ETHICAL BEHAVIOR CONSTRUCT: MEASUREMENT AND PRACTICAL
IMPLICATIONS
Branko BUČAR, Faculty of Economics, University of Ljubljana
Mateja DRNOVŠEK, Faculty of Economics, University of Ljubljana
Address all correspondence to:
Branko Bučar
University of Ljubljana, Faculty of Economics
Kardeljeva ploščad 17, 1000 Ljubljana, Slovenia
Email: branko.bucar@uni-lj.si
ABSTRACT
The primary concern of the article is to validate the scale of ethical behavior
construct developed in previous research. We examined the internal consistency of the
modified Newstrom and Ruch’s (1975) scale for the measurement of the ethical behavior
construct. We based this study on the model by Akaah and Lund (1994) about the
influence of personal and organizational values on ethical behavior in the entrepreneurial
context. We concluded that the six-dimensional construct of ethical behavior cannot be
supported in the same way as proposed by Akaah and Lund (1994) and proposed several
modifications to the measurement scale.
1
KONSTRUKT
ETIČNEGA
OBNAŠANJA:
MERJENJE
IN
PRAKTIČNE
POSLEDICE
Glavni namen tega članka je preveriti meritveno lestvico konstrukta etičnega
obnašanja, ki je bila razvita v prejšnjih študijah. Proučili smo notranjo konsistentnost
spremenjene meritvene lestvice, ki sta jo originalno razvila Newstrom in Ruch (1975).
Študijo smo zasnovali na Akaah in Lundovem (1994) modelu vplivov osebnih in
organizacijskih vrednot na etično obnašanje, pri čemer smo model preučili v
podjetniškem kontekstu. Ugotovili smo, da šest-dimenzionalni konstrukt etičnega
obnašanja, ki sta ga razvila Akaah in Lund (1994), ni podprt s podatki in smo posledično
predlagali spremembe v meritveni lestvici.
1
INTRODUCTION
In today’s uncertain business environment shaken by a myriad of corporate
scandals we must re-evaluate the role of business ethics in the entrepreneurial process
and examine the factors that influence the differences between ethical attitudes of
business managers and entrepreneurs. The construct of ethical behavior is central to an
understanding of moral framework within which business people operate. Ethical
behavior is “the product of personal values, experiences, and the environment in which
one works and lives” (Donaldson & Dunfee, 1999: 86). Recent studies developed various
arguments and employed different research approaches to examine the ethical questions
within business environment. Jackall (1988) argued that ethical views of managers are
2
affected by a complex interaction between the manager’s personal value system and that
of upper management, frequently resulting in manager’s ethical decisions being
influenced by considerations other than their personal value systems. Entrepreneurs, on
the other hand, usually do not face the issue of the separation of ownership and control
(Bucar et al., 2003). As owners-managers entrepreneurs could employ their personal
values to a much greater extent than managers within large businesses (Humphreys et al.,
1993), since they are not constrained by the structure of bureaucratic corporate
organizations. Countering this view, Baumol (1993) cautioned that entrepreneurship
should not be taken as a synonym for virtuousness. He made a clear distinction between
value creating and unproductive, rent seeking entrepreneurial activities.
The results of previous studies point towards higher ethical attitudes of
entrepreneurs due to their higher equity stakes and the higher risks assumed (see
Sarasvathy et al., 1997; Bucar & Hisrich, 2001; Bucar et al., 2003). Other authors
suggested that entrepreneurs are more ethical than managers on some actions and less on
others (see Longenecker et al., 1988, 1989a, 1989b). However, these findings are not
stable across different studies. We argue that they are inconclusive because of the validity
and reliability problems of the existing measurement instruments.
2
VALIDITY AND RELIABILITY OF A CONSTRUCT
The primary concern of the article is to validate the scale of ethical behavior
construct developed in previous research. Validity of a measure basically means that the
3
particular measure indeed measures what it is supposed to measure. Cronbach’s and
Meehl’s (1955) broader view of validity holds that validity describes the meaning of
scores produced by an instrument or assessment procedure. Validity assessment involves
evaluating the inferences made from scores on a test, not the test itself (Cronbach, 1971).
