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IS “THE LOVE OF MONEY” THE ROOT OF ALL EVIL? OR DIFFERENT STROKES FOR DIFFERENT FOLKS: LESSONS IN 12 COUNTRIES

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This study examines a model involving income, Money Ethic (MES), pay satisfaction, ethical culture, organizational commitment, sex, job changes, and unethical behavior based on data from 2,338 full- time employees in 12 countries/cultures and tests the notion: Does Money Ethic (the love of money) or income (money) have a direct and/or indirect impact on unethical behavior? Results suggested that the love of money had a direct impact on unethical behavior. The indirect path was also significant: The love of money caused low pay satisfaction that, in turn, reduced organizational commitment that, in turn, enhanced unethical behavior. However, income had no impact on either the love of money or unethical behavior. Thereby, the indirect and direct paths supported the notion: The love of money is the root of all evil, whereas income is not. Moreover, men were more obsessed with the love of money than women. Ethical culture enhanced commitment. Job changes influenced unethical behavior. We compared data from 12 countries simultaneously using the model and found cross-cultural differences.
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Money Ethic in 12 Countries
1
IS “THE LOVE OF MONEY” THE ROOT OF ALL EVIL? OR DIFFERENT STROKES
FOR DIFFERENT FOLKS: LESSONS IN 12 COUNTRIES
THOMAS LI-PING TANG
Middle Tennessee State University, Murfreesboro, TN 37132 Tel: (615) 898-2005, Fax: (615)
898-5308, e-mail: ttang@mtsu.edu, USA
ADEBOWALE AKANDE, Potch University, deboakande@hotmail.com, South Africa
ABDULGAWI SALIM ALZUBAIDI, SQU, abdulqawi.al-zubaidi@squ.edu.om, Oman
MARK G. BORG, University of Malta, mbor1@duc.um.edu.mt, Malta,
BOR-SHIUAN CHENG, National Taiwan University, chengbor@ccms.nt u.edu.tw, Taiwan,
RANDY K. CHIU, Hong Kong Baptist University, crandy@hkbu.edu.hk, Hong Kong,
CHIN-KANG JEN, National Sun-Yat-Sen University,
ckjen@cm.nsysu.edu.tw, Taiwan
ALI MAHDI KAZEM, SQU, amkazem@squ.edu.om, Oman,
VIVIEN KIM GEOK LIM, National University of Singapore, bizlimv@nus.edu.sg, Singapore,
EVA MALOVICS, University of Szeged, malovics@jgytf.u-szeged.hu, Hungary,
JOHNSTO E. OSAGIE, Florida A & M University, jsqie@yahoo.com, USA
RUJA PHOLSWARD, University of the Thai Chamber of Commerce, aruph@mail.utcc.ac.th,
Thailand
ELISAVETA SARDZOSKA, University St. Cyril and Methodius, elisa@ukim.edu.mk,
Macedonia,
ALLEN F. STEMBRIDGE, Southwestern Adventist University, stem@aiias.edu, USA
TOTO SUTARSO, Middle Tennessee State University, tsutarso@frank.mtsu.edu, USA
Money Ethic in 12 Countries
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THERESA LI-NA TANG, FISI-Cendant, Brentwood, TN, theresa.tang@fisi.cendant.com,
USA
THOMPSON SIAN HIN TEO, National University of Singapore, bizteosh@nus.edu.sg,
Singapore,
PETER VLERICK, Ghent University, peter.vlerick@rug.ac.be , Belgium,
All authors have been arranged alphabetically after the senior author. The authors would
like to thank Donald L. Curry, Dean, College of Graduate Studies and the Faculty Research &
Creative Activity Committee of Middle Tennessee State University for the support of this
research project, Father Wiatt A. Funk and Thomas W. Lee for their suggestions, and Brian
Daughtrey, Emily Thormaehlen, and Nathan Harding for their assistance.
Correspondence concerning this article should be addressed to Thomas Li-Ping Tang,
P.O. Box 516, Department of Management and Marketing, Jennings A. Jones College of
Business, Middle Tennessee State University, Murfreesboro, TN 37132. Tel: (615) 898-2005,
Fax: (615) 898-5308, e-mail: ttang@mtsu.edu [ethics.01.AoM.doc: 2/22/2002]
[mes.ethics.00.1; .00.2]
Money Ethic in 12 Countries
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ABSTRACT
This study examines a model involving income, Money Ethic (MES), pay satisfaction, ethical
culture, organizational commitment, sex, job changes, and unethical behavior based on data from
2,338 full-time employees in 12 countries/cultures and tests the notion: Does Money Ethic (the
love of money) or income (money) have a direct and/or indirect impact on unethical behavior?
Results suggested that the love of money had a direct impact on unethical behavior. The indirect
path was also significant: The love of money caused low pay satisfaction that, in turn, reduced
organizational commitment that, in turn, enhanced unethical behavior. However, income had no
impact on either the love of money or unethical behavior. Thereby, the indirect and direct
paths supported the notion: The love of money is the root of all evil, whereas income is not.
Moreover, men were more obsessed with the love of money than women. Ethical culture
enhanced commitment. Job changes influenced unethical behavior. We compared data from 12
countries simultaneously using the model and found cross-cultural differences.
------------
Key Words: Income, Money Ethic, the Love of Money, Pay Satisfaction, Commitment, Ethical
Culture, Unethical Behavior, and Cross-Culture Differences
Money Ethic in 12 Countries
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IS “THE LOVE OF MONEY” THE ROOT OF ALL EVIL? OR DIFFERENT STROKES
FOR DIFFERENT FOLKS: LESSONS IN 12 COUNTRIES
The adaptation of a “common” currency, the Euro, on January 1, 2002 in 12 European
countries involving 305 million people with “diverse” cultures, China’s accession to WTO, the
provisions of the North American Free Trade Agreement (NAFTA), the formation of the
European Union (EU), economic developments of Pacific Rim countries, reunification of
Germany, and the restructuring of the former Soviet Union have caused significant changes
around the world. The growing integration of the world economy into a single, huge free market
increases the intensity of competition. Only the most efficient and best-managed organizations
can survive. Organizations in the US and around the world are increasingly interested in
reducing labor costs and increasing worker productivity and profits.
Managers use money to attract, retain, and motivate employees and achieve
organizational goals (Milkovich & Newman, 2002; Opsahl, & Dunnette, 1966; Wernimont &
Fitzpatrick, 1972). The meaning of money is “in the eye of the beholder” (McClelland, 1967: 10)
and serves as a “frame of reference” in which they examine their everyday lives (Tang, 1992:
201). There has been a significant increase regarding the importance of money in the US and
around the world (e.g., Chiu, Luk, & Tang, 2001; Mitchell & Mickel, 1999; Rynes & Gerhart,
2000). Managers want to manage human resource and compensation systems effectively and
efficiently across borders. Further, pay dissatisfaction has numerous undesirable consequences
(Heneman & Judge, 2000), such as: low commitment, turnover (Hom & Griffeth, 1995), counter-
productive behaviors, and unethical behavior (Cohen-Charash & Spector, 2001).
Ethics are moral principles or beliefs about what is right or wrong. These beliefs guide
employees in their dealings with other individuals and stakeholders and provide a basis for
Money Ethic in 12 Countries
5
deciding whether behavior is right and proper. Researchers have examined taxonomy and
conceptual models of ethical decision-making (Forsyth, 1980; Michalos, 1995), national culture
(Hofstede, 2001; Trompenaars, 1994), social institutions (Parsons, 1990), personal values, and
cross-cultural ethical phenomena (Wines & Napier, 1992).
In the wake of downsizing and empowerment, employees not only have the “pressure” to
perform (push), but also the “opportunity” to make money and consume products and services
(pull) that may lead to unethical behavior. Opportunity to engage in unethical behavior is a
predictor of unethical behavior (Ferrell & Gresham, 1985). For example, 56% of American
business people have experienced pressure to behave unethically in order to achieve company
goals, 48% admitted having engaged in unethical behavior (Lonkevich, 1997), 31% witnessed
ethical misconduct, and 29% have been forced to use unethical means to get promoted (Gross,
1995). Codes of ethics and business ethics courses are now commonplace among America’s
large corporations and business schools’ curriculums, respectively. Due to globalization,
managers face more ethical dilemmas. Few empirical studies have systematically investigated
the issue of money, money attitude, and unethical behavior in a wide range of countries. This is
a critical omission in the management literature.
The Present Study
In this study, we develop a model of unethical behavior (Figure 1) using constructs
developed by theorists in the US, and test these constructs in 12 countries around the world.
More specifically, we test our model (using structural equations modeling, SEM) that involves
income, Money Ethic, pay satisfaction, ethical culture, organizational commitment, unethical
behavior, sex, and the number of job changes, based on 2,338 full-time employees in 12 cultures
(Belgium, Hong Kong, Hungary, Macedonia, Malta, Oman, Philippines, Singapore, South Africa,
Money Ethic in 12 Countries
6
Taiwan, Thailand, and the USA). (Please note that Hong Kong is a part of People’s Republic of
China (China). Hong Kong differs from Taiwan. Both are different from China. We treat Hong
Kong and Taiwan as two separate samples. For the sake of convenience, we use the term
“country” or “culture” for each sample throughout this paper.)
