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

1
One More Time Is the Love of Money the Root of
All Evil ?

• Shanghai Maritime University
• October 17-19, 24-26, 2008
• March 12-14,19-21, 2010
• Presented by
• Thomas Li-Ping Tang, Ph.D.
• Middle Tennessee State University, the USA 10/8

2
One More Time Is the Love of Money the Root of
All Evil ?

• Shanghai Jiao Tong University
• October 20, 2008
• March 15-17, 2010
• Presented by
• Thomas Li-Ping Tang, Ph.D.
• Middle Tennessee State University, the USA

3

• 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,

4

• 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, France

5
Research Summary

• Journal article 127
• Conference Paper 205
• Language (published) 5
• Language (cited) 8
• Chinese, English, Italian, Romanian, Spanish
• Chinese, English, French, Italian, Romanian,
  Spanish, Turkish, Russian

6
Journal Impact
Factor

• Journal of Applied Psychology (3.769)
• Intelligence (3.757)
• Journal of Management (2.558)
• Journal of Organizational Behavior (2.441)
• Personnel Psychology (2.222)
• Computers Education (2.190)
• Personality and Individual Differences (1.982)
• Human Relations (1.372)
• Journal of Business Ethics (1.023)

7
ISI Web of KnowledgeCitation (2/12/2010)

• Article (127) 66
• Citation 611
• Average citations/year 22.63
• Average citations/item 9.26
• h-index 14
• 14 papers with 14 citations or more

8
Research Interest

• Management, HRM, OB
• Motivation, QC, Compensation
• Individual Differences
• Work Ethic, Leisure Ethic, Self-Esteem,
• Money Ethic, The Love of Money,
• Behavioral Ethics

9
Collaborators in 37 countries

• Australia, Belgium, Brazil, Bulgaria, China,
  Congo, Croatia, Egypt, France, Hong Kong,
  Hungary, Italy, Japan, Kyrgyzstan, Macedonia,
  Malaysia, Malta, Mexico, Nigeria, Oman, Peru, the
  Philippines, Poland, Portugal, Romania, Russia,
  Saudi Arabia, Singapore, Slovenia, South Africa,
  South Korea, Spain, Taiwan, Thailand, Turkey, UK,
  the USA.

10
Service

• Editorial Board 7
• Reviewer (Journal) 33
• Reviewer (Conference) 10

11
Award

• Outstanding Research (PSY) 1991
• Outstanding Research (MGT) 1999
• International Service 1999
• Outstanding Faculty 2008
• Career Achievement Award 2008
• The Best Reviewer Award IM (AoM) 2003, 2007, 2009

12
MTSU

• Visit http//www.mtsu.edu/
• Faculty and Staff (Photo) 2/12/2010
• Virtual Tour (Video)

13
Recent News in the USA

• May 31, 2008. Lehman Brothers had assets of 639
  billion, but was 613 billion in debt. 639 - 613
  26 26/639 4
• CEO Dick Fulds 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.

14
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)

15
Recent News

• On September 23, 2008, several days after Wall
  Street meltdown, FBI started to investigate
  officials at these investment banks (e.g., Fannie
  Mae, Freddie Mac, Lehman Brothers, and AIG).

16
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).

17
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.

18
Illinois Governor Charged with Corruption
12/9/2008

• Gov. Rod Blagojevich and his chief of staff, John
  Harris, were arrested Tuesday for what U.S. Atty.
  Patrick Fitzgerald called a "political corruption
  crime spree" that included attempts to sell the
  U.S. Senate seat to the Highest Bidder, vacated
  by President-elect Barack Obama.

19
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20
Kenneth Lay - Enron

• Quintessential fraudulent executive.
• Indicted on July 7, 2004 for Enron catastrophe.
• Prior to indictment, estimated net worth 40M.
• During trial, Lay claimed net worth was (250k).
• 20,000 employees lost jobs.

21
Enrons Market Share Price
http//ca.encarta.msn.com/media_701610605/the_fall
_of_enron_stock.html
22
Robert Nardelli Home Depot

• Amassed approx. 500M in six-year tenure.
• January 3, 2007 received 210M severance golden
  parachute (part of 500M).
• Meanwhile, investors saw minimal improvement in
  share price.
• Now CEO of Chrysler LLC.

