This chapter presents the statistical analyses of the data and the results of the research hypotheses that were tested. This chapter provides the general characteristics of the sample with the aid of descriptive statistical analysis. It then further presents the comparison of means by using One-way Anova to test the first hypothesis, and later it proceeds to show the results of correlation analysis, simple linear regression and simultaneous multiple regression to test the other three remaining hypotheses. The result of the correlation analysis show a basic explanation about the relationships and the simple linear regression was used to illustrate the causal relationship between the independent and dependent variables. Finally, the chapter concludes with discussions based on the findings from the data.
Hence, we start with presenting the descriptive statistic of the demographic data.
General Characteristics of the Sample Sample Characteristics
Two hundred and sixteen questionnaires were distributed among six commercial banks in The Gambia. A total of 210 questionnaires were received, which resulted in a response rate of 97. 22%. It is important to note that of the 210 questionnaires received, 10 had both invalid and missing data and were not included in the study and hence the total of number of usable questionnaires for this study was 200.
The demographic variables obtained from each respondent included: gender, age, marital status, level of education and organizational tenure. Table 4.1 provides details of the demographical characteristics of the sample.
In line with this, of the total participants, 112 (56.0%) were male. Although this result is expected in a country whose economic spheres was mainly male dominated but programs by the Government aimed at empowering its female citizenry over the past years is paying heed and hence , it is well noting that the differences with females in this sample is not so large as they constitute 44% of the total respondents. The age of the participants varied from 23 or below to 36 and above. The sample aged 30 to 35 represented 66 or 33% but it is worth noting that nearly half (86 or 43%) of the sample were between 24 to 29 years of age. Though the proportion of ages between these two groups was relatively equal, it is still evident that most of the country’s workforce comprises of College certificate or diploma holders (97 or 48.5%) who are mostly between these age brackets (24 to 29). From table 4.1, we can see that 42.5% of the sample hold bachelors degree and only 9% of the sample have masters and
above. This is also a real representation of the level of education of people in the country since there is only one University with almost all of the programs offered are only Bachelors.
Hence, only those few who are fortunate enough to go abroad could earn qualifications higher than Bachelors. Of the sample 52% is single while 48% are married. Thus, the percentage of single and married is nearly distributed and this is expected since most people still hold traditional values that at the age of 20 or above, one should consider getting married.
The participant’s tenure with their organization varied from six months to more than five years. The majority of the participants (89 or 44.5%) were found to have been employed with their organization between 1to 3 years. The number of participants who were employed with their organization between 3 to 5 years was 49 (24.5%) while a total of 34( 17%) were found to have worked with their organization for over 5 years and only 28 (14%) had been employed with their organization between six months to one year. The results are shown in the Table 4.1 below.
After a brief analysis of the sample characteristics, the next step is to address the research questions and hypotheses.
Comparison of Means of the Demographic Variables
The differences in the mean scores of the demographic variables (gender, age, marital status, level of education and organizational tenure) on organizational commitment was tested using One – Way ANOVA which is a statistical technique that can be used to evaluate whether there are differences between the average or mean of a variable across several population groups ( Villeneuve, 2001) and if there was a significant difference then a Post- Hoc Scheffé Test was done to determine between which groups were significantly different on organizational commitment. It is important to note that the purpose of ANOVA is to test for significant differences between means and if one is comparing two means, ANOVA would give the same results as the t test for independent samples. Hence, in this study, it was primarily utilized to test the first hypothesis which is:
H1 Demographic Data would make no significant differences on Organizational Commitment.
Hence results about the differences between demographic groups on organizational commitment are shown in this section.
Differences between Demographic Groups on OC
The results of the ANOVA test as depicted in Table 4.2 shows that there are no significant differences in mean scores between the two gender groups: Male and female on organizational commitment since the F value and P value reported was (F= 0.027, p >0.05) which was greater than the cut-off point under which the p-value (p < 0.05) was set for the finding to be considered statistically significant. The result of this study is so contrary to the mindset of most employers in the banking sector of The Gambia where most employers have prejudices on female employees been lazy, uncommitted to their work because they regard them as putting their filial duties as wives and mothers more essential than their jobs. Thus, this finding shows with regards to organizational commitment, they were no statistical gender differences.
According to the results on Table 4.2, there are no significant differences in mean scores between the different age groups on organizational commitment (F = 1.053, p > 0.05).
This means that been younger, middle aged or older does not differentiate one from been committed to the organization.
