One More Time: Is the Love of Money the Root of All Evil ?

Shanghai Maritime UniversityOctober 17-19, 24-26, 2008

Presented by

Thomas Li-Ping Tang, Ph.D.

Middle Tennessee State University, the USA 10/1

One More Time: Is the Love of Money the Root of All Evil ?

Shanghai Jiao Tong UniversityOctober 20, 2008

Presented by

Thomas Li-Ping Tang, Ph.D.

Middle Tennessee State University, the USA

Toto Sutarso, Middle Tennessee State University, U.S.A.,Adebowale Akande, International Institute of Research, South Africa,Michael W. Allen, University of Sydney, Australia,Abdulgawi Salim Alzubaidi, Sultan Qaboos University, Oman,Mahfooz A. Ansari, University of Lethbridge, Canada, Fernando Arias-Galicia, Universidad Autónoma del Estado de Morelos,

Mexico,Mark G. Borg, University of Malta, Malta,Luigina Canova, University of Padua, Italy,Brigitte Charles-Pauvers, University of Nantes, France,Bor-Shiuan Cheng, National Taiwan University, Taiwan,Randy K. Chiu, Hong Kong Baptist University, Hong Kong, Linzhi Du, Nankai University, China,Ilya Garber, Saratov State Socio-Economic University, Russia,Consuelo Garcia De La Torre, Technological Institute of Monterrey,

Mexico,Rosario Correia Higgs, Polytechnic Institute of Lisbon – Portugal,

Portugal, Abdul Hamid Safwat Ibrahim, Iman University, Saudi Arabia, Chin-Kang Jen, National Sun-Yat-Sen University, Taiwan,Ali Mahdi Kazem, Sultan Qaboos University, Oman,Kilsun Kim, Sogang University, South Korea,Vivien Kim Geok Lim, National University of Singapore, Singapore,Roberto Luna-Arocas, University of Valencia, Spain,Eva Malovics, University of Szeged, Hungary,

Anna Maria Manganelli, University of Padua, Italy,Alice S. Moreira, Federal University of Pará, Brazil, Richard T. Mpoyi, Middle Tennessee State University, the U.S.A., Anthony Ugochukwu Obiajulu Nnedum, Nnamdi Azikiwe University, Nigeria, Johnsto E. Osagie, Florida A & M University, U.S.A.,AAhad M. Osman-Gani, Nanyang Technological University, Singapore,Francisco Costa Pereira, Polytechnic Institute of Lisbon – Portugal, Portugal,Ruja Pholsward, Rangsit University, Thailand, Horia D. Pitariu, Babes-Bolyai University, Romania,Marko Polic, University of Ljubljana, Slovenia,Elisaveta Sardzoska, University St. Cyril and Methodius, Macedonia, Petar Skobic, Middle Tennessee State University, U.S.A.Allen F. Stembridge, Andrews University, U.S.A.,Theresa Li-Na Tang, Affinion Group, Brentwood, TN, U.S.A., Thompson Sian Hin Teo, National University of Singapore, Singapore,Marco Tombolani, University of Padua, Italy,Martina Trontelj, University of Ljubljana, Slovenia,Caroline Urbain, University of Nantes, FrancePeter Vlerick, Ghent University, Belgium

Recent News in the USAMay 31, 2008. Lehman Brothers had

assets of $639 billion, but $613 billion in debt. 639 - 613 = 26; 26/639 = 4%

CEO Dick Fuld’s one-year total pay was $71.9 million, five-year total pay was $354 million. He received a $39 million (€24.7 million) bonus in cash and restricted stock in 2007.

Recent News On Monday, September 15, 2008,

Lehman Brothers, a 158-year-old investment bank, filed for Chapter 11 protection in the US.

It was the biggest corporate bankruptcy in history with $639 billion, followed by WorldCom with $126 billion and Enron with $81 billion. (former Chief Financial Officer Andrew Fastow and former Chief Executive Officer Jeffrey Skilling)

Recent News On September 23, 2008, several

days after Wall Street meltdown, FBI started to investigate the officials and these investment banks (e.g., Fannie Mae, Freddie Mac, Lehman Brothers, and AIG).

Recent News Lloyd Blankfein, CEO of Goldman

Sachs, took home $74 million in salary, bonuses and other awards in 2007.

James E. Cayne, former CEO of Bear Stearns made $49.31 million over the last two years (2006-2007).

