Job Satisfaction

23 Pages   |   3,661 Words

Table of Contents

Introduction. 1
Exposition of Variables under Study. 2
Training. 2
Supervision. 2
Benefits / Compensation. 2
Frequency Tables, Descriptive Statistics and Bar Charts. 4
Age. 4
Gender 4
Salary. 5
Work Experience. 6
Training. 6
Relationship with Supervisor 7
Satisfaction and Benefits. 9
Associations & Correlations. 10
Age and Training. 10
Age and Relationship with Supervisor 10
Age and Satisfaction about Benefits. 11
Gender and Training. 11
Gender and Relationship with Supervisor 12
Gender and Satisfaction about Benefits. 12
Work Experience and Training. 13
Work Experience and Relationship with Supervisor 13
Work Experience and Satisfaction about Benefits. 14
Regression. 15
Linear Regression Model of Salary on Age. 15
Linear Regression Model of Salary on Work Experience. 17
Generalization of Results to the Target Population. 19
Recommendations. 19
Bibliography. 20

Introduction

According to Oxford Dictionary (Oxford University Press, 2011), the literal meaning of word “Satisfaction” is “the good feeling that you have when you have achieved something or when something that you wanted to happen does happen; something that gives you this feeling”.When this satisfaction is derived from work, it is termed as Job Satisfaction. One of the most acknowledged tools of measuring Job Satisfaction is Job Descriptive Index which terms five factors as most important in measuring job satisfaction. These factors are
  • Job
  • Excellence of Command
  • Affiliation with Colleagues
  • Career Development Opportunities
  • Salary
Besides these most common attributes of measuring job satisfaction, there are other criteria as well such as Training programs. Organizational Culture and Consciousness also plays a vital role in Job satisfaction of employees (Organ, 1988). Job satisfaction not only directly impacts the performance of an individual at workplace but results in the mental wellbeing of the individual itself (Arnold & Silvester, 2005).
The motivation to pursue research on the identification of factors that influence Job Satisfaction comes from the researches that identify the crucial nature of Job Satisfaction and its impact on organizational performance (Katzell, Yankelovich, Fein, Ornati, & Nash, 1975)(Nanda & Browne, 1977). These researchers have identified factors such as Absenteeism, Employee Turnover, and Job Performance as possible consequences of Job Dissatisfaction. Therefore, a study is conducted to study the impact and association of factors such as Training, Supervisory Role and Benefits & Compensation on Job Satisfaction in an organization.
The investigation was limited to a population of 1000 local companies in a specific industry in South Africa consisting of 30% larger (more than 200 employees) and 70% smaller companies (50 to 200 employees).
 

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Exposition of Variables under Study

Training

Trainings create a sense of organizational commitment among employees by enhancing their cognitive and practical skills for improved job performance. Today, Employee Training methods such as Job rotation helps the employees in adjusting to the right workplace depending on their skills & preferences. Which results in improved job satisfaction as employees turn to become more productive if they have identified the right place for them to work during these job rotations.
In our study to measure the extent of training, we asked the survey participants if there is scope to develop skills and abilities at existing organisation, do they receive formal evaluation of work and are sufficient training programs offered to update skills.

Supervision

Supervisors at workplaces play a vital role in the motivation levels of employees. Regular boosts of appreciation for the work done increases the motivation levels of subordinates. Moreover, taking a personal interest in employees work and empowering employees to take major decisions also results in a higher degree of job satisfaction. Kim (2002) exmained the effect of participative management on job satisfaction and found out that it has a fairly positive impact on job satsfaction to the extent that it reduces turnover and absentisim. Effective communication between supervisors and employees increases the productivity  of the workplace environment.
We analyzed the role of supervision in job satisfaction by asking if the superviors are offering constructive feedback to employees? If the goals and job requiremnets are communicated effectively, and if regular performance appraisals have been conducted.

Benefits / Compensation

Benefits and Salary directly influence Job Satisfaction levels and is the prime factor in fulfilling basic human needs such as Shelter, Food, and Clothing etc. Employees, who are more aware of the value of monetary benefits that they receive, are likely to have high levels of job satisfaction than employees who fail to recognize the actual value of these benefits. Igalens and Roussel (1999) found out that motivation for performance is greatest when there is a regular rise in fixed compensation on superior job performance. Furthermore, employees are more motivated to deliver performance work when they perceive existing salary to be fair.
In our study we explored if a fair compensation package is offered to employees, and are employees satisfied by the existing compensation packages.