As shown in the ANOVA results in Table 4.2, been single or married had no significant differences on organizational commitment (F = 1.133, p > 0.05). The reason might be that in The Gambia, the mindset of most parents is so traditional that they believe their children should find employment upon completion of their education to look or take care of them. Hence, the rationale might be that those single employees might be committed to their organization because they have responsibilities towards their parents and those married bank employees might be also committed because they have financial or family responsibilities and thus they need more stability and security in their jobs and so this might be the reason why there were no statistical significant difference found between the two levels on organizational commitment.
Moreover, a one-way ANOVA was conducted to see whether there are statistically significant differences between the different levels of educational groups. The results in Table 4.2 revealed statistically significant differences among the educational groups on organization commitment (F =3.190, p < 0.05). Thus, from the ANOVA results, it seems that at least one group means is significantly different from the others or that at least two of the groups’ means are significantly different from each other. In line with this, since Scheffé Post- Hoc Test look at mean differences between different pairs or from the total number of groups, hence this test was conducted to determine which groups means differ significantly from each other on organizational commitment. The Post- hoc Scheffé Test revealed statistically significant differences between employees who hold college certificate or diploma (M = 4.06, SD = 0.25) and those employees with bachelors degree (M= 4.15, SD = 0.24). Hence, the results shows that employees with bachelors degree reported significantly higher commitment (0.9297*) with their organization than those who hold college certificate or diploma (-0.9297*). The reason might be that in The Gambia banking sector, those employees with college certificate or diploma are mostly the junior staff and they could possibly be finding new opportunities elsewhere while those with bachelors degree who are mostly in the middle management may feel that they are already stably placed in positions and hence might not have the urge to be uncommitted or leave. There were no other significant differences with the other group (master’s and above)
As shown in Table 4.2, there are no significant differences in mean scores between the different organizational tenure groups’ on organizational commitment (F= 1.518, p< 0.05).
The reason may perhaps be that newly employed employees may have expectations from the
job and hence these expectations might make them want to stay while tenured or those employees who spent more time with the organization may stay because of past experiences that are favorable to them and hence the willingness to stay. In essence, the results show that the length of service with the organization does hardly make any significant difference on exist between the proposed variables in Hypotheses 2 – 4. It is measured by what is called the coefficient of correlation (r). Its numerical value ranges from (-1) to (+1). In essence, this
gives an indication of the strength and direction of the relationships between the variables under study. In general, r > 0 indicates positive relationship, r < 0 indicates negative relationship while r = 0 indicates no relationship (or that the variables are independent and not related).
Having briefly discussed the test that was used to examine the relationships between the variables, let’s now move to the second hypotheses which is:
Emotional Intelligence is positively related to Organizational Commitment
According to Table 4.3, the results revealed that emotional intelligence is positively and significantly related to organizational commitment (r =.845, p<0.01). It also reveals that the relationship between these two variables is strong. This result suggests that employees with higher levels of emotional intelligence tend to show increased levels of organizational commitment. Similarly, people with lower levels of emotional intelligence tend to have correspondingly lower levels of organizational commitment. This result is in line with Abraham’s (2000) study which states that emotional intelligence is related to the ability to interact with others and that emotionally intelligent employees might be committed to their organization. It is important to note that it was only predicted that there would be a positive relationship between these two variables but the results shows that there exist not only a positive association but also a significant relationship. Hence the second hypothesis of this study was confirmed and supported. The results on Table 4.3 are as follows:
Table 4.3.
Correlation of Emotional Intelligence and Organizational Commitment (N=200)
Variables M SD 1 2
1.Emotional Intelligence Pearson Correlation 4.21 0.26
2.Organizational Commitment
Pearson Correlation 4.11 0.25 .845**
*p<0.05, ** p< 0.01.
Furthermore, to test the third hypothesis, the same Pearson-product correlation was conducted to examine whether there was a positive relationship between the below proposed hypothesis:
Emotional Intelligence is positively related to Job satisfaction
As shown in the correlational analysis in Table 4.4, the results indicates there is a positive and significant relationship between emotional intelligence and job satisfaction (r =.864, p<0.01). It also shows that the observed relationship were strong. This result suggests that employees with emotional intelligence are also more likely to experience high levels of job satisfaction since they can utilize their ability to appraise and manage emotions in others. In the same vein, emotionally intelligent individuals can experience continuous positive moods and feelings that can generate levels of satisfaction (Carmeli, 2003).This result is also in line with other studies (Carmeli, 2003; Sy et al., 2006; Wong & Law, 2002) who indicated that emotional intelligence has a positive strong impact on job satisfaction. In line with this, we can see that hypothesis 3 was clearly supported and hence purpose 3 of this research was achieved. The results are shown in Table 4.4.