We examine the following paths: (1) two direct paths (Income à Unethical Behavior
and Money Ethic à Unethical Behavior), (2) the indirect path (Income à Money Ethic à Pay
Satisfaction à Organizational Commitment à Unethical Behavior), (3) Ethical Culture à
Organizational Commitment, and (4) Sex à Money Ethic and Job Changes à Unethical
Behavior. The indirect path, (2) mentioned above, tests the notion: Income may or may not
have an impact on Money Ethic (the love of money). The love of money will lead to low pay
satisfaction. People with low pay satisfaction will have low organizational commitment that, in
turn, will lead to unethical behavior. We will (1) perform separate confirmatory factor analyses
(CFA) for all measures, (2) test the model using structural equations modeling (SEM) based on
the whole sample, and (3) compare 12 countries simultaneously using the SEM model. We will
present relevant literature below.
The Money Ethic Scale (The Love of Money)
Money is the instrument of commerce and the measure of value (Smith, 1776/1937).
There is a spirited debate: On the one hand, money is a hygiene factor (Herzberg, 1987).
“People do work for moneybut they work even more for meaning in their lives” (Pfeffer, 1998:
112). On the other hand, money is a motivator (Gupta & Shaw, 1998; Kohn, 1993). “No other
incentive or motivational technique comes even close to money” (Locke, Feren, McCaleb, Shaw,
& Denny, 1980: 381). Money has been examined directly in some motivation theories (Herzberg,
1987) but not in others (Alderfer, 1972; Maslow, 1970). Further, individual difference has been
Money Ethic in 12 Countries
7
emphasized in some models (Hackman & Oldham, 1980; Porter & Lawler, 1968) but not in
others (Adams, 1963; Herzberg, 1987). One construct that should not be overlooked is the
meaning of money.
It is beyond the scope of this paper to discuss all different meanings of money and
measures of money attitudes (see Furnham & Argyle, 1998). Among many perspectives in the
current literature on money, the one consistent thread in this body of work is “the emphasis on its
importance” (Mitchell & Mickel, 1999: 569). “Learning more about the relative importance of
pay to people is an imperative” (Heneman & Judge, 2000: 96). In an Academy of Management
Review article, Mitchell and Mickel have considered the Money Ethic Scale as one of the most
“well-developed” and systematically used measures of money attitude (1999: 571).
Tang and his associates have developed the Money Ethic Scale (MES) according to the
ABC (affective, behavioral, and cognitive) model of an attitude (Ajzen & Fishbein, 1977). The
MES reflects multi-dimensional constructs that tap on “the meaning of money”. Definition of
factors, test-retest reliability, Cronbach’s alpha, the nomological network of correlations, and
validity of the different versions of Money Ethic Scale can be found in the literature (e.g.,
Furnham & Argyle, 1998; Tang, 1992; Tang, Kim, & Tang, 2000). MES has been discussed in
Chinese, French, Italian, Spanish, Russian, and other languages (see Tang, Luna -Arocas, &
Whiteside, in press).
We will focus on cognitive Factors (Motivator, Success, Importance, and Rich) related to
the “importance” of money (Mitchell & Mickel, 1999: 569) in this study. (1) Factor Motivator
taps on the notion that money is a motivator (Gupta & Shaw, 1998). (2) Factor Success
represents people’s “obsession with money as a sign of success” (Furnham and Argyle, 1998, p.
148). “In America, money is how we keep score” and “income is used to judge success”
Money Ethic in 12 Countries
8
(Rubenstein, 1981: 34). (3) Factor Importance stresses that money is important. (4) Finally,
Factor Rich reflects that most people want to be rich and have a lot of money (i.e., Richins &
Rudmin, 1994). We will label these four “cognitive” constructs of MES, in plain English, as
“the love of money” in this paper. We will examine the paths of our model (Figure 1) below.
Income
à
Money Ethic
Rich or poor is a state of mind. People may be financially poor but psychologically rich
and vice versa. According to needs theories, satisfied needs are no longer important, whereas
unsatisfied needs are important (Alderfer, 1972; Maslow, 1970). Higher incomes are related to
lower marginal utility of money (Brandstatter & Brandstatter, 1996)(a negative Income à
Money Ethic path). People who have experienced financial hardship are obsessed with
money (Lim & Teo, 1997; Lynn, 1991) (a positive path). People in developing countries
consider money important due to the “newness” of having money and the availability of new
products and services in the markets (Tang, Furnham, & Davis, 2000).
Tang and his associates have found a non-significant Income à Money Ethic path among
fairly paid Spanish professors (Tang, Luna-Arocas, Tang, Homaifar, & Tang, 2001) and a
positive path for underpaid American professors (Tang, Sutarso, Tang, & Luna-Arocas, 2001).
Income influences Money Ethic more than vice versa. The Income à Money Ethic path is non-
significant for Caucasian and males but is positive for African-Americans and females who have
lower income than their counterparts (Tang, Beasley-Emele, Tang, & Tang, 2002). We assert
the Income à Money Ethic path can be positive, non-significant (neutral), and negative (in the
form of an inverted U) for low-, median-, and high-income people in the society. When positive,
non-significant, and negative paths among 12 countries are combined into one whole sample, the
overall path will be non-significant.
Money Ethic in 12 Countries
9
Hypothesis 1: The Income à Money Ethic path will be positive, neutral, and negative
for low-, median-, and high-income individuals, compared to the market in the society,
respectively. The same path will be non-significant for the whole sample.
Money Ethic
à
Pay Satisfaction
The two most widely known and used models of pay satisfaction are the equity model
(Adams, 1965) and the discrepancy model (Lawler, 1971) (Heneman & Judge, 2000). The
equity theory examines the Income à Equity Comparison à Pay Satisfaction relationship,
where the discrepancy model focuses on the difference in pay between expectation and reality
(Rice, Phillips, & McFarlin, 1990). People assess the adequacy of their rewards through a
process of social comparison. The value of a given reward is not absolute, but is relative to other
rewards with which it is compared. What is fair or just is open to interpretation. We argue that
the Money Ethic Scale reflects individuals’ “cognitive standards”, “frame of reference”, or
“expectation” of social comparison in judging pay satisfaction (Tversky & Kahneman, 1981). A
large discrepancy between “expectation” (the love of money) and reality will lead to high pay
dissatisfaction.
Hypothesis 2: Money Ethic will negatively influence pay satisfaction.
Pay Satisfaction
à
Organizational Commitment
à
Unethical Behavior
Commitment. Organizational commitment reveals employees’ identification with and
involvement in an organization (Mowday, Porter, & Steers, 1982; Meyer & Allen, 1997). Two
cross-sectional studies using structural equations modeling (SEM) found commitment to be a
consequence of job satisfaction (Williams & Hazer, 1986). “There is reciprocal and
synchronous causality between commitment and satisfaction, with satisfaction influencing
commitment more than vice versa” (Hom & Griffeth, 1995: 98, emphasis added). “To the
Money Ethic in 12 Countries
10
extent employees perceive their organization to be unfair because it uses unfair procedures for
resource allocations, employees will develop negative attitudes toward the organization (e.g.,
lower trust and commitment and greater anger)” (Cohen-Charash & Spector, 2001: 288).
Hypothesis 3: Pay satisfaction will positively influence organizational commitment.
Unethical behavior, corruption, and evil. Business leaders around the world suggest
that a more ethical business climate is the next step toward becoming a more integral part of the
global economy. Corruption Perceptions Index (CPI), published by Transparency
International (TI), reflects the degree to which corruption is perceived to exist among public
officials and politicians. It ranks 91 countries around the world. According to the CPI 2001,
“the new Index illustrates once more the vicious circle of poverty and corruption”
(http://www.transparency.org/documents/cpi/2001/cpi2001.html). Peter Eigen, Chairman of
Transparency International, asserts: “There is no end in sight to the misuse of power by those in
public office and corruption levels are perceived to be as high as ever in both the developed
and developing worlds”. The richest countries in the world (e.g., Finland, Denmark, New
Zealand, Iceland, Singapore, and Sweden), scored 9 or higher, have very low levels of perceived
corruption, whereas the world’s poorest, scored less than 5, have high levels of corruption.
