Doing Well by Doing Good 1

Doing Well by Doing Good: Does Economic Development Make a Difference?

Thomas Li-Ping Tang and Toto Sutarso, U.S.A.; Peter Vlerick, Belgium; Vivien Kim Geok

Lim, Singapore; Ilya Garber, Russia, Fernando Arias-Galicia, Mexico; Adebowale Akande,

South Africa; Michael W. Allen, Australia; Abdulgawi Salim Alzubaidi, Oman; Mahfooz A.

Ansari, Canada; Mark G. Borg, Malta; Brigitte Charles-Pauvers, France; Bor-Shiuan Cheng,

Taiwan; Randy K. Chiu, Hong Kong; Linzhi Du, China; Consuelo Garcia De La Torre,

Mexico; Rosario Correia Higgs, Portugal; Abdul Hamid Safwat Ibrahim, Saudi Arabia;

Chin-Kang Jen, Taiwan; Ali Mahdi Kazem, Oman; Kilsun Kim, South Korea; Roberto

Luna-Arocas, Spain; Eva Malovics, Hungary; Alice S. Moreira, Brazil; Richard T. Mpoyi,

U.S.A.; Anthony Ugochukwu Obiajulu Nnedum, Nigeria; Johnsto E. Osagie, U.S.A., AAhad

M. Osman-Gani, Singapore; Francisco Costa Pereira, Portugal, Ruja Pholsward, Thailand;

Horia D. Pitariu, Romania; Marko Polic, Slovenia; Elisaveta Sardzoska, Macedonia; Petar Skobic,

Allen F. Stembridge, and Theresa Li-Na Tang, U.S.A.; Thompson Sian Hin Teo, Singapore,

Martina Trontelj, Slovenia; Caroline Urbain, France.

The final version of this paper was published in:

Tang, T. L. P., Sutarso, T., Vlerick, P., Lim, V. K. G., Garber, I., Arias-Galicia, F., Akande, A.,

et al. (2007). Doing Well by Doing Good: Does Economic Development Make a Difference?

Academy of Management Index on Corporate and Personal Values, 66, 392.

ABSTRACT

Using survey data from managers of 29 geopolitical entities (7 geopolitical entities in the high

GDP group, 12 in the median GDP group, and 10 in the low GDP group) across six continents

around the world (N = 6,081), we propose and test a model of “doing well by doing good”.

Results supported the precept: High corporate ethical values and low love of money were related

to high ethical behavior that was related to low job stress that, in turn, was related to high life

satisfaction. Corporate ethical values had a positive “double-whammy” effect: increasing ethical

behavior and reducing job stress. Moreover, our results varied across the three GDP groups: The

relationship between corporate ethical values and ethical behavior and between low love of

money and ethical behavior existed for the high and median GDP groups but not for the low

GDP group. The high GDP group had the lowest unethical behavior, as expected, whereas the

median GDP group had the lowest corporate ethical values, the highest unethical behavior, the

highest percentage of bad apples, the highest job stress, and the strongest relationship between

love of money and unethical behavior. Our theory provides new insights regarding doing

business at different levels of economic development around the world.

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Key words: Doing Well by Doing Good, the Love of Money, Corporate Ethical Values,

Propensity to Engage in Unethical Behavior, Job Stress, Life Satisfaction, Levels of Economic

Development, Measurement Invariance

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Doing Well by Doing Good 2

Doing Well by Doing Good: Does Economic Development Make a Difference?

I know what I want, I have a goal, and opinion. If God lets me live, I shall not remain

insignificant. I shall work in the world and for mankind! And now I know that first and

foremost I shall require courage and cheerfulness!

-Anne Frank, April 11, 1944 (Vermeulen, 2007, p. 754)

Introduction

In this paper, we develop a model of doing well by doing good and limit ourselves to

only one social success component of this issue. Our model, grounded in substantive theory of

reasoned action (Ajzen & Fishbein, 1980) and framed in stewardship theory (Davis, Schoorman,

& Donaldson, 1997), investigates two antecedents (individual attitude and social norms) and two

consequences (job stress and life satisfaction) of ethical behavioral intention. We assert that

organizations that do good by promoting ethical values as well as reducing the love of money are

able to do well by enhancing managers’ ethical behavior, reducing job stress, and improving life

satisfaction. Our model stems from a small set of research ideas and addresses puzzling

omissions.

Due to an ever-expanding list of scandals and corruptions in the USA, ethics has become

an interesting topic for research and spirited debate (Evans, Treviño, & Weaver, 2006). Since

many executives received their training at the best business schools (Merritt, 2002), it is not lack

of “intelligence” (brains) but lack of “wisdom” (Feiner, 2004, p. 85) or virtue (Giacalone, 2004;

Tang & Chen, in press) that caused these scandals. Researchers continue to identify means for

improving ethical behavior. Some scholars argue that one of the real root causes of this ethics

crisis is “maximizing shareholder value” (Kochan, 2002, p. 139) or “the bottom-line-mentality”

(Sims, 1992, p. 508). The social responsibility of business is to increase its profits (Freidman,

1970). Agency theory suggests that owners (principals) of many corporations have profit-sharing

programs for top-level executives (agents) to align management interests with the owners’ value

maximization goals (Eisenhardt, 1989; Tosi & Gomez-Mejia, 1989). The tremendous amount of

pressure to maximize profits and opportunities to earn exorbitant bonuses may “push” and

“pull”, respectively, executives to engage in unethical behavior. Those who want to be rich fall

into temptation and a trap and into many foolish and harmful desires that plunge men into ruin

and destruction. De Tocqueville traced love of wealth to the root of all that Americans do.

However, greed is not good (Sloan, 2002). The adage that power corrupts and absolute power

corrupts absolutely once again has proven true (Kochan, 2002). We assert that managers’ high

aspiration for money (love of money) may lure them to engage in these scandals (Tang & Chiu,

2003). One puzzling omission is that very little research has investigated people’s attitude toward

money, love of money and money as power, in particular, as related to corruption or unethical

behavior.

According to the theory of reasoned action (TRA, Ajzen & Fishbein, 1980), or the

expanded theory of planned behavior (TPB, Ajzen, 1991; Armitage & Conner, 2001), behavior is

determined by intention, which is a function of attitude toward the behavior and subjective

norms: Attitude toward the behavior deals with the individual’s global positive or negative

evaluations of performing a particular behavior; subjective norms refer to the individual’s

perceptions of general social pressure to perform (or not to perform) the behavior. The person-

situation interactionist model of ethical decision making (Treviño, 1986) suggests that managers’

ethical behavior is influenced by the situational variables in organizations. Following these

Doing Well by Doing Good 3

suggestions (Ajzen, 1991; Treviño, 1986), we investigate two antecedents of unethical behavior

and examine the extent to which (1) high love of money, a personal attitude at the individual

level, may “pull” people to engage in unethical behavior and (2) corporate ethical values,

managers’ perceptions of ethical values at the organizational level, may “push” people to engage

in ethical behavior. In other words, for the former, low love of money may “pull” people to

engage in ethical behavior in organizations.

Expanding the stewardship notion, unethical behavior in an organization may cause a

high level of job stress because most people would like to conceal their unethical behavior and

those who with a “concealable stigma” face considerable stressors and psychological challenges

(Pachankis, 2007). American businesses lose an estimated $200-$300 billion per year due to

stress. When managers behave ethically, they may experience a low level of job stress that, in

turn, may lead to a high level of life satisfaction (Diener & Lucas, 2000). We argue that job

stress may serve as a mediator of the relationship between ethical behavior and life satisfaction.

Very little research has examined these issues. With a low level of job stress and a high level of

life satisfaction, managers may focus on creativity, innovation, improving effectiveness and

efficiency in producing products and services, and the satisfaction of many stakeholders.

Due to globalization, multinational corporations employ more than 1.3 million expatriates

from the USA alone. About 80 percent of mid-size and large corporations send expatriates

abroad (Black & Gregersen, 1997). Doing business in different parts of the world has become

very important to researchers and executives because bribery is illegal (Foreign Corruption

Practices Act) in the USA but is commonly practiced in other geopolitical entities. According to

Transparency International’s Corruption Perceptions Index (CPI), corruption is the abuse of

public office for private gain (http://www.infoplease.com/ipa/A0781359.html). CPI measures the

degree to which corruption is perceived to exist among a country’s public officials and

politicians. The CPI Index (http://www.transparency.org/documents/cpi/2001/cpi2001.html)

illustrates once more the vicious circle of poverty and corruption. The richest countries (Finland,

Iceland, New Zealand, Denmark, and Singapore) have very low levels of perceived corruption;

the poorest countries (e.g., the Philippines, Nigeria) are the greatest victims of corruption

(Campbell, 2007; Organization for Economic Co-operation and Development, the OECD).

However, we know very little about managers’ unethical behavior in different geopolitical

entities and at different levels of economic development. This is another puzzling omission.

We examine unethical behavior, of which corruption is an important component, across

cultures. Recent advances in analytic tools and measurement theories enable researchers to

examine measurement invariance and test management theories across cultures (Tsui, Nifadkar,

& Ou, 2007; Vandenberg & Lance, 2000). The bulk (64%) of cross-cultural research has covered

only two countries and little (23%) involved more than two countries (Sin, Cheung, & Lee,

1999). Studies involving an insufficient number of cultures may have limited usefulness.

We attempt to identify not only stable, distinctive, and generalizable models based on

data from three levels of economic development but also a culture-free theory for all geopolitical

entities involved in this study. We argue that within the same level of economic development,

geopolitical entities may have different yet similar level of ethical values at organizational and

national levels, attitudes toward money, and propensity to engage in unethical behavior. At this

convergence, we trust that the time is ripe to address this omission, investigate these issues

across developed, developing, and underdeveloped economies, and treat economic development

as a moderator (Baron & Kenny, 1986). We believe that this research is useful for theory and

practice and may make relevant and responsible contributions to the literature.

Doing Well by Doing Good 4

The present study. We develop a model (Figure 1) with five constructs. We explore the

relationships among (1) attitude at the individual level, as measured by the love of money, (2)

social norms or ethical values at the organizational level, as measured by individuals’

perceptions of corporate ethical values, (3) behavioral intention, as measured by the propensity

to engage in ethical/unethical behavior, (4) job stress, and (5) life satisfaction and test the same

model across three levels of economic development simultaneously, using a proxy variable

developed based on GDP per capita. We use data from managers of 29 geopolitical entities (7

geopolitical entities in the high GDP group, 12 in the median GDP group, and 10 in the low GDP

group) across six continents around the world (N = 6,081) to test the model.

--------Insert Figure 1 about here--------

Theory and Hypotheses

Deeply grounded in the theory of reasoned action (Ajzen, 1991) and the person-situation

interactionist model of ethical decision making (Treviño, 1986), we test our model of doing well

by doing good and also explore the “bad apples” (individual characteristics) versus “bad barrel”

(social variables) debate (Treviño & Youngblood, 1990): Is ethical/unethical behavior a direct

result of personal characteristics of the individual decision maker? Or, rather, is ethical/unethical

behavior more heavily dependent upon organizational or social variables (Brass, Butterfield, &

Skaggs, 1998)? Researchers have examined (1) characteristics of the individual: cognitive and

moral development (Treviño & Youngblood, 1990), economic, political, and religious value

orientation (Hegarty & Sims, 1978), ego strength (Stead, Worrell, & Stead, 1990), ethical

philosophy, locus of control (Jones & Kavanagh, 1996), Machiavellianism (Hegarty & Sims,

1978), nationality, and gender (Stead et al., 1990) and (2) organizational and cultural influences:

competition (Hegarty & Sims, 1978), economic conditions (Stead et al., 1990), managerial

influences (Jones & Kavanagh, 1996), reinforcement contingencies, subsequent responsibilities

(Treviño, 1986), relationships among actors (Brass et al., 1998), scarcity of resources, and

stakeholders (Hunts & Vitell, 1986). We selectively examine the love of money (attitude) and

corporate ethical values (social norms) as related to propensity to engage in unethical behavior

(behavioral intention) and provide the logical interconnectedness (Sutton & Staw, 1995) of our

theory below. We introduce the love of money construct first.

