Springer 2006
Journal of Business Ethics (2007) 72:375–393 DOI 10.1007/s10551-006-9176-4
Income and Quality of Life: Does the Love of Money Make a Difference?
ABSTRACT. This paper examines a model of income and quality of life that controls the love of money, job satisfaction, gender, and marital status and treats employment status (full-time versus part-time), income
Thomas Li-Ping Tang (Ph.D., Case Western Reserve University) is a Full Professor of Management in the Department of Management and Marketing, Jennings A. Jones College of Business at Middle Tennessee State University (MTSU), Murfreesboro, Tennessee, 37132. He has taught Industrial and Organizational Psychology at National Taiwan University and at MTSU. Professor Tang teaches (has taught) EMBA courses in China and France. He serves (has served) on the editorial review board of six journals and as a reviewer for 26 journals around the world. Professor Tang’s research interests focus upon people’s work motivation, compensation, money attitudes, the Love of Money, pay satisfaction, turnover, stress, and cross-cultural issues. He has published more than 100 journal articles in top behavior sciences and management journals, including Journal of Applied Psychology, Personnel Psychology, Human Relations, Journal of Management, Management Research, Management and Organization Review, Journal of Organizational Behavior, Journal of Business Ethics, Journal of Managerial Psychology, European Sport Management Quarterly, Journal of Higher Education, and others. He has presented more than 185 papers in professional conferences and invited seminars in Austria, China, Czech Republic, Finland, France, Greece, Hong Kong, Italy, Mexico, New Zealand, Singapore, Spain, Taiwan, the UK, the US, and other countries. His research has been cited in many languages, textbooks of several fields (e.g., Management Organizational Behavior, Human Resources Management, Industrial and Organizational Psychology, Human Relations, Compensation, and Statistics), and popular books. He was the winner of two Outstanding Research Awards (1991, 1999), and Distinguished International Service Award (1999) at Middle Tennessee State University. He also received the Best Reviewer Award from the International Management Division of the Academy of Management in Seattle, WA (2003).
Thomas Li-Ping Tang
level, and gender as moderators. For the whole sample, income was not significantly related to quality of life when this path was examined alone. When all variables were controlled, income was negatively related to quality of life. When (1) the love of money was negatively correlated to job satisfaction and (2) job satisfaction was positively related to both income and quality of life, income was negatively related to quality of life for fulltime, high-income, and male employees. When these two conditions failed to exist, income was not related to quality of life for part-time, median- or low-income, and female employees. This model provides new insights regarding the impact of the love of money and job satisfaction on the income–quality of life relationship. KEY WORDS: income, quality of life, the love of money, job satisfaction, employment status, income level, gender, marital status
Research suggests that in general, there is a positive relationship between income and subjective wellbeing (SWB) or happiness (Diener, 1984). However, in the U.S. income and happiness correlate only 0.13 (Diener et al., 1993). As societies grow wealthy, differences in well-being are less frequently due to income and are more frequently due to social relationships and enjoyment at work (Diener and Seligman, 2004). Materialism is negatively correlated with life satisfaction, subjective well-being, and general affect (Belk, 1985; Richins and Rudmin, 1994; Solberg et al., 2004). Moreover, pursuing extrinsic goals or achieving the American Dream of financial success (e.g., extrinsic rewards, other’s approval, and having instead of being) may distract people form achieving meaningful life satisfaction and may have a dark side. However, the negative impact of the goal for financial success on overall life satisfaction diminished as household income increased (Nickerson et al., 2003).
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‘‘Understanding individual differences in adaptation will help illuminate when and why adaptation does or does not occur’’ (Diener et al., 2006, p. 310). One construct that should not be overlooked is the ‘‘meaning of money’’ (Barber and Bretz, 2000, p. 45). Very little research has examined the income to quality of life relationship from the meaning of money perspectives, the love of money, in particular. This study will fill in the void and incorporate the individual difference variable, the love of money in investigating these issues. More specifically, the major purpose of this study is to identify conditions under which higher income will fail to produce higher quality of life perceptions, and to identify subsets of populations where this may occur. The present study This study investigates a model of income and quality of life relationship (Figure 1) and offers the following unique theoretical and empirical contributions to the literature. Intuitively, it is reasonable to expect that for many people, income will be positively related to quality of life. However, when we incorporate the love of money into a model of income–quality of life relationship, it is plausible that Hypothesized Model Control Variable
income may lead to negative quality of life for some people who are obsessed with money and that income may not be related to quality of life at all for others. There is a commonly accepted belief: Happiness, or satisfaction with different aspects of our life, does not depend on what we have, but it does depend on how we feel towards what we have. We can be happy with little and miserable with much. Thus, one’s love of money may play an important role and serve as one’s frame of reference or standards in evaluating one’s quality of life here (e.g., Easterlin, 2006; Michalos, 1985). Further, research suggests that one’s satisfaction with pay depends on one’s love of money and how one compares (Tang et al., 2005). I trust that research on one’s satisfaction with pay may be expanded (to a larger extent) to one’s subjective well-being or one’s quality of life. More specifically, the proposed model controls (1) two important attitudinal variables: (a) job satisfaction (Smith et al., 1975) and (b) positive affect toward money (Tang, 1992), the construct of the love of money (LOM), in particular (Tang and Chiu, 2003), and (2) demographic variables (i.e., gender and marital status) in a cross-sectional sample of 458 employees in the US. Moreover, since there might be differences in income–quality of life relationship between full-time and part-time employees, high-, median-, and low-income workers, and men and women, this study treats employment status, income level, and gender as moderators. A brief review of the literature is presented below.
Gender Major Path Income Marital Status
Job Satisfaction Quality of Life The Love of Money
Figure 1. The hypothesized model.
Subjective well-being Subjective well-being (SWB) may involve happiness, life satisfaction, and positive affect (Diener, 1984). Most measures of SWB correlate moderately with each other and have adequate reliability and internal consistency (Eid and Diener, 2004). For example, physical pleasure is associated with daily satisfaction (Oishi et al., 2001). People with a high level of subjective well-being are satisfied with marriage, work, health, finances, and friendship (Diener and Diener, 1996) and are associated with frequent positive affect, infrequent negative affect, and a global sense of satisfaction with life (Myers and Diener, 1995). This study examines the subjective well-being using the 15 categories of quality of life (Flanagan, 1978).
