2011_AoM_Best _LOM_Pay_CPI_Corrupt

AoM 2011 10690: LOM Pay CPI (Best Paper Proceedings)

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The final version of this paper was presented at the Academy of Management Annual Meeting San Antonio, Texas, USA, August 12-16, 2011A. The 6-page version of this paper was published as: The love of money is the root of all evil: Pay satisfaction and CPI as moderators. In Leslie A. Toombs (Ed.), Best paper proceedings of the 2011 Annual Meeting of the Academy of Management.

The Love of Money is the Root of All Evil: Pay Satisfaction and CPI as ModeratorsTHOMAS LI-PING TANG and TOTO SUTARSO, U.S.A.; MAHFOOZ A. ANSARI, Canada; VIVIEN KIM GEOK LIM, Singapore; THOMPSON SIAN HIN TEO, Singapore; FERNANDO ARIAS-GALICIA, Mexico; ILYA GARBER, Russia; PETER VLERICK, Belgium; JIAN LIANG, China; ADEBOWALE AKANDE, South Africa; MICHAEL W. ALLEN, Australia; ABDULGAWI SALIM ALZUBAIDI, Oman; MARK G. BORG, Malta; BRIGITTE CHARLES-PAUVERS, France; BOR-SHIUAN CHENG, Taiwan; RANDY K. CHIU, Hong Kong; LINZHI DU, China; CONSUELO GARCIA DE LA TORRE, Mexico; ROSARIO CORREIA HIGGS, Portugal; ABDUL HAMID SAFWAT IBRAHIM, Saudi Arabia; CHINKANG JEN, Taiwan; ALI MAHDI KAZEM, Oman; KILSUN KIM, South Korea; ROBERTO LUNA-AROCAS, Spain; EVA MALOVICS, Hungary; ALICE S. MOREIRA, Brazil; RICHARD T. MPOYI, USA; ANTHONY UGOCHUKWU OBIAJULU NNEDUM, Nigeria; JOHNSTO E. OSAGIE, USA; MEHMET FERHAT ZBEK, Turkey; AAHAD M. OSMAN-GANI, Malaysia; FRANCISCO COSTA PEREIRA, Portugal; RUJA PHOLSWARD, Thailand; HORIA D. PITARIU, Romania; MARKO POLIC, Slovenia; ELISAVETA GJORGJI SARDOSKA, Macedonia; PETAR SKOBIC, U.S.A.; ALLEN F. STEMBRIDGE, U.S.A.; THERESA LI-NA TANG, U.S.A.; MARTINA TRONTELJ, Slovenia; CAROLINE URBAIN, France; LUIGINA CANOVA and ANNA MARIA MANGANELLI, Italy

ABSTRACT By incorporating pay satisfaction at Level 1 and Corruption Perceptions Index (CPI) at Level 2, we investigated the relationship between the love of money and self-reported corrupt intent among 6,382 managers in 31 countries across six continents. Our significant cross-level three-way interaction effect showed that for managers with high pay satisfaction, the intensity (slope) of the love of money to corrupt intent relationship was almost identical in high or low CPI countries but the former had the lowest magnitude of corrupt intent, whereas the latter had the highest. For those with low pay satisfaction, the slope was the steepest in high CPI entities, but was flat in the low CPI entities and the difference between the two was significant. -----------Keywords: Theory of Planned Behavior (TPB), Attitude (Love of Money and Pay Satisfaction), Money Ethics, Subjective Norms (Corruption Perceptions Index), GDP per capita, Behavioral Intention (Corruption), Business Ethics, Cross-Cultural, Mplus


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INTRODUCTION Corruptiondefined as the illicit use of ones position or power for perceived personal or collective gainis both a state and a process that reflects not only the corrupt behavior of an individual but also the dangerous, viruslike infection of a group, organization, industry, nation/country (Ashforth, Gioia, Robinson, & Trevio, 2008; Tepper, Carr, Breaux, Geider, Hu, & Hua, 2009). Further, the legal enforcement policies and ethical cultures at the local or entity level have a significant impact on corruption which was measured by parking violations among United Nations diplomats living in New York City (Fisman & Miguel, 2007). Although corruption is not only a multilevel real-world phenomenon from the perspectives of bad apples, bad cases, and bad barrels (Kish-Gephart, Harrison, & Trevio, 2010) but also a psychological construct heavily influenced by cultures and values, most research uses a single level of analysis (applying a micro or macro lens alone) which yields incomplete understanding at either level (Hitt, Beamish, Jackson, & Mathieu, 2007). According to the theory of planned behavior (TPB, Ajzen, 1991), attitudes toward the behavior, subjective norms, and perceived behavioral control predict behavioral intention. TPB has been widely examined across different fields (Armitage & Conner, 2001; Manning, 2009) including management (Cordano & Frieze, 2000). Only limited research, however, has applied TPB in investigating behavioral ethics. Very few studies have been conducted outside the US and even fewer in entities at the bottom of the income pyramid (Prahalad & Hammond, 2002). The contribution of TPB is not as ubiquitous as most researchers once thought, particularly in underresearched areas of the world (Kirkman & Law, 2005). People around the world have unique histories, cultures, beliefs, and values as well as economic, legal, political, and social infrastructures, yet they all speak one common language that everyone understands: money. Corruption is about money, directly or indirectly. Money is an instrument of commerce and a measure of value (Smith, 1776/1937). Although money is universally accepted across cultures, the meaning of money (Colquitt, LePine, & Wesson, 2011) is in the eye of beholder (McClelland, 1967). Following the attitude-to-behavioral-intention aspect of TPB, we assert that money attitudes, such as the love of money, in particular, contribute to our understanding of corruption. Only limited research has tested the validity of the oft-cited propositionthe love of money is the root of all evil. To date, no research has investigated this issue empirically in a large scale cross-cultural study. Perceptions of inequity provoke dishonesty, theft, or corruption (Gino & Pierce, 2009a; Greenberg, 1993, 2002; Huseman, Hatfield, Miles, 1987). Managers pay satisfaction (Heneman & Judge, 2000) reflects their pay equity perceptions. Following the adage, whoever loves money is never satisfied with his or her income, some (but not all) high love-of-money people have low pay satisfaction (Tang et al., 2006). We explore pay satisfaction as a moderator and assert that the combination of high (low) love-of-money and low (high) pay satisfaction may lead to the strongest (weakest) perceptions of pay inequity and the highest (lowest) propensity to become corrupt. Transparency Internationals Corruption Perceptions Index, CPI, a composite of perceived levels of public sector corruption, serves as a proxy for the ethical/unethical climate, culture, or