The Love of Money

Researchers have examined the meaning of money from different perspectives such as

the psychology of money (Furnham & Argyle, 1998; Vohs, Mead, & Goode, 2006),

compensation and pay satisfaction (Rynes & Gerhart, 2000), consumer behavior (Vitell, Paolillo,

& Singh, 2006), materialism (Belk, 1985), and subjective well-being (Diener & Seligman, 2004;

Srivastava, Locke, & Bartol, 2001; Tang, 2007). Among numerous money constructs, we select

the Love of Money Scale (LOM) for the present study. The Love of Money Scale, LOM, a

subset of the Money Ethic Scale (MES), has been considered one of the most well-developed and

systematically used measures of money attitude (Lea & Webley, 2006; Mitchell & Mickel,

1999). The love of money is (1) one’s attitudes toward money including affective, behavioral,

and cognitive components; (2) the meaning one attributes to money; (3) one’s desire or

aspiration for money; (4) not one’s need, greed, or materialism; (5) a multi-dimensional

individual difference variable; and (6) a second-order latent construct with several first-order

latent sub-constructs (Tang et al., 2006). The love of money and materialism are different but

Doing Well by Doing Good 5

related constructs. We assert that the love of money (aspiration for money) is more fundamental

and more related to unethical behavior (Tang & Chiu, 2003) than materialism (pursuit of the

good life through consumption or possessions, Belk, 1985). The measurement and functional

equivalence, reliability, and validity of the LOM and MES have been examined in many studies

(Tang & Chiu, 2003; Tang et al., 2006; Vitell et al., 2006), in several languages (Chinese,

English, French, Italian, Spanish, Romanian, Russian, see Luna-Arocas & Tang, 2004), and in

various books (Furnham & Argyle, 1998; Milkovich & Newman, 2008; Rynes & Gerhart, 2000).

We select four sub-constructs of LOM specifically for this study: (1) the desire to be rich

(affective), (2) the view of money as a motivator (behavioral), (3) the view of money as

important (cognitive), and (4) the view of money as power (cognitive) and present them below.

Most people love money and want to be rich; few hate money and want to be poor. This notion

(I want to be rich), the most important sub-construct of the love of money, is a predictor of

unethical behavior (Tang & Chen, in press; Tang et al., 2006; Vitell et al., 2006). Children from

poor economic backgrounds tend to overestimate the size of a coin more than their counterparts

from rich families (Lea & Webley, 2006). People with financial hardship are obsessed with

money (Lim & Teo, 1997). If money is a motivator (Harpaz, 1990; Jenkins, Mitra, Gupta, &

Shaw, 1998), one may do whatever it takes to make money. Regarding improving performance

in organizations, “no other incentive or motivational technique comes even close to money”

(Locke, Feren, McCaleb, Shaw, & Denny, 1980, p. 381). Money can also motivate people to

behave unethically: For example, in response to a bonus plan that paid people for finding insect

parts in a food processing plant, innovative “employees brought insect parts from home to add to

the peas just before they removed them and collected the bonus” (Milkovich & Newman, 2008,

p. xiii). To some, money plays an important role in their lives. The most consistent thread of the

money attitude literature is the “emphasis on its importance” (Mitchell & Mickel, 1999, p. 569).

Money represents power (Tang, 1992, 1993). People use money to buy goods, services, or

freedom and to bribe, control, and corrupt others (Furnham & Argyle, 1998). In summary, the

love of money may induce people to engage in unethical behavior. Moreover, high income may

reduce managers’ love of money which, in turn, may enhance ethical behavior (Tang & Chiu,

2003). We present subjective/social norms next.

Corporate Ethical Values (CEV)

Stewardship theory (Davis et al., 1997) becomes a special case of agency theory in which

the agent has inculcated social norms, values, and expectations whereby stewardship behaviors

supplant opportunism by the agent. Ignoring society’s restrictions on behavior and self-interest

results in the loss of things of value (reputation, wealth, trust, and liberty) (Campbell, 2007;

Gomez-Mejia, Wiseman, & Dykes, 2005). From the institutional perspective, the imperative of

maximizing profit and shareholder value is the root cause that may prevent corporations from

acting in socially responsible ways. However, when managers become concerned with

preserving the reputation of their firms for the sake of continued business success, they may act

in socially responsible ways by “treating their workers and customers decently, abiding by the

law, and generally maintaining standards of honesty and integrity” (Campbell, 2007, p. 947). We

incorporate corporate ethical values in this study.

Strong cultures enhance firm performance (O’Reilly & Chatman, 1996). Corporate

ethical values or culture (Deal & Kennedy, 1982; Schein, 1985) may deter unethical behavior

(Baker, Hunt, & Andrews, 2006; Victor & Cullen, 1987) because most people do look to the

social context or culture to determine what is ethically right and wrong (Bandura, 1977; Thomas,

Doing Well by Doing Good 6

Schermerhorn, & Dienhart, 2004), obey authority figures (Litzky, Eddleston, & Kidder, 2006;

Milgram, 1974), are influenced by others (Westphal & Stern, 2006), and do what is rewarded

(Skinner, 1972; Treviño & Brown, 2004) in organizations. An organization’s values serve to

convey a sense of identity to its members, enhance the stability of its social system, direct

managers’ attention to important issues, and guide subsequent decisions by managers. Corporate

ethical values are the organization’s formal and informal policies on ethics (Wimbush, Shepard,

& Markham, 1997) that may help establish the standards that delineate the right things to do, or

the things worth doing, and promote ethical behavioral intentions (Shih & Chen, 2006). The

possible effect of corporate ethical values on ethical behavior is particularly interesting and

important because the Sarbanes-Oxley Act, signed into law in 2002, requires American

organizations to adopt a code of ethics (Anand, Ashforth, & Joshi, 2004).

Among many constructs (e.g., Peterson, 2002; Victor & Cullen, 1987), we select Hunt,

Wood, and Chonko’s (1989) Corporate Ethical Value Scale, which attempts to capture the

perception that (1) managers are acting ethically, (2) managers are concerned about issues of

ethics, and (3) ethical (unethical) behavior is rewarded (punished) in their organization. They

examined the broader principles of the degree to which organizations take an interest in ethical

issues and act in an ethical manner. Based on the aforementioned theory (Ajzen, 1991; Treviño,

1986), corporate ethical values (subjective/social norms) may guide managers to engage in

ethical behavior, whereas low love of money (attitude toward the behavior) may attract them to

engage in ethical behavior (behavioral intention). We discuss behavioral intention below.

Propensity to Engage in Unethical Behavior (PUB)

It is difficult to observe and measure unethical behaviors directly because they are

usually performed in private or behind closed doors. People are more willing to provide accurate

information answering an anonymous paper-and-pencil survey or computer-administered

questionnaire than in a face-to-face interview (Richman, Kiesler, Weisband, & Drasgow, 1999).

The incumbent’s self-report and the coworker’s peer-report converged significantly on

counterproductive work behavior toward other persons and work stressors (Fox, Spector, Goh, &

Bruursema, 2007). Behavioral intentions and self-reports are arguably adequate surrogate

measures of actual unethical behavior (Fox et al., 2007; Jones & Kavanagh, 1996).

Researchers have examined workplace deviance (Greenberg, 2002; Robinson & Bennett,

2000), counterproductive behavior (Cohen-Charash & Spector, 2001), corruption (Anand et al.,

2004), whistle blowing (Dozier & Miceli, 1985), organizational misbehavior (Vardi & Weitz,

2004), and unethical behavior (Chen & Tang, 2006; Tang & Chiu, 2003). Ivancevich,

Konopaske, and Matteson (2005) examined 23 misbehaviors at work, some of those may have

nothing to do with the love of money (e.g., sexual harassment). Researchers examined a variety

of unethical behaviors and then combine them as one construct (Grover, 1993), a potential

deficiency in studying these constructs in the literature. Among these measures, we select the

Propensity to Engage in Unethical Behavior Scale (PUB) (Chen & Tang, 2006) with four sub-

constructs that are related to publicized scandals and white-collar crime. The cumulative effect of

these unethical behaviors may hurt organizations’ bottom line. For example, shoplifting costs

$196 per incident and $10.23 billion annually, and theft costs $1,446 per incident and $15.2

billion annually (Greenberg, 1993; Ivancevich et al., 2005). Corruption is costly to managers

(loss of jobs and pension), corporations (in 2001, Arthur Andersen collapsed and Enron stock

dropped from $2.1 billion to $10 million), and society. In 2000, among 8,766 defendants charged

with white-collar crime by the US government, 78 percent were convicted, and 46 percent of

Doing Well by Doing Good 7

whom were sentenced to prison for an average of 16 months (Ivancevich et al., 2005). We

introduce these sub-constructs of PUB below.

Because the cumulative effects of resource abuse, such as pilfering office supplies or

wasting company time, including cyberloafing (Lim, 2002), on the bottom line can be huge,

many organizations electronically monitor managers. Not whistle blowing means not taking any

action against misbehavior/wrongdoing. Some managers implicitly condone theft by “looking

the other way” or may consider it “an invisible wage structure” (Tang & Chiu, 2003). In the

USA, theft is a $200 billion-a-year problem. Many people steal money or merchandise and

falsify expense accounts (Greenberg, 1993). Corruption is the misuse of organizational position

or authority for personal or organizational gain (e.g., bribery, kickbacks) and may include acts

committed against or on behalf of the organization (Anand et al., 2004). Executives need to

prevent such problems because all may lead to financial losses and hurt the organization’s

bottom line. We turn to the relationships among the three major constructs below.

The Relationship between the Love of Money (LOM) and Unethical Behavior (PUB) In a nationwide survey, American adult consumers who desire to be rich (Factor Rich of

the Love of Money Scale) are likely to condone questionable consumer activities (Vitell et al.,

2006). The instrumental climate (looking out for one’s own self-interest) of the Ethical Climate

Questionnaire (ECQ) is the most related to unethical behavior (Peterson, 2002; Wimbush et al.,

1997). The love of money (17 items with Factors Rich, Motivator, Success, and Importance) is

related to unethical behavior (15-item, 4-factor scale) among Hong Kong managers (Tang &

Chiu, 2003). Among business and psychology students, the love of money is indirectly related to

unethical behavior through Machiavellianism (the Love of Money Machiavellianism

Unethical Behavior) (Tang & Chen, in press). Further, this mediating effect existed for business

students but not for psychology students, for male students but not for female students, and for

male business students but not for female business students. Moreover, when examined alone,

the direct effect (the Love of Money Unethical Behavior) existed for business students but not

for psychology students.

We argue that when opportunities present themselves, high love-of-money people may

have a higher propensity to adopt devious strategies, take advantage of the situation, do whatever

it takes to make money (Milkovich & Newman, 2008), and engage in activities related to their

self-interest, financial benefits, and personal gains than their low love-of-money counterparts. To

the best of our knowledge, the Factor Power of the Love of Money (examined in the present

study) has not been studied in the context of unethical behavior and may contribute to our

understanding of unethical behavior. We test our Hypothesis 1 (Path 1, Figure 1) below.

Hypothesis 1: Managers’ love of money is positively related to their unethical behavior.

The Relationship between Corporate Ethical Values (CEV) and Unethical Behavior (PUB)

People obtain and process information that is available from the social context (Bandura,

1977), authority figures, role models (Litzky et al., 2006), and the reward system (Treviño &

Brown, 2004). Organizational ethical values are negatively related to organizational misbehavior

(Vardi & Weitz, 2004) and counterproductive behavior (Wimbush et al., 1997). Organizations

with strong ethical values have strong policies to reward ethical behavior and punish or deter

unethical behavior (O’Reilly & Chatman, 1996). Managers’ ethical behavior is influenced by the

situational variables in organizations (Treviño, 1986) and ethical values at the “organizational”

level (social norms, Ajzen, 1991). Borrowing the general ideas above, we argue that managers

Doing Well by Doing Good 8

with a strong perception of corporate ethical values are more likely to behave ethically, in

general (Path 2).

