Summarize

How Ethical Behavior of Firms is Influenced by the Legal and Political Environments: A Bayesian Causal Map Analysis Based on Stages of Development

Ahmet Ekici • Sule Onsel

Received: 26 July 2011 / Accepted: 25 June 2012 / Published online: 12 July 2012

� Springer Science+Business Media B.V. 2012

Abstract Even though potential impacts of political and

legal environments of business on ethical behavior of firms

(EBOF) have been conceptually recognized, not much

evidence (i.e., empirical work) has been produced to clarify

their role. In this paper, using Bayesian causal maps

(BCMs) methodology, relationships between legal and

political environments of business and EBOF are investi-

gated. The unique design of our study allows us to analyze

these relationships based on the stages of development in

92 countries around the world. The EBOF models struc-

tured through BCMs are used to explain how EBOF in a

given country group are shaped by how managers perceive

political, legislative, and protective environments of busi-

ness in these countries. The results suggest that irregular

payments and bribes are the most influential factors

affecting managers’ perceptions of business ethics in rel-

atively more advanced economies, whereas intellectual

property protection is the most influential factor affecting

managers’ perceptions of business ethics in less-advanced

economies. The results also suggest that regardless of

where the business is conducted in the world, judicial

independence is the driving force behind managers’ per-

ceptions of business ethics. In addition, the results of this

study provide further support for scholars who argue that

business ethics is likely to vary among countries based on

their socio-economic factors. In addition to its managerial

implications, the study provides directions for policy

makers to improve the ethical conduct of businesses in

their respective countries.

Keywords Bayesian causal map � Ethical behavior of firms � Judicial independence � Bribery � Intellectual property protection � Country development stage

Abbreviations

WEF World Economic Forum

BCM Bayesian causal map

EOS Executive Opinion Survey

GCI Global Competitiveness Index

EBOF Ethical behavior of firms

IPP Intellectual property protection

IPAB Irregular payments and bribes

JI Judicial independence

FDGO Favoritism in decisions of government officials

TGP Transparency of government policymaking

SARS Strength of auditing and reporting standards

ECB Efficacy of corporate boards

SIP Strength of investor protection

IPR Intellectual property rights

Introduction

Ethical issues are a major concern for businesses because

they have significant impacts on various stakeholders

including the company, customers, employees, sharehold-

ers, and society in general. Considering the significance of

this impact, a great deal of scholarly work (both theoretical

and empirical) has been devoted to advance our under-

standing of the factors affecting ethical behavior of firms

(EBOF).

A. Ekici (&) Faculty of Business Administration, Bilkent University,

06800 Bilkent, Ankara, Turkey

e-mail: [email protected]

S. Onsel

Dogus University, Engineering Faculty, Acibadem, Istanbul,

Turkey

e-mail: [email protected]

123

J Bus Ethics (2013) 115:271–290

DOI 10.1007/s10551-012-1393-4

Especially since the 1980s, scholars from various areas

of business have introduced models of ethical (or unethi-

cal) decision making (e.g., Bommer et al. 1987; Ferrell and

Gresham 1985; Hunt and Vitell 1986, 1993; Jones 1991;

Stead et al. 1990; Trevino 1986; Vitell 1986). These

models identified various factors influencing EBOF. A

review of these models suggest that factors influencing

ethical behavior can be grouped into individual character-

istics of the decision maker, organizational (company-

specific) factors, situational and contextual factors, social

and cultural environment, business/industry environment,

and governmental and legal environments.

There is a significant amount of research within mar-

keting, organization studies, and international management

that empirically demonstrates the impacts of most of these

factors on business ethics. As can be clearly seen in a

number of comprehensive review papers such as Ford and

Richardson (1994), Loe et al. (2000), Nill and Schibrowsky

(2007), O’Fallan and Butterfield (2005), and Tsalikis and

Fritzsche (1989), the role that individual, organizational,

and contextual factors play in the ethical decisions made in

organizations has been well established. In addition,

scholars of international business ethics in particular have

demonstrated the role of certain social and cultural factors

(including family and religion) on ethical decision making

in organizations (e.g., Jing and Graham 2008; Pitta et al.

1999; Srnka 2004; Stajkovic and Luthans 1997). However,

even though conceptually recognized by the ethical deci-

sion-making models mentioned above, relatively less

empirical attention has been paid to the impacts of legal,

political, and professional/business environments on the

EBOF.

The purpose of this study is to advance our under-

standing of how certain institutional factors related to

political and legal environments of business affect EBOF.

In addition to personal, organizational (company-specific),

and social and cultural factors, business ethics is also

shaped by the legal, political, and business institutions of a

society (e.g., Bommer et al. 1987; Hunt and Vitell 1986,

1993; Stead et al. 1990). As noted earlier, most of the

empirical work to date has dealt with either micro (indi-

vidual and/or company) level or uncontrollable macro level

issues (such as culture) of business ethics. The role that

legal, political, and business institutions play in the ethical

decisions businesses make has received relatively less

attention. We believe that understanding the impacts of the

institutions on the EBOF is very important. Once the

relationships between political and legal institutional fac-

tors and business ethics are established, by using appro-

priate policy interventions, the performances of these

institutions can be improved. Consequently, the ethical

performances of businesses can also be improved. This, in

turn, leads to a well-served and competitive economy. As

noted also in the WEF report (Sala-i-Martin et al. 2011),

existence of strong ethical practices for a firm in their

dealings with the government, other firms, and the public is

the main component of a competitive economy. It is

obvious that concerns about the protection of property

rights, ethics and corruption, undue influence, and gov-

ernment inefficiencies lead to an institutional environment

that does not support a well-served economy.

More specifically, using the World Economic Forum

(WEF) data and through the Bayesian causal map (BCM)

methodology, we study how EBOF in a given country

group are shaped by how managers perceive the political,

legislative, and protective environments of business in

these countries. The data that was used to compute the

Global Competitiveness Index (GCI) were collected from

13,000 executives in 139 countries. The unique design of

our study allows us to compare these relationships based on

the country classification (stages of development) indenti-

fied by WEF. This way, we are able to demonstrate how

issues related to legal and political environments of busi-

ness are linked to EBOF operating in countries with dif-

ferent stages of development.

In their analysis of global competitiveness, by using the

well-established economic theory of stages of develop-

ment, WEF identifies three distinct groups of economies:

factor driven, efficiency driven, and innovation driven.

More specifically, the Global Competitiveness Report

(Sala-i-Martin et al. 2010) states that:

In the first stage, the economy is factor driven and

countries compete based on their factor endowments:

primarily unskilled labor and natural resources.

Countries then move into the efficiency-driven stage

of development, when they must begin to develop

more efficient production processes and increase

product quality. Finally, countries move into the

innovation-driven stage, where companies must

compete by producing new and different goods using

the most sophisticated production processes and

through innovation (p. 9).

The rest of the paper is structured as follows: First, we

review existing conceptualizations and empirical work that

deal with factors affecting EBOF. Then we outline the

research methodology, including the rationale behind the

selection of the BCM method and the procedure followed

for selection of institutional factors used in the study. The

findings section discusses how the BCM models are shaped

for each of the three country groups. The paper concludes

by discussing its conceptual and methodological contribu-

tions, managerial and public policy implications, and future

research areas.

272 A. Ekici, S. Onsel

123

Factors Affecting Ethical Behavior

Scholars from diverse disciplines in business offered con-

ceptual models that delineate various factors affecting

business ethics and ethical decision making. Some of these

models focus on a narrow range of factors such as indi-

vidual and situational variables (e.g., Trevino 1986) or the

characteristics of the ethical issue itself (e.g., Jones 1991).

In this section, we briefly review models that are recog-

nized as more comprehensive in nature (Wyld and Jones

1997).