Recent News Martin J Sullivan, former CEO of

American International Group (AIG), raked in $39.6 million in the last three years (2005-2007). Sullivan oversaw two quarters of record losses as the insurance

giant's head. Shareholders pressured him to quit in June. Severance package plus bonus: $19 million.

Thailand: Bangkok Post

Prosecutors in Thailand seek the confiscation of 76 billion baht in cash and assets from the former Prime Minister Thaksin Shinawatra and his family.

The former Prime Minister was accused of abusing his power by changing tax and telecommunications policies to benefit his own business empire, Shin Corp., while he was in office from 2001 to 2006. Shares were sold to Temasek of Singapore.

Russia Corruption in Russia is the rule rather

than the exception. Nikolai Zlobin, a former political

adviser to the Kremlin living in Washington, DC: The formula of modern power in Russia is: “The size of ‘otkat’ (kickback, 佣金 /回扣 ) must correspond to the strength of ‘naezd’ (racketeering, 敲诈 )”.

Russia Liberal Democratic Party of Russia

Leader Vladimir Zhirinovsky: every post in Russia -- with the exception of the five or six highest ones, is available for sale. A governorship costs about €5 to €7 million.

A department head or the head of a federal agency, costs €3 to €4 million.

China Two former Bank of China managers

and their wives were convicted in Las Vegas for money laundering/ 洗钱 and racketeering/ 敲诈 in the US involving at least $485 million over 13 years.

A Chinese national was repatriated ( 被遣返回国 ) from Canada for prosecution.

Singapore Ng Teck Lee failed in his duties as

CEO of a waste recycling company, Citiraya, and committed fraud against the firm.

Investigators believe that Ng skipped town with S$72 million ($51 million), making it one of Singapore’s biggest ever corporate scandals and frauds.

Nigeria 10% of the daily supply of oil in Nigeria is

stolen from pipelines and other facilities by criminals and militants and sold off illegally (blood diamond/oil)

Leaders of People Democratic Party in Oyo State: Governor Alao-Akala has embezzled ( 盗用 ) $24 billion excess crude oil fund released to the State by the Federal Government

Corruption After a thief was nabbed by the police and the stolen

goods retrieved, a Staff Sergeant contacted the businessman indicating that his $7,000 worth of stolen jewelry had been pawned and was about to be melted.

The police officer asked for $2,000. The businessman could offer only $1,000 and two bottles of whisky.

Judge: The police officer had violated the public’s trust, abused his position, and asked for extra incentives to do essentially his job. The officer was sentenced to 14 months’ jail for corruption and penalized $1,110 for the cash and the value of the whisky he received.

Money People around the world have

different economic, legal, political, and social infrastructures, history, cultures, beliefs, values, attitudes, and behavior; yet they all speak one language that everyone understands: Money.

Professional Wrestlers as Ushers: Increased Collection Plate Donations by 72%

Money

• The instrument of commerce and the measure of value (Smith,

1776/1937). • Attract, retain, and motivate

employees and achieve organizational goals (Chiu, Luk, & Tang, 2002; Milkovich & Newman, 2005; Tang, Kim, & Tang, 2000).

• Objective

The Meaning of Money

is “in the eye of the beholder” (McClelland, 1967, p. 10)

and can be used as the “frame of reference” (Tang, 1992) in which people examine their everyday lives (Tang & Chiu, 2003; Tang, Luna-Arocas, & Sutarso,

2005). Subjective

The Importance of Money*10 Job Preferences, Pay was ranked:

(Jurgensen, 1978) No. 5 by Men

No. 7 by Women

*11 work goals, Pay was ranked: (Harpaz, 1990). No. 1 in GermanyNo. 2 in Belgium, UK, and the US

The ABCs of Money Attitudes

Affective: Do you “love or hate” money?

Behavioral: What do you “do” with your money?

Cognitive: What does money “mean” to you?

The Love of Money ScaleFactor 1: Rich (Affective)

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

Factor 2: Motivator (Behavior)4. I am motivated to work hard for money.5. Money reinforces me to work harder.6. I am highly motivated by money.

Factor 3: Importance (Cognitive)7. Money is good.8. Money is important.9. Money is valuable.

Factor 4: Power (Cognitive)10. Money is power.11. Money gives one considerable power.12. Money can buy the best products and services

Affective: Rich Love or Hate Most people love Money. I want to be Rich. Whoever loves money never has

money enough; whoever loves wealth is never satisfied with his income (Ecclesiastes 5: 10).