Hypothesis 2: Corporate ethical values are negatively related to managers’ unethical

behavior.

Three Paths to Job Stress

Following the stewardship theory, we focus on the extent to which low love of money

and high corporate ethical values may reduce job stress and enhance life satisfaction. Stress is

one’s physiological or psychological response to an external event or stressor mediated by one’s

characteristics (Brief, Burke, Robinson, George, & Webster, 1985; Kreitner & Kinicki, 2007).

Although a moderate amount of job stress is related to a high level of performance, most

individual, organizational, and extra-organizational stressors may lead to a wide range of

physiological, psychological, cognitive, and behavioral reactions and to illness or to low

satisfaction, commitment, and performance that may hurt the bottom line of an organization

(Caplan, Cobb, French, Van Harrison, & Pinneau, 1975; Ivancevich et al., 2005). We examine

three paths to stress.

The love of money and job stress. Individual differences (e.g., hardiness, locus of

control) play a role in responses to stressors, and the love of money is no exception. Research

suggests that people with a strong love-of-money orientation are more likely to compare

themselves with the rich, experience relative deprivation, be dissatisfied with their pay (Tang et

al., 2006), change jobs (Tang, Kim, & Tang, 2000), and have high turnovers (Hom & Griffeth,

1995) that may lead some to a high level of frustration and stress. Since high love-of-money

individuals want to be rich, their thoughts are controlled by money or the reward system (cf.

Lawler, 1971). Association of money with achievement and respect in the community can cause

high irritation, i.e., symptoms of stress (Tang, 1993). One who equates money with power

becomes the slave rather than the master of money and has an external locus of control (Tang,

1993), leading to a high level of stress and a low level of subjective well-being (Siu, Spector,

Cooper, & Lu, 2005). High love-of-money managers’ large gap between expectation and reality

regarding money may lead to a high level of frustration. In summary, high love-of-money

individuals may experience symptoms of job stress (Hypothesis 3), in general.

Hypothesis 3: Managers’ love of money is positively related to job stress.

Unethical behavior and job stress. Organizational injustice leads to counterproductive

behavior (Cohen-Charash & Spector, 2001) and stress (Judge & Colquiott, 2004). We reason that

counterproductive behavior may cause job stress (examined in this study) and stress leads to

psychological strain. The love of money may entice people to engage in unethical behavior,

whereas corporate ethical values may drive people to behave ethically. The conflict between the

two may motivate people to resolve inconsistencies/dissonances. Ethically troubled individuals

experience a high level of job-related tension, frustration, and anxiety. Individuals who do not

share the values of their organization experience high levels of job anxiety and tension (Posner,

Kouzes, & Schmidt, 1985). Individuals who perceive injustice at work report a higher degree of

strain (Francis & Barling, 2005).

Individuals with a “concealable stigma” face considerable stressors and psychological

challenges (Pachankis, 2007). This cognitive-affective-behavioral model may be applicable to

people’s unethical behavior because most people would like to conceal their unethical behavior

and not attract others’ attention. Salience of unethical behavior, threat of discovery, and

Doing Well by Doing Good 9

consequences of discovery may have cognitive, affective, and behavioral implications. The

preoccupation, vigilance, and suspiciousness of unethical behavior (cognitive component) may

be related to anxiety, depression, hostility, demoralization, guilt, and shame (affective

component) as well as social avoidance and isolation and impaired close relationships

(behavioral component). This may lead to a negative view of self and diminished self-efficacy.

We argue that managers trying to conceal their unethical behavior experience many affective,

cognitive, and behavioral consequences (Pachankis, 2007) and high job stress. Following these

arguments, unethical behavior may be related to a high level of job stress (Hypothesis 4).

Hypothesis 4: Managers’ unethical behavior is positively related to job stress.

Corporate ethical values and job stress. Work overload, role ambiguity, role conflict,

time pressures, and poor person-environment fit are job-related stressors. Corporate ethical

values are positively related to role clarity and organizational commitment (Hunt et al., 1989) but

negatively related to role conflict and role ambiguity (Jackson & Schuler, 1985; Shih & Chen,

2006). We assert that when organizations have strong ethical values, managers know what is

right and wrong, what is rewarded, and what is punished, managers have a clear direction

regarding what to do and what not to do, less role conflict and role ambiguity, and low job stress.

We present our Hypothesis 5 below.

Hypothesis 5: Corporate ethical values are negatively related to job stress.

The Relationship between Job Stress and Life Satisfaction

Job stress may incur many costs in an organization: premature deaths of managers, higher

rates of accidents, violence in the workplace, lower performance, burnout, absenteeism, turnover

(Hom & Griffeth, 1995), and lower job and life satisfaction (Ivancevich et al., 2005). Life

satisfaction is related to subjective well-being, happiness, and positive affect. People with a high

level of life satisfaction are satisfied with marriage, work, health, finances, and friendship and

experience frequent positive affect, a global sense of satisfaction with life, and infrequent

negative affect (Diener & Oishi, 2000). In occupational health research literature, relationships

leading from stressors to well-being are found more often than a reverse causal relationship.

People with high job stress may develop mental and physical health problems. Stress has a

negative impact on people’s work-related well-being in Hong Kong and Beijing (Siu et al.,

2005). Job stress has strong effects on work-family conflict and cross-domain satisfaction (Ford,

Heinen, & Langkamer, 2007). Low job stress is related to high life satisfaction.

Valuing money as a means to show off, get power, compare themselves to others, or

overcome self doubts (negative money motives) can lead to money being viewed as important

and to low subjective well-being (Srivastava et al., 2001). One’s high love of money may

contribute indirectly to low life satisfaction (Tang, 2007). People with a high sense of ethics

experience low satisfaction with other group members of a deviant workgroup (Robinson &

O’Leary-Kelly, 1998). With high corporate ethical values, people may behave ethically,

experience less job stress, feel peaceful, and have high life satisfaction. Money attitudes and

ethical values may have indirect impacts on life satisfaction. In summary, people with a low

level of job stress may experience high subjective well-being (Hypothesis 6). The combination of

Hypotheses 4 and 6 suggests that job stress is a mediator of the relationship between unethical

behavior and life satisfaction.

Hypothesis 6: Job stress is negatively related to life satisfaction.

Doing Well by Doing Good 10

Levels of Economic Development (A Moderator)

We examine the issue of economic development because firms and managers may behave

differently across different levels of economic development. Levels of economic development

usually translate to income per capita and human development. Geopolitical entities in the

developed economy have dominate economies in the tertiary (service) and quaternary

(intellectual services such as health, education, culture, research, and entertainment) sectors of

industry, a more stable environment (cf. Sorensen, 2002), and higher income and ethical values

than those in other developing or underdeveloped economies. There is no well established

convention for the developed, developing, or underdeveloped designations. Since increases in

income do not increase one’s happiness when one has an annual income of $20,000 (Layard,

2003), we set our cutoff criteria at $20,000 for the high GDP group and at $5,000 (25% of

$20,000) for the low GDP group and create the high, median, and low GDP groups.

High GDP group (GDP per capita > $20,000). According to Campbell (2007),

organizations are less likely to engage in socially irresponsible practices or unethical behaviors

and are more concerned with preserving the reputation of their firms for the sake of continued

business success than their counterparts when they are under normal competitive conditions

where at least a modest profit is assured and firm survival per se is not at stake. Regulation is not

always the responsibility of the state or geopolitical entity. Sometimes, the most effective means

of facilitating increased corporate social responsibility is through corporate peer pressure

(Martin, 2003). Some organizations go to great lengths to organize a system of self-regulation

designed to set standards (treating their workers and customers decently, abiding by the law, and

generally maintaining standards of honesty and integrity). We assert that organizations in the

high GDP group may fall into this category and have strong corporate ethical values.

At the individual level, when managers advance in the organizational hierarchy, money

may become less important (Harpaz, 1990). We posit that people with money are more likely to

find their needs satisfied, feel comfortable (rich) financially and psychologically, have a sense of

self-sufficiency (Vohs et al., 2006), may have reached Kohlberg’s conventional level of moral

development (Greenberg, 2002), maintain their dignity and integrity, and are law-abiding

citizens. People in developed entities with well established economic, legal, political, and social

infrastructures follow the rule (the spirit or the letter) of the law; those in developing and

underdeveloped entities follow the rule of the man, or no rule (no law or order) at all (Veiga,

Golden, & Dechant, 2004). Laws may be strictly enforced, loosely enforced, and ignored

completely in the high, median, and low GDP groups, respectively. Corporate ethical values

promote ethical behavior through reward and punishment (Hunt et al., 1989). Its efficacy

depends on economic, legal, political, and social infrastructures at geopolitical entity level

(Treviño & Brown, 2004). People in the richest entities have low levels of corruption. In

summary, people in the high GDP group may have the lowest level of unethical behavior;

corporate ethical values may have the strongest impact on ethical behavior. The relationship

between corporate ethical values and ethical behavior and between the love of money and

unethical behavior exists. Managers usually respect and obey laws and rules. Due to cultural

differences at geopolitical entity level, corporate ethical values may reduce unethical behaviors

for people in the high GDP group, but to a much lesser extent for those in the other GDP groups.

Median GDP group ($5,000-$20,000). When organizations are in extreme competition

and the profit margins are narrow enough to put shareholder value and firm survival at risk, the

incentive to cut corners and act in unethical ways may increase (engage in corruption,

compromise product safety and quality, and cheat customers) (Campbell, 2007; Hegarty & Sims,

Doing Well by Doing Good 11

1978; Schneiberg, 1999). We argue that managers who experience the most substantial changes

and economic developments in emerging markets (Drori, Jang, & Meyer, 2006) may have

greater pressure to meet economic expectations of the organization/society and a better

opportunity to make money than those in other GDP groups. Economic structures are more

developed and have a higher priority than legal, political, and social considerations. These

infrastructures are, relatively speaking, weaker than those in developed entities. Some

geopolitical entities, such as Russia, have changed from a controlled economy to a market

economy. In that environment, managers turn a blind eye to unethical behavior; people with

connections may manipulate the situation to their advantage and become rich quickly, legally or

illegally.

Due to the stronger appeal for economic development and weaker urge for ethical values

and institutional voids in these entities with the highest level of volatility, competition, pressure,

and opportunity, high love-of-money and opportunistic individuals are more likely to take risks

and engage in unethical behavior than those in the high GDP group. It is plausible that people

have a vicious streak and it will come out when they are given a chance (Christie & Geis, 1970).

We assert that in the median GDP group, the relationship between the love of money and

unethical behavior may be strong, whereas corporate ethical values may have a very weak

impact on deterring unethical behavior.

Low GDP group (GDP < $5,000). According to slack resource theory, firms that are less

profitable have fewer resources to spare for socially responsible activities than those more

profitable counterparts (Waddock & Graves, 1997). These former firms may be less inclined to

meet even the minimum threshold of socially responsible behavior (Campbell, 2007). Managers

in these firms may act opportunistically with self-interest and guile and think that they can get

away with it. These ideas may be applicable to managers in the low GDP group. In

underdeveloped geopolitical entities, the solid economic, legal, political, and social

infrastructures may not exist. People for the most part are poor and jobless and have significantly

fewer resources and opportunities to make money than those in the high or median GDP groups.

Corruption is a way of life. Reputation and liberty, stressed in the stewardship theory and in the

developed economy, may be of less value.