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good/bad barrel at the entity level (Martin & Cullen, 2006; Victor & Cullen, 1988). Following the person-situation interactionist model of ethical decision-making (Trevio, 1986), very little research has expanded the TPB framework to include a prevalent social norm, CPI, into a multilevel theory of corruption examined corrupt intent around the world (Rousseau & Fried, 2001). This study fills the void and makes three unique contributions. First, we ascertain that the relationship between love of money and corrupt intent is moderated by pay satisfaction at Level 1 (attitude) and CPI (social norm) at Level 2 using a multi-level theoretical model. Second, we test our theoretical model based on data collected from 6,382 managers in 31 countries. Third, our significant cross-level three-way interaction results offer counterintuitive, novel, and original theoretical, empirical, and practical contributions (Colquitt & Zapata-Phelan, 2007) and furthers theory development, theory testing, and improved practice. THEORY AND HYPOTHESIS Money Attitude: The Love of Money (LOM) Attitudes predict behavior effectively only when there is a high correspondence between the attitude object and the behavioral option (Ajzen, 1991; Grant, 2008; Tang & Baumeister, 1984). Among individual variables, researchers have investigated the effect of several predispositions on corruption (integrity, moral identity, self-control, empathy, cognitive moral development, and psychopathology) (Dineen, Lewicki, & Tomlinson, 2006; Kish-Gephart et al., 2010). Researchers have studied effects of money and its meaning (Ariely, 2008a, 2008b; Gino & Pierce, 2009b) on cheating and corruption. We turn to the meaning of money next. Money and its meaning. Children from poor economic backgrounds overestimate the size of a coin (Bruner & Goodman, 1947). In dual-career families, college students money anxiety is influenced by both paternal and maternal money anxiety (Lim & Sng, 2006). Money, as a tool, is instrumental in satisfying biological and psychological needs (Lea & Webley, 2006). To some, money is metaphorically a powerful, addictive, insatiable drug because drug addicts require larger dosages to maintain the same level of high or utility of money. Thinking about money activates feelings of self-sufficiency leading to the desire to be independent, reduce requests for help, donate less money to charity, and keep a large physical distance between themselves and others (Vohs, Mead, & Goode, 2006). Counting 80 $100 bills (compared to 80 pieces of paper) reduces peoples physical pain (Zhou, Vohs, & Baumeister, 2009). Anticipation of pain heightens the desire for money (Zhou & Gao, 2008). The visible presence of abundant wealth ($7,000 in $1 bills piled on two tables) provokes a feeling of envy toward wealthy others that, in turn, causes a significantly higher percentage of participants to engage in and a much larger magnitude of cheating for personal gains than without such abundance of money (Gino & Pierce, 2009b: 142). Getting Harvard, MIT, Yale, and Princeton students to contemplate their own ethical values by recalling the Ten Commandments or signing an honor code eliminates cheating completely, while offering poker chips to redeem for cash, a few seconds later, doubles the level of cheating (Ariely, 2008a: 24; Aquino, Freeman, Reed, Lim, & Felps, 2009). Therefore, financial resources,


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experiences, and culture at the individual, organizational, and country levels shape our deeplyrooted monetary beliefs, provoke or curb self-interest, and incite ethical or unethical behaviors. We now turn to the love of money. The love of money. For the past several decades, researchers have examined numerous money-related attitudes and measures in the literature (Furnham & Argyle, 1998; Mitchell & Mickel, 1999; Srivastava, Locke, & Bartol, 2001; Tang, 1992; Wernimont & Fitzpatrick, 1972; Yamauchi & Templer, 1982). Most lay people are familiar with the love-of-money construct due to one of the most well-cited propositionsthe love of money is the root of all evil (Tang & Chiu, 2003). Tang and his associates (2006) have examined different meanings of money, or money ethic (Tang, 1992), and defined a subset of the constructthe love of money conceptually as subjective and positive attitudes toward money with affective, behavioral, and cognitive components, or aspiration for money (Easterlin, 2001). The love of money (LOM) is empirically defined as a multi-dimensional individual difference variable with several sub-constructs. This scale captures peoples desire to be rich (Rich), behavioral intention energized by money (Motivator), and cognitions that money is important and power (Importance, Power). It is one of the most well-developed and systematically used constructs of money attitude (Colquitt et al., 2011; Mitchell & Mickel, 1999), mildly related to materialism (Belk, 1985; Kasser, 2002), and differs from greed (Cozzolino, Sheldon, Schachtman, & Meyers, 2009). It is related to a winner-take-all mentalitythe Matthew Effect (Merton, 1968; Tang, 1996) and predicts unethical behavioral intention in a panel study (Tang & Chen, 2008). This construct has been substantiated in empirical studies across 32 countries (Lim & Teo, 1997; Tang, et al., 2006) and cited in influential reviews (Kish-Gephart et al., 2010; Mickel & Barron, 2008; Mitchell & Mickel, 1999; Zhang, 2009) and in numerous books (Colquitt et al., 2011; Furnham & Argyle, 1998; McShane & Von Glinow, 2008; Milkovich, Newman, & Gerhart, 2011; Rynes & Gerhart, 2000). Corruption Corruption implies a willful perversion of order, ideals, and, perhaps most important, trusta moral deterioration (Ashforth et al., 2008: 671; Gilbert & Tang, 1998). It is impossible to directly measure managers actual corruption because most behaviors are performed in private, except in formal criminal investigations of corruption cases, police records (e.g., Fisman & Miguel, 2007), and laboratory experiments (Ariely, 2008a). However, people are willing to provide accurate information for specific questions in an anonymous survey (Richman, Kiesler, Weisband, & Drasgow, 1999; Schoorman & Mayer, 2008). The convergence of the incumbents self-report and the coworkers peer-report on counterproductive behavior suggests that selfreported corrupt intent (intention) is a reasonable surrogate measure of corruption (behavior) (De Jonge, & Peeters, 2009; Fox, Spector, Goh, & Bruursema, 2007; Martin, Cullen, Johnson, & Parboteeah, 2007). Among workplace deviance (Bennett & Robinson, 2000; Tepper et al., 2009), counterproductive behavior (Cohen-Charash & Spector, 2001; Spector & Fox, 2010), corruption


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(Anand, Ashforth, & Joshi, 2004), and misbehavior (Ivancevich, Konopaske, & Matteson, 2005), researchers examined corrupt and deviant (CAD) intent (Tang & Chiu, 2003) that is a subset of organizational deviances performed against organizations (Robinson & Bennett, 1995). The scale includes misuse of position, power, or authority for personal or organizational gain (receiving gifts, money, bribery, and kickbacks); acts committed against the company (sabotage and theft); and acts conducted on behalf of the organization (laying off employees for personal gain) (Ashforth et al., 2008; Robinson & Bennett, 1995). This construct has been tested empirically in Hong Kong, Macedonia (Sardoska & Tang, 2009), and the US (Tang & Tang, 2010) and cited in textbooks (Bateman & Snell, 2011). The Love of Money (LOM) and Corruption High love-of-money people have a large discrepancy between their desire and earning (Lawler, 1971; Michalos, 1985) that leads to low pay satisfaction and many unmet needs. Unmet needs are motivators (Kahneman & Deaton, 2010; Maslow, 1954). These people are vulnerable to foolish and harmful desires, likely to fall into a trap or temptation (Baumeister, 2002), reduce their moral and ethical standard, and have a high corrupt intent. We illustrate our rational below. Factor Rich (the affective component of LOM) is the most important sub-construct because most people want to be rich and have an abundance of money (Gino & Pierce, 2009b). To some, money is an insatiable drug: The more they have, the more they want. Monetary experience is relative but consumption is absolute (Hsee, Yang, Li, & Shen, 2009). When people compare, the rich envy the superrich (Nickerson & Zenger, 2008). Feelings of inequity (Gino & Pierce, 2009b) lead them to steal in the name of justice (Greenberg, 1993; Simons & Roberson, 2003). American adult consumers who desire to be rich condone questionable consumer activities (Vitell, Paolillo, & Singh, 2006). Similarly, public employees high love of money leads to compromised ethical standards in Swaziland (Gbadamosi & Joubert, 2005). Money is a motivator (the behavioral component) (Maslow, 1954) that leads to movements (Herzberg, 1987). Whatever gets measured (paid) gets done (Ariely, 2010; Jenkins, Mitra, Gupta, & Shaw, 1998). Among four motivational techniques, nothing comes even close to money (Locke, Feren, McCaleb, Shaw, & Denny, 1980). When people were rewarded for finding insect parts in a food processing plant, innovative employees brought insect parts from home to add to the food just before they removed them and collected the bonus (Milkovich et al., 2011). High turnover helps people get paid at the current market rate (Tang, Kim, & Tang, 2000). Malaysian Christians with the most favorable attitudes toward money are less critical of unethical practices (Wong, 2008). Cognitively, money is important and represents power. Many consider money as a score card (Adams, 1963). In order to keep receiving money, power, success, and life style that come with the job (Badaracco, 2006), they can not resist nor control the power of money. That leads to the illicit use of their position or power for personal or collective gain (Ashforth et al., 2008). Hong Kong managers love of money is related to unethical behavior (Tang & Chiu, 2003). Love of money predicts unethical intention in a panel study. In sum, following the TPB (Ajzen, 1991), people with strong love-of-money attitude will have stronger corrupt intent than those without.