23
Extrinsic Ways to Mitigate Agency Theorys Effects
24
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 in office from 2001 to 2006. Shares
  were sold to Temasek of Singapore.

25
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
  (kickbacks, ??/??) must correspond to the
  strength of naezd (racketeering, ??).

26
Russia

• Leader of the Liberal Democratic Party of Russia,
  Vladimir Zhirinovsky every post in Russiawith
  the exception of the five or six highestis
  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.

27
China

• Two former Bank of China managers and their wives
  were convicted in Las Vegas for money
  laundering/?? and racketeering/??in the US
  involving at least 485 million over 13 years.
• A Chinese national was repatriated (?????) from
  Canada for prosecution.

28
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
  S72 million (51 million), making it one of
  Singapores biggest ever corporate scandals and
  frauds.

29
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 Peoples Democratic Party in Oyo
  State Governor Alao-Akala has embezzled (??) 24
  billion from the excess crude oil fund released
  to the State by the Federal Government

30
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
  publics trust, abused his position, and asked
  for extra incentives to do essentially his job.
  The officer was sentenced to 14 months in jail
  for corruption and penalized 1,110 for the cash
  and the value of the whisky he received.

31
Corruption

• is both a state and a process.
• It reflects not only the corrupt behavior of an
  individualdefined as the illicit use of ones
  position or power for perceived personal or
  collective gainbut also the dangerous, viruslike
  infection of a group, organization, industry, or
  country or geopolitical entity (Ashforth, Gioia,
  Robinson, Treviño, 2008).

32
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.

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34
Professional Wrestlers as Ushers Increased
Collection Plate Donations by 72
35
What is the difference?

• 1
• 100

36
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

37
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

38
The Meaning of Money

• Children from poor economic backgrounds
  overestimate the size of a coin than their
  affluent counterparts (Bruner Goodman, 1947).
• College students money anxiety is influenced by
  both paternal and maternal money anxiety (Lim
  Sng, 2006).

39
Voh, Meed, Goode (2006)

• 1. Descrambling task 5 words A high paying
  salary, See Monopoly Money, vs. Neutral
• 2. Read aloud An Abundance of Money vs. Meager
  resources
• 3. Screensaver Currency floating underwater vs.
  fish swimming underwater, no screen
• 4. Monopoly Money 4000, 200, 0 Imagine
  Abundance of Money, Strained Finances
• 5. Poster Money, Leisure, Flower

40
Money--Self-Sufficiency

• Worked longer before asking help
• Volunteered less (5.10/8.47 sheets)
• Volunteered less (67.35/147.81 sec.)
• Gathered less pencils (18/20, 27 pencils)
• Donated less money (.77/1.34, 2.00)
• Played alone--individually focused leisure
• Kept a larger distance two chairs

41

• Thinking that time is money leads people to
  volunteer less (DeVoe Pfeffer, 2007).
• Counting 80 100 bills (compared to counting 80
  pieces of paper) reduces peoples physical pain
  (Zhou, Vohs, Baumeister, 2009).
• Anticipation of pain heightens the desire for
  money (Zhou Gao, 2008).

42
Presence of Money

• The presence of abundant wealth (with visible
  7,000 in real 1 bills on a table) provokes
  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,
  2009 142).

43
Money as tool and as drug

• Money (as tool) is instrumental in satisfying
  biological and psychological needs.
• Metaphorically, money is a functionless,
  powerful, addictive, and insatiable drug
  (motivator)(Lea Webley, 2006)
• Drug addicts require larger dosages to maintain
  the same level of high (Mason, 1992), most
  people want more money in order to achieve the
  same original level of utility.

44
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 Germany
• No. 2 in Belgium, UK, and the US

45
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?

46
The Love of Money Scale

• Factor 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

47
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).

48
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)

49
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).

50
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)

51
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 Germany
• No. 2 in Belgium, the UK, and the US
  (Harpaz, 1990)

52
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).
• The Matthew Effect (Gabris Mitchell, 1988
  Heneman, 1992, Merit pay Tang, 1996)

53
Cognitive Power

• Power tends to corrupt and absolute power
  corrupts absolutely (Lord Acton, Letter to Bishop
  Mandell Creighton, 1887).
• Money talks.

