minola, T. (Eds), Entrepreneurship and Technological Change,
Edward-Elgar Publishing, New York, pp. 73-94.
Helms, M.M. and Renfrow, T.W. (1994), “Expansionary process of the small business: a life cycle
profile”, Management Decision, Vol. 32 No. 9, pp. 43-6.
Houghton, K.A. (1984), “Accounting data and the prediction of business failure, the setting of
priors and age of data”, Journal of Accounting Research, Vol. 22 No. 1, pp. 361-8.
Jain, B.A. and Nag, B.N. (1997), “Performance evaluation of neural network decision models”,
Journal of Management Information Systems, Vol. 14 No. 2, pp. 201-16.
Jovanovic, B. (1982), “Selection and the revolution of industry”, Econometrical, Vol. 50 No. 3,
pp. 649-70.
Keasey, K. and Watson, R. (1991), “Financial distress prediction models: a review of their
usefulness”, British Journal of Management, Vol. 2 No. 2, pp. 89-102.
Keasey, K. and Watson, R. (1993), Small Firm Management: Ownership,, Finance and
Performance, Blackwell Publishers, Oxford.
Keasey, K., McGuinness, P. and Short, H. (1990), “Multilogit approach to predicting corporate
failure, further analysis and the issue of signal consistency”, Omega, Vol. 18 No. 1,
pp. 85-94.
Klofsten, M. (1992a), “Tidiga Utvecklingsprocesser I Teknikbaserade Fo
¨retag (early development
processes in technology-based firms)”, No. 24, PhD dissertation, Department of
Management and Economics, Linko
¨ping University, Linko
¨ping.
Klofsten, M. (1992b), “Tidiga Utvecklingsprocesser I Teknikbaserade Fo
¨retag – En
literaturgenomga
˚ng”, Working Paper No. Code: LIUEKI/WPS-92-9203, University of
Linko
¨ping, Linko
¨ping.
Klofsten, M. (2010), The Business Platform: Entrepreneurship & Management in the Early Stages
of a Firm’s Development, 3rd ed., TII asbl, Luxemburg.
Libby, R. and Lewis, B.L. (1982), “Human information processing research in accounting: the
state of the art in 1982”, Accounting, Organization and Society, Vol. 7 No. 3, pp. 231-85.
141
Implementing
the SIV model

Lindholm, A
˚. (1994), “The economics of technology-related ownership changes: a study of
innovativeness and growth through acquisitions and spin-offs”, published PhD
dissertation, Chalmers University of Technology, Gothenburg.
McPherson, M.A. (1995), “The hazards of small firms in Southern Africa”, The Journal of
Development Studies, Vol. 32 No. 1, pp. 31-55.
Mazzarol, T. and Rebound, S. (2008), “The role of complementary actors in the development of
innovation in small firms”, International Journal of Innovation Management, Vol. 12 No. 2,
pp. 223-53.
Motwani, J., Dandridge, T., Jiang, J. and Soderquist, K. (1999), “Managing innovation in French
small and medium-sized enterprises”, Journal of Small Business Management, Vol. 37
No. 2, pp. 106-14.
Nelson, R.R. and Winter, S.G. (1978), “Forces generating and limiting concentration under
Schumpeterian competiton”, The Bell Journal of Economics, Vol. 9 No. 2, pp. 524-48.
Qian, G. and Li, L. (2003), “Profitability of small and medium-sized enterprises in high-tech
industries: the case for biotechnology industry”, Strategic Management Journal, Vol. 24
No. 9, pp. 881-7.
Schumpeter, J.A. (1934), The Theory of Economic Development, Harvard University Press,
Cambridge, MA.
Scott, W.R. (2003), Organizations, Rational, Natural, and Open Systems, 5th ed., Prentice Hall,
Upper Saddle River, NJ.
Soni, P.K., Lilien, G.L. and Wilson D.T., D.T. (1993), “Industrial innovation and firm performance:
a reconceptualization and structural equation analysis”, International Journal of Research
in Marketing, Vol. 10 No. 4, pp. 365-80.
Teirlinck, P and Spithoven, A. (2008), “The spatial organization of innovation: open innovation,
external knowledge relations and urban structure”, Regional Studies,Vol.42No.5,pp.689-704.
Trott, P. (20 08) , Innovation Management and New Product Development,4thed.,PrenticeHall,London.
