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Small Business Entrepreneurship
and the Internal Determinants of International Behaviour
Michela C. Mason,
Department of Biology and Agro-industrial Economics, University of Udine, Italy, +39 0432 558325
Rubens Pauluzzo,
Department of Economic Sciences, University of Udine, Italy, +39 0432 249590
ABSTRACT
The role of Italian small and medium enterprises (SMEs) in the international economic
environment has attracted many scholars for its peculiarities. In particular, their development
and longevity paths are critical to a better understanding of the main characteristics of the
global economy. Although they manage many of the same issues of other companies, they
must also face specific problems, mainly related to their status and to the entrepreneurial
behaviour of the firm owners.
Nevertheless, few empirical studies have stressed the importance of human resources and
entrepreneurial behaviour for the performance and growth of Italian SMEs, especially in the
international environment, where keen competition and difficulties of reaching the necessary
resources are often insuperable barriers for SMEs.
The main goal of this study is to suggest a “holistic” model, in order to analyze the relations
between the internal determinants of the internationalization process of SMEs, by integrating
the individual-level with the firm-level features. The analysis, based on the theories of
individual cognitive aspect, international expansion and business strategy, explores the
process of internationalization of SMEs from a cognitive perspective. Therefore, the study
aims to provide a better understanding of the deepest reasons that lead entrepreneurs towards
distinctive paths of international development.
Finally, the present study stresses the central role played by individual expectations and risk
perception in understanding the international expansion strategies of SMEs. In particular, it
considers some significant relationships between international processes, human capital and
the role played by the firm owner.
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1. INTRODUCTION
Export Marketing literature has distinguished the main determinants of firm export behaviour
in two categories: internal and external determinants. Macroeconomic literature has widely
considered the external determinants, whereas Management scholars have mainly focused on
the internal determinants. The growing importance of these internal factors, to support the
international development of SMEs, offers interesting research ideas. The organizational
structure, together with management characteristics, entrepreneurial behaviour and human
capital represent some of the most relevant elements tightly linked to the international
performance of the firms.
Although SMEs manage many of the same issues as other companies, they must also face
specific problems, mainly related to their status and to the entrepreneurial behaviour of firm
owners.
Nevertheless, few empirical studies have stressed the importance of human resources and
entrepreneurial behaviour for the performance and growth of Italian SMEs, especially in the
international environment, where keen competition and difficulties of reaching the necessary
resources are often insuperable barriers for SMEs.
The main goal of this study is to suggest a “holistic” model, in order to analyze the relations
between the internal determinants of the internationalization process of SMEs, by integrating
the individual-level with the firm-level features. The analysis, based on the theories of
individual cognitive aspect, international expansion and business strategy, explores the
process of internationalization of SMEs from a cognitive perspective. Therefore, the study
aims to provide a better understanding of the deepest reasons that lead entrepreneurs towards
distinctive paths of international development.
Finally, the present study stresses the central role played by individual expectations and risk
perception in understanding the international expansion strategies of SMEs. In particular, it
considers some significant relationships between international processes, human capital and
the role played by the firm owner.
Our sample frame consisted of 150 SMEs from the Italian region of Friuli Venezia Giulia,
with particular regard to the provinces of Udine and Pordenone, associated with the family
business centers of the most significant sectors. The surveys were submitted to a panel of
1509 firms, with an overall response rate of about 10%.
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2. LITERATURE REVIEW
The analysis aimed at deepening what are the main factors of international success for SMEs.
In fact, the openness towards foreign markets is a strategic choice for SMEs, aimed at, on the
one hand, supporting fundamental development paths, and, on the other hand, protecting their
businesses (Skrt and Antoncic, 2004). The literature offers a vast and composite theoretical
framework, in which the various approaches agree in suggesting the central role and
influence played by the family ownership in this process. In particular, at the SMEs level, the
human capital, mainly due to the owner, is the main source of competitive advantage. In these
companies, in fact, the owner does not only invest in the firm activity, but also acts as the
main, though not exclusive, decision maker and business manager (Glancey, 1998;
Miesenbock, 1988; Prince and Van Dijken, 1998; Reid, 1981; Westhead, Binks, Ucbasaran,
and Wright, 2002). Several contributions on this matter highlight different aspects of the
entrepreneurial role, such as the strategy followed (Baird, Lyles, and Orris, 1994; Tyebjee,
1994), managers attitudes (Bijmolt and Zwart, 1994; Ogbuehi and Longfellow, 1994),
responsibilities (Dhanaraj and Beamish, 2003) and international experiences (Qian, 2002;
Reuber and Fischer,1997) as well as other elements of human capital (Andersson, 2000;
Bilkey and Tesar, 1977; Cavusgil, 1993; Herrmann and Datta, 2002; Manolova, Brush,
Edelman, and Greene, 2002; McAuley, 1999; Moini, 1995; Trevino and Grosse, 2002)1.