A large percentage of financial loss, according to loss-prevention executives, is attributed
to employee theft (38.4%), shoplifting (35.6%), administrative error (19.4%), and vendor theft
(6.4%). Firms reported an average loss of US$142.49 per shoplifting, $737.31 per employee
theft, and $2,410 per armed robbery (Mathews, 1997). In the restaurant industry, internal theft
accounts for 7% to 10% of all sales losses and 75% of inventory shrinkage. In a paper entitled:
“stealing in the name of justice”, underpaid undergraduate subjects took (i.e., they stole) more
than they were permitted to take for their participation in a laboratory study, whereas
Money Ethic in 12 Countries
11
equitably/fairly paid participants did not (Greenberg, 1993). Employee theft is conceived as
resulting from the net strength of individual-, group-, and organizational-level forces that both
encourage and inhibit acts of taking (stealing) (Greenberg, 1998). “From a procedural justice
perspective, perceived injustice will lead to negative perceptions of the organization and, hence,
to counterproductive behaviors that will hurt the organization” (Cohen-Charash & Spector, 2001:
287). Dissatisfaction will lead to low commitment and low commitment will lead to high
unethical behavior. We propose that organizational commitment will mediate the pay
dissatisfaction-unethical behavior relationship. In this study, we will label unethical behavior
(i.e., abuse position (theft), abuse power (corruption), abuse resources (office supply), and take
no action for unethical behavior) as “evil”.
Hypothesis 4: Organizational commitment will reduce unethical behavior.
Ethical Culture
à
Organizational Commitment
Corporate culture has been defined as the assumptions, beliefs, goals, knowledge, and
values that are shared by organizational members (Deal & Kennedy, 1982). Corporate ethical
values help establish and maintain the standards that delineate the “right” things to do and the
things “worth doing”. All excellent companies have a well-defined set of shared values,
particularly ethical values (Peters & Waterman, 1982). Top-level managers can reduce the
ethical problems by discouraging unethical behavior. The attraction, selection, and attrition
process (Chatman, 1989) may cause individuals unable to fit into that culture to leave. There are
significant differences regarding the effect of organizational culture on ethical decision-making
across offices of different firms and across offices within the same firm (Jeffrey & Weatherholz,
1996). There is a strong link between corporate ethical culture and organizational commitment
(Hunt, Wood, & Chonko, 1989). Organizational ethical culture influences ethical behavior:
Money Ethic in 12 Countries
12
“Ethical culture affects individual values (i.e., idealism) and idealism affects judgments”
(Douglas, Davidson, & Schwartz, 2001: 111). Enhancing employees’ personal responsibility for
their actions (volition) helps establish and maintain their commitment to task and the
organization. We propose that ethical culture will enhance organizational commitment
(Hypothesis 5) and organizational commitment will deter unethical behavior (Hypothesis 4).
Hypothesis 5: Corporate ethical culture will enhance organizational commitment.
Money Ethic
à
Unethical Behavior
Our Hypotheses 1 to 4 present the indirect path from income, to Money Ethic, to pay
satisfaction, to commitment, and to unethical behavior. We now turn to the direct path and test
the commonly held belief: The love of money is the root of all evil. With more money, people
may experience the satisfaction of higher-order needs and enjoy a higher standard of living; they
simply have to make more money in order to maintain their life style, further improve their status,
and outperform others. Some people may have an unlimited hunger for more and more goods
and engage in a ceaseless pursuit of the good life through consumption. The love of money
escalates upward and becomes a moving target. It is the love of money that motivates people to
engage in unethical behavior.
Hypothesis 6: Money Ethic will positively influence unethical behavior.
Income
à
Unethical Behavior
It will be totally inappropriate for us to assume that low-income people will automatically
have a higher tendency to engage in unethical behavior than high-income people. We argue that
the love of money have a much stronger impact on unethical behavior (Hypothesis 6) than
income (money) itself. We predict that the Income à Unethical Behavior path will be non-
significant for the whole sample. However, due to the vicious circle of poverty and corruption,
Money Ethic in 12 Countries
13
low-income people are more obsessed with money and more likely to engage in unethical
behavior. There are many exceptions. In developing countries, high-income people may have
more opportunity to perform the unethical behavior than low-income employees, relatively
speaking, because only high-status people will have the opportunity to control resources, internal
and external contacts (e.g., customers, suppliers, and distributors), the power and authority, and
the opportunity to abuse their power and authority. In developing countries, the biggest problem
in doing business is corruption because “the rule of man” is much more powerful than “the rule
of law”. We will test our null hypothesis on an exploratory basis as follows.
Hypothesis 7: Income will have no impact on unethical behavior for the whole sample.
Gender. Research suggests that equity for males and equality for females do exist across
cultures (Tang, 1996; Tang, Furnham, & Davis, 2000). Women tend to rate social needs as more
important than do men, while men tend to consider pay more important than do women (Lawler,
1971). Women are subjectively satisfied with their pay in spite of objective underpayment, i.e.,
the paradox of the contented female worker (Crosby, 1982). Women have lower pay
expectations than men and have a tendency to be equally as satisfied as men with lower pay
(Smith, Kendall, & Hulin, 1969) or more satisfied than men with equivalent pay (Major & Konar,
1984; Sauser & York, 1978). We expect that men value money more than women.
Hypothesis 8: Men will have a stronger Money Ethic endorsement than women.
Job changes. The most important reason for voluntary turnover is higher wages/career
opportunity (Campion, 1991). “Leavers” tend to have “lower pay satisfaction” than stayers and
receive about 20% increases in pay on their new jobs. On average, an extra five years of service
with a company means earning 6% less. In order to avoid wage compression and to increase
their pay, many employees may be motivated to change their jobs. The number of job changes is
Money Ethic in 12 Countries
14
a significant predictor of management faculty's pay (Gomez-Mejia & Balkin, 1992). We assert
that, people with frequent job changes are concerned about their self-interest and income and
may have low organizational commitment that may lead to unethical behavior. Due to low pay
satisfaction, some leavers may try to “get even” by taking the advantages of their position and
power and engaging in unethical behavior before they change their jobs. Following this rationale
and Hypothesis 4, we explore the following prediction.
Hypothesis 9: Employees with frequent job changes are more likely to commit unethical
behavior.
METHODS
Participants
The senior author developed a survey questionnaire, identified researchers from the
membership directory of several professional organizations: (1) Academy of Management, (2)
Academy of Human Resource Development, (3) Society for Industrial and Organizational
Psychology, (4) International Association for Research in Economic Psychology, and (5)
International Association of Applied Psychology, contacted them by e-mail or in person, and
recruited researchers in approximately 47 countries to collect data for this cross-cultural project.
Researchers receive a 19-page package that contains a six-page research survey (instruction and
informed consent) and a 13-page instruction (items, factors, references, translation procedures,
etc.) as well as several published journal articles and conference papers.
This paper reports the first wave of data that were collected in 12 countries. All
researchers were asked to collect data from 200 full-time, white-collar employees in several large
corporations/organizations and/or MBA students who have full-time work experiences. The
combined sample has 2,338 participants, e.g., the US (n = 210), Belgium (201), Hong Kong
Money Ethic in 12 Countries
15
(203), Hungary (100), Macedonia (204), Malta (200), Oman (204), Philippines (200), Singapore
(203), South Africa (211), Taiwan (200), and Thailand (202). We will present our results using
the above order (the US and other countries arranged alphabetically). Our small convenience
sample does not represent a random sample (the average citizen) of the whole population (the
country).
Measures
Researchers in each country either adopt and modify the original survey or translate the
English version of the survey questionnaire to their own native languages using a multi-stage
translation-back-translation procedure (Brislin, 1980). Researchers in different countries with
the same language are encouraged to contact the lead researcher(s) of that language to facilitate
the translation and modification process and the senior investigator for clarification. Items have
been modified to fit the culture, language, religion, and context of different countries.
We collected data regarding participants’ demographic variables (age, sex (male = 1,
female = 0), work experience (in years), income (US$), the number of job changes), 58-item
Money Ethic Scale (cf. Tang, 1992) with strongly disagree (1), neutral (3) and strongly agree (5)
as anchors, 18-item Pay Satisfaction Questionnaire (PSQ) (Pay, Benefits, Raises, and Pay
Administration)(Heneman & Schwab, 1985) with very dissatisfied (1), neutral (3), and very
satisfied (5) as anchors, 15-item Organizational Commitment Questionnaire (Mowday, Steers, &
Porter, 1979), five-item Corporate Ethical Culture (Hunt, Wood, & Chonko, 1989), and 15-item
Unethical Behavior Tendency (cf. Tang & Weatherford, 1997) with very low probability (1),
average (3), and very high probability (5) as anchors. For our SEM analysis, we use two
indicators for Corporate Ethical Culture: Policy Rewarding Ethical Behavior (three items) and
Ethical Behavior (two items), two indicators for OCQ (Factor 1, nine items (commitment), and
Money Ethic in 12 Countries
16
Factor 2, six reverse scored items (not to leave) (cf. Mowday, Porter, & Steers, 1982), and four
indicators for unethical behavior: Abuse Position (theft, 5 items), Abuse Power (corruption, 5
items), Abuse Resources (office supply, 3 items), and Take No Action (look the other way, 2
items).
Participants complete the survey voluntarily and anonymously. The mean, standard
deviation, and correlations of all major variables for the whole sample are presented in Table 1.
