Ethical Behaviour Construct

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: [email protected]

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

8 should be explained by the factors) and finally scree test criterion – graphically shows the 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.

9 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 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).

10 Based on the results of IRT analysis, we suggest alternative specifications of the scale for the measurement of the ethical behavior construct.

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

11 female entrepreneurs is not quite representative of the actual share of female entrepreneurs in Russia.

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

12 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 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

13 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, 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.

14 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 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

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.

15 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

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

16 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).

IRT is a full information technique, therefore normality is not a required assumption in

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

17 degree, but still very similar are information functions for items ETHIC3 and ETHIC4, with ETHIC3 having considerably higher discriminability. A separate analysis of 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

18 consistency indices, which are traditionally used to assess the performance of items, can 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 (sum of standardized loadings)2 (sum of standardized loadings)2 + sum of indicator measurement reliability error Source: Hair et al, 1995.

19 Table 2. Formula for the calculation of the variance extracted by a factor

Variance Sum of squared standardized loadings Sum of squared standardized loadings + sum of indicator measurement extracted 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 Description of the item and an operational label used Personal use 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) Passing blame Pass blame for errors to an innocent co-worker (ETHIC8) Claim credit for peer’s work (ETHIC9) Bribery Give gifts/favors in exchange for preferential treatment (ETHIC6) Accept gifts/favors in exchange for preferential treatment (ETHIC7) Falsification 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) Padding expenses Overstate expense accounts by more than 10% of the correct amount (ETHIC3) Overstate expense accounts by less than 10% of the correct amount (ETHIC4) Deception 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

Chi square Df BNFI BNNFI CFI RMR RMSEA Independence model 2567 120 M1 214 89 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 Correlations Personal use  Passing blame 0.76 Personal use  Bribery 0.57 Personal use  Falsification 0.84 Personal use  Deception 0.78 Personal use  Expenses 0.6 Passing blame  Bribery 0.41 Passing blame  Falsification 0.92 Passing blame  Deception 0.71 Passing blame  Padding expenses 0.54 Bribery  Falsification 0.52 Bribery  Deception 0.5 Bribery  Padding expenses 0.46 Falsification  Deception 1.00 Falsification  Padding expenses 0.59 Deception  Padding expenses 0.59

Table 7. Comparison between items loading on six-dimension ethical behavior construct.

Ethical sub scale Description of the item and an operational label used The proposed model Falsification 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) Deception 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 Falsification Authorize subordinates to violate company’s policy (ETHIC13) Fail to report a co-worker’s violation of company’s policy (ETHIC14) Deception 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

Chi p- square Df value BNFI BNNFI CFI RMR RMSEA Independence model 2567 120 M1 214 89 0 0.92 0.93 0.95 0.07 0.07 M2 160 89 0 0.94 0.96 0.97 0.06 0.05

22 M1: Proposed model - M2: Refined model

Table 9. Correlations among six subscale factors in the refinement of the proposed model

Sub scale factors Correlations Personal use  Passing blame 0.76 Personal use  Bribery 0.58 Personal use  Falsification 0.60 Personal use  Deception 0.75 Personal use  Expenses 0.60 Passing blame  Bribery 0.42 Passing blame  Falsification 0.54 Passing blame  Deception 0.71 Passing blame  Padding expenses 0.54 Bribery  Falsification 0.54 Bribery  Deception 0.44 Bribery  Padding expenses 0.47 Falsification  Deception 0.47 Falsification  Padding expenses 0.52 Deception  Padding expenses 0.52

Table 10. Variance shared, variance extracted within six dimensions of ethical behavior construct

Sub scale factors Variance Factor Variance shared extracted Personal use  Passing blame 0.58 Personal use 0.34 Personal use Bribery 0.34 Bribery 0.67 Personal use  Falsification 0.36 Passing blame 0.50 Personal use  Deception 0.56 Falsification 0.46 Personal use  Expenses 0.36 Deception 0.41 Passing blame  Bribery 0.17 Padding expenses 0.53 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 0.27

23 expenses Deception Padding expenses 0.27

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 a b1 ETHIC1 1.80 0.66 ETHIC2 2.70 1.31 ETHIC3 2.18 1.53 ETHIC4 1.90 1.46 ETHIC5 1.74 0.86 ETHIC6 1.41 0.52 ETHIC7 2.19 0.94 ETHIC8 3.85 1.50 ETHIC9 4.00 1.47 ETHIC10 1.68 1.48 ETHIC11 2.01 0.74 ETHIC13 2.58 0.90 ETHIC14 1.25 0.27* ETHIC15 2.33 1.67 ETHIC18 2.96 1.56 ETHIC19 1.99 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 ETHIC3 4 ETHIC4 ETHIC9 ETHIC5

