2006 MOR LOM&PaySat MeasInvariance

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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 29 Geopolitical 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; Ilya Garber, Russia; Consuelo Garcia De La Torre, Mexico, Rosario Correia Higgs, Portugal; Chin-Kang Jen, Taiwan; Ali Mahdi Kazem, Oman; Kilsun Kim, South Korea; Vivien Kim Geok Lim, Singapore; Roberto Luna-Arocas, Spain; Eva Malovics, Hungary; Anna Maria Manganelli, Italy; Alice S. Moreira, Brazil; Anthony Ugochukwu Obiajulu Nnedum, Nigeria; Johnsto 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

ABSTRACT Demonstrating the equivalence of constructs is a key requirement for crosscultural empirical research. The major purpose of this paper is to demonstrate how to assess measurement and functional equivalence or invariance using the 9-item, 3-factor Love of Money Scale (LOMS, a second-order factor model) and the 4-item, 1-factor Pay Level Satisfaction Scale (PLSS, a first-order factor model) across 29 samples in six continents (N = 5973). In step 1, we tested the configural, metric and scalar invariance of the LOMS and 17 samples achieved measurement invariance. In step 2, we applied the same procedures to the PLSS and nine samples achieved measurement invariance. Five samples (Brazil, China, South Africa, Spain and the USA) passed the measurement invariance 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. We achieved functional equivalence for these two scales in all five samples. The results of this study suggest the critical importance of evaluating and establishing measurement equivalence in cross-cultural studies. Suggestions for remedying measurement nonequivalence are offered. KEYWORDS the love of money, pay level satisfaction, measurement invariance, functional equivalence, cross-cultural empirical research, 29 geopolitical entities

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

INTRODUCTION Money is the instrument of commerce and the measure of value (Smith, 1776/1937). For the past several decades, the importance of money has been recognized in the literature. For example, only 49.9 percent of freshman in 1971 said that the important reason in deciding to go on to college is “to make more money”. That number increased to 75.1 percent in 1993 (The American Freshman, 1994). Men ranked pay the fifth and women ranked pay the seventh in importance, among 10 job preferences in the US (Jurgensen, 1978). Among 11 work goals, pay was ranked the second in importance in Belgium, the UK, and the US and the first in Germany (Harpaz, 1990). In Hong Kong and China, most Chinese prefer cash among 35 components of compensation, i.e., the cash mentality (Chiu, Luk, & Tang, 2001). The lack of money has become the number one cause of dissatisfaction among university students on campus (out of 10 causes) for the past seven years (1997-2003), up from the third and the second place of two earlier periods (1990-1996, 1981-1987, respectively) (Bryan, 2004). People in the US and around the world are keenly aware of the importance of money. Money has been used to attract, retain, and motivate their employees and achieve organizational goals around the world (Milkovich & Newman, 2005). Researchers and managers have great interests in compensation, money, and pay satisfaction in organizations 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 literature that “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 the meaning 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 has not been systematically examined. According to Riordan and Vandenberg (1994), it does little good to test a theoretical and conceptual relationship across cultures “unless there is confidence that the measures operationalizing the constructs of that relationship exhibit both conceptual and measurement equivalence across the comparison groups” (p. 645). Researchers should “systematically examine the assumption of stable and transferable measurement continua” 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).

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The major purpose of this study is to examine the configural and metric invariance of the Love of Money Scale (LOMS, Tang & Chen, 2005; Tang & Chiu, 2003) across 30 convenience samples of full-time employees and managers in 29 geopolitical entities from six continents around the world. Most of the geopolitical entities 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. In order to establish the “functional equivalence” (Cavusgil & Das, 1977) and the nomological network of this measure, we will also examine the configural and metric invariance of the Pay Level Satisfaction of the Pay Satisfaction Questionnaire (PSQ, Heneman & Schwab, 1985) and investigate the relationship between the Love of Money and Pay Level Satisfaction across samples. We will define the constructs, review the literature, discuss the issues of measurement invariance, develop a model, and propose a hypothesis 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 in their lives” (Pfeffer, 1998: 112). On the other hand, money is a motivator (Gupta & Shaw, 1998; Kohn, 1993; Locke, Feren, McCaleb, Shaw, & Denny, 1980). Although money has been used universally around 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) have considered “the Money Ethic Scale” (MES, Tang, 1992) as one of the most “welldeveloped” and systematically used measures of money attitude in their article published in the Academy of Management Review. Tang and his associates have investigated the meaning of money based on the ABC model of an attitude with affective, behavioral, and cognitive components and research suggested in the literature (e.g., Furnham & Argyle, 1999; Opsahl & Dunnette, 1966; Wernimont & Fitzpatrick, 1972). They have developed several versions of multidimensional Money Ethic Scale (MES) and the Love of Money Scale (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 the rationale 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 matter regarding the meaning of money is the love of money. Thus, we would like to simplify the 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 the literature and is thoroughly grounded in theory. Second, researchers attempting to model relationships among a large number of latent variables have found it difficult to fit such models to even predictions with strong theoretical support (Joreskog & Sorbom, 1986). In

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order to decrease the number of indicators used in the model (achieve the need for parsimony), yet maintain the estimation of measurement error given by using multipleitem indicators; researchers must reduce the number of items and constructs to a manageable level (e.g., the 9-item-3-factor LOMS, Tang & Tang, 2003), or to use parcels. 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 Love of Money Scale (a subset of MES) in a series of studies. Let us summarize selected research findings briefly below. Research on the Love of Money Scale First, researchers have examined measurement invariance of the Love of Money Scale across gender and college major (law, sociology, and political science) among Chinese students (Du & Tang, 2005), across college major (business vs. psychology) among American students (Chen & Tang, 2005; Tang & Chen, 2005), across gender and culture (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-related behaviors and tendencies. Mental health professionals with high Love of Money have high income and high voluntary turnover 18 months later, regardless of their intrinsic job satisfaction (Tang et al., 2000). Among university students in the US, the Love of Money is directly related to Evil (Tang & Chiu, 2003; Tang & Tang, 2003) and indirectly related to Evil through Machiavellianism (the Love of Money  Machiavellianism  Evil) for Business students, but not for Psychology students (Tang & Chen, 2005). Third, the following studies are related to pay satisfaction and may provide direct implications for the present study. American professors experience pay dissatisfaction because 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 is negatively 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 Spanish professors (Tang, Luna-Arocas, Sutarso, & Tang, 2004). In summary, the Love of Money Scale (LOMS) is more direct and effective than the Money Ethic Scale (MES) in making theoretical and empirical contributions to the management and organization literature across cultures. The time is ripe to examine the issue of measurement invariance of the Love 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 study the 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 between the love of money and pay satisfaction, i.e., the major focus of this study: “People who want to get rich fall into temptation and a trap and into many foolish and harmful desires

