Factor analysis

Factor analysis was performed for all scales used in this study. Principal Components analysis was executed. Varimax Rotation was used. Initially, the items which have factor loadings less than .50 (Possibilities for development-5,7; Career competencies-17) were excluded. Then, factor loadings for all the study variables ranged between .50 and .86. The sampling adequacy is tested by Kaiser- Meyer Olkin (KMO) coefficient. KMO Scores, except social support (KMO=.59), exceed the expected value (.60); and Bartlett’s test of Sphericity were significant. Factor analysis result of Work Engagment Scale (UWES-9) is presented in Table 5.5. and the result of the other scales are presented in the Appendix-6.

Table 5.5. Factor Analysis Results and Reliability of Work Engagement Scale Item

Nu. Scale Factor

Loading

Cronchbach

’s alpha

Explained Variance

Work Engagement .89 55.57

1 At my work, I feel bursting with energy. .82 2 At my job, I feel strong and vigorous. .81 3 I am enthusiastic about my job. .84

4 My job inspires me. .83

5 When I get up in the morning, I feel

like going to work. .75

6 I feel happy when I am working intensely. .61 7 I am proud of the work that I do. .60

8 I am immersed in my work. .64

9 I get carried away when I am working. .77 KMO=.82; Chi-Square Bartlett's Test=525.73; P=.000

87 5.4.3.!Pearson-moment correlation

The relationship between variables was determined by performing the Pearson-moment correlation. Intercorrelations of the study variables are presented in Table 5.6.

Table 5.6. Intercorrelations of the variables.

S/N Variables Mean SD 1 2 3 4 5 6 7 8 9 10

1 _Resources ^Job 3.46 .49 1 2 Role Clarity 3.73 .62 .75^** 1 3 Possibilities

For Dev. 3.47 .74 .84^** .52^** 1 4 _Support ^Social 3.19 .63 .64^** .18 .29^** 1 5 _Demands ^Job 3.40 .59 -.01 -.08 .05 .00 1 6 _Conflict ^{Role 3.00 .80 -.11 -.14 -.05 -.07 .88** 1} 7 _Overload ^Work 3.79 .62 .13 .03 .16 .09 .79^** .39^** 1 8 _Engagement^Work _3.55 .58 .44^** .44^** .31^** .24^* -.11 -.04 -.16 1 9 Leadership

Competency 3.75 .67 .13 .27^* .14 -.12 -.09 -.03 -.13 .23^* 1 10 _Competency^Career _3.56 .57 .14 .17 .18 -.05 -.10 -.10 -.07 .14 .69^** 1

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

5.4.4.! Role of job demand and job resources on work engagement in military context Inspection of Pearson-moment correlation scores (Table 5.6.) showed that job resources measured as role clarity, possibilities for development and social support displayed a statistically significant positive relationship with work engagement (p<.01;p<.01 p<.05). Thus, Hypothesis-1, which states that there is a positive relationship between perceived job resources and work engagement of officers was supported.

Inspection of variables which measure job demands showed that role conflict and work overload displayed a statistically negative relationship with work engagement. Thus, Hypothesis-2, which states that there is a negative relationship between perceived job demands and work engagement of officers was not supported. The direction of the relationship was as expected; however, the effect was not significant. This may result because of the sample size.

We expected that our hypohthesis would be supported, if we increased the sample size.

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A multiple regression analysis was used to define the predictors of work engagement (Table 5.7.). A significant regression equation was found (F (3, 84) = 7.90, p < .001), with a R² of .22. Participants’ predicted work engagement is equal to 1.59 + .37 (Role Clarity). Results showed that role clarity measured as job resource is significant predictor of work engagement in this model and the model predicts 22 % of the variance in work engagement.

Table 5.7. Regression analyses of job resources (Role clarity, possibilities for development and social support) as predictor variables on work engagement as criterion variable.

Criterion variable: Work Engagement

Predictor variables β p

Role Clarity .37 .001*

Possibilities for

Development .08 .519

Social Support .15 .139

R² .22***

*p < 0.05; ***p < 0.001 – statistically significant

5.4.5.! Relationship between leadership competencies and job resources, job demands and work engagement

According to correlation scores (Table 5.6.), leadership competencies displayed a statistically significant positive relationship with role clarity (p< 0.05), positive relationship with possibilities for development and negative relationship with social support; statistically negative relationship with role conflict and work overload; statistically significant positive relationship with work engagement (p< 0.05).

These scores showed that Hypothesis-3, which states that there is a positive relationship between proficiency level of officers on leadership competencies and job resource, was partially supported; Hypothesis-4, which states that there is a positive relationship between proficiency level of officers on leadership competencies and work engagement of officers, was supported, and Hypothesis-5, which states that there is a negative relationship between proficiency level of officers on leadership competencies and job demands (work overload and role conflict), was not supported.

