Does the Love of Money Cause Pay Dissatisfaction?

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Does the Love of Money Cause Pay Dissatisfaction?

• Thomas Li-Ping Tang
• Middle Tennessee State University, the USA
• Fernando Arias-Galicia
• Universidad Autonoma del Estado de Morelos,
  Mexico
• Ilya Garger
• Saratov State Social-Economic University, Russia
• Theresa Li-Na Tang
• Affinion Group, the USA
• The 26th International Congress of Applied
  Psychology
• Athens, Greece, July 16-21, 2006

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• TOTO SUTARSO, Middle Tennessee State University,
  USA,
• ADEBOWALE AKANDE, International Institute of
  Research, South Africa,
• MICHAEL W. ALLEN, Griffith University, Australia,
• ABDULGAWI SALIM ALZUBAIDI, Sultan Qaboos
  University, Oman,
• MAHFOOZ A. ANSARI, University Science Malaysia,
  Malaysia,
• FERNANDO ARIAS-GALICIA, National University of
  Mexico, 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,
• IOANA CODOBAN, Babes-Bolyai University, Romania,
• LINZHI DU, Nanjing University, China,
• ILIA GARBER, Saratov State Social-Economic
  University, Russia,
• CONSUELO GARCIA DE LA TORRE, Technological
  Institute of Monterrey, Mexico,
• ROSARIO CORREIA HIGGS, Polytechnic Institute of
  Lisbon Portugal, Portugal,
• CHIN-KANG JEN, National Sun-Yat-Sen University,
  Taiwan,
• ALI MAHDI KAZEM, Sultan Qaboos University, Oman,
• KILSUN KIM, Sogang University, South Korea,

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• 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,
• ANTHONY UGOCHUKWU O. NNEDUM, Nnamdi Azikiwe
  University, Nigeria,
• JOHNSTO E. OSAGIE, Florida A M University, USA,
• FRANCISCO COSTA PEREIRA, Polytechnic Institute of
  Lisbon Portugal, Portugal,
• RUJA PHOLSWARD, University of the Thai Chamber of
  Commerce, 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,
  Croatia,
• ALLEN F. STEMBRIDGE, Southwestern Adventist
  University, USA,
• THERESA LI-NA TANG, Cendant Marketing Group,
  Brentwood, TN, USA,
• 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,

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Outline

• The Meaning of Money
• The Love of Money Scale
• The Pay Level Satisfaction Scale
• Method
• Results
• Discussion, Implications, Limitations

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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).

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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).

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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, the UK, and the US

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Why Do Students Go to College?

• In 1971, 49.9 of freshman said They want to
  make more money.
• In 1993, 75.1
• (The American Freshman, 1994).

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Major Cause of Dissatisfaction Among University
Students

• The Lack of Money ?
• 1981-1987 No. 2
• 1990-1996 No. 3
• 1997-2003 No. 1 (Bryan, 2004).

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Pay Dissatisfaction

• Has numerous undesirable consequences (Heneman
  Judge, 2000 77)
• Turnover (Hom Griffeth, 1995 Tang, Kim,
  Tang, 2000),
• Counterproductive Behavior (Cohen-Charash,
  Spector, 2001 Luna-Arocas Tang, 2004), and
• Unethical Behavior (e.g., Chen Tang, 2006 Tang
  Chen, 2005 Tang Chiu, 2003).

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The Love of Money ?Pay Level Satisfaction

• Some Oldest References
• Poverty consists, not in the decrease of ones
  possessions, but in the increase of ones greed
  (Plato, 427-347 BC).

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Some Oldest References

• Whoever loves money never has money enough
  whoever loves wealth is never satisfied with his
  income (Ecclesiastes, 5 10, New International
  Version).
• The Love of Money ? Pay Level Satisfaction

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Some Oldest References

• People who want to get rich fall into temptation
  and a trap and into many foolish and harmful
  desires that plunge men into ruin and
  destruction. For the love of money is a root of
  all kinds of evil (Bible 1 Timothy, 6 9-10
  Tang Chiu, 2003).
• ? The Love of Money Scale

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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?

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Money Is a Motivator ()

• 4 Methods Improvement in Productivity
• Participation 0
• Job design 9
• Goal setting 16
• Contingent Pay 30
• No other incentive or motivational technique
  comes even close to money (Locke, Feren,
  McCaleb, Shaw, Denny, 1980 381)
• Money is a motivator (Stajkovic Luthans, 2001).
  