Vermeulen, P.A.M., Jong, J.P.De. and O’Shaughnessy, K.C. (2005), “Identifying key determinants
for new product introductions and firm performance in small service firms”, The Service
Industries Journal, Vol. 25 No. 5, pp. 625-40.
Veugelers, R. (1997), “Interval R&D expenditures and external technology sourcing”, Research
Policy, Vol. 26 No. 3, pp. 303-15.
Vossen, R.W. (1998), “Relative strengths and weaknesses of small firms in innovation”,
International Small Business Journal, Vol. 16 No. 3, pp. 88-94.
Wincent, J. and Westerberg, M. (2005), “Personal traits of CEO, inter-firm networking and
entrepreneurship in their firms: investigating strategic SME network participants”,
Journal of Development entrepreneurship, Vol. 10 No. 3, pp. 271-84.
Wincent, J., Anokin, S. and Boter, H. (2009), “Network board continuity and effectiveness of open
innovation in Swedish strategic small-firm networks”, R&D Management, Vol. 39 No. 1,
pp. 55-67.
Wolff, J.A. and Pett, T.L. (2006), “Small-firm performance: modeling the role of the product and
process improvements”, Journal of Small Business Management, Vol. 44 No. 2, pp. 268-84.
Yazdipour, R. and Constand, R.L. (2010), “Predicting firm failure: a behavioral finance
perspective”, The Journal of Entrepreneurial Finance, Vol. 14 No. 3, pp. 90-104.
Further reading
Globerman, S., Roehl, T.W. and Standifird, S. (2001), “Globalization and electronic commerce:
inferences from retail brokering”, Journal of International Business Studies, Vol. 32 No. 4,
pp. 749-68.
142
WJEMSD
8,2/3

Storey, D.J., Keasey, K., Watson, R. and Wynarczyk, P. (1987), The Performance of Small Firms,
Croom-Helm Ltd, London.
Appendix 1. History of Autoadapt AB
Before 1991
.Handi Sverige AB was established.
.The company operated a commercial activity to modifying (different applications)
to handicapped people. The activities centered on the low platform Chrysler Voyger
cars. The customer target group where mostly wheelchair-bound people who drove cars.
Later on, when Autoadapt AB, took over, it widened that category to include families
with a member which has handicapped status, even when the person is not bound to chair.
Autoadapt AB even worked with taxi cars to modify them for handicapped usage.
.Beram AB bought the bankrupted firm Handi Sverige AB.
1991-1994
.In 1992, Beram AB took over the distribution of Invacare products in the marker. Invacare
is an American company which produced rehabilitation products such as wheelchairs and
ceiling-fixed lifting cranes.
1995-1998
.The activities of car modification for handicapped people were taken over from
Invacare by Beram Autoadapt AB which was built by Staffan Ramer and Peter Wahlsten.
That occurred in 1996. With the building up of Beram Autoadapt a decision was taken to
widen the company’s activities to include cars of Volvo model, beside the American
car models. A decision was also taken to seek ISO 9002 certification. At that time Peter
Wahl ten was responsible for the car modification activities as well as being the service
chief for electrical rolling chairs while Staffan Ramer was the general manager of the
company. In January 1998, took Ha
˚kan Sandberg that position after the resignation of
Peter Wahlsten.
.In summer of 1998, Staffan Ramer solid all his shares in the company to Peter Wahlsten.
The shares were sold immediately to 6:e AP-fonden.
.Peter Sandberg, the financial manager of Volvo AB, was on the board of the company
between September 1995 and March 1997.
.After Peter Sandberg left the board Ha
˚kan Sandberg was elected as a general manager
and owner in the company. The company is owned by three partners: Peter Wahlsten,
Ha
˚kan Sandberg and 6:e AP fonden.
.On October 1999, took Autoadapt AB over BEV Euroaid AB and its daughter company,
Fastighetsbolaget Sanna AB. BEV has been the largest competitors of Autoadapt in the
field of production and selling of adapted personal cars.
2000-2004
.During August 2000, a fusion occurred between Autoadapt AB and BEV Euroaid AB.
The resulting company became the leading actor in Europe in the area of adapting
143
Implementing
the SIV model

personal cars to the usage of the handicapped people. Audoadapt became a company with
overall strategy of manufacturing products to move handicapped people from a place to
another.
.Autoadpt won the prize of being the year growth company in 2002.
.In December 2002 a new stage in the development of Autoadpt occurred when Bruno
Independent Living Aids bought 6:e AP Fondens shares in the company. From that date
the company is owned by Bruno, Ha
˚kan Sandberg and Peter Wahlsten. Fo
¨retaget Bruno
was at that point one of the largest customers of Autoadapt.