The General Theory of Marketing Ethics (Hunt and

Vitell 1986, 1993; one of the most cited and tested models

in the business ethics literature) posits that ethical behav-

iors of managers are influenced by a host of environmental,

situational, and contextual factors such as cultural envi-

ronment (e.g., religion, legal system, and political system),

general business environment (professional, industry, and

organizational) as well as personal characteristics of the

decision maker. The model also argues that how decision

makers perceive the ethical problem, available alternatives,

and the probability of resulting consequences shape their

ethical behavior.

Various components of this theory have been tested

since its introduction. Most empirical work has focused on

the personal characteristics of the decision maker, with

relatively less attention being paid to the impact of ‘‘gen-

eral business’’ and ‘‘cultural and political environment’’

components. For example, Singhapakdi and Vitell (1990,

1991) studied the impact of personal (background) char-

acteristics such as Machiavellianism and locus of control

on ethical decision making. Hunt and Vasquez-Parraga

(1993) study found gender as a personal characteristic that

shapes managers’ ethical decisions. Burns and Kiecker

(1995), Donoho et al. (1999), Mayo and Marks (1990),

Menguc (1998), and Vitell et al. (2001) studied the impact

of deontological versus teleological orientations of the

decision maker.

Bommer et al. (1987) proposed a normative ethical

decision model and argued that individual attributes of

the decision maker, the work environment, professional

environment, personal environment, social environment,

and government/legal environment affect ethical/unethi-

cal behavior. Over the last 25 years, this model has been

an inspiration to more than 300 empirical studies pub-

lished in business journals and book chapters. As in the

case of the General Theory of Marketing Ethics, most

studies investigated the role that individual, personal,

and work/professional environments play in ethical/

unethical behavior (e.g., Akaah 1989; Giacalone and

Jurkiewicz 2003; McCabe et al. 2006; Soutar et al.

1994). The role of ‘‘governmental/legal environment’’

(described as legislation, administrative agencies, and

judicial system within the proposed model) largely

remained untouched.

Another widely cited model for understanding ethical

behaviors of business organizations was proposed by Stead

et al. (1990). The model involves three broad categories of

factors: individual, organizational, and external. Individual

factors encompass both personality (such as ego, Machia-

vellianism, and locus of control) and socialization (such as

sex roles, religion, age, work experience, and significant

others). Organizational factors include issues related to

managerial philosophy and behavior, reinforcement sys-

tem, and characteristics of the job. External factors are

composed of economic conditions, scarce resources, com-

petition, stakeholders, and political and social institutions

(p. 237). Similar to other models, over the years,

researchers have primarily investigated individual and

organizational aspects of this model (e.g., Barnett et al.

1994; Cleek and Leonard 1998; Lere and Gaumnitz 2003;

Mitchell et al. 1996; Winter et al. 2004).

Scholars of international business ethics suggested that

company-specific factors such as existence of a written

code of ethics, opportunities to discuss ethical issues within

the company, size and the ownership type of the company,

and extent of international involvement affect the ethical

practices of companies that operate in different countries

(Batten et al. 1999). In addition, culture accounts for a

large part of the differences among the ethical decisions

made by companies in different countries (e.g., Jing and

Graham 2008; Lam and Shi 2008; Pitta et al. 1999; Sims

and Gegez 2004; Srnka 2004). International business

textbooks are full of examples warning managers of pos-

sible cultural conflicts regarding business ethics. Norms

such as integrity, loyalty, honesty, and self-discipline vary

greatly among people (Prasad and Rao 1982). People raised

in different cultures hold different value systems and eth-

ical understandings. As a macro factor affecting business

decisions, culture is usually considered as given and the

managers are expected to align themselves and their

decisions based on cultural differences.

The above review suggests that even though potential

impacts of political and legal environments of business on

EBOF have been recognized, little empirical work has been

produced to clarify their role. With respect to the ‘‘business

environment,’’ the existing literature suggests that industry

type (e.g., Dornoff and Tankersley 1975; Oz 2001) and the

level of competition (e.g., Hegarty and Sims 1978; Rob-

erston and Rymon 2001) may have an impact on business

ethics-related perceptions of managers. Most empirical

work has focused on illustrating the impacts of other

(individual, organizational, and situational) factors and, as

a result, especially the influences of legal and political

issues on business ethics have largely remained unknown.

It may be unrealistic to expect all aspects of these

How Ethical Behavior of Firms is Influenced by the Legal and Political Environments 273

123

comprehensive models to be investigated in a single study.

Therefore, we do not suggest the exclusion of legal and

political issues as a weakness of the previous studies.

However, as suggested by Hunt and Vitell (2006), in order

to advance our understanding of the EBOF, we believe that

‘‘researchers [should] focus attention on legal systems and

political systems’’ (p. 147).

Methodology

This section provides information regarding the data-set

used in the study, overview of the BCM method, proce-

dures followed to identify institutional (political and legal)

factors included in the study, and determination of the

causal links among the institutional factors affecting

EBOF.

Data Source

The data for this study come from a larger data-set of the

Global Competitiveness Network of the WEF. The net-

work, which has 150 partners around the world, collects

data from two main sources: (1) standard international

indicators (such as GDP) and (2) Executive Opinion Sur-

vey (EOS). The data used in this study come from a part of

the EOS conducted with 13,000 executives in 139 countries

(with an average of 95 respondents from each country)

between January and May 2010.

As noted previously, WEF classifies countries into three

distinct stages of development: factor-, efficiency-, and

innovation-driven countries. Even though they are not

exactly the same, this classification can roughly be treated

as similar to the more commonly known classification of

economic development, namely underdeveloped, develop-

ing, and developed countries. In WEF classification, two

criteria are used to allocate countries into the stages of

development prosperity (Sala-i-Martin et al. 2010). The

first is the level of GDP per capita at market exchange

rates. This widely available measure is used as a proxy for

wages, because internationally comparable data on wages

are not available for all countries covered. The thresholds

identified by WEF are shown in Table 1. The second cri-

terion measures the extent to which countries are factor

driven. This is measured by the share of exports of mineral

goods in total exports (goods and services), assuming that

countries that export more than 70 % of mineral products

(measured using a 5-year average) are to a large extent

factor driven. Besides, any country falling in between two

of the three stages is considered to be ‘‘in transition.’’ As

noted earlier, the entire WEF data were collected in 139

countries. However, since our analysis does not include the

countries ‘‘in transition,’’ the actual data for this study

come from a total of 92 countries (31 Stage 1, 29 Stage 2,

and 32 Stage 3 countries).

Bayesian Causal Maps (BCM)

This study utilizes BCM methodology. The BCM aids

decision-making processes while accounting for uncer-

tainty associated with the variables in the map. It makes it

possible to structure the cause–effect relations as perceived

by individuals and to make inferences using these per-

ceived conditional relations (Nadkarni and Shenoy 2001).

The first component of the BCM is causal maps. The

construction of the causal maps requires capturing the

causal knowledge of experts about a domain. Causal

knowledge is especially important in the context of deci-

sion making because decision problems are described and

understood through causal connections (Nadkarni and

Shenoy 2004). These maps represent domain knowledge

more descriptively than other models such as regression or

structural equations. They express the judgment that certain

events or actions will lead to particular outcomes and are

formed by nodes representing causal concepts and links

representing causal connections among causal concepts.

The second component of the BCM is Bayesian nets.

The Bayesian nets, based on probability theory, are used

where expert knowledge is uncertain and ambiguous. A

fundamental assumption of the Bayesian net is that when

we multiply the conditionals for each variable, we obtain

the joint probability distribution for all variables in the

network (Kemmerer and Shenoy 2007). As in causal maps,

nodes represent the variables of the map, but this time, the

links represent the conditional dependencies between the

variables. If there is a directed link from a variable X1 to a

variable X2, then X1 is called as the parent of X2 and X2 as

the child of X1. Each variable in a Bayesian network X1, …, XN has a probability distribution given its parents and the

product of these conditional probability distributions con-

stitute the joint probability distribution of the network. The

related formula can be written as in Eq. 1.

PðX1; . . .; XNÞ¼ YN

i¼1 PðXijPaðXiÞÞ ð1Þ

where Pa(Xi) denotes the set of parents of Xi. Based on this

formula, it can be seen easily that the inference process is

not based on one output variable in a Bayesian net.