There are however several facets of validity (Pedhazur & Schmelkin, 1991): 1) face
validity – questions whether the measures are appropriate for a particular use, 2) internal
structure validity, often referred to in terms of convergent validity and 3) cross structure
validity, more often named discriminant validity. Construct validation is an exhaustive
never-ending process, which can be however captured within a sizeable framework.
Pedhazur and Schmelkin (1991) propose the subsequence of the following steps to
construct validation – logical analysis, internal structure analysis, and cross structure
analysis. The main aim of logical analysis is to generate counterhypotheses as alternative
explanations regarding the construct presumably being measured, relations between the
constructs, and the like. The internal structure analysis assesses the validity of treating a
set of indicators as reflecting the same construct (Pedhazur & Schmelkin, 1991). Internal
structure is usually assessed by the means of factor analysis – exploratory factor analysis
when the nature of the construct has not been theoretically defined yet and confirmatory
factor analysis in cases when theory of the construct has been already established and a
researcher wants to test it. Finally, the cross structure analysis determines the
correspondence between the structure of a set of indicators and the construct they are said
to reflect. It is important to recognize that evidence from internal structure analysis is
necessary but not sufficient condition to lend support to the construct validity of a
measure or a set of indicators (Pedhazur & Schmelkin, 1991).
4
In their seminal paper, Campbell and Fiske (1959) proposed the concepts of
convergent and discriminant validity of a construct. Convergent validity refers to
convergence among different methods, preferably as different as possible, designed to
measure the same construct. Discriminant validity refers to the distinctiveness of
constructs, demonstrated by the divergence of methods designed to measure different
constructs (Pedhazur & Schmelkin, 1991). The discriminant validity of the construct is an
empirical test to address redundancy problem of the construct. A researcher measuring
facets of a latent construct should, theoretically and logically alike, try to define targeted
facets by nonredundant constructs. The issue of redundancy is a very important one, since
it leads to so often addressed multicollinearity problem, a substantive pitfall of empirical
analysis, preventing a researcher from finding statistically significant results, especially
when doing regression analysis.
In the case of ethical behavior construct, internal consistency of items will be
examined by calculation of reliability coefficients of the six focal sub scales. Broadly
speaking, reliability is related to measurement errors idiosyncratic to a particular
research. Reliability refers to the degree to which test scores are free from errors in
measurement (American Psychological Association, 1985). Two kinds of errors may
occur in the process of measurement; systematic – occurring over again upon repeated
measurements and unsystematic - random, error not predictable upon repeated
measurement (Pedhazur & Schmelkin, 1991). Social science researchers often treat
reliability and validity of constructs interchangeably. However, reliability is a necessary
but not a sufficient condition for validity. The measure cannot be valid if it is not reliable,
5
but being reliable it is not necessarily valid for the purpose its user has in mind. The
concept of reliability is particularly important in cross-national research, where different
levels of construct’s reliability so often contribute to a researcher’s failure to find
statistically significant results (see Table 1 for formula of construct reliability).
Literature gives different suggestions on the targeted value of reliability
coefficient. Nunnally (1967) claims that relatively low reliability coefficients (<0.5) are
tolerable in early stages of research on predictor test or hypothesized measures of the
construct, higher reliabilities are required when the measure is used to determine
differences between groups (i.e. cross national research) and very high reliability scores
are essential when the scores are used for making important decisions about individuals.
Another point should be added here – the reliability estimate is the interplay between the
number of items comprising a construct – given a sufficiently large number of items, a
measure may show a high internal consistency, even if it is composed of items which
share a little among themselves (Pedhazur & Schmelkin, 1991).
Discriminant validity, implying the nonredundancy of constructs, can be
investigated by a set of statistical evidence: a) the value of correlation coefficient
between focal constructs – in order to be distinct they should not correlate perfectly; b)
the share of variance a focal construct shares with other constructs; c) the share of
variance extracted by a focal construct. The discriminant validity is established when
variance extracted by a construct is higher than the variance the construct shares in a
combination with any other construct within the research framework. The variance shared
6
by two constructs is simply a square of their correlation coefficient (see Table 2 for the
formula of the variance extracted by a factor).