Due to the lack of resources and wealth, they have nothing to lose. Simply thinking about

money (Vohs et al., 2006) may cause people to experience a sense of injustice (Cohen-Charash

& Spector, 2001) and see themselves as helpless victims. Financial hardship enhances the

importance of money (Lim & Teo, 1997) and the victim mentality. The combination of a poor

economy and lack of law and order may lead to a state of anarchy that leaves a society powerless

to suppress unethical behavior. However, people in a poor economy must survive with limited

choices or no choice at all. When opportunities exist, most people may ignore all the existing

laws, orders, or values in the bad barrels (values at the organizational level, Campbell, 2007;

Treviño & Youngblood, 1990), ignore reputation and liberty (values at the personal level,

Gomez-Mejia et al., 2005), throw ethics out the window, take matters into their own hands, steal

otherwise become corrupt in the name of justice (Greenberg, 1993), and do whatever it takes to

survive or get even. Addressing the issue of imbalance by playing Robin Hood--robbing the rich

(faceless organizations) to give to the poor (themselves, the victims of society) may be

considered ethical by many because seemingly no real person with a name and a face is hurt and

faceless corporations can write it off as a business expense (Anand et al., 2004). The love of

money may have nothing to do with unethical behavior. Corporate ethical values may have very

little power, if any, to curb unethical behavior. Relationships among corporate ethical values, the

Doing Well by Doing Good 12

love of money, and unethical behavior may be predictable in developed economies, but may not

exist in underdeveloped economies. Hypotheses 1 and 2 may not exist in the low GDP group.

Other considerations. Material aspirations are initially fairly similar among income

groups (Easterlin, 2001). Those with more income are, on average, happier than those with less.

Aspirations grow along with income and undercut the favorable effect of income growth on

happiness. A low level of aspiration for money leads to a high level of happiness (Srivastava et

al., 2001). Some believe that wealthy nations are happier than less wealthy ones (Khamsi, 2006),

whereas others argue that money does not buy happiness (Easterlin, 2001). Higher incomes are

related to the lower marginal utility of money. Low love of money may be directly and indirectly

related to low job stress and high life satisfaction (Tang, 2007); corporate ethical values may be

directly and indirectly related to low job stress and high life satisfaction. These connections have

not been examined empirically. Money is important but assumes decreasing importance as a

person advances in the organizational hierarchy (Harpaz, 1990). “Poverty consists, not in the

decrease of one’s possessions, but in the increase of one’s greed” (Plato, 427-347 BC). High love

of money and one’s comparison with others cause low happiness (Luna-Arocas & Tang, 2004).

From a global perspective, as nations get richer, increases in wealth are associated with

diminishing increases in well-being. Within nations, increased income is associated with well-

being primarily for the poor; once the poverty threshold is crossed, increased income matters

little for happiness (Csikszentmihalyi, 1999).

Research suggests that income reduces the love of money among highly paid

professionals (Tang & Chiu, 2003), enhances the love of money among underpaid employees

(Tang, Luna-Arocas, & Sutarso, 2005; Tang, Tang, & Homaifar, 2006), and is not related to the

love of money among adequately paid people (Tang et al., 2005, 2006). The culture at

organizational and national levels and objective income may have impacts on managers’ love of

money. A fairly-paid income at the market value may lead to needs satisfaction and a sense of

self-sufficiency (Vohs et al., 2006). Without financial hardship (Lim & Teo, 1997), some

managers may reduce their love of money, enjoy what they have with a gratifying mindset,

behave ethically, reduce the temptation to engage in unethical behavior in organizations (Tang &

Chiu, 2003), experience low stress and high life satisfaction.

We assert that without comparing income levels, full-time managers in the three GDP

groups may have a similar sense of self-sufficiency in their respective economies. Unethical

behavior creates job stress for most people (Hypothesis 4) and most people with high job stress

have low life satisfaction (Hypothesis 6). We predict that Hypotheses 4 and 6 may be universal.

Using the terms in cross-cultural literature, we expect to find some culture-free (etic) and

culture-specific (emic) paths. Hypotheses 1 and 2 may not be applicable to the low GDP group.

Hypothesis 7: The level of economic development is a moderator in this study. More

specifically, the relationships among variables in our model may vary across the three

levels of economic development.

Method

Procedure and Sample

The senior author contacted collaborators in several geopolitical entities and developed a

survey questionnaire in English and several key languages (Chinese, French, Spanish, etc.) in

2000. The senior researcher recruited additional collaborators through personal contacts of

friends and participants in several international conferences (Academy of Management,

Academy of Human Resource Development, International Association for Research in Economic

Doing Well by Doing Good 13

Psychology, International Association of Applied Psychology, and Society for Industrial and

Organizational Psychology) and offered to each collaborator a package including a six-page

survey (informed consent and items), a 12-page instruction guide (references, websites,

translation procedures), and background literature including published articles and conference

papers. He asked collaborators to collect data from at least 200 full-time managers in large

organizations. Researchers secured their own funding. We attempted to collect cross-sectional

data in different geopolitical entities (cf. Hofstede, 1980).

We started our data collection in January 2001. As of August, 2006, we had obtained 32

samples from 30 geopolitical entities (N = 6,659) in six continents around the world. For this

study, we deleted two samples with missing key variables and employed 30 samples (N = 6,081)

from 29 geopolitical entities with two samples from Singapore. Most of these entities are nation-

states. Since we have data from People’s Republic of China (PRC), Hong Kong, and Taiwan, we

use the term geopolitical entity and treat each one as a separate entity.

Measures

Researchers in each geopolitical entity used the original survey or translated from English

to their own native language involving either people fluent in both languages or professional

translation services at the university and followed the multi-stage translation-back-translation

procedure (Brislin, 1980). The senior author responded to researchers’ questions and ensured the

quality of translation and data collection. Appendix 1 shows all the individual items, first-order

latent factors, and second-order latent factor of major measures in this study.

We selected the 4-factor, 12-item Love of Money Scale, LOM, for this study (Furnham &

Argyle, 1998; Mitchell & Mickel, 1999; Tang & Chiu, 2003; Tang et al., 2006). Hunt et al.

(1989) developed a 5-item Corporate Ethical Values Scale, CEV. In order to accommodate our

diverse samples and use a confirmatory factor analysis (CFA), we deleted the two reverse-scored

CEV items. We used a five-point Likert scale with strongly disagree (1), neutral (3) and strongly

agree (5) as anchors.

We selected 12 items from the Propensity to Engage in Unethical Behavior Scale (PUB,

Tang & Chiu, 2003; Chen & Tang, 2006). We asked participants to rate each item with the

following instructions and a 5-point scale with very low probability (1), neutral (3) and very high

probability (5) as anchors: If you were in that situation, what is the probability that you would

take action as suggested in the vignette? This is a measure of self-prediction and is a strong

predictor of behavior. For job stress, we adopted three items of irritation, symptoms of stress

(Caplan et al., 1975). Life satisfaction was measured by three items similar to those in the United

States’ General Social Survey (GSS) conducted by the National Opinion Research Center since

1972 with very dissatisfied (1), neutral (3), and very satisfied (5) as anchors (Easterlin, 2001).

Mangers completed the survey voluntarily and anonymously.

On the basis of Layard’s (2003) claim mentioned earlier, the time of our data collection,

and 2005 GDP per capita (International Monetary Fund, IMF, http://www.imf.org/external/

np/ds/matrix.htm), we identified (1) 8 samples (2 from Singapore) in the High GDP (GDP >

$20,000), (2) 12 samples in the Median GDP ($5,000 - $20,000), and (3) 10 samples in the Low

GDP (GDP < $5,000) groups. When we applied other GDP data sets, we obtained different but

similar results (World Bank’s Top GDP 2005, http://siteresources.worldbank.org/

DATASTATISTICS/Resources/GDP.pdf and United Nations’ Human Development Report

2006, http://hdr.undp.org/hdr2006/statistics/ indicators/133.html). Table 1 shows a list of 30

samples, sample size, 2005 GDP per Capita, 2005 Corruption Perceptions Index, self-reported

Doing Well by Doing Good 14

income, mean, and standard deviation of major variables for 30 samples (N = 6,081) and for the

three GDP groups (high, n = 1,756; median, n = 2,371; low, n = 1,954). Table 2 shows the mean,

standard deviation, correlations, and Cronbach’s alpha of variables for the whole sample, and the

high, median, and low GDP groups.

We did not report the average self-reported income for Australia and Spain in Table 1

due to missing data. Average GDP per capita and average self-reported income for the whole

sample (GDP = $13,862 vs. income = $13,384) and the high ($31,595 vs. $30,148) and median

GDP groups ($11,225 vs. $11,845) were quite similar (Table 1). For the low GDP group, the

average self-reported income ($4,880) was a little higher than GDP per capita ($2,544) due to

high income reported in Peru and Thailand. The correlation between 2005 GDP per capita and

self-reported income (N = 28) was .69 (p < .001), between 2005 GDP and 2005 Corruption

Perceptions Index (CPI) (N = 30) was .85 (p < .001), and between income and 2005 CPI (N = 28)

was .68 (p < .001). Both GDP and income were related to CPI. We found significant correlation

between the GDP of International Monetary Fund 2005 and GDP of United Nations’ Human

Development Report 2006 (N = 30, r = .98, p < .001). GDP in 2006 was also significantly related

to self-reported income (r = .74, p < .001). Our current samples were similar to GDP per capita

and were reasonable representations of the respective geopolitical entities. Our participants had

the following job titles: executive, senior manager, logistics coordinator, key accountant,

financial director, product manager, sales manager, architect, director of communication,

engineer, R&D supervisor, HR manager, purchasing officer, health care worker, assistant

marketing manager, etc. Managers (N = 6,081) were 34.68 years old (SD = 9.92), male (51%,

male = 1, female = 0), and had 15.42 years of education (SD = 2.56) on average in this study.

Measurement Invariance

Among nine steps of measurement invariance, tests for configural and metric invariance

were most often reported. Configural invariance refers to the equality of factor structures or

equal number of factors and factor patterns. The same item must be an indicator of the same

latent factor across groups. We used the criteria below for evaluating configural invariance: (1)

χ2, df, and p value, (2) χ

2/df < 5, (3) incremental fit index, IFI > .90, (4) Tucker-Lewis Index, TLI

> .90, (5) comparative fit index, CFI > .90, (6) standardized root mean square residual, SRMSR

< .10, and (7) root mean square error of approximation, RMSEA < .10 (Vandenberg & Lance,

2000). Metric invariance is achieved when the differences between unconstrained and

constrained (all factor-loading parameters are set to be equal) multi-group confirmatory factor

analyses (MGCFAs) are non-significant. Changes in chi-square are sensitive to sample size.

Cheung and Rensvold (2002) recommended using changes in CFI (< .01) as a rule of thumb for

metric invariance (if ΔCFI = .01 or less: differences between models do not exist).

Results

Step 1: Measurement Model

Configural invariance. We established a measurement model with 33 items and latent

constructs for all five scales (Appendix 1). For example, the love of money (a second-order

latent construct) has four first-order latent sub-constructs/factors and 12 items. We examined the

goodness of fit between our model and data for each GDP group in three separate confirmatory

factor analyses (CFAs). There was a good fit for each of the three GDP groups (Table 3, Step 1,

Models 1, 2, and 3). We achieved configural invariance because results passed the

aforementioned criteria.

Doing Well by Doing Good 15

-----Insert Tables1, 2, and 3 about here-----

Metric invariance. For the unconstrained model, we examined the model across three

GDP groups using a multi-group confirmatory factor analysis (MGCFA) (Table 3, Step 1, and

Model 4). For the constrained model, we used Model 4 as the baseline model and constrained the

first-order factor loadings to be the same across the three GDP groups (Model 5) in a MGCFA.

We achieved metric invariance because the change between Models 5 and 4 was not significant

(ΔCFI = .0070) (Cheung & Rensvold, 2002).

Step 2: Common Method Variance (CMV)

According to Spector (2006), the common method variance (CMV) problem may have

been overstated and reached the status of urban legend in the literature. There is little credible

evidence that common method variance exists, and much evidence to the contrary. Due to the

nature of our cross-sectional data collected at one time, we followed suggestions in the literature

and examined this issue in two steps (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003).

