2006 MOR LOM&PaySat MeasInvariance

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Management and Organization Review (2006) 2:3 423–452 The Love of Money 1

The final version of this paper was publish in Management and Organization

Review (2006), 2 (3), 423-452.

The Love of Money and Pay Level Satisfaction:

Measurement and Functional Equivalence in 29Geopolitical Entities around the World

Thomas Li-Ping Tang and Toto Sutarso, USA; Adebowale Akande, South Africa;Michael W. Allen, Australia; Abdulgawi Salim Alzubaidi, Oman; Mahfooz A. Ansari,Malaysia;Fernando Arias-Galicia, Mexico; Mark G. Borg,Malta; Luigina Canova,Italy; Brigitte Charles-Pauvers, France;Bor-Shiuan Cheng, Taiwan; Randy K. Chiu,Hong Kong;Linzhi Du, China; I lya Garber, Russia;Consuelo Garcia De La Torre, Mexico, Rosario Correia Higgs, Portugal; Chin-Kang J en, Taiwan; Ali MahdiKazem, Oman; Kilsun Kim, South Korea; Vivien Kim Geok L im, Singapore; RobertoLuna-Arocas, Spain; Eva Malovics, Hungary;Anna Maria Manganelli, Italy; Alice S.Moreira,Brazil; Anthony Ugochukwu Obiajulu Nnedum, Nigeria; J ohnsto E.Osagie,USA; AAhad M. Osman-Gani, Singapore; Francisco Costa Pereira, Portugal;Ruja Pholsward, Thailand; Horia D. Pitariu,Romania;Marko Polic,Slovenia;Elisaveta Sardzoska, Macedonia; Allen F. Stembridge, USA; Theresa Li-Na Tang,USA ; Thompson Sian Hin Teo, Singapore; Marco Tombolani, Italy;Martina Trontelj, Slovenia; Caroline Urbain, France;Peter Vlerick, Belgium

ABSTRACTDemonstrating the equivalence of constructs is a key requirement for crossculturalempirical research. The major purpose of this paper is to demonstrate how toassess measurement and functional equivalence or invariance using the 9-item, 3-factorLove of Money Scale (LOMS, a second-order factor model) and the 4-item, 1-factor PayLevel Satisfaction Scale (PLSS, a first-order factor model) across 29 samples in sixcontinents (N =5973). In step 1, we tested the configural, metric and scalar invarianceof the LOMS and 17 samples achieved measurement invariance. In step 2, we appliedthe same procedures to the PLSS and nine samples achieved measurement invariance.Five samples (Brazil, China, South Africa, Spain and the USA) passed the measurementinvariance criteria for both measures. In step 3, we found that for these two measures,common method variance was non-significant. In step 4, we tested the functional

equivalence between the Love of Money Scale and Pay Level Satisfaction Scale. Weachieved functional equivalence for these two scales in all five samples. The results of this study suggest the critical importance of evaluating and establishing measurementequivalence in cross-cultural studies. Suggestions for remedying measurementnonequivalence are offered.

KEYWORDS the love of money, pay level satisfaction, measurement invariance,functional equivalence, cross-cultural empirical research, 29 geopolitical entities




The Love of Money and Pay Level Satisfaction: Measurement and FunctionalEquivalence in 29 Geopolitical Entities around the World

INTRODUCTIONMoney is the instrument of commerce and the measure of value (Smith,1776/1937). For the past several decades, theimportanceof money has been recognizedin the literature. For example, only 49.9 percent of freshman in 1971 said that theimportant reason in deciding to go on to college is “to make more money”. That numberincreased to 75.1 percent in 1993 ( The American Freshman, 1994). Men ranked pay thefifthand women ranked pay theseventhin importance, among 10 job preferences in theUS (Jurgensen, 1978). Among 11 work goals, pay was ranked the second in importancein Belgium, the UK, and the US and the first in Germany (Harpaz, 1990). In Hong Kongand China, most Chinese prefer cash among 35 components of compensation, i.e., thecash mentality (Chiu, Luk, & Tang, 2001). Thelack of moneyhas become the number

one cause of dissatisfaction among university students on campus (out of 10 causes) forthe past seven years (1997-2003), up from the third and the second place of two earlierperiods (1990-1996, 1981-1987, respectively) (Bryan, 2004). People in the US andaround the world are keenly aware of the importance of money.

Money has been used to attract, retain, and motivate their employees and achieveorganizational goals around the world (Milkovich & Newman, 2005). Researchers andmanagers have great interests in compensation, money, and pay satisfaction inorganizations because pay dissatisfaction has “numerous undesirable consequences”(Heneman & Judge, 2000: 77), such as: turnover (Hom & Griffeth, 1995; Tang, Kim, & Tang, 2000), low commitment, counterproductive behavior (Cohen-Charash, & Spector,2001; Luna-Arocas & Tang, 2004), and unethical behavior (e.g., Tang & Chen, 2005;

Tang & Chiu, 2003). Moreover, it has been suggested in the pay satisfaction literaturethat “one construct that should not be overlooked is the meaning of money” (Barber &Bretz, 2000: 45) because the meaning of money can be used as the “frame of reference”(Tang, 1992) in which people examine their everyday lives, such as pay satisfaction(Tang & Chiu, 2003; Tang, Luna-Arocas, & Sutarso, 2005).

There are several studies using the Love of Money Scale (LOMS) to examine themeaning of money and other work-related attitudes and behaviors in different cultures(e.g., Du & Tang, 2005; Tang & Chen, 2005; Tang & Chiu, 2003; Tang, Luna-Arocas, &Sutarso, 2005). However, the measurement invariance/equivalence (MI/E) of the Love of Money Scale across different cultures is unknown and hasnotbeen systematicallyexamined. According to Riordan and Vandenberg (1994), it does little good to test atheoretical and conceptual relationship across cultures “unless there is confidence that themeasures operationalizing the constructs of that relationship exhibit both conceptual andmeasurement equivalence across the comparison groups” (p. 645). Researchers should“systematically examine the assumption of stable and transferable measurementcontinua” and should not take the measurement invariance for granted (Riordan &Vandenberg, 1994, p. 666). This study is an initial step in the cross-cultural validation of the Love of Money Scale using these new techniques of measurement invariance(Cheung, 2002; Riordan & Vandenberg, 1994; Vandenberg & Lance, 2000).




The major purpose of this study is to examine the configural and metricinvariance of the Love of Money Scale (LOMS, Tang & Chen, 2005; Tang & Chiu,2003) across 30 convenience samples of full-time employees and managers in 29geopolitical entities from six continents around the world. Most of the geopoliticalentities are nation-states. We use the term geopolitical entity and sample interchangeably

and treat data from China (PRC), Hong Kong, and Taiwan as independent samples. Inorder to establish the “functional equivalence” (Cavusgil & Das, 1977) and thenomological network of this measure, we will also examine the configural and metricinvariance of the Pay Level Satisfaction of the Pay Satisfaction Questionnaire (PSQ,Heneman & Schwab, 1985) and investigate the relationship between the Love of Moneyand Pay Level Satisfaction across samples. We will define the constructs, review theliterature, discuss the issues of measurement invariance, develop a model, and propose ahypothesis to test the model below.

THEORETICAL BACKGROUND AND LITERATURE REVIEW

Money and the Love of Money There is a spirited debate on money. On one hand, money is a hygiene factor(Herzberg, 1987). “People do work for money—but they work even more for meaning intheir lives” (Pfeffer, 1998: 112). On the other hand, money is a motivator (Gupta &Shaw, 1998; Kohn, 1993; Locke, Feren, McCaleb, Shaw, & Denny, 1980). Althoughmoneyhas been useduniversallyaround the world, the meaning of money, however, is“in the eye of the beholder” (Tang, 1992).

Among many studies of the meaning of money, Mitchell and Mickel (1999) haveconsidered “the Money Ethic Scale” (MES, Tang, 1992) as one of the most “well-developed” and systematically used measures of money attitude in their article publishedin theAcademy of Management Review. Tang and his associates have investigated the

meaning of money based on the ABC model of an attitude with affective, behavioral, andcognitive components and research suggested in the literature (e.g., Furnham & Argyle,1999; Opsahl & Dunnette, 1966; Wernimont & Fitzpatrick, 1972). They have developedseveral versions of multidimensional Money Ethic Scale (MES) and the Love of MoneyScale (LOMS, a subset of MES). Researchers have cited the Money Ethic Scale (MES)and the Love of Money Scale (LOMS) and published their findings in Chinese (e.g., Du& Yue, 2002; Du, Xu, & Tang, 2004), English (Furnham & Argyle, 1999; Mitchell &Mickel, 1999), French (Urbain, 2000), Italian (Tang, 1996), Spanish (Luna-Arocas, 1998;Luna-Arocas, & Tang, 1998; Quintanilla, 1997), Romanian (Tang & Weatherford, 2005),Russian (Fenko, 2000), and other languages.

This study investigates the Love of Money Scale (LOMS) and provides therationale below. First, the Money Ethic Scale (e.g., the 58-item-14-factor MES, Tang & Tang, 2002) is too long to be practical in empirical studies. The crux of the matterregarding the meaning of money is the love of money. Thus, we would like to simplifythe measure, focus on the basic core of the meaning of money, and propose a short,simple, specific, and easy-to-use construct that is developed based on suggestions in theliterature and is thoroughly grounded in theory. Second, researchers attempting to modelrelationships among a large number of latent variables have found it difficult to fit suchmodels to even predictions with strong theoretical support (Joreskog & Sorbom, 1986). In



Management and Organization Review (2006) 2:3 423–452 The Love of Money

order to decrease the number of indicators used in the model (achieve the need forparsimony), yet maintain the estimation of measurement error given by using multiple-item indicators; researchers must reduce the number of items and constructs to amanageable level (e.g., the 9-item-3-factor LOMS, Tang & Tang, 2003), or to useparcels. Using parcels (raw item responses combined into sub-scales) may have

detrimental effects on tests of measurement invariance of factor loadings (Meade &Kroustalis, 2005). Third, researchers have recognized the importance of using the Loveof Money Scale (a subset of MES) in a series of studies. Let us summarize selectedresearch findings briefly below.

