The data collected is subjected to the Exploratory Factor Analysis (EFA) and EFA has reduced the ten measures to two factors. After confirming that the sample is suitable and adequate for EFA using KMO, the extracted factors are labeled based on the set of variables grouped for each factor.
The conceptual model presented in Diagram 1 is validated using Exploratory Factor Analysis (EFA).
The factor loading and communalities have helped us to validate the hypotheses set by us. The findings are presented below.
KMO Bartlett’s Test: The first step is to validate the survey instrument. The KMO and Bartlett’s test is recommended for the instrument validation. The KMO & Bartlett’s test results are given below as Table 1.
Table 1
KMO and Bartlett’s Test Kaiser-Meyer-Olkin Measure of
Sampling Adequacy. .852
Bartlett’s Test
of Sphericity Approx.
Chi-Square 225.096
df 45
Sig. .000
Note: df – degree of freedom Sig. -- significance
The value obtained for Kaiser-Meyer-Olkin Measure of Sampling Adequacy is 0.852. It is recommended that the calculated value of KMO to be greater than 0.5. The KMO test has confirmed that the sample size is adequate for conducting EFA. We have 10
variables considered and the sample size is 72. The sample size is seven times the number of variables and within the range of five to ten as per the thumb rule followed. Thus, our sample size was adequate for conducting EFA based on KMO measure as well as popular thumb rule. The approximate value of Chi square obtained by conducting Bartlett’s Test of Sphericity was 225.09 (df=45, Sig. = .000) which is much higher than critical value of Chi square. (Chi square = 30.61, df 45, α =.95) The test assured us that there is no linear relation among the variables and the survey instrument is suitable for EFA.
Exploratory Factor Analysis: The data is subjected to EFA using principal component analysis. The factors are extracted using Varimax Rotation. The factor selection is done using Eigen values. The Eigen values are given in Table 2 below:
Table 2
Total Variance Explained Component Initial Eigenvalues Extraction Sums of
Squared Loadings Rotation Sums of Squared Loadings
Total Per cent of
Vari-ance
Cumula-tive Per
cent
Total Per cent of
Vari-ance
Cumula-tive Per
cent
Total Per cent of
Vari-ance
Cumula-tive %
1 3.947 39.471 39.471 3.947 39.471 39.471 3.536 35.359 35.359
2 1.358 13.576 53.047 1.358 13.576 53.047 1.769 17.689 53.047
3 .918 9.183 62.231
4 .867 8.672 70.903
5 .743 7.428 78.331
6 .644 6.436 84.767
7 .549 5.491 90.258
8 .439 4.387 94.645
9 .307 3.075 97.720
10 .228 2.280 100.000
Extraction Method: Principal Component Analysis.
Only two factors have Eigen value more than 1 and hence two factors can be considered for further analysis. The selection is supported by Scree plot.
The Scree plot graph is flatter after two factors. The Scree plot i.e plot between Eigen value and factor number is given in Graph 1 below:
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Graph 1
The component Score Covariance matrix is calculated and it is an identity matrix. These two factors have no correlation and independent to one another. The Component Score covariance matrix is given below as Table 3.
Table 3
Component Score Covariance Matrix
Component 1 2
1 1.000 0.000
2 0.000 1.000
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
The communalities are given in Table 4 below. The lower value of communality indicates that the effect is low on the factors.
Table 4 Communalities
Initial Extraction
Pay Satisfaction 1.000 .564
Ease of Job 1.000 .342
Job for Growth 1.000 .595
Supervisor Support O 1.000 .319 Supervisor support P 1.000 .352
Job Satisfaction 1.000 .788
Like Employer 1.000 .738
Intention to Quit 1.000 .644
Job Challenge 1.000 .452
Job Stress 1.000 .511
Extraction Method: Principal Component Analysis.
Four variables have communality less than 0.5.
These four variables are discussed along with the associated factors. The two factors identified are labeled as Perceived Organizational Support (POS) and Perceived Supervisor Support (PSS) based on the variables attached. The Factor loadings along with their communality are given below as Table 5:
Table 5
Factor Loadings and Communalities
S. No Factor Factor
Loading Communality Remarks 1. POS 35% of Variance Pay Satisfaction .746 .564 Highly Significant
Job Satisfaction .855 .788 Highly Significant
Ease of Job .516 .342
Like Employer .799 .738 Highly Significant
Intention to Quit -.788 .644 Highly Significant
Job Challenge .655 .452
2 PSS 17.6 % of variance Job for Growth .574 .595 Significant
Supervisor Support O .555 .319 Supervisor support P .589 .352
Job Stress -.706 .511 Highly Significant
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 3 iterations.
*S K Sastry Akella **Arif A Waqif ***K.Venketeswara Rao
The variables with factor loadings of more than 0.7 are very significant. The communality value of less than .5 indicates that its effect is slightly less than the other variables.
