Results of the data analysis are presented in this chapter. The chapter includes a discussion of sample characteristics and the results of the hypothesis testing, sub-scale analyses, confounding variables analyses, supplemental analyses, summary of the research findings and discussions of the result.
Sample Characteristics
The participants in this study were employees of a German based semiconductor company in Taiwan. Participants included employees in all positions. The participation was voluntary and confidential.
A total of 300 participants (60 contractors and 240 employees) were invited to participate. A total of 270 participants (54 contractors and 216 employees) actually participated in the study (90% response rate). Among the returned questionnaires, one contractor survey was discarded because all the questions were responded in the same answer (neural). The number of questionnaires that were suitable to use was 269 (53 temporary and 216 permanent). The reliability evidence, Cronbach’s Alpha, for this research is showed at Table 4.1.
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Table 4.1. Cronbach’s Alphas for Each Subscale and Total of JSS and OCB Scale
Measure/ Sub-Scale α
Job Satisfaction .95
Pay .89 Promotion .90 Supervision .90 Benefits .89
Contingent Rewards .91
Operating Procedure .90
Co-workers .89
Nature of Work .90
Communication .90
OCB .79 Altruism .90
General Compliance .88
A cross-tabulation for gender and employment status is presented in Table 4.2 Overall, the majority of employees were male (74.1 %). There was a significant association between gender and employment status χ2 (1, N = 269) = 55.77, p < .01.
As can be seen in Table 4.2, there are much more female employees in contractors than in employees.
Table 4.2. Cross-Tabulation for Gender and Employment Status
Contractors Employees Total
Gender N % N % N %
Males 10 18.9 160 74.1 170 63.2
Females 43 84.1 56 25.9 99 36.8
Total 53 100 216 100 269 100
Two Independent T-tests were calculated to test for significant differences in gender groups’ means on each of the variables in this study. The descriptive statistics are displayed in Table 4.3 and Table 4.4, and the results are displayed in Table 4.5 and Table 4.6. As can be seen in these tables, males scored significantly higher on job
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satisfaction and OCB.
Table 4.3. Descriptive Statistics of Job Satisfaction by Gender
Gender N M SD SE
Males 170 3.24 .68 .88
Females 99 3.01 .87 .86
Total 269 3.16 .54 .88
Table 4.4.Descriptive Statistics of OCB by Gender
Gender N M SD SE
Males 170 3.74 .53 .69
Females 99 3.41 .07 .70
Total 269 3.62 .04 .71
Table 4.5. Independent-Sample T test : Gender on Job Satisfaction
Levene’s Test t-test
F p t df p
Equal Variance assumed .12 .73 2.00 267 .04
Table 4.6. Independent-Sample T test : Gender on OCB
Levene’s Test t-test
F p t df p
Equal Variance not assumed .12 .73 3.70 210.95 .00
A cross-tabulation for age group and employment status is presented in Table 4.7. There was a significant association between age group and employment status χ2 (1, N = 269) = 20.55, p<.01. As can be seen in Table 4.7, contractors tended to be younger than employees.
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Table 4.7. Cross-Tabulation for Age Groups and Employment Status
Contractors Employees Total
Age Group N % N % N %
24 to 29 yrs 16 30 39 18 55 20
30 to 35 yrs 34 64 95 44 129 48
36 to 41 yrs 3 6 55 25 58 22
42 and older 0 0 27 13 27 10
Total 53 100 216 100 269 100
Two Oneway ANOVAs were calculated to test for significant difference in age groups’ means on each of the variables in this study. The descriptive statistics are displayed in Table 4.8 and Table 4.9 and the results are displayed in Table 4.10 and Table 4.11.