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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 will define the Love of Money construct, a higher-order (latent) construct, using three selected latent sub-constructs: Rich, Motivator, and Important. We will discuss each construct below. See also the left side of Figure 1. -------------------------------Insert Figure 1 about here -------------------------------Rich. The affective components of an attitude examine one’s love and/or hate orientations, feelings, or emotions. Factor Rich is the affective component of the Love of Money construct. Following the aforementioned rationale (Tang & Chiu, 2003), we speculate 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 is good. Rich is better than poor. Very few want to be poor. We use the notion that “people want to get rich” to define the Rich construct that, in turn, is the most important component of the Love of Money construct. Empirical research shows that Factor Rich has the highest factor loading of the Love of Money Scale and the Love of Money is directly and indirectly related to Evil (Tang & Chen, 2005; Tang & Chiu, 2003). Motivator. The behavioral component refers to how one intends or expects to act toward 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, they are 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 important beliefs or ideas one has about an object or situation. Money is important to people in the US and around the world, (Jurgensen, 1978; Harpaz, 1990; Bryan, 2004). The most consistent thread of the money attitude literature is the “emphasis on its importance” (Mitchell & Mickel, 1999, p. 569). The importance of money is formed early in one’s childhood and maintained in adult life (Furnham & Argyle, 1998). It is probably influenced by the way one was raised, the social and economic background, and workrelated experiences. Most people work for money for the most part of their lives (e.g., 40 hours/week for about 40 years). Factor Important stresses that money is important. We now turn to the construct of pay satisfaction. Pay Level Satisfaction

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Job satisfaction and pay satisfaction may be defined as “a pleasurable or positive emotional 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 management and organization research (Iaffaldano & Muchinsky, 1985; Spector, 1997) with an estimated 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 job satisfaction. On November 16, 2005, we checked the primary index for business literature, ABI/Inform, and found 9,065 references on job satisfaction and 129 on pay satisfaction and the world’s literature in psychology and related fields, PsycINFO and found 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 equity model (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 gap between 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 equity model of pay satisfaction depends on the comparison of the person’s outcome-input ratio to the outcome-input ratio of a comparison other and predicts that the greater the similarity 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. For example, children from poor economic background tend to over estimate the size of a coin 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 few people 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 a point. It has been argued that money (salary) is a hygiene factor because satisfaction with money goes back to zero and the zero point escalates (Herzberg, 1987; Tang, LunaArocas, & Sutarso, 2005). Those who with high love of money (harmful desires) may be never satisfied with their income. Thus, the love of money will be negatively related to pay satisfaction (Tang & Chiu, 2003). These models of pay satisfaction will help us to examine the relationship between the love of money and pay level satisfaction. There are many multidimensional measures of job satisfaction (e.g., the Minnesota 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., the multidimensional Pay Satisfaction Questionnaire (PSQ, Heneman & Schwab, 1985) that has four factors: pay level, benefits, pay raises, and pay structure and administration. The generic term pay satisfaction may be something of a “misnomer” (Heneman & Judge, 2000: 64). “The consistency of the pay level-pay satisfaction relationship is probably the most robust (though hardly surprising) finding regarding the causes of pay satisfaction” (Heneman & Judge, 2000: 71). Actual pay level (income) is consistently and positively related to pay satisfaction. The magnitude of the relationship varies from study to study,

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with correlations ranging from .13 to .46. It makes intuitive sense that the higher the pay level (income), the higher the pay satisfaction. It is easy to understand the relationships of 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 the four 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 Level Satisfaction). 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 pay satisfaction (Tang et al., 2005). If money is important to them, they may pay more attention 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 discrepancy theory). This leads to a lower output/input ratio compared to others (referents) or a large gap between the expectation and the reality. Both theories predict that those with high love of money will have high pay dissatisfaction. Whoever loves money never has money enough and is never satisfied with his income. Empirical research suggests that there is a significant 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 Level Satisfaction. Hypothesis 1: The Love of Money will be negatively related to Pay Level Satisfaction. Measurement Invariance Researchers 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 across geopolitical entities (e.g., Vandenberg & Lance, 2000). There are nine steps involved in measurement invariance: (1) an omnibus test of equality of covariance matrices across groups, (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 are invariant across groups, (6) a test of the null hypothesis that factor variances were invariant across groups, (7) a test of the null hypothesis that factor covariances were invariant 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 invariance were most often reported” (p. 35, emphasis added). Following their suggestions, this paper reflects a “modest” attempt to address only two of nine steps: configural and metric invariance. Configural invariance exists when the same factor structures are identified across all groups. Metric invariance is achieved when all factor-loading parameters are equal across groups using a multi-group confirmatory factor analysis (MGCFA). Full metric invariance is rarely achieved in

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cross-cultural research (Vandenberg & Lance, 2000). Four strategies may deal with items that 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) invoke partial 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, the factor level, and the item level (e.g., Cheung, 2002; Cheung & Rensvold, 1999, 2002). On one hand, non-invariance may not be undesirable because items may provide the specific information about the nature of differences across groups (Cheung, 2002) due to differences 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 across geopolitical entities. On the other hand, in order to achieve measurement invariance and obtain a cultural-free (etic) scale, researchers may eliminate several samples or items with significant 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 performance ratings (Maurer, Raju, & Collins, 1998), and different time periods in longitudinal research (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 Level Satisfaction 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 and metric invariance across the majority of samples or geopolitical entities. METHODS Participants The senior author recruited researchers in approximately 50 geopolitical entities through personal friends, contacts, or networking at professional conferences of the Academy of Management, Academy of Human Resource Development, International Association for Research in Economic Psychology, International Association of Applied Psychology, and Society for Industrial and Organizational Psychology. Researchers received a 19-page package involving a six-page survey (informed consent and items) and instructions (references, websites, translation procedures). He asked collaborators to collect 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 from China. Our convenience samples may not represent the whole population or the average citizens of the geopolitical entities. On average, participants in this study were 34.70 years old (SD = 9.92) with 50% male and had 15.46 years of education (SD = 3.26).