Harman’s single-factor test. First, we conducted Harman’s one-factor test and examined

the unrotated factor solution involving 33 items of all five variables of interest in an exploratory

factor analysis (EFA) and found 10 factors. The amount of variance explained and items or

factors of a scale were listed as follows: 18.69 percent (unethical behavior, PUB), 13.50 percent

(Factors Rich, Motivator, and Important of LOM), 8.29 percent (life satisfaction), 5.67 percent

(corporate ethical values), 5.21 percent (job stress), 4.39 percent (Factor Power of LOM), 4.31

percent (factor with cross-loadings), 4.03 percent, 3.25 percent, and 3.04 percent, respectively

(total = 70.37%). Factor Power made unique contributions. No single factor accounted for the

majority of the covariance in the data. The concern for CMV was not warranted.

Latent common method variance (CMV) factor. Based on our measurement model, we

included a direct arrow from the unmeasured latent common method variance factor (CMV) to

each of the 33 items. The measurement model with the addition of an unmeasured latent common

method variance (CMV) factor must not significantly improve the fit over our measurement

model without a CMV factor. With a CMV factor, “the variance of the responses to a specific

measure is partitioned into three components: (a) trait, (b) method, and (c) random error”

(Podsakoff et al., 2003, p. 891). The difference between (1) the measurement model without

CMV (Table 3, Step 2, Model 1) and (2) the measurement model with CMV (Model 2) was not

significant (ΔCFI = .0128). The factor loadings of these items remained significant. The CMV

effect was indeed negligible (e.g., Podsakoff et al., 2003; Spector, 2006).

Step 3: Structural Equation Modeling (SEM) Results

Based on our measurement model, we connected variables with six paths and examined

our hypothesized SEM (Figure 1) in six models (Table 3, Step 3). Model 1 was the unconstrained

baseline model. Using Model 1 as the baseline model, we constrained all first-order and second-

order factor loadings of the Love of Money (LOM) to be equal across the three GDP groups in

Model 2. The non-significant CFI change (.0033) between Models 2 and 1 revealed the

invariance of the Love of Money across the three GDP groups. Using Model 2 as the baseline

model, we further constrained the first-order factor loadings of the Corporate Ethical Values

(CEV) to be equal across the three GDP groups (Model 3) and found that CEV was invariant

across the three groups (Models 3 vs. 2 = .0022). Following the same procedure, we found that

the Propensity to Engage in Unethical Behavior (PUB) (Models 4 vs. 3 = .0038), job stress

Doing Well by Doing Good 16

(Models 5 vs. 4 = .0001), and life satisfaction (Models 6 vs. 5 = .0000) were all invariant across

the three GDP groups. We now examine the six paths of the SEM model (Model 6).

Model 6 results. A path is significant at the .05, .01, and .001 levels when the critical

ratio, C.R., is greater than or equal to 1.96, 2.58, and 3.50, respectively. Table 4 (Step 3, Model

6, Part 1) displayed the standardized direct effect of the six paths. For the high GDP group, the

love of money was positively related to unethical behavior (.15, C.R. = 4.848, p < .001,

Hypothesis 1), while corporate ethical values were negatively related to unethical behavior (-.16,

p < .001, Hypothesis 2). The love of money was positively related to job stress (.09, p < .01,

Hypothesis 3), while corporate ethical values were negatively associated with job stress (-.20, p

< .001, Hypothesis 5). Unethical behavior was positively related to job stress (.21, p < .001,

Hypothesis 4) that, in turn, was negatively related to life satisfaction (-.27, p < .001, Hypothesis

6). All six hypotheses were supported.

For the median GDP group, results were similar to the high GDP group (Table 4).

Hypotheses 1, 2, 3, 4, 5, and 6 were supported.

For the low GDP group, the love of money (.00) and corporate ethical values (-.01) were

not significantly related to unethical behavior. Further, the love of money was negatively related

to job stress (-.07, p < .01), and corporate ethical values were positively associated with job

stress (.09, p < .01). Unethical behavior was positively related to job stress (.33, p < .001),

which, in turn, was negatively related to life satisfaction (-.33, p < .001). Hypotheses 4 and 6

were supported, but Hypotheses 1, 2, 3, and 5 were not. Due to differences across the three GDP

groups, Hypothesis 7 was supported: Economic development is a moderator.

Comparison among three groups. For Path 1 (Table 4, Part 1), the median GDP group

(.25) was stronger than the high GDP group (.15), which, in turn, was stronger than the low GDP

group (.00) (high vs. median: C.R. = 4.294, p < .001; median vs. low: C.R. = 6.413, p < .001;

high vs. low: C.R. = 2.937, p < .01). For Path 2, the high and median GDP group were

significantly different from the low GDP group (high vs. low: C.R. = 3.675, p < .001; median vs.

low: C.R. = 3.711, p < .001, respectively). For Path 3, the high and median GDP groups were

significantly different from the low GDP group (high vs. low: C.R. = 3.996, median vs. low:

C.R. = 3.859, ps < .001, respectively). For Path 5, the high (-.20), median (-.12), and low (.09)

GDP groups were all significantly different from each other (high vs. median: C.R. = 2.988, p <

.01; median vs. low: C.R. = 6.091, p < .001; high vs. low: C. R. = 8.098, p < .001).

Table 4 shows standardized indirect effect (Part 2), total effect (Part 3), squared multiple

correlation (Part 4), and factor loading (Part 5). The indirect effect of the love of money on job

stress for the high GDP group (the Love of Money Unethical Behavior Job Stress) was .03.

The total effect (Part 3) was exactly the same as the direct (Part 1) or indirect (Part 2) effect

when the same number of variables was involved in the path. Two total effects were underlined

(Part 3): For the high GDP group, the total effect of the love of money on job stress (.12)

consisted of (1) a direct path (the Love of Money Job Stress = .09) and (2) an indirect path

(the Love of Money Unethical Behavior Job Stress = .03). The total effect of corporate

ethical values on job stress (-.23) also had two components: the direct effect (-.20) and the

indirect effect (-.03). The total effects of the love of money on life satisfaction (the Love of

Money Unethical Behavior Job Stress Life Satisfaction) for the high, median, and low

GDP groups were -.03, -.04, and .02, respectively. The total effect of corporate ethical values on

life satisfaction (Corporate Ethical Values Unethical Behavior Job Stress Life

Satisfaction) was .06, .05, and -.03 for the high, median, and low GDP groups, respectively. The

squared multiple correlation of unethical behavior for the high, median, and low GDP groups

Doing Well by Doing Good 17

was .05, .08, and .00 respectively (Table 4, Part 4). The predictors of job stress explained 11

percent, 14 percent, and 11 percent of its variance for the high, median, and low GDP groups,

respectively. For the predictors of life satisfaction, the results were 7 percent, 10 percent, and 11

percent, respectively.

------------Insert Table 4 and Figure 2 about here------------

The second-order factor loadings of the love of money and unethical behavior across the

three GDP groups are listed in Table 4 (Step3, Model 6, Part 5). For the love of money, Factor

Rich had the highest factor loading, relatively speaking, among the four factors for the high

(.88), median (.84), and low (.82) GDP groups, supporting the literature (Tang & Chiu, 2003;

Tang et al., 2006). For unethical behavior, Factor Theft had the highest factor loading among the

four factors for the high GDP group (.86), whereas Factor Corruption had the highest factor

loading for the median (.95) and low GDP (.96) groups.

In summary, we ask the question: Does the level of economic development make a

difference using our model? Our answer is a resounding yes. We have identified four culture-

specific (emic) and two culture-free (etic) paths using our model. Thus, the level of economic

development is a moderator. We turn to our next question: Can researchers summarize all these

findings in an overall culture-free (etic) model that is, in a sense, universal across all three GDP

groups? We examine this issue in our Step 4 below.

Step 4: The Final Model (Constrain All Paths to be Equal, Model 7) Using Model 6 as the baseline model, we constrained all paths to be equal across the

three GDP groups in Model 7. The non-significant difference between Models 7 and 6 (.0019)

revealed functional equivalence across the three GDP groups. We focused on the unstandardized

estimates that were exactly the same across all three GDP groups (see the last column of Table 4

and Figure 2). High corporate ethical values and low love of money were related to high ethical

behavior, which, in turn, was related to low job stress, which, in turn, was related to high life

satisfaction. Corporate ethical values were negatively related to job stress. Hypotheses 1, 2, 4, 5,

and 6 were supported, but Hypothesis 3 was not. Figure 2 is our final culture-free (etic) model.

Our model is generalizable to entities in all three levels of economic development.

Additional Results

We examined five variables across the three GDP groups in a multivariate analysis of

variance (F (10, 12146) = 32.72, p < .001, Wilks’ Lambda = .948, partial eta squared = .026).

Tests of between-subjects effects showed significant differences in unethical behavior (F (2,

6078) = 102.68, p < .001), corporate ethical values (F (2, 6078) = 21.54, p < .001), and job stress

(F (2, 6078) = 50.72, p < .001) but no differences in the love of money and life satisfaction. The

median GDP group reported (1) the lowest corporate ethical values (3.40) (lower than the low

(3.51) and high GDP (3.55) groups, ps < .05, Tukey HSD); (2) the highest unethical behavior

(1.78) (higher than the low (1.60) and high GDP group (1.53)); and (3) the highest amount of job

stress (2.44) (similar to the high (2.41), but higher than the low GDP group (2.16)).

Based on individuals’ scores on Resource Abuse, Not Whistle Blowing, Theft, and

Corruption, we applied a cluster analysis and found two clusters for the whole sample. People in

Cluster 2 had higher scores of unethical behavior (bad apples, 29.39% of the sample) than those

in Cluster 1 (good apples, 70.61%). Second, we conducted the same cluster analysis for each of

Doing Well by Doing Good 18

the three GDP groups and found that 28.30 percent of the high GDP group, 66.55 percent of the

median GDP group, and 29.74 percent of the low GDP group were bad apples.

We took a closer look at four geopolitical entities in the low GDP group. The love of

money was strongly related to unethical behavior for China (.21, p = .018) and Peru (.43, p <

.001) but not for Bulgaria (.04) or Thailand (.04). Corporate ethical values were associated with

ethical behavior for China (-.19, p = .020) and Peru (-.34, p = .001) but not for Bulgaria (.00) or

Thailand (.05). It appears that China and Peru may belong to the median GDP group, whereas

Bulgaria and Thailand may belong to the low GDP group in terms of behavior patterns.

Discussion

This study proposes and tests a model of doing well by doing good and provides

important theoretical, empirical, and practical contributions to the literature. First, we discuss our

final etic model. Low love of money, the personal attitude at the “individual” level, and

corporate ethical values, perceptions of ethical social norms at the “organizational” level, are

significantly related to ethical behavioral intention. Corporate ethical values have a positive

“double-whammy” effect: increasing ethical behavior and reducing job stress. The strongest

paths of our model show that people with high propensity to engage in ethical behavior have

high life satisfaction because they experience low job stress: Job stress is a mediator of the

relationship between ethical behavior and life satisfaction. High corporate ethical values and low

love of money are also indirectly related to high life satisfaction (Srivastava et al., 2001). Our

findings support our theory of doing well by doing good (Davis et al., 1997; Freeman, 1984).

Low love of money and high corporate ethical values are related to propensity to engage in

ethical behavior that is related to low job stress that, in turn, is related to high life satisfaction.

Second, across the three GDP groups, our overall model fits the high and median GDP

groups but not the low GDP group. Therefore, the level of “economic development” is a

moderator (Baron & Kenny, 1986). Our results offer indirect support of Campbell’s (2007)

propositions. Since we collected data from different geopolitical entities around the world, this

significant moderating effect may not be affected by the common method variance (CMV)

biases.

Third, we turn to similarities and differences across three levels of economic

development. There are no significant differences in the love of money and life satisfaction

across the three GDP groups. Although income levels vary significantly across the high, median,

and low GDP groups, the love of money may be equally important to people in all GDP groups.