Research on the Love of Money ScaleFirst, researchers have examined measurement invariance of the Love of Money

Scale across gender and college major (law, sociology, and political science) amongChinese students (Du & Tang, 2005), across college major (business vs. psychology)among American students (Chen & Tang, 2005; Tang & Chen, 2005), across gender andculture (the US vs. Spain) among professors (Tang, Luna-Arocas, & Sutarso, 2005), and

across 26 geopolitical entities among full-time employees (Tang & Tang, 2003). Second,besides measurement invariance, the Love of Money is related to other work-relatedbehaviors and tendencies. Mental health professionals with high Love of Money havehigh income and high voluntary turnover 18 months later, regardless of their intrinsic jobsatisfaction (Tang et al., 2000). Among university students in the US, the Love of Moneyis directly related to Evil (Tang & Chiu, 2003; Tang & Tang, 2003) and indirectly relatedto Evil through Machiavellianism (the Love of MoneyMachiavellianism Evil) forBusiness students, but not for Psychology students (Tang & Chen, 2005).

Third, the following studies are related to pay satisfaction and may provide directimplications for the present study. American professors experience pay dissatisfactionbecause they use the Love of Money to judge Pay Equity Comparison and Pay

Satisfaction, while Spanish professors do not (Tang et al., 2005). The Love of Money isnegatively related to Pay Satisfaction (the Love of Money Pay Satisfaction, -.27,p<.01) among Hong Kong professionals using a structural equation model (SEM) (Tang &Chiu, 2003). The Love of Money mediates the Income to Pay Satisfaction relationship(Income the Love of Money Pay Satisfaction) among American and Spanishprofessors (Tang, Luna-Arocas, Sutarso, & Tang, 2004). In summary, the Love of MoneyScale (LOMS) is more direct and effective than the Money Ethic Scale (MES) in makingtheoretical and empirical contributions to the management and organization literatureacross cultures. The time is ripe to examine the issue of measurement invariance of theLove of Money Scale across cultures. We now turn to the love of money construct itself.

What is the Love of Money? The first question a scientific investigator must ask is not “How can I measure it?”

but rather, “What is it” (Locke, 1969: 334)? First of all, we trace the inspiration to studythe love of money construct in the literature. It shows the importance of the “rich”construct as related to “the love of money” and “evil” as well as the relationship betweenthe love of money and pay satisfaction, i.e., the major focus of this study: “People whowant to get rich fall into temptation and a trap and into many foolish and harmful desires




that plunge men into ruin and destruction. For the love of money is a root of all kinds of evil” (Tang & Chiu, 2003).

Researchers have defined the love of money (LOM) as (1) one’s wants, “desires”(Sloan, 2002), and values of money, (2) one’s attitudes toward money (Tang, 1992), (3)one’s meaning of money (Mitchell & Mickel, 1999), (4) not one’s needs, greed (Sloan,

2002), nor materialism (Belk, 1985; Richins & Rudmin, 1994; Tang, Luna-Arocas, &Quintanilla, 2001), (5) a multi-dimensional individual difference variable with affective,behavioral, and cognitive components (Tang, 1992), and (6) the combined notion of several sub-constructs or factors (e.g., Du & Tang, 2005; Luna-Arocas & Tang, 2004; Tang & Chen, 2005; Tang & Chiu, 2003; Tang, Luna-Arocas, & Sutarso, 2005). We willdefine the Love of Money construct, ahigher-order (latent) construct, using threeselected latent sub-constructs: Rich, Motivator, and Important. We will discuss eachconstruct below. See also the left side of Figure 1.

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

Rich. Theaffectivecomponents of an attitude examine one’s love and/or hateorientations, feelings, or emotions. Factor Rich is the affective component of the Love of Money construct. Following the aforementioned rationale (Tang & Chiu, 2003), wespeculate that most people love money and very few hate money. If one loves money,one wants to have a lot of money. That leads to one’s desire to get rich. Being rich isgood. Rich is better than poor. Very few want to be poor. We use the notion that “peoplewant to get rich” to define the Rich construct that, in turn, is the most importantcomponent of the Love of Money construct. Empirical research shows that Factor Richhas the highest factor loading of the Love of Money Scale and the Love of Money isdirectly and indirectly related to Evil (Tang & Chen, 2005; Tang & Chiu, 2003).

Motivator. The behavioral component refers to how one intends or expects to acttoward someone or something. Regarding improving performance in organizations, “no

other incentive or motivational technique comes even close to money” (Locke, Feren,McCaleb, Shaw, & Denny, 1980: 381). Money is a strong motivator for people (e.g.,Stajkovic & Luthans, 2001). When people are motivated to work hard for money, theyare likely to take actions in order to achieve their goals. Money is a motivator for some.

Important. The cognitive component of an attitude examines the most importantbeliefs or ideas one has about an object or situation. Money is important to people in theUS and around the world, (Jurgensen, 1978; Harpaz, 1990; Bryan, 2004). Themostconsistent threadof the money attitude literature is the “emphasis on its importance”(Mitchell & Mickel, 1999, p. 569). The importance of money is formed early in one’schildhood and maintained in adult life (Furnham & Argyle, 1998). It is probablyinfluenced by the way one was raised, the social and economic background, and work-related experiences. Most people work for money for the most part of their lives (e.g., 40hours/week for about 40 years). Factor Important stresses that money is important. Wenow turn to the construct of pay satisfaction.

Pay Level Satisfaction




Job satisfaction and pay satisfaction may be defined as “a pleasurable or positiveemotional state resulting from the appraisal of one’s job or job experiences” (Locke,1976, p. 1300). These topics have been two of the most popular topics in managementand organization research (Iaffaldano & Muchinsky, 1985; Spector, 1997) with anestimated 3,350 research articles (or dissertations) published in 1976 (Locke, 1976) and

5,000 in 1992 (Cranny, Smith, & Stone, 1992). Pay satisfaction is only a part of jobsatisfaction. On November 16, 2005, we checked the primary index for businessliterature, ABI/Inform, and found 9,065 references on job satisfaction and 129 on paysatisfaction and the world’s literature in psychology and related fields, PsycINFO andfound 19,844 on job satisfaction and 155 on pay satisfaction. It is beyond the scope of this study to review all the research in the literature.

The two most widely known and used models of pay satisfaction are the equitymodel (Adams, 1965) and the discrepancy model (Lawler, 1971) (Heneman and Judge,2000). “The idea that net satisfaction is a function of the perceived discrepancy or gapbetween what one has and wants is at least as old as the stoic philosophy of Zeno of Citium around 300 B.C.” (Michalos, 1985, p. 348). The pay discrepancy model focuses

on the difference between “expectation” and “reality” in pay (e.g., Crosby, 1982; Rice,Phillips, and McFarlin, 1990; Sweeney, McFarlin, and Inderrieden, 1990). The equitymodel of pay satisfaction depends on the comparison of the person’s outcome-input ratioto the outcome-input ratio of a comparison other and predicts that the greater thesimilarity of the ratios; the greater the person’s pay satisfaction (e.g., Adams, 1965;Heneman & Judge, 2000).

Money attitudes may play a role when one considers pay satisfaction. Forexample, children from poor economic background tend to over estimate the size of acoin and place greater importance on money than those from rich families (Bruner &Goodman, 1947). There is a Chinese expression: The raising tides lift all boats. Very fewpeople feel that they are rich enough. When one’s income increases, one raises the bar

and changes the standard. The more money they have, the more they want it, up to apoint. It has been argued that money (salary) is a hygiene factor because satisfaction withmoney goes back to zero and the zero point escalates (Herzberg, 1987; Tang, Luna-Arocas, & Sutarso, 2005). Those who with high love of money (harmful desires) may benever satisfied with their income. Thus, the love of money will be negatively related topay satisfaction (Tang & Chiu, 2003). These models of pay satisfaction will help us toexamine the relationship between the love of money and pay level satisfaction.

There are many multidimensional measures of job satisfaction (e.g., theMinnesota Satisfaction Questionnaire, MSQ, Weiss, Dawis, England, Lofquist, 1967; the Job Descriptive Index, JDI, Smith, Kendall, & Hulin, 1969; and Job Satisfaction Survey, JSS, Spector, 1985). Other researchers focused specifically on pay satisfaction, e.g., themultidimensional Pay Satisfaction Questionnaire (PSQ, Heneman & Schwab, 1985) thathas four factors: pay level, benefits, pay raises, and pay structure and administration. Thegeneric term pay satisfaction may be something of a “misnomer” (Heneman & Judge,2000: 64).

“The consistency of the pay level-pay satisfaction relationship is probably themost robust (though hardly surprising) finding regarding the causes of pay satisfaction”(Heneman & Judge, 2000: 71). Actual pay level (income) is consistently and positivelyrelated to pay satisfaction. The magnitude of the relationship varies from study to study,




with correlations ranging from .13 to .46. It makes intuitive sense that the higher the paylevel (income), the higher the pay satisfaction. It is easy to understand the relationshipsof pay level and pay satisfaction because both are dealing with the same domain, i.e., pay. This study will investigate specifically the Pay Level Satisfaction that is only one of thefour components of the 18-item-4-factor Pay Satisfaction Questionnaire (PSQ, Heneman

& Schwab, 1985). This study differs from previous studies and examines the Love of Money and Pay Level Satisfaction relationship (the Love of Money Pay LevelSatisfaction).

The Love of Money to Pay Level Satisfaction Relationship The Love of Money reflects individuals’ “standards”, “frame of reference”

(Tversky & Kahneman, 1981), or “expectation” of pay that will be used in judging paysatisfaction (Tang et al., 2005). If money is important to them, they may pay moreattention to and are constantly aware of others’ pay in the society (Pfeffer & Langton,1993). There is a Chinese saying: He who compares with others gets upset and is angered

to death. If one has high love of money, then, one expects to have a large output (pay) for

one’s work (the equity theory), or high “expectation” for one’s pay (the discrepancytheory). This leads to a lower output/input ratio compared to others (referents) or a largegap between the expectation and the reality. Both theories predict that those with highlove of money will have high pay dissatisfaction. Whoever loves money never has moneyenough and is never satisfied with his income. Empirical research suggests that there is asignificant and negative path (-.27) from the Love of Money to Pay Satisfaction (Tang &Chiu, 2003). We assert: The Love of Money will be negatively related to Pay LevelSatisfaction.

Hypothesis 1: The Love of Money will be negatively related to Pay LevelSatisfaction.