• Money is NOT a motivator (Herzberg, 1987).

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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 2 Importance (Cognitive)
• 7. Money is good.
• 8. Money is important.
• 9. Money is valuable.

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Pay Level Satisfaction

• The 18-item-4-factor Pay Satisfaction
  Questionnaire (PSQ, Heneman and Schwab, 1985)
• Pay, Bonus, Pay Raise, Administration
• One of the most well-known multidimensional
  measures of Pay Satisfaction (e.g., Williams,
  McDaniel, Nguyen, 2006).

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Measurement Invariance

• It does little good to test a theoretical and
  conceptual relationship across cultures unless
  there is confidence that the measures
  operationalizing the constructs of that
  relationship exhibit both conceptual and
  measurement equivalence across the comparison
  groups (Riordan Vandenberg, 1994, p. 645).

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Measurement Invariance 9 Steps

1. an omnibus test of equality of covariance
   matrices across groups,
2. a test of configural invariance,
3. a test of metric invariance,
4. a test of scalar invariance,
5. a test of the null hypothesis that like items
   unique variances are invariant across groups,
6. a test of the null hypothesis that factor
   variances were invariant across groups,
7. a test of the null hypothesis that factor
   covariances were invariant across groups,
8. a test of the null hypothesis of invariant factor
   means across groups, and
9. other more specific test.

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Measurement Invariance

• Tests for configural and metric invariance were
  most often reported (Vandenberg and Lance (2000)
  p. 35).
• Configural invariancefactor structures
• Metric invariancefactor loadings

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Method
Researchers collected data from 200 full-time
white-collar employees and managers in large
organizations. Translation-back translation The
Love of Money Scale The Pay Level Satisfaction
Scale
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32 Samples, N 6,659

• 1. Australia (n 262), 11. Hungry (100)
• 2. Belgium (201), 12. Italy (204)
• 3. Brazil (201), 13. Macedonia (204)
• 4. Bulgaria (162), 14. Malaysia (200)
• 5. China-1 (319 students), 15. Malta (200)
• 6. China-2 (204 employees), 16. Mexico (295)
• 7. Croatia (165), 17. Nigeria
• 8. Egypt (200), 18. Oman
• 9. France (135), 19. Peru
• 10. Hong Kong (211), 20. Philippines (200)

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32 Samples, N 6,659

• 21. Portugal (200), 31. Thailand (202)
• 22. Romania (200), 32. the USA (274)
• 23. Russia (200),
• 24. Singapore-1 (203),
• 25. Singapore-2 (336),
• 26. Slovenia (200),
• 27. South Africa (211),
• 28. South Korea (203),
• 29. Spain (183),
• 30. Taiwan (200),

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Measurement Invariance 8 Steps

• 1. Configural invariance (Factor Structures,
  Form)
• ?2, df ,
• TLI gt .95,
• CFI gt .95,
• SRMSR lt .08,
• RMSEA lt .08 and
• 2. Metric invariance (Factor Loadings, Unit)
• chi-square change (??2/?df)
• fit index change ?CFI

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• 3. Item-level metric invariance
• The Z statistic for all pair-wise comparisons
  can be calculated from the parameter estimates,
  standard errors, and the asymptotic covariance
  matrix of the unconstrained model.
• 4. First-order scalar invariance (Intercept,
  Origin)

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• 5. First-order latent mean comparison
• 6. Second-order metric invariance
• 7. Second-order scalar invariance
• 8. Second-order latent mean comparison

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• Table 1. Major Variables of the Study across 29
  Geopolitical Entities
• __________________________________________________
  __________________________________________________
  __
• Sample N Age Sex Education
  Rich Motivator Important
  LOM Pay Level
• 
  ( Male) (Year) M SD M SD
  M SD M SD M SD
• __________________________________________________
  __________________________________________________
  __
• 1. Australia 262 26.81 29 12.50
  3.73 .81 3.23 .90 3.79 .73
  3.58 .66 3.14 .94
• 2. Belgium 201 38.97 57 16.09
  3.40 .79 3.04 .84 3.68 .72
  3.37 .61 3.30 .85
• 3. Brazil 201 37.71 45 16.92
  3.59 .91 3.05 .98 3.73 .81
  3.45 .63 2.68 .95
• 4. Bulgaria 162 27.36 64 16.91
  3.92 .71 3.57 .85 3.82 .65
  3.78 .61 2.65 .84
• 5. China 204 31.57 60 15.38
  3.69 .80 3.28 .85 3.79 .76
  3.59 .66 2.72 .81
• 6. Egypt 200 40.26 50
  14.88 3.75 1.05 2.90 1.04 4.08
  .74 3.57 .70 3.37 1.08
• 7. France 135 32.30 56 16.19
  3.79 .78 3.38 .92 3.61 .70
  3.59 .66 2.86 1.04
• 8. HK 211 30.68 49 15.67 4.06
  .69 3.33 .90 4.07 .59
  3.82 .58 3.00 .83
• 9. Hungary 100 34.06 55 15.96
  3.83 .73 3.55 .90 3.98 .71
  3.79 .67 3.05 1.08