3
SCALE ASSESMENT AND DEVELOPMENT USING CLASSICAL
TEST THEORY AND ITEM RESPONSE THEORY PROCEDURES
The research goal of our paper is the assessment of reliability, discriminant and
convergent validity of the hypothesized 6-factor sub scale of ethical behavior construct.
In the first part of the analysis, we examine the underlying assumptions of the current
scale for ethical behavior construct and propose alternative options for scale
development. This logical analysis deals with definitions and conceptual formulations
that we had to clarify prior to the central stages of our research. Second, we explore the
internal structure validity of the existing measurement scale. Third, we refine the scale
using the procedures from classical test theory and item response theory.
3.1
General Method – Classical Test Theory
The hypothesized sub-scale factors of an ethical behavior construct were
operationally translated into a set of measurement equations estimated using a maximum
likelihood procedure by structural equation program EQS 6.0 (Bentler, 2000). The use of
structural equation modeling is desirable, because the recovered relationships are
between theoretic constructs rather than among some linear combinations of observables.
Hence, it is eminently suited for internal and cross structure analysis in the process of
construct validation. Of various estimation procedures, maximum likelihood estimation is
7
the most frequently used, since it is based on a search for estimates of parameters most
likely to have generated the observed data given specific distributional assumptions
(Pedhazur & Schmelkin, 1991). Additionally, the obtained solution can be evaluated by
several criteria. First a 2 goodness-of-fit test indicates whether or not the model fits the
data. Second, several indicators (relative and absolute) of the goodness-of-fit (BNFI,
BNNFI, GFI, RMR, RMSEA) are available to assess the relative amount of variance covariance explained by the model. Finally, measurement parameters can be examined
for statistical significance (t-test) (Singh, 1991). The coefficients’ standardized loadings
and estimated measurement errors are also the necessary input into the calculation of the
discriminant validity of constructs and their reliabilities.
3.2
Refinement of the Construct Using Classical Test Theory Procedures
In the central part of our analysis, we try to refine the instrument to measure the
dimensions of ethical behavior construct. A multivariate statistical technique –
exploratory factor analysis will be run for that purposes. The primary purpose of
exploratory factor analysis is data reduction of a large number of variables by defining a
set of common underlying dimensions known as factors. When a large set of variables is
factored, some a priori criteria should be established in order to arrive to a specific
number of factors extracted. The most commonly used a priori criteria involve: latent
root criteria (eigenvalues higher than 1), a priori criteria – when applying it, the analyst
already knows how many factors to extract before undertaking the factor analysis,
percentage of variance criterion (a priori set value of a percentage of variance that it
should be explained by the factors) and finally scree test criterion – graphically shows the
8
number of factors that can be extracted before the unique amount of variance begins to
dominate the common variance structure (Hair et al. 1995). When coming to the point of
interpretation of the factor solution, rotation of factors is a very helpful tool. Prior to
factor rotation, a researcher has to decide whether to use oblique or orthogonal rotations.
The decision is purely theoretically based – orthogonal rotation methods are based on the
theoretical conceptualization of factors not being correlated, whereas oblique rotations
allow factors to correlate. In the case of sub scale of ethical behavior construct refinement
we conceptualize the factors to correlate. Oblique factor rotation will be used, more
specifically, Promax rotation. In interpretation of the factors, criteria must be made
regarding the item loadings that are worth considering. The literature suggest the
following rule of thumb – item loadings greater than +/- 0.30 are considered to meet the
minimal level, loadings of +/- 0.40 are considered more important, and if the loadings are
higher than +/- 0.50 factors are considered especially important. The proposed values
however vary with the sample size, risk level and power of the statistical test. Shortly, in
smaller samples (up to 100 cases), values should be higher for the item loadings to be
considered important (Hair et al., 1995). Exploratory factor analysis was run in statistical
software package SPSS 10.0. After the refinement of the ethical behavior construct
instrument, confirmatory factor analysis in EQS was run again in order to validate the
results.