The Love of Money Those who want to get rich fall

into temptation and a trap and into many foolish and harmful desires that plunge people into ruin and destruction. For the love of money is a root of all kinds of evil. (1 Timothy 6: 9-10)

Behavioral: Motivator Strategy Performance

Improvement Pay: 30% Goal Setting: 16% Job Design: 9% Participation: 0%

(Locke, E. A., Feren, D. B., McCaleb, V. M., Shaw, K. N., & Denny, A. T. 1980).

Behavioral: Money is a Motivator

Herzberg: Money Movement (no motivation)

A clear link: Performance Rewards (Nohria, Groysberg, & Lee, HBR, 2008)

When people were paid 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 & Newman, 2008)

Cognitive: Importance*10 Job Preferences, Pay was

ranked:No. 5 by Men

No. 7 by Women (Jurgensen, 1978)

*11 work goals, Pay was ranked:No. 1 in GermanyNo. 2 in Belgium, the UK,

and the US (Harpaz, 1990)

Cognitive: Importance How we compare Outperform Others “For to him who has shall be given,

and he shall have abundance; but from him who does not have, even that which he has shall be taken away” (Matthew, 13: 12).

Cognitive: Power Power tends to corrupt; and

absolute power corrupts absolutely (Lord Acton, Letter to Bishop Mandell Creighton, 1887).

Money talks.

Pay Satisfaction

Job satisfaction: A pleasurable or positive emotional state resulting from the appraisal of one’s job or job experiences (Locke, 1976: 300).

Pay Satisfaction: Pay Level Pay Satisfaction relationship is the most robust finding (Heneman & Judge, 2000: 71).

Pay Satisfaction Questionnaire

1. Pay Level 2. Pay Raise3. Benefit4. Pay Administration (Heneman & Schwab,

1985) Time 1-Time 2 (Judge & Welbourne, 1994)

Majority of studies included only Pay Level Satisfaction of PSQ (Williams, McDaniel, & Nguyen, 2006)

Pay Level Satisfaction

1. My take home pay2. My current salary3. My overall level of pay4. Size of my current salary

The Love of Money-Pay Satisfaction

Adam (1963): Equity modelLawler (1971): Discrepancy modelEasterlin (2001): Relative theoryVeenhoven (1984): Absolute theoryBrickman & Campbell (1971):

Adaptation theoryMichalos (1985): Aspiration theory

The Love of Money-Pay Satisfaction

Poverty consists, not in the decrease of one’s possessions, but in the increase of one’s greed. Plato (427-347 BC)

Whoever loves money never has money enough; whoever loves wealth is never satisfied with his income. (Ecclesiastes 5:10)

High Income High Pay Level SatisfactionHigh Love of Money Low Pay Level Satisfaction

Pay Satisfaction, So-What?

Larger Context:Corporate Ethical Values (social norms)Unethical Behavior Intentions (consequence of pay dissatisfaction)

Theory of Planned Behavior

Unethical Behavior Intentions

The incumbent’s self-report and the coworker’s peer-report converged significantly on counterproductive work behavior toward other persons and work stressors (Fox, Spector, Goh, & Bruursema, 2007).

Self-reported behavioral intentions are arguably adequate surrogate measures of actual unethical behavior (Jones & Kavanagh, 1996).

Unethical Behavior Intentions Schoorman, F. D., & Mayer, R. C.

2008. The value of common perspectives in self-reported appraisals: You get what you ask for. Organizational Research Methods, 11, 148-159.

Unethical Behavior Intentions Workplace deviance (Greenberg, 2002;

Robinson & Bennett, 2000),

Counterproductive behavior (Cohen-Charash & Spector, 2001),

Corruption (Anand, Ashforth, & Joshi, 2004), Whistle-blowing (Dozier & Miceli, 1985), Misbehavior (Ivancevich, Konopaske, & Matteson,

2005; Vardi & Weitz, 2004).

Unethical Behavior IntentionsPropensity to Engage in Unethical Behavior (PUB)

We developed a short self-reported measure (PUB) and ask managers:

If you were in that position, what is the probability that you may engage in that activity?