The Love of Money Income and subjective well-being The main purpose of this investigation is to ascertain the income to subjective well-being relationship. It makes intuitive sense that the higher the income, the higher the life satisfaction because money can satisfy one’s needs and improve one’s quality of life. Income had a positive correlation with general affect (subjective well-being) (LaBarbera and Gurhan, 1997). After basic need fulfillment is controlled, income correlated with subjective well-being (Diener et al., 1995). Other variables may have impacts on this relationship. Desires Individuals’ desires play a pivotal role in determining people’s satisfaction with income (Crawford Solberg et al., 2002). People with higher material goals or materialism are substantially less happy (Solberg et al., 2004). The income to subjective well-being relationship depends on the amount of material desires that people’s income allows them to fulfill. Srivastava et al. (2001) incorporated motives of making money in studying the relationship between importance of money and subjective well-being. If people valued money to show off, get power, compare oneself to others or overcome self doubts (negative money motives), then it led to money being viewed as important and to low subjective well being. When motives were controlled, money importance and subjective well being were not related. Having healthy motives for wanting money did not affect either subjective well being or importance of money. The positive and negative money motives are different from materialism. Tang et al. (2001) have found that the love of money is significantly correlated to materialism; it is similar to but not the same as materialism. It is also different from motives, negative motives, in particular, examined by Srivastava et al. (2001). Employment status Young people enrolled in schools are an important source of part-time employees and are voluntary part-timers. In general, part-time workers earn lower
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mean hourly wages, less fringe benefits (Tilly, 1992); have little opportunity for promotions, and higher turnover than full-time employees. Part-time employees tend to work at the entry-level and in unskilled and dead-end jobs with little responsibility and promotional opportunities. From the employer’s perspectives, part-time workers are difficult to motivate and are only in their jobs for the money (Still, 1983). Part-time employees are less satisfied with their pay, benefits, the job in general, career, job security, and promotion and have higher role strain, role conflict, and overload than their full-time counterparts (Bennett et al., 1994). Others found no significant differences between these two groups of employees on pay satisfaction and overall job satisfaction (Levanoni and Sales, 1990). Still others found that part-timers have higher satisfaction with social context of work, supervision, and working conditions (Bennett et al., 1994) than full-timers. It is reasonable to expect that part-time workers may bring to their work a different set of expectations and ‘‘frames of reference’’ (the love of money and job satisfaction) and their satisfaction with work and life may be more detached than that of full-time employees (Tang et al., 2002). Due to part-time workers’ detached notion about their job and life, many of them work for the money only and do not care for their jobs. Their work may contribute very little to their quality of life. Therefore, an increase in income may have very little impact on their subjective well being or quality of life. This research will treat employment status (full-time versus part-time) as a moderator.
Income level As income increases, social relationships and enjoyment at work tend to play an important role toward subjective well-being (Diener and Seligman, 2004). Higher incomes are related to lower marginal utility of money. Money assumes decreasing importance as a person advanced in the organizational hierarchy. From a global perspective, as nations get richer, increases in wealth are associated with diminishing increases in well-being (Ahuvia and Friedman, 1998). Within nations, increased income is associated with well-being primarily for the poor; once the poverty threshold is crossed, increased income
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matters little for happiness (Csikszentmihalyi, 1999; Diener and Oishi, 2000). Importance of money was negatively correlated with life satisfaction among college students from 41 nations (Diener and Oishi, 2000). Nickerson et al. (2003) examined household-income midpoint from $500 to $290,000 and found that the negative impact of the goal for financial success on overall life satisfaction diminished as household income increased. The hidden costs of high income (i.e., work-related stress) and the work– family conflict can be a source of stress that influences psychological and physical outcomes and diminishes the quality of life (Adams et al., 1996; Bacharach et al., 1991; Rousseau, 1978). Since time is money, one’s dedication of time to work may lead to higher income, but at the same time may decrease the time, relatively speaking, to enjoy other activities and the fruits of one’s labor, such as leisure, family, social, and learning and exploration. Middle-class employees with high income will have more duties and responsibilities than those with low income. Since achieving the American Dream of financial success has a dark side (Nickerson et al., 2003), it is plausible that income may deteriorate the quality of life more for the highincome middle-class employees (not the super rich people in the society) than the low-income workers. This study examines income as a major variable and treats income level (high, median, and low) as a moderator.
Marital status Relations between marital status and subjective wellbeing are similar across 42 nations in the world (Diener et al., 2000). Married people have high life satisfaction (Mroczek and Spiro, 2005). Never married men have unfavorable subjective well-being. Men and women in formal marriages exhibit higher life satisfaction than other forms of family arrangements. A life-long marriage is the most satisfying (Evans and Kelley, 2004). Thus, marital status may contribute to our understanding of life satisfaction and will be treated as a control variable in the model. In summary, these findings seem to suggest that the income to quality of life relationship may depend on one’s desires or the importance of money and importance attached to job satisfaction. For full-time employees, high-income employees, and males, their job is a more important part of their lives than their counterparts. Their job satisfaction will be related to income and the quality of life. For part-time employees, low-income workers, and females, job satisfaction may have very little impact on income and quality of life and their income to quality of life relationship will be weak. This study controls job satisfaction, the love of money, gender, and marital status in investigating the income to quality of life relationship and treats employment status, income level, and gender as moderators. Our literature review leads us to conclude that omitting the love of money in research has produced very specific prior anomalies in the literature. We will now turn to the literature on the love of money.
Gender Gender is a statistically significant predictor of subjective well-being. The relationship between social class and subjective well-being is reduced when gender is used as a covariate (Haring et al., 1984). Women tend to rate social needs as more important than do men, while men tend to consider pay more important than do women. Women are subjectively satisfied with their pay in spite of objective underpayment, the paradox of the contented female worker (Crosby, 1982; Major and Konar, 1984). Male employees tend to have high income than female employees. This study will examine gender as a control variable and also treat gender (male versus female) as a moderator.