3.3
Refinement of the Construct Using Item Response Theory Procedures
The same scale was examined using IRT procedures. Item response theory
includes three central concepts: a) a scale is intended to measure individual differences in
9
the level of some unobservable construct; b) we infer the existence of the construct
through observation of covariation of the responses to different items; and c) the IRT
models explain all of the observed covariation among the items, “attributing that
covariation to the relation of each item separately to the common underlying construct”
(Steinberg & Thissen, 1996: 82). While classical test theory models account for the
covariance between the items, IRT models account for examinee item responses (Reise et
al., 1993).
The data analysis under IRT is an iterative, two-step procedure:
1. Using item response data, we select a unidimensional IRT model and estimate
the parameters for that model. Unidimensionality implies that the set of items assesses a
single underlying trait dimension.
2. In the next step, we examine the residuals of the data from the model to ensure
that the item responses are locally independent. The two items are said to be locally
dependent if they are more related to each other than can be explained by their mutual
relation with the underlying construct that the test is supposed to measure. Then through
interpretation of parameter estimates we examine if they are “consonant with the idea that
the construct that we intend to measure is an explanatory variable for the item responses”
(Steinberg & Thissen, 1996: 82).
Based on the results of IRT analysis, we suggest alternative specifications of the
scale for the measurement of the ethical behavior construct.
10
4
SAMPLE AND DATA COLLECTION
The data were obtained by self-administered questionnaire mailed to two different
samples of entrepreneurs - in the United States and Russia respectively. In the United
States, mailing lists were obtained from COSE (Council of Smaller Enterprises of the
Cleveland Growth Association) and EDI (Enterprise Development, Inc.), an incubator
also providing seminars for entrepreneurs in Cleveland. The response rate of the mail
survey was 24% (165 usable responses). In Russia, a list of 200 entrepreneurs associated
with the Academy of the National Economy was obtained. The entrepreneurs were from
various regions in Russia – Siberia, Urals, and the Central Region – including Moscow.
Due to anonymity being guaranteed and the fact that the academy is well known for its
high quality academic programs, 159 responses were obtained – an extremely high (80%)
response rate.
4.1
Sample Composition
The sample size is balanced between the two countries. However, the male/female
percentages in both countries indicate lower percentage of women in each of the samples
(see Table 3). The share of women entrepreneurs in the US sample is closer to the actual
figures for the US; it might be a little smaller (Brush, 1997), however the structure of
female entrepreneurs is not quite representative of the actual share of female
entrepreneurs in Russia.
11
When comparing the age of the entrepreneurs - the Russian entrepreneurs are
relatively young. This difference reflects the characteristics of the former economic
systems, in Russia; the socialist system was in existence for a longer period, which helps
explain the phenomenon of the new generation quickly grasping the option of
establishing a private business. Considering the income levels, the two groups differ
significantly. In the USA, significantly more entrepreneurs (47%) were in the upper
income brackets, with a $100.000 and over income level, although in Russia, in spite of
its poor average income, entrepreneurs were doing surprisingly well in terms of income.
4.2
Measures
Ethical behavior construct was measured using the scale developed by Newstrom
and Ruch’s (1975) comprised of seventeen items. Each item reflects a facet of unethical
behavior and it was originally measured on a 7-point scale with descriptive anchors
ranging form “never would” to “definitely would”. The scale was originally developed
for the purposes of measuring unethical behavior if engaged in by a marketing
professional. The particular survey instrument was then often used in different research
studies measuring unethical behavior.
Our research is based on the model by Akaah and Lund (1994) on the influence of
personal and organizational values on marketing professionals’ ethical behavior. On the
basis of principal factor analysis they reduced the scale of ethical behavior to six subscales reflecting six facets of ethical behavior – “personal use”, “passing blame”,
“bribery”, “padding of expenses”, “falsification” and “deception”. We wanted to validate
12
the subscales of ethical behavior proposed in their analysis on a different sample of
respondents and thus contribute towards a refinement and generalizability of the ethical
behavior measurement. Further in the text we refer to their model as to the “proposed
model”.
Our survey used the same items, however measured by binary scale asking
respondents to decide whether a certain action is considered to be ethical or not (yes/no
answers). The decision to collapse the 7-point Likert scale into binary scale came after a
lengthy analysis of the meaning of different answers on the ethical behavior scale. We
came to the agreement that answers “maybe would,” “sometimes would,” “quite often
would,” to questions regarding theft, bribery, deception, falsification and others, all
indicate that a respondent is unethical and that there is no need to look for “different
grades or levels of unethical.” The questions were thus phrased as “do you consider it
ethical for someone to” (see the text for each item in Table 4) and answers as yes/no.