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Table 1 shows the sample size, the basic demographic information and the means and standard deviations of the two measures for each of these 29 samples. Measures Researchers in each geopolitical entity organized small focus groups and translated the English version to their own native languages using a multi-stage translation-back-translation procedure (Brislin, 1980). We collected data regarding participants’ demographic variables (age, sex (male = 1, female = 0), education (in years), income (US$)), 58-item Money Ethic Scale (MES, Tang & Tang, 2002) with strongly disagree (1), neutral (3) and strongly agree (5) as anchors, the 18-item-4-factor Pay Satisfaction Questionnaire (PSQ, Heneman & Schwab, 1985) with strongly dissatisfied (1), neutral (3) and strongly satisfied (5) as anchors, and many other measures. Participants completed the survey voluntarily and anonymously. For the purposes 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. All items are presented in Table 2. Evaluation Criteria Researchers have recommended the following most rigorous criteria for evaluating (1) configural invariance: χ2/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 (Δχ2/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 a better fit and should be non-significant. However, for large sample sizes, this statistic may 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 chisquares. Vandenberg and Lance (2000) have suggested that researchers are (1) to interpret the chi-square test of model fit only in conjunction with other practical fit indices, (2) to select a variety of practical fit indices to supplement the chi-square test and not just one, and (3) to use four indices (e.g., TLI, RNI, RMSEA, and SRMR) because no single index emerging as one to embody all trade-offs (p. 43). Both the SRMSR and the RMSEA are referred to as absolute fit indices. Due to the large number of samples with diverse language, culture, religion, economic development, income, ability to answer a Likert-type survey, and practical considerations, we will retain a sample if it satisfies ¾ of 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 the literature (Epitropaki & Martin, 2004; Srivastava, Locke, & Bartol, 2001). RESULTS Step 1: Reliability Alpha and the Measurement Model First, 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

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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-item3-factor model (χ2 = 201.06, df = 24, p < .001, χ2/df = 8.38, TLI = 1.00, CFI = 1.00, SRMSR = .02, RMSEA = .03) and (2) a 9-item-1-factor model (χ2 = 7321.89, df = 27, p < .01, χ2/df = 271.18. TLI = .93, CFI = .96, SRMSR = .11, RMSEA = .21). We adopted the 9-item-3-factor model because it passed all four most rigorous criteria and was significantly better (Δχ2 = 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 fit between our model and our data from 30 samples around the world (N = 6,175). Factor Rich had the highest factor loading (.89), followed by Factors Important (.68) and Motivator (.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: χ2, df, p, χ2/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 one criterion, we highlighted the sample (underlined). If a sample failed two criteria, we eliminated the sample (printed in bold*). Results suggested that 16 samples failed to pass the RMSEA criterion. We eliminated only 2 samples (Malta and Nigeria) out of 30 in this analysis. Second, in order to identify the possible reasons for the non-invariance, we used exploratory factor analysis (EFA) to examine the data from Malta and Nigeria. For the sample 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) and was not related to Factor Motivator that had only two items. It appears that items with relatively high cross factor loadings and items associated with non-intended first-order factor may be the main cause of non-configural invariance in Malta and Nigeria. In summary, 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). The differences between the unconstrained MGCFA (χ2 = 1443.02, df = 672, p < .01, χ2/df = 2.15, TLI = .99, CFI = 1.00, SRMSR = .06, RMSEA = .01) and the constrained MGCFA (χ2 = 2106.85, df = 834, p < .01, TLI = .99, CFI = .99, SRMSR = .06, RMSEA = .02) was significant based on chi-square change (Δχ2 = 663.83, Δdf = 162, p < .05), but not significant based on fit index change (ΔCFI = .01, n.s.). Full metric invariance is rarely found in cross-cultural management research (Vandenberg & Lance, 2000). Due to nonsignificant fit index change, we have achieved both configural and metric invariance, in general, and may stop our analysis here. We conducted further analyses to advance our understanding of the measurement scale.