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• Table 2. Configural Invariance of the
  9-Item-3-Factor Love of Money Scale (LOMS)
• __________________________________________________
  __________________________
• ?2 df p TLI
  CFI SRMSR RMSEA
• __________________________________________________
  __________________________
• 1. Australia 74.47 24 .00 .9874
  .9933 .0561 .0898
  
• 2. Belgium 27.41 24 .29
  .9988 .9994 .0416 .0266
  
• 3. Brazil 26.49 24 .33 .9992
  .9996 .0412 .0228
• 4. Bulgaria 34.37 24 .08
  .9973 .9986 .0386 .0428
• 5. China 34.82 24 .07
  .9965 .9981 .0337 .0471
  
• 6. Egypt 29.64 24
  .20 .9979 .9989 .0369
  .0344
• 7. France 37.98 24 .03
  .9929 .9962 .0480 .0659
  
• 8. HK 46.43 24 .00 .9939
  .9968 .0437 .0667
• 9. Hungary 107.09 24 .00 .9501
  .9734 .0760 .1870
  
• 10. Italy 51.98 24 .00 .9905
  .9950 .0424 .0758
  
• 11. Macedonia 60.84 24 .00 .9885
  .9939 .0518 .0870
• 12. Malaysia 106.90 24 .00
  .9772 .9879 .0520 .1317
  
• 13. Malta 445.66 24 .00
  .8931 .9430 .1197 .2971
  
• 14. Mexico 79.35 24 .00 .9873
  .9932 .0506 .0886

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• Table 3 Summary of Fit Statistics
• __________________________________________________
  __________________________________________________
  _________________________________________
• 
  
  Model
• Model ?2 df p
  TLI CFI SRMSR RMSEA
  Comparison ??2 ?df ?CFI
• __________________________________________________
  __________________________________________________
  _________________________________________
• Step 1. Testing Measurement Invariance of
  Second-Order Factor Model of the Love of Money
• Model 1 Configural invariance (see results for
  each geopolitical entity (sample) in Table 2)
• Model 2 Construct-level metric invariance
• A. Unconstrained
  615.95 408 .01
  .9960 .9979 .0416 .0123
• B. Constrained (first-order factor
  loading) 982.98 504 .01
  .9926 .9951 .0478 .0168 2A
  vs. 2B 367.02 96 .0034
• Model 3 Item-level metric invariance (Item 1
  constrained) 716.89 424 .01
  .9946 .9970 .0486 .0143 3
  vs. 2A 100.94 16 .0014
• Model 4 Scalar invariance
  2835.35 648 .01 .9736
  .9776 .0488 .0317 4 vs. 2B
  1852.37 176 .0175
• 2B intercepts of measured variables
  invariance
• Model 5 First-order latent mean comparison
  

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• Table 3 Summary of Fit Statistics
• __________________________________________________
  __________________________________________________
  ___________________________________
• 
  
  Model
• Model ?2 df
  p TLI CFI SRMSR RMSEA
  Comparison ??2 ?df ?CFI
• __________________________________________________
  __________________________________________________
  ___________________________________
• Step 2 Testing Measurement Invariance of First
  -Order Factor Model of the Pay Level Satisfaction
• Model 1 Configural invariance (see results for
  each geopolitical entity (sample) in Table 4)
• Model 2 Construct-level metric invariance
• A. Unconstrained
  19.73 18 .35 .9997 .9999
  .0030 .0067
• B. Constrained (first-order
  factor loading) 89.46 42
  .01 .9959 .9981 .0113
  .0229 2A vs. 2B 69.73 24 .0018
• Model 3 Item-level metric invariance (Item 1
  constrained) 49.01 26 .01
  .9968 .9991 .0032 .0203
  3 vs. 2A 29.28 8 .0010
  