Considering the studies focused on identifying the success factors of internationalization, it is
therefore possible to detect an evolutionary path characterized by the gradual transition from
tangible determinants (foreign markets, export incentives) to more intangibles dimensions,
such as demographic features and psycho-cognitive aspects of management (Dichtl,
Koglmayr and Muller 1990; Muller 1991).
In order to better understand the main reasons for success in the international markets, it is
necessary to develop a model able to consider several variables explaining the complexity of
the phenomenon. The number of variables is a fundamental prerequisite in business studies,
because the development of these organizations is influenced by various factors which
constantly interact with each other and with the external environment (Macharzina and
Engelhard, 1991).
In order to analyze the international performance of SMEs is therefore necessary to, on the
one hand, select a suitable set of variables and, on the other hand, to apply a specific
statistical method. Several studies suggested different methods to identify the dependent
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variable (international performance) because, since its introduction (Tookey, 1964), a
universally shared conceptualization has not been developed (Cavusgil e Zou 1994; Shoham,
1998).
As a result, we would like to suggest a frame of reference of the most important studies
focused on the determinants of export success. Some of these analysis are presented in table
1.
With reference to the theoretical framework presented, we have developed a multidimensional model able to integrate two levels of analysis: the firm level and the individual
characteristics level. In particular, the analysis has distinguished between the “observable” or
objective management dimension and the subjective one, emphasizing the relevance of
psycho-cognitive aspects. In accordance with the upper echelon theory (Hambrick and
Mason, 1994), the study has therefore accepted the assumption that the individual
characteristics affect business attitudes, strategic choices and business performance.
Revising the approach suggested by some authors (Aabay and Slater, 1989; Bijmol and
Zwart, 1994; Holzmüller and Stottinger 1996, 2001), the model identifies several explanatory
variables, emphasizing the importance of subjective entrepreneurial characteristics,
international activity, firm characteristics and objective entrepreneurial characteristics that
have a direct and mediated effect on international performance. The study also deepens an
issue only marginally considered by the literature: the bond between firm characteristics and
international activity.
In particular, with reference to the results of various studies (Katsikeas, Deng and Wortzel,
1997; Wolff and Pett, 20002), the analysis has accepted the assumption that smaller
dimensions (small number of employees) are positively related to international activity. This
suggests that SMEs do not follow competitive paths based on their dimensions, as larger
companies usually do, but, in order to find the needed resources at lower costs, they show an
interesting orientation to international activity. This supports the hypothesis that the
international activity of SMEs is dominated by a resource seeking orientation, influenced by
the comparative advantages that can be reached in terms of factor costs and especially of
labour costs.
The model analyses the relationships between the following set of variables and international
performance3:
Firm Characteristics (company size, age, degree of innovation/technology);
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Objective Entrepreneurial Characteristics OGG (education, age, work experience,
international experience);
Subjective Entrepreneurial Characteristics SOG (proactivity, risk acceptance, tolerance for
ambiguity, initiative, personal development, relationship with uncertainty);
International Activity INT (internationalization methods, expansion strategies, international
planning,
internationalization
motivations,
resource
seeking
orientation,
foreign
procurement).
International Performance IP (export sales/total turnover, international market share) 4.
Structural equation modeling (SEM) was used to test the relationships between observed and
latent variables. In particular, the analysis focused on a latent structure model with explicit
causal relations, developed with LISREL 8.51 (Jöreskog and Sörbom, 2001, Corbetta, 2002).
LISREL is particularly suitable for the purpose of the analysis, because the explanatory
investigation requires an identification of those variables that can be considered as “cause”
and those variables that can be considered as “effect”.
The model identified with the relevant determinants of international performance is presented
in Figure 1.