It is a measure of “self-prediction” of their unethical behavior which is highly related to actual behavior

Propensity to Engage in Unethical Behavior: Unethical Behavior Intentions

Factor Resource Abuse 1. Use office supplies (paper, pen), Xerox machine, and stamps for personal purpose

2. Make personal long-distance (mobile phone) calls at work 3. Waste company time surfing on the Internet, playing computer games, and

socializing Factor Not Whistle Blowing 4. Take no action against shoplifting by customers 5. Take no action against managers who steal cash/merchandise Factor Theft 6. Abuse company expense accounts and falsify accounting records 7. Take merchandise and/or cash home 8. Borrow $20 from a register overnight without asking Factor Corruption 9. Accept money, gifts, and kickbacks from others 10. Reveal company secrets when a person offers several million dollars 11. Sabotage the company to get even due to unfair treatment 12. Lay off 500 managers to save the company money and increase my personal

bonus

Corporate Ethical Values (CEV)

Strong cultures enhance firm performance (O’Reilly & Chatman, 1996) and deter unethical behavior (Baker, Hunt, & Andrews, 2006): Most people do look to the social context to determine what is ethically right and wrong (Bandura, 1977; Thomas, Schermerhorn,

& Dienhart, 2004), obey authority figures (Litzky,

Eddleston, & Kidder, 2006; Milgram, 1974), do what is rewarded (Skinner, 1972; Treviño & Brown, 2004), and follow the code of ethics (Bethoux, Didry, & Mias,

2007).

Cross-Cultural Study

64%: only 2 countries 23%: > 2 countries (Sin, Cheung, & Lee, 1999).

72.43%: did not report Measurement Invariance (He, Merz, & Alden, 2008)

Configural Invariance: Factor structureMetric Invariance: Factor Loading

The Income Pyramid

Prahalad & Hammond, 2002, HBR $ N---------------------------------------------------------1. > $20,000 100 Million2. $2,000 – $20,000 2,000 Million

3. < $2,000 4,000 Million

Level of Economic Development

1. GDP > 20,0002. GDP 5,000 – 20,0003. GDP < 5,000

We treat GDP as a “Moderator”

Method N = 6,285(1) High GDP Group (n = 1,960, 8 entities):

the USA, Belgium, Australia, France, Italy, Spain, Singapore, Hong Kong;

(2) Medium GDP Group (n = 2,371, 12 entities): Portugal, Slovenia, South Korea, Taiwan, Malta, Oman, Hungary, Croatia, Mexico, Russia, South Africa, Malaysia;

(3) Low GDP Group (n = 1,954, 10 entities): Romania, Brazil, Bulgaria, Peru, Macedonia, Thailand, China, Egypt, the Philippines, and Nigeria.

TABLE 1 Descriptive Statistics of All Variables and SEM Path of the Relationship between the Love of Money to Pay Level Satisfaction across 31 Samples (30 Geopolitical Entities)

High GDP Group     GDP Age Sex Education Income LOM PLS Path Sample N M % M M M SD M SD LOM PLS     1. The USA (H) 274 42,000 35.03 45 15.08 35,357 3.85 .65 2.83 1.00

-.11 2. Belgium 201 35,712 38.85 57 14.83 20,269 3.37 .61 3.30 .85 -.04 3. Australia 262 34,740 26.50 29 12.74 - 3.58 .66 3.14 .94

-.17* 4. France 87 33,918 36.63 63 15.74 16,735 3.39 .64 2.86 1.04

-.11 5. Italy 204 30,200 37.65 40 14.14 15,303 3.22 .72 3.04 .88

-.27*** 6. Spain 183 27,226 33.82 58 14.20 - 3.40 .72 3.12 .86 -.12 7. Singapore 1 202 26,836 33.70 53 15.12 31,746 3.80 .66 3.18 .88 -.22** 8. Singapore 2 336 26,836 33.22 57 15.01 29,277 3.85 .58 3.26 .82 -.08 9. HK 211 25,493 30.79 49 15.66 47,509 3.82 .58 3.00 .83

-.34***

Medium GDP Group    

GDP Age Sex Education Income LOM PLS Path

Sample N M % M M M SD M SD LOM PLS

10. Portugal (M) 200 17,456 35.16 39 15.44 3,386 3.36 .61 2.70 .90 -.24** 11. Slovenia 200 16,986 38.68 44 13.68 7,025 3.34 .57 2.93 1.00