Table 4.4.
Correlation of Emotional Intelligence and Job Satisfaction (N=200)
Variables M SD 1 2
1.Emotional Intelligence Pearson Correlation 4.21 0.26
2.Job satisfaction Pearson Correlation 4.13 0.21 .864**
*p<0.05, ** p< 0.01.
Moreover, the same correctional analysis was used to test the last hypothesis which is stated below:
Job satisfaction is positively and significantly related to Organizational Commitment As shown in Table 4.5, it is seen that the Pearson product-moment correlation (r =.820, p<0.01) indicates a positive and significant relationship between job satisfaction and organizational commitment. It is interesting to note that this study was in line with previous studies done in well advanced countries and hence Miller (1997) uses job satisfaction as the independent variable and found a close significant relationship between job satisfaction and organizational commitment. Similarly, research results indicate that satisfied employees tend to be committed to an organization, and employees who are satisfied and committed are more likely to attend work, stay with an organization, arrive at work on time, perform well and
engage in behaviors helpful to the organization (Aamodt, 2007). Thus, hypothesis 4 was also confirmed and accepted. Table 4.5 is shown below:
Table 4.5.
Correlation of Job Satisfaction and Organizational Commitment (N=200)
Variables M SD 1 2
1.Job Satisfaction Pearson Correlation 4.13 0.21
2.Organizational Commitment
Pearson Correlation 4.11 0.25 .820**
Note: *p < 0.05, ** p< 0.01.
Cause - Effect Relationship between Variables
The Pearson product-moment correlational analysis above supports a significant relationship between the variables under this study but since correlation does not imply causation which means that the correlation between two variables does not imply that one causes the other and also since we are concerned with the relationship between one dependent variable and one independent variable, we used simple linear regression to find if there is a cause-effect relationship or not between the variables in hypotheses 2 – 4. Hence, this test was employed to specify the nature of the relationship between these hypothesized variables.
We will examine each of these hypotheses below:
Cause Effect Relationship between Emotional Intelligence and Organizational Commitment As mentioned above, simple linear regression was conducted to examine whether emotional intelligence have any influence on organizational commitment. The results as depicted in Table 4.6 revealed that the overall model was statistically significant (F= 492.80, p<0.01) with a R2 value of 0.713. This means that 71% of the variation in organizational commitment might be explained by emotional intelligence. Hence the results revealed that emotional intelligence (β=0.845, p<0.01) might be a significant predictor of organizational commitment. In essence, higher emotional intelligence might be associated with higher levels of organizational commitment. In line with this, the value of R (0.845) shows that the strength of the relationship between emotional intelligence and organizational commitment was strong. Thus, this supports our previous correlational analysis between emotional
intelligence and organizational commitment. Hence our second hypothesis was supported.
The results are shown in Table 4.6 below.
Table 4.6.
Linear Regression Analysis Summary between Emotional Intelligence and Organizational Commitment (N=200)
Variable Unstandardized coefficients ______________________
Standardized coefficients
B Std Error Beta t Sig.
(Constant)
EI
0.732 0.152
0.801 0.036 0.845**
4.809
22.199 .000 Note: R2 = 0.713, F = 492.80, R= 0.845.
*p < 0.05, **p < 0.01
Moreover, the same test was conducted between the two variables below:
Cause-effect Relationship between Emotional Intelligence and Job Satisfaction
The results in Table 4.7 show that the regression model with one predictor variable namely, emotional intelligence was statistically significant (F =581.29, p<0.01) with a R2 value of 0.746. The value of R2 indicates that 75% of the variance in emotional intelligence predicts job satisfaction. Thus, the results revealed that emotional intelligence (β=0.864, p<0.01) is a significant predictor of job satisfaction. Hence, higher emotional intelligence could be associated with higher levels of job satisfaction. In addition, based on the value of R (0.864), the strength of the relationship between emotional intelligence and job satisfaction was found to be strong. Thus, this further affirms the results produced from our correlational analysis on these two variables. Hence our third hypothesis was supported. The results are shown in Table 4.7 below.
Table 4.7.
Linear Regression Analysis Summary between Emotional Intelligence and Job Satisfaction (N=200)
Variable Unstandardized coefficients ______________________
Standardized coefficients
B Std Error Beta t Sig.