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55
Pay Satisfaction

• Job satisfaction A pleasurable or positive
  emotional state resulting from the appraisal of
  ones job or job experiences (Locke, 1976 300).
• Pay Satisfaction
• Pay Level ? Pay Satisfaction relationship is the
  most robust finding (Heneman Judge, 2000 71).

56
Pay Satisfaction Questionnaire

• 1. Pay Level
• 2. Pay Raise
• 3. Benefit
• 4. 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)

57
Pay Level Satisfaction

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

58
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59
The Love of Money-Pay Satisfaction

• Poverty consists, not in the decrease of ones
  possessions, but in the increase of ones greed.
  Plato (427-347 BC)
• Whoever loves money never has money enough
  whoever loves wealth is never satisfied with his
  income. (Ecclesiastes 510)
• High Income ? High Pay Level Satisfaction
• High Love of Money ? Low Pay Level Satisfaction

60
The Love of Money-Pay Satisfaction

• Adam (1963) Equity model
• Lawler (1971) Discrepancy model
• Easterlin (2001) Relative theory
• Veenhoven (1984) Absolute theory
• Brickman Campbell (1971) Adaptation theory
• Michalos (1985) Aspiration theory

61
Pay Satisfaction, So-What?

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

62
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64
Unethical Behavior Intentions

• The incumbents self-report and the coworkers
  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).
• 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.

65
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).

66
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

67
Theft, Corruption

• Pilfering office supplies, wasting company time,
  cyber-loafing,
• In 1997, shoplifting 10 billion annually in the
  US,
• In 2006, 40.5 billion,
• A 32-country study, 98.6 billion,
• Employee theft and commercial bribery, 100
  billion annually, the American Management
  Association
• Corruption 1 trillion world economy/year
• Impact on an organizations bottom line and on
  economies in general

68
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

69
Corporate Ethical Values (CEV)

• Strong cultures enhance firm performance
  (OReilly 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).

70
Cross-Cultural Study

• 64 only 2 countries
• 23 gt 2 countries (Sin, Cheung, Lee, 1999).
• 72.43 did not report Measurement Invariance
  (He, Merz, Alden, 2008)
• Configural Invariance Factor structure
• Metric Invariance Factor Loading

71
The Income Pyramid

• Prahalad Hammond, 2002, HBR
• 
  N
• --------------------------------------------------
  -------
• 1. gt 20,000
  100 Million
• 2. 2,000 20,000
  2,000 Million
• 3. lt 2,000
  4,000 Million

72
Level of Economic Development

• 1. GDP gt 20,000
• 2. GDP 5,000 20,000
• 3. GDP lt 5,000
• We treat GDP as a Moderator

73
Method N 6,285

• 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.

74
AoM 2008
75

• 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

76

• 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

77

• 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

78

• Multiple Regression Results
•  
• __________________________________________________
  __________________________________________________
  ____________
•  
• Variable R R2 R2
  Change F Change df p
• __________________________________________________
  __________________________________________________
  ____________
•  

79

• Model ?2 df
  p ?2/df IFI TLI
  CFI SRMSR RMSEA Models ?CFI
• __________________________________________________
  __________________________________________________
  
•  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

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•  
• 
  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 lt L
  Model 13 -.10
• Model 11 LOM ? PLS -.16
  -.14 -.02 W/O HM lt L
  Model 12 -.11
• Part 2 Squared Multiple Correlation (SMC)
• Model 10 PLS .026
  .020 .006
• Model 11 PLS .025
  .020 .000

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Main Findings

• Love of Money ? Pay Level Satisfaction
• High GDP Group -.16
• Medium GDP Group -.14
• 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)

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Groups

• High, Medium, Low-GDP Groups
• High, Medium, Low-Income Groups
• High, Medium, Low-Love of Money Groups
• Good (70.6) vs. Bad Apples (29.4)

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SEM Results ?2 df
p ?2/df IFI TLI CFI RMSEA

• The Whole Sample
• 4386.69 450 .00 9.7482 .9528 .9480
  .9528 .0379
• Across 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 .0257
• Across 3 Income Levels, Medium GDP Group
• 3698.22 1404 .00 2.6341 .9283 .9239
  .9282 .0263
• Across 3 Income Levels, Low GDP Group
• 4329.91 1404 .00 3.0840 .9088 .9031
  .9086 .0237
• Across Good and Bad Apples
• 5602.92 927 .00 6.0441 .9254 .9201
  .9254 .0288

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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

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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.