Table 4.8. Descriptive Statistics of Age Groups on Job Satisfaction
Age Group N M SD SE
24 to 29 yrs 55 3.09 .87 .12
30 to 35 yrs 129 3.19 .84 .07
36 to 41 yrs 58 3.16 .95 .13
42 yrs and older 27 3.10 .97 .19
Total 269 3.16 .88 .05
Table 4.9. Descriptive Statistics of Age Groups on OCB
Age Group N M SD SE
24 to 29 yrs 55 3.55 .70 .09
30 to 35 yrs 129 3.57 .72 .06
36 to 41 yrs 58 3.74 .69 .09
42 yrs and older 27 3.72 .69 .13
Total 269 3.62 .71 .04
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Table 4.10. Oneway ANOVA: Age Group on Job Satisfaction
SS df MS F p
Between Groups .43 3 .14 .18 .91
Within Groups 207.52 265 .78
Total 207.95 268
Table 4.11.Oneway ANOVA: Age Group on OCB
SS df MS F p
Between Groups 1.67 3 .56 1.12 .34
Within Groups 132.12 265 .50
Total 133.79 268
As can be seen in the tables, there was no significant difference across age groups on the variables.
Education levels of the participants are shown in Table 4.12. Of the employees who responded, 48 (17.8 %) had a college degree or certificate, 126 had a bachelor’s degree, 90 (33.5 %) had a master’s degree and only five had a doctor’s degree. There was an association between educational level and employment status χ2 (3, N = 269) = 19.55, p < .01.
Table 4.12. Cross-Tabulation for Education and Employment Status
Contractors Employees Total
Education N % N % N %
College degree or Certificate 14 26.4 34 15.7 48 17.8 Bachelor’s degree
Two Oneway ANOVAs were calculated to test for differences of group means by education. This descriptive statistics in Table 4.13 and Table 4.14 and the results are displayed in Table 4.15 and Table 4.16. As can be seen in the tables, there was no significant difference across educational groups on job satisfaction and OCB.
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Table 4.13. Descriptive Statistics of Education Groups on Job Satisfaction
Education Group N M SD SE
College degree or Certificate 48 3.23 .79 .11
Bachelor’s degree 126 3.13 .87 .77
Master’s degree 90 3.15 .94 .10
Doctor’s degree 5 3.10 1.13 .50
Table 4.14. Descriptive Statistics of Education Groups on OCB
Education Group N M SD SE
College degree or Certificate 48 3.56 .71 .10
Bachelor’s degree 126 3.55 .71 .06
Master’s degree 90 3.74 .70 .07
Doctor’s degree 5 3.79 .75 .34
Table 4.15. Oneway ANOVA: Education Groups on Job Satisfaction
SS df MS F p
Between Groups 0.33 3 .11 .14 .94
Within Groups 207.62 265 .78
Total 207.95 268
Table 4.16. Oneway ANOVA: Education Groups on OCB
SS df MS F p
Between Groups 2.07 3 .69 1.39 .25
Within Groups 131.72 265 .50
Total 133.79 268
The employment lengths of the participants are found in Table 4.17. Of the employees who responded, 31 (11.5 %) had been with their organization for less than six months and 41 (15.2 %) had been with their organization for more than six months to one year, 91 (33.8 %) had been with their organization more than one year to three years, 73 (27.1 %) had been with their organization for more than three years to five years. There are 33 (12.3 %) employees had been with their organization for more than five years. There was an association between employment length and
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employment status χ2 (4, N = 269) = 43.18, p < .01. In general, the contractors had been in their current positions for a shorter period of time than the employees.
Table 4.17. Cross-tabulation for Employment Length and Employment Status Contractors Employees Total
Employment Length N % N % N %
Less than 6 months 5 9.4 26 12.0 31 11.5
More than 6 months to 1 year 11 20.8 30 13.9 41 15.2 More than 1 year to 3 years 35 66.0 56 25.9 91 33.8 More than 3 years to 5 years 2 3.8 71 32.9 73 27.1
More than 5 years NA NA 33 15.3 33 12.3
Total 53 100.0 216 100.0 269 100.0
Two Oneway ANOVAs were calculated to test for differences by employment length on each of the variables in this study. The descriptive statistics are displayed in Table 4.18 and Table 4.19 and the results are displayed in Table 4.20 and Table 4.21.
There was no significant difference by employment length on OCB.