5
RESULTS
5.1
Validation of the six dimensional ethical behavior construct
The first model that we have estimated is based on the theoretical model of ethical
behavior construct measured by 6 subs scale factors. The table below shows the results of
the model fit. One important remark should be made at that point; the program output
reported that the results may not be appropriate due to the condition code occurred in the
model. The condition code informs on linear dependency of factors in the model,
13
reflecting redundancies among variables. To fix the problem, it is generally advisable to
find those variables that are linear combination of other variables and remove them form
the input (Bentler, Chou, 1988). Hence, excluding of linear dependent variables from the
proposed scale basically implies alternating the proposed theoretical model. Thus, we
conclude that the six-subscale model of ethical behavior construct cannot be supported in
the same way as proposed by Akaah and Lund (1994).
It is quite obvious that the hypothesized model doesn’t fit the data well, as there is
a perfect correlation between deception and falsification subdimensions, which means
that those two factors lack discriminant validity and are thus redundant (see Tables 5 and
6). Further, high correlation between passing blame and falsification also predicts
possible redundancy problems. The model could be further analyzed in terms of item
loadings on subscale factors and reliability of sub scale factors. However, since the
criterion of the model fit has been rejected in the very beginning we will not examine it
further but rather focus on its refinement.
5.2
The refinement of the six dimensional ethical behavior construct
The refinement of the construct involved two steps: in the first step we have more
closely checked the items loading on six sub scale factors through exploratory factor
analysis. Exploratory factor analysis, using Promax rotation has been run using a priori
criterion of 6 dimensional structure.
In the next step we tried to solve the redundancy problem identified in previous
analysis of sub-scale factors. To tackle the problem, we ran exploratory factor analysis of
14
the total set of items and estimated 6 factors. We theorize the factors to be correlated,
thus oblique rotation methods were used to clarify the underlying dimensions. The items
measuring the four factors – personal use, bribery, passing blame and padding expenses
loaded in the same way as in the proposed model. Items measuring falsification and
deception loaded somewhat differently compared to the proposed model. In the table 7
we compare the loadings of the items. The most obvious conclusion drawn from the table
7 can be that the items related to some aspect of individual’s behavior towards an
organization load on the same factor - deception, whereas the items related to office
interpersonal relations load on the another factor – falsification. Confirmatory factor
analysis of the reshaped six-factor sub-scale was run. Results are reported in tables 8 and
9.
The comparison of indexes of fit shows the increase of indexes of goodness of fit.
The chi square comparison shows that M1 has the higher chi square value at the same
number of the degrees of freedom as model M2, indicating the greater discrepancy
between the data and the model M1. Also, we don’t have the redundancy problem
anymore. The next step in the assessment of the model fit is investigation of the
convergent and discriminant validity, and calculation of construct’s coefficients of
reliability.
The comparison between values of variance shared by two factors and variance
extracted values shows that factors Personal use and Passing blame share a higher
proportion of variance than it is individually extracted by the factor Personal use (see
15
Table 10). And further, the same relation between Personal use and Deception and
Passing blame and Deception consequently. Again, two factors of the six dimensional
ethical behavior sub-scale seem to be redundant. Indeed, the fact that we have already
noticed when doing exploratory factor analysis – only 4 factors satisfied the criteria of
having eigenvalues higher than one and thus representing a meaningful sub dimension.
The measures of reliability are significantly higher in our refined model than in the model
reported by Akaah and Lund (see Table 11).
5.3
Towards new sub dimensions of the ethical behavior construct
Results of the analysis have revealed that the current scale of ethical behavior
construct cannot be supported very well. In the next step of the analysis, we have tried to
propose a new sub dimensional model of ethical behavior construct. Based on the
exploratory factor analysis and previously identified problems with the six-dimensional
scale we examined the properties and model fit of four and five-dimensional scales. They
both proved inferior to the six-dimensional scale (somewhat lower indexes of goodness
of fit; χ3= 175, df3= 94; χ4= 223, df4= 98).