-.32** 12. S. Korea 203 16,308 37.15 73 15.91 45,647 3.97 .52 3.02 .82

-.10 13. Taiwan 201 15,203 34.94 51 16.50 22,567 4.02 .56 3.03 .86 -.11 14. Malta 200 13,803 36.91 51 16.47 14,922 3.81 .66 2.56 1.02 -.39*** 15. Oman 204 12,664 29.91 64 14.68 5,816 3.59 .61 3.56 .94 -.30*** 16. Hungary 100 10,814 34.06 55 15.96 2,700 3.79 .67 3.05 1.08

-.11 17. Croatia 165 8,675 37.48 42 14.73 14,336 3.47 .59 2.93 .86 -.05 18. Mexico 295 7,298 30.95 55 14.32 7,416 3.49 .71 2.97 .93 -.03 19. Russia 200 5,349 35.92 42 17.58 2,901 3.73 .61 2.76 .92 -.25** 20. S. Africa 203 5,106 43.32 50 15.61 5,247 3.69 .44 2.28 .56 -.15 21. Malaysia 200 5,042 31.80 53 15.23 10,180 3.93 .54 3.12 .89 .01

Low GDP Group

   

GDP Age Sex Education Income LOM PLS Path Sample N M % M M M SD M SD LOM PLS

22. Romania (L) 200 4,539 38.02 27 16.69 1,723 3.75 .63 2.56 .94 -.05 23. Brazil 201 4,320 37.50 45 16.87 5,006 3.45 .63 2.68 .95 .20*** 24. Bulgaria 162 3,459 27.48 43 16.76 2,148 3.78 .61 2.64 .84 .30** 25. Peru 183 2,841 31.98 68 16.93 13,060 3.57 .65 3.07 .87 .08 26. Macedonia 204 2,810 41.60 44 13.31 2,176 3.86 .61 2.87 .97 .02 27. Thailand 200 2,659 33.32 55 16.84 10,985 3.68 .65 3.19 .63 -.19* 28. China 204 1,709 31.86 60 15.38 2,553 3.59 .66 2.72 .81 -.05 29. Egypt 200 1,265 40.41 50 14.88 7,181 3.57 .70 3.37 1.08 -.07 30. The Philippines 200 1,168 33.45 60 16.96 2,027 3.71 .65 3.44 .74 .09 31. Nigeria 200 678 34.80 60 15.74 1,909 4.09 .42 3.45 .84 1.00***† ______________________________________________________________________________________________________ 1. High GDP 1,960 31,595 33.61 49 14.65 27,314 3.63 .68 3.10 .90 -.16*** 2. Medium GDP 2,371 11,225 35.37 52 14.45 11,995 3.68 .64 2.91 .95 -.14*** 3. Low GDP 1,954 2,544 35.22 51 16.01 7,764 3.71 .65 3.01 .93 .08** (-.02) † Whole Sample 6,285 13,862 34.77 51 15.37 15,434 3.67 .66 3.00 .93 -.10***(-.11***)†

______________________________________________________________________________________________________

Multiple Regression Results   ________________________________________________________________________________________________________________   Variable R R2 R2 Change F Change df p ________________________________________________________________________________________________________________  

Step 1 Sex, Age, Education .051 .003 .003 5.41 3, 6211 .001 Z Income .219 .048 .045 296.19 1, 6210 .001 The Love of Money (LOM) .232 .054 .006 37.59 1, 6209 .001 GDP .233 .054 .000 3.05 1, 6208 .081 LOM x GDP .238 .057 .002 15.98 1, 6207 .001   _________________________________________________________________________________________________________________     Note. Sample size: N = 6,285. Due to large income differences, we calculated standardized Z income for each entity.    

Model χ2 df p χ2/df IFI TLI CFI SRMSR RMSEA Models ΔCFI ____________________________________________________________________________________________________  Step 2: Measurement model Configural Invariance: 1. High GDP 271.04 62 .0000 4.3716 .9864 .9829 .9864 .0548 .0415 2. Medium GDP 374.65 62 .0000 6.0428 .9802 .9750 .9802 .0498 .0461 3. Low GDP 711.68 62 .0000 11.4705 .9471 .9334 .9471 .0563 .0732   Metric Invariance (3 GDP Groups): 4. Unconstrained 1,356.87 186 .0000 7.2950 .9731 .9661 .9730 .0548 .0317 5. Constrained 1,631.70 204 .0000 7.9859 .9671 .9623 .9671 .0564 .0334 5 vs. 4

.0059   Step 3: Measurement Model Without and With Latent Common Method Variance (CMV) Factor (3 GDP

Groups): 6. Model 1,356.87 186 .0000 7.2950 .9731 .9661 .9730 .0548 .0317 7. Model 6 + CMV 1,460.75 159 .0000 9.1871 .9701 .9559 .9700 .0422 .0361 7 vs.