Measurement InvarianceResearchers have become increasingly interested in measurement invariance due

to recent advances in analytic tools, measurement theories, and significant increases of studies that test management theories using psychological measurements acrossgeopolitical entities (e.g., Vandenberg & Lance, 2000). There are nine steps involved inmeasurement invariance: (1) an omnibus test of equality of covariance matrices acrossgroups, (2) a test of “configural invariance”, (3) a test of “metric invariance”, (4) a test of “scalar invariance”, (5) a test of the null hypothesis that like items unique variances areinvariant across groups, (6) a test of the null hypothesis that factor variances wereinvariant across groups, (7) a test of the null hypothesis that factor covariances wereinvariant across groups, (8) a test of the null hypothesis of invariant factor means across

groups, and (9) other more specific test. Vandenberg and Lance (2000) concluded that“tests for configural and metric invariancewere most often reported” (p. 35, emphasisadded).

Following their suggestions, this paper reflects a “modest” attempt to address onlytwo of nine steps: configural and metric invariance. Configural invariance exists whenthe same factor structures are identified across all groups. Metric invariance is achievedwhen all factor-loading parameters are equal across groups using a multi-groupconfirmatory factor analysis (MGCFA). Full metric invariance israrely achieved in




cross-cultural research (Vandenberg & Lance, 2000). Four strategies may deal with itemsthat are not metric invariant: (1) ignore the non-invariance because the comparison of data is not meaningful, (2) eliminate non-invariance items from the scale, (3) invokepartial metric invariance that allows the factor loading of non-invariant items to vary, and(4) interpret the source of non-invariance (Poortinga, 1989; Cheung, 1999). We will

discuss these strategies in this paper.Recent research has examined measurement invariance at the scale level, thefactor level, and the item level (e.g.,Cheung, 2002; Cheung & Rensvold, 1999, 2002). Onone hand, non-invariance maynotbe undesirable because items may provide the specificinformation about the nature of differences across groups (Cheung, 2002) due todifferences in culture, value, religion, GDP per Capita, economic development,demographic variables, and familiarity with survey questionnaire developed in the US.An excellent item should be able to detect these cross-cultural differences acrossgeopolitical entities. On the other hand, in order to achieve measurement invariance andobtain a cultural-free (etic) scale, researchers may eliminate several samplesor itemswithsignificant factor loading differences (Cheung, 2002).

The issue of measurement invariance is not limited to only cross-cultural studies.Researchers have investigated measurement invariance across gender (Eagle, Miles, &Icenogle, 2002), income level, profession (Idaszak, Bottom, & Drasgow, 1988; Tang,Moser, & Austin, 2002), employment status (full-time vs. part-time) (Tang, Kim, & Tang, 2002), experimental treatments (Chan & Schmitt, 1997), sources of performanceratings (Maurer, Raju, & Collins, 1998), and different time periods in longitudinalresearch (Riordan, Richardson, Schaffer, & Vandenberg, 2001). Due to limited space,this study will not examine measurement invariance across demographic variables.

This study will examine configural (factor structures) and metric (factor loadings)invariance of the 9-item-3-factor Love of Money Scale and the 3-item-1-factor Pay LevelSatisfaction across 30 samples. We will examine the metric invariance at the scale level,

factor level, and also item level and predict that both scales will achieve configural andmetric invariance across the majority of samples or geopolitical entities.

METHODSParticipants

The senior author recruited researchers in approximately 50 geopolitical entitiesthrough personal friends, contacts, or networking at professional conferences of theAcademy of Management, Academy of Human Resource Development, InternationalAssociation for Research in Economic Psychology, International Association of AppliedPsychology, and Society for Industrial and Organizational Psychology. Researchersreceived a 19-page package involving a six-page survey (informed consent and items)and instructions (references, websites, translation procedures). He asked collaborators tocollect data from 200 full-time white-collar employees/managers in large organizations.

We received 31 samples from 30 geopolitical entities (N =6659) in the period of December 2002 to January 2005. We selected 29 samples of full-time employees (N =5973) and eliminated a duplicate sample from Singapore and a student sample fromChina. Our convenience samples may not represent the whole population or the averagecitizens of the geopolitical entities. On average, participants in this study were 34.70years old (SD =9.92) with 50% male and had 15.46 years of education (SD =3.26).




Table 1 shows the sample size, the basic demographic information and the means andstandard deviations of the two measures for each of these 29 samples.

MeasuresResearchers in each geopolitical entity organized small focus groups and

translated the English version to their own native languages using a multi-stagetranslation-back-translation procedure (Brislin, 1980). We collected data regardingparticipants’ demographic variables (age, sex (male =1, female =0), education (inyears), income (US$)), 58-item Money Ethic Scale (MES, Tang & Tang, 2002) withstrongly disagree(1), neutral (3) andstrongly agree(5) as anchors, the 18-item-4-factorPay Satisfaction Questionnaire (PSQ, Heneman & Schwab, 1985) with stronglydissatisfied(1), neutral (3) andstrongly satisfied(5) as anchors, and many othermeasures. Participants completed the survey voluntarily and anonymously. For thepurposes of this paper, we selected the 9-item-3-factor Love of Money Scale (Tang &Chen, 2005) from the MES and the 4-item-1-factor Pay Level Satisfaction from PSQ. Allitems are presented in Table 2.

Evaluation CriteriaResearchers have recommended the following most rigorous criteria for

evaluating (1) configural invariance: χ 2/df <3.0, Tucker-Lewis Index, TLI >.95,

comparative fit index, CFI >.95, the standardized root mean square residual, SRMSR <.08, root mean square error of approximation, RMSEA <.06 and (2) metric invariance:(a) chi-square change (Δ χ

2/Δdf ) and (b) practical fit index change (i.e., ΔCFI, if Δ = .01

or less: differences between models do not exist) (Cheung & Rensvold, 2002; Hu &Bentler, 1999; Vandenberg & Lance, 2000). A lower value of chi-square indicates abetter fit and should be non-significant. However, for large sample sizes, this statisticmay lead to rejection of a model with good fit (Schumacker & Lomax, 1998). Models

with many variables and degrees of freedom will almost always have significant chi-squares. Vandenberg and Lance (2000) have suggested that researchers are (1) tointerpret the chi-square test of model fit only in conjunction with other practical fitindices, (2) to select a variety of practical fit indices to supplement the chi-square test andnot just one, and (3) to use four indices (e.g., TLI, RNI, RMSEA, and SRMR) because nosingle index emerging as one to embody all trade-offs (p. 43). Both the SRMSR and theRMSEA are referred to as absolute fit indices. Due to the large number of samples withdiverse language, culture, religion, economic development, income, ability to answer aLikert-type survey, and practical considerations, we will retain a sample if it satisfies ¾of the four most rigorous criteria (TLI >.95, CFI >.95, SRMSR <.08, and RMSEA <.06).1 These criteria are more rigorous than those used by many researchers in theliterature (Epitropaki & Martin, 2004; Srivastava, Locke, & Bartol, 2001).

RESULTS

Step 1: Reliability Alpha and the Measurement ModelFirst, we examined the reliability alpha for each of these 30 samples. For Factor

Rich, Cronbach’s alpha varied between .73 and .94, except Bulgaria (.64), France (.18),and South Africa (.46). For Factor Motivator, alpha varied between .74 and .93, except




Nigeria (.69) and South Africa (.38). For Factor Important, alpha varied between .70 and.93, except Bulgaria (.04), France (.63), Russia (.66), and South Africa (.27).

Second, we examined two separate models using the whole sample: (1) a 9-item-3-factor model ( χ

2 =201.06,df =24, p<.001, χ 2/df =8.38, TLI =1.00, CFI =1.00,

SRMSR =.02, RMSEA =.03) and (2) a 9-item-1-factor model ( χ 2 =7321.89, df =27, p<

.01, χ

2

/df =271.18. TLI =.93, CFI =.96, SRMSR =.11, RMSEA =.21). We adopted the9-item-3-factor model because it passed all four most rigorous criteria and wassignificantly better (Δ χ

2=7321.89 - 201.06 =7,120.83, Δdf =27 – 24 =3, p < .001, ΔCFI

=1.00 - .96 =.04) than the one-factor model. We concluded that there was a good fitbetween our model and our data from 30 samples around the world (N=6,175). FactorRich had the highest factor loading (.89), followed by Factors Important (.68) andMotivator (.64). We conducted additional analyses below.

Step 2: Configural invariance For configural (factor structures) invariance, we examined the fit between the

measurement model of the Love of Money Scale and data from each sample separately

and repeated the procedure 30 times. Table 3 shows the results for each sample: χ

2

, df , p, χ 2/df , TLI, CFI, SRMSR, and RMSEA. First, we will apply the four most rigorous criteria

(i.e., TLI >.95, CFI >.95, SRMSR <.08, and RMSEA <.06). If a sample failed onecriterion, we highlighted the sample (underlined). If a sample failed two criteria, weeliminated the sample (printed inbold*). Results suggested that 16 samples failed to passthe RMSEA criterion. We eliminated only 2 samples (Malta and Nigeria) out of 30 in thisanalysis.

Second, in order to identify the possible reasons for the non-invariance, we usedexploratory factor analysis (EFA) to examine the data from Malta and Nigeria. For thesample in Malta, Item 3 (see Table 2) had strong factor loadings for Factors Rich (.855)and Important (.420); Item 6 was strongly related to Factors Motivator (.755) and Rich

(.456); and Item 9 was strongly associated with Factors Important (.757) and Rich (.432).For the sample in Nigeria, Item 6 was negatively related to Factor Important (-.401) andwas not related to Factor Motivator that had only two items. It appears that items withrelatively high cross factor loadings and items associated with non-intended first-orderfactor may be the main cause of non-configural invariance in Malta and Nigeria. Insummary, our data in 28 samples survived the analyses using the most rigorous criteria.

Step 3: Metric invariance We examined metric (factor loadings) invariance by performing MGCFA based

on data from 28 geopolitical entities at the “scale” level (9-item-3-factor LOMS). Thedifferences between the unconstrained MGCFA ( χ

2 =1443.02, df =672, p<.01, χ 2/df =

2.15, TLI =.99, CFI =1.00, SRMSR =.06, RMSEA =.01) and the constrained MGCFA( χ

2 =2106.85, df =834, p<.01, TLI =.99, CFI =.99, SRMSR =.06, RMSEA =.02) wassignificant based on chi-square change (Δ χ

2=663.83, Δdf =162,p<.05), but notsignificant based on fit index change (ΔCFI = .01, n.s.). Full metric invariance is rarelyfound in cross-cultural management research (Vandenberg & Lance, 2000). Due to non-significant fit index change, we have achieved both configural and metric invariance, ingeneral, and may stop our analysis here. We conducted further analyses to advance ourunderstanding of the measurement scale.