The ability to reflect the complex dependency structure

between the variables without compromising on any of the

variables is the main advantage of Bayesian networks.

They are especially useful in modeling uncertainty in a

domain and have been applied particularly to problems that

require diagnosis of problems from a variety of input data.

They not only provide clear graphical structure with natural

274 A. Ekici, S. Onsel

123

causal interpretation that most people find intuitive to

understand; but also provide good estimates even when

some predictors are missing (Nicholson et al. 2008).

Two different approaches have been used to construct

Bayesian networks: one is data-based approach and the

other one, that is also used in this study, is knowledge-

based approach (Nadkarni and Shenoy 2004). The data-

based approaches use conditional independence theory to

conduct models from data. The knowledge-based approach

uses causal knowledge of domain experts in constructing

networks. Nadkarni and Shenoy (2004) defines such maps

as the most effective nets since they combine the quali-

tative structure based on expert knowledge with the

quantitative probabilities identified and revised using hard

data.

Thus, as a combination of causal maps and Bayesian

nets, the BCM capture the causal knowledge of experts

about a domain while allowing robust probabilistic infer-

ences based on both causality and conditional indepen-

dence. Although it resembles multiple regression in the

way that there are dependent and independent variables,

multiple regression treats only one item as a dependent

variable, whereas the BCM has the ability to analyze more

than one dependent variable at a time. Moreover, by the

help of commercial software like Netica (the one that is

used in this study), it is possible to conduct several what-if

scenario analyses. The BCM methodology has been suc-

cessfully used in various areas of economics and business,

including the analysis of the complex and dynamic struc-

ture of inflation (Onsel-Sahin et al. 2006), environmental

economics (Ülengin et al. 2010), venture capital decision

making (Kemmerer and Shenoy 2007), and services (health

care) management (Aktas et al. 2005).

In summary, the BCM methodology involves two main

steps: (1) identification of a conceptual-causal map based

on an expert panel and (2) ‘‘training’’ (analysis) of the

conceptual-causal map with the actual data. In other words,

using an expert panel, initially a conceptual-causal map is

constructed. Then, the loops as well as redundant causal

relations are eliminated from the map. Finally, the data for

each concept are fed to the map in order to train the BCM.

The following two sections provide details about the expert

panel and the data analysis parts of this methodology.

Expert Panel: Identification of Factors and Causal

Relationships

For the first stage of the BCM method, a survey was

conducted to determine the variables to be used in the

model which will serve as the basis for the analysis of

EBOF. Seven academics who have expertise on business

ethics were asked to choose the concepts that they thought

had an influence on the EBOF among the 20 concepts of

the first pillar of the GCI, namely ‘‘institutions.’’ (Please

see Appendix for the list of 20 concepts of the GCI) These

experts were selected from different parts of the world (two

from the United States, two from Europe, two from the

Middle East, and one from Asia) and the survey procedure

was performed through electronic (email) communication.

The common characteristic of the expert panel members is

that they either teach undergraduate and/or graduate levels

business/marketing ethics courses and/or publish regularly

in major business ethics journals such as the Journal of

Business Ethics. The list of the resulting concepts that has

been chosen by all of the experts is given in Table 2.

After the determination of the concepts influencing

EBOF, the experts compared these concepts in a pairwise

matrix where the rows represented causes and the columns

represented effects. The experts were asked to specify

whether the relation between each pair of concepts was

‘‘positive,’’ ‘‘negative,’’ or ‘‘zero’’ (no relation). They were

instructed to enter a ‘‘0’’ for no relation, ‘‘?’’ for a positive

relation, and ‘‘-’’ for a negative relation in each cell to

specify the relation between two concepts in the matrix. A

total consensus was sought in order to gather the different

points of view.

The relations were then analyzed in order to reveal

reciprocal/circular causal relationships (i.e., the loops). The

existence of loops is an indicator of the dynamic structure

of any map (Eden and Ackermann 1998). However, the

circular relationships or causal loops destroy the hierar-

chical form of the graph and violate the acyclic structure

that is required by a BCM. For causal maps, no calculation

has been developed that can cope with feedback cycles

(Jensen 2002). Therefore, the map should not contain

cycles. As a result, in the third stage, the identified loops

were analyzed by subset of our expert team.

More specifically, if two concepts had reciprocal influ-

ences, then the one with the more dominant causal influ-

ence was determined. For example, it was stated that there

was a causal link both from ‘‘irregular payments and

bribes’’ (IPAB) to ‘‘strength of auditing and reporting

standards’’ (SARS) and from ‘‘SARS’’ to ‘‘IPAB.’’ After

analyzing the situation, it was decided by the expert panel

that the more dominant causal influence was the one from

‘‘SARS’’ to ‘‘IPAB.’’ As a result, the link from ‘‘IPAB’’ to

‘‘SARS’’ was eliminated. In addition, one factor (strength

Table 1 Income thresholds for establishing stages of development

Stage of development GDP per capita

Stage 1: Factor driven (*underdeveloped) \2,000 Transition from Stage 1 to Stage 2 2,000–3,000

Stage 2: Efficiency driven (*developing) 3,000–9,000

Transition from Stage 2 to Stage 3 9,000–17,000

Stage 3: Innovation driven (*developed) [17,000

How Ethical Behavior of Firms is Influenced by the Legal and Political Environments 275

123

of investor protection, SIP) that had been indentified in the

first stage as a potential cause of EBOF was eliminated

from further consideration as this factor was not considered

as a cause for any other factors by our experts in the second

round. The final pairwise matrix reached after above

mentioned revisions is given in Table 3.

Data Analysis Using BCM

The BCM, as proposed by Nadkarni and Shenoy (2001), is

a special type of causal map with an associated set of

probability tables. In other words, the BCM is a combi-

nation of causal maps used for deterministic modeling and

Bayesian networks used for uncertainty based modeling

(Nadkarni and Shenoy 2001, 2004).

The map consists of the nodes, representing the con-

cepts, and the arcs, representing relations between the

concepts. The nodes of a BCM represent uncertain con-

cepts and the arcs are the causal links between them

(Fenton and Neil 2000). The BCM is a type of graphical

model, which uses probability theory to manage uncer-

tainty and complexity by explicitly representing the con-

ditional dependencies between the nodes (concepts)

(Jensen 2002). The visual representation of a BCM can be

very useful in clarifying previously opaque assumptions or

reasoning hidden in an expert’s mind. From a mathematical

point of view, the basic property of a BCM is the chain

rule: a BCM is a compact representation of the joint

probability table over its universe. In a simple Bayes net

where A affects B and B affects C; it is assumed that

P A; B; Cð Þ ¼ P Að Þ� P B j Að Þ� P C j Bð Þ;

where � denotes pointwise multiplication of tables. In fact, the rule of total probability tells us that

P A; B; Cð Þ ¼ P Að Þ� P B j Að Þ� P C j A; Bð Þ

The difference between these two expressions depends

on the assumption that P(C | A,B) = P(C | B), hence C is

conditionally independent of A given B. In other words, in

Bayes nets, it is assumed that it is conditionally

independent of its predecessors in the sequence given its

parents meaning that missing arcs (from a node to its

successors in the sequence) signify conditional

independence assumptions. The fundamental assumption

of a Bayesian network is that when the conditionals for

each variable are multiplied, the joint probability

distribution for all variables in the network is obtained

(Mishra et al. 2001). In practice, such an approach is

computationally intractable when there is an extensive

number of variables since the joint distribution will have an

exponential number of states and values.