6 .0030

Step 4: Main SEM Model (3 GDP Groups) 8. Model 1,280.22 183 .0000 6.9957 .9748 .9677 .9747 .0262 .0309 9. Model 8 + LOM 1,521.09 199 .0000 7.6437 .9696 .9642 .9695 .0324 .0325 9 vs. 8

.0052 10. Model 9 + PLS 1,579.68 205 .0000 7.7058 .9684 .9639 .9683 .0324 .0327 10 vs. 9

.0012 11. Model 10 – Nigeria 847.40 205 .0000 4.1337 .9848 .9826 .9848 .0308 .0277 11 vs. 9

-.0153

Step 5: Set the Path to be Equal 12. Model 11 + Path 861.83 207 .0000 4.1635 .9845 .9824 .9845 .0312 .0228 12 vs. 11

.0003 13. Model 10 + Path 1,624.84 207 .0000 7.8495 .9674 .9631 .9673 .0361 .0330 13 vs. 10

.0010

 

Step 4, Model 10/11 Step 5, Model 12/13

Path High Medium Low Across Three GDP Groups

____________________________________________________________________________________________________   Part 1: Direct Effect Standardized Comparison Unstandardized Model 10 LOM PLS -.16*** -.14*** .08** HM < L Model 13 -.10*** Model 11 LOM PLS -.16*** -.14*** -.02 W/O HM < L Model 12 -.11*** Part 2: Squared Multiple Correlation (SMC) Model 10 PLS .026 .020 .006 Model 11 PLS .025 .020 .000 Part 3: Factor Loading Model 10 The Love of Money (LOM) 1. Rich .92 .88 .84 2. Motivator .66 .65 .61 3. Important .69 .71 .64 Model 11 The Love of Money (LOM) 1. Rich .92 .87 .84 2. Motivator .68 .66 .66 3. Important .68 .70 .63 _______________________________________________________________________________________________________

Main Findings Love of Money Pay Level Satisfaction

1. High GDP Group: -.16***2. Medium GDP Group: -.14***3. Low GDP Group: -.02

The Whole Sample: -.11*** (functional equivalence)

High GDP + Low LOM The Highest Pay Level Satisfaction

Medium GDP + High LOM The Lowest Pay Level Satisfaction

Low GDP + High LOM High Pay Level Satisfaction (Corruption)

Groups

1. High, Medium, Low-GDP Groups2. High, Medium, Low-Income

Groups3. High, Medium, Low-Love of Money

Groups4. Good (70.6%) vs. Bad Apples

(29.4%)

SEM Results χ2 df p χ2/df IFI TLI CFI RMSEA

The Whole Sample 4386.69 450 .00 9.7482 .9528 .9480 .9528 .0379Across 3 GDP Groups 7508.97 1404 .00 5.3483 .9298 .9255 .9297 .0267 Across 3 Income Levels, High GDP Group 3035.00 1404 .00 2.1617 .9330 .9287 .9327 .0257Across 3 Income Levels, Medium GDP Group 3698.22 1404 .00 2.6341 .9283 .9239 .9282 .0263Across 3 Income Levels, Low GDP Group 4329.91 1404 .00 3.0840 .9088 .9031 .9086 .0237Across Good and Bad Apples 5602.92 927 .00 6.0441 .9254 .9201 .9254 .0288

Main Findings(1). Income Pay Satisfaction(2). Income Low Love of Money(3). Love of Money Low Pay Satisfaction(4). Love of Money Evil(5). Pay Satisfaction Low Evil (6). Money –X Evil (7). Corporate Ethical Values Low Evil (8). Love of Money Low Pay Satisfaction

High Evil

Main Findings

The love of money is the root of evil, however, money is not.

The love of money is directly and indirectly (through pay dissatisfaction) the root of evil.

Corporate ethical values deter evil.