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Step 4: Partial Metric Invariance at the “Factor” Level We now investigated the scale at the “factor” level. We compared the results of the unconstrained 28-country MGCFA with three separate partially constrained 28country 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 (χ2 = 1443.02, df = 672, p < .01, χ2/df = 2.15, TLI = .99, CFI = 1.00, SRMSR = .06, RMSEA = .01) with (1) Factor Rich constrained (χ2 = 1764.17, df = 726, p < .01, χ2/df = 2.43, TLI = .99, CFI = .99, SRMSR = .06, RMSEA = .02), (2) Factor Motivator constrained (χ2 = 1546.78, df = 726, p < .01, χ2/df = 2.13, TLI = .99, CFI = 1.00, SRMSR = .06, RMSEA = .02), and (3) Factor Important constrained (χ2 = 1689.20, df = 726, p < .01, χ2/df = 2.33, TLI = .99, CFI = .99, SRMSR = .06, RMSEA = .02). Please note that all these results passed the most rigorous criteria. Further, we achieved partial metric invariance based on fit index change but not on chi-square change for Factor Rich constrained (Δχ2 = 321.15, Δdf = 54, p < .001; ΔCFI = .01), Factor Motivator constrained (Δχ2 = 103.76, Δdf = 54, p < .001; ΔCFI = .00), and Factor Important constrained (Δχ2 = 246.18, Δdf = 54, p < .001; ΔCFI = .01), respectively. Factor Rich was the major source of non-invariance since it had the largest chi-square change, whereas Factor Motivator had the smallest chi-square change 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 to vary: (1) Factors Motivator and Important constrained (Factor Rich unconstrained) (χ2 = 1793.26, df = 780, p < .01, χ2/df = 2.30, TLI = .99, CFI = .99, SRMSR = .06, RMSEA = .02), (2) Factors Rich and Important constrained (Factor Motivator unconstrained) (χ2 = 2005.18, df = 780, p < .01, χ2/df = 2.57, TLI = .99, CFI = .99, SRMSR = .06, RMSEA = .02), and (3) Factors Rich and Motivator constrained (Factor Important unconstrained) (χ2 = 1865.55, df = 780, p < .01, χ2/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 Rich unconstrained (Δχ2 = 350.24, Δdf = 108, p < .001; ΔCFI = .01), Factor Motivator unconstrained (Δχ2 = 562.16, Δdf = 108, p < .001; ΔCFI = .01), and Factor Important unconstrained (Δχ2 = 422.53, Δdf = 108, p < .001; ΔCFI = .01), respectively. Relatively speaking, when Factors Rich and Important were constrained, we found the largest chisquare change (Δχ2 = 562.16). When Factors Rich and Motivator were constrained, we found the second largest chi-square change (Δχ2 = 422.53). Factor Rich was the cause of non-invariance. Step 5: Partial Metric Invariance at the “Item” Level Further, Factor Rich has three items. We applied the same procedure and examined partial metric invariance at the “item” level in three partially constrained 28country MGCFAs in that only one item of the model was constrained while allowing the other eight items to vary. We compared the unconstrained model (χ2 = 1443.02, df = 672, p < .01, χ2/df = 2.15, TLI = .99, CFI = 1.00, SRMSR = .06, RMSEA = .01) with Item 3 of Factor Rich constrained (χ2 = 1596.87, df = 699, p < .01, χ2/df = 2.28, TLI = .99, CFI = .99, SRMSR = .05, RMSEA = .01) and found the largest chi-square change. In order to identify the differences in factor loadings between or among samples for all items, we analyzed data across samples at the item level using the Z test in Step 6.

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Step 6: Additional Analysis at the “Item” Level Using the Z test According to Cheung (2002), the Z test can be used to determine the significant difference of parameter estimates. When comparing factor loading across groups, the Z statistic is defined as: λˆ(i1) − λˆ(i 2) (1) S λ2ˆ(1) + S λ2ˆ( 2 ) i

i

where the factor loadings are estimates in the unconstrained model and the parenthetical number in superscript denotes the group number. Further, the Z test is superior to the Likelihood Ratio (LR) test because it is computationally simpler (Cheung, 2002). The Z 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 calculated 378 pair-wise comparisons (i.e., n (n-1)/2, n = the number of samples) for each item and a total 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 Z value of 4.18 (http://math.uc.edu/~brycw/classes/148/tables.htm). The goal of this analysis is to prove that the null hypothesis of no difference in parameter estimates is correct. Our extreme correction described above benefits the goal of this research by making it very difficult to reject the null hypothesis. Using the critical value mentioned above only protects the overall Type I error, but not the Type II error. Therefore, the procedure used to adjust the alpha level may be conducive to making a Type II error (i.e., incorrectly concluding that the 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, we investigated Item 3 of Factor Rich (having a lot of money (being rich) is good) across 28 samples 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 parameter estimates (first row of Table 4, L), standard errors (second row of Table 4, S) of the unconstrained model of Item 3 across 28 samples, apply the formula (1) above, and calculate a Z test for each of the pair-wise comparisons. We applied the critical Z value of 4.18 (Cheung, 2002) and found 23 significant differences (i.e., 6.08%) in factor loading parameter estimates among 378 pair-wise comparisons (Table 4, printed in bold). In general, Belgium (.547, the first row of Table 4), 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 factor loading 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), and South 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,

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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 analysis because Item 1 (1) starts with the word “I” and is an item with the “I orientation”, i.e., “I want 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 and performed additional analyses for Item 1 using Item 2 as the “marker item”. Due to space limitations, we will not present detailed findings in this paper (available upon request from the first author). There were 14 significant pair-wise Z test results. Australia had a much 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 noninvariance, mentioned earlier. First, we may ignore the significant factor loading differences 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 in factor 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 a non-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 loading than 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 same items 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), China and South Korea rank medium (29) and low (43), respectively. On one hand, people in Australia may have a much stronger concern over themselves and items with an “I orientation” that causes a much stronger factor loading than those in collectivistic cultures. On the other hand, people in China and South Korea do not focus on themselves, 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”, is not acceptable, while the notion, “having a lot of money (being rich) is good”, is acceptable. Our findings support the theory regarding the “I orientation” and culture (Riordan & Vandenberg, 1994). Thus, non-invariant items (e.g., Items 1 and 3 of Factor Rich) may not be undesirable because they might provide useful information 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.

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

Pay Level Satisfaction Scale In 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 Pay Satisfaction Questionnaire (PSQ). The reliability of the Pay Level Satisfaction, based on data for the whole sample (N = 6,175), was .903. We calculated alpha for each of the 30 samples and found that alpha varied between .712 and .962 and was higher than .90 in 22 samples. 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 rigorous criteria, 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 two factors for the 4-item Pay Level Satisfaction in Nigeria, the cause of non-invariance. Metric invariance. We examined metric (factor loadings) invariance by performing MGCFA based on data from 28 geopolitical entities (N = 5,771) at the “scale” level. The differences between unconstrained MGCFA (χ2 = 218.40, df = 56, p < .01, χ2/df = 3.90, TLI = .99, CFI = 1.00, SRMSR = .003, RMSEA = .02) and constrained MGCFA (χ2 = 472.86, df = 137, p < .01, χ2/df = 3.45, TLI = .99, CFI = 1.00, SRMSR = .0081, RMSEA = .02) was significant based on chi-square change (Δχ2 = 254.46, Δdf = 81, p < .05), but not significant based on fit index change (ΔCFI = .00, n.s.). Due to non-significant fit index change, we have achieved both configural and metric invariance, in general, and stop 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 Satisfaction Nigeria 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 relationship between 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, and Oman removed) (χ2 = 355.73, df = 61, p < .01, χ2/df = 5.83, TLI = 1.00, CFI = 1.00, SRMSR = .02, RMSEA = .03). Figure 2 showed that the love of money was negatively related to pay level satisfaction (-.09, C.R. = - 5.502, p < .001), supporting our hypothesis. Factor Rich had the highest factor loading (.87), followed by Factors Motivator (.68) and Important (.68). We investigated the same model for each of these 27 samples and presented the SEM results and the path (the Love of Money  Pay Level) in Table 5. In summary, the Love 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 there were significant differences in Factors Rich, Motivator, Important and Pay Level across