The means of all major variables across 12 countries are presented in Table 2. The average
income in Thailand was US$73,777. After we remove one outlier case, the average income was
US$20,802. We analyzed the data using multivariate analysis of variance (MANOVA) for 20
variables across 12 countries. Results suggested significant differences in these 20 variables
across 12 countries: F = 16.50, p < .001, Wilks’ Lambda = .18, partial Eta squared = .15 (Table
2). Tests of between-subjects effects suggested that there were significant differences in all 20
variables across 12 countries. For subsequence SEM analyses, due to large differences across 12
countries, we transform income to z scores for participants within each country. We dropped age
and work experience from our SEM model due to non-significant findings.
We adopt GDP per Capita data (available for 143 countries) from CIA World Factbook
(http://www.photius.com/wfb1999/rankings/gdp_per_capita_0.html). We list participants’ self-
reported income and GDP per Capita for the 12 countries below (following the order of GDP
data from high to low): the US (self-reported income = US$34,661; GDP per Capita =
US$31,500; income/GDP ratio = 1.10), Singapore ($32,006; $26,300; 1.22), Hong Kong
($47,502; $25,100; 1.89), Belgium ($19,683; $23,400; .84), Taiwan ($22,839; $16,500; 1.38),
Malta ($14,923; $13,000; 1.15), Oman ($5,888; $7,900; .75), Hungary ($224; $7,400; .03),
South Africa ($5,946; $6,800; .87), Thailand ($20,802; $6,100; 3.41), Philippines ($4,018;
Money Ethic in 12 Countries
17
$3,500; 1.15), and Macedonia ($2,176; $1,050; 2.07). Compared to the GDP per Capita, we
have high-income participants in Hong Kong, Thailand, and Macedonia and low-income people
in Hungary.
The Money Ethic Scale. The senior author used existing factors, items, and the literature
and developed an expanded 58-item Money Ethic Scale (MES) specifically for this study. First,
we employed the exploratory factor analysis (EFA) using only US data (n = 210) and identified a
49-item, 14-factor Money Ethic Scale. For the present study, we select only four cognitive
factors (17 items) related to the importance of money (cf. Mitchell & Mickel, 1999): Factors
Importance, Success, Motivator, and Rich. Second, on the basis of preliminary US results, we
applied EFA to the whole sample (N = 2,338) using these 17 items and, again, identified a four-
factor structure: Factors Importance (5 items, Eigenvalue = 6.18, explained variance = 36.36%),
Success (4 items, 2.36, 13.87%), Motivator (4 items, 1.47, 8.67%), and Rich (4 items, 1.14,
6.69%). The Cronbach’s alphas were listed below: Importance (.80), Success (.84), Motivator
(.86), and Rich (.83). Then, we applied confirmatory factor analysis (CFA) using data from the
whole sample to analyze the four factors of MES (see Appendix 1).
According to Corruption Perception Index, the country rank and 2001 CPI score for each
country are listed below: Singapore (Rank = 4, Score = 9.2), Hong Kong (14, 7.9), the USA (16,
7.6), Belgium (24, 6.6), Taiwan (27, 5.9), Hungary (31, 5.3), South Africa (38, 4.8), Thailand (61,
3.2), and Philippines (65, 2.0). The country rank and CPI score for Macedonia, Malta, and
Oman are not available from the CPI list. Countries with a score of 5 or less, such as South
Africa, Thailand, and Philippines, are considered as high in corruption.
Money Ethic in 12 Countries
18
Data Analysis
Step one. We employ SPSS (10.1) and Amos (4.0) and follow the "two -step procedure
(Anderson & Gerbing, 1988; Williams & Hazer, 1986). We examine the psychometric
equivalence of all measures across 12 countries using CFA in a two-step process. Step 1 is a
simultaneous test of invariance among the 12 countries regarding the number of factors
underlying the factor structures. Step 2 is a simultaneous test of invariance in factor loadings
across the 12 samples. In this process, a model is estimated in which the chi-square parameters
are constrained to be equal among the 12 countries. If the chi-square change between Steps 1
and 2 is not significant, then, both factor structures and factor loadings are equivalent.
Amos provides several measures of fit between the model and the data: NFI, the Bentler-
Bonett’s normed fit index; RFI, Bollen’s relative fit index; IFI, Bollen’s incremental fit index;
TLI, the Tucker-Lewis index; CFI, Bentler’s comparative fit index; and RMSEA, root mean
square error of approximation. The comparative fit index (CFI) assesses the improvement in the
fit of a model relative to the baseline model. CFI is truncated to fall in the range from 0 to 1. If
CFI is greater than .90, it indicates a very good fit. For RMSEA, a value of about .05 or less
indicates a close fit of the model in relation to the degrees of freedom. In this study, we focused
mainly on the CFI because it prevents the underestimation of fit likely to occur in small samples.
Step two. We estimate a series of nested structural models using the sequential chi-
square difference tests (SCDTs) (Anderson & Gerbing, 1988). For a saturated structural model
(or a confirmatory measurement model, Model 1) (Ms), all parameters relating the constructs to
one another are estimated. For the null structural model (Mn), all parameters relating the
constructs to one another are fixed at zero. Model 2 (a structural model) represents all the paths
Money Ethic in 12 Countries
19
of researcher’s hypothesized model (theoretical model of interest, Mt) are fixed at zero. Model 3
reveals the hypothesized model (Mt).
Method Variance and Measurement Error in Path Analysis
Researchers have examined the biasing effects of “method variance” and “random
measurement error” in path analysis models. In the present application, the path from any
construct to its measured variable (indicator) equals the square root of the reliability of the
measured variable, while the amount of random error variable is the quantity one minus the
reliability (cf. Williams & Hazer, 1986). For example, the Cronbach’s alpha for Factor
Motivator of the Money Ethic Scale (MES) is .86 for the whole sample. We fix the path from
Money Ethic Scale to Factor Motivator to .93 and the path from random error to Factor
Motivator to .14. We apply this procedure for all indicators of all measures. When we compare
data across 12 countries simultaneously using the model, we calculate the Cronbach’s alpha for
each indicator of these constructs for all 12 countries.
RESULTS
Step One: Separate Confirmatory Factor Analyses
Results of confirmatory factor analysis (CFA) for the Money Ethic Scale showed that
there was a good fit between the model and our data for the whole sample (N = 2,338) (chi-
square = 863.92, df = 113, p < .01, comparative fit index (CFI) = .99, RMSEA = .05). We used
CFAs to analyze the psychometric equivale nce of the 12 countries in two steps. The Chi-square
change (565.53, df = 143, p < .001) between Step 1 (chi-square = 5951.76, df = 1356, p < .01,
CFI = .96, RMSEA = .04) and Step 2 (chi-square = 6517.29, df = 1499, p < .01, CFI = .96,
RMSEA = .04) was significant. In summary, we developed a model for the Money Ethic Scale
based on the US sample. We, then, replicated the model in the whole sample using EFA.
Money Ethic in 12 Countries
20
Results of CFA showed that there was a good fit between our model and our data for the whole
samp le. Further, the “factor structures” of MES were equivalent across 12 countries, whereas
“factor loadings” were not, reflecting possible cross-cultural differences in money attitudes.
We apply the same procedure for all measures and summarize the results (items, factor
loadings for the whole sample, and psychometric equivalence of the measure across 12 countries)
in Appendix 1. There was a good fit between our model and our data for the whole sample for
all measures (all CFIs were greater than or equal to .98). Regarding psychometric equivalence of
all measures across 12 countries, all CFIs were greater than .90. For all measures, the “factor
structures” were equivalent across 12 countries, but “factor loadings” were not.
Step Two: Structural Equations Analysis (The Whole Sample)
The difference between Model 1 (with all paths)(chi-square = 6,022.71, df = 156, p < .01,
CFI = .95) and Model 2 (chi-square = 6,678.82, df = 165, p < .01, CFI = .94) was significant
(chi-square change = 656.11, df = 9, p < .001). Model 2 (with hypothesized paths fixed at zero)
became significantly “worse” than Model 1. Thereby, Model 3 (with all hypothesized paths) was
quite significant (chi-square = 6,162.27, df = 160, p < .01, CFI = .95, RMSEA = .13) (see Figure
1). CFI was greater than .90, suggesting a good fit. Since 12 different cultures and 2,338
employees were combined into one whole sample, RMSEA suggested a poor fit (.13).
Income was not significantly related to Money Ethic (-.06, C.R. = -1.735)(Hypothesis 1,
Figure 1). (A path is significant, when C.R. is greater than or equal to 1.96.) The significant
Money Ethic à Pay Satisfaction path suggested that high Money Ethic (the love of money) led
to low pay satisfaction (-.13, C.R. = -5.034). Hypothesis 2 was supported. Pay Satisfaction
enhanced Organizational Commitment (.55, C.R. = 21.062), supporting Hypothesis 3.
Organizational Commitment deterred Unethical Behavior (-.28, C.R. = -10.189). Hypothesis 4
Money Ethic in 12 Countries
21
was supported. Corporate Ethical Culture also increased Organizational Commitment (.68, C.R.
= 7.897) (i.e., our Hypothesis 5). Money Ethic (the love of money) was positively related to
Unethical Behavior (evil) (.14, C.R. = 5.353). The love of money is the root of all evil,
supporting Hypothesis 6. Income was not related to Unethical Behavior (.01, C.R.
