International Journal of Management, Knowledge and Learning, 2(1), 25–44
Job Performance, Job Satisfaction
and Human Capital in the Labour
Market in Bosnia
Alexi Danchev
Fatih University, Turkey
Erkan Ilgün
International Burch University, Bosnia and Herzegovina
The paper analyses the effect of job performance, job satisfaction and human
capital. It shows that together with monetary factors, such factors as the
perception of the social importance of the job, the ability to meet good friends
in the team, and the atmosphere within which the respondents work, may also
have a high level of impact on labour supply through human capital. The paper
demonstrates the power of non-monetary factors in achieving improvements
in the context of the ‘job performance-job satisfaction-human capital’ chain,
thus bringing about positive changes in labour market supply in Bosnia.
Keywords: job performance; human capital; job satisfaction; Bosnia
Introduction
During the past 20 years, dramatic changes have taken place in the labour
market in Bosnia. The transition from a centrally planned economy to a
market economy has created new conditions for the supply and demand
of labour. Job performance is a component of this market, affecting both
supply and demand. One of the conditions for good job performance is the
level of job satisfaction, which is expected to have a positive influence on it.
There are many aspects of this relationship that have not yet been suffi-
ciently explored. For example, a high level of job satisfaction creates incen-
tives for activity with many external effects, which result in the encourage-
ment of economic agents to improve their individual performance, and, in
particular, to expand their knowledge and skills, to work better, to increase
their creativity, etc. Job satisfaction is one of the central factors that creates
incentives for human capital improvement and thus for a better quality of
labour supply. The decision to pursue such improvement is strongly related
to the value systems of individuals and is defined by a complex of social,
economic, psychological, ethical, and other elements, which is reflected in
their job satisfaction. As Hamermesh (1999) indicates, ‘Only one measure,
the satisfaction that workers derive from their jobs, might be viewed as re-
flecting how they react to the entire changing panoply of job characteristics.’
www.issbs.si/press/ISSN/2232-5697/2_25-44.pdf
26 Alexi Danchev and Erkan Ilgün
Thus, job satisfaction can be regarded as a pivot point between job per-
formance and human capital. The increase in knowledge and skills as the
main characteristic of human capital is expected to contribute to a greater
job satisfaction, which in turn should lead to improved job performance.
Certainly in real life the link is not so linear, but we apply this simplification
to facilitate the analysis of this complex problem. All these three elements
– job performance, job satisfaction and human capital – are reflections of
various facets of the labour supply. For this reason, we initially concentrate
on the link between job performance and increased knowledge and skills
– that is, on the quality of the human capital. We then try to observe the
effect of job satisfaction on this relationship.
Based on this perspective, we can outline the following research objec-
tives:
1. To construct the theoretical background of the study examining
the links within the labour market triangle, job performance-job
satisfaction-human capital, with the aim of capturing significant new
characteristics of this market.
2. To identify suitable analytical methods for the quantitative measure-
ment of the links within this triangle and the factors affecting its com-
ponents and their relationships.
3. To test the formulated theoretical hypotheses using concrete data
from the labour market in Bosnia and to outline further problems of
studying labour markets from the perspective of job performance, job
satisfaction and human capital.
In the first part of this paper, we present a critical review of the litera-
ture on the basic relationships. Theoretical models used in further work,
as a result of the review, are then outlined. The statistical data and the
features of the sample are explored by means of factor analysis. Next, we
examine some of the basic characteristics of the relationship between job
performance and the quality of human capital, using a logistic approach. As
a result of the features of the collected data, most of the models are tested
by means of the Maximum Likelihood technique, followed by a detailed anal-
ysis of job satisfaction and the factors influencing its extent. Consequently,
the link between job performance and human capital is analysed, taking
into account the effect of job satisfaction on both, job performance as well
as human capital.
The discussion of some of the main issues that are not fully examined in
this paper highlights problems such as the expected biases and the search
for more objective methods of data collection, i.e. looking at the sample
‘from outside.’ Finally, conclusions are presented and recommendations
outlined for further studies.
International Journal of Management, Knowledge and Learning
Job Performance, Job Satisfaction and Human Capital 27
Brief Critical Review
Job performance is certainly an important feature of the labour market.
Introducing three categories of job performance, namely organizational citi-
zenship behaviour (OCB), counterproductive work behaviour (CWB) and task
performance, Ong (2012) indicates the way in which ‘job performance could
benefit organizations and their members.’ From a psychological viewpoint,
job performance is presented as a significant ingredient of the personality
dimensions in the work of Barrick and Mount (1991), who outline three cri-
teria of job performance: job proficiency, training proficiency and personal
data. The Human Resources project at the University of California, Berkeley
(Human Resources at UC Berkley, 2011) made great strides in the method-
ology of Job Performance Standards, formulating them as describing ‘the
scope, key responsibilities, and knowledge and skill requirements of a spe-
cific job level within a family.’ Morrison et al. (2008) indicate that ‘job perfor-
mance standards are critical to help ensure the success of the employee,
supervisor, work unit and agency.’