The love of money Money is the instrument of commerce and the measure of value. There has been a significant increase regarding the importance of money in the US and around the world (e.g., Milkovich and Newman, 2005; Rynes and Gerhart, 2000). In 1971, only 49.9% of freshman said that the important reason in deciding to go on to college is ‘‘to make more money’’. In 1993, that number increased to 75.1% (The American Freshman, 1994). In 1978, men ranked pay the fifth and women ranked pay the seventh in importance, among 10 job preferences (Jurgensen, 1978). In 1990, among 11 work goals,
The Love of Money pay was ranked the second in importance in the US and the UK and the first in Germany (Harpaz, 1990). The lack of money has become the number one cause of dissatisfaction among university students for the past 7 years (1997–2003), up from the second place at an earlier period (1981–1987) (Bryan, 2004). Managers are keenly aware of the importance of managing human resource effectively and efficiently in the global market. Money has become more important than ever in attracting, retaining, and motivating employees around the world. Pay and job dissatisfaction may have many negative consequences in organizations, e.g., theft, turnover, counterproductive behavior, and misbehavior (e.g., Greenberg, 1993; Robinson and Bennett, 1995; Tang et al., 2000; Vardi and Weitz, 2004). Although money is used universally, the meaning of money is in the eye of the beholder and is a ‘‘frame of reference’’ to examine their everyday lives. Among many measures of money attitudes in the literature (e.g., Furnham and Argyle, 1998; Wernimont and Fitzpatrick, 1972), 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. This study adopts the love of money scale (LOMS, a subset of MES) and selects Factors Rich, Budget, Important, and Success (Tang, 1992; Tang and Chiu, 2003). A high love of money score means one’s positive affect toward money: one wants to be rich (affective), one budgets money carefully (behavioral), and one considers money important and as a sign of success (cognitive). Researchers have examined these measures in more than 30 countries and cited MES and LOMS in more than seven languages (Tang et al., 2005). Researchers have defined the love of money as (1) one’s wants, desires, values and ‘‘aspirations’’ of money (Easterlin, 2006), (2) one’s attitudes toward money, (3) one’s meaning of money, (4) not one’s needs, greed (Sloan, 2002), or materialism (Belk, 1985; Richins and Rudmin, 1994), (5) a multidimensional individual difference variable with affective, behavioral, and cognitive components (i.e., the ABC model), and (6) the combined notion of several constructs or factors (Du and Tang, 2005; Luna-Arocas and Tang, 2004; Tang and Chiu, 2003; Tang et al., 2005; Tang et al., in press).
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The love of money and job satisfaction Research suggests that the love of money is negatively related to pay satisfaction (Tang and Chiu, 2003; Tang et al., in press) that, in turn, may lead to unethical behavior (Chen and Tang, 2006; Luna-Arocas and Tang, 2004; Tang and Chen, 2006; Tang and Chiu, 2003). Further, the income to pay satisfaction relationship depends on (1) the importance of the love of money and (2) the extent to which the love of money is applied to evaluate pay equity comparison satisfaction (Tang et al., 2005). If the love of money is used as one’s frame of reference (or evaluation standards) in evaluating pay comparison, income leads to low pay satisfaction. It is possible that if one is obsessed with money and values money highly (i.e., high love of money), then, one may judge everything from the perspective of money. If one values money highly, one is less likely satisfied with pay, based on the discrepancy model and equity model of pay satisfaction. If it is not, income is not related to pay satisfaction. The income to pay satisfaction relationship depends on one’s love of money and how one compares. Pay satisfaction is a part of job satisfaction. Following the above argument, if the love of money is related to job satisfaction, then, income may have a negative impact on quality of life in this study. If the love of money is not related to job satisfaction, then, income may not be related to quality of life.
The love of money and income Income will be positively related to the love of money when people are extremely obsessed with money due to pay compression or financial hardship. They may feel poor financially and psychologically (Tang et al., 2005). The more money they have, the more they want it, up to a point. The income to the love of money relationship is negative among Hong Kong employees because their income is higher than the GDP per Capita and they feel rich financially and psychologically (Tang and Chiu, 2003). When people are paid at the market level and feel adequate financially and psychologically, income will not be related to the love of money (Tang et al., 2005).
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The love of money and quality of life Those with positive dispositions are more willing to proactively change their lives. The love of money reflects one’s positive disposition regarding money. Mental health workers with high love of money have higher voluntary turnover regardless of their intrinsic job satisfaction than those without (Tang et al., 2000). Materialism is negatively correlated with happiness and life satisfaction (Richins and Rudmin, 1994). The love of money, however, is not exactly the same as materialism. We argue that both the love of money and quality of life measure one’s positive dispositions. They will be positively correlated for some people. The love of money to quality of life relationship has not been examined. This study will investigate this relationship on an exploratory basis.
Job satisfaction
makes intuitive sense that the higher the pay, the higher the pay satisfaction. Pay satisfaction is only a subset of job satisfaction. Income will be positively related to job satisfaction for most people. However, for part-time and low-income individuals, if job is not a critical part of their life, then, income and job satisfaction may not be significantly correlated. In summary, this study examines the income to quality of life relationship and controls gender, marital status, job satisfaction, and the love of money. I argue that a high love of money may lead to low job satisfaction. Job satisfaction is related to life satisfaction, in general. Job satisfaction is also correlated with income. I predict a negative relationship between income and quality of life if the following two conditions exist: (1) the love of money is negatively related to job satisfaction and (2) job satisfaction is related to both income and quality of life. If the above two conditions do not exist, then, income is not related to quality of life.
Job satisfaction and quality of life Job satisfaction is related to subjective well-being (Judge and Chandler, 1996). Job satisfaction has a stronger effect on life satisfaction than vice versa (Iverson and Maguire, 2000). Others suggest that life satisfaction causes job satisfaction (Judge and Watanabe, 1993). A causal influence from life satisfaction to job satisfaction is supportive of the ‘‘dispositional’’ perspective (Staw et al., 1986). Job and life satisfaction are significantly and reciprocally related (Judge and Watanabe, 1993). This study does not examine the cause-and-effect relationship between the two variables. Based on this literature review, it is reasonable to expect that job satisfaction and quality of life will be positively related. If one is happy with one’s job, then, one will be happy with one’s quality of life because job satisfaction is a component of one’s quality of life.
Job satisfaction and income 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 and Judge, 2000). It
Hypothesis 1: There is a negative relationship between income and quality of life, if (1) the love of money is negatively related to job satisfaction and (2) job satisfaction is related to both income and quality of life.
Method Participants and measures Research data were collected from a convenience sample of 458 participants in the Southeastern US (personnel managers attending professional seminars, employees at Arnold Engineering Development Center, university faculty and staff, local schools, banks, churches, and other establishments). All participants were asked to indicate their annual income (full-time US$33,982 and part-time $12,665), age, sex (235 (51.3%) male and 223 (48.7%) female), employment status (265 (57.9%) full-time and 193 (42.1%) part-time), marital status (214 (46.7%) married and 235 (51.3%) non-married), and other variables. The majority of part-time employees in this sample were university students. They were
The Love of Money mostly paid at the market level. Full-timer’s average income was close to the GDP per Capita at the time of data collection. In this study, I employed the original 30-item Money Ethic Scale (Tang, 1992), the Job Descriptive Index (JDI) (Smith et al., 1975), and the 15 categories of quality of life (QOL) (Flanagan, 1978). All these three measures have very well established reliability and validity in the literature. For the Love of Money Scale (LOMS), I selected 10 items for Factors Rich, Budget, Important, and Success for this study. A 7-point Likert scale was used to measure the LOMS and quality of life (QOL) with disagree strongly (1), neutral (4), agree strongly (7) and very dissatisfied (1), neutral (4), and very satisfied (7) as anchors, respectively. JDI employs the 3-point format: ‘‘Yes, ?, No’’. A scoring key was used to evaluate their satisfaction that produced a score of 3, 1, or 0 for each item. The number of items and Cronbach’s alpha for the Love of Money (LOMS), JDI, and quality of life (QOL) are listed below: the LOM: Important (4 items, 0.56), Budget (2 items, 0.83), Success (2 items, 0.69), and Rich (2 items, 0.60), JDI: work (18 items, 0.76), pay (9 items, 0.80), promotions (9 items, 0.90), supervision (18 items, 0.89), and coworkers (18 items, 0.89), and Quality of Life (15 items, 0.79): leisure (4 items), personal development and fulfillment (4 items), social activities (4 items), family and relatives (3 items).