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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

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Main Findings

• High GDP Group
• The highest Corporate Ethical Values,
• The lowest Evil (PUB)
• The lowest of bad apples 20.9
• Medium GDP Group
• The lowest Corporate Ethical Values,
• The highest Evil (PUB),
• The highest of bad apples 38.0
• The strongest path The Love of Money ? Evil

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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

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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

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(1) High GDP Group (n 1,960/3, 8 entities)
the USA, Belgium, Australia, France, Italy,
Spain, Singapore, Hong Kong(2) Medium GDP Group
(n 2,371/3, 12 entities) Portugal, Slovenia,
South Korea, Taiwan, Malta, Oman, Hungary,
Croatia, Mexico, Russia, South Africa, Malaysia
(3) Low GDP Group (n 1,954/3, 10 entities)
Romania, Brazil, Bulgaria, Peru, Macedonia,
Thailand, China, Egypt, the Philippines, and
Nigeria.
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(1) High GDP Group (n 1,960/3, 8 entities) the
USA, Belgium, Australia, France, Italy, Spain,
Singapore, Hong Kong(2) Medium GDP Group (n
2,371/3, 12 entities) Portugal, Slovenia, South
Korea, Taiwan, Malta, Oman, Hungary, Croatia,
Mexico, Russia, South Africa, Malaysia (3) Low
GDP Group (n 1,954/3, 10 entities) Romania,
Brazil, Bulgaria, Peru, Macedonia, Thailand,
China, Egypt, the Philippines, and Nigeria.
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(1) High GDP Group (n 1,960/3, 8 entities) the
USA, Belgium, Australia, France, Italy, Spain,
Singapore, Hong Kong(2) Medium GDP Group (n
2,371/3, 12 entities) Portugal, Slovenia, South
Korea, Taiwan, Malta, Oman, Hungary, Croatia,
Mexico, Russia, South Africa, Malaysia (3) Low
GDP Group (n 1,954/3, 10 entities) Romania,
Brazil, Bulgaria, Peru, Macedonia, Thailand,
China, Egypt, the Philippines, and Nigeria.
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Main Findings

• 1. High Income Low Love of Money High GDP
  Group ? Highest Pay Satisfaction
• 2. Low Income High Love of Money
• Medium GDP Group ? Lowest Pay Satisfaction
• 3. High/Medium/Low Income High Love of Money
  Medium GDP Group ? Highest Evil (PUB)

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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)

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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.

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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.

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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.

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Implications

• 1. 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

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Limitations

• Convenience samples from H, M, L GDP Groups
• from each entity, from 1 Source, at 1 Time
• Extraneous/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)

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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.

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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.

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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

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Conclusion

• Understanding and learning to control the reins
  that harness the love of money and corporate
  ethical values at the individual, organizational,
  and entity levels properly may help executives
  successfully manage the most stubborn, tenacious,
  and challenging beast in businesspay
  dissatisfaction and corruptionacross developed,
  developing, and underdeveloped economies.

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SEM Results ?2 df
p ?2/df IFI TLI CFI RMSEA

• The Whole Sample
• 4386.69 450 .00 9.7482 .9528 .9480
  .9528 .0379
• Across Gender (Male vs. Female) Groups
• 5093.23 927 .00 5.4943 .9501 .9466
  .9501 .0272
• Across Gender, High GDP Group
• 2150.23 927 .00 2.2710 .9505 .9469
  .9504 .0259
• Across Gender, Medium GDP Group
• 2896.14 927 .00 3.1242 .9378 .9333
  .9377 .0299
• Across Gender, Low GDP Group
• 3372.08 927 .00 3.6376 .9248 .9193
  .9246 .0348

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Thank You

• Danke ??????
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• Muchas Gracias
• Obrigado
• Takk Deg