Judge, Thoresen, Bono and Patton (2001) add to this discussion the
effect of job satisfaction on job performance, providing a reliable qualitative
and quantitative review of the relationship between the two, and underlining
the difficulty of producing a clear estimation of this relationship.
Although both job performance and job satisfaction are psychological en-
tities, they have important socio-economic dimensions and without doubt
can be regarded as a part of labour market studies. Bartel (1992) looks
for the link between on-the-job training, wages and job performance, and
indicates how biases can be avoided in such estimations. Bartel’s conclu-
sion is that training (the growth of human capital) leads to wage growth and
improvement in job performance.
During the 1960s, an important observation was formulated by Lawler
and Porter (1967). According to their work, ‘much of the interest in job
satisfaction seems to have come about because of its presumed relation-
ship to job performance.’ In more recent times, this relationship remained
somewhat neglected, as most of related studies are oriented either toward
various aspects of job performance or toward job satisfaction. The level of
job performance, as one of the indicators of the quality of labour supply, is
most probably determined largely by job satisfaction and the whole spec-
trum of factors influencing it. Pugno and Depedri (2009) not only confirm
the positive relationship between job performance and job satisfaction, but
also indicate that the economic incentives may not be the main motivations
of job performance. This is an additional piece of evidence that job perfor-
mance is influenced by a complex of factors and it is difficult to outline a
universal scheme of their interrelationships.
Volume 2, Issue 1, 2013
28 Alexi Danchev and Erkan Ilgün
While the analysis of job performance includes a broad range of studies
at individual and company level, research on job satisfaction has been more
directly oriented towards individuals. Advances have been made in both the-
oretical and empirical aspects. Brayfield and Rothe (1951) construct one of
the first indices of job satisfaction and incorporate differences in work val-
ues, presenting job characteristics as ‘key explanatory variables.’ They em-
pirically test the theoretical background, examining the relationship between
job satisfaction and such indicators as work values and job rewards associ-
ated with ‘six dimensions of work-intrinsic, convenience, financial, relations
with co-workers, career opportunities and resource adequacy’ (Kalleberg,
1977).
This concept has been enriched in the modern vision of job satisfaction,
which indirectly traces out the link with job performance. Both job-related
issues are analysed in relation to the monetary (wage, income, etc.) and
non-monetary factors. Arvey, Bouchard, Segal and Abraham (1989) study
the role of intrinsic, extrinsic, and genetic satisfaction with the current (or
major) job. Weiss (2002) underlines the role of overall evaluative judgments
about jobs, affective experiences at work, and beliefs about jobs in job
satisfaction. The complexity of the link between job satisfaction and the
monetary factors affecting it is demonstrated in the work of Grund and
Schmitt (2013) on the effects of works councils on employees’ wages and
job satisfaction in general and in subgroups with respect to sex and occupa-
tional status. These studies enable the formulation of the hypothesis that
the monetary reasons for job performance/satisfaction are not always the
most important and that non-monetary factors may also play a significant
role in this chain.
Several studies seem to be more directly relevant to our paper, thus
helping to define the choice of the variables for testing the models in part
3. Special attention has been paid to the choice of non-monetary variables,
due to a wide variety of possible approaches. Among the numerous studies
in this area, we selected indicators following the results of Troup and Rose
(2012), examining the link between job satisfaction and the way the time
is spent during working hours, and the detailed study by Jones (2005) of
various aspects of job satisfaction and communication with amiable people,
which were divided into two parts, namely the satisfaction gained through
meeting friends on the job (Krug & Rebien, 2012), and the satisfaction of
working in a good team, even if this does not necessarily turn into friendship
(Gockel, Robertson & Brauner, 2013).
Other social factors that are relevant to our study are analysed by Kulka-
rni and Nithyanand (2013) and include the level of friendship in the unit in
which the respondent works, and the perception of the social importance
of the job. However, account must also be taken of the factors that could
International Journal of Management, Knowledge and Learning
Job Performance, Job Satisfaction and Human Capital 29
have a negative influence on job satisfaction (Fraňek & Večěra, 2008), such
as poor social environment, perception that the work is of low value, etc.
In terms of the choice of suitable methods of analysis, we followed Judge
et al. (2001), who underline the importance of using advanced analytical
methods, since both job performance and job satisfaction are multifaceted
phenomena that not only exist per se, but also depend on the point of view
from which they are analysed. This problem is discussed in detail in part 4.