Further, we examined the same scale using item response theory procedures.
MULTILOG was used to analyze all the items of the ethical behavior scale. A selection
of items that tapped into Falsification and Deception was analyzed separately and
compared to results from classical test theory procedures. Same as in the previous part of
the analysis, we used a combined sample of Russian and the U.S. entrepreneurs, because
of sample size considerations (MULTILOG requires large samples for stable analysis).
16
IRT is a full information technique, therefore normality is not a required assumption in
IRT.
Graded model was used in the estimation of discrimination parameters (a) and
category difficulties (b) for each item in the analysis (see Table 12). Discrimination
parameters range from 1.25 to 4.0, all being statistically significant at 0.05 level (t>1.96).
χ2 tests were assessed for individual items: values for the test ranged from 0.27 to 1.86.
Comparison to the critical value (χ2=3.84, df=1) indicated good fit of the model for the
individual items. We also used graphical representations in the ethical behavior scale
analysis. Information functions I(θ) for all items were plotted in a graph, where we tried
to assess which items could be redundant in the measurement of ethical behavior (see
Figures 1 and 2).
Information functions indicated possible formation of testlets between items
ETHIC8 and ETHIC9, and between items ETHIC3 and ETHIC4. A testlet is “a group of
items related to a single content area that is developed as a unit and contains a fixed
number of predetermined paths that an examinee may follow” (Wainer & Kiely, 1987:
190). The analysis of information functions pointed out the item ETHIC8 as the item with
high discriminability, and also indicated possible redundancy of item ETHIC9, because it
taps into the same dimension as item ETHIC8, but has lower discriminability. To a lesser
degree, but still very similar are information functions for items ETHIC3 and ETHIC4,
with ETHIC3 having considerably higher discriminability. A separate analysis of
17
information functions of Falsification and Deception items (Figure 2) revealed a great
overlap between the information functions of ETHIC15 and ETHIC18 items. Because of
this and previous issues with ETHIC15 item in the classical test theory procedures, we
considered it for elimination. The shorter measurement scale for ethical behavior
construct was analyzed. Additional MULTILOG run indicated improved fit of the model
for the individual items. The reliability calculated as Cronbach’s α was still at high level
of 0.87 (compared to initial 0.88) with smaller number of items. The analysis of the
deleted item (“Falsify internal time/quality/quantity reports”) also raised some questions
about the labeling of dimensions Falsification and Deception, which will have to be
resolved in further research.
6
CONCLUSIONS
In this paper we analyzed the existing scale for the measurement of the ethical
behavior construct. Based on the analysis we proposed several changes to the scale
including deleting some of the items and constructing a shorter scale, which is the desired
outcome in the construction of questionnaires. More importantly, the preceding analysis
of the ethical behavior scale uses a specific procedure to examine measurement properties
of questionnaires in entrepreneurship research. The analysis follows the steps of construct
validation – logical analysis, internal structure analysis, and cross structure analysis –
described by Pedhazur and Schmelkin (1991) using a combination of item response
theory and classical test theory procedures. We have seen that factor analysis and internal
consistency indices, which are traditionally used to assess the performance of items, can
18
be misleading and that the IRT-based approach more efficiently solves the problems of
local dependence among items.
The research was performed on data from two very diverse environments
(Russian and American entrepreneurs), however the two samples were not large enough
to allow testing measurement invariance across different groups. One of the goals for
future research should be establishing measurement invariance of the ethical behavior
scale across distinct groups of business people (e.g. entrepreneurs and managers, male
and female entrepreneurs, entrepreneurs from different countries). Data collection will
have to be grounded on the findings of the previous research.
The implications for practitioners are two-fold. More precise measurement of the
intended constructs will lead to more accurate research involving ethical behavior in
business environment, which will in turn produce more relevant recommendations for
practical training in this particular area. Also, improved measurement scales may resolve
some of the ambiguities of the previous research and help create better understanding of
the role of ethical behavior in the economic development.
Table 1. Formula for construct reliability
Construct
reliability
(sum of standardized loadings)2
(sum of standardized loadings)2 + sum of indicator measurement
error
Source: Hair et al, 1995.