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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 tests revealed 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 South Korea. Group 1 had scores significantly higher than Group 2: Philippines (3.71), South Africa (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 grouped into three distinctive groups (F (26, 5595) = 14.48, p < .001). The first Group had the highest 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 between Group 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 CONCLUSION In 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 major theoretical 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 whole sample and good reliability for the measure. In Step 2, we focus on configural and metric invariance of the scale across 30 samples. Twenty eight (28) samples survived and two samples failed (Malta and Nigeria) our configural invariance analysis based on the most rigorous criteria. Third, metric invariance was achieved based on fit index change, but not achieved based on chi-square change for all 28 samples. Then, partial metric invariance is further analyzed at the factor level (Step 4) and at the item level (Step 5). In order to identify the differences in factor loading parameter estimates across samples for all items, we apply the Z test in Step 6. Factor Rich is the affective component of the Love of Money Scale, shows one’s emotions and value-laden orientation, and is the most critical component of LOMS because those who want to be rich may fall into temptation and engage in unethical behavior 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 invariance perspective. 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 the item. 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 geopolitical entities.

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Moreover, it is interesting to know that the wording of an item, such as the “I orientation” in Item 3 of the Love of Money Scale—“I want to be rich”, may cause people to respond to the same items differently based on their collectivistic or individualistic 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 may not be acceptable. Factor Rich has several diverse items that eventually capture these cultural differences. We offered our discussion regarding the four strategies regarding possible solutions for non-invariance (Poortinga, 1989; Cheung, 1999). As mentioned, we may delete 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 22 samples (28 - 6 = 22) using the 9-item-3-factor LOMS. It should be pointed out that our data from 28 geopolitical entities do provide not only “acceptable” but also “desirable” and “meaningful” culture differences (Cheung, 2002). Thus, we want to keep these items and 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 relationship between the Love of Money and another meaningful variable, we pick Pay Level Satisfaction in this study. Again, we apply the same rigorous procedure and achieve configural and metric invariance for the Pay Level Satisfaction across 28 samples after dropping two samples (Nigeria and Oman). We find that the Love of Money is negatively related to Pay Level Satisfaction for the whole sample (27 samples after eliminating Malta, Nigeria, and Oman) and for nine (9) geopolitical entities. Thus, researchers in cross-cultural studies can not take the wording of an item and culture 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 the literature. Implications. First, researchers should not take the measurement invariance for granted. Riordan and Vandenberg strongly encourage researchers “to systematically examine the assumption of stable and transferable measurement continua” (1994: 666). It is necessary to identify measurement properties before testing theories and models developed in the US in different geopolitical entities around the world. This study is an initial step in the cross-cultural validation of the Love of Money Scale using these new techniques (Cheung, 2002; Riordan & Vandenberg, 1994; Vandenberg & Lance, 2000). Second, this study examines the love of money across samples and “money” is the focus of our attention. Most items include the word “money”. The meanings of money reflect the culture, language, history, people, political systems and representation, and the value of the currency in each nation and may vary across geopolitical entities. We do have 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 to the research questionnaire in the context of culture, i.e., the “I” orientation, varies across geopolitical entities. Researchers have to examine the wording or phrasing of items

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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 in examining the psychometric properties of a measurement scale. Cronbach’s alpha examines inter-item reliability, whereas measurement invariance identifies equivalence across populations. Both contribute to our understanding of the measurement instrument. Some samples may pass the measurement invariance criteria but fail the internal reliability 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 the samples. Malaysia and Singapore-2 samples have Chinese, Malays, Indians, and Caucasians. The Nigeria sample has Igbo, Yoruba, Housa, and members of other ethnic groups. Singapore-1 sample has Chinese. Ethnic groups within one sample differ significantly in their history, culture, religion, language, social-economic status, and values toward the love of money. Our China-2 sample (full-time employees) achieves measurement invariance, while China-1 sample (the student sample) does not (i.e., 3.0 < χ2/df < 5.0). It is plausible that one’s employment status (employee vs. student) may play a 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 caused invariance 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 the literature (e.g., Bulgaria, Hungary, Malaysia, Malta, and Nigeria) may have difficulties in understanding and answering survey questionnaires using Liker-type rating scales, developed in the US. Demographic variables may contribute to poor reliability and noninvariance. Finally, researchers need to consider the four strategies that deal with metric noninvariant items (Cheung, 2002; Cheung & Rensvold, 1999, 2002; Vandenberg & Lance, 2000). Our results show that the significant factor loading differences are all related to data from three geopolitical entities. Thus, it is also important to examine the meanings of significant factor loading differences across samples and identify the ideal balance between (1) eliminating (a) samples or (b) items that cause significant factor loading differences and (2) achieving measurement invariance across samples carefully. Limitations. First, the senior author has taken every precaution and offered detailed instructions regarding the translation-back-translation procedure to all collaborators. There is no guarantee that the original meaning of these items is perfectly translated to the other languages. Due to cultural and language differences, researchers need to examine the sender, message, encoding, medium, decoding, receiver, and the noise in the communication process and translation differences in future studies. Second, we have collected data from full-time employees in these geopolitical entities. However, we do not have perfect control of many nuisance variables (e.g., the type of industry, the type of organization, the size of the organization, organizational culture, economy of the nation/region, unemployment rate, and participants’ knowledge of the English language, management literature, and the purpose of this research project).