Discussion: We have identified two factors that account for more than 50 percent of employee commitment to the organization. The POS has six variables namely Job satisfaction, Like employer, Intention to quit, Pay satisfaction, Job challenge and Ease of job. The PSS has four variables namely Job Stress, Job growth, Supervisor support P and Supervisor support O. Both these factors are discussed below.
Perceived Organizational Support (POS): There are six variables under POS and account for around 39 per cent of total variance in organizational commitment.
Job satisfaction and Like employer have very high factor loading as well as communality. Higher communality means that both Job satisfaction and Like employer account for significant effect on other variables. Job satisfaction has very high positive relation with POS. Similarly Like employer has high communality and factor loading. Like the employer is also very significant and highly related with POS.
Intention to quit is negatively correlated with POS and correlation is very high with POS. The Pay satisfaction has positive and moderate effect on other variables. Pay satisfaction has high positive relation with POS. The Job characteristics namely ease of Job and Job challenges have low communality and have very low effect on other variables. Both the variables, ease of job and job challenge have positive relation with POS. The effect of Job characteristics on POS is not significant, even though job characteristics are related with POS. Employees’ organizational commitment is dependent on their Perception of Organizational Support (POS).
Perceived Supervisor Support (PSS): Perceived Supervisor Support accounts for 13.5 per cent of variance. Job Stress has significant negative effect on PSS. Job stress reduces PSS and consequently reduces organizational commitment. The Job assignment by the supervisor that helps in career growth increases PSS and there by organizational commitment.
Both Job stress and Job assignment for growth are controlled by the supervisor. However, supervisor support for official or personal problems have low communality. The effect of these two variables on organizational commitment is low.
The Model Diagram with Factor Loadings is given below as Diagram 2.
Diagram 2
Model Diagram with Factor Loadings
Hypotheses: Based on the discussion above and the diagram the hypotheses formulated are verified and our findings are given below.
Hypothesis 1 (H1): Perceived Organization Support has around 39 per cent effect on Organizational commitment. This clearly shows that POS increases organizational commitment and hence H1 is considered true.
H1a: The Pay satisfaction has a factor loading of more than .7 and communality more than .5.
Pay satisfaction has a positive relation with POS.
Hypothesis H1a is statistically true.
H1b: The job satisfaction and employee liking for the employer are the two variables measured for assessing job satisfaction. Both have very high loading factors and communality. The Job satisfaction is very significantly and positively related with POS. There is evidence that H1b is true.
H1c: The job characteristics measured have communality less than .5 and factor loadings less than 0.7. The job challenge and ease of the Job have shown relation with POS but the relation is
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not statistically significant. There is no evidence that H1c is true.
H2: The Perceived Supervisor Support is having 13.5 percent effect on Organizational commitment.
PSS has a positive and considerable influence on organizational commitment. We have evidence that H2 is true.
H2 a: We have collected Supervisor support in case of personal problems (Supervisor support P) and Supervisor support in official problems (Supervisor support O). Both have a low communality and low factor loadings. We have no statistical evidence that H2 a is true.
H2b: Job stress has communality more than .5 showing that stress plays significant role in PSS. The stress has a factor loading of -.706 showing that stress is highly and negatively related to PSS. High job stress decreases with PSS. The supervisor has to put efforts to decrease job stress to increase organizational commitment. H2b is not true and the relation is negative.
H2c: Another measure studied is whether the job assignment by supervisor helps in career growth.
The Job growth is having communality more than 0.5 and hence effect is significant. The factor loading is .574 (>.5 and <.7) indicating a moderate effect on PSS. We do have evidence that H2c is true.
H3: Intention to quit has a communality .644 (>.5) and has significant influence on POS. The intention to quit has factor loading of -.788. The relation is very strong and negative. The increase in POS reduces intention to quit or reduction in intention to quit increases POS. There is a strong negative relation between POS and intention to quit. We conclude that H3 is not statistically true.
The myth that the IT worker is not influenced by organization is not correct. Attrition can be controlled by increasing POS among employees.
Conclusion
Attrition in the Indian IT industry can be controlled and both management and supervisor can play key role in controlling attrition. Management can focus on initiatives to increase employee liking of the organization and job satisfaction thereby reduce intention to quit. Pay satisfaction helps but not significantly compared with other factors. The job characteristics may not have significant effect
on employee commitment. The supervisor can also play significant role in reducing the attrition.
Reducing the job stress and assigning job that help career growth ensure employee commitment to the organization. Supervisor support either in official or personal problems has not shown any significant effect on employee commitment.
Limitations: The study is based on survey method and results are based on self-reporting. The participants do not have adequate representation from female professionals.
Future Research: The current study is conducted in Hyderabad and participants belong to multiple organizations. Future research should focus on organization wise study and comparative study will throw more light on the control of attrition.
From this study, we can conclude that employee’s organizational commitment can be increased by the managements. The immediate supervisor can help the management in controlling attrition.