Table 4.18. Descriptive Statistics of Employment Length on Job Satisfaction
Employment Length N M SD SE
Less than 6 months 31 3.35 .81 .15
More than 6 months to 1 year 41 3.24 .83 .13
More than 1 year to 3 years 91 3.08 .83 .09
More than 3 years to 5 years 73 3.14 .95 .11
More than 5 years 33 3.12 1.01 .18
Total 269 3.16 .88 .05
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Table 4.19. Descriptive Statistics of Employment Length on OCB
Employment Length N M SD SE
Less than 6 months 31 3.77 .68 .12
More than 6 months to 1 year 41 3.50 .70 .11
More than 1 year to 3 years 91 3.46 .71 .07
More than 3 years to 5 years 73 3.75 .68 .08
More than 5 years 33 3.76 .70 .12
Total 269 3.63 .71 .04
Table 4.20. Oneway ANOVA: Employment Length on Job Satisfaction
SS df MS F p
Between Groups 2.10 4 .52 .67 .61
Within Groups 205.86 264 .78
Total 207.95 268
Table 4.21 Oneway ANOVA: Employment Length on OCB
SS df MS F p
Between Groups 5.48 4 1,37 2.82 .03
Within Groups 128.30 264 .49
Total 133.79 268
Table 4.22 summarizes the relationships between the demographic variables and employment status by displaying the chi-square values and significance. Table 4.23 summarizes the relationships between the demographic variables and job satisfaction and OCB by displaying the results of the ONEWAY ANOVA F-tests and significance.
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Table 4.22 Pearson Chi-Square Tests of Association: Demographics and Employment Status.
Demographics χ2 df N p
Gender 55.77 1 269 <.01
Age 20.55 1 269 <.01
Education 19.55 3 269 <.01
Employment Length 43.18 4 269 <.01
Table 4.23. Summary of Oneway ANOVAs: Demographics on JS and OCB
Demographics Variables F p
Age Job Satisfaction .02 .89
OCB 3.33 .07
Education Job Satisfaction .14 .94
OCB 1.39 .25
Employment Job Satisfaction .67 .61
Length OCB 2.82 .03
An important result that can be seen in Table 4.14 and 4.15 is that gender, age, education, and employment length confound employment status in the planned hypotheses. More specifically gender, age, education, and employment length are related to employment status. Gender has an effect on job satisfaction and OCB.
Employment length has an effect on OCB. Multiple regression was performed in order to deal with these confounds. First, all hypotheses were analyzed as planned, and then all hypotheses are re-analyzed while controlling for gender, age, education and employment length.
Testing of the Hypotheses
In this section, the findings are described. To test Hypothesis One and Two, the independent sample t tests were computed to determine if contractors and employees differed on their mean job satisfaction and OCB. In each analysis, Levene’s Test for Equality Variances was used to determine whether equal population variances could be assumed.
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Null hypothesis one
Contractors will score the same or higher on job satisfaction than employees as measured by the Job Satisfaction Survey (Spector, 1985)
An independent sample t test was used to test this hypothesis. Table 4.24 displays the results of the analysis and Table 4.25 displays the independent sample t-test result for Hypothesis One. Levene’s Test for Equality of Variances shows that equal population variances could not be assumed, F = 10.05, p < .01. Contractors scored lower on job satisfaction than employees. (Means = 2.78 and 3.25, respectively). The analysis rejected Null Hypothesis One (T (108.25) =4.31, p< .001).
Table 4.24. Descriptive Statistics of Job Satisfaction by Employment Status
Employment Status N M SD SE
Contractors 53 2.78 .65 .09
Employees 216 3.25 .91 .62
Total 269 3.16 .54 .88
Table 4.25. Independent Sample T-test: Employment Status on Job Satisfaction
t df p
Mean Diff.
St. Err.
Diff.
Equal variances not assumed 4.31 108.25 <.01 .47 .11
Levene’s Test F = 10.05 <.01
Null hypothesis two
Contractors will score the same or higher on OCB than employees as measured by the OCB Scale (Smith et al., 1983).