Data analyses Vandenberg and Lance (2000) have provided the following criteria for using CFI and TLI (0.90 = the lower bound of a good fit, 0.95 or higher = excellent fit) and RMSEA (0.08 = the upper limit of a good fit, 0.06 or less = excellent fit) and testing the difference between models, i.e., the chi-square change (Dv2/Ddf) and the fit index change (DCFI) as a ‘‘supplement’’ (i.e., D = 0.01 or less: differences between models do not exist; between 0.01 and 0.02: differences between models may suspiciously exist; and greater than 0.02, differences between models definitely exist) (see also Cheung and Rensvold, 2002). Researchers have examined the ‘‘random measurement error’’ in path analysis models. In the present application, the path
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(factor loading) from any construct to its measured variable equals the square root of the reliability of the measured variable, while the amount of random error variable is the quantity one minus the reliability.
Results Step 1: Common Method Variance Harman’s one-factor test Since the common method variance is a potential problem in this study (Podsakoff et al., 2003), Harman’s one-factor test was conducted to examine the unrotated factor solution involving all 13 variables of interest (four factors of the love of money, five factors of the JDI, and four factors of quality of life) in an exploratory factor analysis. There were four factors (explained variance: 22.43%, 12.97%, 10.94%, and 8.034%, respectively, total explained variance = 54.37%). The scree plot suggested that three factors should be retained. No single factor accounted for the majority of the covariance in the independent (controlled) variables and the criterion variable. Thus, common method variance was non-significant in this study. Controlling for the effects of a single unmeasured latent method factor To demonstrate that the results are not due to common method variance, the measurement model with the addition of a latent common method variance factor (v2=233.78, df = 62, v2/df = 3.77, p = 0.00, TLI = 0.98, CFI = 0.99, RMSEA = 0.08) must not significantly improve the fit over the measurement model (v2=335.25, df = 75, v2/ df = 4.47, p = 0.00, TLI = 0.98, CFI = 0.98, RMSEA = 0.09). The difference was significant based on the v2 change (Dv2 = 335.25 ) 233.78 = 101.47, Ddf = 75–68=7, p < 0.05), but was not significant based on the practical fit index change (DCFI = 0.01). With the latent common method variance factor, the factor loadings of these items continued to be significant. Thereby, the method effects were not significant. Thus, common method bias could not account for all of the relationships among the scales in this study.
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Step 2: Measurement invariance of the love of money scale The measurement invariance of the love of money scale was examined and presented as an example below. Results of a confirmatory factor analysis (CFA) suggested a good fit between the love of money model and the data for the whole sample (v2 = 91.19, df = 31, p = 0.00, CFI = 0.99, TLI = 0.98, RMSEA = 0.09). I examined the configural (factor structures) invariance using CFA and metric (factor loadings) invariance of the love of money scale across employment status (full-time versus part-time) using a Multi-Group Confirmatory Factor Analysis (MGCFA). The configural invariance was achieved (full-time: v2=212.21, df = 64, p = 0.00, CFI = 0.99, TLI = 0.99, RMSEA = 0.06; part-time: v2=174.04, df = 64, p = 0.00, CFI = 0.99, TLI = 0.99, RMSEA = 0.05). The metric invariance was not achieved based on v2 change (Dv2=37.87, Ddf = 6, p < 0.05) between unconstrained (v2=150.67, df = 64, p = 0.00, CFI = 0.99, TLI = 0.99, RMSEA = 0.05) and df = 70, constrained MGCFA (v2=188.54, p = 0.00, CFI = 0.99, TLI = 0.98, RMSEA = 0.06) but was achieved based on the fit index change (DCFI = 0.00) (Vandenberg and Lance, 2000). Further, configural invariance was achieved across gender (men: v2=209.29, df = 64, p = 0.00, CFI = 0.99, TLI = 0.98, RMSEA = 0.06; women: v2=187.40, df = 64, p = 0.00, CFI = 0.99, TLI = 0.99, RMSEA = 0.05). Metric invariance across gender was achieved for both v2 change (Dv2=2.82, Ddf = 6, p > 0.05) and also fit index change (DCFI = 0.00). Thus, this study has achieved measurement invariance across employment status and gender for the love of money scale. The mean, standard deviation, and correlations of major variables for the whole sample are presented in Table I.
Step 3: The SEM for the whole sample Results revealed a good fit between the proposed model and the data for the whole sample (v2=521.10, df = 102, p = 0.00, CFI = 0.98, TLI = 0.97, RMSEA = 0.09) (see Table II and Figure 2). Notice that RMSEA was slightly greater than 0.08 and was acceptable. The main focus of this
study is to ascertain the proposed path between income and quality of life. A path is significant at the 0.05, 0.01, or 0.001 levels, when the critical ratio (C.R.) is greater than or equal to 1.96, 2.58, or 3.50, respectively. The proposed path was significant and negative (Income fi Quality of Life = ) 0.25, C.R. = ) 3.808, p < 0.001). For control variables, male and married people had higher income. Job satisfaction was significantly related to income. Job satisfaction and the love of money were significantly related to quality of life. Among control variables, most males were married. Married people had higher job satisfaction. Next, all measured factors as related to the major variables were examined. For the five aspects of job satisfaction, satisfaction with work (0.72) had the highest regression weight, followed by pay (0.55), supervision (0.54), co-workers (0.54), and promotions (0.45). For the Love of Money Scale, Factor Important had the highest regression weight (0.56), followed by Factors Budget (0.39), Success (0.39), and Rich (0.34). Regarding life satisfaction, satisfaction with leisure appeared to carry slightly more weight (0.68) than knowledge (0.66), social (0.61), and family (0.35). Overall, 32% of income can be explained by control variables and 26% of the quality of life can be explained by income and control variables. When all the paths from control variables to income and quality of life and correlations among control variables were set to zero (v2=734.84, df = 115, p = 0.00, CFI = 0.96, TLI = 0.96, RMSEA = 0.11), the income to quality of life path alone ( ) 0.02) was not significant. RMSEA increased from 0.09 to 0.11 and became worse than the original model. In summary, the significant and negative relationship between income and quality of life prevailed when variables were controlled in the model.