Next we narrowed down our analysis of the literature by focusing on
human capital; the literature on this is vast, and a full survey would fill many
volumes. For the purposes of this study, we have constrained it by using
knowledge and skills as a proxy for human capital, although in our previous
publications it was analysed in a broader sense that included individuals’
health and value systems (Danchev, 2010).
Among the vast literature that connects the different elements of
our triangle, we should also mention Blinder and Weiss’s (1976) study
on human capital and labour supply, and the projects within the Cen-
ter for Effective Organizations at the University of Southern Carolina’s
(http://ceo.usc.edu/research/human_capital.html) research programme
on strategic talent management and human capital, combining ‘organi-
zational, managerial, and economic perspectives to bridge the strategic,
business, and organizational aspects of human resource management.’
In the wider research context, studies on the link between human capital
and its affect on labour supply reveal various other aspects of the prob-
lem, including life cycle issues, effects of human capital on wages, and
the determinants of labour supply in developing countries (Sahn & Alder-
man, 1988). López-Bazo and Motellón (2012) report the results of a study
of human capital and regional wage gaps, thus indicating the necessity of
research on the role of both monetary and non-monetary factors of human
capital formation.
These studies provide evidence of the advances in research on the com-
plexity of human capital and the factors influencing its formation, which help
to better orientate its link with job performance and job satisfaction.
For this reason, in our study we considered human capital to only be
the starting point for analysing the relationships between job performance
and job satisfaction. These relationships are presented in this way merely
in order to simplify the analysis with a view of moving toward more realistic
models in the future.
The Theoretical Background
As previously stated, job performance is defined by many external and in-
ternal components, and it is not possible to construct a model that could
encompass all of them. For this reason, we narrowed down our analysis and
Volume 2, Issue 1, 2013
30 Alexi Danchev and Erkan Ilgün
Job performance Willingness
to work
Human capital
Job satisfaction
Factors affecting
job satisfaction
Figure 1 The Scheme of the Study
formulated the hypothesis that job performance primarily depends on the
Willingness to Work (WTW), which in turn depends on job satisfaction and
human capital:
JOBPN = f(WTW,SATISF), (1)
WTW = h(PSKILL,SATISF), (2)
where JOBPN is job performance and PSKILL is the present level of knowl-
edge and skills as a proxy for human capital. It should be noted that, similar
to Willingness To Pay (WTP) in demand studies, we regard WTW as a basic
characteristic of labour supply reflecting a complex of social, economic, psy-
chological, political, and other elements. On the other hand, the rationale
of including job satisfaction and human capital in this relationship is based
on the results from the abovementioned studies on the role of both job
satisfaction and human capital in the labour market. Combining 1 and 2
gives:
JOBPN = f[h(PSKILL,SATISF)]. (3)
In our opinion, such an interpretation of the problem reveals new aspects
of the labour market that have not previously been presented. The scheme
of our study is presented in Figure 1.
In order to more adequately outline the influence of human capital and
job satisfaction on job performance, we initially defined the factors influ-
encing job satisfaction. It is a process that includes many possible factors,
and as shown in the critical review, there are many approaches to it. For the
purpose of our analysis, we selected several factors and divided them into
two groups:
•Monetary factors – the level of income and related factors (if these
increase, the level of job satisfaction increases).
•Non-monetary (some of these can be defined as hedonic) factors,
which can increase or decrease the level of job satisfaction.
International Journal of Management, Knowledge and Learning
Job Performance, Job Satisfaction and Human Capital 31
To facilitate our analysis we assumed a continuous, concave and twice-
differentiable function of job satisfaction (the mathematical reasoning of
these conditions are not presented for technical reasons) so that:
SATISF = f[exp(α,β)]. (4)
Correspondingly, we can decompose monetary and non-monetary factors
as follows:
α= f(x1,x2,. . . xn), (5)
where x1,x2,. . . xn are monetary-related factors; and
β= z(z1,z2,. . . zn), (6)
where z1,z2,. . . zn are non-monetary (hedonic)-related factors.
We can combine monetary and non-monetary characteristics into one
equation as:
SATISF = f(α,β, t) = f(x1,x2,. . . xn),z(z1,z2,. . . zn). (7)
If we assume a non-linear form, the equation would be:
SATISF =α0x
α1
1 x
α2
2 , . . . x
αn
n z
β1
1 z
β2
2 , . . . z
βn
n . (8)
The next problem was to select the concrete indicators for the empir-
ical test of (8). As can be seen from the critical review, there are many
approaches to different aspects of the problem, which allows a selection of
various indicators to cover the most essential part of the model.