= .209)(Hypothesis 7). A significant Sex à Money Ethic path (.17, C.R. = 5.064) suggested
males considered money more important than females. Hypothesis 8 was supported. Finally,
people with more job changes tended to perform more unethical behavior (.12, C.R. = 3.944), i.e.,
our Hypothesis 9. Pay Satisfaction and Corporate Ethical Culture explained 76% of the variance
for Organizational Commitment. Commitment, Money Ethic, Income, and Job Changes together
explained 12% of the variance for Unethical Behavior.
----------------------------------------------------------
Insert Tables 1, 2, 3, and Figure 1 about here
----------------------------------------------------------
The regression weights from the Money Ethic to different factors, in order of importance,
were listed as follows: Factors Importance (.72), Rich (.68), Motivator (.62), and Success (.53).
Factor Importance seems to be the most important cognitive element of the Money Ethic Scale,
supporting the literature (Mitchell & Mickel, 1999). The construct of Pay Satisfaction was
related to Pay Administration (.83), Raises (.78), Benefits (.75), and Pay (.75). Corporate Ethical
Culture consisted of Policy Rewarding Ethical Behavior (.43) and Ethical Behavior (.38).
Organizational Commitment had two indicators: Factor 1 (.66) and Factor 2 (.61). Finally,
Unethical Behavior was related to Abuse Power (corruption)(.85), Abuse Position (theft)(.84),
Abuse Resources (office supply)(.59), and Take No Action (.58).
In summary, we have identified the following profound findings in this study. First, the
direct (Money Ethic à Unethical Behavior) path is significant. Second, the indirect path
(Money Ethic à Pay Satisfaction à Organizational Commitment à Unethical Behavior) is also
Money Ethic in 12 Countries
22
significant. This suggests: The love of money leads to low pay satisfaction that, in turn, causes
low organizational commitment that, in turn, enhanced unethical behavior. Therefore, Money
Ethic does have a significant direct and indirect impact on Unethical Behavior. We label
Money Ethic as “the love of money” and unethical behavior as “evil”. Then, we have strong
support for the notion that “the love of money is the root of all evil”. Second, Income is related
to neither Money Ethic nor Unethical Behavior. Thereby, money (income) is not the root of all
evil. Third, Ethical Culture does have an impact on Organizational Commitment. Fourth, men
are more obsessed with the love of money than women. Those with more job changes are more
likely to engage in unethical behavior.
Step Three: Structural Equations Analysis (Across 12 Countries)
In Step Three, we test the model, established in Step Two for the whole sample, across 12
countries simultaneously. Results showed a good fit between the model and our data (chi-square
= 10,588.38, df = 1920, p < .01, CFI = .93, RMSEA = .04). In fact, RMSEA (root mean square
error of approximation) has improved significantly from Step 2 (.13) to Step 3 (.04). When we
compared these 12 different cultures simultaneously, we found a better fit between our model
and data from 12 countries than between our model and the whole sample. Results of all major
paths and regression weights for each Factor (indicator) of major variables are presented below
(see Table 3).
Hypothesis 1 examines the Income à Money Ethic path. The Income à Money Ethic
path was positive in one country (Thailand), negative in three countries (Hong Kong, Hungary,
and Oman), and neutral in eight countries (the US, Belgium, Macedonia, Malta, Philippines,
Singapore, South Africa, and Taiwan). Compared to the GDP per Capita, we have high-income
participants in Hong Kong, Thailand, and Macedonia and low-income people in Hungary. Thus,
Money Ethic in 12 Countries
23
we expect to find a negative path for the former (three countries), a positive path for the latter
(one country), and a non-significant for eight countries. Our results support Hypothesis 1 in nine
out of 12 countries: a negative path (Hong Kong, Macedonia) and a non-significant path (the US,
Belgium, Malta, Philippines, Singapore, South Africa, and Taiwan). However, Thai people
(with high income) in this sample are obsessed with money (the love of money), suggesting the
newness of having money leads to the love of money in Thailand.
We found that those with the love of money have low pay satisfaction in five countries
(Hong Kong, Malta, Oman, Singapore, and Thailand) (Hypothesis 2). The opposite was true for
people in South Africa. The significant Pay Satisfaction à Organizational Commitment path
was found in all countries except South Africa (Hypothesis 3). For South Africans, the Amos’
model does not provide estimate of the path in the diagram (C.R. not significant). The
Cronbach’s alpha for all measures of the South Africa sample was the lowest among 12
countries. Researchers were present when participants completed the survey in several large
organizations in South Africa. These participants may perceive all these items differently within
one factor or change their response pattern significantly in answering the survey that causes low
internal consistency for variables and several missing paths in the model.
Hypothesis 4 predicted that organizational commitment reduces unethical behavior. This
path was supported by our data from seven countries (the US, Belgium, Hungary, Malta,
Philippines, Singapore, and Taiwan). Corporate Ethical Culture enhanced Organizational
Commitment in four countries (the US, Belgium, Hungary, and Taiwan) (Hypothesis 5). For
employees in Hong Kong, Malta, and Oman, the love of money is the root of all evil (Hypothesis
6). High-income Hungarians and low-income South Africans tended to commit unethical
Money Ethic in 12 Countries
24
behavior. Both Hungary (US$224) and South Africa (US$5,946) are less developed countries
and the latter has more income than the former. Hypothesis 7 was supported.
In the US, Belgium, Hong Kong, Hungary, Oman, Singapore, and South Africa, men
considered money more important than women (Hypothesis 8), while the reverse was true for
people in Macedonia and Thailand. Employees with high job changes tended to commit more
unethical behavior in Oman but not in any other countries (Hypothesis 9).
When we compared the four regression weights (Table 3) regarding Abuse Position,
Abuse Power (Corruption), Abuse Resources, and No Action, we found that the Unethical
Behavior à Abuse Power (Corruption) path was “the strongest” path among the four for the
whole sample and also for Philippines, South Africa, and Thailand. Our results indirectly
support the country rank and 2001 CPI score in that South Africa (Rank = 38, Score = 4.8),
Thailand (Rank = 61, Score = 3.2), and Philippines (Rank = 65, Score = 2.0) have scores less
than 5 and are considered as high in corruption.
In summary, when we examine 12 countries simultaneously, our results have partially
supported the different paths, proposed in the model. No single country has fully supported the
model and no single country has fully rejected the model. There are significant cultural
differences across these 12 countries.
DISCUSSION
Our results reveal that “factor structures” are equivalent across 12 countries, but “factor
loadings” are not for all these measures, reflecting possible cross-cultural differences. It is
possible that researchers may find cross-cultural equivalence in two countries, e.g., the US vs.
Canada (Johns & Xie, 1998). Riordan and Vandenberg (1994) have found equivalence using the
short nine-item Organizational Commitment Questionnaire (OCQ) between the US and Korea.
Money Ethic in 12 Countries
25
In this study, we have used the 15-item OCQ across 12 countries. However, due to culture
differences, it is almost impossible to find cross-cultural equivalence across 12 countries. This
will be a new challenge for researchers. Researchers may want to examine people in different
countries with the same language; the possible impacts of the culture may be investigated (after
the language is controlled). More research is needed for identifying culture-free (etic) and
culture-specific (emic) items and constructs in the future.
When we test our model using a large sample (N = 2,338), results provide a full picture
and strong support for our hypothesized paths. These results are better than the findings of these
12 cultures examined separately and simultaneously. Due to the combination of 12 cultures into
one whole sample, our RMSEA index is not as good as testing the same model using 12 separate
data sets. These findings are reasonably easy to understand and in the expected direction. A
large sample size with 12 different cultures has its strengths and weaknesses.
For the whole sample, income (money, an objective measure) and the love of money
(Money Ethic, a subjective measure) are two separate constructs. In plain English, one’s income
(i.e., rich or poor) is not related to “the love of money” (Money Ethic). Income is not directly
related to unethical behavior. However, the love of money (Money Ethic) is both directly and
indirectly related to unethical behavior. The indirect path suggests that the love of money leads
to low pay satisfaction that, in turn, undermines commitment, that, in turn, promotes unethical
behavior. Our results suggest: The love of money is the root of all evil, whereas money is not.
We analyze data from 12 countries simultaneously and find cross-cultural differences.
Income decreased the love of money in three countries (Hong Kong, Hungary, and Oman) but
increased the love of money in Thailand. Only Hong Kong employees have higher income than
GDP per Capita, thus, our Hong Kong results are in the predicted direction. For employees in
Money Ethic in 12 Countries
26
Thailand, the more money they have, the more they want. One possibility is that people in Hong
Kong, Hungary, and Oman do not experience “significant economic developments and changes”
in the society, whereas those in Thailand do. Thus, the “newness” of having money in a
developing economy (Thailand) may enhance the importance of money (Tang, Furnham, &
Davis, 2000). Due to these cultural differences, the Income à Money Ethic path fails to reach
significance for the whole sample.