5.4.6.! Role of leadership competencies in the motivational process of JD-R Model

In order to explore the role of leadership competencies in the motivational process of JD-R model, we compared two structural models. First, a direct effect only model: a structural

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model in which job resources and competencies only had direct effects on work engagement (Figure 5.3.).

Figure 5.3.: Direct effect model (Job resource and leadership competencies have direct effect on work engagement.)

A multiple regression analysis was performed in order to see how job resources (Social support, possibilities for development and role clarity) and leadership competencies effect and explain work engagement (Table 5.8.). A significant regression equation was found (F (4, 83)

= 6.54, p < .001), with a R² of .24. Participants’ predicted work engagement is equal to 1.18 + .33 (Role clarity). Results showed that role clarity is the significant predictor of work engagement in this model and the model predicts 24 % of the variance in work engagement.

Table 5.8. Regression analysis of leadership competencies and job resources (role clarity, Possibilities for development and social support) as predictor variables on work engagement as criterion variable.

Criterion variable: Work Engagement

Predictor variables β p

Leadership competencies .15 .146

Role Clarity .33 .005*

Possibilities for

Development .07 .559

Social Support .18 .084

R² .24***

*p < 0.05; ***p < 0.001 – statistically significant

Second a mediation model: As stated above, results of the first model (Figure 5.3.) showed that job resources and competencies had motivational role on JD-R model, and role clarity was the predictor of work engagement. Then, we investigated the role of job resources between relationship competencies and work engagement. Considering the intercorrelation scores (Table 5.6.), which showed that competencies only had significant relationships with role

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clarity and work engagement (r = 0.27, p < 0.05; r = 0.23, p < 0.05, respectively), we tested a model in which leadership competencies had an effect on work engagement via role clarity.

The model is presented in the Figure 5.4.

Figure 5.4.: Mediation model of job resource (Hypothesis-6: Job resources mediate the relationship between leadership competencies and work engagement.)

In order to see how role clarity, effect and explain the relationship between leadership competencies and work engagement, a multiple regression/mediation analysis was performed.

Table 5.9. covers 3 of the 4 steps suggested by Baron and Kenny’s (1986) procedure.

Table 5.9. Summary of mediation analysis on role clarity between relationships of leadership competencies and work engagement

Variables Criterion:

Predictors: Role clarity Work engagement

β β

Step 1

Leadership competencies .270 ^*

R² adjusted .062

F 6.76*

Step 2

Leadership competencies .225 *

R² adjusted .039

F 4.76*

Step 3

Leadership competencies .116

Role clarity .404 *

R² adjusted .183

F 10.74***

Note: *p<. 05; ***p<. 001

Step 1 of the analysis showed that leadership competencies had a positive and significant effect on role clarity (β = 0.27, p < 0.05), Step 2 of the analysis showed that leadership competencies had a positive and significant effect on work engagement (β = 0.225, p < 0.05), Step 3 of the analysis showed that when we entered the role clarity (mediator) in to the model,

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role clarity was also positive and significantly related to work engagement (β = 0.404, p < 0.05), however, effect of leadership competencies on work engagement (β = 0.116, p > 0.05) was not significant anymore, confirming mediator role of role clarity between the relationship leadership competencies and work engagement. A Sobel test was performed in order to see whether the beta of the meditational model is significant (Goodman, 1960). The test revealed that this beta is significant (z = 2.17, p = .0029). Results of Sobel Test are presented is in the Appendix-7.

5.4.7.! Relationship between leadership competencies and career competencies

As stated above, the second purpose of our study was to investigate whether leadership competencies were associated with career competencies. The correlations scores (Table 5.6.) showed that the leadership competencies displayed a statistically significant positive relationship career competencies (r=0.69, p<0.01). Considering the correlations scores, we concluded that our hypothesis which states that leadership competencies are positively associated with career competencies was supported.

Then, we performed a regression analysis to define whether leadership competencies

predict career competencies (Table 5.10.). A significant regression equation was found (F (1, 86) = 79.53, p < .000), with the R² of .48. Participants’ predicted career competencies

were equal to 1.34 + .69 (Leadership competencies).

Table 5.10. Regression analysis of leadership competencies as predictor variables on career competencies as criterion variable.

Criterion variable: Career competencie

Predictor variables β p

Leadership competencies .69 .000

R² .48***

*p < 0.05; ***p < 0.000 – statistically significant

Results showed that leadership competencies were the significant predictor of career competencies in our model, and the model predicted 48% of the variance in career competencies.