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Pay Satisfaction as a Moderator (Level 1) Pay Satisfaction Questionnaire (PSQ) is one of the most often used measures of pay satisfaction (Heneman & Judge, 2000; Heneman & Schwab, 1985; Williams, McDaniel, & Nguyen, 2006; Sweeney, McFarlin, & Inderrieden, 1990). People with low pay satisfaction expect financial hardship (Lim & Teo, 1997), high anticipation of pain (Zhou & Gao, 2008), strong emotions of envy (Gino & Pierce, 2009b), and a strong desire to become rich. They are likely to take action and become corrupt in the name of justice (Gino & Pierce, 2009a; Greenberg, 1993). The relationship between income and pay satisfaction was significant for professor with high love of money, but non-significant for those without (Tang, Luna-Arocas, Sutarso, & Tang, 2004). High love-of-money professionals tend to have high voluntary turnover, regardless of intrinsic job satisfaction. We assert: Pay satisfaction moderates the relationship between love of money and corrupt intent. Managers with high (low) love of money and low (high) pay satisfaction will have the strongest (weakest) feeling of inequity and corrupt intent. Corruption Perceptions Index, CPI, as Moderator (Level 2) Most people look to the social context to determine what is ethically right and wrong (Bandura, 1986), obey authority figures (Litzky, Eddleston, & Kidder, 2006; Milgram, 1974) and laws, and do what is rewarded. Following TPB (Ajzen, 1991), CPI is a proxy of the social norms, ethical/unethical climate, or good/bad barrel at the country level (Kish-Gephart et al., 2010; Martin & Cullen, 2006). Transparency International defines corruption as the abuse of entrusted power for private gain in the public sector. When the public sector is corrupt, it may spillover to the private sector because corruption occurs between the government in the public sector and business organizations in the private sector. High CPI countries. Entities with high CPI scores have high levels of economic development (Gross Domestic Product, GDP per capita), long-established liberal democracy, free press, power sharing, information transparency, socio-political stability, strong ethical culture of law abidance, and low corruption (Sorensen, 2002; Treisman, 2007). Economic, legal, political, and social infrastructures promote ethically responsible practices (Campbell, 2007) and stewardship behaviors; supplant opportunism; inculcate social norms, values, and expectations (Davis, Schoorman, & Donaldson, 1997); and deter corruption (Victor & Cullen, 1988). With high pay satisfaction, the cost of corruption outweighs the benefit (Tepper, Moss, Lockhart, & Carr, 2007). Executives corruption and scandals have been publicized in the media, textbooks, and case studies. The financial gains do not justify the loss of freedom, dignity, integrity, and reputation in their lives (Gomez-Mejia, Wiseman, & Dykes, 2005). We assert that managers with high pay satisfaction in high CPI countries have the lowest magnitude of corrupt intent. Managers with low pay satisfaction compare themselves with the rich, are very sensitive to inequity, anticipate pain that intensifies the desire for money, and become corrupt in the name of justice (Gino & Pierce, 2009a; Greenberg, 1993; Huseman et al., 1987). We predict that the intensity of the relationship between love of money and corrupt intent is the strongest for those


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with low pay satisfaction in high CPI entities. Thus, pay satisfaction has a significant impact on managers corrupt intent in high CPI countries. Low CPI countries. We assert that the effect of pay satisfaction on managers corrupt intent in low CPI countries differs from that in high CPI entities. Due to meager resources, it is difficult for managers to attain additional money legally. Therefore, they take corruption for granted as a way of life. They also have nothing to lose (freedom, dignity, integrity, and reputation), but much temptation to covet a thing (Drori, Jang, & Meyer, 2006; McCarthy & Puffer, 2008). In Ukraine, bribery accounted for at least 20% of total wage compensation in the public sector because public employees have lower pay but the same level of consumption as those in the private sector (Gorodnichenko & Peter, 2007; Vynoslavaska, McKinney, Moore, & Longenecker, 2005). On the one hand, high love-of-money managers climb to the top and get rich first. All but the richest feel underpaidthe rich envy the superrich. Political or public office has become a viable means of extending personal wealth and political power of the ruling class (kleptocracy rule by thieves, Grossman, 1999). Bad apples crowd out good apples (Frey & Jegen, 2001). The bad apples (managers with high pay satisfaction due to money obtained legally or illegally) develop a winner-take-all mentality, consider money as a powerful, addictive, insatiable drug; treat corruption as an entitlement in a highly corrupted environment, and engage in corruption. It is plausible that the magnitude of corrupt intent is the highest for mangers with high pay satisfaction in low CPI countries. On the other hand, those with low pay satisfaction sadly missed the boat of corruption. Taken together, we test our three-way interaction effect (love of money, pay satisfaction, and CPI) on corrupt intent below: Hypothesis 1: There is a significant cross-level, three-way interaction effect. Specifically, managers with high pay satisfaction in low CPI countries have the highest magnitude of corrupt intent, those with high pay satisfaction in high CPI countries have the lowest. The intensity of the relationship between love of money and corrupt intent is the strongest for managers with low pay satisfaction in the high CPI group. METHODS Sample and Procedure Collaborators adopted the English survey or translated it to their native language using the multistage translation/back-translation procedure (Brislin, 1980), and collected data using random sampling, convenience samples, or a systematic snowball approach (asking full-time managers who were in various graduate (MBA/PhD) programs to collect data from their colleagues in various organizations) in a single or multiple cities in both public and private sectors. Participants completed the surveys voluntarily, anonymously and without financial reward and forwarded them directly to the researchers. The return rate varied between 45% and 100%. Researchers achieve higher levels of power (1 ) and contributions by employing larger samples at the group level (Level 2) rather than the individual level (Level 1) (Scherbaum &