Figure 1 shows a very simple BCM that consists of four

variables, namely, judicial independence (JI), favoritism in

decisions of government officials (FDGO), IPAB, and

intellectual property protection (IPP). The dependence

relations are expressed in terms of conditional probability

distributions for each variable. Each variable has a set of

possible values, called states. For illustrative purposes,

these four variables have only three states, namely, low,

medium, and high (please note that in the actual analysis

each variable was expressed in five states; see Table 4). If

there is an arrow from one variable to another, this means

that there is a conditional dependence between these two

variables, whereas the lack of an arrow signifies that the

two variables are independent from each other. Thus, in our

example JI and FDGO are independent of IPAB while IPP

is dependent to FDGO and IPAB. Figure 1 also shows the

conditional probability tables of P(JI), P(FDGO\JI),

P(IPAB), and P(IPP\FDGO, IPB). From these conditional

probability tables, one can easily analyze the relations. For

example, let us assume that the state of JI is known to be

‘‘low,’’ then the probability of FDGO’s being low is

88.97 %, being medium is 10.08 %, and being high is

0.94 %. Now suppose that the state of FDGO is known as

‘‘medium’’ and IPAB as ‘‘high’’, then IPP will be ‘‘low’’

with 0.52 % probability, ‘‘medium’’ with 48.13 % proba-

bility, and ‘‘high’’ with 51.34 % probability.

Although the BCMs create an efficient language for

building models of domains with inherent uncertainty, it

may be time consuming to calculate conditional probabil-

ities, even for a very simple BCM like the one given in

Fig. 1, not to mention the model used in this study which

has 8 variables, 20 conditional relations, and 32,680 con-

ditional probabilities. Fortunately, there are several com-

mercial software tools such as Hugin and Netica that can

perform this operation. In the current research, Netica

version 4.02 was used. It is a complete software package

designed to work with BCMs, decision networks, and

influence diagrams.

Results

The multiple iterations of the expert panel method revealed

the final BCM illustrated in Fig. 2. The figure depicts the

Table 2 List of the variables directly/indirectly effecting EBOF

Intellectual property protection

Irregular payments and bribes

Judicial independence

Favoritism in decisions of government officials

Transparency of government policymaking

Strength of auditing and reporting standards

Efficacy of corporate boards

Strength of investor protection

276 A. Ekici, S. Onsel

123

direct and indirect relationships between EBOF and the

remaining seven factors. The resulting BCM was then

analyzed three times for each of the three different country

(development) groups (Figs. 3, 4, 5) Before the analysis,

each variable which was measured on a seven-point scale

was divided into five probability categories (previously

mentioned as states), ranging from ‘‘very low’’ to ‘‘very

high’’. The cut-off ranges for each category are shown in

Table 4.

The interpretation of the models derived from the BCM

analysis can be done in two main ways: bottom-up and top-

down approaches. Each type of interpretation provides

researchers and decision makers a different perspective on

how to improve the system which is composed of both

dependent and independent variables. The bottom-up

approach is useful in illustrating how the dependent vari-

able is affected by the changes in each of the independent

variables. In other words, through the use of the bottom-up

approach, the researchers can identify how the possibilities

of each state change in each of the factors influence the

system as a whole. For example, when a state change from

medium to very high in the JI occurs, then, the states of

IPP, efficacy of corporate boards (ECB), and transparency

of government policymaking (TGP) change from medium

to high level, whereas EBOF changes from medium to very

high.

The top-down approach, on the other hand, informs

researchers and decision makers about what improvements

need to be made in each of the variables so that the desired

level of dependent variable can be achieved. For example,

one can easily see that in order to change the level of

EBOF from medium to high, there have to be changes in

the states of IPP, IPAB, ECB, TGP, and JI from medium to

high level as well as a change in FDGO from low to

medium. Both bottom-up and top-down approaches pro-

vide information about the system in general.

More specific analyses can be done using the ‘‘sensi-

tivity to findings’’ option of Netica. Sensitivity analysis

identifies the variables having the most important impact

on a dependent variable. A detailed investigation of these

factors extracted from the sensitivity analysis is crucial

because positive or negative changes in these factors have

substantial impacts on the dependent variable. In the fol-

lowing sections, we first report the results of the BCM

models and then provide results of the sensitivity analyses

for each country group.

Table 3 Final pairwise matrix of the model

Intellectual

property

protection

Irregular

payments

and bribes

Judicial

independence

Favoritism in

decisions of

government

officials

Transparency

of government

policymaking

Strength of

auditing and

reporting

standards

Efficacy

of

corporate

boards

Ethical

behavior

of firms

Intellectual

property

protection

0 0 0 0 0 0 0 1

Irregular

payments and

bribes

1 0 0 0 0 0 0 1

Judicial

independence

1 1 0 1 1 0 0 0

Favoritism in

decisions of

government

officials

1 1 0 0 0 1 1

Transparency of

government

policymaking

0 1 0 1 0 1 0 0

Strength of

auditing and

reporting

standards

0 1 0 1 0 0 1 1

Efficacy of

corporate

boards

0 1 0 0 0 0 0 1

Ethical behavior

of firms

0 0 0 0 0 0 0 0

How Ethical Behavior of Firms is Influenced by the Legal and Political Environments 277

123

Results for Stage 3 Countries (Innovation-Driven,

Advanced Economies)

Figure 3 suggests that given the current BCM model which

consists of various political, legal, and business institutions,

EBOF operating in innovation-driven (advanced) econo-

mies is generally perceived as high. In other words, based

on the existing variables and the causal relationships,

there is a 75.8 % (high: 33.1 % ? very high: 42.7 %)

probability that managers who work in innovation-driven

(advanced) economies perceive the behavior of their fel-

low managers who work in the same type economies as

‘‘highly’’ ethical.

In addition, the model suggests that each factor affecting

EBOF in these advanced economies is perceived quite

positively. The overall review of the model reveals that the

only partially problematic variable appears to be FDGO.

The probability that managers perceive FDGO as medium

or lower in these innovation-driven economies is 66.5 %

(medium: 39 % ? low: 26.6 % ? very low: 0.93 %).

Intellectual property protection

Favoritism in decisions of government officials Transparency of government policymaking

Judicial independence

Ethical Behaviours of Firms

Efficacy of corporate boardsStrength of auditing and reporting standards

Irregular payments and bribes

Fig. 2 The BCM for the EBOF

Table 4 Probability categories (states)

Range Category

1.00–2.20 Very low

2.30–3.40 Low

3.50–4.60 Medium

4.70–5.80 High

5.90–7.00 Very high

Fig. 1 An Illustration of BCM with conditional probability tables

278 A. Ekici, S. Onsel

123

Intellectual property protect...

verylow low medium high veryhigh

1.45 1.45 26.4 49.1 21.7

5.06 ± 1

Favoritism in decisions of government...

verylow low medium high veryhigh

0.93 26.6 39.0 30.5 3.06

4.1 ± 1.1

Transparency of government policyma...

verylow low medium high veryhigh

0.22 0.21 26.5 57.7 15.3

5.05 ± 0.86

Judicial independence

verylow low medium high veryhigh

0.18 0.18 23.6 27.5 48.5

5.49 ± 1.1

Ethical Behaviours of Fir...

verylow low medium high veryhigh

2.30 5.01 16.9 33.1 42.7

5.31 ± 1.2

Efficacy of corporate boar...

verylow low medium high veryhigh

0.63 0.63 27.2 68.5 2.99

4.87 ± 0.75

Strength of auditing and reporting stan...

verylow low medium high veryhigh

0.21 0.21 7.83 62.8 29.0

5.44 ± 0.79

Irregular payments and bri...

verylow low medium high veryhigh

1.66 1.68 12.5 28.1 56.1

5.62 ± 1.1

Fig. 3 The BCM of EBOF for Stage 3 countries

Intellectual property protect...

verylow low medium high veryhigh

5.17 56.8 21.3 11.7 5.05

3.46 ± 1.2

Favoritism in decisions of government...

verylow low medium high veryhigh

17.1 53.4 19.1 6.36 4.01

3.12 ± 1.2

Transparency of government policyma...

verylow low medium high veryhigh

0.18 7.38 60.4 31.8 0.19

4.29 ± 0.79

Judicial independence

verylow low medium high veryhigh

3.30 49.4 27.4 19.6 0.24

3.57 ± 1.1

Ethical Behaviours of Fir...