3. RESEARCH METHODOLOGY
From a panel of 1509 small and medium enterprises from the Italian region of Friuli Venezia
Giulia (853 from the province of Udine and 656 from the province of Pordenone), 150 SMEs,
associated with the family business centers of the most significant sectors, have been
surveyed. Data were collected by submitting a questionnaire to firm owners in the period
January-December 2007.
The empirical analysis is based on multivariate statistics. Factor analysis with varimax
rotation has been performed to identify the five dimensions: firm characteristics (IMP),
objective entrepreneurial characteristics (OGG), subjective entrepreneurial characteristics
(SOG), international activity (INT) and international performance of the firm (IP).
The reliability of each factor has been tested with Cronbach’s α coefficient.
Table 2 shows the results of factor and reliability analysis
4. MEASURES AND RESULTS
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The estimated structural equation modeling (SEM), analyzed with LISREL 8.51, follows a
logic based on two steps. The first one is related to the process for estimating parameters,
which takes place through an interactive procedure aimed at minimizing the gap between data
produced by the model and observed data. The second step is based on a comparison of the
theoretical model with the data observed. If the gap between the matrix of the observed
covariance and the expected matrix, generated by the program, is higher than the gap
attributable to the stochastic error, the model is rejected. The analysis will then determine if
the model is able to represent the examined phenomenon, through four different kinds of fir
indices.
These indices are represented by the χ ² test, the Overall model fit indices5, the Incremental fit
indices6 and the Residuals indices7.
LISREL model consists of two parts: the structural model and the measurement model. The
structural model submits all the causal relations between endogenous and exogenous latent
variables. The measurement model analyzes the links between latent and observed variables.
The abbreviations commonly used by LISREL are listed in table 3.
The measurement model aims at explaining if the observed variables can effectively measure
the latent variables. With reference to our study, the measurement model analyzes the bonds
between the subjective entrepreneurial characteristics (SOG), the international activity of the
firm (INT), the performance of the firm on the international markets (IP), the objective
entrepreneurial characteristics (OGG), the firm characteristics (IMP) and their observed
variables.
As we stated above, Structural equation modeling (SEM) is a statistical technique for testing
and estimating causal relationships using a combination of statistical data and qualitative
causal assumptions. In order to provide the fullest evidence of measurement efficacy, before
testing the complete model, it should be useful to test also each latent variable.
Subjective entrepreneurial characteristics (SOG) has been conceptualized as a second-order
model, explained by six observed variables. The fit statistics for the model are RMSEA = 0,
90% Confidence Interval for RMSEA (0; 0.0062), NFI = 0.96, NNFI = 1, GFI = 0.99, AGFI
= 0.97, confirming a very good model-data fit.
International activity (INT) has been conceptualized as a second-order model, explained by
six observed variables. The fit statistics for the model are RMSEA = 0.046, 90% Confidence
Interval for RMSEA (0.02; 0.0097), NFI = 0.77, NNFI = 0.74, GFI = 0.94, AGFI = 0.87,
confirming a reasonable model-data fit.
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International performance (IP) has been conceptualized as a second-order model, explained
by two observed variables. The fit statistics for the model has not been calculated by LISREL
due to the small number of observed variables.
Firm characteristics (IMP) has been conceptualized as a second-order model, explained by
three observed variables. The fit statistics for the model are RMSEA = 0.058, 90%
Confidence Interval for RMSEA (0; 0.25), NFI = 0.88, NNFI = 0.86, GFI = 0.99, AGFI =
0.96, confirming a good model-data fit.
Objective entrepreneurial characteristics (OGG) has been conceptualized as a second-order
model, explained by four observed variables. The fit statistics for the model are RMSEA =
0.017, 90% Confidence Interval for RMSEA (0; 0.18), NFI = 0.93, NNFI = 0.98, GFI = 0.99,
AGFI = 0.96, confirming a very good model-data fit.
Tables 4 and 5, and figures 2 and 3, show the relationships between observed and latent
variables and the fit indices of latent variables.
The structural model confirms, on the one hand, the existence of a direct relationship between
the firm characteristics (IMP), the objective entrepreneurial characteristics (OGG) and the
international performance (IP) and, on the other hand, the existence of an indirect link,
mediated by the subjective entrepreneurial characteristics (SOG) and by the international
activity (INT).
It seems useful to stress the importance of the mediating role played by the subjective
entrepreneurial characteristics (SOG) and by the international activity (INT). In fact, the firm
characteristics (IMP) and the objective entrepreneurial characteristics (OGG) affect directly
and indirectly the international performance (IP).