Step 4: Partial Metric Invariance at the “Factor” LevelWe now investigated the scale at the “factor” level. We compared the results of

the unconstrained 28-country MGCFA with three separate partially constrained 28-country MGCFAs in that only one factor of the three-factor model was constrained while

allowing the other two factors to vary. That is, we compared the unconstrained model ( χ

2

=1443.02, df =672,p<.01, χ 2/df =2.15, TLI =.99, CFI =1.00, SRMSR =.06, RMSEA

=.01) with (1) Factor Rich constrained ( χ 2 =1764.17, df =726,p<.01, χ

2/df =2.43, TLI=.99, CFI =.99, SRMSR =.06, RMSEA =.02), (2) Factor Motivator constrained ( χ

2 =1546.78, df =726, p<.01, χ

2/df =2.13, TLI =.99, CFI =1.00, SRMSR =.06, RMSEA =.02), and (3) Factor Important constrained ( χ

2 =1689.20, df =726, p<.01, χ 2/df =2.33,

TLI =.99, CFI =.99, SRMSR =.06, RMSEA =.02). Please note that all these resultspassed the most rigorous criteria. Further, we achieved partial metric invariance based onfit index change but not on chi-square change for Factor Rich constrained (Δ χ

2=321.15,Δdf =54, p < .001; ΔCFI = .01), Factor Motivator constrained (Δ χ

2= 103.76, Δdf =54, p

< .001; ΔCFI = .00), and Factor Important constrained (Δ χ 2= 246.18, Δdf =54, p<.001;

ΔCFI = .01), r espectively. Factor Rich was the major source of non-invariance since ithad the largest chi-square change, whereas Factor Motivator had the smallest chi-squarechange and was the best Factor of the scale from the measurement invariance perspective.

Next, we constrained two factors of the scale while allowing the third factor tovary: (1) Factors Motivator and Important constrained (Factor Rich unconstrained) ( χ

2 =1793.26, df =780, p<.01, χ

2/df =2.30, TLI =.99, CFI =.99, SRMSR =.06, RMSEA =.02), (2) Factors Rich and Important constrained (Factor Motivator unconstrained) ( χ

2 =2005.18, df =780, p<.01, χ

2/df =2.57, TLI =.99, CFI =.99, SRMSR =.06, RMSEA =.02), and (3) Factors Rich and Motivator constrained (Factor Important unconstrained) ( χ

2 =1865.55, df =780,p<.01, χ

2/df =2.39, TLI =.99, CFI =.99, SRMSR =.06, RMSEA=.02). Again, all these results passed the most rigorous criteria. We achieved partial

metric invariance based on fit index change but not on chi-square change for Factor Richunconstrained(Δ χ 2=350.24, Δdf =108,p < .001; ΔCFI = .01), Factor Motivator

unconstrained(Δ χ 2= 562.16, Δdf =108,p < .001; ΔCFI = .01), and Factor Important

unconstrained(Δ χ 2=422.53, Δdf =108,p < .001; ΔCFI = .01), respectively. Relatively

speaking, when Factors Rich and Important were constrained, we found the largest chi-square change(Δ χ

2=562.16). When Factors Rich and Motivator were constrained, wefound the second largest chi-square change(Δ χ

2=422.53). Factor Rich was the cause of non-invariance.

Step 5: Partial Metric Invariance at the “I tem” L evelFurther, Factor Rich has three items. We applied the same procedure and

examined partial metric invariance at the “item” level in three partially constrained 28-country MGCFAs in that only one item of the model was constrained while allowing theother eight items to vary. We compared the unconstrained model ( χ

2 =1443.02, df =672,p<.01, χ

2/df =2.15, TLI =.99, CFI =1.00, SRMSR =.06, RMSEA =.01) with Item 3 of Factor Rich constrained ( χ

2 =1596.87, df =699, p<.01, χ 2/df =2.28, TLI =.99, CFI =

.99, SRMSR =.05, RMSEA =.01) and found the largest chi-square change. In order toidentify the differences in factor loadings between or among samples for all items, weanalyzed data across samples at the item level using theZtest in Step 6.




Step 6: Additional Analysis at the “I tem” Level Using theZ testAccording to Cheung (2002), theZtest can be used to determine the significant

difference of parameter estimates. When comparing factor loading across groups, theZ statistic is defined as:

2ˆ

2ˆ

)2()1(

)2()1(

ˆˆ

ii

SS

ii

λ λ

λ λ

+

− (1)

where the factor loadings are estimates in the unconstrained model and the parentheticalnumber in superscript denotes the group number. Further, theZtest is superior to theLikelihood Ratio (LR) test because it is computationally simpler (Cheung, 2002). TheZ statistic for all pair-wise comparisons can be calculated from the parameter estimates,standard errors, and the asymptotic covariance matrix of the unconstrained model.

We examined nine (9) items of the LOM scale across 28 samples. We calculated378 pair-wise comparisons (i.e., n (n-1)/2, n =the number of samples) for each item and atotal of 3,402 pair-wise comparisons for nine items (i.e., 378 x 9). To obtain a balance

between Type I and Type II errors, we will adopt the alpha value of .0000146 (alpha =.05/3,402) for each pair-wise comparison. This translates into a critical Zvalue of 4.18 (http://math.uc.edu/~brycw/classes/148/tables.htm). The goal of this analysis is to provethat the null hypothesis of no difference in parameter estimates is correct. Our extremecorrection described above benefits the goal of this research by making it very difficult toreject the null hypothesis. Using the critical value mentioned above only protects theoverall Type I error, but not the Type II error. Therefore, the procedure used to adjust thealpha level may be conducive to making a Type II error (i.e., incorrectly concluding thatthe null hypothesis of measurement invariance is correct). We use Item 3 (having a lot of money (being rich) is good) of Factor Rich as an example.

Item 3 of Factor Rich. What is invariant with respect to one marker item can be

non-invariant with respect to another marker item. To simplify the procedure, weinvestigated Item 3 of Factor Rich (having a lot of money (being rich) is good) across 28samples using only Item 1 as the “marker item” (factor loading fixed at 1 in our model,see also Figure 1). By using a spreadsheet, we input the factor loading parameterestimates (first row of Table 4, L), standard errors (second row of Table 4, S) of theunconstrained model of Item 3 across 28 samples, apply the formula (1) above, andcalculate aZtest for each of the pair-wise comparisons.

We applied the critical Zvalue of 4.18 (Cheung, 2002) and found 23 significantdifferences (i.e., 6.08%) in factor loading parameter estimates among 378 pair-wise comparisons (Table 4, printed inbold). In general, Belgium (.547, the first row of Table4), Hungary (.459), Oman (.651), and Spain (.711) had lower factor loading parameter

estimates, relatively speaking, whereas Bulgaria (1.098), China (1.383), Egypt (1.033),Italy (1.153), Philippines (1.283), Singapore 1 (1.198), South Korea (1.284) and Taiwan(1.267) had higher factor loading parameter estimates. Belgium had a lower factorloading parameter estimate than Bulgaria (4th row of Table 4, Z=-4.286,), China (Z =-5.351), Egypt (-4.454), Italy (-5.587), Philippines (-4.536), Singapore 1 (-5.219), andSouth Korea (-5.377). China had higher factor loading parameter estimate than Slovenia(4.185) and Spain (4.485), but was not significantly different from other Asian samples,

http://math.uc.edu/~brycw/classes/148/tables.htm







e.g., Hong Kong, Singapore 1, Singapore 2, Philippines, South Korea, Thailand, and Taiwan. Readers may follow the same procedure and examine other significant results.

Item 1 of Factor Rich. We also picked Item 1 of Factor Rich for further analysisbecause Item 1 (1) starts with the word “I” and is an item with the “I orientation”, i.e., “Iwant to be rich”, (2) may provide additional and meaningful findings, and (3) can be used

for comparison with Item 3 discussed above. We applied the same procedure andperformed additional analyses for Item 1 using Item 2 as the “marker item”. Due to spacelimitations, we will not present detailed findings in this paper (available upon requestfrom the first author). There were 14 significant pair-wiseZtest results. Australia had amuch stronger factor loading parameter estimate (1.448) than, for example, China (.833,Z=4.394), Malaysia (.835, 4.368), South Korea (.766, 5.312), and Taiwan (.777, 5.209).

Strategies With the above results, we now consider the four strategies of dealing with non-

invariance, mentioned earlier. First, we may ignore the significant factor loadingdifferences and conclude that the comparison of data is not meaningful. Second, we may

delete the Item 3 of Factor Rich from the scale that causes the most non-invariance.Alternatively, if we keep Item 3 and eliminate six samples (i.e., Belgium, China,Hungary, South Korea, Spain, and Taiwan), then, we have no significant differences infactor loading across 22 samples (28 - 6 =22) using the 9-item-3-factor LOMS. Third,we may invoke partial metric invariance, which constrains the factor loading of invariance factors or items to equality across samples and allows the factor loading of anon-invariant Factor (Step 4, Factor Rich) or a non-invariant item to vary (Steps 5 and 6).Fourth, we may interpret the source of non-invariance and offer our presentations below:Australia had a much higher factor loading than China, Malaysia, South Korea, and Taiwan for Item 1 (“I want to be rich”), whereas Belgium had much lower factor loadingthan China, Philippines, Singapore 1, and South Korea for Item 3 (“having a lot of money

(being rich) is good”). This reflected the culture differences of these samples.Research suggests that when the “individual self” is the center of the respondents’psychological field for items of a scale (“I orientation”), people respond to the sameitems differently based on their collectivistic or individualistic values (Riordan &Vandenberg, 1994; Tang, Furnham, & Davis, 2003). Regarding Individualism (Hofstede& Bond, 1988; Yu & Yang, 1994), Australia and Belgium rank high (ranks =2, 8), Chinaand South Korea rank medium (29) and low (43), respectively. On one hand, people inAustralia may have a much stronger concern over themselvesand items with an “Iorientation” that causes a much stronger factor loading than those in collectivisticcultures. On the other hand, people in China and South Korea donot focus onthemselves, or boast their achievement and financial success to others in public,according to the Confucius doctrine (Tang, Furnham, & Davis, 2000). For these people,the notion, “I want to be rich”, isnotacceptable, while the notion, “having a lot of money(being rich) is good”, is acceptable. Our findings support the theory regarding the “Iorientation” and culture (Riordan & Vandenberg, 1994). Thus, non-invariant items (e.g.,Items 1 and 3 of Factor Rich) maynotbeundesirablebecause they might provide usefulinformation about the nature of differences across samples examined in this study (e.g.,Cheung, 1999). We concluded that we do not want to eliminate these items, nor samples.