19
Table 2. Formula for the calculation of the variance extracted by a factor
Variance
extracted
Sum of squared standardized loadings
Sum of squared standardized loadings + sum of indicator measurement
error
Source: Hair et al, 1995.
Table 3. Sample characteristics (in %)
Characteristic
USA
Russia
Entrepreneurs Entrepreneurs
SAMPLE Respondents
165
159
SEX
Male
77
55
Female
22
35
No answer
1
10
AGE
- 30 years
7
35
30 - 39 years
28
29
40 - 49 years
37
25
50 - 59 years
20
60 and more
8
3
No answer
1
9
EDUCATIO Less
than
N
secondary
Secondary
9
1
College
27
40
University
29
10
MBA, Ph.D.
34
35
No answer
1
14
COMPANY Small 1)
77
55
SIZE
Medium
19
28
Large
2
7
No answer
2
9
INCOME up to 20.000
7
77
LEVEL
20.000-39.999
14
11
40.000 and more
79
2
No answer
10
1)
The company size: - small up to 99 employees, medium 100-999; large over 1.000 employees
20
Table 4. Measurement scale for the ethical behavior construct
Ethical sub scale
Personal use
Passing blame
Bribery
Falsification
Padding expenses
Deception
Description of the item and an operational label used
Use company services for personal use (ETHIC1)
Remove company supplies for personal use (ETHIC2)
Use company time for no-company benefits or for personal business
(ETHIC5)
Taking extra personal time (lunch hour, break, early departures)
(ETHIC11)
Pass blame for errors to an innocent co-worker (ETHIC8)
Claim credit for peer’s work (ETHIC9)
Give gifts/favors in exchange for preferential treatment (ETHIC6)
Accept gifts/favors in exchange for preferential treatment (ETHIC7)
Call in sick in order to take a day off (ETHIC10)
Authorize subordinates to violate company’s policy (ETHIC13)
Falsify internal time/quality/quantity reports (ETHIC15)
Overstate expense accounts by more than 10% of the correct amount
(ETHIC3)
Overstate expense accounts by less than 10% of the correct amount
(ETHIC4)
Fail to report a co-worker’s violation of company’s policy
(ETHIC14)
Divulge confidential information to parties external to the firm
(ETHIC18)
Take longer than necessary to do a job (ETHIC19)
**We had three additional items included into our research (ETHIC12, ETHIC16 AND ETHIC17)
respectively.
Table 5. Goodness of fit indexes in the proposed model
Independence model
M1
Chi
square
2567
214
Df
120
89
BNFI
BNNFI
CFI
RMR
RMSEA
0.92
0.93
0.95
0.07
0.07
21
Table 6. Correlations among six subscale factors in the proposed model
Sub scale factors
Personal use  Passing blame
Personal use  Bribery
Personal use  Falsification
Personal use  Deception
Personal use  Expenses
Passing blame  Bribery
Passing blame  Falsification
Passing blame  Deception
Passing blame  Padding expenses
Bribery  Falsification
Bribery  Deception
Bribery  Padding expenses
Falsification  Deception
Falsification  Padding expenses
Deception  Padding expenses
Correlations
0.76
0.57
0.84
0.78
0.6
0.41
0.92
0.71
0.54
0.52
0.5
0.46
1.00
0.59
0.59
Table 7. Comparison between items loading on six-dimension ethical behavior construct.