Table 6, and figure 4, show the relationships between latent variables.
The integrated exam of the fit indices allows to confirm whether the model is able to fit the
data or not.
χ ² scores 298.53 with 180 degrees of freedom, p-value 0. Overall model fit indices show
reasonable fit results. GFI (Goodness of fit index) scores 0.82, while AGFI (Adjusted
goodness of fit index) 0.77. CN (Critical N) scores 290.
Incremental fit indices show lower values, but able to support the conceptual model. NFI
(Normed fit index) scores 0.59, NNFI (Non-normed fit index) 0.71, while CFI (Comparative
fit index) scores 0.75 .
Residuals indices show low values, confirming that the assumed model is able to explain
most of the observed data. RMR (Root mean square residual) index scores 0.15, while
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RMSEA (Root mean square error of approximation) index 0.042, with 90 % confidence
interval between 0.027 and 0.086 (Figure 5 and 6).
5. CONCLUSION
The analysis carried out with LISREL allowed us to examine more precisely the determinants
of international performance of SMEs, through the assumptions made with reference to the
structural model.
The results are as follows:
1) There is a positive direct effect between the firm characteristics (firm size, age, degree
of innovation/technology) and the international performance.
The direct positive effect between firm characteristics and the international performance can
be explained considering that larger companies, with more experience, especially on the
international markets, together with a higher degree of innovation tend to achieve better
results on the international markets.
2) Objective entrepreneurial characteristics (education and age of the owner, his/her
work and international knowledge) are positively and directly linked to the
international performance of the firm.
The education level and the experience gained by the owner tend to have particular
importance in fostering better results on the international markets. This confirms the core role
played by the owner in leading SMEs towards successful development paths, especially at the
international level.
3) Some firm characteristics have a positive direct effect on the subjective
entrepreneurial characteristics (proactivity, risk acceptance, tolerance for ambiguity,
initiative, personal development, relationship with uncertainty).
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The positive direct effect between some firm characteristics and the subjective
entrepreneurial characteristics can be explained considering that in larger firms, with higher
degrees of experience and innovation, the owner tends to be more proactive and tolerant for
ambiguity and risk.
4) Objective entrepreneurial characteristics are positively and directly linked to
subjective entrepreneurial characteristics.
The model confirms that owners with a higher level of education, maturity and experience
tend to have a more tolerant approach for risk and ambiguity and to be more oriented towards
personal development.
5) Subjective entrepreneurial characteristics have a direct positive effect on the
international performance of the firm.
The analysis has confirmed the hypothesis that higher degrees of proactivity, risk acceptance
and initiative by the owner tend to encourage better international results of the SMEs. The
confirmation of this hypothesis stresses the importance that, not only objective factors, but
also subjective determinants have in supporting SMEs’ activities towards successful growing
paths on the international markets.
6) International
activity
(internationalization
methods,
expansion
strategies,
international planning, internationalization motivations, resource seeking orientation,
foreign procurement) has a positive direct effect on international performance of
SMEs.
The development of a clear internationalization strategy and the complete awareness of the
reasons that lead the company towards international activities tend to promote the
achievement of better results, in terms of turnover achieved and market share gained.
7) Firm characteristics are negatively and directly linked to the international activity.
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The analysis has revealed that some firm characteristics, such as firm size, have a negative
effect on the international activity of SMEs. In particular, SMEs with larger size but without
a sufficient level of resources can find several obstacles along their international development
paths. Larger sizes involve more complex organizational structures, often characterized by a
lower degree of flexibility. Under these circumstances, it can be more difficult to quickly
meet market changes. As stated before, SMEs tend not to follow competitive paths based on
their dimension, as larger companies usually do, but tend to have an international orientation
based on resource seeking approaches. This confirms that SMEs tend to implement strategies
based on foreign countries comparative advantages in terms of factor costs, with particular
reference to labour costs.
8) Objective entrepreneurial characteristics have a positive direct effect on the
international activity of SMEs.
Higher levels of education, maturity and experience of the owner, especially on the
international stage, tend to promote the development of strategies or action plans able to
support the international activity of the firm and better awareness of the reasons of
international investments. This analysis can have different managerial implications. On the
one hand, better international performances are not only influenced by hard factors, such as
firm and objective entrepreneurial characteristics, but also by determinants linked to the
cognitive sphere, such as values, attitudes and norms. On the other hand, the flexibility of
SMEs, due to their small sizes, tend to promote internationalization strategies based on jointventures, cooperation agreements, technology transfers, rather than those based on direct and
indirect exports or FDIs.
