23 Pages | 3,661 Words

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

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

- Job
- Excellence of Command
- Affiliation with Colleagues
- Career Development Opportunities
- Salary

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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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.

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.

In our study we explored if a fair compensation package is offered to employees, and are employees satisfied by the existing compensation packages.

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 |
|||||
---|---|---|---|---|---|

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 |

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 |
|||||
---|---|---|---|---|---|

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 |

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 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 |

Chi-Square Tests |
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---|---|---|---|

Value | df | Asymp. Sig. (2-sided) | |

Pearson Chi-Square | 22.133^{a} |
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. |

Chi-Square Tests |
|||
---|---|---|---|

Value | df | Asymp. Sig. (2-sided) | |

Pearson Chi-Square | 18.975^{a} |
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. |

Chi-Square Tests |
|||
---|---|---|---|

Value | df | Asymp. Sig. (2-sided) | |

Pearson Chi-Square | 27.024^{a} |
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.

Chi-Square Tests |
|||
---|---|---|---|

Value | df | Asymp. Sig. (2-sided) | |

Pearson Chi-Square | 7.745^{a} |
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. |

Chi-Square Tests |
|||
---|---|---|---|

Value | df | Asymp. Sig. (2-sided) | |

Pearson Chi-Square | 11.043^{a} |
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. |

Chi-Square Tests |
|||
---|---|---|---|

Value | df | Asymp. Sig. (2-sided) | |

Pearson Chi-Square | 5.471^{a} |
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. |

Chi-Square Tests |
|||
---|---|---|---|

Value | df | Asymp. Sig. (2-sided) | |

Pearson Chi-Square | 11.319^{a} |
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. |

Chi-Square Tests |
|||
---|---|---|---|

Value | df | Asymp. Sig. (2-sided) | |

Pearson Chi-Square | 9.619^{a} |
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. |

Chi-Square Tests |
|||
---|---|---|---|

Value | df | Asymp. Sig. (2-sided) | |

Pearson Chi-Square | 13.347^{a} |
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. |

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 | .062^{a} |
.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.

ANOVA^{b} |
||||||
---|---|---|---|---|---|---|

Model | Sum of Squares | df | Mean Square | F | Sig. | |

1 | Regression | 4415660.623 | 1 | 4415660.623 | .232 | .631^{a} |

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.

Coefficients^{a} |
||||||
---|---|---|---|---|---|---|

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) |

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.

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 | .093^{a} |
.009 | -.008 | 4,348.150 |

a. Predictors: (Constant), Work Experience (Years) |

ANOVA^{b} |
||||||
---|---|---|---|---|---|---|

Model | Sum of Squares | df | Mean Square | F | Sig. | |

1 | Regression | 9807484.919 | 1 | 9807484.919 | .519 | .474^{a} |

Residual | 1.134E9 | 60 | 18906410.641 | |||

Total | 1.144E9 | 61 | ||||

a. Predictors: (Constant), Work Experience (Years) b. Dependent Variable: Monthly Salary (Rands) |

Coefficients^{a} |
||||||
---|---|---|---|---|---|---|

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) |

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.

- 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.
- 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.
- 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.
- 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.
- 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.
- Job Satisfaction must be periodically measured and analyzed at workplace and any possible factors which are negatively impacting job satisfaction must be removed accordingly.

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