Following the results of the critical review, our study considered such
factors as the way the time is spent during working hours (z1), together with
the satisfaction derived from communication with amiable people, which
has two aspects: the satisfaction gained through meeting friends on the
job (z2), and the satisfaction of working in a good team, even if this does
not necessarily turn into friendship (z3). In our opinion, another important
non-monetary characteristic is the perception of social significance of one’s
work (z4), which is also included in equation 8.
However, the analysis would be incomplete if we did not take into ac-
count the negative aspects of this relationship. Thus, the basic dependent
variable SATISF was regressed against the following factors:
Factors that are expected to have a positive influence on job satisfaction,
namely (the number in parenthesis reflects the corresponding number in
the questionnaire):
• JOBMON – level of income sufficient ‘to enjoy my life’ (1.7.1),
• JOBFR – ability to make friends on the job (1.7.2),
Volume 2, Issue 1, 2013
32 Alexi Danchev and Erkan Ilgün
• JOBSOC – the perception of social importance of the job (1.7.3),
• JOBNICE – working hours pleasantly spent (1.7.4.),
• JOBTEAM – the pleasure of working within a team (1.7.5).
It is envisaged that these variables, except JOBMON, reflect the main
social factors of job satisfaction outside the influence of income level. The
choice of these variables was on one hand inspired by the work of Pugno
and Depedri (2009), which emphasizes the role of interest in the job, and
Helliwell and Huang’s (2005) study on the role of social capital in the work-
place, which finds a significant and substantial relationship between trust
in management and job satisfaction, etc. On the other hand, the choice was
influenced by factors such as the perception of social importance of the job,
which have so far not been sufficiently discussed, but which could also be
expected to have significant influence on job satisfaction.
Factors that are expected to negatively affect job satisfaction. The choice of
factors that negatively affect job satisfaction is difficult due to the complex-
ity of the relationships. Analysis of the various factors that have negative
effects on job satisfaction is presented in the studies of Redman and Snape
(2006), who suggest that ‘age discrimination acts as a stressor, with [. . .]
negative effects of perceived age discrimination on job and life satisfaction,
perceived power and prestige of the job.’ Willem, Buelens and de Jonghe
(2007) describe the negative effect of centralization on job satisfaction.
Hsu (2011) indicates that work-family conflict has a negative effect on job
satisfaction.
In order to allow comparison of the effects of the factors affecting job
satisfaction positively and negatively, we selected negative factors as op-
posites to the positive ones. In this way, we expect to be able to present a
more comprehensive analysis by comparing the extremes. For example, if
we choose a factor such as a level of income sufficient to ‘enjoy my life,’ we
correspondingly formulate a factor at the other extreme – too low a level of
payment; to the perception of the social importance of the job, we counter-
vail the perception that the job is useless for society, and so on.
Next, we formulated the following factors with negative effects on job
satisfaction:
•NJOBMON – the level of payment is too low (1.8.1),
•NJOBATM – dislike the atmosphere in the work team (1.8.2),
•NJOBUL – the perception that the job is useless for society (1.8.3),
•NJOBBOR – the job is boring and irritating (1.8.4),
•NJOBIP – there are no interesting people in the firm (1.8.5).
Our expectation is that these two groups of variables will encompass a
International Journal of Management, Knowledge and Learning
Job Performance, Job Satisfaction and Human Capital 33
broad spectrum of the basic factors defining the level of job satisfaction.
Finally, we added socio-economic indicators, such as age, gender, working
experience, educational level and health status, to the model.
Research Strategy
The theoretical background presented above imposes the requirement of a
search for a suitable strategy to test the theory with empirical data. The
starting point is the three basic dependent variables – job performance,
job satisfaction and human capital – and a complex of other variables that
are expected to affect the dependent variables in various ways. Several
problems can be outlined, the solution of which influences the research
strategy of the study.
Data Collection
The nature of the variables in the models is such that it requires the partic-
ipants to be interviewed in the labour market. This entails the construction
of a suitable questionnaire and the organization of the interview process.
Since telephone and postal interviewing normally have low response rates,
and taking into account the features of the questions that require more
direct links with the respondents, the survey took the form of personal in-
terviews with the respondents. Despite the need to collect a large volume
of information to cover the details of the problems, the questionnaire was
controlled, so that it took no more than 15–20 minutes of the respondents’
time. The selection of the respondents was random.
Defining the Methods of Measuring the Relationships
Data collection in the form of interviews allows a large number of interre-
lated performance measures to be collected, with this number subsequently
being reduced (Kalogeras et al., 2013). In this case it is normal to apply the
principal component (factor) analysis to outline the main groups (eigenval-
ues) of relationships. The merits of factor analysis are multifarious: it not
only defines the basic groups of interrelated components, but also provides
useful information on the links between the components, which can be used
as a basis of further in-depth analysis, an approach demonstrated by many
studies (Papadimitriou, Theofilatos & Yannis, 2013; Broberg, Salminen, &
Kyttä, 2013; Siddiqui & Ahmed, 2013). Such an approach also provides
information that is useful as an orientation measure in the search for suit-
able methods of analysis for the next, more detailed, stages of the study of
these relationships.