Frequency Tables, Descriptive Statistics and Bar Charts

Age

Most of the participants in our study fell in the age bracket of 45 – 54 years (38% of total). The second largest category was participants in the age bracket 35 – 44.
Age (Years)
Age Frequency Percent Valid Percent Cumulative Percent
Valid 15  - 24 4 6.5 6.5 6.5
25  - 34 9 14.5 14.5 21.0
35  - 44 15 24.2 24.2 45.2
45  - 54 24 38.7 38.7 83.9
> 54 10 16.1 16.1 100.0
Total 62 100.0 100.0  

Gender

Gender
  Frequency Percent Valid Percent Cumulative Percent
Valid Female 51 82.3 82.3 82.3
Male 11 17.7 17.7 100.0
Total 62 100.0 100.0
 
There were 51 female participants and 11 Males participants in our study.

Salary

Most of the participants had a salary range of 5,000 to 10,000 rand. Only a few exceeded in Earning Salaries more than that. The mean salary was 7,058 rand with a Standard Deviation of 4,330 rand.

Work Experience

Since most of the participants belonged to a senior 45-54 age groups hence it was a normal phenomenon to observe most of the applicants having 31 years and above work experience.
Work Experience (Years)
Years Frequency Percent Valid Percent Cumulative Percent
Valid 10 and below 13 21.0 21.0 21.0
11- 20 9 14.5 14.5 35.5
21 - 30 12 19.4 19.4 54.8
31 and above 28 45.2 45.2 100.0
Total 62 100.0 100.0  

Training

Training
Scope for Development
Formal Evaluation
Training Programs
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 7 11.3 11.7 11.7
Disagree 20 32.3 33.3 45.0
Neutral 12 19.4 20.0 65.0
Agree 8 12.9 13.3 78.3
Strongly Agree 13 21.0 21.7 100.0
Total 60 96.8 100.0  
Missing System 2 3.2    
Total 62 100.0    
 
Most of the participants (32%) disagreed to the notion that they have been provided with opportunities to develop their skills and abilities. However nearly 21% agreed that a scope exists to develop skills and abilities at the organization where they work. Another 19% stayed neutral. Such analysis shows that some companies are trying to provide an employee productive environment where they can grow professionally but most have failed to initiate employee development programs which is definitely resulting in job dissatisfaction.

Relationship with Supervisor

Relationship with Supervisor
Constructive Feedback
Periodic Performance Review
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 7 11.3 11.7 11.7
Disagree 19 30.6 31.7 43.3
Neutral 12 19.4 20.0 63.3
Agree 14 22.6 23.3 86.7
Strongly Agree 8 12.9 13.3 100.0
Total 60 96.8 100.0  
Missing System 2 3.2    
Total 62 100.0    
 
The study also found out that most of the employees (30% of total) believe that their supervisors are not helpful and does not offer constructive feedback in performance review. Furthermore, they also disagree to the notion that their supervisor has a clear set of policies and enforces them consistently. Also these 30% employees believe that their supervisors do not take employees performance reviews seriously.However 22% of employees have very positive views about their supervisors and consider them helpful and motivating.

Satisfaction and Benefits

Satisfaction about Benefits
Competitive & Fair Compensation
 
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 10 16.1 17.5 17.5
Disagree 13 21.0 22.8 40.4
Neutral 12 19.4 21.1 61.4
Agree 14 22.6 24.6 86.0
Strongly Agree 8 12.9 14.0 100.0
Total 57 91.9 100.0  
Missing System 5 8.1    
Total 62 100.0    
21% of the participants consider their compensation packages as competitive, appropriate and fair. However, an equal number of participants 22% were not satisfied with their compensation packages at all. Another 19% showed a neutral attitude towards existing compensation packages.

Associations & Correlations

Age and Training

Chi-Square Tests
  Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 22.133a 16 .139
Likelihood Ratio 24.519 16 .079
Linear-by-Linear Association .470 1 .493
N of Valid Cases 60    
a. 23 cells (92.0%) have expected count less than 5. The minimum expected count is .47.
 
 
Using   and degrees of freedom = 16, the critical value of   from the Chi-Square table is 26.296 which is greater than the calculated value of 22.133. We have failed to reject Null Hypothesis. Thus we conclude that there is no association between Age and Training Variable.

Age and Relationship with Supervisor

 
Chi-Square Tests
  Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 18.975a 16 .270
Likelihood Ratio 21.075 16 .176
Linear-by-Linear Association .041 1 .839
N of Valid Cases 60    
a. 23 cells (92.0%) have expected count less than 5. The minimum expected count is .47.
 
 
Using   and degrees of freedom = 16, the critical value of   from the Chi-Square table is 26.296 which is greater than the calculated value of 18.975. We have failed to reject Null Hypothesis. Thus we conclude that there is no association between Age and Relationship with Supervisor Variable.

