Multiple regression model fitted for job satisfaction of employees working in saving and cooperative organization

Introduction

The dictionary meaning of job satisfaction is feeling of fulfillment or enjoyment that a person derives from their job. Job satisfaction can be defined in various ways it is simply how content individual is with his or her job. In other words, whether or not they like the job or individual This means that, spiritual leadership does not have as much considerable effect as the cultural dimensions on employee satisfaction (Aydin, 2009) [3] . The employees under organization are more or less satisfied with the job. The organization should consider on the salary, relationship of employees and supervisors, grievance handling and give more opportunity for the new employees (Raja, 2013) [12] . The Satisfaction of human resource finds close links to highly motivated employees. Motivated employees then develop loyalty or commitment to the organization resulting to greater productivity and lower turnover rates (Mulugeta, 2018) [10] . The findings of this study emphasized that each of the work life balance policies on its own is a predictor of job satisfaction. The result explored that managers in banks should improve the work life balance policies offered to employees in order to increase their job satisfaction, to improve staff commitment and productivity (Maurya V.N., et al., 2015) [9] . The results indicate a positive relationship between working environment and employee job satisfaction. The study concludes with some brief prospects that the businesses need to realize the importance of good working environment for maximizing the level of job satisfaction (Abdul R., et al., 2015) [1] . The Results reveal statistically significant differences in pay, promotion, fringe benefits, contingent rewards, supervision, co-workers and nature of work subscales (Zirwatul R., et al., 2014) [13] . 'Job security' is the most significant factor of job satisfaction to the employees of commercial banks in Nepal. Level of job satisfaction does not differ significantly between male and female employees. However, there are significant differences in level of job satisfaction among various age groups of employees (Pathak H., 2015) [11] . Job satisfaction is very important because most of the people spend a major portion of their life at working place. Moreover, job satisfaction has its impact on the general life of the employees also, because a satisfied employee is a contented and happy human being. Today in this growing world people are at different kinds of job which includes Savings and Credit Cooperative organization even. In Nepal, even there are 13578 SACCOS established yet which has provided employment opportunities to 35447 employees www.deoc.gov.np/downloadfile/Coop data 2074). Every employees have their own feeling about their job and this proposed research work intends to analyze job satisfaction through selected parameters of cooperatives in Nepalgunj sub metropolitan city, Banke. The employees are the most precious and important asset of any organization, hence employee job satisfaction has now become one of the top priority issues in every the industry (Kumari & Pandey, 2011) [8] . So the issue is: What are the most significant factors of influencing job satisfaction that are highly perceived by employees particularly in the context of cooperatives in Nepalgunj Sub-metropolitan city?

Objectives

The general objective of the study is to assess the job satisfaction status of employees and identify the significant factors that influence the job satisfaction of the employees working in saving and cooperative organizations in Nepalgunj sub-metropolitan city, Banke; Nepal. The specific objectives of the study are as follows: i. To identify the factors associated with job satisfaction level of employees in saving and cooperative organizations in Nepalgunj sub metropolitan city. ii. To examine the relationships between job satisfaction and different socio-economic, demographic factors.

Limitations of study

The study confined only on the basis of data collected through employees working in offices of selected Savings and Credit Cooperative organizations of Nepalgunj sub-metropolitan city, Banke. The findings of this study may not be generalized.

Materials and Methods

Research design

A cross-sectional and descriptive statistical research designs have been used for the research to identify and analyse various factors influencing on employees' job satisfaction working in saving and credit cooperative organization in Nepalgunj Sub-metropolitan city of Banke District, Nepal.

Population and sample

Target population for this study is the total number of employees who are working in Savings and Credit Cooperative organization of Nepalgunj sub-metropolitan city of Banke District, Nepal. The population frame was obtained from the record of each office and the sample size for every organization was determined by proportionate allocation and finally sampling units were selected by using simple random sampling without replacement technique. The representative sample of size 161 was collected by using a structured questionnaire to each respondent.

Data analysis tools and techniques

Different statistical computational /analytical tools such as Ms-Excell, SPSS version 21 were used throughout the entire computation and analysis process. In this study regression analysis, along with t-test, F-test and Adjusted R 2 , VIF are used to analyse the data.

Multivariate Regression Analysis

The data consist of n observations on a dependent or response variable Y and p predictor or explanatory variables, X 1 , X 2 , X 3,. . . . . . . . , X P . The relationship between Y and X 1 , X Table 1 shows that male employees are equal 37.3% of the study participants while female employees are equal 62.7% of the study's participants. As p-value = 0.481 > 0.05, the independent t-test reveals that there is no significant difference in job satisfaction among male and female.