Step 4: Employment status (full-time versus part time) as a moderator I analyzed full-time and part-time employees simultaneously using the same model (v2=611.79, df = 204, p = 0.00, CFI = 0.98, TLI = 0.97, RMSEA = 0.07). Please note that RMSEA in Step 4 (0.07) was better than the first SEM analysis in Step
Age Gender Education Income Status Important Budget Success Rich Work Pay Promotion Supervision Co-Worker QOLife
29.93 1.49 15.40 24.99 1.42 4.83 4.77 4.34 4.80 32.95 30.81 25.26 39.54 39.61 4.89
M
10.84 0.50 2.13 19.12 0.49 0.82 1.38 1.37 1.35 11.50 14.35 17.81 14.10 14.28 0.82
SD ) 0.09* 0.26* 0.57* ) 0.50* ) 0.00 0.12* ) 0.00 ) 0.12* 0.35* 0.07 ) 0.06 0.15* 0.23* 0.19*
1
0.22* 0.36* ) 0.13* 0.02 ) 0.01 0.09 ) 0.02 0.07 0.08 0.14* ) 0.10 ) 0.06 0.04
2
4
0.41* ) 0.25* ) 0.61* ) 0.02 0.03 ) 0.03 ) 0.06 0.07 0.08 ) 0.09 ) 0.09 0.21* 0.30* 0.08 0.37* 0.06 0.16* 0.01 0.11* 0.08 0.18* 0.10* 0.07
3
6
7
0.04 ) 0.05 0.09 0.08 0.44* 0.05 0.03 0.12* 0.06 ) 0.31* ) 0.06 ) 0.01 ) 0.20* ) 0.13* ) 0.05 ) 0.12* 0.02 0.10 ) 0.07 0.06 0.06 ) 0.19* ) 0.02 0.03 ) 0.13* 0.05 0.16*
5
0.20* ) 0.13* ) 0.08 ) 0.03 ) 0.05 ) 0.08 ) 0.03
8
) 0.10 ) 0.14* ) 0.03 ) 0.02 ) 0.10 ) 0.05
9
0.28* 0.34* 0.45* 0.53* 0.37*
10
12
13
14
0.38* 0.21* 0.25* 0.22* 0.18* 0.52* 0.21* 0.18* 0.24* 21*
11
Note: N = 458. Sex: Male (n = 235) = 1, Female (n = 223) = 0. Income: Expressed in $1000. Employment Status: Full-time (n = 265) = 1, Part-time (n = 192) = 2. For correlations, N varies between 318 and 458. *p < 0.05.
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.
Variable
Mean, standard deviation, and correlations of variables
TABLE I
The Love of Money 383
) 0.29*** 0.32*** 0.20** 0.24*** 0.06 0.04 0.17* 0.60*** 0.14 0.25*** 0.07 ) 0.01 0.12 ) 0.02 ) 0.21* 0.75 0.53 0.42 0.53 0.56 0.37 0.40 0.42 0.63 0.66 0.66 0.61 0.38
0.26*** 0.34*** 0.27*** 0.03 0.04 0.11 0.52*** 0.18* 0.17*** 0.04 0.04 0.19** ) 0.01 ) 0.14 0.72 0.55 0.45 0.54 0.54 0.34 0.39 0.39 0.56 0.68 0.66 0.61 0.35
Full
) 0.25***
Whole
Status
0.71 0.66 0.61 0.35
0.31 0.37 0.35 0.49
0.70 0.71 0.60 0.41
0.36 0.47 0.40 0.64
0.71 0.57 0.39 0.52 0.55
0.48*** 0.17 ) 0.09 0.13 0.06 ) 0.36*
) 0.05 ) 0.10 0.15 0.00 ) 0.11 ) 0.01 0.65 0.52 0.45 0.51 0.47
0.10 ) 0.03 0.36* 0.27 ) 0.11 0.14 0.68*** 0.34
) 0.29*
High
0.17* 0.20* 0.10 0.08 ) 0.01 0.00 0.35** 0.30*
) 0.14
Part
0.73 0.70 0.58 0.46
0.34 0.37 0.47 0.69
0.81 0.59 0.45 0.55 0.62
0.13 ) 0.21 0.18 ) 0.11 0.22 ) 0.11
0.06 0.02 0.15 0.13 0.04 0.22 0.81*** 0.13
0.03
Median
Income
0.66 0.65 0.63 0.35
0.38 0.43 0.39 0.55
0.65 0.49 0.42 0.48 0.46
) 0.11 ) 0.16 0.12 ) 0.01 ) 0.13 ) 0.09
0.09 0.41*** 0.07 0.07 0.06 0.12 0.38*** 0.16
) 0.03
Low
0.70 0.66 0.63 0.37
0.37 0.43 0.42 0.64
0.79 0.58 0.48 0.56 0.56
0.53*** 0.34*** ) 0.12 0.24** ) 0.12 ) 0.21*
0.33*** 0.31*** 0.25*** 0.10 0.12 ) 0.04 0.59*** 0.27*
) 0.32**
Male
Female
0.67 0.66 0.60 0.34
0.31 0.34 0.34 0.48
0.64 0.51 0.41 0.51 0.53
0.27** 0.24** 0.00 0.12 0.14 ) 0.08
0.53*** 0.06 0.08 0.02 ) 0.06 0.24** 0.46*** 0.05
) 0.09
Gender
Note. *p < 0.05, **p < 0.01, ***p < 0.001. Whole: v2 = 521.10, df = 102, p = 0.00, CFI = 0.98, TLI = 0.97, RMSEA = 0.09. Status: v2 = 611.79, df = 204, p = 0.00, CFI = 0.98, TLI = 0.97, RMSEA = 0.07. Income: v2 = 625.28, df = 306, p = 0.00, CFI = 0.98, TLI = 0.97, RMSEA = 0.05. Gender: v2 = 632.55, df = 204, p = 0.00, CFI = 0.98, TLI = 0.97, RMSEA = 0.07. In this analysis, variable Status (Full-Time versus Part-time) replaced variable Gender for the variable list.