The analysis tends to confirm that the owner is still a fundamental determinant in leading
SMEs’ international strategies.
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 Wolff, J. A. & Pett, T. L.(2000), “Internationalization of Small Firms: An Examination of Export
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TABLES
Table 1: Frame of reference
Variables
Dependent variable
Author
Independent varible
Method
OGG
International Performance
EV IMP
and
INT
SOG
Axinn (1988)
Regression
ER
no
yes
yes
no
Diamantopoulos and Inglis (1988)
Discriminant
ER (dichotomous)
no
yes
no
yes
Koh and Robicheaux (1988)
ANOVA
EP perceived vs. domestic
no
no
no
yes
Culpan (1989)
Discriminant
composite
no
yes
no
no
ER
no
no
no
yes
∆ER
no
yes
yes
no
no
no
no
yes
Regression
Bourantas and Halikias (1990)
Regression
Dichtl, Köglmayr and Müller (1990)
Average ER, average ∆ES vs. domestic,
evaluation ∆ES vs domestic,
Lee and Yang (1990)
evaluation ∆ES vs industry,
ANOVA
average EP vs. domestic,
evaluation EP vs. industry
Samiee and Walters (1991)
T-test / χ2
ER, number of markets, EP
no
yes
no
yes
Holzmüller and Kasper (1991)
Regression
ER
no
yes
yes
yes
Koh (1991)
SEM: PLS
composite (ER and ∆ER)
yes
yes
yes
yes
Lellan (1993)
χ2 and ANOVA
EP perceived vs. domestic
no
yes
yes
yes
Donthu and Kim (1993)
Correlation
yes
no
no
yes
no
yes
yes
yes
yes
yes
yes
yes
no
no
no
yes
Beamish, Craig and OGG e SOG
ER in UK, EP in UK, ER in Canada,
EP in Canada
Discriminant
∆ES
Kaynak and Kuan (1993)
Discriminant
De Luz (1993)
Walters (1993)
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Annual ES, annual EP, ER,EP -ratio
5 year ∆EP
Correlation
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T-test, ANOVA
no
yes
no
yes
5 year ES and satisfaction with ES)
no
yes
yes
yes
ES, ER, after tax EP margins
Evangelista (1994)
composite ( ER, relative EP, development
SEM: LISREL
Naidu and Prasad (1994)
Discriminant
satisfaction with IP
no
yes
yes
yes
Johnson and Arunthanes (1995)
Logistic regression
3 year trend ES and EP
yes
yes
yes
no
Moini (1995)
Regression
EP and EMS, ES
yes
no
no
yes
Wagner (1995)
ANOVA, Duncan’s test
composite ( ER and ∆ER)
no
yes
yes
yes
Das (1994)
Tobit
ER
no
yes
no
no
Holzmüller and Stöttinger (1996)
Discriminant
ER
yes
yes
yes
yes
Katsikeas, Piercy and Ioannidis (1996)
SEM: PLS
composite ( ER and ∆ER)
yes
yes
yes
yes
Shoham (1996)
Regression ( 3 steps)
composite ( EMS, ES,EP)
no
yes
yes
yes
Katsikeas, Deng and Wortzel (1997)
Regression
composite ES, EP, ∆ES, ∆EP
no
no
no
yes
Wood and Robertson (1997)
MANCOVA,ANOVA
international degree
yes
yes
no
yes
Leonidou and Kaleka (1998)
Regression
no
yes
yes
no
ER, expected ER, composite (actual ER
Expected ER)
Nakos, Brouthers and Brouthers (1998) ANOVA, Scheffe’s test
international degree
no
no
no
yes
Stump, Athaide and Axinn (1998)
ER, perceived EP
si
si
si
si
ER, EP
no
si
si
si
no
no
no
yes
no
yes
yes
yes
Regression
Regression
Shoham and Kropp (1998)
Regression
Composite ES (ER and satisfaction ,
ES and satisfaction., EMS) composite ∆ES
( ER and satisfaction ES and satisfaction
EMS)
Composite EP ( ROA and satisfaction ROI
Thirkell and Dau (1998)
and
profit margin and satisfaction) composite
∆EP
( ∆ROA, ∆ROI and satisfaction ∆ profit
margins)
composite (5 year ER, 5 year ∆ER,
Beamish, Karavis, Goerzen and Lane
Strategic importance and self-assessment
Regression ( 2 steps)
(1999)