_________________________________________________

Insert Tables 1, 2, 3, 4, 5, 6, 7 and Figures 2 and 3 about here _________________________________________________

Pay Level Satisfaction ScaleIn order to establish “functional equivalence” of the Love of Money Scale, we

incorporated a meaningful measure, the 4-item-1-factor Pay Level Satisfaction of the PaySatisfaction Questionnaire (PSQ). The reliability of the Pay Level Satisfaction, based ondata for the whole sample (N=6,175), was .903. We calculated alpha for each of the 30samples and found that alpha varied between .712 and .962 and was higher than .90 in 22samples. The only exception was South Africa (alpha =.266).Configural invariance. Table 5 shows the results of configural (factor structures)invariance of the Pay Level Satisfaction Scale for each sample. Using the most rigorouscriteria, we eliminated two samples, Nigeria and Oman, and retained 28 samples out of

30. Again, we employed exploratory factor analysis (EFA) and found that there were twofactors for the 4-item Pay Level Satisfaction in Nigeria, the cause of non-invariance.Metric invariance. We examined metric (factor loadings) invariance by performingMGCFA based on data from 28 geopolitical entities (N=5,771) at the “scale” level. Thedifferences between unconstrained MGCFA ( χ

2 =218.40,df =56, p<.01, χ 2/df =3.90,

TLI =.99, CFI =1.00, SRMSR =.003, RMSEA =.02) and constrained MGCFA ( χ 2 =

472.86, df =137, p<.01, χ 2/df =3.45, TLI =.99, CFI =1.00, SRMSR =.0081, RMSEA

=.02) was significant based on chi-square change (Δ χ 2=254.46, Δdf =81, p<.05), but

not significant based on fit index change (ΔCFI = .00, n.s.). Due to non-significant fitindex change, we have achieved both configural and metric invariance, in general, andstop our analysis here. We now turn to the relationship between the Love of Money and

Pay Level Satisfaction.

The Love of Money and Pay Level SatisfactionNigeria was the only sample that failed configural invariance for both the Love of

Money Scale and the Pay Level Satisfaction scale. Next, we examined the relationshipbetween the love of money and pay level satisfaction using the 27 samples (N=5,571)that passed measurement invariance criteria for both measures (with Malta, Nigeria, andOman removed) ( χ

2 =355.73,df =61, p<.01, χ 2/df =5.83, TLI =1.00, CFI =1.00,

SRMSR =.02, RMSEA =.03). Figure 2 showed that the love of money was negativelyrelated to pay level satisfaction (-.09, C.R. =- 5.502, p<.001), supporting ourhypothesis. Factor Rich had the highest factor loading (.87), followed by FactorsMotivator (.68) and Important (.68).

We investigated the same model for each of these 27 samples and presented theSEM results and the path (the Love of Money Pay Level) in Table 5. In summary, theLove of Money to Pay Level Satisfaction path was significant and negative for 9 samples(i.e., Australia, Bulgaria, Hong Kong, Italy, Portugal, Russia, Singapore-1, Slovenia, and Thailand), supporting our Hypothesis, but positive for one sample (Brazil).

Finally, multivariate analysis of variance (MANOVA) results showed that therewere significant differences in Factors Rich, Motivator, Important and Pay Level across




27 samples (F (104, 21789) =14.462, p <.001, Wilks’ Lambda =.767, partial eta square=.064). We investigated the average Love of Money score (the average of Factors Rich,Motivator, and Important) across 27 samples using a one-way analysis of variance(ANOVA) (F (26, 5544) =23.083, p<.001). Results of Tukey HSD post hoc testsrevealed that these 27 samples were divided into three distinctive groups. Group 1 had

the highest Love of Money scores (Taiwan (4.02), South Korea (3.97), Malaysia (3.93),Macedonia (3.86), Singapore-2 (3.847), the US (3.846), Hong Kong (3.82), Singapore-1(3.80), Hungary (3.79)). Three samples, Bulgaria (3.78), Romania (3.75), and Russia(3.73), fell between Group 1 and Group 2 and were also lower than Taiwan and SouthKorea. Group 1 had scores significantly higher than Group 2: Philippines (3.71), SouthAfrica (3.69), Thailand (3.68), France (3.592), China (3.585), Australia (3.583), Egypt(3.57), Peru (3.55), and Mexico (3.49). Group 3 had the lowest scores: Brazil (3.45),Spain (3.40), Belgium (3.37), Portugal (3.36), Slovenia (3.34), and Italy (3.22).

Following the same procedure, people’s pay level satisfaction can be also groupedinto three distinctive groups (F (26, 5595) =14.48, p<.001). The first Group had thehighest pay level satisfaction, Philippines (3.44), Egypt (3.37), Belgium (3.30),

Singapore-2 (3.26), Thailand (3.19), Singapore-1 (3.17), Australia (3.14), Spain (3.12),and Malaysia (3.12). Group 2 had 12 samples: Peru (3.08), Hungary (3.05), Italy (3.04), Taiwan (3.03), South Korea (3.027), Hong Kong (3.00), Mexico (2.97), Slovenia (2.93),Macedonia (2.87), France (2.86), the US (2.83), Russia (2.76). Four samples fell betweenGroup 2 and Group 3: China (2,72), Portugal ( 2.70), Brazil (2.68), and Bulgaria (2.64).Group 3 had the lowest scores: Romania (2.56) and South Africa (2.28).

DISCUSSION AND CONCLUSIONIn this paper, we examine the 9-item-3-factor Love of Money Scale (LOMS)

among full-time employees in 30 geopolitical entities around the world. Our majortheoretical and empirical contributions of this paper can be listed below. First, we find a

good fit between our 9-item-3-factor Love of Money Scale (LOMS) and our wholesample and good reliability for the measure. In Step 2, we focus on configural and metricinvariance of the scale across 30 samples. Twenty eight (28) samples survived and twosamples failed (Malta and Nigeria) our configural invariance analysis based on the mostrigorous criteria. Third, metric invariance was achieved based on fit index change, but notachieved based on chi-square change for all 28 samples. Then, partial metric invariance isfurther analyzed at the factor level (Step 4) and at the item level (Step 5). In order toidentify the differences in factor loading parameter estimates across samples for all items,we apply theZtest in Step 6.

Factor Rich is the affective component of the Love of Money Scale, shows one’semotions and value-laden orientation, and is the most critical component of LOMSbecause those who want to berichmay fall into temptation and engage in unethicalbehavior or evil (Tang & Chen, 2005; Tang & Chiu, 2003). Item 3 (having a lot of money(being rich) is good) is the worst item of Factor Rich, from the measurement invarianceperspective. The original intent of this item is to focus on the notion of having a lot of money. Since it is an item of Factor Rich, the wording “being rich” becomes a part of theitem. It is plausible that “having a lot of money” (material possession) and “being rich”(mental state) may provide different meanings to some people in some geopoliticalentities.




Moreover, it is interesting to know that the wording of an item, such as the “Iorientation” in Item 3 of the Love of Money Scale—“I want to be rich”, may causepeople to respond to the same items differently based on their collectivistic orindividualistic values (Riordan & Vandenberg, 1994; Tang, Furnham, & Davis, 2003).Combined results of Items 1 and 3 of Factor Rich reveal that in the Chinese context for

example, having a lot of money may be acceptable, but I want to be rich maynotbeacceptable. Factor Rich has several diverseitems that eventually capture these culturaldifferences. We offered our discussion regarding the four strategies regarding possiblesolutions for non-invariance (Poortinga, 1989; Cheung, 1999). As mentioned, we maydelete the Item 3 of Factor Rich from the scale that causes the most non-invariance. Or,alternatively, we may eliminate six samples (i.e., Belgium, China, Hungary, South Korea,Spain, and Taiwan) and declare that we have achieved measurement invariance across 22samples (28 - 6 =22) using the 9-item-3-factor LOMS. It should be pointed out that ourdata from 28 geopolitical entities do provide not only “acceptable” but also “desirable”and “meaningful” culture differences (Cheung, 2002). Thus, we want to keep these itemsand geopolitical entities in our data set. When we consider all factors of the Love of

Money Scale, we have three culture-specific (emic) factors across 28 geopolitical entities.In order to establish functional invariance and investigated the relationshipbetween the Love of Money and another meaningful variable, we pick Pay LevelSatisfaction in this study. Again, we apply the same rigorous procedure and achieveconfigural and metric invariance for the Pay Level Satisfaction across 28 samples afterdropping two samples (Nigeria and Oman). We find that the Love of Money is negativelyrelated to Pay Level Satisfaction for the whole sample (27 samples after eliminatingMalta, Nigeria, and Oman) and for nine (9) geopolitical entities.

Thus, researchers in cross-cultural studies can not take the wording of an item andculture for granted.

Factor Rich has the highest factor loading in the 31-sample analysis (Figure 2)

and in the 12-sample analysis with four controlled demographic variables (Figure 3),supporting previous findings (Tang & Chiu, 2003). This deserves further attention in theliterature.

Implications. First, researchers should not take the measurement invariance forgranted. Riordan and Vandenberg strongly encourage researchers “to systematicallyexamine the assumption of stable and transferable measurement continua” (1994: 666). Itis necessary to identify measurement properties before testing theories and modelsdeveloped in the US in different geopolitical entities around the world. This study is aninitial step in the cross-cultural validation of the Love of Money Scale using these newtechniques (Cheung, 2002; Riordan & Vandenberg, 1994; Vandenberg & Lance, 2000).

Second, this study examines the love of money across samples and “money” is thefocus of our attention. Most items include the word “money”. The meanings of moneyreflect the culture, language, history, people, political systems and representation, and thevalue of the currency in each nation and may vary across geopolitical entities. We dohave three items, however, with the “I orientation” (i.e., an item that starts with “I”). Therefore, the relationship between (1) the major subject of the research questionnaire,i.e., money, and (2) the extent to which people’s personal involvement in responding tothe research questionnaire in the context of culture, i.e., the “I” orientation, varies acrossgeopolitical entities. Researchers have to examine the wording or phrasing of items




carefully when they design future measurement instruments and define the“operationalizations of constructs” of measurements (Riordan & Vandenberg, 1994: 667)and take the culture into consideration. Australia had a much stronger factor loading(1.448, GDP per Capita =$30,700) than, for example, China (.833, Z$5,600), Malaysia(.835, $9,700), South Korea (.766, $19,200), and Taiwan (.777, $25,300).