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Nuisance variables are potential independent variables that if left uncontrolled could exert a systematic influence on the measurements in a study. However, with the large sample from 29 geopolitical entities, these nuisance variables are distributed randomly and may not have a systematic impact on the results of this study. We do not measure culture-related variables. We do, however, control demographic variables, e.g., age, gender, educational level, and income in examining the love of money. Our other variables (e.g., job title) are difficult to compare across organizations, industries, and samples and are not examined in this study. Third, our whole sample is reasonably large, but the sample from each geopolitical entity is quite small and does not represent a random sample or the average citizen of the geopolitical entity. We cannot generalize the findings to the whole population with full confidence. We do not examine differences (e.g., mean differences of Factors of LOMS) among samples. This may be achieved by combining samples with similar languages or cultures, etc. A heterogeneous sample from one entity may contribute to measurement non-invariance. Our understanding of the Love of Money Scale 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 ($5,600) and high corruption (CPI Rank = 57, Score = 3.5). As China continues its economic development and becomes the world’s largest market and economy, Chinese people will have the opportunity to make more money, increase consumptions, and realize the importance of money than before. With all these economic changes, the importance of money and the love of money (¥€$) in the world market may also change overtime and across cultures. The love of money may play a pivotal role in our understanding of people’s work-related attitudes and behaviors 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 offers researchers some confidence and insights in using the Love of Money Scale. When researchers and practitioners apply the same tools consistently across organizations and geopolitical entities overtime, the cumulative results will enhance our abilities to understand, predict, and control the role of the love of money in organizations.

Footnote 1

We 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 ____________________________________________________________________________ Age Sex Education Income Rich Motivator Important Pay N ____________________________________________________________________________

1. Australia 262 26.81 .29 12.50 12,008.99 3.73 3.23 3.79 3.14 2. Belgium 201 38.97 .57 16.09 20,269.40 3.40 3.04 3.68 3.30 3. Brazil 201 37.71 .45 16.92 5,006.64 3.59 3.05 3.73 2.68 4. Bulgaria 162 27.36 .64 16.91 2,148.70 3.92 3.57 3.82 2.65 5. China 204 31.57 .60 15.38 3,537.86 3.69 3.28 3.79 2.72 6. Egypt 200 40.26 .50 14.88 7,181.00 3.75 2.90 4.08 3.37 7. France 135 32.30 .56 16.19 16,753.70 3.79 3.38 3.61 2.86 8. HK 211 30.68 .49 15.67 47,509.06 4.06 3.33 4.07 3.00 9. 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 3.04 11. Macedonia 204 41.60 .44 13.31 2,176.48 3.97 3.54 4.07 2.87 12. Malaysia 200 31.80 .53 15.23 10,180.21 3.99 3.64 4.17 3.12 13. Malta 200 36.91 .51 16.47 14,922.60 3.95 3.13 4.33 2.56 14. Mexico 295 30.79 .54 14.31 7,416.59 3.42 3.26 3.80 2.97 15. 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 3.07 18. Philippines 200 33.45 .51 17.13 4,027.27 3.80 3.26 4.08 3.44 19. Portugal 200 35.18 .40 15.44 3,386.23 3.50 2.78 3.81 2.70 20. 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 2.76 22. Singapore-2 336 33.23 .57 15.01 29,277.07 3.95 3.52 4.07 3.26 23. Slovenia 200 38.72 .43 13.68 7,025.55 3.37 3.00 3.66 2.93 24. S. Africa 203 46.52 .46 15.76 5,247.93 3.88 3.16 4.03 2.28 25. S. Korea 203 37.21 .73 15.92 45,647.02 4.21 3.67 4.24 3.03 26. Spain 183 33.81 .59 14.15 4,142.85 3.56 2.91 3.72 3.12 27. Taiwan 201 34.95 .48 16.56 22,567.04 4.10 3.81 4.15 3.03 28. Thailand 200 33.29 .54 16.98 10,950.04 3.88 3.30 3.87 3.19 29. 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 ____________________________________________________________________________

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

______________________________________________________________________________ The Love of Money Scale Factor Rich 1. I want to be rich. 2. It would be nice to be rich. 3. Have a lot of money (being rich) is good. Factor Motivator 4. I am motivated to work hard for money. 5. Money reinforces me to work harder. 6. I am highly motivated by money. Factor Important 7. Money is good. 8. Money is important. 9. Money is valuable. 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. ________________________________________________________________________

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Table 3. Configural Invariance of the 9-Item-3-Factor Love of Money Scale (LOMS) ____________________________________________________________________________ df p χ2/df TLI CFI SRMSR RMSEA χ2 ____________________________________________________________________________ 1. Australia 74.47 24 .00 3.10 .99 .99 .06 .09 2. Belgium 27.41 24 .29 1.14 1.00 1.00 .04 .03 3. Brazil 26.49 24 .33 1.10 1.00 1.00 .04 .02 4. Bulgaria 34.37 24 .08 1.43 1.00 1.00 .04 .05 5. China 34.82 24 .07 1.45 1.00 1.00 .03 .05 6. Egypt 29.64 24 .20 1.24 1.00 1.00 .04 .03 7. France 37.98 24 .03 1.58 .99 1.00 .05 .07 8. HK 46.43 24 .00 1.93 .99 1.00 .04 .07 9. Hungary 107.09 24 .00 4.46 .95 .97 .08 .19 10. Italy 51.98 24 .00 2.17 .99 .99 .04 .08 11. Macedonia 60.84 24 .00 2.53 .99 .99 .05 .09 12. Malaysia 106.90 24 .00 4.45 .98 .99 .05 .13 13. Malta 445.66 24 .00 18.57 .89 .94 .12 .30* 14. Mexico 79.35 24 .00 3.31 .99 .99 .05 .09 15. Nigeria 92.67 24 .00 3.86 .98 .99 .12 .12* 16. Oman 15.26 24 .91 .64 1.00 1.00 .03 .00 17. 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 .04 20. Romania 60.24 24 .00 2.51 .99 .99 .05 .09 21. Russia 33.59 24 .09 1.40 1.00 1.00 .04 .04 22. Singapore-1 36.66 24 .05 1.53 1.00 1.00 .03 .05 23. Singapore-2 95.95 24 .00 4.00 .99 .99 .05 .09 24. Slovenia 41.30 24 .02 1.72 .99 1.00 .06 .06 25. S. Africa 37.64 24 .04 1.57 .99 1.00 .06 .05 26. S. Korea 43.74 24 .01 1.82 1.00 1.00 .04 .06 27. Spain 41.08 24 .02 1.71 .99 1.00 .05 .06 28. Taiwan 72.01 24 .00 3.00 .99 .99 .05 .10 29. Thailand 30.64 24 .16 1.28 1.00 1.00 .03 .04 30. 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 a sample failed two or more criteria, we eliminated the sample and printed results in bold* (Malta and Nigeria).