Age and Satisfaction about Benefits

Chi-Square Tests
  Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 27.024a 16 .041
Likelihood Ratio 26.521 16 .047
Linear-by-Linear Association .946 1 .331
N of Valid Cases 57    
a. 23 cells (92.0%) have expected count less than 5. The minimum expected count is .56.
 
 
 
Using   and degrees of freedom = 16, the critical value of   from the Chi-Square table is 26.296 which is less than the calculated value of 27.024. We can reject Null Hypothesis. Thus we conclude that there is association between Age and Satisfaction about Benefits.

Gender and Training

Chi-Square Tests
  Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 7.745a 4 .101
Likelihood Ratio 9.221 4 .056
N of Valid Cases 60    
a. 5 cells (50.0%) have expected count less than 5. The minimum expected count is 1.28.
 
 
Using   and degrees of freedom = 4, the critical value of   from the Chi-Square table is 9.488 which is greater than the calculated value of 7.745. We cannot reject Null Hypothesis. Thus we conclude that there is no association between Gender and Training.

Gender and Relationship with Supervisor

Chi-Square Tests
  Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 11.043a 4 .026
Likelihood Ratio 10.726 4 .030
N of Valid Cases 60    
a. 5 cells (50.0%) have expected count less than 5. The minimum expected count is 1.28.
 
 
Using   and degrees of freedom = 4, the critical value of   from the Chi-Square table is 9.488 which is less than the calculated value of 11.043. We can reject Null Hypothesis. Thus we conclude that there is association between Gender and Relationship with Supervisor.

Gender and Satisfaction about Benefits

Chi-Square Tests
  Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 5.471a 4 .242
Likelihood Ratio 7.083 4 .132
N of Valid Cases 57    
a. 5 cells (50.0%) have expected count less than 5. The minimum expected count is 1.54.
 
 
Using   and degrees of freedom = 4, the critical value of   from the Chi-Square table is 9.488 which is greater than the calculated value of 5.471. Thus, we cannot reject Null Hypothesis. Thus we conclude that there is no association between Gender and Satisfaction about Benefits.

Work Experience and Training

Chi-Square Tests
  Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 11.319a 12 .502
Likelihood Ratio 12.155 12 .433
Linear-by-Linear Association 1.426 1 .232
N of Valid Cases 60    
a. 17 cells (85.0%) have expected count less than 5. The minimum expected count is .93.
 
 
Using   and degrees of freedom = 12, the critical value of   from the Chi-Square table is 21.026 which is greater than the calculated value of 11.319. Thus, we cannot reject Null Hypothesis. Thus we conclude that there is no association between Training and Work Experience.

Work Experience and Relationship with Supervisor

Chi-Square Tests
  Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 9.619a 12 .649
Likelihood Ratio 11.665 12 .473
Linear-by-Linear Association 1.314 1 .252
N of Valid Cases 60    
a. 17 cells (85.0%) have expected count less than 5. The minimum expected count is .93.
 
Using   and degrees of freedom = 12, the critical value of   from the Chi-Square table is 21.026 which is greater than the calculated value of 9.619. Thus, we cannot reject Null Hypothesis. Thus we conclude that there is no association between Relationship with Supervisor and Work Experience.

Work Experience and Satisfaction about Benefits

Chi-Square Tests
  Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 13.347a 12 .344
Likelihood Ratio 14.294 12 .282
Linear-by-Linear Association 5.195 1 .023
N of Valid Cases 57    
a. 17 cells (85.0%) have expected count less than 5. The minimum expected count is 1.12.
 
 
Using   and degrees of freedom = 12, the critical value of   from the Chi-Square table is 21.026 which is greater than the calculated value of 13.347. Thus, we cannot reject Null Hypothesis. Thus we conclude that there is no association between Satisfaction about Benefits and Work Experience.

Regression

Linear Regression Model of Salary on Age

Correlations
  Monthly Salary (Rands) Age (Years)
Pearson Correlation Monthly Salary (Rands) 1.000 -.062
Age (Years) -.062 1.000
Sig. (1-tailed) Monthly Salary (Rands) . .316
Age (Years) .316 .
N Monthly Salary (Rands) 62 62
Age (Years) 62 62
 
 
The correlation between Salary and Age is negative and insignificant (-0.062) at 0.05 level.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .062a .004 -.013 4,358.472
a. Predictors: (Constant), Age (Years)
 
 
The small R-Square value of 0.004 implies that 0.4% variation in the dependent variable Salary is explained by the independent variable Age. The Adjusted R Square, the lowest R-Square after taking into account the variation in sample size and number of variables, is about -0.013 and shows that nearly -1.3% variation in Salary is explained by Age. The SSE also known as Standard Error of Estimate or Standard Deviation of Residuals is very large.
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 4415660.623 1 4415660.623 .232 .631a
Residual 1.140E9 60 18996274.379    
Total 1.144E9 61      
a. Predictors: (Constant), Age (Years)
b. Dependent Variable: Monthly Salary (Rands)
 