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Ferreter, 2009). We collected data from 6,586 managers (Level 1) in 32 countries (Level 2) across six continents. For the present study, we deleted missing data (Italy) and used data from 6,382 managers (Level 1) in 31 countries (Level 2). Participants had job titles such as executive, senior manager, logistics coordinator, accountant, financial director, product manager, sales manager, director of communication, engineer, R&D supervisor, HR manager, purchasing officer, and assistant marketing manager. In general, managers were 34.66 years old (SD = 9.87), 50.6 percent male, with 15.35 years of education (SD = 2.65) and an average income of US$14,199.15 (SD = $18,035.51). The average sample size was 205.9 per country. Measures LOM. We selected the 12-item, 4-factor Love of Money Scale (Tang & Chiu, 2003) with Factors Rich, Motivator, Importance, and Power (sample items: I want to be rich; I am motivated to work hard for money; money is an important factor in everyones life; money gives one considerable power). We used a 5-point Likert scale with the following anchors: strongly disagree (1), disagree (2), neutral (3), agree (4), and strongly agree (5). PSQ. We adopted 18-item, 4-factor Pay Satisfaction Questionnaire (PSQ) with Factors Pay Level, Raises, Benefits, and Pay Administration (Heneman & Schwab, 1985; Judge & Welbourne, 1994) and the following anchors: strongly dissatisfied (1), dissatisfied (2), neutral (3), satisfied (4), and strongly satisfied (5). Sample items include: My take-home pay, my most recent raise, my benefit package, and the organizations pay structure. Corrupt intent (CAD). We measured corrupt intent asking the following question (Tang & Chiu, 2003; Tang & Tang, 2010): If you were in a given situation, what is the probability that you would engage in this activity? We used the following anchors: very low probability (1), low probability (2), average (3), high probability (4), and very high probability (5). Using three different anchors for these three scales was a deliberate attempt to avoid common method bias (CMV, Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). We selected 7 items: reveal company secrets for several million dollars; accept money, gifts, and kickbacks from others; sabotage the company to get even due to unfair treatment; lay off managers to save the company money and increase my personal bonus; abuse the company expense accounts and falsify accounting records; overcharge customers to increase sales and to earn higher bonus; and take merchandise and/or cash home. A short 4-item version is correlated with the four-item corruption measure from the World Values Survey (i.e., claiming government benefits to which you are not entitled, avoiding a fare on public transportation, cheating on taxes if you have a choice, accepting a bribe in the course of their duties) among 151 MBA students in China (r = .74, p < .01), providing construct validity. CPI. CPI reflects corruption perceptions for the public sector. Since corruption occurs between the government in the public sector and business organizations in the private sector, corruption may spillover from the public sector to the private sector. There are several indices related to ethical values/cultures in different countries: Transparency Internationals (TI) Corruption Perceptions Index (CPI), the Bribe Payers Index (BPI), the Global Corruption Barometer (GCB), Global Corruption Report (Global Index of Bribery, GIB) and the World


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Values Survey. Due to our large sample at the country level (Level 2, N = 31), we selected World Banks GDP per capita and TIs CPI for our 31 countries. In this study, the difference between average self-reported income at the country level ($12,189.48) and GDP per capita ($12,736.23) was not significant (t = .447, p > .05) and the correlation between the two was significant (r = .68, p < .001). It was concluded that our sample represented the population reasonably well. Average income (r = .66, p < .001) and GDP per capita were correlated with CPI (M = 4.89) (r = .86, p < .001). Table 1 shows the entity name, sample size, GDP per capita, CPI, and managers age, sex, education, career experience, service/production, and private/public for each of the 31 countries in this study. Analysis. We used (SPSS/PASW/Amos, Version 18) and the following criteria for configural invariance (passing 5 out of 6 criteria): (1) chi-square and degrees of freedom (2/df), (2) incremental fit index (IFI > .90), (3) Tucker-Lewis Index (TLI > .90), (4) comparative fit index (CFI > .90), (5) standardized root mean square residual (SRMSR < .10), and (6) root mean square error of approximation (RMSEA < .10) (Vandenberg & Lance, 2000). Metric invariance is achieved when the differences between unconstrained and constrained multi-group confirmatory factor analyses (MGCFAs) are not significant (CFI/RMSEA < .01, Cheung & Rensvold, 2002). We employed Mplus (Muthn & Muthn, 2010, Version 6) for our cross-level analysis. RESULTS Confirmatory Factor Analysis (CFA) Results Table 2 (Model 1) showed a good fit between our measurement model of love of money (LOM) and data for the whole sample (2 = 436.29, df = 50, p < .01, IFI = .99, TLI = .98, CFI = .98, SRMSR = .03, RMSEA = .03). Factor Rich (.84) had the highest factor loading followed by Motivator (.67), Importance (.67), and Power (.51), supporting findings in the literature. Due to the results for our measurement model above and the use Mplus for our cross-level analysis, we combined all items into an overall index (Cronbachs = .83, see also Tale 3). For the pay satisfaction questionnaire (Model 2), Raises ( = .97) had the highest factor loading followed by Pay Administration (.88), Pay Level (.84), and Benefits (.84). We combined all items of PSQ into an overall index (.94). There was a good fit for the 7-item corrupt intent measure (Model 3) (.87). Measurement invariance. We examined measurement invariance across three CPI groups using a three-way split (high CPI: CPI > 5.5, n = 2,761; medium CPI: 5.5 > CPI > 3.5, n = 1,269; low CPI: 3.5 > CPI, n = 2,352). Table 2 shows configural invariance of love of money, pay satisfaction, and corruption for the three CPI groups in nine analyses (Models 4-12). For metric invariance, we used multiple group confirmatory factor analysis (MGCFA) and compared unconstrained and constrained models across three CPI groups simultaneously in six analyses (Models 13-18). All results provided good support for our measurement models. For pay satisfaction, results showed excellent fit between our measurement model and our data for the whole sample (Model 2) and the high and medium CPI groups for configural invariance, but weak results in the low CPI group (Model 9). This was probably due to smaller sample size and huge