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Table 4. Z-Test Results for Factor Rich, Item 3 —“Having a lot of money (being rich) is good” Across 28 Geo-political Entities ______________________________________________________________________________________________________ 2

3

6

7

11

12

L

0.773

1

0.547

0.904

1.098

1.383

1.033

0.787

0.899

0.459

1.153

1.048

1.044

0.927

0.658

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

0.058

2.173

-1.493

-2.757

-4.133

-2.699

-0.120

-1.193

2.907

-3.975

-2.383

-2.087

-1.636

1.331

-3.514

-4.286

-5.351

-4.454

-1.878

-2.999

0.736

-5.587

-3.968

-3.560

-3.546

-1.105

-1.676

-3.283

-1.376

1.018

0.048

4.211

-2.679

-1.272

-1.094

-0.252

2.948

-0.418

0.111

0.495

0.329

1.048

-0.094

0.080

0.083

0.295

0.785

0.547

0.877

0.735

1.393

0.360

0.495

0.483

0.717

1.173

0.240

1.018

-0.224

-0.026

-0.018

0.200

0.735

-0.186

0.539

-0.630

-0.419

-0.396

-0.242

0.231

0.752

-0.456

-0.248

-0.231

-0.051

0.452

-1.235

-0.974

-0.925

-0.838

-0.370

0.182

0.180

0.427

0.975

0.006

0.211

0.703

0.194

0.660

1

Australia

2

Belgium

3

Brazil

4

Bulgaria

5

China

6

Egypt

7

France

8

H.K.

9

Hungary

10

Italy

11

Macedonia

12

Malaysia

13

Mexico

14

Oman

15

Peru

16

Philippines

17

Portugal

18

Romania

19

Russia

20

Singapore1

21

Singapore2

22

Slovenia

23

S. Africa

24

S. Korea

25

Spain

26

Taiwan

27

Thailand

28

USA

4

5

0.422

8

9

10

13

14

0.534

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15

The Love of Money

16

17

18

19

20

21

22

23

24

25

28

26

27

28

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

1.106

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

0.124

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

-2.386

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

-3.760

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

-1.466

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

-0.050

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

1.523

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

-0.509

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

-2.018

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

-1.382 -4.271

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

0.329

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

-0.370

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

-0.370

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

-1.261

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

-3.273

-0.389

0.282

0.025

0.044

-0.299

0.119

2.013

0.853

-1.839

2.192

-1.661

0.236

-0.597

0.698

0.451

0.454

0.124

1.878

3.437

1.745

-0.006

3.665

0.090

2.007

0.946

-0.295

-0.256

-0.642

-1.602

1.126

0.287

-3.783

1.319

-3.419

-1.513

-1.929

0.023

-0.362

0.010

2.434

0.843

-2.413

2.818

-2.143

0.167

-0.812

-0.367

-0.107

2.125

0.783

-2.329

2.396

-2.091

0.028

-0.852

1.783

3.742

1.558

-0.630

4.176

-0.482

1.970

0.591

2.438

0.841

-2.433

2.828

-2.160

0.158

-0.822

-0.163

-4.283

0.000

-3.955

-2.381

-2.578

-1.851

0.167

-1.771

-0.790

-1.186

4.748

0.116

2.630

1.122

-4.338

-2.795

-2.784

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

2.334

27 28

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

0.979 -0.946

Management and Organization Review (2006) 2:3 423–452

The Love of Money

29

TABLE 5. Configural Invariance of the 3-Item-1-Factor Pay Level Satisfaction Scale (PSQ) ____________________________________________________________________________ df p χ2/df TLI CFI SRMSR RMSEA χ2 ____________________________________________________________________________ 1. Australia .31 2 .86 .16 1.00 1.00 .00 .00 2. Belgium 4.82 2 .00 2.41 1.00 1.00 .01 .08 3. Brazil 2.25 2 .33 1.13 1.00 1.00 .01 .03 4. Bulgaria 14.49 2 .00 7.25 .97 .99 .02 .19 5. China 2.84 2 .24 1.42 1.00 1.00 .02 .05 6. Egypt 5.06 2 .08 2.53 .99 1.00 .02 .09 7. France 13.23 2 .00 6.62 .97 .99 .02 .20 8. HK 5.49 2 .06 2.75 .99 1.00 .02 .09 9. Hungary 11.46 2 .00 5.73 .97 .99 .01 .22 10. Italy 13.11 2 .00 6.56 .98 1.00 .02 .17 11. Macedonia 13.52 2 .00 6.76 .97 .99 .04 .17 12. Malaysia 17.00 2 .00 8.50 .97 .99 .02 .19 13. Malta 25.48 2 .00 12.47 .95 .99 .02 .24 14. Mexico 4.04 2 .13 2.02 1.00 1.00 .01 .06 15. 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 .10 18. Philippines 10.21 2 .01 5.10 .98 1.00 .03 .14 19. Portugal 5.92 2 .05 2.96 .99 1.00 .01 .10 20. Romania 9.11 2 .01 4.56 .98 1.00 .01 .13 21. Russia 5.53 2 .06 2.77 .99 1.00 .02 .09 22. Singapore-1 21.30 2 .00 10.65 .97 .99 .02 .22 23. Singapore-2 2.23 2 .33 1.11 1.00 1.00 .01 .02 24. Slovenia 7.33 2 .03 3.67 .99 1.00 .01 .12 25. S. Africa .05 2 .07 .07 1.00 1.00 .00 .00 26. S. Korea 5.53 2 .06 2.76 .99 1.00 .01 .09 27. Spain 4.01 2 .13 2.01 1.00 1.00 .01 .07 28. Taiwan 2.17 2 .34 1.09 1.00 1.00 .01 .02 29. Thailand 5.24 2 .07 2.62 .99 1.00 .02 .09 30. 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 a sample failed two or more criteria, we eliminated the sample and printed results in bold* (Nigeria and Oman). We retained 28 samples in this analysis.