(Constant)
EI
0.013 0.203
0.991 0.049 0.820**
0.062
20.451 .000 Note: R2 = 0.679, F = 409.99, R= 0.820.
*p < 0.05, **p < 0.01
Moreover, the relationship between job satisfaction and organizational commitment was further explored using the same statistical test.
Cause-effect Relationship between Job Satisfaction and Organizational Commitment
As mentioned above, a linear regression was used to examine whether job satisfaction has any influence on organizational commitment. The overall model was statistically significant (F = 406.99, p<0.01) with a R2 value of 0.679. This means that 68% of the variance in organizational commitment might be explained by job satisfaction. Thus, the results revealed that job satisfaction (β=0.820, p<0.01) might be a significant predictor of organizational commitment. This means that higher levels of job satisfaction could be associated with higher levels of organizational commitment. The value of R (0.820) suggests that the strength of the relationship between job satisfaction and organizational commitment was strong. Hence, this supports our previous correlational analysis between job satisfaction and organizational commitment. Hence our fourth hypothesis was supported. The results are shown in Table 4.8 below.
Table 4.8.
Linear Regression Analysis Summary between Job Satisfaction and Organizational Commitment (N=200)
Variable Unstandardized coefficients ______________________
Standardized coefficients
B Std Error Beta t Sig.
(Constant)
EI
1.276 0.119
0.678 0.028 0.864**
10.749
24.110 .000 Note: R2 = 0.746, F = 581.29, R= 0.864,
*p < 0.05, **p < 0.01
Influence of Emotional Intelligence and Job Satisfaction on Organizational Commitment As mentioned above, a simultaneous regression was used to examine whether emotional intelligence and job satisfaction combined would influence organizational commitment. The results as highlighted in Table 4.9 reveals that the overall model which specifically includes emotional intelligence and job satisfaction have statistically significant influence on organizational commitment (F = 288.95, p<0.01) with a R2 value of 0.748. This means that 75% of the variance in organizational commitment might be explained by emotional intelligence and job satisfaction of employees working in the banking sector.
Hence, an inspection of the individual predictors revealed that emotional intelligence (β=0.536, p<0.01) and job satisfaction (β=0.357, p<0.01) are significant predictors of organizational commitment. A summary of the analysis is presented in Table 4.9 below.
Table 4.9.
Simultaneous Multiple Regression Analysis Summary of Emotional Intelligence, Job Satisfaction on Organizational Commitment (N =200) with organizational commitment. In line with this, the ANOVA results revealed that gender make no significant difference with organizational commitment. This research supports the findings of Blau and Boal (1989); Cohen and Lowenberg (1990) and Kacmar and Carlson (1999) who found no significant differences between gender and organizational commitment.
This result is also contradictory to the findings of Grusky (1966) who found a significant difference between gender and organizational commitment. In addition, according to the studies conducted to reveal whether gender has a significant difference on organizational commitment, no consensus has been reached in terms of the difference between organizational commitment levels of men and women. Some studies (Fry & Grenfeld, 1980;
Cromie, 1981) have shown organizational commitment do not vary according to gender.
Some other studies, on the other hand (Angle &Perry, 1981; Hrebiniak &Alutto, 1972) have pointed out that women have a higher level of organizational commitment. Still in another study conducted by (Graddick & Farr, 1983), it has been found that women have a lower level of organizational commitment compared to men. Similarly, Mattieu and Hamel (1989) support this in their study on professional employees that men were more committed to the organization than women. Nevertheless, our study found that the organizational commitment of the employees working in the Gambia banking sector do not vary with regards to gender.
The reason might be as Grusky (1966) states women have to overcome more barriers to be part of the organization and therefore their membership and commitment to the organization is more important to them. On the other hand, men might be committed to the organization because society looks at them as the breadwinners of their families, hence the need for them to maintain that role and stayed with their jobs. Thus, this might explained why there was no significant difference found between the genders with regards to organizational commitment.
Moreover, the results of this study indicated that there was no significant difference between the different age groups and organization commitment. This finding supports the findings of Cohen and Lowenberg (1990); Kanungo (1982) and Wiedmer (2006). Similarly, in a study conducted on sales assistants, Sager and Johnston (1989) have stated that there is
Moreover, the results of this study indicated that there was no significant difference between the different age groups and organization commitment. This finding supports the findings of Cohen and Lowenberg (1990); Kanungo (1982) and Wiedmer (2006). Similarly, in a study conducted on sales assistants, Sager and Johnston (1989) have stated that there is