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differences in benefits (e.g., food, housing) across countries in the low CPI group. The root mean square error of approximation (RMSEA) tends to over-reject a true model due to small sample size and model complexity (Tang et al., 2006). Factor loadings of pay level, benefit, raises, and pay administration for high, medium, and low CPI groups are listed belowHigh: .82, .77, .98, and .86; medium: .84, .87, .95, and .88; and Low: .89, .93, .95, and .89, respectively. We achieved metric invariance for all three measures due to non-significant differences between unconstrained and constrained models using MGCFA. Common method variance bias (CMV). The common method variance (CMV) problem should not be a concern for our cross-level interaction effect in this study because we obtained data from managers (Level 1) in 31 different countries (Level 2). We also used scales with different anchors for predictors and a criterion to reduce the CMV effect which is not a source of spurious interactions. We examined this CMV issue in two steps (Podsakoff et al., 2003). First, Harmans single-factor test examines the unrotated factor solution involving all items of interest in an exploratory factor analysis (EFA). Results showed eight factors with eigenvalues greater than one. We listed the scale and the amount of variance explained (total = 65.04%) as follows: PSQ (24.74%), LOM (12.98%), Corruption (10.02%), and items with cross loading (3.99%, 3.80%, 3.75%, 2.98%, and 2.80%). Our predictors and criterion were separate factors in our EFA. Second, the measurement model with the addition of an unmeasured latent common method variance (CMV) factor (2 = 6,740.47, df = 584, p = .01, 2/df = 11.54, IFI = .95, TLI = .94, CFI = .95, SRMSR= .04, RMSEA = .04) did not improve the fit over our measurement model without a CMV factor (2 = 4,163.72, df = 387, p = .01, 2/df = 11.32, IFI = .96, TLI = .96, CFI = .96, SRMSR= .03, RMSEA = .04) (CFI = .01, RMSEA = .00, respectively). Thus, the common method variance (CMV) was not a concern in this study (Spector, 2006). Table 3 shows the mean, standard deviation, correlations, and Cronbachs alpha of major variables. Corruption was related to high love of money and low pay satisfaction. It was also related to people who were young, male, highly educated, in the manufacturing sector, and in the public sector. Corruption was also negatively related to GDP and CPI, as expected. Cross Level Analysis We had a large sample size at Level 1 (6,382 managers) and at Level 2 (31 countries). In our within level analysis, we used love of money and pay satisfaction to predict corrupt intent (CAD). The between-level variable was Corruption Perceptions Index (CPI, Level 2). The random slope S1 was defined by the linear regression of corrupt intent on the interaction effect between love of money and pay satisfaction. We regressed S1 on CPI in our between level analysis. ------------Insert Tables 1, 2, 3, and 4 and Figure 1 about here-----------Cross-level, three-way interaction effect. Our cross-level, three-way interaction effect on corrupt intent was significant (-2.61, p < .01) (see Table 4). Following suggestions in the literature (Aiken & West, 1991; Dawson & Richter, 2006), we plotted the three-way interaction effect and compared six pairs of slopes using t tests. The slope reveals the intensity of the relationship between love of money and corrupt intent, whereas the intercept suggests the magnitude of its


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impact on the corrupt intent. A steep (gentle) slope implies high (low) intensity, whereas a high (low) intercept suggests high (low) magnitude of corrupt intent. Magnitude. Overall, managers in low CPI (high corruption) countries had higher magnitude of corrupt intent than those in high CPI countries, as expected. Managers with high pay satisfaction in high CPI entities had the lowest magnitude of corrupt intent (Slope/Line 1 at the bottom of Figure 1); those with high pay satisfaction in low CPI entities had the highest magnitude of corrupt intent (Slope/Line 2 at the top of Figure 1) that confirms the notion of kleptocracy (Grossman, 1999). We may label the former (Slope 1) as good apples and the latter bad apples (Slope 2). Intensity. For those with low pay satisfaction, the difference between Slopes 3 and 4 was significant (t = 3.09, p < .01, see Figure 1). Slope 3 showed the strongest intensity in high CPI countries; Slope 4 had the weakest intensity in low CPI countries. For those with high pay satisfaction, the intensity of corrupt intent was almost identical regardless of whether they were in the low or the high CPI countries (Slope 1 vs. Slope 2: t = .31, p > .05). The intensity was slightly higher for managers with high pay satisfaction in both high and low CPI countries than those with low pay satisfaction in low CPI countries, but the differences was not significant (Slope 1 vs. Slope 4: t = 1.81, p > .05; Slope 2 vs. Slope 4: t = 1.63, p > .05; respectively). The differences between Slope 2 and Slope 3 (Figure 1) (t = -1.38, p > .05) and also between Slope 1 and Slope 3 (t = -1.12, p > .05) were not significant. Our results supported Hypothesis 1. DISCUSSION We craft a multilevel theoretical model and identify a significant three-way interaction effect: The moderating effect of pay satisfaction on the relationship between love of money and corrupt intent varies across levels of corrupt culture (CPI). Our new and value-added theoretical, empirical, and practical implications are presented below. Theoretical implications. We provide the following theoretical contributions. First, our significant three-way interaction effect shows that the relationship between love of money and corrupt intent is moderated by pay satisfaction at the micro level and CPI at the macro level, supporting and expanding the interactionist theory of ethical decision making (Kish-Gephart et al., 2010). Second, love of money contributes to corrupt intent, supporting the attitude-to-behavioral intention aspect of theory of planned behavior (Ajzen, 1991; Grant, 2008; Tang & Baumeister, 1984) and the proposition that the love of money is the root of all evil, given the fact that corrupt intent is a small part of evil examined in this study (Tang & Chiu, 2003; Tang & Chen, 2008). Third, we focus on the magnitude (intercept) of corrupt intent first. In the high CPI countries (the good barrels), high pay satisfaction enhances integrity and curbs corruption, because managers have the lowest magnitude of corrupt intent (Slope 1), as expected. Interestingly enough, managers with high pay satisfaction in low CPI countries have the highest magnitude of corrupt intent (Slope 2). The slopes for those with high pay satisfaction are almost identical regardless of CPI at the entity level. We can clearly identify good and bad apples.


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Fourth, contrary to the conventional wisdom that pay satisfaction is important to all people, our results reveal that pay satisfaction matters differently for managers in high or low CPI countries. Thus, incorporating the social norm, CPI, enhances our understanding of peoples corrupt intent across cultures. Fifth, with high pay satisfaction, the intensity (slope) for the relationship between love of money and corrupt intent is almost identical for managers in high or low CPI countries. However, the magnitude (intercept) of corrupt intent for high or low CPI groups is significantly different. The former (high pay satisfaction and high CPI) has the lowest magnitude, whereas the latter (high pay satisfaction and low CPI) has the highest. CPI is a moderator. We provide our analysis below: Sixth, for managers with high pay satisfaction in high CPI countries, they are less likely to become corrupt because a satisfied need is no longer a motivator, supporting the hierarchy of needs theory (Maslow, 1954). They do not want to take the risks and lose their freedom, dignity, integrity, and reputation in their lives (Gomez-Mejia et al., 2005). The cost of corruption is too high for the benefit (Tepper et al., 2007). Seventh, for people with high pay satisfaction in the low CPI countries, pay satisfaction may fuel the fire of corruption (Grant, 2008) causing them to have the highest magnitude of corrupt intent in this study. In low CPI entities, managers with high pay satisfaction (money earned legally or illegally) take corruption for granted, have the strongest winner-take-all mentality (the Matthew Effect; Merton, 1968)winning the most in a corrupt environment, treating corruption as a game, considering corruption as an entitlement, considering money as an addictive, insatiable drug (the more they have, the more they want), requiring larger dosages to maintain the same level of high or utility of money (Lea & Webley, 2006), and becoming members of the kleptocractic class (Grossman, 1999). Bad apples appear by the bushel in bad barrels (Pinto, Leana, & Pil, 2008) (Slope 2). They probably do not mind the risks of losing their freedom, dignity, integrity, and reputation (Gomez-Mejia et al., 2005). Even incarcerated, some may live comfortable with all the luxuries. Money talks everywhere, even in prison. Pay satisfaction is a double-edged sword with both positive and negative consequences. These novel findings have not been explored before. Eighth, the intensity of corrupt intent changes for managers with low pay satisfaction in high or low CPI countries. The pattern of change is significantly different for high or low CPI groups. The intensity increases for managers in the high CPI countries; but decreases for those in the low CPI group. Thereby, the intensity (slope) of corrupt intent for the high CPI group is significantly stronger than that for the low CPI group. One important implication is that pay satisfaction matters more in high CPI countries than in low CPI entities. Low pay satisfaction in high CPI countries greatly enhances managers intensity of corrupt intent (slope) due to their higher sensitivity to pay equity and justice (Huseman et al., 1987). Results support the notion people will steal, become dishonest, and have high corrupt intent in the name of justice (Gino & Pierce, 2009a; Greenberg, 1993). On the other hand, managers in a corrupt environment with low pay satisfaction may be expected by many lay people to have the highest intensity and magnitude of corrupt intent. But, that is not the case. We speculate that in a corrupt culture, the notion of equity or justice may or