EMS, EP, market diversification,
customer satisfaction)
Shoham (1999)
Regression
ES,ER,∆ES
no
yes
no
yes
Baldauf, Cravens and Wagner (2000)
SEM: LISREL
Composite IP and 5 year composite ∆IP
yes
no
no
yes
Dean, Mengüç and Myers (2000)
Regression
ES, ER, composite Export effectivness
yes
yes
no
yes
Francis and Collins–Dodd (2000)
Discriminant
ES, ∆ES, ER
no
yes
yes
yes
ER, ES, ∆ER, Export gross profit margins
no
yes
no
yes
yes
no
no
yes
ER
no
no
no
yes
composite (self assessment EP, ROI, IP
yes
yes
yes
no
Regression
Robertson and Chetty (2000)
composite (impostance and satisfaction
Regression
EP,ER,
Shoham (2000)
diversification, ∆ES and evaluation IP)
Regression
Wolff and Pett (2000)
Balabanis and Katsikea (2003)
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Overall 5 years vs competitors)
Julien and Ramangalahy (2003)
composite (reputation, ∆ES, EP, ER).
SEM: PLS
no
yes
yes
Notes: EV = Environment, IMP = Firm characteristics, OGG e SOG = Objective and subjective entrepreneurial
characteristics, INT = International activity, IP = International performance, ER = Export ratio/intensity, ES =
Export sales, EP = Export profitability, EMS = Export market share.
Table 2: Factor and reliability analysis
ITEMS
M
SD
Number of employees 2004-2006
1,606
0,546
Total turnover 2004-2006
7,002
0,498
1,589
0,856
4,437
7,413
1,943
0,663
1,722
0,086
1,667
0,474
FL
CA
FIRM CHARACTERISTICS - IMP
DIMENSION 1 – Firm size (X1)
0,903
DIMENSION 2 – Age (X2)
Number of generations of the firm
DIMENSION 3 – Degree of innovation/technology (X3)
% R&D expenses/total costs
OBJECTIVE ENTREPRENEURIAL CHARACTERISTICS - OGG
DIMENSION 1 – Education (X4)
Education of the owner
DIMENSION 2 – Age (X5)
Age of the owner
DIMENSION 3 – Work experience (X6)
Work experiences of the owner
DIMENSION 4 – International experience (X7)
0,621
Number of languages spoken by the owner
1,679
0,471
0,805
International experiences of the owner
1,634
0,485
0,690
SUBJECTIVE ENTREPRENEURIAL CHARACTERISTICS - SOG
0,789
DIMENSION 1 - Proactivity (Y1)
To resolve a problem I have to study each part of it in detail
5,195
1,207
0,805
People consider me to be a logical thinker
4,509
1,329
0,673
I believe that rational thinking is the only basis for decision making
4,698
1,353
0,603
I always look for better ways of doing things
5,525
1,019
0,592
I can see opportunities way before others do
4,602
1,347
0,480
DIMENSION 2 – Risk acceptance (Y2)
0,898
My firm has a high probability of success in foreign markets
4,460
1,961
0,909
International activity is a positive thing in my business
4,898
1,738
0,902
Exports are an important opportunity for my firm
4,857
1,921
0,870
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DIMENSION 3 – Tolerance for ambiguity (Y3)
0,754
I enjoy the challenges of uncertain situations
3,453
1,511
0,777
I enjoy working in uncertain situations
2,313
1,459
0,746
I enjoy the challenges of uncertain situations
3,543
1,574
0,742
Selling products in foreign markets implies high risks
3,639
1,445
0,574
Most of my decisions are based on my intuition
4,034
1,231
0,440
DIMENSION 4 – Initiative (Y4)
0,724
If I believe in an idea, no obstacle will prevent me from implementing it
4,698
1,403
0,678
I love when my idea win, even when opposing other views
4,675
1,526
0,670
I am very good at identifying opportunities
4,672
1,304
0,614
No matter the odds, if I believe in something I will make it happen