Management and Organization Review (2006) 2:3 423–452

The Love of Money

30

TABLE 5 The Love of Money to Pay Level Satisfaction Relationship ____________________________________________________________________________ χ2 df p χ2/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 -.04 3. 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 -.04 6. Egypt 58.40 61 .57 .96 1.00 1.00 .04 .00 -.07 7. France 99.22 61 .00 1.63 .99 .99 .06 .07 -.17 8. 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 .02 12. Malaysia 198.39 61 .00 3.25 .98 .99 .06 .11 .01 13. Mexico 127.66 61 .00 2.09 .99 .99 .05 .06 -.03 14. Peru 107.26 61 .00 1.76 .99 .99 .06 .06 .10 15. Philippines 157.02 61 .00 2.57 .99 .99 .09 .09 .09 16. 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 -.05 18. 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 -.15 23. S. Korea 80.18 61 .00 1.31 1.00 1.00 .04 .04 -.10 24. Spain 87.56 61 .01 1.44 .99 1.00 .05 .05 -.12 25. Taiwan 120.99 61 .00 1.98 .99 .99 .05 .07 -.11 26. 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

31

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

The Love of Money and Pay Satisfaction Relationship

The Love of Money 0, 0,

e1 0,

e2 0,

e3

1

1

1

M1 1 M2

e21 1 0

Rich

Pay Level Satisfaction

M3 1 1

0, 0,

e4 0,

e5 0,

e6

1

M4 1

1

M5

1

0,

Motivator

e7 0,

e8 0,

e9

1

M6

M7 1

1

1

The Love of Money

S2

0

e66

S3

M8 M9

1

0

Important

0,

e32

1

0,

S3

e23

1

Pay Level 1

0, 0,

e31

e22 0

1

S1

0,

1

1

0,

e33 0,

e34

The Love of Money: 31 Samples FIGURE 2

32

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

The Love of Money and Pay Satisfaction Relationship

Chi-Square = 355.73 df = 61 p = .00 Chi-Square/df = 5.83 TLI = 1.00 CFI = 1.00 SRM SR = .02 RM SEA = .03

The Love of Money .63

e1

.61

e2

e21

M1 .80 M2

.76

.78

Rich

Pay Level Satisfaction

.60

e3

M3

.78

.63

.87 .79 .68

e4

M4 .82 .72

e5

M5

e22

Motivator

.68 The Love of Money

-.09

.01

.88

Pay Level

.86

M6 .80 .49

M7 .70 .45

e8

M8

.67

.58

e9

M9

e32 .74

.76

e33

e66

.86 e7

S2 S3

.63

e6

e31 .77

.47

.85

S1

.68

e23 .47

Important

.74

S3

e34

The Love of Money: 31 Samples

33

The Love of Money and Pay Satisfaction Relationship

Chi-Square = 355.73 df = 61 p = .00 Chi-Square/df = 5.83 TLI = 1.00 CFI = 1.00 SRM SR = .02 RM SEA = .03

The Love of Money .63

e1

.61

e2

e21

M1 .80 M2

.76

.78

Rich

Pay Level Satisfaction

.60

e3

M3

.78

.63

.87 .79 .68

e4

M4 .82 .72

e5

M5

e22

Motivator

.68 The Love of Money

-.09

Pay Level

M7 .70 .45

M8

.67

.58

e9

.74

e33

M6 .80 .49

e8

e32

S3 .86

e7

S2

.86

.63

e6

e31 .77

.88

.47

.85

S1

M9

.76

.68

e23 .47

Important

.74

S4

e34

The Love of Money: 31 Samples

34

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

1.090

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

0.081

-0.103

4.238

0.897

4.394

4.736

-1.049

1.783

-1.107

3.960

3.569

4.368

2.867

2.530

2.832

0.797

3.105

3.196

-0.833

1.379

-0.892

2.716

2.566

3.091

2.044

1.845

-2.299

0.904

1.058

-3.374

-2.335

-3.342

-0.167

-0.240

0.876

-1.591

-1.848

0.685

-0.507

0.146

-0.528

0.537

0.497

0.655

0.334

0.277

-0.010

-1.125

-0.573

-1.138

-0.182

-0.187

-0.004

-0.404

-0.455

-1.161

-0.603

-1.174

-0.188

-0.192

0.006

-0.427

-0.481

0.669

-0.026

1.032

0.983

1.121

0.851

0.796

-0.69

0.434

0.39

0.568

0.204

0.141

1.047

0.999

1.135

0.869

0.815

-0.019

0.178

-0.243

-0.302

0.183

-0.208

-0.264

-0.399

-0.451

1

Australia

2

Belgium

3

Brazil

4

Bulgaria

5

China

6

Egypt

7

France

8

H.K.

9

Hungary

10

Italy

11

Macedonia

12

Malaysia

13

Mexico

14

Oman

15

Peru

16

Philippines

17

Portugal

18

Romania

19

Russia

20

Singapore1

21

Singapore2

22

Slovenia

23

S. Africa

24

S. Korea

25

Spain

26

Taiwan

27

Thailand

28

USA

0.659

-0.063

The Love of Money: 31 Samples

35

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

1.013

0.107

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

-0.4

-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

-0.011

4.34

0.486

2.02

-4.522

-1.912 1.75

The Love of Money: 31 Samples

36

The Love of Money and Pay Satisfaction Relationship

Chi-Square = 98.14 df = 61 p = .00 Chi-Square/df = 1.61 TLI = .99 CFI = 1.00 SRM SR = .02 RM SEA = .06

The Love of Money .77

e1

.91

e2

e21

M1 .88 M2

.05

.95

Rich

Pay Level Satisfaction

.66

e3

M3

.82

.60

.23 .78 .60

e4

M4 .77 .82

e5

M5

e22 .04

Motivator

1.93 The Love of Money

.20

Pay Level

.85 S2 .87

M6 .75 .66

M7 .82 .70

e8

M8

.84

.57

e9

M9

.75

.75

e33

e66

.90 e7

e32

S3

.56

e6

e31 .72

3.74

.90

S1

.00

e23 .00

Important

.81

S3

e34