Due to the lack of mean differences in life satisfaction, our findings do not support the notion

that wealthy nations are happier than less wealthy ones (Khamsi, 2006). Happiness is similar

across entities (Easterlin, 2001). We discuss the differences below.

The high GDP group. With the highest level of economic development, the high GDP

group has the lowest unethical behavior, as expected. On one hand, since individual

characteristic (love of money) is positively related to unethical behavior, our results support the

notion that the love of money is a root of all kinds of evil (Tang & Chen, in press; Tang & Chiu,

2003; Vitell et al., 2006). “Bad apples” exist in good barrels (the high GDP group). On the other

hand, corporate ethical values are significantly related to ethical behavior. “Good apples” and

ethical behavior exist in good barrels. Individuals do look to the social context to determine what

is ethically right and wrong, obey authority figures, and do what is rewarded in organizations

(e.g., Treviño & Youngblood, 1990). Although “bad apples” exist in the high GDP group, the

percentage of bad apples (28.30% of the managers) is the smallest and overall unethical behavior

Doing Well by Doing Good 19

is also the lowest in “good barrels” among the three GDP groups. Most managers (71.7%) in the

high GDP group are “good apples”; therefore, it is easier and safer, relatively speaking, to do

business in these high GDP entities. Our results seem to support the literature regarding

managers in the high GDP group (Campbell, 2007).

According to the 17th

-century French playwright Jean-Baptiste Molière, “it is not only for

what we do that we are held responsible but for what we do not do”. Executives need to be aware

of what managers have done (Resource Abuse, Theft, and Corruption) and what they have failed

to do (Not Whistle Blowing) in organizations. Among four sub-constructs of unethical behavior,

Factor Theft (not Corruption) is the most important concern for managers in the high GDP

group. Corruption, in fact, is a more serious problem than theft around the world. It is possible

that managers in the high GDP groups may have less power, authority, and opportunity to

engage in corruption than those in other GDP groups. Doing well by doing good may be easier to

achieve in this group than other GDP groups due to the higher perception of corporate ethical

values and solid social infrastructures.

The median GP group. This group has the lowest corporate ethical values, the highest

unethical behavior, the highest percentage of bad apples (66.55%), the highest job stress, and the

strongest relationship between the love of money and unethical behavior (Path 1). Due to the

highest level of volatility and competition in the global environment (Campbell, 2007; O’Reilly

& Chatman, 1996; Sorensen, 2002), 66.55 percent of managers may experience the most

“pressure” (maximizing profits) and abundant “opportunities” (earning exorbitant bonuses) and

become “bad apples”. Managers in the median GDP groups may have power, authority, and

opportunity to engage in corruption; corruption is the most important element of unethical

behavior. These “bad apples” display the highest level of unethical behavior in “bad barrels”.

Thus, ethical values at organizational and cultural level may cause unethical behavior (Treviño &

Youngblood, 1990). On the positive side, people still respect laws and organizational ethical

values. On the negative side, when corporate ethical values (Baker et al., 2006; Victor & Cullen,

1987) are weak and the need to survive and competition are both high, high love-of-money

managers take risks, succumb to temptation, and engage in the most unethical behavior

(Badaracco, 2006; Campbell, 2007).

President Bush signed the Sarbanes-Oxley Act into law on July 30, 2002. Entities in the

developing economy may or may not have such laws. The implications are clear: Ethical values

at the organizational level may not exist in a vacuum. Strong and coherent economic, legal,

political, and social infrastructures must exist at the geopolitical entity level. Corporate ethical

values may be too weak to curb managers’ unethical behavior. Due to low ethical values at the

corporate and geopolitical entity levels, unethical behavior is the highest and the relationship

between love of money and unethical behavior is also the strongest. Bad apples’ vicious streak

will come out when they are given a chance, in bad barrels (Christie & Geis, 1970). It is difficult

to do business in the developing economy. Executives and expatriates need to be aware of the

possible gaps between espoused values and actual practices, i.e., behavioral integrity (Simons,

Friedman, Liu, & Parks, 2007). Doing well by doing good is difficult to achieve. Due to

institutional voids, managers experience the highest level of job stress and human “costs” in the

developing economy.

The low GDP group. For these “good” and “bad” apples (29.74% of the managers)

mixed in the “poorest barrels”, no rules and/or behavior patterns exist. That is, ethical decision

making has nothing to do with the love of money at the individual level and perceptions of

ethical values at the organizational level. We suspect that these managers may have adequate pay

Doing Well by Doing Good 20

(Tang et al., 2006), much more power, authority, and opportunity to engage in corruption, and

are much better off than the majority of people in society (mostly illiterate, unemployed, living

in remote or rural areas, and perhaps living on less than one dollar a day) than those in the other

GDP groups. Perhaps due to a poor economy and pervasive corruption in society, managers do

not have a sense of self-sufficiency (Vohs et al., 2006) but rather a strong sense of injustice

(Baker et al., 2006) or a victim mentality. In a state of chaos, some victims do not care about

anything and just act out their frustration, ignore all the laws, orders, and values in the poorest

barrels, become corrupt in the name of justice, and do whatever it takes to get even. Promoting

corporate ethical values may have very little or no impact on ethical behavior in these entities.

People have very little or no money. Disparity is acute. Without wealth, perhaps reputation and

liberty have very little value (Campbell, 2007; Gomez-Mejia et al., 2005). When needs are not

satisfied, money is a motivator (Lim & Teo, 1997). To some, the most important mentality is:

How I can get the most benefit out of the situation for myself now?

Here is a case in point. A business professor in the USA sent enough money to his

younger brother, who was caring for their ill father in one of the poorest countries in Africa. He

instructed his brother to buy the best medicine and supplements and bring them to their father

every day so that their father’s health might improve. The professor was surprised to learn later

that his brother brought medicine and food to their father only twice a week. The young brother’s

rationale was that he needed the money more and could make much better use of it than his

father. When he received the money, he considered it his. He brought medicine and supplements

to his father twice a week, which was much better than doing nothing at all. Later, the father

passed away. In a dire situation, people ignored social norms, love for their parents, and the

stewardship of wealth, reputation, and liberty. The corruption that occurred in this family may be

applicable to managers in organizations.

Those with a high love of money may engage in unethical behavior in an effort to reduce

their perceptions of injustice, vent frustration, voice concerns, get even, and reduce job stress.

Path 3 is negative. When we constrain all the paths to be equal across the three GDP groups, the

two positive paths (high and median GDP groups) and one negative path (low GDP group)

cancel each other out, creating a non-significant path.

Corporate ethical values increase job stress for the low GDP group. We speculate that (1)

people engage in unethical behavior regardless of their love of money or corporate ethical

values, and (2) corporate ethical values exist. With a high level of corruption and unethical

behavior, the above two conditions create conflict, confusion, and chaos that cause job stress

(Path 5). When we impose equality constraints, the two negative paths for the high and median

GDP groups are much stronger than the positive path, creating a negative path.

Bulgaria, China, Peru, and Thailand belong to the same low GDP group. The behavior

patterns of people in China and Peru are similar to those people in the median GDP group (i.e.,

Paths 1 and 2 are both significant) but with a large difference in self-reported income (China =

$2,553 vs. Peru = $13,060). People in Bulgaria and Thailand have a significant difference in

income (Bulgaria = $2,148 vs. Thailand = $10,985), yet their behavior patterns are similar to

those of people in the low GDP group (i.e., both Paths 1 and 2 are not significant). We speculate

that managers in China and Peru may respect and obey the laws but that managers in Bulgaria

and Thailand do not because corporate ethical values do reduce unethical behavior for the former

but do not for the latter. The love of money, ethical values at the organizational and entity level,

and the level of the economic development all play a role here. Future researchers need to

identify qualitative data and test this proposition empirically.

Doing Well by Doing Good 21

Due to globalization, outsourcing, and foreign direct investment (FDI), China (hourly

compensation = US$0.57, Milkovich & Newman, 2008, p. 3) may become one of the most

volatile markets in the world: It has experienced an eight percent increase of GDP per capita for

the past several years (10.1% in 2004, 9.9% in 2005, 8.6% in 2006, and expected 8.2% in 2007)

and will move from the low GDP group, based on 2005 GDP data, to the median or high GDP

groups quickly. According to the United Nation’s 2006 GDP per capita, China ($5,896) is

already in the median GDP group. Chinese managers respect and obey laws but may face tough

challenges. Due to extremely low ethical values at organizational and geopolitical entity level,

they may engage in unethical behavior. In the low GDP group, doing well by doing good may be

difficult to achieve at the present time. Researchers need to investigate the impact of change in

GDP per capita and self-reported income on ethical/unethical behavior over time. We present our

practical implications below.

Practical Implications

Doing well by doing good may be applicable to people at individual, organizational, and

national levels. First, at individual level, people enter the business field to make money (Bok,

1993; Cunningham, Frauman, Ivy, & Perry, 2004). Managers bring dispositional values (Staw,

Bell, & Clausen, 1986), such as the love of money, to an organization (McCabe et al., 2006). As

mentioned, income reduces the love of money among highly paid (Tang & Chiu, 2003),

enhances the love of money among underpaid (Tang et al., 2005, 2006), and is not related to

the love of money among adequately paid employees (Tang et al., 2005, 2006). When people are

paid fairly at the market value, they have a sense of self-sufficiency (Vohs et al., 2006) and do

not experience financial hardship (Lim & Teo, 1997). Some managers may reduce their love of

money, enjoy what they have with a gratifying mindset, behave ethically, reduce the temptation

to engage in unethical behavior in organizations (Tang & Chiu, 2003), and experience low stress

and high life satisfaction. Although few may thrive on chaos, many prefer non-stressful and

happy lives. This may not only improve performance, physical and psychological health, and

quality of life but also reduce human costs at individual level (Tang, 2007). We turn to our

second implication at organizational level below.

Compensation. It may be difficult or impossible to control managers’ love of money in

organizations. The rising tide lifts all boats. After a while, satisfaction with money returns to

zero, and the zero point escalates (Herzberg, 1987). On the basis of suggestions mentioned above

(Campbell, 2007; Tang & Chiu, 2003, Tang et al., 2005, 2006), top executives need to pay

managers fairly and well because they are important stakeholders in the organization (Freeman,

1984). Satisfaction with internal equity, external competitiveness, individual equity, and pay

administration (Milkovich & Newman, 2008) enhances perception of pay equity and justice that

may reduce unethical behavior (Cohen-Charash & Spector, 2001; Greenberg, 2002). When

managers are pampered with excellent pay as well as perceptions of justice in organization and

society, they feel rich financially and psychologically, are able to take their minds off money

(Herzberg, 1987; Kohn, 1993), have low love of money, elevate their virtue and wisdom,

inculcate their stewardship behaviors, and supplant opportunism and self-interest (Davis et al.,

1997; Gomez-Mejia et al., 2005). An effective, efficient, fair, and just compensation system may

help good apples in good barrels preserve the reputation of their firms and ensure continued

business success at organizational level (Campbell, 2007). Researchers may investigate the effect

of managers’ income on the change in their love of money and perception of justice regarding

compensation and identify the critical melting point at which few bad apples fail to resist the

Doing Well by Doing Good 22

temptations of their success and allow their rising ambition for money to push aside their

wisdom or virtue and engage in unethical behavior.

Corporate ethical values. Executives need to strongly promote corporate ethical values at

the organizational level and enhance people’s perceptions of an ethical environment (Baker et

al., 2006) that may promote people to engage in ethical behavior. They can encourage corporate

ethical values by considering (1) prevention (identifying and rejecting job applicants and

managers who are “prone” to engage in unethical behaviors); (2) control (the use of normative

force--code of ethics, internal control systems, role models, and social norms and instrumental

force--proper checks and balances, electronic surveillance devices, and rewards and

punishment); and (3) deterrence (dismissing managers in business organizations or providing a

strong response to harmful misbehavior). It is not easy to change people’s ethical orientation

overnight; executives may strengthen corporate ethical values through reward and punishment

and serve as role models to crystallize them in managers’ minds (Treviño & Brown, 2004).