Third, measurement invariance and Cronbach’s alpha are different criteria inexamining the psychometric properties of a measurement scale. Cronbach’s alphaexamines inter-item reliability, whereas measurement invariance identifies equivalenceacross populations. Both contribute to our understanding of the measurement instrument.Some samples may pass the measurement invariance criteria but fail the internalreliability criterion, and vice versa. The leniency of the criteria may make a difference.

Fourth, one of the possible reasons of non-invariance is the composition of thesamples. Malaysia and Singapore-2 samples have Chinese, Malays, Indians, andCaucasians. The Nigeria sample has Igbo, Yoruba, Housa, and members of other ethnicgroups. Singapore-1 sample has Chinese. Ethnic groups within one sample differsignificantly in their history, culture, religion, language, social-economic status, and

values toward the love of money. Our China-2 sample (full-time employees) achievesmeasurement invariance, while China-1 sample (the student sample) does not (i.e., 3.0 < χ

2/df <5.0). It is plausible that one’s employment status (employee vs. student) may playa role here regarding the meaning of money as measured by the Love of Money Scale(Tang, Kim, & Tang, 2002). The comparability of the samples may have causedinvariance across some of the groups. These groups have been eliminated in the process.Future research may treat these sub-groups separately within the sample in a MGCFA, if the sample is large enough. Participants in some under-represented samples in theliterature (e.g., Bulgaria, Hungary, Malaysia, Malta, and Nigeria) may have difficulties inunderstanding and answering survey questionnaires using Liker-type rating scales,developed in the US. Demographic variables may contribute to poor reliability and non-

invariance.Finally, researchers need to consider the four strategies that deal with metric non-invariant items (Cheung, 2002; Cheung & Rensvold, 1999, 2002; Vandenberg & Lance,2000). Our results show that the significant factor loading differences are all related todata from three geopolitical entities. Thus, it is also important to examine the meanings of significant factor loading differences across samples and identify the ideal balancebetween (1) eliminating (a) samples or (b) items that cause significant factor loadingdifferences and (2) achieving measurement invariance across samples carefully.

Limitations. First, the senior author has taken every precaution and offereddetailed instructions regarding the translation-back-translation procedure to allcollaborators. There is no guarantee that the original meaning of these items is perfectlytranslated to the other languages. Due to cultural and language differences, researchersneed to examine the sender, message, encoding, medium, decoding, receiver, and thenoise in the communication process and translation differences in future studies.

Second, we have collected data from full-time employees in these geopoliticalentities. However, we do not have perfect control of many nuisance variables (e.g., thetype of industry, the type of organization, the size of the organization, organizationalculture, economy of the nation/region, unemployment rate, and participants’ knowledgeof the English language, management literature, and the purpose of this research project).




Nuisance variables are potential independent variables that if left uncontrolled couldexert a systematic influence on the measurements in a study. However, with the largesample from 29 geopolitical entities, these nuisance variables are distributed randomlyand may not have a systematic impact on the results of this study. We do not measureculture-related variables. We do, however, control demographic variables, e.g., age,

gender, educational level, and income in examining the love of money. Our othervariables (e.g., job title) are difficult to compare across organizations, industries, andsamples and are not examined in this study.

Third, our whole sample is reasonably large, but the sample from eachgeopolitical entity is quite small and doesnot represent a random sample or the averagecitizen of the geopolitical entity. We cannot generalize the findings to the wholepopulation with full confidence. We do not examine differences (e.g., mean differencesof Factors of LOMS) among samples. This may be achieved by combining samples withsimilar languages or cultures, etc. A heterogeneous sample from one entity maycontribute to measurement non-invariance. Our understanding of the Love of MoneyScale will not be complete without additional analyses regarding measurement invariance

across subgroups within a sample such as gender, income level, employment status,possible change over time, and other variables.One of the most difficult aspects of doing business in China is “corruption”.

Chinese people have, relatively speaking, low income ($5,600) and high corruption (CPIRank =57, Score =3.5). As China continues its economic development and becomes theworld’s largest market and economy, Chinese people will have the opportunity to makemore money, increase consumptions, and realize the importance of money than before.With all these economic changes, the importance of money and the love of money (¥€$)in the world market may also change overtime and across cultures. The love of moneymay play apivotal role in our understanding of people’s work-related attitudes andbehaviors in the competitive world market, e.g., job satisfaction, voluntary turnover,

helping behavior in organizations, organizational misbehavior, and unethical behavior(e.g., Tang & Chiu, 2003) in the Chinese context, in particular. This study offersresearchers some confidence and insights in using the Love of Money Scale. Whenresearchers and practitioners apply the same tools consistently across organizations andgeopolitical entities overtime, the cumulative results will enhance our abilities tounderstand, predict, and control the role of the love of money in organizations.

Footnote1We would like to thank the Editor and one reviewer for this suggestion.




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Table 1.Major Variables of the Study across 30 Geopolitical Entities

____________________________________________________________________________

N Age Sex Education Income Rich Motivator Important Pay

____________________________________________________________________________

1. Australia 262 26.81 .29 12.50 12,008.99 3.73 3.23 3.79 3.142. Belgium 201 38.97 .57 16.09 20,269.40 3.40 3.04 3.68 3.303. Brazil 201 37.71 .45 16.92 5,006.64 3.59 3.05 3.73 2.684. Bulgaria 162 27.36 .64 16.91 2,148.70 3.92 3.57 3.82 2.655. China 204 31.57 .60 15.38 3,537.86 3.69 3.28 3.79 2.726. Egypt 200 40.26 .50 14.88 7,181.00 3.75 2.90 4.08 3.377. France 135 32.30 .56 16.19 16,753.70 3.79 3.38 3.61 2.868. HK 211 30.68 .49 15.67 47,509.06 4.06 3.33 4.07 3.009. Hungary 100 34.06 .55 15.96 2,700.29 3.83 3.55 3.98 3.05

10. Italy 204 37.88 .39 14.12 15,303.46 3.37 2.86 3.43 3.0411. Macedonia 204 41.60 .44 13.31 2,176.48 3.97 3.54 4.07 2.8712. Malaysia 200 31.80 .53 15.23 10,180.21 3.99 3.64 4.17 3.1213. Malta 200 36.91 .51 16.47 14,922.60 3.95 3.13 4.33 2.5614. Mexico 295 30.79 .54 14.31 7,416.59 3.42 3.26 3.80 2.9715. Nigeria 200 34.80 .61 15.74 1,909.35 4.48 3.24 4.57 3.45 16. Oman 204 29.74 .64 14.67 5,816.32 3.81 2.82 4.15 3.56.17. Peru 190 31.89 .64 17.30 13,060.30 3.62 3.27 3.77 3.0718. Philippines 200 33.45 .51 17.13 4,027.27 3.80 3.26 4.08 3.4419. Portugal 200 35.18 .40 15.44 3,386.23 3.50 2.78 3.81 2.7020. Romania 200 38.02 .27 16.69 1,726.19 3.83 3.56 3.85 2.56

21. Russia 200 35.92 .42 17.58 2,901.83 3.96 3.34 3.88 2.7622. Singapore-2 336 33.23 .57 15.01 29,277.07 3.95 3.52 4.07 3.2623. Slovenia 200 38.72 .43 13.68 7,025.55 3.37 3.00 3.66 2.9324. S. Africa 203 46.52 .46 15.76 5,247.93 3.88 3.16 4.03 2.2825. S. Korea 203 37.21 .73 15.92 45,647.02 4.21 3.67 4.24 3.0326. Spain 183 33.81 .59 14.15 4,142.85 3.56 2.91 3.72 3.1227. Taiwan 201 34.95 .48 16.56 22,567.04 4.10 3.81 4.15 3.0328. Thailand 200 33.29 .54 16.98 10,950.04 3.88 3.30 3.87 3.1929. US 274 35.04 .45 15.08 35,357.08 3.85 3.59 4.10 2.83 ____________________________________________________________________________

Whole Sample 5,973 34.70 .50 15.46 14,513.09 3.80 3.67 3.95 3.00 ____________________________________________________________________________




Table 2.Items of the Love of Money Scale and Pay Level Satisfaction Scale ______________________________________________________________________________

Item ______________________________________________________________________________

The Love of Money Scale

Factor Rich1. I want to be rich.2. It would be nice to be rich.3. Have a lot of money (being rich) is good.

Factor Motivator4. I am motivated to work hard for money.5. Money reinforces me to work harder.6. I am highly motivated by money.

Factor Important7. Money is good.8. Money is important.9. Money is valuable.

Pay Level Satisfaction Scale

1. My take-home pay.2. My current salary.3. My overall level of pay.4. Size of my current salary.

________________________________________________________________________




Table 3.Configural Invariance of the 9-Item-3-Factor Love of Money Scale (LOMS) ____________________________________________________________________________

χ 2 df p χ

2/df TLI CFI SRMSR RMSEA

____________________________________________________________________________

1. Australia 74.47 24 .00 3.10 .99 .99 .06 .092. Belgium 27.41 24 .29 1.14 1.00 1.00 .04 .033. Brazil 26.49 24 .33 1.10 1.00 1.00 .04 .024. Bulgaria 34.37 24 .08 1.43 1.00 1.00 .04 .055. China 34.82 24 .07 1.45 1.00 1.00 .03 .056. Egypt 29.64 24 .20 1.24 1.00 1.00 .04 .037. France 37.98 24 .03 1.58 .99 1.00 .05 .078. HK 46.43 24 .00 1.93 .99 1.00 .04 .079. Hungary 107.09 24 .00 4.46 .95 .97 .08 .19

10. Italy 51.98 24 .00 2.17 .99 .99 .04 .0811. Macedonia 60.84 24 .00 2.53 .99 .99 .05 .09 12. Malaysia 106.90 24 .00 4.45 .98 .99 .05 .1313. Malta 445.66 24 .00 18.57 .89 .94 .12 .30* 14. Mexico 79.35 24 .00 3.31 .99 .99 .05 .0915. Nigeria 92.67 24 .00 3.86 .98 .99 .12 .12* 16. Oman 15.26 24 .91 .64 1.00 1.00 .03 .0017. Peru 60.03 24 .00 2.50 .99 .99 .05 .09 18. Philippines 73.16 24 .00 3.05 .99 .99 .05 .10 19. Portugal 30.39 24 .17 1.27 1.00 1.00 .03 .0420. Romania 60.24 24 .00 2.51 .99 .99 .05 .09

21. Russia 33.59 24 .09 1.40 1.00 1.00 .04 .0422. Singapore-1 36.66 24 .05 1.53 1.00 1.00 .03 .0523. Singapore-2 95.95 24 .00 4.00 .99 .99 .05 .0924. Slovenia 41.30 24 .02 1.72 .99 1.00 .06 .0625. S. Africa 37.64 24 .04 1.57 .99 1.00 .06 .0526. S. Korea 43.74 24 .01 1.82 1.00 1.00 .04 .0627. Spain 41.08 24 .02 1.71 .99 1.00 .05 .0628. Taiwan 72.01 24 .00 3.00 .99 .99 .05 .1029. Thailand 30.64 24 .16 1.28 1.00 1.00 .03 .0430. US 56.46 24 .00 2.35 .99 1.00 .04 .07 ____________________________________________________________________________

Whole Sample 201.06 24 .00 8.38 1.00 1.00 .02 .03 ____________________________________________________________________________

Note. We retained a sample if it satisfied ¾ of the following four most rigorous criteria (i.e., TLI >.95, CFI >.95,SRMSR < .08, and RMSEA <.06). If a sample failed one criterion, results were underlined (16 samples). If asample failed two or more criteria, we eliminated the sample and printed results inbold* (Malta and Nigeria).