3,5 ETHIC6 ETHIC7 ETHIC8 3 ETHIC9 ETHIC10 2,5 ETHIC11 ETHIC18 ETHIC13 ETHIC14 2 ETHIC15 ETHIC13 ETHIC18 1,5 ETHIC19

ETHIC15 1 ETHIC3 ETHIC4 0,5 ETHIC10 ETHIC19

ETHIC14 0 -2,00 -1,50 -1,00 -0,50 0,00 0,50 1,00 1,50 2,00

25 Figure 2. Information functions for items of Falsification and Deception sub-dimensions

0,8 ETHIC10 ETHIC13 ETHIC14 0,6 ETHIC15 ETHIC18 0,4 ETHIC19

0 -2,00 -1,50 -1,00 -0,50 0,00 0,50 1,00 1,50 2,00

7 REFERENCES

Akaah, Ishmael P., & Lund Daulatram. (1994) “The Influence of Personal and Organizational Values on Marketing Professionals’ Ethical Behavior.” Journal of Business Ethics 13: 417-430.

American Psychological Association. (1985) Standards for Educational and Psychological Testing. Washington, DC: American Psychological Association.

26 Baumol, W.J. (1993) Entrepreneurship, Management and the Structure of Payoffs. Cambridge, MA: MIT Press.

Bentler, P. (1982) “Confirmatory Factor Analysis via Noniterative Estimation: A Fast, Inexpensive Method.” Journal of Marketing Research 19: 417-424.

Bentler, P. (1985) Theory and Implementation of EQS. A Structural Equations Program. Los Angeles, CA: BMDP Statistical Software.

Bentler, P., & C. Chou (1988) “Practical Issues in Structural Modeling.” In Lang S. (ed.): Common Problems, Proper Solutions, 161-192.

Bucar B., M. Glas, & R. Hisrich (2003) “Ethics and Entrepreneurs: An International Comparative Study.” Journal of Business Venturing 18: 261-281.

Bucar B. & R. Hisrich (2001) “Business Ethics of Entrepreneurs vs. Managers.” Journal of Developmental Entrepreneurship 6(1).

Campbell, D.T. & D. Fiske (1959) “Convergent and Discriminant Validity by the Multitrait-multimethod Matrix.” Psychological Bulletin 56: 81-105.

Cronbach, L.J. & P.E. Meehl (1955) “Construct Validity in Psychological Test.” Psychological Bulletin 52: 281-302.

Cronbach, L.J. (1971) “Test Validation.” In R.L. Thorndike (ed.): Educational Measurement (2nd edition). Washington, DC: American Council on Education.

Donaldson, Thomas & Thomas W. Dunfee (1999) Ties that Bind: A Social Contracts Approach to Business Ethics. Boston, MA: Harvard Business School Press.

27 Duane, A. (1988) “Measurement and the Interpretation of Effects in Structural Equation Modeling.” In Lang S. (ed.): Common Problems, Proper Solutions 15-45.

Foster, S.L. & J.D. Cone (1995) “Validity Issues in Clinical Assessment.” Psychological Assessment 7(3): 248-260.

Hair, J.F., R.E. Anderson, R.L. Tatham & W.C. Black (1995) Multivariate Data Analysis. New Jersey: Prentice Hall.

Humphreys, N., D.P. Robin, R.E. Reidenbach, & D.L. Moak (1993) “The Ethical decision Making Process of Small Business Owner-Managers and Their Customers.” Journal of Small Business Management 31(3):9-22.

Jackall, R. (1988) Moral Mazes: The World of Corporate Managers. New York, NY: Oxford University Press.

Longenecker, Justin G., J. McKinney, & C.W. Moore (1988) “Egoism and Independence: Entrepreneurial Ethics.” Organizational Dynamics 16(3): 64-72.

Longenecker, Justin G., J. McKinney, and C.W. Moore (1989a) “Do Smaller Firms Have Higher Ethics?” Business and Society Review 71: 19-21.

Longenecker, Justin G., J. McKinney, and C.W. Moore (1989b) “Ethics in Small Business.” Journal of Small Business Management 27(1): 27-31.

Newstrom, J.W., W.A. Ruch (1975) “Marketing Ethics of Management and the Management of Ethics”, MSU Business Topics, Winter, 31.

Nunnally, J. (1967) Psychometric theory. New York: McGraw Hill.

28 Pedhazur, E. & L. Schmelkin (1991) Measurement Design, and Analysis. New Jersey: LEA.

Sarasvathy, D.K., H.A. Simon & L. Lave (1998) “Perceiving and Managing Business Risks: Differences between Entrepreneurs and Bankers.” Journal of Economic Behavior and Organization 33:207-225.

Singh, J. (1991) “Redundancy in Constructs: Problem, Assessment, and Illustrative Example.” Journal of Business Research 22: 255-280.

Wainer, H. & G.L. Kiely (1987) “Item Clusters and Computerized Adaptive Testing: A Case of Testlets.” Journal of Educational Measurement 24: 185-201.