Major path Income fi QOL Control variable fi Major variable Gender fi Income Married fi Income Job satisfaction fi Income LOM fi Income Gender fi QOL Married fi QOL Job satisfaction fi QOL LOM fi QOL Between control variables Gender ‹ fi Married Gender ‹ fi Job satisfaction Gender ‹ fi LOM Married ‹ fi Job satisfaction Married ‹ fi LOM Job satisfaction ‹ fi LOM Job satisfaction (regression wt) Work Pay Promotions Supervision Co-workers The Love of Money Rich Success Budget Important Quality of life Leisure Knowledge Social Family
Variable
SEM results
TABLE II
384 Thomas Li-Ping Tang
The Love of Money
385 Chi-Square = 521.10 df = 102 p = .00 CFI = .98 TLI = .97 RMSEA = .09
Hypothesized Model The Whole Sample
Sex
e31
1.00 1.00
Gender e11
e32 e51 e52
1.00 1.00
Marital .69 .84
.17 Status
1.00
.26
Income
.52 Work .30
.72
.04
.19
Pay
.55 .20 .89 .45 Promotions e53 .54 .29 .84 .54 Supervision e54 .29 .84 e55 Co-workers
1.00
.34 .27
e1
Income
Job Satisfaction
.32 .03
-.25 -.14
e61 e62 e63 e64
.94 .92 .92 .83
.12 Rich
-.01
.04
.04
.34 .15
.26
.39
Success .15
.11
.52 .39
.18
The Love of Money
Quality of Life
.56
Budget .32 Important
.47
.68 .66 .44
Leisure Knowledge .73 e21
.75 e22
e2 .35
.61 .38 Social .79 e23
.12 Family .94 e24
Figure 2. Results for the whole sample.
3 (0.09), suggesting that full-time employees were different from part-time employees. We will present results of full-time employees first (see Table II). Full-time employees Income was negatively related to quality of life ( ) 0.29, p < 0.001) (Figure 3). Full-time employees’ love of money was negatively related to job satisfaction. Job satisfaction, in turn, was significantly related to both income and quality of life. Male fulltime employees and married employees have higher income. Married employees had higher quality of life than non-married people. Part-time employees Income was not related to quality of life ( ) 0.14) (Figure 4). Further, job satisfaction was positively related to quality of life only. However, as expected,
job satisfaction was not related to income for part-time employees. Further, the relationship between the love of money and job satisfaction failed to reach significance. Thus, employment status is a moderator because full-time and part-time employees do have different patterns of results using this model. Finally, when we set all the correlations of control variables to zero (v2=791.71, df = 232, p = 0.00, CFI = 0.97, TLI = 0.96, RMSEA = 0.07), the income to job satisfaction path was not significant: full-time employees ( ) 0.06, p > 0.05), part-time employees ( ) 0.07), respectively. Step 4: Income level as a moderator I selected full-time employees and divided them into high-, median-, and low-income groups (one
Thomas Li-Ping Tang
386
Chi-Square = 611.79 df = 204 p = .00 CFI = .98 TLI = .97 RMSEA = .07
Hypothesized Model The Full-Time Sample Sex
e31
e52
Gender e11
.25 1.00 1.00 Marital Status
e32 e51
1.00 1.00
.66 Work .85
1.00
.32
Income
.56 .28
.75
.12
.07
Pay
.53 .17 .42 e53 Promotions .53 .28 .85 .56 Supervision e54 .32 .83 Co-workers e55 .91
1.00
.20 .24
Income
Job Satisfaction
e1 .25
.06
-.29 -.21
.93 e61 .92 e62 .91 e63 e64
.78
.14 Rich
-.03
-.01
.04
.37
.17
.16
.60 .40
Success .18
.42
.35
The Love of Money
.14
Quality of Life
.63
Budget .40 Important
.44
.66 .66 .43
Leisure Knowledge .75 e21
.75 e22
e2 .38
.61 .38 Social .79 e23
.14 Family .93 e24
Figure 3. Results for full-time employees.
third each) and then applied the cut-off points for the whole sample. Part-time employees had lower income and thus were mostly in the low-income group. Results for the whole sample were presented in Table II (v2 = 625.28, df = 306, p = 0.00, CFI = 0.98, TLI = 0.97, RMSEA = 0.05). High-income employees This sample represented the high-income employees and was a part of the full-time employee sub-group. The results of high-income employees were similar to that of the full-time employees. In short, the income to quality of life relationship was significant and negative ( ) 0.29). Job satisfaction was significantly correlated with both income and quality of life. Further, the love of money was negatively associated with job satisfaction.
Median- and low-income employees These two groups of employees were different from high-income employees and were similar to parttime employees. The income to quality of life path was not significant for median- (0.03) and for lowincome ( ) 0.03) employees. Further, job satisfaction was not related to income and the love of money was not related to job satisfaction. Finally, when I set all the correlations of control variables to zero, the income to job satisfaction path was not significant (v2 = 788.19, df = 347, p = 0.00, CFI = 0.97, TLI = 0.97, RMSEA = 0.07): high-income ( ) 0.07), median-income (0.15), or low-income (0.04) employees, respectively. Income level among full-time employees Next, when only full-time employees were selected in the analysis for high-, median-, and low-income
The Love of Money
387 Chi-Square = 611.79 df = 204 p = .00 CFI = .98 TLI = .97 RMSEA = .07
Hypothesized Model The Part-time Sample
Sex
e31
Marital
e32
1.00 1.00 1.00 1.00
e11 1.00
.17
Status
Income
.42
.76 Work
e51
Gender -.05
.27 .65 Pay e52 .52 .21 .89 .45 Promotions e53 .51 .26 .86 .47 e54 Supervision .22 .88 Co-workers e55
-.10
.00
1.00
.20
.85
.10
Income
Job Satisfaction
e1 .09
.08
-.14 -.01
.95 e61 e62 e63 e64
.93 .94 .87
.10 Rich
.11
-.01
.15
.31 .14 .37
Success .12
.00
.35 .35
.21 .30
The Love of Money
Quality of Life
.49
Budget .24 Important
.51
.71 .66 .44
Leisure Knowledge .70 e21
.75 e22
e2 .35
.61 .37 Social .79 e23
.13 Family .94 e24
Figure 4. Results for part-time employees.
groups, similar results were found (v2 = 516.47, df = 306, p = 0.00, CFI = 0.98, TLI = 0.97, RMSEA = 0.06). The income to quality of life path was significant for high-income employees ( ) 0.27, p < 0.05), but was not significant for median- ( ) 0.01) or low-income ( ) 0.08) employees, respectively. When I examined the income to quality of life path alone (v2 = 642.21, df = 347, p = 0.00, CFI = 0.97, TLI = 0.96, RMSEA = 0.06), the path was not significant for these three income groups ( ) 0.04, 0.23, and ) 0.07, respectively).