Main Findings Good for High-, Medium-GDP Groups;

but not for Low-GDP Group. Income Pay Satisfaction, All Groups Whole: 29.4% Bad Apples (70.6%) High GDP: 20.9% Medium GDP: 38.0% Low GDP: 26.6%

Main Findings High GDP Group

The highest Corporate Ethical Values,

The lowest Evil (PUB)The lowest % of bad apples: 20.9%

Medium GDP GroupThe lowest Corporate Ethical Values,

The highest Evil (PUB), The highest % of bad apples: 38.0%The strongest path: The Love of Money Evil

Main Findings 1. Bad apples, 2. managers in the

underdeveloped economies in general, and 3. all low-income managers across all three levels of economic development have one thing in common:

Corporate ethical values have very little power, if any, to curb managers’ unethical behavior intentions. CEV --X Evil

Across 3 Income Groups The Love of Money and GDP on

Pay Satisfaction The Love of Money and GDP on

Unethical Behavior Intentions 6 Figures

Main Findings1. High Income + Low Love of Money + High

GDP Group Highest Pay Satisfaction2. Low Income + High Love of Money +

Medium GDP Group Lowest Pay Satisfaction3. High/Medium/Low Income + High Love of

Money + Medium GDP Group Highest Evil (PUB)

Implications Valuing money as a means to show off, get

power, compare oneself to others, or overcome self-doubt low satisfaction (Srivastava, Locke, & Bartol, 2001) $1 Million, $2 Million, $3 Million Locke

Forbes’ 946 Billionaires: Bill Gates III ($56 billion) 178 new Billionaires: 19 Russians, 14 Indians, 13

Chinese, 10 Spaniards, and 1 from Cyprus, Oman, Romania, and Serbia. (Emerging/Transition markets)

Implications The highly visible disparity, the

disproportionately greater financial benefits for being number one (CEO) (the tournament theory), and corporate boards’ inclination to look outside for a new CEO can result in number two executives eager to jump ship and become CEOs elsewhere.

High Income + High Love of Money + Low GDP Group High Pay Satisfaction

Low GDP Group: Love of Money –X Evil Turnover, Positive Affect, Adjust

Standards,Boehm & Lyubomirsky, JCA, 2008; Howell & Howell, PB, 2008; Lyubomirsky, King, & Diener, PB, 2005;

High Position, High Income, High Pay Satisfaction may include Corruption,

Underreport Unethical Behavior Intentions, Corruption is the norm.

Implications In Nigeria (the lowest GDP, the lowest CPI),

democracy has turned into a form of “kleptocracy” (rule by thieves).

Nigeria lost about N3.5 trillion to corruption High-level position, authority, power, and

money, the ruling class (kleptocrates), are able to take advantage of the situation

According to Governor Timipre, corruption is a way of life in Nigeria.

Implications1. Prevention (identifying and rejecting job applicants and

managers who are prone to make unethical decisions);

2. Control (the use of normative force--code of ethics, internal control systems, role models, and social norms and instrumental force--proper checks and balances, electronic surveillance devices, and rewards and punishment);

3. Deterrence (dismissing managers in business organizations or providing a strong response to harmful misbehavior) (Ivancevich, Konopaske, & Matteson, 2005).

Slow down but can never Stop disobedient managers with high love of money from engaging in corruption

LimitationsConvenience samples from H, M, L GDP Groups

from each entity, from 1 Source, at 1 TimeExtraneous/Nuisance variables: the size of the

organization, organizational culture, economy of the nation/region, unemployment rate, SD, etc.

Any arbitrary categorization of a continuous variable (GDP) is problematic, undermines statistical power.

Measure Unethical Behavior Intentions; NOT actual behavior (Greenberg, 2002)

Conclusion Whoever loves money is never

satisfied with his or her income. We need to keep our lives free

from the love of money and be content with what we have.

Conclusion The love of money is directly and

indirectly (through pay dissatisfaction) related to evil, whereas money is not.

Corporate ethical values enhance ethical behavior intentions.

Conclusion We identify not only these principles

but also boundaries and exceptions of these principles around the world.

What, How, Why, Who, Where, and When

Conclusion Considering shafts and the harness that

drives aspiration for money at the individual, organizational, and entity level as well as corporate ethical values and controlling the reins properly may help executives manage the most stubborn and challenging beast in business organizations, pay satisfaction and corruptions, across different levels of economic development around the world.

Thank YouDanke Спасиб Dankeshen ありがとうGrazie 너를 감사하십시요 Merci 謝謝 谢谢 Muchas GraciasObrigadoTakk Deg