verylow low medium high veryhigh

7.45 31.3 38.1 15.5 7.61

3.81 ± 1.3

Efficacy of corporate boar...

verylow low medium high veryhigh

1.66 1.62 60.4 32.4 3.90

4.42 ± 0.87

Strength of auditing and reporting stan...

verylow low medium high veryhigh

0.25 0.26 44.8 47.3 7.43

4.74 ± 0.84

Irregular payments and bri...

verylow low medium high veryhigh

6.17 20.7 48.8 18.4 5.97

3.97 ± 1.2

Fig. 4 The BCM of EBOF for Stage 2 countries

How Ethical Behavior of Firms is Influenced by the Legal and Political Environments 279

123

A more detailed look at the relationships given in Fig. 3

require a sensitivity analysis. In this way, we can easily

analyze how much our belief in a particular node (factor) is

influenced by the findings at other nodes (factors). The

sensitivity analysis of the BCM for innovation-driven

economies has shown that the most significant factor on the

EBOF is IPAB, followed by FDGO, and IPP. Furthermore,

a detailed sensitivity analysis identifies JI as the most

influential factor on IPAB in Stage 3 countries. We will

provide a more detailed discussion about these most

influential factors later in this section.

Results for Stage 2 Countries (Efficiency-Driven,

Developing Economies)

An overview of the BCM for the Stage 2 countries (i.e., the

efficiency driven, developing economies) suggests that

managers who work in these countries perceive ethical

behavior of fellow managers who also work in similar

countries generally as medium or low (but definitely not

high). A closer look at each factor indicates that issues

related to IPP, FDGO, and JI are particularly perceived to

be low in these countries.

The sensitivity analysis of the BCM for Stage 2 coun-

tries has yielded almost the same results as the Stage 3

countries in that the most significant factor on the EBOF is

IPAB, followed by FDGO, and IPP. Furthermore, as in the

case of the Stage 3 countries, a detailed sensitivity analysis

has identified JI as the most influential factor on IPAB for

the Stage 2 countries.

Results for Stage 1 Countries (Factor-Driven,

Underdeveloped Economies)

As can be seen in Fig. 5, given the existing model and

the causal relationships among the variables, managers in

factor-driven economies perceive the EBOF operating in

similar countries generally as low. Issues related to

FDGO, IPP, IPAB, and JI appear particularly problematic

(‘‘low’’ probabilities are 85.6, 78.2, 63, and 56 %,

respectively). However, without a sensitivity analysis,

any conclusion regarding the most influential factors on

EBOF operating in factor-driven economies would be

misleading.

As compared to the Stage 3 and Stage 2 countries, for

Stage 1 countries, the order resulting from the sensitivity

analysis reveals a different pattern, making IPP the most

influential factor affecting ethical behavior, followed by

FDGO and JI. However, as in the case of the Stage 2 and

the Stage 3 countries, a further sensitivity analysis on the

Intellectual property protect...

verylow low medium high veryhigh

2.16 78.2 12.6 4.95 2.15

3.12 ± 0.89

Favoritism in decisions of government ...

verylow low medium high veryhigh

1.54 85.9 4.52 6.51 1.56

3.05 ± 0.85

Transparency of government policyma...

verylow low medium high veryhigh

0.13 4.23 83.6 11.9 0.13

4.09 ± 0.6

Judicial independence

verylow low medium high veryhigh

9.09 56.0 22.5 12.1 0.28

3.26 ± 1

Ethical Behaviours of Fir...

verylow low medium high veryhigh

2.46 49.6 37.8 7.71 2.50

3.5 ± 0.99

Efficacy of corporate boar...

verylow low medium high veryhigh

0.99 0.95 63.7 30.3 4.09

4.43 ± 0.83

Strength of auditing and reporting stan...

verylow low medium high veryhigh

0.22 8.83 65.8 24.9 0.21

4.19 ± 0.77

Irregular payments and bri...

verylow low medium high veryhigh

2.56 63.0 26.8 5.08 2.57

3.31 ± 0.96

Fig. 5 The BCM of EBOF for Stage 1 countries

280 A. Ekici, S. Onsel

123

IPP variable reveals once again JI as the key factor

affecting perceptions of IPP in Stage 1 countries.

Detailed Analysis for Stage 3 and Stage 2 Countries

When we employ the top-down approach to the results of

the sensitivity analysis for Stage 3 and Stage 2 countries

(Figs. 6, 7), we see that any improvement on EBOF is

closely linked to the improvements these countries can

make in IPAB. As a result, if countries would like to

improve EBOF operating in their countries, they should

improve their performance on IPAB (i.e., they should take

measures to lower perceptions of corruption and bribery

among the business people in the country).

Similarly, when we focus on the IPAB and again use the

top-down approach, the sensitivity analysis reveals JI as the

most influential factor on IPAB. As can be seen in Figs. 8

and 9, in order to improve IPAB both in Stage 3 and in

Stage 2 countries, a significant improvement in the JI

would be required.

JI, located at the bottom of all the models, is considered

as the critical factor (the ‘‘policy variable’’) as it affects all

the other factors yet is not affected by any other factor in

the system. Therefore, since changes in policy variables

have considerable impacts on the entire system, their

analyses may reveal important results. The following two

figures delineate potential changes among the most crucial

three factors in the business ethics systems in Stage 3 and

Stage 2 countries. As can be seen in Fig. 10, when JI is

elevated from medium to high, and to very high levels, the

factor it ultimately affects the most (EBOF) improves from

medium to high, and to very high immediately.

Similarly, if JI can be improved to medium and to high

from low, EBOF operating in Stage 2 countries improve

from low to medium, and to high (see Fig. 11).

Detailed Analysis for Stage 1 Countries

As indicated earlier, factor-driven economies (Stage 1

countries) show a different pattern than both efficiency-

driven economies (Stage 2 countries) and innovation-dri-

ven economies (Stage 3 countries). In other words, when

we employ the top-down approach to the results of the

sensitivity analysis for Stage 1 countries, we see that any

Ethical Behaviours of Firms

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Irregular payments and bribes

verylow low medium high veryhigh

1.99 1.97 51.1 41.5 3.38

4.51 ± 0.89

Ethical Behaviours of Firms

verylow low medium high veryhigh

0 0 0

100 0

5.2 ± 0.35

Irregular payments and bribes

verylow low medium high veryhigh

1.01 1.01 0.85 56.7 40.5

5.62 ± 0.85

Ethical Behaviours of Firms

verylow low medium high veryhigh

0 0 0 0

100

6.4 ± 0.35

Irregular payments and bribes

verylow low medium high veryhigh

0.79 0.80 0.67 1.69 96.1

6.3 ± 0.68

Fig. 6 Relationship between EBOF and IPAB in Stage 3 countries

Ethical Behaviours of Firms

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Irregular payments and bribes

verylow low medium high veryhigh

3.88 51.0 35.2 6.10 3.79

3.46 ± 1

Ethical Behaviours of Firms

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Irregular payments and bribes

verylow low medium high veryhigh

3.25 3.10 81.1 9.44 3.13

4.07 ± 0.81

Ethical Behaviours of Firms

verylow low medium high veryhigh

0 0 0

100 0

5.2 ± 0.35

Irregular payments and bribes

verylow low medium high veryhigh

8.03 7.43 18.3 58.6 7.65

4.6 ± 1.3

Fig. 7 Relationship between EBOF and IPAB in Stage 2 countries

How Ethical Behavior of Firms is Influenced by the Legal and Political Environments 281