Ethical sub scale
Falsification
Deception
Falsification
Deception
Description of the item and an operational label used
The proposed model
Call in sick in order to take a day off (ETHIC10)
Authorize subordinates to violate company’s policy (ETHIC13)
Falsify internal time/quality/quantity reports (ETHIC15)
Fail to report a co-worker’s violation of company’s policy (ETHIC14)
Divulge confidential information to parties external to the firm (ETHIC18)
Take longer than necessary to do a job (ETHIC19)
Changes to the proposed model
Authorize subordinates to violate company’s policy (ETHIC13)
Fail to report a co-worker’s violation of company’s policy (ETHIC14)
Call in sick in order to take a day off (ETHIC10)
Falsify internal time/quality/quantity reports (ETHIC15)
Divulge confidential information to parties external to the firm (ETHIC18)
Take longer than necessary to do a job (ETHIC19)
Table 8. Indexes of goodness of fit
Independence model
M1
M2
Chi
square
2567
214
160
pDf value BNFI BNNFI CFI RMR RMSEA
120
89
0
0.92 0.93 0.95 0.07 0.07
89
0
0.94 0.96 0.97 0.06 0.05
M1: Proposed model - M2: Refined model
22
Table 9. Correlations among six subscale factors in the refinement of the proposed model
Sub scale factors
Personal use  Passing blame
Personal use  Bribery
Personal use  Falsification
Personal use  Deception
Personal use  Expenses
Passing blame  Bribery
Passing blame  Falsification
Passing blame  Deception
Passing blame  Padding expenses
Bribery  Falsification
Bribery  Deception
Bribery  Padding expenses
Falsification  Deception
Falsification  Padding expenses
Deception  Padding expenses
Correlations
0.76
0.58
0.60
0.75
0.60
0.42
0.54
0.71
0.54
0.54
0.44
0.47
0.47
0.52
0.52
Table 10. Variance shared, variance extracted within six dimensions of ethical behavior
construct
Sub scale factors
Variance
shared
Personal use  Passing blame
0.58
Personal use Bribery
0.34
Personal use  Falsification
0.36
Personal use  Deception
0.56
Personal use  Expenses
0.36
Passing blame  Bribery
0.17
Passing blame  Falsification
0.29
Passing blame  Deception
0.60
Passing blame  Padding
expenses
0.29
Bribery  Falsification
0.30
Bribery  Deception
0.19
Bribery  Padding expenses
0.22
Falsification  Deception
0.22
Falsification  Padding
expenses
0.27
Deception Padding expenses
0.27
Factor
Variance
extracted
Personal use
0.34
Bribery
0.67
Passing blame
0.50
Falsification
0.46
Deception
0.41
Padding expenses
0.53
23
Table 11. The reliability of the six dimensional ethical behavior construct
Reliability –
Factor
Reliability Akaah & Lund
Personal use
0.67
0.73
Bribery
0.80
0.52
Passing blame
0.66
0.57
Falsification
0.61
0.73
Deception
0.70
0.54
Padding expenses 0.70
0.36
Table 12. Discrimination parameters (a) and category difficulties (b) for ethical behavior
items
Item
ETHIC1
ETHIC2
ETHIC3
ETHIC4
ETHIC5
ETHIC6
ETHIC7
ETHIC8
ETHIC9
ETHIC10
ETHIC11
ETHIC13
ETHIC14
ETHIC15
ETHIC18
ETHIC19
a
1.80
2.70
2.18
1.90
1.74
1.41
2.19
3.85
4.00
1.68
2.01
2.58
1.25
2.33
2.96
1.99
b1
0.66
1.31
1.53
1.46
0.86
0.52
0.94
1.50
1.47
1.48
0.74
0.90
0.27*
1.67
1.56
1.15
* Test value <1.96, parameter is not significant at 0.05 level.
24
Figure 1. Information functions for ethical behavior items
ETHIC1
4,5
ETHIC2
ETHIC8
4
ETHIC9
ETHIC3
ETHIC4
ETHIC5
ETHIC6
3,5
ETHIC7
ETHIC8
3
ETHIC9
ETHIC10
2,5
ETHIC11
ETHIC18
ETHIC13
ETHIC14
2
ETHIC15
ETHIC18
ETHIC13
1,5
ETHIC19
ETHIC15
ETHIC3
ETHIC4
ETHIC10
ETHIC19
1
0,5
0
-2,00
ETHIC14
-1,50
-1,00
-0,50
0,00
0,50
1,00
1,50
2,00
-0,5
25
Figure 2. Information functions for items of Falsification and Deception sub-dimensions
1,4
1,2
1
ETHIC10
0,8
ETHIC13
ETHIC14
0,6
ETHIC15
ETHIC18
0,4
ETHIC19
0,2
0
-2,00
-1,50
-1,00
-0,50
0,00
0,50
1,00
1,50
2,00
-0,2
7
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