An Independent sample t test was used to test this hypothesis. Table 4.26 displays the result of the analysis and Table 4.27 displays the independent sample t-test result for Hypothesis Two. Levene’s Test for Equality of Variances shows that equal population variances could not be assumed F = 114.53, p < .001. Contractors scored lower on OCB than employees (Means = 2.93 and 3.79, respectively). The analysis rejected Null Hypothesis Two (T (207.16) = 14.34, p<.001).
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Table 4.26. Descriptive Statistics of OCB by Employment Status
Employment Status N M SD SE
Contractors 53 2.93 2.78 .04
Employees 216 3.79 .68 .05
Total 269 3.62 .71 .04
Table 4.27. Independent Sample T-test: Employment Status on OCB
t df p
Mean Diff.
St. Err.
Diff.
Equal variances not assumed 14.34 207.16 <.01 .86 .06
Levene’s Test F = 114.53 <.01
Null hypothesis three
There will be either a significant negative relationship or no relationship between job satisfaction and OCB among both contractors and employees.
To test Hypothesis Three, the Pearson’s product moment correlation was computed to determine if there was a statistically significant relationship between job satisfaction and OCB for both contractors and employees. Table 4.28 summarizes the result of this hypothesis for contractors. Table 4.29 summarizes the result of this hypothesis for employees.
Table 4.28. Pearson’s Product Moment Correlation Coefficients between Job Satisfaction and OCB for Contractors
Job Satisfaction OCB
Job Satisfaction 1 .70**
OCB .70** 1
** p <.01
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Table 4.29. Pearson’s Product Moment Correlation Coefficients between Job Satisfaction and OCB for Employees
Job Satisfaction OCB
Job Satisfaction 1 .88**
OCB .88** 1
** p <.01
Null Hypothesis Three was rejected because there were significantly positive relationships between job satisfaction and OCB among employees (r (N=216) = .88, p<.01) and contractors (r (N=53) = -.70, p<.01).
Table 4.30 summarizes the results for the Null Hypotheses. Null Hypothesis One, Two and three were not supported.
Table 4.30. Summary of Null Hypotheses
Hypothesis Number Results
One Not supported
Two Not supported
Three Not supported
Sub-scale Analyses
In this section, the hypotheses above were re-analyzed for each sub-scale. Table 4.31 and 4.32 display the descriptive statistics and t test results for Hypotheses One and Two using subscales. The results of Hypotheses One and Two did not change.
Two very interesting finding emerged from these subscale analyses. Firstly, standard deviation for each subscale is more than one and that is large scale for 5-Likert scale. The reason might be that the distributions for subscale measures are usually wider than overall measure. Secondly, Contractors did not score significantly lower than employees did on all subscales of JSS. Of all subscales of JSS, contractors scored slightly but not statistically lower on operating procedure, coworkers and communication than employees did. However, this finding did not affect the overall results. The Sub-Scale analysis rejected these null hypotheses.
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Table 4.31. Descriptive Statistics of Sub-scales of Job Satisfaction and OCB by Employment Status
Sub-scale
Employment
Status N M SD SE
Pay Contractor 53 2.67 1.27 .17
Employee 216 3.32 1.21 .08
Promotion Contractor 53 2.55 1.14 .16
Employee 216 3.30 1.23 .08
Supervision Contractor 53 2.67 1.22 .17
Employee 216 3.29 1.24 .08
Benefits Contractor 53 2.64 1.20 .17
Employee 216 3.36 1.19 .08
Contingent Rewards Contractor 53 2.62 1.17 .16
Employee 216 3.29 1.27 .09
Operating Procedure Contractor 53 2.63 1.18 .16
Employee 216 2.70 1.25 .09
Co-workers Contractor 53 3.30 1.19 .16
Employee 216 3.34 1.23 .08
Nature of Work Contractor 53 2.58 1.14 .16
Employee 216 3.30 1.24 .08
Communication Contractor 53 3.37 1.23 .17
Employee 216 3.32 1.22 .08
Altruism Contractor 53 2.55 1.25 .17
Employee 216 3.81 0.97 .07
General Compliance Contractor 53 3.22 1.27 .17
Employee 216 3.77 0.86 .06
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Table 4.32. Independent Sample t test: Employment Status on Sub-Scales of Job Satisfaction and OCB
Sub-Scale t df p
Mean Diff.