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may not be valued as high as those in high CPI countries. With scarce resources and income, managers with low pay satisfaction in low CPI countries may have (1) missed the opportunity for becoming a member of the good ole boys club, (2) missed key ingredients of corruption position, resources, personal contact, and power, (3) been controlled by a strong culture of kleptocracy (rule by thieves, Grossman, 1999), (4) low sensitivity to inequity, (5) very little hope for justice, and (6) developed the learned helplessness in that environment (Abramson, Seligman, & Teasdale, 1978). Their love of money exists. They have high magnitude but low intensity of corrupt intent. They do not do it for the love of money. The consequences of low pay satisfaction may vary across high or low CPI countries. Finally, we summarize our contributions below. Our finding helps researchers (1) recognize the importance of testing the three-way interaction effect on corrupt intent, (2) expand the theory of planned behavior (TPB) from an individual level theory to a multi-level theory; (3) integrate TPB with the person-situation interactionist theory by incorporating pay satisfaction and CPI at the micro and macro level, respectively (Kish-Gephart et al., 2010; Trevio, 1986), (4) tease out good and bad apples unseen behavioral patterns in good or bad barrels and make them clearly visible in a new perspective, (5) identify not only contributions but boundaries of several theories (Ajzen, 1991; Ashforth et al., 2008; Herzberg, 1987; Milkovich et al., 2011), and (6) incorporate contextualization, help develop robust theories, capture the complexity of organizational phenomena, and offer real-world relevance in the management field (Bamberger, 2008). Empirical contributions. Our pay satisfaction and CPI help practitioners understand love of money to corrupt intent relationship based on a large sample of 6,382 managers in 31 countries across six continents. Results regarding measurement models for the whole sample and measurement invariance across CPI groups enhance the generalizability of our findings and offer confidence to future researchers in using these scales in underresearched areas of the world (Kirkman & Law, 2005). Our counterintuitive discoveries (Bartunek, Rynes, & Ireland, 2006) are impossible to achieve without a large sample size at both levelsLevel l and Level 2. Practical implications. Our results provide partial support for the proposition that the love of money is the root of all evil, if we consider corrupt intent as a small part of evil. It is difficult to manage managers love of money because people bring dispositional values to organizations (Staw, Bell, & Clausen, 1986). Although executives cannot manipulate or change directly managers love of moneya deeply rooted value or attitudethey can still properly manage it in organizations. People need money (a hygiene factor) continuously to maintain their lives (Herzberg, 1987). Research suggests that the relationship between income and love of money is negative among highly-paid professionals in Hong Kong (Tang & Chiu, 2003), not significant among adequately-paid males and Caucasians, but positive among underpaid females and AfricanAmericans in the US (Tang, Tang, & Homaifar, 2006). Females and African-Americans have lower pay than their male and Caucasian counterparts, respectively. Underpaid people lack feelings of self-sufficiency and love money more than those who are not. Therefore, objective income, financial experiences, and ethical cultural values may shape or modify ones love of money and behavior (Tang, Luna-Arocas, & Sutarso, 2005). Executives need to conduct annual


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surveys in order to understand peoples love of money and pay satisfaction, i.e., money-related feelings or attitudes, and corrupt intent. People do work for moneybut they work even more for meaning in their lives (Pfeffer, 1998: 112). Executives have strong control over their malleable compensation systems and can pay managers fairly and well. Managers with high pay satisfaction have a sense of selfsufficiency (Vohs et al., 2006), take their minds off money (Kohn, 1993), elevate their virtue and wisdom, inculcate their stewardship behaviors, and supplant opportunism and self-interest (Gomez-Mejia et al., 2005). Over the years, multinational corporations (MNCs) have become increasingly interested in improving productivity and profits, reducing labor costs, and managing human resource effectively across borders. Due to economic down turn, downsizing, and pay cuts, fewer employees shoulder more responsibility with lower pay. Consequently, organizational trust decreases and dishonesty is on the rise (Gilbert & Tang, 1998). These contextual variables may trigger desperate and disgruntled people to retaliate (Skarlicki & Folger, 1997; Tepper et al., 2009), get even, or become corrupt. In order to eradicate corruption, boost business ethics globally, and maintain sustainable development in the competitive market, executives must be aware of this trap (love of money, pay satisfaction, and CPI are three critical ingredients of corruption), avoid the most commonly-recognized and deeply-rooted temptations (i.e., paying managers as little as possible or less than they deserve), and manage all stakeholders (stockholders, managers, employees, suppliers, and customers) fairly and well to reduce corruption and increase profits. It pays to enhance managers pay satisfaction and curb corruption. Executives may accumulate resources, adopt administrative innovations, and implement an effective and efficient compensation system to cultivate a sense of justice or a healthy level of love of money. Executives need to monitor love of money, pay satisfaction, firm-level pressures (Martin et al., 2007), and different forms of temptations across boarders; valorize corporate ethical values; and inspire personal integrity (Feiner, 2004; Simons, 2002). In addition, they need to implement organizational corruption control elementsbureaucratic control, punishment, incentive alignments, legal/regulatory sanctioning, social sanctioning, vigilance controls, selfcontrol, and concertive controls (Lange, 2008)to curb corruption. Doubling the civil servants wage may improve two points on the Corruption Perceptions Index (CPI) (Rijekeghem & Weder, 1997). We argue that this strategy may work for managers in the most affluent countries. It may also work for those in low CPI (the least affluent) countries when entities at the bottom of the income pyramid establish proper infrastructures, reach a high level of economic development, and create an ethical culture or environment. Or, regardless of CPI, increasing corporate ethical values in organizations may also greatly reduce corruption because most people do want to receive reward and avoid punishment in the social context (Fisman & Miguel, 2007). Executives have more control over corporate ethical values than the CPI (country level). China, for example, the second largest economy and one of the most volatile markets in the world, has had an eight percent increase of GDP per capita for the past several years, and will soon be in the high GDP group. Researchers must monitor the changes in GDP per