Traditionally, the results of the factor analysis are consequently used
to construct the econometric models applying ordinary least square (OLS)
or other techniques. As most of the observations in our study are in the
Volume 2, Issue 1, 2013
34 Alexi Danchev and Erkan Ilgün
form of ranked variables, following the practice of Al-Najjar and Elgammal
(2013) and Zhang, Duysters and Cloodt (2013), we selected the Ordered
Probit technique (quadratic hill climbing), which is expected to help produce
relatable estimations of the relationships.
Search for Suitable Interpretation
The methods presented above allow many sides of the estimations to be
revealed, and the presentation of these would fill many pages of comments
and interpretation. We restricted the interpretation within the following basic
reasonable boundaries:
•First of all there is a need to analyse the descriptive statistics of the
main variables to determine their suitability for further analysis.
•For the results of the factor analysis, we restricted our exposition
within the comments of the scree plot to determine the degree of
explanations of the variables by the eigenvalues. To reduce the num-
ber of variables we applied the Kaiser-Meyer-Olkin (KMO) Measure of
Sampling Adequacy and Barlett’s test to determine the proportion of
the variance that is due to the basic factors.
• It also makes sense to analyse the scatter plot of the matrices in
order to test for outliers and other deviations.
•As most of our variables are in the form of rankings, it is important
to apply the Ordered Probit (quadratic hill climbing) method for calcu-
lating the regression coefficients and their statistical significance, as
well as such characteristics as Pseudo R-squared coefficient, Schwarz
criterion, LR statistics and other characteristics.
Research Results
The data were collected by interviewing Bosnian citizens, who were ran-
domly selected from various parts of Bosnia. Taking into account the fea-
tures of the study, the enumerators were trained by the authors to carry
out semi-structured interviews, recording both the respondents’ answers to
the basic questions and their idiosyncratic comments. In such a way, we
collected data that allowed us to apply both positivist and phenomenolog-
ical strategies in the research. For technical reasons, this paper presents
only the results of the econometric models, leaving the phenomenological
analysis for further presentations.
During the process of data gathering, quota control was applied in line
with the structure of those parts of the population that represent the main
characteristics of job performance, job satisfaction and human capital for
the country. The quota control was constructed in such a way as to retain
only the main information necessary for the purposes of the model. Special
International Journal of Management, Knowledge and Learning
Job Performance, Job Satisfaction and Human Capital 35
attention was paid to the structure of education and work experience as the
main components needed for the next stage of the study, as well as such
socio-economic indicators as age, gender and place of residence.
The sample consists of interviews with 287 citizens from Bosnia as part
of the project ‘Managing human capital for the aims of sustainable develop-
ment (case study of some Balkan countries),’ which was funded by the Fatih
University (Istanbul, Turkey). The questionnaire also covered some general
issues relating to human capital and sustainable behaviour.
Not all respondents gave answers to all the questions asked. Thus, out
of the 287 respondents, only 256 gave relatively complete answers. Of
these, 0.7% have primary education, 50.2% secondary education, 45.1%
a university degree, 3.3% a master’s degree and 0.7% a doctorate. With
regard to working experience, out of 256 respondents, 31.3% have up to 3
years of work experience, 21.9% between 3 and 6 years, 12.6% between 6
and 10 years, 10.8% between 10 and 15 years, and 23.4% more than 15
years.
Socio-economic indicators were also collected, namely place of resi-
dence, age and gender. With regard to the regional aspect, most of the
respondents were from Sarajevo (76.3%). The age distribution within the
sample was: 2.1% aged 16–19 years, 47.0% aged 20–29, 23.2% aged 30–
39, 9.8% aged 40–44, 11.9% aged 45–49, 4.9% aged 50–59 and 1.1%
aged 60 or over. In terms of gender, 53.2% were male and 46.8% female.
These features characterize the sample as sufficiently indicative for out-
lining a general picture of the ‘job performance–job satisfaction–human cap-
ital’ triangle, as well as the factors influencing various aspects of this chain
in Bosnia.
We selected several indicators for characterizing these relationships. Job
performance and job satisfaction are represented on the 5-point Likert scale
JOBPN and SATISF, while human capital is defined by the level of education
EDUCATION, and the present level of knowledge and skills necessary to
complete the respondents’ jobs PSKILL. The descriptive statistics of these
variables are presented in Table 1.