4,670
1,197
0,504
DIMENSION 5 – Personal development (Y5)
0,750
I am always at the lookout for things that will improve my life
5,274
1,215
0,839
At any situation I have always been an important factor for constructive change
5,137
1,159
0,748
If I see something I don’t like it, I fix it
5,703
1,007
0,548
Nothing is more exciting than seeing my ideas turn into reality
5,783
1,106
0,512
DIMENSION 6 – Relationship with uncertainty (Y6)
0,650
The uncertainty surrounding my firm prevents me from doing my best
3,684
1,775
0,793
I often get irritated when unexpected events ruin my plans
4,052
1,798
0,781
1,114
0,319
2,351
1,316
1,526
0,503
Competitors already in the foreign market
2,650
1,132
0,785
Wide market
1,862
1,116
0,733
Follow customers in their internationalization processes
2,883
1,121
0,723
Geographical position for further investments in the region
2,850
1,102
0,694
INTERNATIONAL ACTIVITY - INT
DIMENSION 1 – Internationalization methods (Y7)
Internationalization method
DIMENSION 2 – Expansion strategies (Y8)
Development of internationalization strategy
DIMENSION 3 – International planning (Y9)
Strategy or action plan
DIMENSION 4 – Internationalization motivations (Y10)
0,727
DIMENSION 5 – Resource seeking orientation (Y11)
0,767
Low raw materials costs
2,855
1,006
0,866
Low semi finished goods costs
3,066
0,981
0,796
Low labour costs
2,787
1,226
0,787
1,760
0,722
0,326
0,305
2,683
0,534
DIMENSION 6 – Foreign procurement (Y12)
Foreign procurement
INTERNATIONAL PERFORMANCE - IP
DIMENSION 1 – Export sales/total turnover (Y13)
% Export sales/total turnover (average years 2004-06)
DIMENSION 2 – International market share (Y14)
International market share
Notes: M=mean, SD=standard deviation, FL=factor loading, CA= Cronbach’s α.
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Table 3: Main LISREL abbreviations
ABBR.
MEANING
ABBR.
MEANING
Y
Endogenous observed variables
β
Structural coefficients between η and η
X
Exogenous observed variables
γ
Structural coefficients between ξ and η
η
Endogenous latent variables
ζ
Stochastic errors η
ξ
Exogenous latent variables
ε
Stochastic errors Y
λx
Structural coefficients between η and Y
δ
Stochastic errors X
λy
Structural coefficients between ξ and X
Table 4: The bonds between observed and latent variables
Observed variables/Latent Variables
SOG (η1)
Y1
λy11
Y2
λy21
Y3
λy31
Y4
λy41
Y5
λy51
Y6
λy61
INT (η2)
Y7
λy72
Y8
λy82
Y9
λy92
Y10
λy102
Y11
λy112
Y12
λy122
IP (η3)
Y13
λy133
Y14
λy143
IMP (ξ1)
X1
λx11
X2
λx21
X3
λx31
OGG (ξ2)
X4
λx42
X5
λx52
X6
λx62
X7
λx72
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Table 5: Fit indices of latent variables
Latent variables
Observed variables
Proactivity
RMSEA = 0
Risk acceptance
Subjective entrepreneurial
characteristics
Tolerance for ambiguity
Iniziative
Relationship with uncertainty
Internationalization methods
Expansion strategies
NFI = 0.96
GFI = 0.99
AGFI = 0.97
RMSEA = 0.046
90 % CI RMSEA = (0.02 ; 0.097)
International planning
NFI = 0.77
Internationalization motivations
NNFI = 0.74
Resource seeking orientation
International performance
90 % CI RMSEA = (0 ; 0.062)
NNFI = 1
Personal development
International activity
Fit indices
GFI = 0.94
Foreign procurement
AGFI = 0.87
Export sales/total turnover
Not calculated by LISREL due to
International market share
the small number of observed
variables.