123

Irregular payments and bribes

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Judicial independence

verylow low medium high veryhigh

7.60 74.5 8.08 9.64 0.23

3.05 ± 0.93

Irregular payments and bribes

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Judicial independence

verylow low medium high veryhigh

0.85 56.7 38.3 4.04 .097

3.35 ± 0.79

Irregular payments and bribes

verylow low medium high veryhigh

0 0 0

100 0

5.2 ± 0.35

Judicial independence

verylow low medium high veryhigh

2.26 11.6 20.9 64.9 0.26

4.59 ± 1

Fig. 9 Relationship between JI and IPAB in Stage 2 countries

Irregular payments and bribes

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Judicial independence

verylow low medium high veryhigh

0.29 0.29 89.5 6.91 3.05

4.15 ± 0.63

Irregular payments and bribes

verylow low medium high veryhigh

0 0 0

100 0

5.2 ± 0.35

Judicial independence

verylow low medium high veryhigh

0.13 0.13 40.4 58.1 1.29

4.72 ± 0.72

Irregular payments and bribes

verylow low medium high veryhigh

0 0 0 0

100

6.4 ± 0.35

Judicial independence

verylow low medium high veryhigh

.064

.063 0.62 15.5 83.8

6.19 ± 0.6

Fig. 8 Relationship between JI and IPAB in Stage 3 countries

Ethical Behaviours of Firms

verylow low medium high veryhigh

1.43 12.5 63.2 21.4 1.52

4.11 ± 0.87

Irregular payments and bribes

verylow low medium high veryhigh

1.58 1.62 47.2 48.1 1.47

4.55 ± 0.84

Judicial independence

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Ethical Behaviours of Firms

verylow low medium high veryhigh

4.60 5.02 4.79 58.2 27.4

5.18 ± 1.2

Irregular payments and bribes

verylow low medium high veryhigh

3.05 3.05 3.13 59.2 31.5

5.36 ± 1.1

Judicial independence

verylow low medium high veryhigh

0 0 0

100 0

5.2 ± 0.35

Ethical Behaviours of Firms

verylow low medium high veryhigh

1.29 1.26 1.29 24.6 71.5

5.97 ± 0.91

Irregular payments and bribes

verylow low medium high veryhigh

0.77 0.78 0.79 0.75 96.9

6.31 ± 0.67

Judicial independence

verylow low medium high veryhigh

0 0 0 0

100

6.4 ± 0.35

Fig. 10 Relationship between JI and EBOF in Stage 3 countries

282 A. Ekici, S. Onsel

123

improvement on EBOF will be closely linked to the

improvements these countries can make in IPP. As a result,

if Stage 1 countries would like to improve EBOF operating

in their countries, they would have to improve their per-

formance on IPP (see Fig. 12).

Similarly, when we focus on IPP and again use the top-

down approach, the sensitivity analysis reveals JI as the

most influential factor on IPP. As can be seen in Fig. 13, in

order to improve IPP in Stage 1 countries, a significant

improvement in the JI would be required.

As in the case of Stage 2 and Stage 3 countries, JI is also

the policy variable in Stage 1 countries, and therefore, a

further analysis of JI is likely to reveal fruitful results.

Figure 14 delineates potential changes among the most

crucial three factors in the business ethics system in Stage 1

countries. As can be seen in Fig. 14, when JI is elevated

from low to medium and to high, the factor it ultimately

affects the most (EBOF) improves immediately.

Conclusion

The objective of this study was to advance our current

understanding of the factors affecting EBOF. Toward this

end, we particularly focused our attention on relatively

less-studied aspects of business ethics: the political and

legal environments of business. Using a panel of business

ethics experts, we identified a model that depicts how

certain legal and political factors affect EBOF. The model,

then, was applied to three distinct country types: innova-

tion-, efficiency-, and factor-driven countries. We observed

Ethical Behaviours of Firms

verylow low medium high veryhigh

4.65 45.3 40.8 4.58 4.68

3.51 ± 1.1

Irregular payments and bribes

verylow low medium high veryhigh

4.30 31.1 56.1 4.33 4.14

3.67 ± 1

Judicial independence

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Ethical Behaviours of Firms

verylow low medium high veryhigh

7.63 16.5 47.8 19.8 8.20

4.05 ± 1.2

Irregular payments and bribes

verylow low medium high veryhigh

6.06 6.08 68.1 14.0 5.76

4.09 ± 1

Judicial independence

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Ethical Behaviours of Firms

verylow low medium high veryhigh

13.3 14.2 22.1 37.1 13.2

4.27 ± 1.5

Irregular payments and bribes

verylow low medium high veryhigh

9.60 10.1 10.0 60.8 9.43

4.6 ± 1.4

Judicial independence

verylow low medium high veryhigh

0 0 0

100 0

5.2 ± 0.35

Fig. 11 Relationship between JI and EBOF in Stage 2 countries

Ethical Behaviours of Firms

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Intellectual property protection

verylow low medium high veryhigh

0.87 96.2 1.15 0.91 0.88

2.86 ± 0.56

Ethical Behaviours of Firms

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Intellectual property protection

verylow low medium high veryhigh

1.15 76.1 20.4 1.17 1.15

3.1 ± 0.75

Ethical Behaviours of Firms

verylow low medium high veryhigh

0 0 0

100 0

5.2 ± 0.35

Intellectual property protection

verylow low medium high veryhigh

5.64 7.22 40.9 40.6 5.56

4.4 ± 1.1

Fig. 12 Relationship between EBOF and IPP in Stage 1 countries

How Ethical Behavior of Firms is Influenced by the Legal and Political Environments 283

123

that EBOF diminishes as we move from more advanced

(innovation-driven) to less-advanced (efficiency-driven and

factor-driven) countries. Even though the same model is

executed, we notice important differences in the way

political and legal environments (factors) influence EBOF

in each country group. Despite significant differences

across the country groups, one striking similarity, however,

is the importance of JI for a better (or worse) ethical

conduct throughout the world. This section discusses our

major findings, their managerial and public policy impli-

cations, and future research directions.

Significance of JI

The results of the sensitivity analysis reveal a similar pat-

tern for both Stage 3 and Stage 2 countries. In both cases,

the most influential factor affecting EBOF was IPAB,

followed by FDGO. The only difference we observed was

the percentage probabilities of each factor. When IPAB

was put through a further sensitivity analysis, JI was

identified as the most influential factor affecting IPAB. For

Stage 1 (factor-driven) countries, the order resulting from

sensitivity analysis was different, making IPP the most

influential factor affecting ethical behavior, followed by

FDGO. A further sensitivity analysis on the IPP variable

reveals once again JI as the key factor affecting perceptions

of IPP in Stage 1 countries. This result, along with the

results of Stage 3 and Stage 2 countries, may suggest that at

the bottom line, regardless of the development stage, the

level of EBOF is closely tied to the perceptions of JI in a

particular country. In other words, regardless of where the

business is conducted in the world, perceived independence

of courts and judges is critical for an improved ethical

conduct of businesses.

Judicial independence

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Intellectual property protection

verylow low medium high veryhigh

0.43 92.6 6.14 0.39 0.41

2.89 ± 0.53

Ethical Behaviours of Firms

verylow low medium high veryhigh

0.59 65.3 32.9 0.59 0.60

3.22 ± 0.73

Judicial independence

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Intellectual property protection

verylow low medium high veryhigh

1.90 81.3 13.0 1.91 1.86

3.05 ± 0.79

Ethical Behaviours of Firms

verylow low medium high veryhigh

2.70 32.7 58.8 2.96 2.83

3.65 ± 0.91

Judicial independence

verylow low medium high veryhigh

0 0 0

100 0

5.2 ± 0.35

Intellectual property protection

verylow low medium high veryhigh

7.56 7.72 46.4 30.8 7.54

4.28 ± 1.2

Ethical Behaviours of Firms

verylow low medium high veryhigh

7.67 8.58 25.5 50.6 7.70

4.5 ± 1.3

Fig. 14 Relationship between JI and EBOF in Stage 1 countries

Intellectual property protection

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Judicial independence

verylow low medium high veryhigh

8.88 66.4 23.5 1.19 .070

3.01 ± 0.79

Intellectual property protection

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Judicial independence

verylow low medium high veryhigh

4.47 27.3 23.3 44.5 0.45

4.11 ± 1.2

Intellectual property protection

verylow low medium high veryhigh

0 0 0

100 0

5.2 ± 0.35

Judicial independence

verylow low medium high veryhigh

10.7 4.38 8.69 75.0 1.15

4.62 ± 1.3

Fig. 13 Relationship between JI and IPP in Stage 1 countries

284 A. Ekici, S. Onsel

123

The Differentiating Role of Corruption and Bribery

Based on Economic Development

As noted previously, the results of the sensitivity analysis

identified IPAB as the most significant factor on EBOF

operating in Stage 3 and Stage 2 countries. However, when

it comes to the Stage 1 countries, the sensitivity analysis

revealed IPP as the most influential factor on EBOF. In fact,

IPAB does not even appear among the most important

factors affecting business ethics for the managers who work

in these less-developed (factor-driven) economies (listed as

the fourth out of seven factors). Based on the Transparency

International Report (2010), one might expect corruption

and bribery to have a greater impact on business activities

(and correspondingly on business ethics) in less-developed

countries. So, how can we explain our findings?