We observed relatively high levels of job performance and job satisfac-
tion, as well as high levels of education and accumulated knowledge and
skills. Next we applied factor analysis in order to more distinctly outline the
level of correlation between variables.
Factor Analysis of the Variables in the Sample
The theoretical analysis presented above does not take into account all the
variables included in the questionnaire, and outlines only the main relation-
ships that are expected to reveal the link between the theory and empirical
observations. However, by means of factor analysis we tested all obvious
Volume 2, Issue 1, 2013
36 Alexi Danchev and Erkan Ilgün
Table 1 Descriptive Statistics of the Basic Variables
Item JOBPN SATISF EDUCATION PSKILL
Mean 4.027344 3.972656 2.546875 4.269531
Median 4.000000 4.000000 2.000000 4.000000
Maximum 5.000000 5.000000 6.000000 5.000000
Minimum 1.000000 1.000000 1.000000 1.000000
Std. Dev. 0.693976 0.779161 0.655108 0.687143
Skewness –0,45944 –0,65038 1.126210 –0,69478
Kurtosis 3.780489 3.864101 6.037315 3.867689
Jarque-Bera 15.50386 26.01205 152.5192 28.62673
Observations 256 256 256 256
and latent relationships and attempted to enrich the theoretical postulates
with inductive insights.
All extraction communalities in the sample are high, which is evidence
of a good presentation of the variables by the extracted components, and
there is no need for further component extraction. However, we observed
that the 9 initial eigenvalues were above 1, which explains only about 71%
of the variation. This result is the first confirmation of the hypothesis of the
complexity of the relationships between the variables in the model, in which
variance is dispersed among a relatively high number of eigenvalues. We
have to restrict the number of eigenvalues in further analysis, but reducing
this number results in a loss of information, which is high even with 9
eigenvalues (29%).
The problem is exacerbated by the fact that there is not a great differ-
ence between the extraction and rotation sums of the squared loadings,
which indicates that the rotated component matrix would not contribute
much more to the interpretation of the results than the original matrix. This
is illustrated clearly in the scree plot presented in Figure 2.
However, it makes sense to analyse the rotated component matrix to
determine the composition of the components. Due to technical reasons
we are not presenting the results in detail here. Nevertheless, it has been
clearly demonstrated that the first component is most highly correlated with
0.0
0.5
1.0
1.5
2.0
2.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Ei
ge
nv
al
ue
Component Number
Figure 2 The Scree Plot of the Variables in the Model
International Journal of Management, Knowledge and Learning
Job Performance, Job Satisfaction and Human Capital 37
job satisfaction (0.749), job performance (0.717) and WTW (0.697). The
second component, which can be defined as demographic, gives highest
values in relation to age (0.928) and years of working (0.913), with a large
difference between this and the next variable, PSKILL (0.324). A similar
picture is observed for the third component, which can be defined as hu-
man capital: education level has a value of 0.888 and years of schooling
a value of 0.815, while the next variable, the present level of knowledge
and skills, has a value of 0.521. Such a clear difference is not observed
in the next components; therefore, we were able to restrict our analysis to
the first three components. However, it should be noted that restricting the
analysis to the first three components would result in a loss of 69% of the
information, which is unacceptable, although all variables in the model are
well represented in these three components.
The scatter plot of the matrices (Figure 3) illustrates the links between
the components. There is skewed distribution in the first component (the
first plot of the first row) as a result of the skewness of some of the vari-
ables, as indicated above. In all matrices we observed outliers, which were
excluded from further analysis.
The fact that 9 eigenvalues account for around 69% of the variation
requires an attempt to be made to try to reduce the size of the data by
means of extraction of the principal components, which complicates the
analysis as it is already based on a rotated component matrix. In order to
more clearly outline the structure of the sample, the number of variables is
reduced and the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy
is applied to calculate the proportion of the variance that is due to the basic
factors. As it is not clear which variables should be eliminated due to the
structure of the eigenvalues, we calculated the KMO Measure of Sampling
Adequacy for the whole sample; this is known as KMO and Barlett’s test.
The result is 0.296, which is regarded as low (< 0.5), and requires a revision
of the structure of variables included in the model. However, the Bartlett’s
test of sphericity indicates a significance level of 0.000, which provides
encouragement to apply factor analysis for a more detailed study of the
problem.
Despite some fuzziness of the results, the interrelation of job perfor-
mance, job satisfaction and human capital with various variables of the
sample is distinctly observed. This provides the reason to take the next
steps: first, to test the relationships between these three variables, and
then to extend the analysis to include other variables until suitable results
are obtained.