RMSEA = 0.058
Firm characteristics
Firm size
90 % CI RMSEA = (0 ; 0.25)
Age
NFI = 0.88
Degree of innovation/technology
NNFI = 0.86
GFI = 0.99
AGFI = 0.96
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RMSEA = 0.017
Education
90 % CI RMSEA = (0 ; 0.18)
Age
Objective entrepreneurial
characteristics
NFI = 0.93
Work experience
NNFI = 0.98
International experience
GFI = 0.99
AGFI = 0.96
Table 6: The bonds between latent variables
IMP (ξ1)
OGG (ξ2)
SOG (η1)
γ11
γ12
INT (η2)
γ21
γ22
γ31
γ32
Latent varibles/latent variables
SOG (η1)
β31
IP (η3)
INT (η2)
β32
FIGURES
Figure 1: Model of the determinants of International Performance
+
FIRM
CHARACTERISTICS
+
SUBJECTIVE
ENTREPRENEURIAL
CHARACTERISTICS
+
-
INTERNATIONAL
PERFORMANCE
+
OBJECTIVE
ENTREPRENEURIAL
CHARACTERISTICS
+
INTERNATIONAL
ACTIVITY
+
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Figure 2: Y-Measurement model
Figure 3: X-Measurement model
Figure 4:
Structural
model
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Figure 5: Main fit indices of LISREL model
Degrees of Freedom = 180
Minimum Fit Function Chi-Square = 326.57 (P = 0.00)
Normal Theory Weighted Least Squares Chi-Square = 298.53 (P = 0.00)
Minimum Fit Function Value = 2.55
Root Mean Square Error of Approximation (RMSEA) = 0.042
90 Percent Confidence Interval for RMSEA = (0.027 ; 0.086)
P-Value for Test of Close Fit (RMSEA < 0.05) = 0.0092
Normed Fit Index (NFI) = 0.59
Non-Normed Fit Index (NNFI) = 0.71
Comparative Fit Index (CFI) = 0.75
Critical N (CN) = 290.00
Root Mean Square Residual (RMR) = 0.15
Standardized RMR = 0.10
Goodness of Fit Index (GFI) = 0.82
Adjusted Goodness of Fit Index (AGFI) = 0.77
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Figure 6: LISREL model
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1 The influence of managerial characteristics on the internationalization process has been analyzed by Leonidou
and Katsikeas, (1996); Leonidou, Katsikeas and Percy, (1998) and Manolova , Edelman and Greene, (2002).
2
The authors confirm the hypothesis that there is a direct link between the number of employees and the
international activity of the firm.
3
Since SMEs are not able to influence significantly the external environment, due to their small dimension
(Bijmolt e Zwart, 1994), we have excluded the environment dimension from the analysis.
4 See also Sousa C.M.P., 2004.
5
Overall model fit indices include GFI (Goodness of fit index) and AGFI (Adjusted goodness of fit index), by
Jöreskog and Sörbom (1986).
The goodness-of-fit index (GFI) is defined as:
GFI=1-[Ti/max (Ti)] (1)
The numerator in (1) is the minimum of the fit function after the model has been fitted; the denominator is the fit
function before any model has been fitted, or when all parameters are zero.
The goodness-of-fit index adjusted for degrees of freedom, or the adjusted GFI, AGFI, is defined as:
AGFI=1-(k/df)(1-GFI) (2)
where df is the degrees of freedom of the model. This corresponds to using mean squares instead of total sums
of squares in the numerator and denominator of 1 - GFI. Both of these measures should be between zero and
one, although it is theoretically possible for them to become negative. This should not happen, of course, since it
means that the model fits worse than no model at all.
We should also consider CN index (Critical N). It underlines if the sample size is adequate.
CN= [χ²(1-α)/F]-1 (3)
where χ² is the 1 - a percentile of the chi-square distribution. This is the sample size that would make the
obtained chi-square just significant at the significance level a.
6
Incremental fit indices include NFI (Normed fit index), by Bentler and Bonnet (1980), NNFI (Non-normed fit
index), by Bollen (1988) and CFI (Comparative fit index).
NFI compares the improvement in the minimum discrepancy for the specified (default) model to the discrepancy
for the independence model. A value of the NFI below 0.90 indicates that the model can be improved.
NFI=1-F/Fi (4)
NNFI adjusts NFI for the dgrees of freedom.
NNFI=(fi-f)/(fi-1)
with f=nF/df and fi=nFi/dfi (5)
NNFI close to 1 indicates a good fit.
CFI compares the existing model fit with a null model which assumes the latent variables in the model are
uncorrelated. CFI close to 1 indicates a very good fit.
CFI=1-τ/τi
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with τ=max(nF-d,0) and τi=max(nFi-di, nF-d, 0) (6)
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Residuals indices include RMR index (Root mean square residual) and RMSEA index (Root mean square error
of approximation). Residuals are the part of observed data that the estimated model is not able to explain.
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