Results and Discussions

Descriptive Information of Independent Variables:

The demographic, socioeconomic characteristics of the respondents such as education level, designation, salary, age group and working tenure are in the following table. The variable representation is: X 1 = pay facilities, X 2 = supervision, X 3 = Relationship with the co-employees, X 4 = working environment, X 5 = training courses, X 6 = carrier development, X 7 = encouragement factors, X 8 = motivation factors and X 9 = job satisfaction.

The above table shows the correlation matrix. Pearson correlation has been used for the study. Test of significance has been done using two-tailed test. The results exhibit that all 8 variables measuring have significant correlation with job satisfaction. Moreover, all the variables have positive correlation with job satisfaction. The variable job satisfaction is significant with pay facilities, supervision, Relationship with co-employees, working environment, training courses, carrier development opportunities, encouragement factors and motivation factors at 1% level of significance.

Significant variables selected

The following is the list of significant variables obtained (i) Pay facilities (ii) Supervision (iii) Relationship with coemployees (iv) Working environment (v) Training courses (vi) Carrier development opportunities (vii) Motivation factors (viii) Encouragement factors.

Test of presence of Multicollinearity

Multicollinearity refers to the existence of more than or exact linear relationship among some or all explanatory variables of a regression model. There are several techniques for detecting multicollinearity but in this study, tolerance and variance inflation factor (VIF) have been used.

The tolerance and VIF are the important and reliable tests of multicollinearity because if multicollinearity exists between two or more independents variables it can deteriorate the results of multiple regression (Hair Jr et al., 2010) [6] . Therefore, before running the multiple regression model, the presence of co-linearity among the above listed variable has been examined using variance inflation factors (VIF) and the result is shown in the following table 4. The result suggests that the current study does not have any problem with multicollinearity and this allows for standard interpretation of the regression coefficients.

Multiple linear Regression for explaining Job satisfaction of employees

The information about results obtained from the fitted multiple linear regression taking, Job satisfaction as the dependent variable and Pay facilities, Supervision, Relationship with the co-employees, Working environment, Training courses, Carrier development, Encouragement factors and Motivation factors as the independent variables (predictors).

Coefficient of Determination (R-square)

The coefficient of determination with standard error of estimates. [6] , the value of adjusted R square is higher than the benchmark of 0.5 which is sufficiently explainable enough for the regression model. According to Hair Jr et al. (2010) [6] , the tolerance values should be higher than 0.1 and the VIF should be lower than 4.0 to avoid multi-collinearity. As it can be seen from the table 4, the collinearity statistics indicate that all tolerance values are greater than the benchmark indicated in the literature, showing that the eight independent variables are not influenced by each other and ensuring the appropriateness of executing the regression analysis. Consequently, the multicollinearity of the regression model is also examined by Variance Inflation Factor (VIF) with the rule of thumb lower than 4.0.

From the table 4, it is clear that the tolerance of eight independent variables ranges between 0.573 and 0.853 are substantially greater than 0.1 and VIF ranges from 1.197 to 1.746 are lower than 4.Therefore, it concludes that there is no multicollinearity exists among the independent variables in the observed data set. [6] . As it can be seen in the table 6, the value of F statistic 25.998 is highly significant with pvalue = 0.00 < 0.05, which means that there is a linear relationship between the dependent variable (Overall job satisfaction) and the independent variables such as pay facilities (salary), supervision, relationship with coemployees, working environment, training courses, carrier development, encouragement factors and motivation factors.

Analysis of Variance (ANOVA)

In other words, any given change in one of the independent variables will always produce a corresponding change in the dependent variable (job satisfaction), thus all independent variables were confirmed by the analysis to have strong impact on dependent variable. For this model let Y represents the job satisfaction. X 1 represents pay facilities, Supervision (X 2 ), relationship with co-employees (X 3 ), working environment (X 4 ), training courses(X 5 ), Carrier development (X 6 ), encouragement factors (X 7 ) and X 8 represents motivation factors.