Ordered Probit Analysis of the Relationships
As the data for first variable – job performance – were collected in the
form of a ranking, we applied the Ordered Probit technique (quadratic hill
Volume 2, Issue 1, 2013
38 Alexi Danchev and Erkan Ilgün
REGR Factor Score 1 for Analysis 1 REGR Factor Score 3 for Analysis 1 REGR Factor Score 5 for Analysis 1
R
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Figure 3 The Scatter Plot of the Matrices
climbing). The results of econometric calculations of the link between job
performance, job satisfaction, and knowledge and skills are presented in Ta-
ble 2. Job performance and job satisfaction are positively correlated, which
supports the above formulated assumption. Knowledge and skills have less
influence on job performance, but the link is also positive and sufficiently
influential.
The results provide evidence of the fact that both job satisfaction and
the accumulated knowledge and skills positively affect job performance.
However, job satisfaction in turn depends on a number of factors. Following
the literature recommendations (Aitchison & Silvey, 1957; Chimka & Wolfe,
2009), the dependent 5-scale variable SATISF was next regressed against
various combinations of positive and negative factors by means of Ordered
Probit regression. Table 3 displays the parameters of the best combination.
The results indicate that the ‘money to enjoy my life’ factor has the
greatest effect on job satisfaction in terms of probability, a fact that is
International Journal of Management, Knowledge and Learning
Job Performance, Job Satisfaction and Human Capital 39
Table 2 The Influence of Job Satisfaction and Knowledge and Skills on Job Performance
Variable Coefficient Std. error z-Statistic Prob.
SAFISF 0.712619 0.097995 7.271964 0.0000
PSKILL 0.572106 0.105512 5.422184 0.0000
Pseudo R-squared 0.158481 Akaike info criterion 1.759060
Schwarz criterion 1.838180 Log likelihood –234.9912
Hannan-Quinn criterion 1.790817 Restr. log likelihood –279.2466
LR statistic 88.51081 Avg. log likelihood –0.857632
Prob. (LR statistic) 0.000000
Notes Dependent variable: JOBPN; Method: ML – ordered probit (quadratic hill climbing);
Sample: 287; Included observations: 274.
Table 3 The Influence of Various Factors on Job Satisfaction
Variable Coefficient Std. error z-Statistic Prob.
JOBFR 1.010156 0.313283 3.224417 0.0013
JOBNICE 1.139422 0.320520 3.554916 0.0004
JOBTEAM 1.018901 0.287842 3.539793 0.0004
JOBSOC 1.092107 0.277519 3.935256 0.0001
NJOBMON –0.388302 0.200749 –1.934268 0.0531
Pseudo R-squared 0.047635 Akaike info criterion 2.222809
Schwarz criterion 2.353299 Log likelihood –298.9705
Hannan-Quinn criterion 2.275161 Restr. log likelihood –313.9243
LR statistic 29.90762 Avg. log likelihood –1.075433
Prob. (LR statistic) 0.000041
Notes Method: ML – ordered probit (quadratic hill climbing); Sample: 287; Included obser-
vations: 278; Number of ordered indicator values: 5; Convergence achieved after 4 iterations;
Covariance matrix computed using second derivatives.
also reported, albeit ambiguously, by other authors (Clark, 1996; Weaver,
1980). The role of non-monetary factors is also strong in the sample: the
understanding of the social importance of the job is very close to that shown
by the results for monetary factors. We can surmise that non-monetary
factors play a strong role as well; these include the pleasure of working
with the team (1.15), a pleasant time spent during working hours (1.21),
and making very good friends on the job (1.11).
Interestingly, of the factors that were expected to negatively influence job
satisfaction, only the monetary factor was found to be statistically signifi-
cant (at a 10% level of confidence). This indicates that, beyond income, the
lack of positive experiences, rather than the presence of negative experi-
ences, drives down job satisfaction, and by extension job performance, in
our sample. This result is most probably influenced by the fact that most
respondents indicate a high level of job satisfaction.
Overall, the analysis indicates that the respondents are willing to partic-
Volume 2, Issue 1, 2013
40 Alexi Danchev and Erkan Ilgün
ipate in the market and demonstrate a high level of job performance when
they are satisfied with their jobs; furthermore, they assess their knowledge
and skills as high. As a rule, WTW is very sensitive to the level of accu-
mulated knowledge and skills and this demonstrates the power of human
capital to influence the state of the Bosnian labour market.
Discussion
The attempts to shed light on the ‘job performance–job satisfaction–human
capital’ triangle raise several questions.
First of all, as job performance is based on self-estimation it includes
a large element of subjectivity. There is a need to improve the methodol-
ogy for estimating job performance, in spite of significant progress in this
area during the past few decades. Most of the studies that are similar to
the discussed study are based on respondents’ self-assessment, a pro-
cess that inevitably suffers from subjective drawbacks. As Lawler (2010)
indicates, ‘There are also numerous examples of situations where individu-
als thought they were doing the right thing and performing well, only to find
out they were mistaken when they had their annual appraisals.’ It is nec-
essary to apply a methodology that produces more objective views on this
issue.