In general, people who are obsessed with the love of money tend to experience a high
level of pay dissatisfaction, i.e., the discrepancy model of pay satisfaction. Results also seem to
support Herzberg’s notion that salary has more potency as a job dissatisfier than as a job satisfier
and money is a hygiene factor. High-income Hungarians and low-income South Africans are
more likely to engage in unethical behavior. Both Hungary and South Africa are low-income
countries, relatively speaking. For people in these two countries, there is a direct Income à
Unethical Behavior path. Thus, money does make a difference in unethical behavior for low-
income countries. In less developed countries (communist society and controlled market), high-
income Hungarians may have more “opportunity” to perform the unethical behavior than low-
income employees, relatively speaking, because only high-income people may have the
opportunity to control resources, internal and external contacts (e.g., customers, contractors), the
power and authority, and the opportunity to abuse their power and authority. These cultures may
actually encourage theft and corruption due to poor wages in the system. Thus, managers need to
pay employees as much as they can so that employees’ basic needs (hygiene needs) are satisfied.
Corporate ethical culture enhances organizational commitment in four countries. The
implication is that managers need to establish proper policy (code of ethics, ethics hotline) to
reward ethical behavior, punish unethical behavior, and use role models to promote ethical
Money Ethic in 12 Countries
27
behavior in organizations. Moreover, volition, enhancing employees’ personal responsibility for
their actions, helps establish and maintain their commitment to the organization.
On the other hand, the Corporate Ethical Culture à Organizational Commitment path is
not significant for eight countries (Hong Kong, Macedonia, Malta, Oman, Philippines, Singapore,
South Africa, and Thailand). The Commitment à Unethical Behavior path is not significant for
five countries (Hong Kong, Macedonia, Oman, South Africa, and Thailand). Hong Kong
employees have high income in our sample and should behave ethically. Our current findings
may be explained by the fact that Hong Kong, Kowloon, and the New Territories were all
returned back to China in July 1997. Since then, the culture of corruption from China (GDP per
Capita = $3,600, Corruption Perception Index Rank = 57, Score = 3.5) may have invaded Hong
Kong and forced employees to conform to the culture of corruption. Managers in these
countries may have limited influence in controlling employees’ unethical behavior. Researchers
and managers need to identify other constructs and avenues to enhance commitment and reduce
unethical behavior in countries with a culture of corruption. In Hong Kong, Malta, Oman,
Singapore, and South Africa, the love of money is the root of all evil.
Finally, people with many job changes tend to perform unethical behavior. Employees
with high turnover rate have higher concern over their income (self interest) than over their
organization. They may perform unethical behavior during their tenure or immediately before
they quite their jobs to avoid being caught. People with many job changes are questionable at
best. Nowadays human resource personnel are unwilling to provide accurate and/or negative
written reference letters. Hiring people who have many job changes (some may not provide full
employment records) will prove to be a challenge for managers in the future.
Money Ethic in 12 Countries
28
Employee theft is a $200 million-dollar a year problem in the US. Employees try to
redress grievances, seek revenge, or get even by “taking things” from a company when they
sense injustice and unfairness (Greenberg, 1993). In some organizations, managers implicitly
condone employee theft by looking the other way and consider it as “an invisible wage structure”
(Global Assignment Americans Abroad, http://www.globalassignment.com/4-12-01/corporate.
html) (Kilpatrick, 2002). In fact, some companies try to use this strategy to compensate
employees for their lower than average wages. It is a short-term solution that may cause a bigger
problem: “Taking” company materials, information, and intellectual properties may contribute to
many business failures. In many developing (low income) countries, their cultures may have
encouraged theft and corruption as “an invisible wage structure”. That may help us explain our
non-significant results regarding commitment, ethical culture, and unethical behavior in many
countries. Therefore, mangers need to consider the whole economic, political, and legal systems
in these countries when they encounter unethical behaviors in the global environment.
Employees’ unethical behavior is related to the perceptions of their pay, pay fairness
within the organization, and CEO’s pay. The most recent case of corporate corruptions shows
that many employees have lost their jobs and $1 billion deferred compensation and pension plan,
while former Enron chairman Kenneth Lay and his wife own over 30 million dollars worth of
real estates and stocks. CEO’s pay and ethical behavior set the tone for the whole organization.
In 1998, Michael Eisner (CEO of Walt Disney Company) made US$575,592,000, whereas the
average worker made $30,000. The pay differential (ratio) between Michael Eisner and the
average worker is 19,320 to 1 (Tang, Luk, & Chiu, 2000). This is also a significant culture-
specific issue in the US and some societies but not in others.
Money Ethic in 12 Countries
29
High-income people in Hong Kong have lower marginal utility of money. Under-paid
people (who have experienced financial hardship) tend to increase the love of money (or are
obsessed with money). It is the love of money that causes unethical behavior. Income is not
related to unethical behavior except in low-income countries (South Africa and Hungary).
On the basis of Factors Evil, Budget, Charity, Important, Motivator, and Success of the
Money Ethic Scale, researchers have identified four “money profiles” using cluster analysis:
Money Worshiper, Frugal Budgeter, Careless Handler, and Conscientious Steward. (Factors
Evil, Budget, and Charity are not examined in this study.) Conscientious Stewards (the highest
score on Charity (give money to charity) and the lowest scores on Important, Motivator, and
Success) tend to have the least job changes, the highest satisfaction (pay and benefits), and the
lowest tendency to commit any forms of unethical behavior. Frugal Budgeters (the highest on
Budget (careful money management) and the lowest on Evil (money leads to evil and unethical
acts)) have the highest satisfaction (raises and pay administration). People in other money
profiles, however, are prone to engage in unethical behaviors. For example, Careless Handlers
(the lowest scores on Budget and Charity) tend to change jobs most often and have the highest
tendency to Abuse Position and Resources. Money Worshipers (the highest on Evil, Important,
Motivator, and Success) tend to Abuse Power and Take No Action for unethical behavior. These
results seem to have important and direct implications for managers and researchers in the
management field. More research is needed in this direction.
Managers need to not only discourage but also prevent unethical behavior in
organizations. All employees are paid fairly regarding internal equity, external competitiveness,
and individual equity. Further, procedural justice and distributive justice must exist in
organizations (Cropanzano & Folger, 1996). Managers also need to focus on strategies to reduce
Money Ethic in 12 Countries
30
unethical behavior, such as: developing profiles of dishonest employees and using that (e.g.,
money profiles) in the personnel selection process, installing surveillance systems, and providing
training to employees to spot and catch unethical behaviors. Managers need to establish an
ethical culture and reinforce that culture by using role models and also reward and punishment in
order to reduce theft, shoplifting, and unethical behavior (Cohen-Charash & Spector, 2001).
Limitations. In this study, our full-time employees in 12 countries are not perfectly
matched and do not represent a random sample of the whole population of a nation or the
average citizens of the count ry. The sample size is also relatively small. There is no reason to
believe, however, that participants are atypical for survey research in these countries. Future
research may want to focus on matched samples for comparisons. We also have collected our
data from one source at one time. Thus, results may reflect the common method variance and
may not provide true cause and effect relationships. For all the measures in this study, developed
by theorists in the US, the “factor structures” are equivalent across 12 countries, but “factor
loadings” are not. Researchers may want to consider these possible limitations for using these
measures and identify culture-free (etic) and culture-specific (emic) items and constructs in
future cross-cultural research. Our unethical behavior measure reflects behavioral tendency or
intentions, and not “actual behaviors”; thus, our results should be interpreted in that light. We
have followed the procedure suggested in the literature regarding the biasing effects of “method
variance” and “random measurement error” in path analysis models. This is the fist attempt to
incorporate the meaning of money (Money Ethic) in studying unethical behavior. Managing
people’s Money Ethic endorsement serves as a new challenge for managers in human resource
management and organizational behavior. Our results are quite robust and unique in
understanding unethical behavior across cultures.