• Model 4 Scalar invariance 443.64
  74 .01 .9820 .9852
  .0147 .0482 4 vs. 2B 354.18
  32 .0129
• 2B intercepts of measured variables
  invariance

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• Table 4 Configural Invariance of the
  4-Item-1-Factor Pay Level Satisfaction Scale
  (PLSS)
• __________________________________________________
  __________________________
• ?2
  df p TLI CFI
  SRMSR RMSEA
• __________________________________________________
  __________________________
• 1. Australia .31 2
  .86 1.0000 1.0000 .0030
  .0000
• 2. Belgium 4.82 2 .00
  .9951 .9990 .0090
  .0839
• 3. Brazil 2.25 2 .33
  .9994 .9999 .0104
  .0250
• 4. Bulgaria 13.35 2 .00
  .9697 .9939 .0233
  .1878
• 5. China 2.84 2 .24
  .9981 .9996 .0156
  .0455
• 6. Egypt 5.06 2
  .08 .9925 .9985
  .0210 .0877
• 7. France 13.23 2 .00
  .9681 .9936 .0169
  .2047
• 8. HK 5.49 2 .06
  .9933 .9987 .0151
  .0912
• 9. Hungary 11.46 2 .00
  .9657 .9931 .0140
  .2186
• 10. Italy 13.11 2 .00
  .9793 .9959 .0191
  .1654
• 11. Macedonia 13.52 2
  .00 .9722 .9944 .0382
  .1684
• 12. Malaysia 17.00 2
  .00 .9717 .9943 .0207
  .1941

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Implications--1

• This study is an initial step in the measurement
  and functional invariance of the Love of Money
  Scale.
• Using the most rigorous criteria, 17 samples (out
  of 29) pass the configural invariance.
• Using a less rigorous criteria (RMSEA lt .10),
• 25 samples pass the configural invariance test
  
• 4 Samples fail Hungary, Malaysia, Malta,
  Nigeria.

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Implications--2

• Most samples fail the RMSEA (root mean square
  error of approximation) criterion that is
  sensitive to (1) sample size and (2) model
  complexity.
• Most samples that fail have several ethnic groups
  (e.g., Nigeria has Igbo, Yoruba, Housa, and
  others, Malaysia has Chinese, Indian, Malays,
  Caucasian, and others)

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Remedies Configural Invariance

1. Increase sample size (gt 300)
2. Use a simple model
3. Analyze sub-samples using MGCFA
4. Use EFA to identify the causes
5. Revise the model
6. One model does not fit all samples

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Non-Metric Invariant Items

• Four strategies (the unit of the measurement)
• 1. Ignore the non-invariance because the
  comparison of data is not meaningful,
• 2. Eliminate non-invariant items from the scale,
• 3. Invoke partial metric invariance that allows
  the factor loading of non-invariant items to
  vary, and
• 4. Interpret the source of non-invariance
  (Cheung, 2002).

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Eliminate non-invariant items?

• Not only metric non-invariance is desirable but
  also is a source of potentially interesting and
  valuable information about how different groups
  view the world (Cheung and Rensvold, 2002, p.
  252).

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I Orientation

• When the individual self is the center of the
  respondents psychological field for items of a
  scale (I want to be rich), people in
  individualistic cultures (Hofstede and Bond,
  1988 Yu and Yang, 1994) may have different
  perceptions than those in collectivistic cultures
  (Riordan and Vandenberg, 1994 Tang et al.,
  2002).

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I Orientation

• People in high collectivistic cultures (e.g.,
  China, South Korea) may consider I want to be
  rich not acceptable in their cultures
• Researchers have to examine the wording or
  phrasing of items carefully when they design
  future measurement instruments (Riordan
  Vandenberg, 1994 667).

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Limitations

1. Translation equivalence
2. Sample equivalence
3. Common method biases
4. Extraneous or nuisance variables (size or org.
   economy of the region, unemployment rate, etc.)
5. Non-random samples, we cannot generalize the
   findings to the whole population with full
   confidence.

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Final Thoughts and Conclusions

• This study offers researchers some confidence and
  insights in using the Love of Money Scale.
• When researchers and practitioners apply the same
  tools consistently across organizations and
  geopolitical entities overtime, the cumulative
  results will enhance our abilities to understand,
  predict, and control the role of the love of
  money in organizations.

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Thank You

• Danke
• Dankeshen
• Grazie
  
• Merci
• Muchas Gracias
• ??