In relation to job satisfaction, it would be useful to complement the
positivist-based survey with a phenomenological approach and balance the
analysis based on structured data with the idiosyncratic information pro-
vided by the comments of the respondents for every level, including the
scale of job dissatisfaction. Moreover, many elements of job satisfaction –
and in particular the role of job conditions and perceived organizational sup-
port, etc., which may also have double effects, remained outside the scope
of our study. As Eisenberger, Cummings, Armeli and Lynch (1997) indicate,
‘Further research is needed on factors influencing employee perceptions
about the organization’s discretionary control over the favourableness of
job conditions.’
Reducing human capital only to knowledge and skill neglects recent re-
search achievements in this area. Our attempts to extend this vision to
include value systems, health, and other modern components of human
capital could not be supported by empirical data.
The combination of factor analysis and Probit-based calculations reveals
only one approach to the relationship between the variables. Discrepancies
are observed in some correlations and it is not always clear which of the
outlined relationships are closer to the real situation. It would be appro-
priate to complement the present analysis through the use of neuro-fuzzy
and other approaches to produce a more complex and thus more realistic
picture of the problem.
International Journal of Management, Knowledge and Learning
Job Performance, Job Satisfaction and Human Capital 41
Conclusions
This paper is a part of a complex study of the influence of human capi-
tal on sustainable development in the Balkan countries. An important task
in this process is to reveal the influence of human capital on labour sup-
ply. In our opinion, the key contribution of the study is the analysis of the
combined effects of monetary factors (income) and non-monetary factors
(such as the perception of the social importance of the job, meeting friends
and spending a pleasant time during the working hours) on job satisfac-
tion and thus on job performance. The results are an indication that the
‘job performance-job satisfaction-human capital’ chain is influenced by both
monetary and non-monetary factors, which may have positive and negative
effects on the Bosnian labour market.
Although not very strong (low Pseudo R-squared), the positive effects of
job satisfaction and knowledge and skill on job performance are evident.
Job satisfaction is sensitive to the level of payment and is positively af-
fected by a number of non-monetary (hedonic) factors. Nonetheless, as
indicated above, a great deal remains to be done. Further studies should
include broader aspects of human capital. There is a definite need to com-
plement this study with research regarding how individuals’ value systems
affect many idiosyncratic features of human behaviour that create incen-
tives for increasing knowledge and skills, but remain outside the traditional
economic analysis. Our paper presents findings that could benefit further
in-depth studies in this area.
These results have direct practical implications. They are a starting point
for understanding the motivation and factors that influence job satisfaction,
which would allow firms to manage their human resources more effectively
and create favourable preconditions for sustainable and prosperous growth.
The case study on Bosnia reveals interesting relationships between job
performance, job satisfaction and human capital in an area ‘where special-
interest groups and patron-client relations prevail over generalized norms
and networks of reciprocity, trust, and cooperation that promote wider social
welfare and development objectives’ (Christoforou, 2011). Our study is the
first step towards revealing a complex relationship in a rather complicated
setting.
The work presented in this paper has been made possible by the ‘Man-
aging human capital for the aims of sustainable development (case study of
some Balkan countries)’ project, supported by the Scientific Research Fund
of Fatih University, project number P51010901_1. We would like to express
our gratitude for this support, and would also like to thank Berkant Cakir,
the technical secretary of the project. We are grateful to all the respon-
dents from Bosnia who took the questionnaire seriously and completed it
Volume 2, Issue 1, 2013
42 Alexi Danchev and Erkan Ilgün
responsibly with the necessary data. Thanks to all colleagues whose critical
remarks helped us considerably improve our analysis.
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Alexi Danchev is a Professor of Economics at Fatih University, Istanbul,
Turkey. During the 1990s, he specialized in environmental, development, and
cultural economics at the Centre for Social and Economic Research on the
Global Environment (CSERGE) at the University College London (UCL). His
publications are in the areas of the theory of endogenous economic growth,
environmental economics and social capital. adanchev@fatih.edu.tr
Erkan Ilgün graduated from the University of Siegen, Germany, and works
as an Assistant Professor in the Department of Management at the Interna-
tional Burch University, Sarajevo, Bosnia and Herzegovina. His main fields of
interest are macroeconomics and time series analysis. erkan-ilguen@web.de
This paper is published under the terms of the Creative Commons
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License (http://creativecommons.org/licenses/by-nc-nd/3.0/).
International Journal of Management, Knowledge and Learning