Management and business scholars, as well as anthro-

pologists and business analysts have argued that issues

related to corruption and bribery may be a function of

cultural context within which moral decisions regarding

business are made (e.g., Blundo and Olivier de Sardan

2006; Chabal and Daloz 1999; Velasquez 2010). For

example, Velasquez (2010) argues that as corruption in

general and bribery in particular are considered unethical in

most developed and developing countries, they may not

necessarily be viewed as acts of violation of business ethics

in certain cultural contexts. Furthermore, he argues:

…it is morally wrong to offer and/or accept bribe- s…in countries where the norms and expectations that govern official roles are relatively clear and well

known, and where the public–private distinction is

recognized, accepted, and well-entrenched in the

bureaucratic institutions in the society. The devel-

oped nations and many of the developing ones fall

into this category’’ (p. 487).

In such countries, for business people to offer IPAB to

officials may be considered unethical because such an act

may result in the use of public power for private purpose.

However, in societies where the public–private distinction

is neither commonly recognized nor accepted, various

forms of corruption including bribery, favoritism, or nep-

otism may not be considered morally unacceptable. When

government is not understood as a public entity whose

primary purpose is to serve the interests of the public (in

other words, when the boundary between the public and

private interests of the individuals blur), offering IPAB to

either government officials or other fellow business people

may not be viewed as an unethical business practice (or

may be viewed as a morally neutral act).

The results of our study may offer empirical support for

the notion that bribery may not be viewed as a critical issue

of business ethics in certain countries. The countries

depicted in the writings of Blundo and Olivier de Sardan

(2006); Chabal and Daloz (1999) and Velasquez (2010) are

all classified as Stage 1 countries in our study. Many of

these countries are characterized not only by low income

but also by undemocratic, autocratic, or monarch govern-

ments including some of the Arab sheikhdoms and sul-

tanates, Nepal, and postcolonial regimes of sub-Saharan

Africa. Even though according to Transparency Interna-

tional, corruption and bribery is a common practice in these

countries, they may not be perceived as morally wrong

conducts of business. As our results show, for managers of

the companies operating in Stage 1 countries, IPAB is not a

very important factor affecting their perceptions of the

level of business ethics in these countries. Therefore, it

may be concluded that bribery affects business decisions,

but not the perceptions of business ethics in Stage 1

countries.

The Role of IPP

Our study reveals a crucial link between IPP and business

ethics in Stage 1 countries. As can be seen in Fig. 15, when

the current level of IPP (which is quite low, see Fig. 5) can

Intellectual property protection

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Ethical Behaviours of Firms

verylow low medium high veryhigh

0.73 61.0 36.8 0.71 0.75

3.28 ± 0.76

Intellectual property protection

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Ethical Behaviours of Firms

verylow low medium high veryhigh

4.52 4.52 61.2 25.1 4.71

4.25 ± 1

Intellectual property protection

verylow low medium high veryhigh

0 0 0

100 0

5.2 ± 0.35

Ethical Behaviours of Firms

verylow low medium high veryhigh

9.40 9.12 8.92 63.3 9.31

4.65 ± 1.3

Fig. 15 Relationship between IPP and EBOF in Stage 1 countries

How Ethical Behavior of Firms is Influenced by the Legal and Political Environments 285

123

be improved to medium or to high, EBOF improves to

medium or to high immediately. Establishing an effective

IPP system in the underdeveloped world appears as the

most significant factor affecting managers’ perceptions of

business ethics in these countries. However, as recent

reports point out, instituting effective IPP systems in the

underdeveloped world is a daunting task (e.g., Britz and

Lipinski 2001; Kameri-Mbote 2005; Sikoyo et al. 2006).

Considering that most (about 60 %) Stage 1 countries in

our study are from Africa (see Table 5), we would like to

particularly discuss a few important issues and challenges

related to IPP in Africa. As indicated by Sikoyo et al.

(2006), even though most of the countries in Africa have

taken measures to comply with international IPP standards,

the effective implementation of these norms has not

become possible due to various shortcomings including

limited understanding of intellectual property rights (IPRs)

and lack of people and institutions with sufficient experi-

ence and knowledge to handle IPRs, particularly with

respect to investment, trade, and competition.

After reviewing current research, knowledge, practices,

and challenges of IPR issues in various African countries,

Kameri-Mbote (2005) and Sikoyo et al. (2006) have made

a number of recommendations to improve IPP standards

around the region. The main argument these scholars put

forward is that considering historical, cultural, socio-

economic, and resource characteristics, a search for an

alternative IPP regime may need to be considered for

Africa. As specified by Sikoyo et al. (2006):

The search for the alternative IP should be driven by

compatibility with the indigenous alternative rights/

systems. These need not be predetermined, but need

to be well thought out and articulated outside the

fixed IP categories. Flexibility should guide the pro-

cess where the African countries are able to mould

IPR regimes that work for them, and the region as a

whole (p. 28).

Interestingly, even though both of these reports were

written in two different time frames and covered different

parts of (sub-Saharan) Africa, they both concluded exactly

the same set of assessments of IPR needs and made similar

recommendations to meet these needs. In summary, in

order to improve IPP in the region, national infrastructure

(including information technology facilities, good record

keeping practices, accurate statistical data, and internet

access), and human resources capacity (e.g., staffing of

trained IP personnel and scientists with understanding of

law and trade) should be enhanced. In addition, legal

practices related to IPP need to be extended, and as law

schools invest more on programs covering IP issues,

training programs should be offered for those who gradu-

ated from law schools with no IP-law education. On a

related issue, it was noted that since litigation on IPR is

quite low in African countries, judges are not able to

develop experience through practice. As a result, there

seems to be a need for creating greater awareness in the

judiciary to both understand and interpret IP-related laws.

Other Managerial and Public Policy Implications

The models depicted in this paper can potentially provide a

useful diagnostic tool for (international) companies and

public policy makers. However, given inherit limitations of

the expert panel method used in this study both policy

makers and other (managerial) practitioners should use our

models cautiously. First, by using our models, companies

Table 5 Country groupings identified by WEF

Stage 3 countries

(Innovation-driven)

Stage 2 countries

(Efficiency-driven)

Stage 1 countries

(Factor-driven)

Australia Albania Chad

Austria Argentina Côte d’Ivoire

Belgium Bosnia and

Herzegovina

Ethiopia

Canada Brazil Gambia

Cyprus Bulgaria Ghana

Czech Republic Cape Verde Honduras

Denmark China India

Finland Colombia Kenya

France Costa Rica Kyrgyz Republic

Germany Dominican Republic Lesotho

Greece Ecuador Madagascar

Hong Kong SAR El Salvador Malawi

Iceland Jordan Mali

Ireland Lebanon Mauritania

Israel Macedonia FYR Moldova

Italy Malaysia Mongolia

Japan Mauritius Mozambique

Korea Rep Mexico Nepal

Luxembourg Montenegro Nicaragua

Malta Namibia Nigeria

Netherlands Panama Pakistan

New Zealand Peru Philippines

Norway Romania Rwanda

Portugal Russian Federation Senegal

Singapore Serbia Tajikistan

Slovenia South Africa Tanzania

Spain Thailand Timor-Leste

Sweden Tunisia Uganda

Switzerland Turkey Vietnam

United Arab Emirates Zambia

United Kingdom Zimbabwe

United States

286 A. Ekici, S. Onsel

123

who intend to enter into one of the three types of econo-

mies (Stage 1, 2 or 3 countries) may be equipped before-

hand about the dynamics of business ethics in that

particular type of economy. More specifically, managers of

international companies may be able to know how certain

legal and political issues of business affect the business

ethics system in that type of economy. This information, in

turn, may help companies to have more informed decisions

about their entry strategies.