Money Ethic in 12 Countries
31
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Money Ethic in 12 Countries
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Table 1
Mean and Correlations of Major Variables for the Whole Sample
Variable M SD 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1. Age 35.35 9.60 .02 .11* .01 .27* -.01 .02 .01 .00 -.03 -.03 .00 -.03 .02 .01 -.04 .04 .01 .00 .04
2. Sex .54 .54 .07* .02 -.02 .02 .04* .05* .02 .08* .06* .07* .10* .03 .01 -.03 .06* .08* .04 .04
3. Experience 12.37 9.16 .06* .24* -.01 -.03 -.05* -.05* -.05* -.03 -.05* -.07* .09* .02 .02 -.00 -.08* -.15* .07*
4. Income US$ 18796.46 29804.21 .05* .00 .05* .02 .02 .02 .01 .04 .02 -.02 .02 -.05* .03 .03 .01 .05*
5. Job Changes 1.90 2.15 .01 .00 .02 -.01 -.15* -.05* -.00 -.09* -.07* -.06* -.14* .16* .12* .05* .08*
6. Importance (MES) 4.05 .62 .20* .39* .50* -.15* -.10* -.10* -.10* .04 .06* .05* -.02 .02 .04* .01
7. Success 2.76 .93 .45* .33* -.04* .02 .05* -.01 -.06* -.05* -.21* .16* .18* .14* .07*
8. Motivator 3.43 .88 .56* -.11* -.03 -.00 -.02 .03 .05* -.11* .07* .11* .14* .00
9. Rich 3.81 .77 -.17* -.11* -.10* -.05* -.00 .01 -.05* .06* .13* .12* .05*
10. Pay (PSQ) 3.04 .94 .62* .63* .64* .36* .12* .15* -.14* -.14* -.09* -.10*
11. Benefits 3.12 .91 .63* .57* .38* .11* .13* -.04 -.08* -.02 -.07*
12. Raises 2.91 .82 .64* .40* .09* .09* .03 -.05* -.03 -.06*
13. Pay Adm. 2.91 .72 .39* .13* .16* -.07* -.07* -.07* -.06*
14. OCQ 3.34 .54 .20* .26* -.12* -.21* -.18* -.09*
15. Policy 3.51 .87 .17* -.14* -.12* -.04* -.09*
16. Ethical 3.65 .97 -.32* -.29* -.19* -.26*
17. Abuse Position 1.45 .67 .72* -.51* .45*
18. Abuse Power 1.54 .65 .53* .47*
19. Abuse Resources 2.07 .85 .23*
20. No Action 1.63 .90
_________________________________________________________________________________________________________________________________
Note. Sex: Male = 1, Female = 0. N = 2,338. *p < .05.
Money Ethic in 12 Countries
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Table 2
Mean of Major Variables Across 12 Countries
Variable Whole US Belgium HK Hungary Macedonia Malta Oman Philippines Singapore S.Africa Taiwan Thailand
1. Age 35.35 36.42 37.81 30.90 32.72 41.60 36.91 29.65 33.48 33.32 46.37 34.84 34.08
2. Sex .54 .38 .61 .49 .58 .44 .50 .65 .82 .52 .38 .52 .53
3. Experience 12.37 14.31 16.34 8.83 10.39 17.29 14.02 8.14 10.30 10.42 17.28 11.38 10.96
4. Income US$ 18769 34661 19683 47502 224 2176 14923 5888 4018 32006 5946 22839 20802
5. Job Changes 1.90 2.66 2.50 2.02 1.36 .94 2.42 .62 1.5 8 1.39 4.52 2.35 1.74
6. MES-Importance 4.05 3.99 3.84 4.09 3.91 4.07 4.35 4.02 4.03 4.04 4.11 4.19 3.83
7. MES-Success 2.76 2.73 2.50 2.98 2.78 2.92 2.46 2.26 2.75 2.85 2.92 3.17 2.90
8. MES-Motivator 3.43 3.52 3.23 3.48 3.69 3.60 3.30 2.83 3.34 3.55 3.33 3.93 3.41
9. MES -Rich 3.81 3.67 3.39 4.08 3.71 3.90 3.91 3.71 3.65 3.73 4.06 3.86 3.91
10. PSQ-Pay 3.04 2.84 3.26 3.02 3.09 2.87 2.56 3.53 3.4 3 2.27 3.02 3.18 3.19
11. PSQ-Benefits 3.12 3.65 3.06 2.98 3.43 2.82 2.48 3.18 3.50 3.07 3.03 3.22 3.13
12. PSQ-Raises 2.91 2.90 2.90 2.89 3.13 2.65 2.45 2.97 3.38 3.13 2.84 3.08 3.02
13. PSQ-Pay Adm. 2.91 2.87 2.99 2.86 3.15 2.67 2.65 3.07 3.24 2.54 2.92 3.08 2.90
14. OCQ 3.34 3.37 3.26 3.35 3.45 3.55 3.05 3.53 3.55 3.36 3.12 3.36 3.20
15. EC-Policy 3.51 3.62 3.51 3.66 3.35 3.33 3.62 3.65 3.66 2.89 3.37 3.31 3.66
16. EC-Ethical 3.65 3.94 3.76 3.30 3.80 3.57 3.95 4.55 3.52 3.01 3.29 3.07 3.60
17. UB-Position 1.44 1.26 1.19 1.36 1.29 1.18 1.30 1.22 1.42 1.26 2.55 1.38 1.90
18. UB-Power 1.54 1.38 1.45 1.59 1.51 1.24 1.49 1.42 1.41 1.37 2.12 1.55 1.96
19. UB-Resources 2.07 2.03 1.97 2.45 2.52 1.71 2.08 1.98 1.81 2.48 2.22 2.27 1.95
20. UB-No Action 1.63 1.50 1.49 1.53 1.48 2.03 1.68 1.42 1.65 2.32 1.45 1.91 1.46
_________________________________________________________________________________________________________________________________
Note. Sex: Male = 1, Female = 0. MES: Money Ethic Scale. PSQ: Pay Satisfaction Questionnaire. OCQ: Organizational Commitment
Questionnaire. EC: Corporate Ethical Culture. UB: Unethical Behavior. Income in Thailand: M = 73,777. If we remove one outlier case,
M= 20,802. MANOVA: F = 16.50, p < .001, Wilks’ Lambda = .18, Partial Eta Squared = .15.
Money Ethic in 12 Countries
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Table 3
Results of Structural Equations Analyses: Regression Weights
Path (Regression Weights) Whole US Belgium HK Hungary Macedonia Malta Oman Philippines Singapore S.Africa Taiwan Thailand
Path:
Income à Money Ethic (Hypothesis 1) -.06 .04 -.09 -.28* -.33* -.13 -.03 -.22* .17 -.04 -.02 -.09 .22*
Money Ethic à Pay Satisfaction (H2) -.13* .03 .09 -.30* -.09 .00 -.25* -.35* .13 -.21* .55* -.13 -.20*
Pay Satisfaction à Commitment (H3) .55* .60* .22* .68* .56* .35* .70* .50* .74* .56* - .69* .60*
Commitment à Unethical Behavior (H4) -.28* -.26* -.22* -.04 -.23* -.12 -.34* -.18 -.33* -.19* - -.22* -.12
Ethical Culture à Commitment (H5) .68* .62* .81* .38 .64* - - .52 .92 .58 - .51* -
Money Ethic à Unethical Behavior (H6) .14* .00 .15 .28* .02 .04 .38* .33* .01 .29* .15* .14 .06
Income à Unethical Behavior (H7) .01 .01 -.07 -.14 .66* .17 -.13 -.14 .07 -.20 -.31* -.12 .15
Sex à Money Ethic (H8) .17* .88* .87* .50* .45* -.26* .11 .96* .04 .22* .25* .14 -.20*
Changes à Unethical Behavior (H9) .12* -.04 .01 .02 -.12 .08 .04 .25* -.13 .04 -.05 .01 .13
Regression Weights:
Money Ethic à Motivator . 62 .63 .73 .71 .70 .61 .57 .64 .61 .67 .52 .69 .67
Money Ethic à Success .53 .56 .63 .62 .59 .46 .51 .60 .51 .59 .31 .57 .58
Money Ethic à Importance .72 .79 .77 .75 .81 .65 .85 .75 .70 .82 .44 .73 .77
Money Ethic à Rich .68 .69 .67 .80 .77 .62 .74 .70 .67 .78 .40 .75 .69
Pay Satisfaction à Pay .75 .81 .76 .67 .75 .85 .84 .81 .87 .75 .47 .78 .79
Pay Satisfaction à Benefits .75 .65 .68 .66 .75 .89 .92 .87 .83 .75 .53 .82 .76
Pay Satisfaction à Raises .78 .82 .82 .76 .87 .89 .96 .86 .47 .86 .47 .81 .74
Pay Satisfaction à Adm. .83 .86 .85 .80 .84 .84 .78 .80 .78 .89 .64 .83 .88
Ethical Culture à Policy .43 .60 .57 .41 .76 .07 .08 .22 .18 .35 - .55 -
Ethical Culture à Ethics .38 .58 .49 .32 .70 .06 .08 .24 .14 .30 - .48 -
Commitment à Com1 .66 .75 .73 .63 .69 .55 .88 .68 .68 .78 - .79 .63
Commitment à Com2 .61 .69 .64 .76 .66 .51 .61 .58 .54 .72 - .73 .48
Unethical à Abuse Position .84 .87 .84 .82 .93 .79 1.00 .84 .89 .81 .61 .84 .91
Unethical à Abuse Power (Corruption) .85 .87 .57 .79 .76 .71 .71 .76 .95 .73 .75 .81 .93
Unethical à Abuse Resources .59 .55 .44 .54 .45 .40 .53 .47 .67 .53 .50 .51 .77
Unethical à No Action .58 .60 .34 .70 .55 .27 .56 .51 .62 .50 .40 .60 .76
Path: *p < .05. All regression weights are significant. -: Amos does not provide the estimate for the model.