.Autoadapt AB now with Bruno is today the largest developer and producers of car
adapting solutions for handicapped people.
.In 2003, the export part of the firm’s activities made 65 percent of the total turnover.
The company had 30 percent of its market in Sweden.
.During April 2004 the company changed the name to Autoadapt AB, it original name.
.In January 2004 the company had 86 employees and production units in Bora
¨s and
Stenkullen as well as a distributor (Autoadapt UK Ltd) in Birmingham. The company
exports roughly 70 percent of its turn over outside Sweden. The company exports to 30
countries around the world. The largest export market being North America (USA,
Canada and Mexico).
2005-2010
.Under the year 2005 the LEAN concept was introduced to the company while at the same
time a lot of re-structuring was performed to allow for future expansion. A lot of the
education and training was done both for the top management as the core workers. A lot
of energy and time was put to create a production development process. The export part of
firm activities was 85 percent.
.In 2006 the company started a new expansion. The board of directors decided to merge the
production units in Bora
˚s and Stenkullen to a combined unit. As a leading process in this
work, a land was bought in Stenkullens Industrial Area. Autoadapt in this point of time
had ownership of its facilities via a 100 percent owned company in Bora
˚s
(Fastighetsbolaget Sanna AB) and in Stenkullen via Fastighetsbolaget Autoadapt AB.
The process of selling the two companies and to construct the new facility was finished
during autumn of the same year. The new project increased the resources for training the
employees continued under 2006. The export part of the activity was roughly 85 percent.
The turn over was 105 million Swedish crowns.
.In August 2007 the company moved to its new facilities (A
˚keriva
¨gen 7, Stenkullen).
Most of that year (2007) went into preparation for the moving into the new facilities.
The company had its best turn-over under the first half. The turn-over for 2007 was
110 millions.
.In year 2008 Autoadapt AB was awarded “Go
¨teborgs Companipris.” The award was to
award the good enterprising of the management team. The basic idea is that enterprising
is good for the customer, colleagues and owners.
.In the subsequent years 2009-2010, the company continued its growth and it is
currently one of the bright of examples of innovative Swedish SME active within
health care.
144
WJEMSD
8,2/3

Appendix 2
Corresponding author
Adli Abouzeedan can be contacted at: adli.abouzeedan@gu.se
Firm
number Company name
Registration date
(D
r
)
Age of firm (years)
(Y
i
)
1 Ahlberg Rehab AB (1) December 23, 1994 5.289
2 Artur Heijel Plastvaru AB (1) October 22, 1962 37.458
3 Atran AB (1) February 2, 1937 63.161
4 Bergdunge Ha
¨st AB (1) March 21, 1996 4.044
5 CombiMobil AB (1) November 5, 1993 6.422
6 Cycleurope AB (1) November 25, 1970 29.367
7 Cycleurope Sverige AB (1) October 20, 1937 62.464
8 Edsbergs Rullstolstillbeho
¨r AB (1) August 25, 1971 28.614
9 Emmaljunga Barnvagnsfabrik AB (1) February 17, 1993 7.139
10 ETAC Supply Center AB (1) January 19, 1981 19.167
11 Handinnova AB (1) December 14, 1988 11.314
12 Hedemora Anpassning AB (1,2) September 18, 1996 3.533
13 Helab Produktion AB (1) July 7, 1989 10.750
14 Invacare Rea AB (1) July 13, 1965 34.706
15 Jatab Care AB (1) September 26, 1989 10.553
16 Minicrosser AB (1) February 20, 1996 4.131
17 OBIK AB (1) December 9, 1994 5.328
18 Permobil AB (1,2) November 27, 1967 33.361
19 Permobil Produktion AB (1) October 28, 1997 2.442
20 Pilen Cykel AB (1) April 30, 1991 8.936
21 Rex Sport AB (1) July 7, 1931 68.750
Summation 456.929
L
j
s
21.758
22 Autoadapt AB (1,2) (before Autoadapt-Bev
AB)
April 7, 2000 0
Table AI.
Age of firm for the refined
list, age of firm at D
0
(2000-04-07), all firms have
o250 employees, and
which been registered on 7
April 2000 or before
To purchase reprints of this article please e-mail: reprints@emeraldinsight.com
Or visit our web site for further details: www.emeraldinsight.com/reprints
145
Implementing
the SIV model