1. 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

2. 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,

3. 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,
PETER VLERICK, Ghent University, Belgium

4. Outline The Meaning of Money The Love of Money Scale
The Pay Level Satisfaction Scale
Method
Results
Discussion, Implications, Limitations

5. 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).

6. 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).

7. 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

8. 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).

9. Major Cause of Dissatisfaction Among University Students
The Lack of Money 
: No. 2
: No. 3
: No. 1 (Bryan, 2004).

10. 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).

11. The Love of Money  Pay Level Satisfaction
Some Oldest References
“Poverty consists, not in the decrease of one’s possessions, but in the increase of one’s greed” (Plato, BC).

12. 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

13. 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

14. 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?

15. Money Is a Motivator (+)
4 Methods: Improvement in Productivity
Participation: 0%
Job design: 9%
Goal setting: %
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).

16. 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.

17. 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).

21. 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).

22. Measurement Invariance: 9 Steps
an omnibus test of equality of covariance matrices across groups,
a test of “configural invariance”,
a test of “metric invariance”,
a test of “scalar invariance”,
a test of the null hypothesis that like items unique variances are invariant across groups,
a test of the null hypothesis that factor variances were invariant across groups,
a test of the null hypothesis that factor covariances were invariant across groups,
a test of the null hypothesis of invariant factor means across groups, and
other more specific test.

23. Measurement Invariance
“Tests for configural and metric invariance were most often reported” (Vandenberg and Lance (2000) p. 35).
Configural invariance—factor structures
Metric invariance—factor loadings

24. 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

25. 32 Samples, N = 6,659 7. Croatia (165), 17. Nigeria
1. Australia (n = 262), Hungry (100)
2. Belgium (201), Italy (204)
3. Brazil (201), Macedonia (204)
4. Bulgaria (162), Malaysia (200)
5. China-1 (319 students), 15. Malta (200)
6. China-2 (204 employees), 16. Mexico (295)
7. Croatia (165), Nigeria
8. Egypt (200), Oman
9. France (135), Peru
10. Hong Kong (211), Philippines (200)

26. 32 Samples, N = 6,659 21. Portugal (200), 31. Thailand (202)
22. Romania (200), 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),

27. Measurement Invariance: 8 Steps
1. Configural invariance (Factor Structures, Form):
χ2, df ,
TLI > .95,
CFI > .95,
SRMSR < .08,
RMSEA < .08 and
2. Metric invariance (Factor Loadings, Unit):
chi-square change (Δχ2/Δdf)
fit index change ΔCFI

28. 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)

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

35. 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
2. Belgium
3. Brazil
4. Bulgaria
5. China
6. Egypt
7. France
8. HK
9. Hungary
10. Italy
11. Macedonia
12. Malaysia
13. Malta
14. Mexico
15. Nigeria
16. Oman
17. Peru
18. Philippines
19. Portugal
20. Romania
21. Russia
22. Singapore
23. Slovenia
24. S. Africa
25. S. Korea
26. Spain
27. Taiwan
28. Thailand
29. US
____________________________________________________________________________________________________
Whole Sample 5,
Note. Age and education were expressed in years. Sex was expressed in % male.

36. Table 2. Configural Invariance of the 9-Item-3-Factor Love of Money Scale (LOMS)
____________________________________________________________________________
χ2 df p TLI CFI SRMSR RMSEA
1. Australia
2. Belgium
3. Brazil
4. Bulgaria
5. China
6. Egypt
7. France
8. HK
9. Hungary
10. Italy
11. Macedonia
12. Malaysia
13. Malta
14. Mexico
15. Nigeria
16. Oman
17. Peru
18. Philippines
19. Portugal
20. Romania
21. Russia
22. Singapore
23. Slovenia
24. S. Africa
25. S. Korea
26. Spain
27. Taiwan
28. Thailand
29. US
Note. We retained a sample if it satisfied all of the following four rigorous criteria (i.e., TLI > .95, CFI > .95, SRMSR < .08, and RMSEA < .08). In this analysis, we eliminated 12 samples (printed in bold) and retained 17 samples.

37. Table 3 Summary of Fit Statistics
_____________________________________________________________________________________________________________________________________________
Model
Model χ df p TLI CFI SRMSR RMSEA Comparison Δχ2 Δdf ΔCFI
Step 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
B. Constrained (first-order factor loading) A vs. 2B *
Model 3 Item-level metric invariance (Item 1 constrained) vs. 2A *
Model 4 Scalar invariance vs. 2B *
2B + intercepts of measured variables invariance
Model 5 First-order latent mean comparison
C. Baseline (with only first-order factor)
D. Estimated latent mean C vs. 5D *
Model 6 Second-order metric invariance
E. Baseline (with second-order factor)
F. E + second-order factor loading invariance E vs. 6F *
Model 7 Second-order scalar invariance
F + second-order intercepts invariance F vs *
Model 8 Second-order latent mean comparison vs *
____________________________________________________________________________________________________________________________________________

38. Step 3: Testing Common Method Variance
Table 3 Summary of Fit Statistics
_______________________________________________________________________________________________________________________________________
Model
Model χ df p TLI CFI SRMSR RMSEA Comparison Δχ Δdf ΔCFI
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
B. Constrained (first-order factor loading) A vs. 2B *
Model 3 Item-level metric invariance (Item 1 constrained) vs. 2A *
Model 4 Scalar invariance vs. 2B *
2B + intercepts of measured variables invariance
Model 5 First-order latent mean comparison vs *
Step 3: Testing Common Method Variance
Model 1 First-order factor model without latent CMV
Model 2 First-order factor model with latent CMV vs *
Step 4: The Love of Money to Pay Level Satisfaction Relationship
Model 1 Constrained the Love of Money Scale
Model 2 Model 1 + Constrained Pay Level Satisfaction vs *
Model 3 Model 2 + Constrained the LOM  PLS Path vs *
Note. *p < .05.

39. Table 4 Configural Invariance of the 4-Item-1-Factor Pay Level Satisfaction Scale (PLSS)
____________________________________________________________________________
χ df p TLI CFI SRMSR RMSEA
1. Australia
2. Belgium
3. Brazil
4. Bulgaria
5. China
6. Egypt
7. France
8. HK
9. Hungary
10. Italy
11. Macedonia
12. Malaysia
13. Malta
14. Mexico
15. Nigeria
16. Oman
17. Peru
18. Philippines
19. Portugal
20. Romania
21. Russia
22. Singapore
23. Slovenia
24. S. Africa
25. S. Korea
26. Spain
27. Taiwan
28. Thailand
29. US
Note. We retained a sample if it satisfied the following four rigorous criteria (i.e., TLI > .95,
CFI > .95, SRMSR < .08, and RMSEA < .08). In this analysis, we eliminated 19 samples
(printed in bold) and retained 10 samples.

43. 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 < .10),
*25 samples pass the configural invariance test
*4 Samples fail: Hungary, Malaysia, Malta, & Nigeria.

44. 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)

45. Remedies: Configural Invariance
Increase sample size (> 300)
Use a simple model
Analyze sub-samples using MGCFA
Use EFA to identify the causes
Revise the model
One model does not fit all samples

46. 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).

47. 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).

48. “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).

49. “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).

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

51. 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.

52. Thank You
Danke
Dankeshen
Grazie
Merci
Muchas Gracias 謝謝