Table 4. Z- Test Results for Factor Rich, Item 3 —“Having a lot of money (being rich) is good” Across 28Geo-political Entities

_________________________________________________________________________________________________

1 2 3 4 5 6 7 8 9 10 11 12 13

L 0.773 0.547 0.904 1.098 1.383 1.033 0.787 0.899 0.459 1.153 1.048 1.044 0.927

S 0.064 0.082 0.060 0.099 0.133 0.072 0.098 0.084 0.087 0.071 0.096 0.113 0.069

1 Australia 2.173 -1.493 -2.757 -4.133 -2.699 -0.120 -1.193 2.907 -3.975 -2.383 -2.087 -1.636

2 Belgium -3.514 -4.286 -5.351 -4.454 -1.878 -2.999 0.736 -5.587 -3.968 -3.560 -3.546

3 Brazil -1.676 -3.283 -1.376 1.018 0.048 4.211 -2.679 -1.272 -1.094 -0.252

4 Bulgaria -0.418 0.111 0.495 0.329 1.048 -0.094 0.080 0.083 0.295

5 China 0.547 0.877 0.735 1.393 0.360 0.495 0.483 0.717

6 Egypt 0.422 0.240 1.018 -0.224 -0.026 -0.018 0.200

7 France -0.186 0.539 -0.630 -0.419 -0.396 -0.242

8 H.K. 0.752 -0.456 -0.248 -0.231 -0.051

9 Hungary -1.235 -0.974 -0.925 -0.838

10 Italy 0.182 0.180 0.427

11 Macedonia 0.006 0.211

12 Malaysia 0.194

13 Mexico

14 Oman

15 Peru

16 Philippines

17 Portugal

18 Romania

19 Russia

20 Singapore1

21 Singapore2

22 Slovenia

23 S. Africa24 S. Korea

25 Spain

26 Taiwan

27 Thailand

28 USA




15 16 17 18 19 20 21 22 23 24 25 26 27

L 1.005 1.283 0.836 0.990 0.977 1.198 0.989 0.711 0.757 1.284 0.711 1.267 0.974

S 0.115 0.140 0.065 0.071 0.087 0.094 0.070 0.090 0.267 0.099 0.069 0.108 0.064

1 -1.763 -3.313 -0.691 -2.270 -1.889 -3.737 -2.277 0.058 0.058 -4.335 0.659 -3.935 -2.221

2 -3.243 -4.536 -2.762 -4.084 -3.597 -5.219 -4.100 -1.347 -0.752 -5.733 -1.530 -5.310 -4.105

3 -0.779 -2.488 0.769 -0.925 -0.691 -2.636 -0.922 1.784 0.537 -3.283 2.111 -2.938 -0.798

4 0.142 -0.268 0.457 0.185 0.198 -0.161 0.899 2.892 1.197 -1.329 3.207 -1.154 1.052

5 0.537 0.135 0.869 0.615 0.612 0.275 2.621 4.185 2.099 0.597 4.485 0.677 2.771

6 0.046 -0.384 0.376 0.080 0.099 -0.286 0.438 2.794 0.998 -2.050 3.229 -1.803 0.612

7 -0.334 -0.719 -0.086 -0.349 -0.312 -0.663 -1.677 0.571 0.105 -3.568 0.634 -3.291 -1.598

8 -0.168 -0.574 0.115 -0.163 -0.133 -0.501 -0.823 1.527 0.507 -2.965 1.729 -2.690 -0.710

9 -0.859 -1.223 -0.684 -0.945 -0.878 -1.228 -4.746 -2.013 -1.061 -6.260 -2.269 -5.826 -4.768

10 0.243 -0.200 0.608 0.306 0.313 -0.078 1.645 3.856 1.433 -1.075 4.464 -0.882 1.873

11 0.066 -0.342 0.374 0.100 0.117 -0.243 0.497 2.561 1.026 -1.711 2.851 -1.516 0.641

12 0.058 -0.336 0.349 0.089 0.106 -0.239 0.414 2.305 0.990 -1.598 2.515 -1.427 0.539

13 -0.129 -0.551 0.176 -0.119 -0.090 -0.475 -0.631 1.905 0.616 -2.958 2.214 -2.653 -0.499

14 -0.590 -0.993 -0.359 -0.654 -0.592 -0.979 -3.641 -0.495 -0.362 -5.456 -0.588 -4.968 -3.659

15 -0.389 0.282 0.025 0.044 -0.299 0.119 2.013 0.853 -1.839 2.192 -1.661 0.236

16 0.698 0.451 0.454 0.124 1.878 3.437 1.745 -0.006 3.665 0.090 2.007

17 -0.295 -0.256 -0.642 -1.602 1.126 0.287 -3.783 1.319 -3.419 -1.513

18 0.023 -0.362 0.010 2.434 0.843 -2.413 2.818 -2.143 0.167

19 -0.367 -0.107 2.125 0.783 -2.329 2.396 -2.091 0.028

20 1.783 3.742 1.558 -0.630 4.176 -0.482 1.970

21 2.438 0.841 -2.433 2.828 -2.160 0.158

22 -0.163 -4.283 0.000 -3.955 -2.381

23 -1.851 0.167 -1.771 -0.790

24 4.748 0.116 2.630

25 -4.338 -2.795

26 2.334

27

28

Note. We examined Item 3 of Factor Rich using theZ test because this item was the major source of non-invarianceand had the most significant differences. Item 1 of Factor Rich was the marker item in this analysis. L =factorloading estimate in the unconstrained model. S =standard error. The critical value for theZ test was 4.18. Therewere 23 significant differences in factor loading among 378 pair-wise comparisons (printed in bold).




TABLE 5.Configural Invariance of the 3-Item-1-Factor Pay Level Satisfaction Scale(PSQ) ____________________________________________________________________________

χ 2 df p χ

2/df TLI CFI SRMSR RMSEA

____________________________________________________________________________

1. Australia .31 2 .86 .16 1.00 1.00 .00 .002. Belgium 4.82 2 .00 2.41 1.00 1.00 .01 .083. Brazil 2.25 2 .33 1.13 1.00 1.00 .01 .034. Bulgaria 14.49 2 .00 7.25 .97 .99 .02 .195. China 2.84 2 .24 1.42 1.00 1.00 .02 .056. Egypt 5.06 2 .08 2.53 .99 1.00 .02 .097. France 13.23 2 .00 6.62 .97 .99 .02 .208. HK 5.49 2 .06 2.75 .99 1.00 .02 .099. Hungary 11.46 2 .00 5.73 .97 .99 .01 .22

10. Italy 13.11 2 .00 6.56 .98 1.00 .02 .1711. Macedonia 13.52 2 .00 6.76 .97 .99 .04 .1712. Malaysia 17.00 2 .00 8.50 .97 .99 .02 .1913. Malta 25.48 2 .00 12.47 .95 .99 .02 .2414. Mexico 4.04 2 .13 2.02 1.00 1.00 .01 .0615. Nigeria 30.86 2 .00 15.43 .94 .99 .09 .27*16. Oman 40.27 2 .00 20.14 .93 .99 .04 .31* 17. Peru 5.87 2 .05 2.93 .99 1.00 .01 .1018. Philippines 10.21 2 .01 5.10 .98 1.00 .03 .1419. Portugal 5.92 2 .05 2.96 .99 1.00 .01 .1020. Romania 9.11 2 .01 4.56 .98 1.00 .01 .13

21. Russia 5.53 2 .06 2.77 .99 1.00 .02 .0922. Singapore-1 21.30 2 .00 10.65 .97 .99 .02 .2223. Singapore-2 2.23 2 .33 1.11 1.00 1.00 .01 .0224. Slovenia 7.33 2 .03 3.67 .99 1.00 .01 .1225. S. Africa .05 2 .07 .07 1.00 1.00 .00 .0026. S. Korea 5.53 2 .06 2.76 .99 1.00 .01 .0927. Spain 4.01 2 .13 2.01 1.00 1.00 .01 .0728. Taiwan 2.17 2 .34 1.09 1.00 1.00 .01 .0229. Thailand 5.24 2 .07 2.62 .99 1.00 .02 .0930. US 1.82 2 .40 .91 1.00 1.00 .01 .00 ____________________________________________________________________________

Whole Sample 172.77 2 .00 86.38 .99 1.00 .02 .12 ____________________________________________________________________________

Note. We retained a sample if it satisfied ¾ of the following four most rigorous criteria (i.e., TLI >.95, CFI >.95,SRMSR <.08, and RMSEA <.06). If a sample failed one criterion, results were underlined (22 samples). If asample failed two or more criteria, we eliminated the sample and printed results in bold* (Nigeria and Oman). Weretained 28 samples in this analysis.