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capita, CPI, and income on corruption empirically over time because all these variables are highly related in this study (Liu & Tang, in press). Future research may test this proposition empirically. The Enron Effect. Many executives in the US were involved in scandals and corruption. We explain the recent scandals of corruption, the Enron effect, below: Many CEOs and top executives are obviously quite rich and are richer then our middle-level managers examined in this study. Most of them may have the highest love-of-money orientation, graduated from the most prestigious universities, moved to the top of the organizational echelon, and are obsessed with money. When executives salary figures are made open to the public, all executives want to surpass the highest pay in the industry leading to escalating pay increases. CEOs and executives have a lot of financial and income data in organizations. When people are primed with different notions of money (e.g., Ariely, 2008a; Gino & Pierce, 2009b; Vohs et al., 2006), it is very easy for the rich to envy the superrich. Due to pay comparison, they experience low pay satisfaction due to pay inequity. They also experience a strong pressure from top CEOs and the unethical corrupt culture to do whatever it takes to make corporations to look good financially in public, on Wall Street, in particular. Personally, they all want to maintain the money, power, and life styles and can not resist the temptation. These factors lead to a higher percentage of executives to engage in and a much larger magnitude of cheating for personal gains. Our results suggest that perceptions of pay inequity lead executives to significantly increase not only the intensity (changing from Slope 1 to Slope 3) but also the magnitude (intercept) of corrupt intent (changing from Slope 1 to Slope 2). No one can serve two masters. You cannot serve both God and Money (Matthew 6: 24; Tang, 2010; Tang & Tang, 2010). If authentic leaders with character and integrity (Simons, 2002), corporate ethical values (CEV) (Baker, Hunt, & Andrews, 2006; Hunt, Wood, & Chonko, 1989), and strong enforcement of laws (Fisman & Miguel, 2007) do not exist, people ignore the ethical culture at the country level (high CPI) and behave like those in low CPI countries with high pay satisfaction due to pressure (Martin et al., 2007) and temptation (Baumeister, 2002) in the environment. Our additional preliminary analyses suggest the following new insights. Managers in low CPI entities with low CEV have the highest magnitude (intercept) of corrupt intent, whereas those in high CPI entities with high CEV have the lowest. Further, people in high CPI entities with low CEV or in low CPI entities with high CEV have almost identical intensity and magnitude of corrupt intent. Furthermore, the intensity (slope) between love of money and corrupt intent is the strongest for managers in high CPI entities with low CEV and the weakest for those in low CPI entities with low CEV. Thereby, we assert that ethical values or social norms at the organizational and entity levels are both important and may be interchangeable. The implications are clear: In high CPI countries, managers with low pay satisfaction or those with low CEV have the strongest intensity of corrupt intent. The combination may suggest that top executives in high CPI countries with low pay satisfaction and low corporate ethical values (CEV) have not only the strongest intensity but also the highest magnitude of corruption and engage in grand corruption (kleptocracy). Some thought they were doing good deeds for the corporation and society (Levine, 2005), yet they become slaves of money (money as a drug) and can not resist the temptation of


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money, power, and life style in corrupt and infectious organizations (Badaracco, 2006). As Ariely pointed out that when cheating is one step removed from cash, people tend to rationalize and justify their dishonesty easily. Such latitude is the force behind the Enrons of the world (2008a: 24). Corruption damages economic efficiency and sustainability. Although the Sarbanes-Oxley Act exists in the US, such laws may not exist in other countries. Without stable infrastructures, the uncertainty associated with economic transactions imposes a heavy risk premium that discourages foreign investment. Executives must be careful when they do business in corrupt entities and not spurn the poor at the bottom of income pyramid because they may become new sources of growth in the earliest stage of rapid economic development. Limitations. We measured managers corrupt intent not the actual behavior. Since the incumbents self-report and the coworkers peer-report converge on deviant behavior (De Jonge, & Peeters, 2009; Fox et al., 2007), self-reported corrupt intent is a reasonable surrogate of corruption. Although we did not select 31 countries or the sample from the population randomly, our samples represent the populations well. Our cross-sectional data did not prove a cause-and-effect relationship. Love of money and pay satisfaction may be best addressed by mono-method self reports. Future researchers may incorporate corruption-control, longitudinal data from multiple sources, unemployment rate, moral development, public/private sector, and religion in research. Conclusion. For managers with high pay satisfaction, the intensity of the love of money to corrupt intent relationship was almost identical in high or low CPI countries; but the former had the lowest magnitude of corrupt intent; while the latter had the highest. For those with low pay satisfaction, the intensity was the strongest in high CPI countries, but was the weakest in the low CPI countries and the difference between the two was significant. We trust that our results provide a new perspective on behavioral ethics, relate to the globalization of the economy and internalization of management research, promote theory development and testing, and further research and improved practice (Colquitt & Zapata-Phelan, 2007).


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AoM 2011 10690: LOM Pay CPI (Best Paper Proceedings)TABLE 1 Demographic Variables across 31 CountriesGeopolitical Entity 1. 2. 3. 4. 5. 6. 7. 8. 9. Australia Belgium Brazil Bulgaria China Croatia Congo Egypt France N 262 201 201 162 204 165 90 200 87 211 100 204 200 200 295 200 204 183 200 200 200 200 538 200 203 203 183 201 200 211 274 GDP ($) 34,740 35,712 4,320 3,459 1,709 8,675 710 1,265 33,918 25,943 10,814 2,810 5,042 13,803 7,298 678 12,664 2,841 1,168 17,456 4,539 5,349 26,836 16,986 5,106 16,308 27,226 15,203 2,659 5,062 42,000 Age (yr.) CPI 8.8 7.4 3.7 4.0 3.2 3.4 2.1 3.4 7.5 8.3 5.0 2.7 5.1 6.6 3.5 1.9 6.3 3.5 2.5 6.5 3.0 2.4 9.4 6.1 4.5 5.0 7.0 5.9 3.8 3.5 7.6 Mean 26.50 38.85 37.50 27.48 31.86 37.48 42.37 40.41 36.63 30.79 34.06 41.60 31.80 36.91 30.95 34.80 29.91 31.98 33.45 35.16 38.02 35.92 33.41 38.68 43.32 37.15 33.82 34.94 33.32 28.87 35.03 SD 13.13 10.79 7.46 5.89 6.85 11.46 9.73 9.12 6.92 7.82 10.59 9.69 6.56 11.28 9.30 9.93 5.86 5.29 7.93 8.49 9.29 9.59 7.78 10.94 7.13 6.08 9.81 7.31 7.05 6.49 10.04 Gender % Male 0.29 0.57 0.45 0.43 0.60 0.42 0.83 0.50 0.63 0.49 0.55 0.44 0.53 0.51 0.55 0.60 0.64 0.68 0.60 0.39 0.27 0.42 0.55 0.44 0.50 0.73 0.58 0.51 0.55 0.61 0.45 Education (yr.) Mean SD 12.74 14.83 16.87 16.76 15.38 14.73 14.61 14.88 15.74 15.66 15.96 13.31 15.23 16.47 14.32 15.73 14.68 16.93 16.96 15.44 16.69 17.58 15.06 13.68 15.61 15.90 14.20 16.50 16.84 14.94 15.08 1.58 Career Work (yr.) Mean SD 9.70 12.05 Type % Ser/Pri

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81.7/58 .8 65.2/63.7 100/37.3/48.5

2.35 17.43 11.55 1.38 14.65 1.44 1.58 3.08 9.90 8.11 6.85

75.6/46.7 90.5/10.5 -

1.62 16.91 11.48 2.71 16.55 1.49 3.11 8.86 7.67 8.82 9.98 5.86 9.25

10. Hong Kong 11. Hungary 12. Macedonia 13. Malaysia 14. Malta 15. Mexico 16. Nigeria 17. Oman 18. Peru 19. Philippines 20. Portugal 21. Romania 22. Russia 23. Singapore 24. Slovenia 25. South Africa 26. South Korea 27. Spain 28. Taiwan 29. Thailand 30. Turkey 31. The USA

79.1/87.7 55.4/60.3 52.5/89.0 99.0/43.0 86.1/69.8 47.2/56.5 85.5/16.2 -

2.11 10.30 1.67 17.28 3.31 8.38

2.12 14.02 10.42 2.43 9.92 8.61

3.72 13.80 12.21 2.51 8.34 2.27 1.63 10.28 3.61 13.07 1.56 15.62 1.20 14.51 1.99 10.20 7.05 8.89 9.90 9.78 6.55 5.59

85.5/83.5 85.5/62.5 2.5/79.5 66.5/53.0 84.9/53.2 44.0/35.0 26.1/27.6 58.1/62.8/63.4 61.2/72.6 54.0/52.0 98.1/71.6 8.91/72.6

2.38 17.75 11.03 2.23 17.36 1.03 12.31 3.26 12.26 2.19 11.55 1.12 10.50 2.44 5.70 7.75 5.91 9.93 7.32 6.92 5.74 9.96

2.01 13.16

Note: N = 6,382. GDP per capita is expressed in US$. Gender = Percentage of Male. Education and total work experience in ones career are expressed in years. Percentage in Service (Production)/Private (Public) Sector.