“People do work for money—but they work even more for meaning in their lives”

(Pfeffer, 1998, p. 112). In the post-Sarbanes-Oxley environment, a sea change of ethical social

norms in schools, organizations, and society, or ethical community-building, is needed to

promote ethical behavior. The combination of organizations’ strong vision, compensation, and

ethical values may instill value, meaning, and purpose in life and help managers know they are

not just “cutting stones” but “building cathedrals”. [It took 182 years (1163-1345) to build the

Cathedral of Notre-Dame in Paris. A strong vision with goals and action plans is needed. This is

very difficult to achieve because many are invited, but few are chosen.] Ultimately, the

combination of “head” and “heart” will be the competitive advantage in the world market

(Ashmos & Duchon, 2000): Productivity and profit are consistent with virtuous behavior

(Waddock & Graves, 1987).

Organizations in the developed economy have outsourced their production or services to

entities in the developing economy in the past (South Korea, Taiwan, and Mexico) and to those

in the underdeveloped economy recently (China). Regarding implications at economic

development level, managers’ love of money seems to be equally important around the world

regardless of the large income differences across geopolitical entities in all three GDP groups.

Researchers and executives need to be aware of the dynamic and changing global competition,

income, and ethical values at organizational and economic development levels, for those in the

median or low GDP groups, in particular (e.g., China).

Although the love of money may be the same, yet the impacts of the love of money and

ethical corporate values on unethical behavior across the three GDP groups are different due to

different corporate peer pressure (Martin, 2003), organizations’ ability to compete and survive in

the global environment (Campbell, 2007; Sorensen, 2002), and economic, legal, political, and

social infrastructures at the geopolitical entity level. Managers are aware of their ability to

control employee compensation (Pfeffer, 1998) and corporate ethical values. However, the

effects of the love of money and ethical corporate values on ethical behavior, job stress, and life

satisfaction are different across different levels of economic development. Bribery and unethical

behaviors damage economic efficiency. There is a surprising amount of agreement throughout

the world that bribery is unethical. International business ethics seldom come in black and white.

Further, good apples in good barrels face the challenges of maintaining their ethical values and

standards while doing business with good/bad apples in good, bad, or the poorest barrels across

the three GDP groups in the rapidly changing economy. They must also respect the rights of

Doing Well by Doing Good 23

other cultures while raising the bar of ethical practices for managers in different GDP groups

around the world.

In summary, organizations may have some direct or indirect control over both the pull,

low love of money, and the push, high corporate ethical values on ethical behavior. Managers

have the opportunity to develop deep meaning and purpose in their lives, achieve goals of

producing products and services, behave ethically, reduce job stress, enhance high quality of life,

obtain success and a good image (Highhouse, Zickar, Thorsteinson, Stierwalt, & Slaughter,

1999), satisfy stakeholders, become stewards of the organization and society, and serve humanity

(Campbell, 2007; Davis et al., 1997). Organizations that do good are able to do well.

Limitations. Although we do not select these geopolitical entities from each of the high,

median, and low GDP groups and from each geopolitical entity randomly, we incorporate 7

geopolitical entities in the high GDP group (n = 1,756), 12 entities in the median GDP group (n

= 2,371), and 10 entities in the low GDP group (n = 1,954) across six continents around the

world (N = 6,081). The number of geopolitical entities and the number of managers in each of

the three GDP groups are large enough to offer reliable results and boost the confidence in

generalizing our research findings to other geopolitical entities (Sin et al., 1999). Differences

between GDP per capita and managers’ self-reported income may show our possible sample

biases in some geopolitical entities. Our significant correlation between GDP per capita and self-

reported income of these samples may mean that our samples are not perfect but reasonable

representations of geopolitical entities in general. Our definition of the three levels of economic

development is by no means perfect but may serve only as a general guide for classifying

geopolitical entities. We do not examine issues related to the economy, unemployment rate,

moral development, and religion of the nation/region. These variables may be distributed

randomly and may not have a systematic impact on the results of this study.

We collected cross-sectional data using a single survey. We incorporated Harman’s one-

factor test, multi-group analyses with and without the latent common method variance (CMV)

factor, and measurement invariance to show that the CMV effect was indeed negligible (e.g.,

Podsakoff et al., 2003; Spector, 2006). Scales with different formats and anchors may be

considered as different methods (Spector, 2006). Attitudes predict self-reports better than

objective or observed behaviors (Armitage & Conner, 2001). We measured only the propensity

to engage in unethical behavior, not the actual unethical behavior. People’s attitude, unethical

behavioral tendency, job stress, and life satisfaction may be best addressed by monomethod self

reports. This study establishes the existence of relationships among variables. Future researchers

may investigate longitudinal changes of income and empirically rule out alternatives of the

current findings (Spector, 2006).

Our results also reveal the importance of investigating our model at the three levels of

economic development. We select the minimum number of variables and items and do not

investigate all different aspects of unethical behavior. Future researchers may use our model,

incorporate managers’ power, authority, and opportunity to engage in corruption, include

additional constructs (e.g., religion, religiosity, Vitell et al., 2006), explore it in other geopolitical

entities, obtain qualitative data from multiple sources, control extraneous variables, and examine

potential differences across geopolitical entities and within each of the three GDP groups.

Conclusion. Our theoretical model suggests that high corporate ethical values and low

love of money are related to ethical behavior, which, in turn, is related to low job stress, which,

in turn, is related to high life satisfaction. Corporate ethical values have a positive “double-

whammy” effect: increasing ethical behavior and reducing job stress. Our model varies across

Doing Well by Doing Good 24

the three GDP groups. Executives need to effectively and efficiently manage compensation

systems to reduce the love of money at the individual level and promote corporate ethical values

at the organizational level and achieve the precept of doing well by doing good. We hope that

researchers and executives may find this cross-cultural applied research relevant in the

globalization of the economy and that this article will move them to further research and

improved practice of this precept. When they do the right thing, they shall not remain

insignificant (Vermeulen, 2007).

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Doing Well by Doing Good 30

Table 1. Major Variables of the Study across 30 Samples (29 Geopolitical Entities)

GDP CPI Income LOM PUB CEV Stress Life

Sample N M M SD M SD M SD M SD M SD

1. The USA 274 42,000 7.6 35,357 3.80 .61 1.55 .53 3.57 .93 2.62 1.12 3.87 .73

2. Belgium 201 35,712 7.4 20,269 3.49 .56 1.51 .45 3.54 .83 1.95 1.05 3.80 .71

3. Australia 262 34,740 8.8 - 3.63 .62 1.72 .48 3.60 .76 2.42 1.06 3.77 .91

4. France 87 33,918 7.5 16,735 3.48 .53 1.56 .34 3.44 .88 2.22 1.06 3.72 .90

5. Spain 183 27,226 7.0 - 3.50 .63 1.55 .47 3.15 .82 2.33 .91 3.82 .69

6. Singapore 1 202 26,836 9.4 31,746 3.76 .63 1.50 .49 3.64 .78 2.64 1.05 3.70 .78

7. Singapore 2 336 26,836 9.4 29,277 3.76 .57 1.29 .45 3.67 .91 2.30 1.07 3.74 .72

8. HK 211 25,493 8.3 47,509 3.78 .58 1.63 .54 3.33 .69 2.69 .91 3.40 .67

----------------------------------------------------------------------------------------------------------------------------- -----------------------

9. Portugal 200 17,456 6.5 3,386 3.44 .58 1.49 .50 3.52 .84 2.11 .97 3.70 .77

10. Slovenia 200 16,986 6.1 7,025 3.48 .51 1.56 .43 3.04 .83 2.19 .90 3.87 .59

11. S. Korea 203 16,308 5.0 45,647 3.97 .59 2.16 .72 3.82 .70 2.76 .94 3.35 .75

12. Taiwan 201 15,203 5.9 22,567 3.86 .56 1.71 .54 3.62 .91 2.43 .97 3.57 .78

13. Malta 200 13,803 6.6 14,922 3.85 .57 1.62 .50 3.62 .83 2.47 .95 3.87 .79

14. Oman 204 12,664 6.3 5,816 3.56 .59 1.50 .45 3.57 1.08 2.40 .99 3.82 .86

15. Hungary 100 10,814 5.0 2,700 3.84 .63 1.71 .52 3.34 .90 2.06 .88 3.92 .75

16. Croatia 165 8,675 3.4 14,336 3.60 .52 1.82 .52 3.14 .85 2.32 .91 3.72 .80

17. Mexico 295 7,298 3.5 7,416 3.57 .66 1.59 .49 3.40 .92 2.36 .98 4.03 .73

18. Russia 200 5,349 2.4 2,901 3.76 .58 2.25 .71 3.17 .72 2.63 .92 3.44 .82

19. S. Africa 203 5,106 4.5 5,247 3.67 .41 2.38 .41 2.78 .60 2.82 .62 3.56 .65

20. Malaysia 200 5,042 5.1 10,180 3.85 .53 1.63 .63 3.28 .84 2.54 .98 3.71 .72

----------------------------------------------------------------------------------------------------------------------------- ---------------------

21. Romania 200 4,539 3.0 1,723 3.77 .57 1.29 .36 3.69 .83 2.13 1.00 3.68 .85

22. Brazil 201 4,320 3.7 5,006 3.54 .61 1.68 .56 3.76 .86 2.04 .84 3.71 .73

23. Bulgaria 162 3,459 4.0 2,148 3.84 .55 1.92 .51 3.33 .70 2.19 .62 3.46 .72

24. Peru 183 2,841 3.5 13,060 3.58 .59 1.83 .86 3.55 .79 2.42 1.02 3.91 .78

25. Macedonia 204 2,810 2.7 2,176 3.91 .55 1.54 .47 3.33 .88 2.60 1.02 3.59 .82

26. Thailand 200 2,659 3.8 10,985 3.65 .60 2.04 .79 3.31 .66 2.37 .78 3.56 .59

27. China 204 1,709 3.2 2,553 3.46 .64 1.44 .54 3.24 .93 2.18 .86 3.28 .77

28. Egypt 200 1,265 3.4 7,181 3.51 .65 1.44 .69 3.94 1.06 2.29 1.11 3.98 .83

29. The Philippines 200 1,168 2.5 2,027 3.69 .62 1.57 .64 3.65 .94 1.85 .90 3.91 .71

30. Nigeria 200 678 1.9 1,909 4.20 .43 1.29 .44 2.68 .63 1.53 .76 4.36 .74

_________________________________________________________________________________________________

1. High GDP 1,756 31,595 7.9 30,148 3.68 .61 1.53 .50 3.55 .77 2.41 1.06 3.73 .77

2. Median GDP 2,371 11,225 5.0 11,845 3.70 .58 1.78 .62 3.40 .78 2.44 .95 3.72 .78

3. Low GDP 1,954 2,544 3.2 4,880 3.71 .62 1.60 .65 3.51 .73 2.16 .95 3.75 .81

Whole Sample 6,081 13,862 5.2 13,384 3.70 .60 1.65 .61 3.44 .88 2.34 .99 3.73 .79

_________________________________________________________________________________________________

Note. N = 6,081. All geopolitical entities were arranged according to GDP per capita from the highest to the lowest and

the 3 GDP groups. LOM = The Love of Money, PUB = Propensity to Engage in Unethical Behavior, CEV = Corporate

Ethical Values. GDP = 2005 GDP per Capita. CPI = 2005 Corruption Perceptions Index. Income = US$. Correlation

(GDP and Income) = .69 (p < .001). Correlation (GDP and CPI) = .85 (p < .001). Correlation (Income and CPI) = .68 (p

< .001).