TABLE 5 The Love of Money to Pay Level Satisfaction Relationship ____________________________________________________________________________

χ 2 df p χ

2/df TLI CFI SRMSR RMSEA Path

____________________________________________________________________________

1. Australia 119.87 61 .00 1.97 .99 .99 .05 .06 -.17*2. Belgium 73.89 61 .12 1.21 1.00 1.00 .05 .03 -.043. Brazil 93.14 61 .00 1.61 .99 1.00 .07 .06 .20***4. Bulgaria 92.93 61 .01 1.52 .99 1.00 .05 .06 -.30**5. China 93.34 61 .00 1.53 .99 1.00 .05 .05 -.046. Egypt 58.40 61 .57 .96 1.00 1.00 .04 .00 -.077. France 99.22 61 .00 1.63 .99 .99 .06 .07 -.178. HK 110.55 61 .00 1.81 .99 .99 .06 .06 -.35***9. Hungary 170.20 61 .00 2.79 .96 .98 .09 .13 -.11

10. Italy 109.64 61 .00 1.80 .99 .99 .05 .06 -.27**11. Macedonia 118.91 61 .00 1.95 .99 .99 .07 .07 .0212. Malaysia 198.39 61 .00 3.25 .98 .99 .06 .11 .0113. Mexico 127.66 61 .00 2.09 .99 .99 .05 .06 -.0314. Peru 107.26 61 .00 1.76 .99 .99 .06 .06 .1015. Philippines 157.02 61 .00 2.57 .99 .99 .09 .09 .0916. Portugal 89.36 61 .01 1.40 .99 1.00 .04 .05 -.24**17. Romania 134.32 61 .00 2.20 .99 .99 .06 .08 -.0518. Russia 109.12 61 .00 1.79 .99 .99 .06 .06 -.25**19. Singapore-1 114.25 61 .00 1.87 .99 .99 .04 .07 -.22**20. Singapore-2 247.90 61 .00 4.06 .98 .99 .07 .10 -.08

21. Slovenia 98.34 61 .00 1.61 .99 1.00 .05 .06 -.32**22. S. Africa 107.11 62 .00 1.73 .99 .99 .07 .06 -.1523. S. Korea 80.18 61 .00 1.31 1.00 1.00 .04 .04 -.1024. Spain 87.56 61 .01 1.44 .99 1.00 .05 .05 -.1225. Taiwan 120.99 61 .00 1.98 .99 .99 .05 .07 -.1126. Thailand 84.91 61 .02 1.39 1.00 1.00 .04 .04 -.19*27. US 128.17 61 .00 2.11 .99 .99 .06 .06 -.11 ____________________________________________________________________________

27 Samples 355.73 61 .00 5.83 1.00 1.00 .02 .03 -.09*** ____________________________________________________________________________

Note. In this analysis, we examined the Love of Money to Pay Level Satisfaction path in 27 samples (i.e., Malta,Nigeria, and Oman were eliminated). *p<.05, **p<.01, ***p<.001.



The Love of Money: 31 Samples 3

FIGURE 1 The Love of Money (9-Item-3-Factor) Model

The Love of Money and Pay SatisfactionRelationship

M4

0,

e4

M5

0,

e51

M6

0,

e61

M7

0,

e71

M8

0,

e8

M9

0,

e9

M10,

e1

M2

0,

e2

1

1

1

1

1

0

Motivator

1

0

Important

0

Rich

1

M3

0,

e31

1

0,

The Loveof Money

0,

e22

0,

e23

0,

e21

1

1

1

1

S3

0,

e33

S2

0,

e32

S1

0,

e31

0

Pay Level

0,e66

1

1

1

1

1


The Love of Money

S3

0,

e341




FIGURE 2 Results of the Love of Money (9-Item-3-Factor) Model

Chi-Square = 355.73df = 61p = .00

Chi-Square/df = 5.83TLI = 1.00CFI = 1.00

SRMSR = .02

RMSEA = .03


.68

M4e4

.72

M5e5

.63

M6e6

.49

M7e7

.45

M8e8

.58

M9e9

.63

M1e1

.61

M2e2

.47

Motivator

.82

.47

Important

.76

Rich.78

.85

.80

.70

.60

M3e3 .78

.67

.76

.80

The Loveof Money

e22

e23

e21

.68

.87

.68.74

S3 e33

.77

S2e32

.63

S1 e31

.01

Pay Level

e66

.86

.88

.79

-.09


The Love of Money

.74

S3 e34.86




Chi-Square = 355.73df = 61p = .00

Chi-Square/df = 5.83TLI = 1.00CFI = 1.00

SRMSR = .02

RMSEA = .03


.68

M4e4

.72

M5e5

.63

M6e6

.49

M7e7

.45

M8e8

.58

M9e9

.63

M1e1

.61

M2e2

.47

Motivator

.82

.47

Important

.76

Rich.78

.85

.80

.70

.60

M3e3 .78

.67

.76

.80

The Loveof Money

e22

e23

e21

.68

.87

.68.74

S3 e33

.77

S2e32

.63

S1 e31

Pay Level .86

.88

.79


The Love of Money

.74

S4 e34.86

-.09




L 1.448 1.471 0.916 1.278 0.833 0.838 1.722 1.175 1.747 0.929 0.939 0.835 1.055 1.09

S 0.116 0.190 0.048 0.150 0.078 0.056 0.234 0.100 0.244 0.061 0.083 0.079 0.073 0.08

1 Australia -0.103 4.238 0.897 4.394 4.736 -1.049 1.783 -1.107 3.960 3.569 4.368 2.867 2.53

2 Belgium 2.832 0.797 3.105 3.196 -0.833 1.379 -0.892 2.716 2.566 3.091 2.044 1.84

3 Brazil -2.299 0.904 1.058 -3.374 -2.335 -3.342 -0.167 -0.240 0.876 -1.591 -1.84

4 Bulgaria 0.659 0.685 -0.507 0.146 -0.528 0.537 0.497 0.655 0.334 0.27

5 China -0.010 -1.125 -0.573 -1.138 -0.182 -0.187 -0.004 -0.404 -0.45

6 Egypt -1.161 -0.603 -1.174 -0.188 -0.192 0.006 -0.427 -0.48

7 France 0.669 -0.026 1.032 0.983 1.121 0.851 0.79

8 H.K. -0.69 0.434 0.39 0.568 0.204 0.14

9 Hungary 1.047 0.999 1.135 0.869 0.81

10 Italy -0.019 0.178 -0.243 -0.30

11 Macedonia 0.183 -0.208 -0.26

12 Malaysia -0.399 -0.45

13 Mexico -0.06

14 Oman

15 Peru

16 Philippines

17 Portugal

18 Romania

19 Russia

20 Singapore1

21 Singapore2

22 Slovenia

23 S. Africa24 S. Korea

25 Spain

26 Taiwan

27 Thailand

28 USA




0.978 0.985 1.234 1.069 1.205 1.269 1.345 0.887 0.478 0.766 1.336 0.777 1.264 1.0130.107 0.112 0.098 0.079 0.108 0.114 0.103 0.094 0.190 0.055 0.116 0.056 0.092 0.110

2.978 2.871 1.409 2.700 1.533 1.101 0.664 4.357 4.357 5.312 0.683 5.209 1.243 2.721

2.261 2.204 1.109 1.954 1.217 0.912 0.583 2.755 3.696 3.564 0.606 3.504 0.981 2.086

-0.529 -0.566 -2.914 -1.655 -2.445 -2.854 -3.775 0.275 2.235 2.055 -3.346 1.885 -3.354 -0.808

0.418 0.405 0.062 0.309 0.102 0.012 -0.368 2.209 3.305 3.205 -0.306 3.129 0.08 1.425

-0.238 -0.246 -0.675 -0.421 -0.609 -0.703 -3.957 -0.441 1.727 0.7 -3.594 0.582 -3.568 -1.333

-0.245 -0.254 -0.714 -0.445 -0.641 -0.739 -4.324 -0.448 1.817 0.917 -3.866 0.77 -3.955 -1.418

0.901 0.886 0.599 0.825 0.625 0.543 1.475 3.311 4.127 3.977 1.478 3.928 1.822 2.742

0.306 0.292 -0.094 0.177 -0.047 -0.144 -1.184 2.098 3.246 3.584 -1.051 3.473 -0.655 1.09

0.918 0.903 0.62 0.844 0.646 0.565 1.518 3.289 4.103 3.922 1.521 3.875 1.852 2.742

-0.085 -0.095 -0.541 -0.265 -0.475 -0.575 -3.475 0.375 2.26 1.985 -3.105 1.836 -3.035 -0.668-0.063 -0.074 -0.49 -0.228 -0.43 -0.526 -3.069 0.415 2.223 1.737 -2.783 1.618 -2.623 -0.537

-0.234 -0.243 -0.671 -0.416 -0.605 -0.699 -3.929 -0.423 1.735 0.717 -3.57 0.599 -3.538 -1.314

0.128 0.115 -0.306 -0.025 -0.249 -0.35 -2.297 1.412 2.835 3.162 -2.05 3.022 -1.78 0.318

0.183 0.169 -0.241 0.037 -0.187 -0.287 -1.946 1.636 2.963 3.309 -1.739 3.179 -1.42 0.564

-0.011 -0.4 -0.149 -0.346 -0.438 -2.471 0.639 2.293 1.762 -2.269 1.664 -2.027 -0.228

-0.384 -0.136 -0.332 -0.422 -2.366 0.67 2.299 1.755 -2.177 1.661 -1.925 -0.178

0.277 0.045 -0.054 -0.781 2.555 3.536 4.164 -0.672 4.049 -0.223 1.5

-0.222 -0.322 -2.126 1.482 2.872 3.148 -1.902 3.015 -1.608 0.414

-0.096 -0.938 2.221 3.326 3.622 -0.827 3.518 -0.416 1.245

-0.495 2.585 3.57 3.974 -0.412 3.874 0.034 1.616

3.284 4.012 4.959 0.058 4.845 0.587 2.203

1.929 1.111 -3.007 1.005 -2.866 -0.871-1.456 -3.854 -1.509 -3.723 -2.437

-4.44 -0.14 -4.646 -2.008

4.34 0.486 2.02

-4.522 -1.912

1.75




Chi-Square = 98.14df = 61p = .00

Chi-Square/df = 1.61TLI = .99

CFI = 1.00SRMSR = .02RMSEA = .06


.60

M4e4

.82

M5e5

.56

M6e6

.66

M7e7

.70

M8e8

.57

M9e9

.77

M1e1

.91

M2e2

3.74

Motivator

.77

.00

Important

.05

Rich.95

.90

.75

.82

.66

M3e3 .82

.84

.75

.88

The Loveof Money

e22

e23

e21

.00

.23

1.93.75

S3 e33

.72

S2 e32

.60

S1 e31

.04

Pay Level

e66

.87

.85

.78

.20


The Love of Money

.81

S3 e34.90

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