 
The insignificant F-test value of 0.232 implies that the model is not significant. The Mean Square value of 4415660 is considerably lower than the residual 18996274, resulting in an F ratio of lower than 1 which shows that the variation in Salary cannot be explained by Age. 
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 7879.860 1791.373   4.399 .000
Age (Years) -239.095 495.915 -.062 -.482 .631
a. Dependent Variable: Monthly Salary (Rands)
 
 
The Unstandardized Coefficient (B) of -239.095 implies that for every 1 year increase in Age, Salary decreases by -239.095. The B (Constant) is the model intercept whose value is 7,879.86.
Regression Equation

After the analysis, it can be observed that the model is not explaining the significance of Salary and Age. High Standard Error of Estimate, insignificant F ratio, and very low R-Square concludes that the model is very weak and cannot explain the variation in Salary by Age factor.
 

Linear Regression Model of Salary on Work Experience

Correlations
  Monthly Salary (Rands) Work Experience (Years)
Pearson Correlation Monthly Salary (Rands) 1.000 -.093
Work Experience (Years) -.093 1.000
Sig. (1-tailed) Monthly Salary (Rands) . .237
Work Experience (Years) .237 .
N Monthly Salary (Rands) 62 62
Work Experience (Years) 62 62
 
 
The correlation between Salary and Age is negative and insignificant (-0.093) at 0.05 level.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .093a .009 -.008 4,348.150
a. Predictors: (Constant), Work Experience (Years)
 
The small R-Square value of 0.009 implies that 0.9% variation in the dependent variable Salary is explained by the independent variable Work Experience. The Adjusted R Square, the lowest R-Square after taking into account the variation in sample size and number of variables, is about -0.008 and shows that nearly 0.8% variation in Salary is explained by Work Experience. The SSE also known as Standard Error of Estimate or Standard Deviation of Residuals is very large.
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 9807484.919 1 9807484.919 .519 .474a
Residual 1.134E9 60 18906410.641    
Total 1.144E9 61      
a. Predictors: (Constant), Work Experience (Years)
b. Dependent Variable: Monthly Salary (Rands)
 
The insignificant F-test value of 0.519 implies that the model is not significant. The Mean Square value of 9807484 is considerably lower than the residual 18906410, resulting in an F ratio of lower than 1 which shows that the variation in Salary cannot be explained by Work Experience with accuracy.
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 8021.132 1446.200   5.546 .000
Work Experience (Years) -333.442 462.963 -.093 -.720 .474
a. Dependent Variable: Monthly Salary (Rands)
 
The Unstandardized Coefficient (B) of -333.442 implies that for every 1 year increase in Work Experience, Salary decreases by 333.442. The B (Constant) is the model intercept whose value is 8,021.132.
Regression Equation

After the analysis, it can be observed that the model is not explaining the significance of Salary and Work Experience as well. High Standard Error of Estimate, insignificant F ratio, and very low R-Square concludes that this model is also very weak and cannot explain the variation in Salary by Work Experience.

Generalization of Results to the Target Population

The results of this study cannot be attributed to target populations with accuracy because of the small sample size which has resulted in an inflated computed value of Chi Square Statistic during the Association Relationship test (Chi Square Test). This happened because too many of the expected counts were less than 5. Also, there are small correlations among the variables under study.

Recommendations

  1. Job Satisfaction can be enhanced by offering performance based regular monetary rewards. In this way employees firmly know in advance that if they show excellent performance at workplace than they are entitled to receive these rewards.
  2. Supervisors play a critical role in Job Satisfaction and Productivity at a workplace. Regular Appraisals, Performance Checks and Constructive Feedback to employees can definitely result in increased motivation among employees and eventually results in higher job satisfaction.
  3. The recruitment process should be designed in such a way as right employees are hired at right positions which improves productivity along with job satisfaction.
  4. It has been found out that Job dissatisfaction results in consequences such as absenteeism and employee turnover. Such practices can be minimized by empowering employees to do the work with authority. Furthermore, management must ensure smooth coordination between employees working in teams together. Such collaboration will increase productivity and job satisfaction among employees.
  5. Trainings and Development programs among employees result in better productivity at workplace. Trained employees have better control over job tasks and can work with authority.
  6. Job Satisfaction must be periodically measured and analyzed at workplace and any possible factors which are negatively impacting job satisfaction must be removed accordingly.

Bibliography

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Oxford University Press. (2011). Oxford Advanced Learner's Dictionary. Retrieved September 15, 2011, from http://www.oxfordadvancedlearnersdictionary.com/dictionary/satisfaction
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