Step 5: Gender as a moderator I selected all employees and investigated the model across gender. In this analysis, the model was revised so that variable gender in Figure 1 was replaced by
the variable employment status (i.e., full-time versus part-time). Thus, I treated employment status as a control variable and gender as a moderator in the model. For Table II, all variables related to gender were also replaced by variables employment status. I reported results for the male employees first (v2 = 632.55, df = 204, p = 0.00, CFI = 0.98, TLI = 0.98, RMSEA = 0.07). Male The income to quality of life path was again negative and significant ( ) 0.32, p < 0.01). Job satisfaction was significantly related to both income and quality of life. The love of money and job satisfaction was negatively correlated ( ) 0.21, p < 0.05). The love of money was positively related to quality of life (0.27, p < 0.05). Full-time and married employees had higher income than their counterparts. Full-time
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Thomas Li-Ping Tang
employees tended to be married and had higher job satisfaction than part-time employees. Female The income to quality of life path was negative but not significant ( ) 0.09). Job satisfaction was significantly related to quality of life, but not related to income. Full-time employees were married and had higher job satisfaction than part-time employees. Finally, I set all the correlations of control variables to zero and examined the proposed path (v2 = 1055.82, df = 232, p = 0.00, CFI = 0.95, TLI = 0.95, RMSEA = 0.09), the income to the quality of life relationship was not significant, for male ( ) 0.05), female (0.03) employees, respectively. Discussion This study examined several control variables in the model. The present findings suggest that job satisfaction is significantly related to quality of life for the whole sample and also all sub-samples examined in this study, making it the most consistent finding using this model. This result supports the significant job satisfaction–quality of life relationship in the literature (e.g., Iverson and Maguire, 2000; Judge and Chandler, 1996). Job satisfaction is correlated with income for the whole sample, full-time, highincome, and male employees. Male employees have higher income than their female counterparts. Married people have higher income than those nonmarried participants. The love of money is correlated with the quality of life for the whole sample, parttime employees, and male participants. For the whole sample, male employees tend to be married. Married people tend to have higher job satisfaction than non-married people. Among all the analyses across employment status, income level, and gender, this study offers the following theoretical and empirical contributions to the literature. When (1) the love of money is negatively correlated with job satisfaction and (2) job satisfaction is positive related to income and quality of life, then, income is negatively related to quality of life for full-time, high-income, and male employees in this study. If these two conditions fail to exist, then, income is not related to quality of life for part-time, median- or low-income employees, and female
employees in our sample. When I examine the major path alone and set all correlations of control variables to zero, then, income is not related to quality of life for all the analyses. Income will not lead to high life satisfaction, but rather life dissatisfaction for full-time, high-income, and male employees in this study, when several variables are controlled in the model. The significant and negative love of money–job satisfaction correlation serves as a harbinger for the significant and negative income–quality of life relationship. Thus, pursuing extrinsic goals (e.g., with a high love-of-money orientation) may distract people from achieving meaningful life satisfaction (Solberg et al., 2004). The love of money construct examined in this study has made significant theoretical and empirical contributes to our understanding of income and quality of life. Srivastava et al. (2001) argue that if people valued money to show off, get power, compare oneself to others or overcome self doubts, then money importance has a negative impact on subjective well being. After controlling for motives, their money importance has no impact on SWB. The present findings suggest that income is not related to quality of life. However, after controlling for job satisfaction and the love of money (as well as gender and marital status), high income significantly undermines the quality of life for full-time, high-income, and male employees and is not significantly related to the quality of life for their counterparts, respectively. Since my measures are different from that of Srivastava et al.’s (2001) study, my results do not support their exact findings. Further, the love of money reflects one’s positive affect toward money and is different from their negative motives or positive motives. In their study, the negative motives (a control variable) are negatively correlated with subjective well-being, whereas positive motives (a control variable) are positively correlated with SWB. However, the love of money (a control variable) is positively correlated with quality of life for the whole sample, parttime employees, and male employees. The present results demonstrate that employment status, income level, and gender are moderators. In Nickerson et al.’s study, there are 10 household income levels from the $500 level up to the $290,000 level. The negative impact of the goal for financial success on overall life satisfaction
The Love of Money diminished as household income increases to the $290,000 level. My sample reflects the middle class in the US (full-time income = $33,982). The negative impact of income on life satisfaction is the strongest for the high-income group. My sample is different from theirs. The average income matches the fourth and fifth levels (below middle levels) of their 10-income-level sample. Income levels do matter. I would like to offer the following speculations. Income comes from one’s hard work on the job. For full-time people, high-income employees, and some male employees in this study, they may work close to 40 h a week in their careers. Note that we have a sample of the working class without the super rich people. Thus, to these people, work is a very important part of their lives and income contributes significantly to their job satisfaction. If one’s income is not strongly related to work, then, the job satisfaction to income relationship does not exist. This is the case for part-time, median- or low-income employees, and female employees in this study. Further, I do not examine the cause-and-effect relationship between job satisfaction and quality of life, but do find a positive job satisfaction-quality of life relationship. If one is happy with the job, one also enjoys one’s quality of life. When job satisfaction is significantly related to both income and quality of life, then, one may expect a strong relationship between income and quality of life. One additional control variable in my model is the love of money. The love of money is significantly and negatively correlated with job satisfaction for full-time, high-income people, and men. Thereby, a person with a high level of the love of money will have a very low level of job satisfaction. Job satisfaction is related to income (a very important link). To them, their pay is quite miserable and inadequate. Job satisfaction is also related to quality of life. With this in mind, one’s hard work on the job may have a significant and negative impact on the quality of life. Full-time employees, relatively speaking, work hard and are committed to the job. This may lead to higher income. Please recall that conflict between the work and family domains can be a source of stress that influences important psychological and physical outcomes (Bacharach et al., 1991). When demands of participation in one domain are incompatible with demands of participation in the other domains, then, the work–family
389
conflict may have a negative effect on the quality of both work and family life (Adams et al., 1996). That, in turn, may lead to a higher amount of work-related stress, less time for activities in other domains, and less time to enjoy their quality of life. Thus, high income (due to long hours on the job and hard work) may contribute to a high level of life dissatisfaction, or poor quality of life. Thus, higher income may lead to lower quality of life for (middleclass) full-time, high-income, and male employees. Moreover, one’s high love of money may create a high expectation (desire) for money that will lead to high pay dissatisfaction (Tang et al., 2005). Low pay satisfaction contributes to low job satisfaction because job satisfaction consists of satisfaction with work, pay, promotions, supervision, and co-workers. The love of money contributes to low job satisfaction. Job satisfaction correlates positively to both income and the quality of life. This paper examines the income–quality of life relationship while controlling for job satisfaction. It appears that when the love of money leads to low job satisfaction, then, income leads to low quality of life. Income is not related to the love of money for all the analyses in this study. It is plausible that participants in this sample may have changed jobs frequently, are being paid at the market level, and feel adequate financially and psychologically. The income to the love of money will be (1) negative when people are extremely obsessed with money due to pay compression or financial hardship (Tang et al., 2005) and (2) positive when their income is higher than the GDP per Capita (Tang and Chiu, 2003). The present finding lends support to the notion that full-time employees and men in this sample place higher importance on money than part-time employees and women, respectively (e.g., Bennett et al., 1994; Crosby, 1982; Major and Konar, 1984). The high-income people in this study are full-time employees. Results for these two analyses are similar. Due to higher importance they place on money and job, full-time employees and men may work harder, have longer hours on the job, and have more financial responsibilities than their counterparts. Their quality of life diminishes. Higher incomes are related to lower marginal utility of money. Increases in wealth are associated with diminishing increases in well-being (Ahuvia and Friedman, 1998;
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Csikszentmihalyi, 1999; LaBarbera and Gurhan, 1997; Nickerson et al., 2003). A significant and positive relationship exists between the love of money and quality of life for the whole sample, part-time employees, and men. Some researchers have considered life satisfaction as a measure of one’s positive ‘‘disposition’’ in life (Staw et al., 1986). The love of money measure reflects one’s positive affect or disposition toward money (Tang et al., 2000) as well as one’s positive disposition toward life. Factor Budget is the behavioral component of the attitude. Those with positive dispositions are more willing to change their lives proactively. When positive disposition in money and life are examined together, it is likely that they are related positively. Thereby, those with higher love of money may have higher quality of life than those without. This deserves further attention in the future. Part-time employees in this sample are students pursuing their degrees at a regional state university. They do want to make money to support themselves while attending school and are quite different from involuntary part-time employees who seek full-time jobs and careers. The implication for mangers is that paying these part-time employees higher wages may have little impact to enhance their job satisfaction. These part-time workers are difficult to motivate (Still, 1983) and ‘‘are only in their jobs for the money’’ (Still, 1983: 58); their income is not related to job satisfaction. Income does not cause life dissatisfaction. Their mentality is quite different from full-time employees in organizations.