Doing Well by Doing Good 31

Table 2. Correlations and Cronbach’s Alpha of Major Variables for the Whole Sample, and the High,

Median, and Low GDP Groups

Variable M SD 1 2 3 4 5 6 7 8 9

Whole

1. Age 34.68 9.63

2. Sex .51 .50 .12**

3. Education 15.42 2.56 .03* .04*

4. Income (Z) .00 .97 .28** .15** .18**

5. LOM 3.70 .60 -.02 .10** .03* -.03*

6. CEV 3.48 .76 .03* -.02 .03** .06** -.01

7. PUB 1.65 .61 -.06** .08** .04** -.04** .10** -.19**

8. Stress 2.34 .99 -.02 -.02 -.07** -.10** .06** -.22** .27**

9. Life 3.73 .79 .02 -.01 -.00 .08** -.00 -.13** -.14** -.28**

Cronbach’s alpha .85 .69 .88 .90 .86

___________________________________________________________________________________________________

High GDP

1. Age 33.14 10.25

2. Sex .50 .50 .22**

3. Education 14.71 2.46 .11** .13**

4. Income (Z) .00 .98 .38** .21** .23**

5. LOM 3.68 .61 -.09** .15** .01 -.03

6. CEV 3.55 .77 .08** .00 .01 .12** -.01

7. PUB 1.53 .50 -.17** .07** -.00 -.04 .13** -.20**

8. Stress 2.41 1.06 -.09** -.08** -.01 -.12** .10** -.30** .23**

9. Life 3.73 .77 .03 .01 -.02 .11** -.07 .16** -.13** -.26**

Cronbach’s alpha .86 .73 .83 .93 .89

___________________________________________________________________________________________________

Median GDP

1. Age 35.37 9.53

2. Sex .52 .50 .08**

3. Education 15.45 2.67 .01 - .03

4. Income (Z) .00 .98 .24** .17** .17**

5. LOM 3.70 .58 -.01 .11** .08* -.03

6. CEV 3.40 .78 -.04 -.01 .05* .04 -.03

7. PUB 1.78 .62 -.03 .09** .07** -.06** .15** -.18**

8. Stress 2.44 .95 .01 -.01 -.02 -.08** .11** -.20** .28**

9. Life 3.72 .78 -.03 -.01 -.03 .07** -.06** -.12** -.16** -.28**

Cronbach’s alpha .84 .71 .86 .86 .82

___________________________________________________________________________________________________

Low GDP

1. Age 35.22 8.99

2. Sex .51 .50 .09**

3. Education 16.01 2.36 -.09** -.04

4. Income (Z) .00 .95 .22** .06** .17**

5. LOM 3.71 .62 -.02 .03 - .02 -.02

6. CEV 3.51 .73 .10** -.06** .07** .03 -.06*

7. PUB 1.60 .65 -.14** .07** -.02 -.02 .03 -.15**

8. Stress 2.16 .95 .03 .04 -.13** -.11** -.04 -.14** .30**

9. Life 3.75 .81 -.06** -.03 .04 .08** .11** .11** -.13** -.31**

Cronbach’s alpha .85 .63 .92 .91 .87

___________________________________________________________________________________________________

Note. Sample size: Whole = 6,081, High GDP = 1,756, Median = 2,371, and Low = 1,954. Sex: Male = 1. Female = 0. Age and Education are

expressed in years. Income = Standardized Z Score. *p < .05, **p < .01

Doing Well by Doing Good 32

Table 3. Main Results

________________________________________________________________________________________________

Model χ2

df p χ2/df IFI TLI CFI SRMSR RMSEA Models ΔCFI

________________________________________________________________________________________________

Step 1: Measurement model

Configural Invariance (3 GDP Groups):

1. High GDP 1593.53 477 .0000 3.3407 .9566 .9519 .9565 .0400 .0365

2. Median GDP 2403.08 477 .0000 5.0379 .9382 .9315 .9381 .0430 .0413

3. Low GDP 2371.66 477 .0000 4.9720 .9449 .9389 .9448 .0604 .0451

Metric Invariance (3 GDP Groups):

4. Unconstrained 6368.25 1431 .0000 4.4502 .9459 .9400 .9458 .0604 .0238

5. Constrained 7049.91 1475 .0000 4.7796 .9389 .9343 .9388 .0644 .0249 2 vs. 1 .0070

Step 2: Measurement Model Without and With Latent Common Method Variance (CMV) Factor (3 GDP Groups):

1. Model 1 6368.25 1431 .0000 4.4502 .9459 .9400 .9458 .0604 .0238

2. Model 1 + CMV 5106.63 1332 .0000 3.8338 .9587 .9507 .9586 .0334 .0216 2 vs. 1 .0128

Step 3: Main SEM Model (3 GDP Groups)

1. Model 1 6468.77 1443 .0000 4.4829 .9449 .9395 .9448 .0639 .0239

2. Model 1 + LOM 6799.17 1465 .0000 4.6411 .9415 .9367 .9415 .0670 .0245 2 vs. 1 .0033

3. Model 2 + CEV 6995.43 1469 .0000 4.7620 .9394 .9346 .9393 .0682 .0249 3 vs. 2 .0022

4. Model 3 + PUB 7355.83 1491 .0000 4.9335 .9357 .9316 .9355 .0684 .0254 4 vs. 3 .0038

5. Model 4 + Stress 7365.64 1495 .0000 4.9269 .9356 .9317 .9356 .0684 .0254 5 vs. 4 .0001

6. Model 5 + Life 7369.98 1499 .0000 4.9166 .9356 .9319 .9356 .0684 .0254 6 vs. 5 .0000

Step 4: Set All Paths to be Equal (Model 7)

7. Model 6 + Paths 7560.46 1511 .0000 5.0036 .9337 .9304 .9336 .0749 .0257 7 vs. 6 .0020

_________________________________________________________________________________________________

Note. N = 6,081. High GDP: n = 1,756, Income over $20,000; Median GDP: n = 2,371, Income $5,000 - $20,000; Low

GDP: n = 1,954, Income under $5,000. LOM: The Love of Money, PUB: Propensity to Engage in Unethical Behavior,

CEV: Corporate Ethical Values. Metric invariance: The difference between the unconstrained and the constrained

models was not significant (ΔCFI = .0070).

Doing Well by Doing Good 33

Table 4 Direct Effect, Indirect Effect, Total Effect, Squared Multiple Correlation, and Factor Loading

_______________________________________________________________________________________________

Step 3, Model 6 Step 4, Model 7 Path High Median Low Across Economic Development

_______________________________________________________________________________________________

Part 1: Direct Effect Standardized Comparison Unstandardized 1. LOM PUB .15*** .25*** .00 M > H > L .11***

2. CEV PUB -.16*** -.12*** .01 HM < L -.10***

3. LOM Stress .09** .07** -.07** HM > L .03

4. PUB Stress .21*** .31*** .31*** H < ML .46***

5. CEV Stress -.20*** -.12*** .09*** H < M < L -.10***

6. Stress Life -.27*** -.32*** -.33*** -.23***

Part 2: Indirect Effect

1. LOM Stress .03 .08 .00

2. LOM Life -.03 -.05 .02

3. CEV Stress -.03 -.04 .00

4. CEV Life .06 .05 -.03

5. PUB Life -.06 -.10 -.10

Part 3: Total Effect

1. LOM PUB .15 .25 .00

2. CEV PUB -.16 -.12 .01

3. LOM Stress .12 .15 -.07

4. PUB Stress .21 .31 .31

5. CEV Stress -.23 -.16 .09

6. Stress Life -.27 -.32 -.33

7. LOM Life -.03 -.04 .02

8. CEV Life .06 .05 -.03

9. PUB Life -.06 -.10 -.10

Part 4: Squared Multiple Correlation (SMC)

PUB .05 .08 .00

Stress .11 .14 .11

Life .07 .10 .11

Part 5: Factor Loading

The Love of Money (LOM)

1. Rich .88 .84 .82

2. Motivator .68 .68 .66

3. Important .67 .70 .65

4. Power .51 .53 .52

Unethical Behavior (PUB)

1. Resource Abuse .64 .76 .85

2. Not Whistle Blowing .49 .57 .59

3. Theft .86 .82 .91

4. Corruption .80 .95 .96

__________________________________________________________________________________________________

Note. LOM: The Love of Money, PUB: Propensity to Engage in Unethical Behavior, CEV: Corporate Ethical Values,

SMC: The amount of variance explained by the predictors of this variable. **p < .01, ***p < .001. Two Total Effects

(LOM Stress and CEV Stress) were underlined: Total Effect = Direct Effect + Indirect Effect. For Step 7, we focus

exclusively on unstandardized paths only.

Doing Well by Doing Good 34

Figure 1. Hypothesized Structural Equation Model

The Love

of Money

Ethical

Values

Unethical

Behavior

Job

Stress

H 1 (+)

H 2 (-)

Hypothesized Structural Equation Model

Life

Satisfaction

H 6 (-)H 4 (+)

H 3 (+)

H 5 (-)

Doing Well by Doing Good 35

Figure 2: Results of the Final etic Model Combining Three GDP Groups (Model 7)

The Love

of Money

Ethical

Values

Unethical

Behavior

Job

Stress

.11***

-.10***

Final Results of the Model:

Unstandardized Estimates

Life

Satisfaction

-.23***.46***

.03

-.10***

Chi-Square = 7560.46, df = 1511, p < .001, Chi-Square/df = 5.0036,

IFI = .9337, TLI = .9304, CFI = .9336, SRMSR = .0749, RMSEA = .0257.

Doing Well by Doing Good 36

Appendix 1: Items of the Key Measures

The Love of Money Scale (Latent Second-Order Construct)

Factor Rich (Latent First-Order Sub-Construct)

1. I want to be rich.

2. It would be nice to be rich.

3. Having a lot of money (being rich) is good.

Factor Motivator

4. I am motivated to work hard for money.

5. Money reinforces me to work harder.

6. I am highly motivated by money.

Factor Important

7. Money is good.

8. Money is important.

9. Money is valuable.

Factor Power

10. Money is power.

11. Money gives one considerable power.

12. Money can buy the best products and services.

Corporate Ethical Values (Latent First-Order Construct)

1. Top management in my company has let it be known in no uncertain terms that unethical

behaviors will not be tolerated.

2. If a manager in my company is discovered to have engaged in unethical behaviors that result

primarily in personal gain (rather than corporate gain), he or she will be promptly

reprimanded.

3. If a manager in my company is discovered to have engaged in unethical behaviors that result

primarily in corporate gain (rather than personal gain), he or she will be promptly

reprimanded.

Propensity to Engage in Unethical Behavior (Latent Second-Order Construct)

Factor Resource Abuse (Latent First-Order Sub-Construct)

1. Use office supplies (paper, pen), Xerox machine, and stamps for personal purpose.

2. Make personal long-distance (mobile phone) calls at work.

3. Waste company time surfing on the Internet, playing computer games, and socializing.

Factor Not Whistle Blowing

4. Take no action against shoplifting by customers.

5. Take no action against managers who steal cash/merchandise.

Doing Well by Doing Good 37

Factor Theft

6. Abuse company expense accounts and falsify accounting records (time cards).

7. Take merchandise and/or cash home.

8. Borrow $20 from a register overnight without asking.

Factor Corruption

9. Accept money, gifts, and kickbacks from others.

10. Reveal company secrets when a person offers several million dollars.

11. Sabotage the company to get even due to unfair treatment.

12. Lay off 500 managers to save the company money and increase my personal bonus.

Job Stress (Irritation) (Latent First-Order Construct)

When you think about yourself and your job nowadays, how do you feel?

1. I get angry

2. I get aggravated.

3. I get irritated or annoyed.

Response scale (1) strongly disagree, (3) neutral, and (5) strongly agree.

Satisfaction with Life (Latent First-Order Construct)

1. My work/family/personal life in general

2. My life as a whole these days

3. My overall life satisfaction

Response scale: (1) strongly dissatisfied, (3) neutral, and (5) strongly satisfied.