St. Err.
Diff
Pay 3.46 267 <.01 .65 .19
Promotion 4.01 267 <.01 .75 .19
Supervision 3.28 267 <.01 .62 .19
Benefits 3.94 267 <.01 .72 .18
Contingent Rewards 3.53 267 <.01 .68 .19
Operating Procedure 0.37 267 .71 .07 .19
Co-workers 0.21 267 .83 .04 .19
Nature of Work 3.83 267 <.01 .72 .19
Communication -.26 267 .80 -.05 .19
Altruism* 6.85 67.91 <.01 1.26 .18
General Compliance* 3.01 64.29 <.01 .55 .18
*Equal Variances not assumed
In the Sub-scale analysis, Hypothesis Three was tested by computing the Pearson product-moment correlations between each job satisfaction sub-scale and OCB for both contractors and employees. The sub-scale analysis result for Hypothesis Three is displayed in Table 4.33 for contractors and Table 4.34 for employees.
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Table 4.33. Pearson Product-Moment Correlation Coefficients between Sub-Scale of Job Satisfaction and OCB for Contractors
Sub-Scale OCB Pay .69**
Promotion .64**
Supervision .76**
Benefits .64**
Contingent Rewards .62**
Operating Procedure .67**
Co-workers .60**
Nature of Work .66**
Communication .65**
** p<.01
Table 4.34. Pearson Product-Moment Correlation Coefficients between Sub-scale of Job Satisfaction and OCB for Employees
Sub-Scale OCB Pay .84**
Promotion .83**
Supervision .84**
Benefits .84**
Contingent Rewards .84**
Operating Procedure .85**
Co-workers .83**
Nature of Work .85**
Communication .83**
** p<.01
For Hypothesis Three, The Sub-Scale analysis still revealed significantly positive relationships between job satisfaction and OCB among employees and contractors. As all of the results did not change, the not supported finding for Null Hypothesis Three still holds true when Sub-Scale Analysis of job satisfaction were used.
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Analysis of Confounding Variables
In this section, the hypotheses above are re-analyzed while attempting to statistically control for the confounding variables gender, age, education and employment length. To test Hypotheses One and Two, multiple regression was performed. To test Hypothesis Three, partial correlation coefficient was calculated.
The researcher conducted multiple regression to test the explanatory levels of each confounding variables discussed above on job satisfaction and OCB. The results of multiple regression for gender, age, education, employment length and employment status on job satisfaction and OCB are showed in Table 4.35 and Table 4.36. The results indicated that the confounding variables have no significant impact on job satisfaction and OCB, thus the results of Hypothesis One and Two still hold true.
Table 4.35. Multiple Regression for Gender, Age, Education, Employment Length and Employment Status on Job Satisfaction
Mod β t p
Gender -.00 -.02 .99
Age .13 1.05 .30
Education -.02 -.18 .86
Employment Length -.20 -1.66 .10
Employment Status -.23 -3.28 <.01
Table 4.36. Multiple Regression for Gender, Age, Education, Employment Length and Employment Status on OCB
Mod β t p
Gender .01 .18 .86
Age -.02 -.22 .83
Education .07 .66 .51
Employment Length -.08 -.71 .48
Employment Status -.50 -7.98 <.01
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The result of Hypothesis Three did not change when gender, age, education and length of employment were controlled for in the covariate analysis. As can be seen in Table 4.37, partial correlation coefficients remained significant and were the same as the results of hypothesis testing above. Table 4.40 summarizes the results of all hypotheses after all analyses.
Table 4.37. Partial Correlations of Job Satisfaction and OCB for Contractors and Employees
Model t p Partial Correlation
Contractors’ JSS Mean 7.97 <.01 .70
Employees’ JSS Mean 26.75 <.01 .88
Supplemental Analysis
As part of the demographics questionnaire, contractors were asked what their reasons were for being a contractor. By far the most common response (24 out of 53, or 45%) was “I hope to eventually obtain a permanent position at this company.” The next most common (42%) was “I want to know what this kind of job/industry is like”
followed by (13%) “I enjoy the flexibility of temporary work.”