Money Ethic in 12 Countries
42
Figure 1: A Theoretical Model of Unethical Behavior (Results for the Whole Sample)
Partially Mediated Model
.03
Money
Ethic
.38
Motivator
e21
.62
.79 .51
Importance
e23
.72
.70
.02
Pay
Satisfaction
.57
Pay
e31
.75
.66 .56
Benefits
e32
.75
.66 .61
Raises
e33
.78
.63 .69
Adm
e34
.83
e3
Income
e11
.50
Z Income
.71
.56
e2
Chi-Square = 6162.27
df = 160
p = .00
CFI = .95
NFI = .95
RFI = .94
IFI = .95
TLI = .94
.12
Unethical
Behavior
.33
No
Action
e64
.35
Abuse
Resources
e63
.73
Abuse
Power
e62
e6
.58
.82
.59
.81
.85
.52
Ethical
Culture
.19
Policy
e41
.14
Ethical
e42
.90
.38
.93
.46
Rich
e24
.74
.68
.43
.70
Abuse
Position
e61
.55
.84
.29
Success
e22
.85
.53
.76
Commitment
.37
Com2
e52
e5
.61
.43
Com1
e51
.79
Sex
e7
.50
Sex
.71
.17
Changes
e8
.50
Job .12
-.13
.68
.55-.06
.01
.71
.14 -.28
.66
.76
Money Ethic in 12 Countries
43
Appendix 1
Confirmatory Factor Analysis (CFA) for the Whole Sample and 12 Countries
___________________________________________________________________________
Item Factor Loading
___________________________________________________________________________
The Money Ethic Scale
Factor 1: Importance (Cronbach’s alpha = .80)
1. Money is important. .76
2. Money is valuable. .75
3. Money is good. .68
4. Money is an important factor in the lives of all of us. .61
5. Money is attractive. .57
Factor 2: Success (Cronbach’s alpha = .84)
6. My represents my achievement. .87
7. Money is a symbol of my success. .84
8. Money reflects my accomplishments. .81
9. Money is how we compare each other. .53
Factor 3: Motivator (Cronbach’s alpha = .86)
10. I am motivated to work hard for money. .84
11. Money reinforces me to work harder. .83
12. I am highly motivated by money. .79
13. Money is a motivator. .67
Factor 4: Rich (Cronbach’s alpha = .83)
14. Having a lot of money (being rich) is good. .78
15. It would be nice to be rich. .73
16. I want to be rich. .77
17. My life will be more enjoyable, if I am rich and have more money. .70
Note. For the whole sample: Chi-square = 863.92, df = 113, p = .00, CFI = .99, RMSEA
= .05. A simultaneous test of invariance among the 12 countries: Step 1, the number of
factors underlying the factor structures (chi-square = 5951.76, df = 1356, p < .00, CFI = .96,
RMSEA = .04). Step 2, factor loadings across the 12 samples (chi-square = 6517.29, df =
1499, p < .01, CFI = .96, RMSEA = .04). The chi-square change between Step 1 and Step 2
(Chi-square change = 565.53, df = 143, p < .001) was significant. The “factor structures” of
MES are equivalent across 12 countries, whereas “factor loadings” are not, reflecting
possible cross-cultural differences in money attitudes.
Money Ethic in 12 Countries
44
Pay Satisfaction Questionnaire
Factor 1: Pay (Cronbach’s alpha = .91)
1. Size of my current salary. .87
2. My overall level of pay. .86
3. My current salary. .85
4. My take-home pay. .78
Factor 2: Benefits (Cronbach’s alpha = .88)
5. The value of my benefits. .84
6. The number of benefits I receive. .82
7. My benefit package. .79
8. Amount the organization pays toward my benefits. .75
Factor 3: Raises (Cronbach’s alpha = .72)
9. How my raises are determined. .76
10. The raises I have typically received in the past. .71
11. My most recent raise. .61
12. Influence my supervisor has on my pay. .45
Factor 4: Pay Administration (Cronbach’s alpha = .83)
13. The organization's pay structure. .73
14. Differences in pay among jobs in the organization. .69
15. Information the organization gives about pay issues. .69
16. Consistency of the organization's pay policies. .65
17. How the organization administers pay. .64
18. Pay of other jobs in the organization. .60
Note. For the whole sample: Chi-square = 1890.09, df = 129, p < .01, CFI = .98, RMSEA
= .08. A simultaneous test of invariance among the 12 countries: Step 1, factor structures
(chi-square = 7483.63, df = 1548, p < .01, CFI = .95, RMSEA = .04). Step 2, factor loadings
(chi-square = 8018.73, df = 1702, p < .01, CFI = .95, RMSEA = .04). The chi-square change
between Step 1 and Step 2 (Chi-square change = 535.10, df = 154, p < .001) was significant.
Organizational Commitment Questionnaire
Factor 1 (Cronbach’s alpha = .84)
1. I am willing to put in a great deal of effort beyond that normally expected
in order to help this organization be successful. .53
2. I talk up this organization to my friends as a great organization to work for. .56
3. I would accept almost any type of job assignment in order to keep
working for this organization. .48
4. I find that my values and the organization's values are very similar. .64
5. I am proud to tell others that I am part of this organization. .76
6. This organization really inspires the very best in me in the way of job
Money Ethic in 12 Countries
45
performance. .65
7. I am extremely glad that I chose this organization to work for over
others I was considering at the time I joined. .67
8. I really care about the fate of this organization. .55
9. For me this is the best of all possible organizations for which to work. .70
Factor 2 (Cronbach’s alpha = .63)
10. I feel very little loyalty to this organization. .47
11. I could just as well be working for a different organization as long as
the type of work was similar. .16
12. It would take very little change in my present circumstances to
cause me to leave this organization. .39
13. There is not too much to be gained by sticking with this
organization indefinitely. .67
14. Often, I find it difficult to agree with this organization's policies
on important matters relating to its employees. .56
15. Deciding to work for this organization was a definite mistake on my part. .55
Note. For the whole sample: Chi-square = 1102.12, df = 89, p < .01, CFI = .98, RMSEA
= .07. A simultaneous test of invariance among the 12 countries: Step 1, factor structures
(chi-square = 4548.32, df = 1068, p < .01, CFI = .96, RMSEA = .04). Step 2, factor loadings
(chi-square = 5300.46, df = 1211, p < .01, CFI = .95, RMSEA = .04). The chi-square change
between Step 1 and Step 2 (Chi-square change = 752.14, df = 143, p < .001) was significant.
Corporate Ethical Values Scale (Cronbach’s alpha = .63)
1. Manager in my company often engages in behaviors that I consider
being unethical.
.22
2. In order to succeed in my company, it is often necessary to compromise
one’s ethics. .15
3. Top management in my company has let it be known in no uncertain
terms that unethical behaviors will not be tolerated. .52
4. 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. .82
5. 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. .71
Note. For the whole sample: Chi-square = 541.57, df = 5, p < .01, CFI = .98, RMSEA = .21.
A simultaneous test of invariance among the 12 countries: Step 1, factor structures (chi-
square = 1374.63, df = 115, p < .01, CFI = .96, RMSEA = .07). Step 2, factor loadings (chi-
square = 773.01, df = 104, p < .01, CFI = .98, RMSEA = .05). The chi-square change
between Step 1 and Step 2 (Chi-square change = 601.02, df = 11, p < .001) was significant.
Two indicators: Items 1 and 2: (Cronbach’s alpha = .64). Items, 3, 4, and 5: (Cronbach’s
alpha = .72)
Money Ethic in 12 Countries
46
The Unethical Behavior Tendency
Factor 1: Abuse Position (Cronbach’s alpha = .86)
1. Take merchandise and/or cash home. .83
2. Abuse the company expense accounts and falsify accounting records. .81
3. Borrow $20 from a register overnight without asking. .77
4. Overcharge customers to increase sales and to earn higher bonus. .71
5. Call in sick while fishing or playing golf. .66
Factor 2: Abuse Power (Cronbach’s alpha = .81)
6. Sabotage the company to get even due to unfair treatment. .75
7. Accept money, gifts, and kickback from others. .71
8. Reveal company secrets when a person offers several million dollars. .71
9. Give merchandise away and do not charge it to the customer. .63
10. Lay off 500 employees to save the company money and increase my
personal bonus. .61
Factor 3: Abuse Resources (Cronbach’s alpha = .67)
11. Waste company time surfing on the Internet, playing computer games,
and socializing. .71
12. Make personal long-distance (mobile phone) calls at work. .67
13. Use office supplies (paper, pen), Xerox machine, and stamps for
personal use. .48
Factor 4: Take No Action (Cronbach’s alpha = .81)
14. Take no action for shoplifting by customers. .85
15. Take no action for employees who steal cash/merchandise. .80
Note. For the whole sample: Chi-square = 1058.43, df = 84, p < .01, CFI = .99, RMSEA
= .07. A simultaneous test of invariance among the 12 countries (Factors 1 and 2 combined):
Step 1, factor structures (chi-square = 6790.26, df = 1044, p < .01, CFI = .92, RMSEA = .05).
Step 2, factor loadings (chi-square = 7680.44, df = 1176, p < .01, CFI = .91, RMSEA = .05).
The chi-square change between Step 1 and Step 2 (Chi-square change = 890.18, df = 132, p
< .001) was significant.
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