Regression model

On the basis of beta coefficients (regression coefficients) from table 7. Where; Constant (intercept) , 𝛽 0 = 0.417 represents when the value of the independent variables are zero, the job satisfaction would take the value 0.417. 𝛽 1 = 0.161 indicates that one unit increase in the pay facilities results in 0.161 units increase in the Job satisfaction and t value = 4.355 with (P-value = 0.00 <0.05) is also significant. So, alternative hypothesis is accepted which states that there is positive relationship between pay facilities and job satisfaction. The 95% confidence interval which is 0.088 to 0.234. Again the model shows 𝛽 2 = 0.017 ; one unit increase in the supervision results in 0.017 units increase in the Job satisfaction and since, t value = 0.395 with (P-value = 0.694 > 0.05) is insignificant which states that there is no significant relationship between supervision and job satisfaction and also the 95% confidence interval is -0.066 to 0.099. Likewise, coefficient 𝛽 3 = 0.030 ; one unit increase in relationship with co-employees results in 0.030 units increase in the Job satisfaction and t value = 0.580 with (P-value = 0.563 > 0.05) is also insignificant which states that there is no significant relationship between relationship with co-employees and job satisfaction. The 95% confidence interval is -0.072 to 0.132. For working environment, 𝛽 4 = 0.158 ; one unit increase in the working environment results in 0.158 units increase in the Job satisfaction. In other words, if the working environment is one times better than before then the level of job satisfaction is increased by 0.158 (15.8%.) and t value = 2.699 with (Pvalue = 0.008 <0.05) is significant which means that there is positive relationship between working environment and job satisfaction. The 95% confidence interval is 0.042 to 0.274. Similarly, the regression coefficient for training courses, 𝛽 5 = 0.069; one unit increase in the training courses results 0.069 units increase in the Job satisfaction and t value = 2.284 with (P-value = 0.024 <0.05) is significant which means that there is positive relationship between the training courses and job satisfaction. The 95% confidence interval is 0.009 to 0.129.

Model adequacy (Residual analysis)

It includes the information how the job satisfaction level is adequately explained by the fitted model.

Test of Normality of residuals

A graphical tool for assessing normality is the normal probability plot, a percentile -percentile plot (P-P Plot) or the cumulative probability plots of residuals (P-P plot) of the standardized data against the standard normal distribution. For normal data, the points plotted in the P-P plot should fall approximately on a straight line, indicating high positive correlation. For the research perspective, we want to show that our data set are normally distributed.

The following figure 1 shows how the residuals are deviate from the normal line. If the Standardized residuals are normally distributed, the scatters should fall on or tightly close to the normal distribution line. Figure .1 shows that the scatters of the residuals basically fall straightly on the normal distribution line, indicating a normal distribution of residual From the above graph, it shows that there is no huge deviation of residuals from normal line. Also, it can be seen that the data set is going through the origin. So, it indicates that the residuals are approximately normally distributed. Hence, it may conclude that the observed data is normally distributed.

Goodness of fit

The goodness of fit of the estimated model is judged by Shapiro-Wilk test. The above table 8 shows that the p-value obtained from the Shapiro-Wilk test is greater than 0.05 (at 5% level of significance) which indicates that the coefficients of residuals are insignificant in overall. So, insignificant value suggests that the estimated model fit is well.

Test of Homoscedasticity of Residuals

The following figure is the scatter plot of residuals versus predicated values from the fitted model. The residual scatter plots could be used to test the assumption of homoscedasticity. If there is no clear relationship between the residuals and predicted values, the assumption of homoscedasticity should also be met. In this study, by plotting the standardized residuals against the predicted values as shown in figure 2, the scatter plot of residual versus predicted value shows that the errors are uniformly distributed over the entire range of the predicted value and it found that there was no clear relationship between the residuals and the predicted values. Therefore, the results suggest that the assumption of homoscedatisity should be met in the study. It is the indication of absence of heteroscedasticity.

Conclusion and Recommendations 7.1 Conclusion

From the results of the study, it can be concluded that the different demographic factor, Socio-economic factor and job related factors have significant influence on job satisfaction of the employees. The fitted multiple linear regression model has shown that the factors pay facilities, working environment, training courses, encouragement factors and motivation factors have significant impact on job satisfaction of the employees under the study.

The finding of the study shows that all the significant variables have positive impact on job satisfaction. Although, the variables like supervision, relationship with co-employees and carrier development opportunities is not significant in the final model but it is important variable and is significant while correlating with job satisfaction.

Recommendation

From the finding of this study following recommendations can be made:  Pay facilities of the employees should be decided in accordance to their education level, tenure of work and working hours; this may help in job satisfaction of employees.  There should not be any discrimination in the working environment in accordance to the education level or job level.  Different schemes like bonus, selecting employee of the month, promotion etc. should be given to the employees that motivates them to the work.

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