Second, by using our models, managers can assess the

overall business ethics climate of the particular country that

they plan to enter, and as a result, take necessary precau-

tions before the entry. For example, let us assume that an

Australian company (currently operating in a Stage 3

economy) is planning to enter into two former-socialist

countries: Romania (a new EU member and a Stage 2

country) and Moldova (a former Soviet Union state and a

Stage 1 country). Based on the classification scheme given

in Table 3, our data indicate that Romania scores ‘‘low’’ on

IPP (3.2), FDGO (2.4), and TGP (2.9); and ‘‘medium’’ on

IPAB (4.4), JI (3.5), SARS (4.6), and ECB (4.4). Moldova,

on the other hand, scores ‘‘low’’ on IPP (2.6), IPAB (3.3),

JI (2.3), and FDGO (2.7); and ‘‘medium’’ on SARS (4.3),

ECB (4.4), and TGP (4.3). When the models we propose in

this study (i.e., models for Stage 2 and Stage 1 economies)

are treated with the above scores, one can observe that

there is a high probability that EBOF in both countries will

be ‘‘low.’’ More specifically, given certain legal and

political factors and the interactions among them, there is a

61.9 % chance that the EBOF in Romania will be low.

Similarly, there is even a greater chance (68.5 %) that the

EBOF operating in Moldova will be low (Fig. 16a, b).

Even though these results may seem discouraging for the

Australian firm, they may indeed help the company to be

better prepared for these new markets. For example, the

Australian company may decide to put more emphasis on

the rules governing their business conduct. If the company

did not have a written ethical code, perhaps it is time to

adopt one so that their managers can be better guided in

these new countries where the business ethics is perceived

to be low. The literature suggests that companies having a

written code of ethics are less likely to engage in unethical

business practices in the international markets (e.g.,

McKinney and Moore 2008). In this example, a hypo-

thetical Australian company was chosen to demonstrate

possible managerial implications of our models. Similar

scenarios can be developed and corresponding managerial

implications can be drawn for any company in the world

that plans to enter into any of the 92 countries included in

this study. Similarly, based on the models depicted in this

study, governments across the world may be able to

develop scenarios and related public policies in order to

improve the business ethics climate in their countries.

Final Remarks and Future Research

This research can be considered as one of the rare examples

that empirically examines the relationships between the

legal and political environments of business and EBOF.

The models reported in this paper clearly delineate how

EBOF is related to each of the seven environmental factors.

Moreover, for each of the three development stages, the

results of the sensitivity analyses pinpoint the most crucial

factors affecting EBOF. Both through the overall models

and through the sensitivity analyses, our study provides

directions for policy makers to improve the ethical conduct

of businesses in their respective countries. In addition, the

results of this study provide further support for scholars

who argue that business ethics is likely to vary among

countries based on their socio-economic factors (e.g.,

Batten et al. 1999; Robertson 2009). The data for our study,

which come from 92 countries in the world, provide a

rather convincing argument for the thesis that business

ethics is related to the countries’ development stage.

Furthermore, the BCM methodology allows researchers

to analyze a domain from a systems perspective. As a

Intellectual property protect...

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Favoritism in decisions of government...

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Transparency of government policyma...

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Judicial independence

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Ethical Behaviours of Fir...

verylow low medium high veryhigh

0.16 61.9 37.6 0.16 0.16

3.26 ± 0.7

Efficacy of corporate boar...

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Strength of auditing and reporting stan...

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Irregular payments and bri...

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Intellectual property protect...

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Favoritism in decisions of government...

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Transparency of government policyma...

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Judicial independence

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Ethical Behaviours of Fir...

verylow low medium high veryhigh

0.17 68.5 31.0 0.17 0.17

3.18 ± 0.68

Efficacy of corporate boar...

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Strength of auditing and reporting stan...

verylow low medium high veryhigh

0 0

100 0 0

4 ± 0.35

Irregular payments and bri...

verylow low medium high veryhigh

0 100

0 0 0

2.8 ± 0.35

Fig. 16 a EBOF probabilities for Romania b EBOF probabilities for Moldova

How Ethical Behavior of Firms is Influenced by the Legal and Political Environments 287

123

result, in addition to explaining how various legal and

political environmental factors affect business ethics, we

are able to delineate causal interrelationships among the

legal and political factors depicted in the models. Finally,

this study, to the best of our knowledge, is the first appli-

cation of the BCM methodology for a business ethics topic.

As noted earlier, the BCM is considered one of the most

powerful techniques in capturing the causal knowledge of

experts about a domain. These maps represent domain

knowledge more descriptively than other models such as

regression and structural equations. We believe that the

adaptation of this methodology by other researchers is

likely to be beneficial for the theory and practice of busi-

ness ethics. This way, we may both advance our under-

standing of the factors affecting ethical decisions and

improve ethical decision-making processes.

Since IPAB are the leading factors affecting managers’

perception of business ethics in Stage 3 and Stage 2

countries, future research that investigates the dynamics of

firm-initiated corruption (i.e., irregular payment and bribes

offered to public officials as well as other firms) may

produce fruitful results in both understanding and com-

bating private corruption in these countries. Such research

may provide valuable insights to the work of other

researchers who have been studying private corruption

(e.g., Argandona 2003, 2005; Fleming and Zyglidopoulos

2008; Gopinath 2007; Luo 2004). Similarly, since IPP

appears as the leading factor affecting managers’ percep-

tion of business ethics in Stage 1 countries, particular

attention may be paid to the study of underlying structural

factors affecting managers’ perceptions of IPP in these

countries. This research identified JI as the critical factor

affecting IPP perceptions. In addition, the discussion sec-

tion provided information regarding cultural underpinnings

of IPP perceptions in many of the Stage 1 countries. Future

research may look into other structural (e.g., political,

natural, and competitive) factors affecting IPP perceptions

in these countries. Furthermore, our analysis was based on

the 92 states comprising the Stage 1, 2, and 3 countries of

the WEF data. Future research can test the model on the

‘‘transition countries’’ which were excluded in the current

study. Future studies may also investigate how perceptions

of managers change over time regarding the business ethics

system in the countries where they work.

Finally, in order to reduce the extent of subjectivity

which is inherit in the expert panel method, as a further

study, a Bayesian net can be developed through structural

learning from data. By this way, qualitative relationships

between variables can be captured solely depending on

Bayes’ probabilities of the variables. The comparison of

the two models (one constructed from data and one based

on the views of experts) can potentially lead to more

interesting results about the EBOF.

Appendix

The list of 20 variables (concepts) comprising the ‘‘Insti-

tutions Pillar’’ of the GCI

1.01 Property rights

1.02 intellectual property protection

1.03 Diversion of public funds

1.04 Public trust of politicians

1.05 Irregular payments and bribes

1.06 Judicial independence

1.07 Favoritism in decisions of government officials

1.08 Wastefulness of government spending

1.09 Burden of government regulation

1.10 Efficiency of legal framework in settling disputes

1.11 Efficiency of legal framework in challenging

regulations

1.12 Transparency of government policymaking

1.13 Business costs of terrorism

1.14 Business costs of crime and violence

1.15 Organized crime

1.16 Reliability of police services

1.17 Strength of auditing and reporting standards

1.18 Efficacy of corporate boards

1.19 Protection of minority shareholders’ interests

1.20 Strength of investor protection

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