As shown in Table 5, all variables have Cronbach’s alpha value ranging from 0.887 to 0.933, which achieved the minimum acceptable level of coefficient alpha above 0.7 (Nunnally’s, 1978). The independent variable of financial rewards has the highest Cronbach’s alphas of 0.933 despite the low number of scale items (8 items), followed by dependent variable of job satisfaction with Cronbach’s alpha of 0.922 (20 items). Both of these variables have value more than 0.9, which are considered excellent. The other independent variable of non-financial rewards has Cronbach’s alpha of 0.887 (10 items), which is considered good. Conclusively, the reliability of the scales used in this study was high with Cronbach’s alpha value close to 1.0.
Table 5. Alpha Coefficient of reliability on variables Variables Cronbach's Alpha Number of Items Financial Rewards 0.933 8 Non-Financial Rewards 0.887 10 Job Satisfaction 0.922 20
Correlation analysis was applied to test the relationships between rewards and job satisfaction as hypothesized in hypotheses 1 and 2. Pearson correlation (r) refers to the degree of association between two variables. It shows the degree of relationship by using readings ranging from -1.00 to +1.00. The value indicates the strength and the sign indicates the direction of a linear relationship between the two variables. Values near to 1 are considered strong relationship, while values near to 0 indicate weak correlations between the two variables (Vignaswaran, 2008).
Table 6. Correlation of the variables Job satisfaction P value Financial rewards 0.819** 0.000 Non-financial rewards 0.740** 0.000 Job satisfaction 1 0.000 Note: **p< 0.01). That is, the more financial rewards given, the more positive would be the perception of the employees towards job satisfaction. Thus, hypothesis 1 (H1), that is, there is a relationship between financial rewards and job satisfaction was accepted.
Hypothesis 2 (H2) was also supported. The relationship between non-financial rewards and job satisfaction are positively and significantly related (r = 0.740**, p < 0.01). This implies that when there is an increase in nonfinancial rewards, there is also a corresponding increase in job satisfaction.
Standard multiple regression analysis measures the simultaneous investigation of the effect of the independent variables and dependent variable (Zikmund, 2000). In this study, financial and non-financial rewards are the independent variables while job satisfaction is the dependent variable. The effects of the types of rewards on employees’ job satisfaction were examined by multiple regression analysis to test hypotheses 3 and 4 of the research.