Also, contractors were asked about their preference for working arrangements.
Forty-six out of 53, or 87% of contractors responded that they preferred a permanent working arrangement over temporary employment. Interestingly, as showed in Table 4.38 and Table 4.39, the contractors who hoped to obtain permanent positions scored slightly higher on job satisfaction and OCB than did contractors who preferred temporary working arrangements.
Table 4.38. Descriptive Statistics of Job Satisfaction on the Preference of Contractor
Preference N M SD SE
Temporary 7 2.68 .75 .28
Permanent 46 2.80 .64 .09
\
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Table 4.39. Descriptive Statistics of OCB on the Preference of Contractor
Preference N M SD SE
Temporary 7 2.92 .35 .13
Permanent 46 2.93 .27 .04
Table 4.40. Summary of Hypotheses
Hypothesis No. Scales Result
One Job Satisfaction Supported
Pay Supported
Promotion Supported
Supervision Supported
Benefits Supported
Contingent Rewards Supported
Operating Procedure Not Supported
Co-workers Not Supported
Nature of Work Supported
Communication Not Supported
Two OCB Supported
Altruism Supported
General compliance Supported
Three Job Satisfaction Supported
Pay Supported
Promotion Supported
Supervision Supported
Benefits Supported
Contingent Rewards Supported
Operating Procedure Not Supported
Co-workers Supported
Nature of Work Supported
Communication Supported
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Summary of Research Findings
The purpose of the present study is to address the problems experienced by both contractors and the organizations that use temporary services. This is done by comparing contractors and employees on job satisfaction and OCB. Additionally, the relationship between job satisfaction and OCB was measured for both contractors and employees.
Contrary to the hypothesis, contractors did not score significantly lower on operating procedure, coworkers and communication subscales of JSS than employees did.
Consistent with the hypotheses, the present study has shown that contractors scored significantly lower than employees did on both overall measures of job satisfaction and OCB. Also, consistent with the hypotheses, the present study found that there are significant positive relationships between job satisfaction and OCB among both employees and contractors.
Multiple regression was performed to examine the explain level of gender, age, education and length of employment on two variables in this research. These analyses did not change any of the overall results discussed above.
In the supplemental analyses, contractors who hoped to obtain permanent positions scored higher than other contractors on job satisfaction and OCB scales.
Discussions of the Results
In this section, the results are discussed to explain the possible reasons behind the results and also assert to possible conclusions that can be drawn from the study.
Job satisfaction and employment status
As discussed above, there is a significant difference across employment status on job satisfaction and OCB. This result is easy to explain given that the fact that contractors at this organization did the same work but for less pay and without compensatory benefits.
For the contrary results of the Sub-Scale Analysis, as German companies always
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have clear guidelines for each of the projects operated in the companies alone with a flat hierarchical ladder in foreign companies, maybe the reason why the operating procedure and communication subscales of JSS were not scored significantly lower by contractors than employees did. In addition, people who would have jobs in this company are mostly equipped with good academic backgrounds and/or several years of great technical experience, being working with the qualified coworkers in this company maybe the reason why contractors did not scored significantly lower on coworkers subscale of JSS.
Attempting to repeat the findings of Krausz et al (1995) the supplemental analyses divided contractors into two groups based on choice of work status.
Surprisingly, those contractors who prefer a permanent work arrangement (involuntary contractors) had a slightly higher job satisfaction than did those contractors who would prefer a temporary work arrangement (voluntary contractors).
Thus, the Krausz et al. (1995) preference hypothesis was not supported by the current research and the questions of why some contractors are more satisfied, why some are less satisfied, and why some are equally satisfied as employees remain to be answered.
One plausible explanation, as Moorman (1993) alludes to, is that difference lies not in the labors, but rather in the measures used. For example, the instrument used in
One plausible explanation, as Moorman (1993) alludes to, is that difference lies not in the labors, but rather in the measures used. For example, the instrument used in