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TABLE 2 Results of Confirmatory Factor Analysis (CFA) ________________________________________________________________________________________________________ 2 df p 2/df IFI TLI CFI SRMSR RMSEA CFI RMSEA ________________________________________________________________________________________________________ I. Whole Sample 1. The Love of Money (LOM) 436.29 2. Pay Satisfaction (PSQ) 5,757.67 3. Corruption (CAD) 313.82 II. Configural Invariance 1. The Love of Money 4. High CPI 5. Medium CPI 6. Low CPI 2. PSQ 7. High CPI 8. Medium CPI 9. Low CPI 3. Corruption 10. High CPI 11. Medium CPI 12. Low CPI III. Metric Invariance 1. The Love of Money (3 CPI, MGCFA) 13. Unconstrained 799.27 150 14. Constrained 1,048.84 166 2. PSQ (3 CPI, MGCFA) 15. Unconstrained 6,630.55 16. Constrained 7,013.20 3. Corruption (3 CPI, MGCFA) 17. Unconstrained 420.36 18. Constrained 160.29

50 131 13

.00 .00 .00

8.73 43.95 24.14

.99 .92 .98

.98 .91 .97

.98 .92 .98

.03 .04 .02

.03 .08 .06

383.41 181.97 233.88

50 50 50

.00 .00 .00

7.67 3.34 4.68

.98 .97 .98

.97 .97 .97

.98 .97 .98

.03 .04 .03

.05 .05 .04

1,795.09 1,152.30 3,683.16

131 131 131

.00 .00 .00

13.70 8.08 28.12

.95 .92 .88

.94 .90 .86

.95 .92 .88

.04 .04 .06

.06 .08 .11

234.72 39.49 146.17

13 13 13

.00 .00 .00

18.06 3.04 11.24

.96 .99 .98

.94 .99 .97

.97 .99 .98

.04 .02 .02

.08 .04 .07

.00 .00

5.33 6.31

.98 .97

.98 .96

.98 .97

.03 .04

.03 .03

.01

.00

393 421

.00 .00

16.87 16.66

.91 .91

.90 .90

.92 .91

.04 .04

.05 .05

.01

.00

39 12

.00 .00

10.78 10.85

.98 .97

.98 .96

.98 .97

.04 .04

.04 .04

.01

.00

________________________________________________________________________________________________________ Note . Sample Size: N = 6,382. High CPI (CPI > 5.5, n = 2,761); Medium CPI (5.5 > CPI > 3.5, n = 1,269); Low CPI (3.5 > CPI, n = 2,352).


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TABLE 3 Means, Standard Deviations, Cronbachs Alpha, and Zero-Order Correlations Among Variables ______________________________________________________________________________________________________________Variable M SD 1 2 3 4 5 6 7 8 9 10 11 ________________________________________________________________________________________________________________________

1. Age 34.56 9.82 2. Sex (% Male) .51 .13** 3. Education (yr.) 15.39 2.60 .03* .03 4. Experience (yr.) 13.34 9.34 .22** .00 .01 5. Service (%) .62 -.05** .00 -.07** -.07** 6. Private (%) .37 -.06** -.05** -.04** -.12** .09** 7. GDP (US$) 13,800.75 12,344.89 -.07** -.03 -.18** -.03 .19** .12** 8. CPI 5.25 2.28 -.10** -.02 -.17** -.08** .21** .09** .88** 9. Money LOM 3.69 .65 -.04* .08** .04* -.01 -.01 .03* -.03* -.01 (.83) 10. Pay PSQ 2.93 .74 -.03* .05* .02 -.05* .03* .01 .02 .02 -.01 (.94) 11. Corruption CAD 1.49 .64 -.07** .10** .04** .01 -.06** -.09** -.11** -.12** .11** -.04** (.87) ______________________________________________________________________________________________________________ Note. N = 6,382. Cronbachs alpha is presented in parentheses. GDP per capita and CPI were assigned to all participants of the same geopolitical entity. *p < .05 **p < .01

AoM 2011 10690: LOM Pay CPI (Best Paper Proceedings) TABLE 4

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Mplus Results of our Cross-Level Analysis of Corrupt Intent ______________________________________________________________________________ Two-Tailed Estimate S.E. Est./S.E. p-Value ______________________________________________________________________________ Within Level Corrupt Intent ON Love of Money Pay Satisfaction Residual Variances Corrupt Intent Between Level S1 ON CPI Corrupt Intent ON CPI Intercepts Corrupt Intent S1 Residual Variances Corrupt Intent S1 -0.01 0.03 0.00 0.02 -2.61 1.57 0.01** 0.12 0.07 0.85

0.14 0.02

0.08 0.08

1.79 0.19

0.31

0.03

10.01

0.00***

1.39 0.03

0.30 0.03

4.69 0.97

0.00*** 0.34

0.05 0.00

0.01 0.00

3.19 2.55

0.00*** 0.01**

_____________________________________________________________________________ Note. Sample size: within level = 6,382 managers; between level = 31 entities. Within level variables: Love of Money and Pay Satisfaction are group mean centered. Between level variable: Corruption Perceptions Index (CPI). Dependent variable = Corrupt Intent. The random slope S1 is defined by the linear regression of the dependent variable corrupt intent on the interaction effect between love of money and pay satisfaction. Test of Model Fit: Loglikelihood, H0 Value = -5,409.22; H0 Scaling Correction Factor for MLR = 5.06. Information criteria: Number of Free Parameters = 9. Akaike (AIC) = 10,836.44. Bayesian (BIC) = 10,897.29. Sample-Size Adjusted BIC (n* = (n + 2) / 24) = 10,868.69. p < .10 *p < .05 **p < .01 ***p < .001

AoM 2011 10690: LOM Pay CPI (Best Paper Proceedings) FIGURE 1 Results of a Cross-Level Model of Corruption

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Comparing Slopes of a Three-Way Interaction Effect ________________________________________________________________________________ Pairs of Slopes (A) (B) (A) and (B) PSQ CPI PSQ CPI t-value p-value ________________________________________________________________________________ (1) and (2) High High High Low .31 .76 (1) and (3) High High Low High -1.12 .26 (1) and (4) High High Low Low 1.81 .07 (2) and (3) High Low Low High -1.38 .17 (2) and (4) High Low Low Low 1.63 .10 (3) and (4) Low High Low Low 3.09 .00** ________________________________________________________________________________ Note. p < .10, **p < .01

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