Limitations I had only small convenience samples of full-time and part-time employees. The average income of our sample was close to the GDP per Capita. My sample did not cover super rich employees in the US. Thus, results may reflect the restriction of range regarding participant’s income. I collected all measures at one point in time and from one source. Results might reflect the common method variance biases. However, Harman’s one factor test, comparison between measurement models with and without the common method variance factor, and measurement (configural and metric) invariance
across employment status and gender (the love of money scale) suggested that the common method variance was non-significant and was not a major concern in this study.
Conclusion My model specifies the conditions under which income will have a negative impact or no impact on quality of life. The income–quality of life relationship depends on the control variables, the love of money and job satisfaction. The love of money plays an important role in our understanding of the income-quality of life relationship. The literature suggests that the love of money is directly related to evil (unethical behavior) (Tang and Chen, 2006; Tang and Chiu, 2003) and indirectly related to evil through pay dissatisfaction (Tang and Chiu, 2003) or Machiavellianism (Tang and Chen, 2006). The love of money is also indirectly related to unethical behavior through Machiavellianism among Business students, male students, and male Business students (Chen and Tang, 2005). The present study reveals also that the love of money leads to job dissatisfaction for some people. Further, for full-time employees and for men, when the love of money leads to job dissatisfaction, then, income also leads to life dissatisfaction. ‘‘From a procedural justice perspective, perceived injustice will lead to negative perceptions of the organization and, hence, to counterproductive behaviors that will hurt the organization’’ (CohenCharash and Spector, 2001, p. 287). It is very likely that the combination of the love of money and dissatisfaction with job and quality of life may lead some people to fall into temptation and engage in many undesirable behaviors in organizations in order to get even, such as: theft, turnover, corruption, workplace deviance, counterproductive behavior, misbehavior, and unethical behavior (e.g., Chen and Tang, 2006; Greenberg, 1993; Robinson and Bennett, 1995; Tang and Chen, 2006; Tang and Chiu, 2003; Tang et al., 2000; Vardi and Weitz, 2004). In the postEnron era, researchers and managers have to pay attention to the important implications and impacts regarding the love of money on many other workrelated attitudes and behavior and identify ways to reduce work–family conflicts and stress (e.g.,
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Cohen-Charash, Y. and P. E. Spector: 2001, ÔThe Role of Justice in Organizations: A Meta-AnalysisÕ, Organizational Behavior and Human Decision Processes 86(2), 278–321. Crawford Solberg, E., E. Diener, D. Wirtz, R. E. Lucas and S. Oishi: 2002, ÔWanting, Having, and Satisfaction: Examining the Role of Desire Discrepancies in Satisfaction with IncomeÕ, Journal of Personality and Social Psychology 83(3), 725–734. Crosby, F.: 1982, Relative Deprivation and Working Women (Oxford University Press, New York). Csikszentmihalyi, M.: 1999, ÔIf We are So Rich, Why aren’t We Happy?Õ, American Psychologist 54, 821–827. Diener, E.: 1984, ÔSubjective Well-BeingÕ, Psychological Bulletin 95(3), 542–575. Diener, E. and C. Diener: 1996, ÔMost People are HappyÕ, Psychological Science 7(3), 181–185. Diener, E. and S. Oishi: 2000, ÔMoney and Happiness: Income and Subjective Well-Being Across NationsÕ, in E. Dinner and E. M. Suh (eds.), Subjective Well-Being Across Cultures (MIT Press, Cambridge, MA), pp. 185– 218. Diener, E. and M. E. P. Seligman: 2004, ÔBeyond Money: Toward an Economy of Well-BeingÕ, Psychological Science in the Public Interest 5(1), 1–31. Diener, E., M. Diener and C. Diener: 1995, ÔFactors Predicting the Subjective Well-Being of NationsÕ, Journal of Personality and Social Psychology 69(5), 851– 864. Diener, E., R. E. Lucas and C. N. Scollon: 2006, ÔBeyond the Hedonic Treadmill: Revising the Adaptation Theory of Well-BeingÕ, American Psychologist 61(4), 305–314. Diener, E., C. L. Gohm, E. Suh and S. Oishi: 2000, ÔSimilarity of the Relations between Marital Status and Subjective Well-Being Across CulturesÕ, Journal of Cross-cultural Psychology 31(4), 419–436. Diener, E., E. Sandvik, L. Seidlitz and M. Diener: 1993, ÔThe Relationship between Income and Subjective Well-Being: Relative or Absolute?Õ, Social Indictors Research 28, 195–223. Du, L. Z. and T. L. P. Tang: 2005, ÔMeasurement Invariance Across Gender and Major: The Love of Money Among University Students in People’s Republic of ChinaÕ, Journal of Business Ethics 59(3), 281–293. Easterlin, R. A.: 2006, ‘Life Cycle Happiness and Its Sources Intersections of Psychology, Economics, and Demography’, Journal of Economic Psychology 27(4), 463–482. Eid, M. and E. Diener: 2004, ÔGlobal Judgments of Subjective Well-Being: Situational Variability and Long-Term StabilityÕ, Social Indicators Research 65(3), 245–277.
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Department of Management and Marketing, Jennings A. Jones College of Business, Middle Tennessee State University, P.O. Box 516, Murfreesboro, TN 37132, USA E-mail:
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