Editorial Board

A Refereed Monthly International Journal of Management

A Study on the Factors Influencing the Rate of Attrition in IT Sector: Based on Indian Scenario

Archita Banerjee Assistant Professor Institute of Management Study Email: haritika.arora@gmail.com Contact No.- +91 9051627827

Rahul Kumar Ghosh ResearchScholarr Seacom Skills University Email: rahulghosh.0101@Gmail.Com Contact No.- +91 9163029676

Meghdoot Ghosh Assistant Professor Institute of Management Study Email: meghdoot.ghosh@gmail.com Contact No. - +91 9830839124

Abstract

As per Darwin’s Law of Survival in this highly cutthroat service industry, most organizations realize that a ‘highly contented’ employee does not inevitably mean a dedicated employee and a high performer.The study mainly revolves around the present cases of IT industry which face the maximum employee churn-out every year. Though IT industry is a key player in generating employment, offering lucrative salary package thereby strengthening the national economy as a whole, yet it fails to retain employees for longer service period. The study tries to explore and establish a relation between various factors which might be responsible for retention risks.Also the study tries to bring under notice the change in strategies adopted and evolving with the concept of employee engagement to lower the risk of retention.

Keywords: - Employee, Attrition, Retention, IT Industry

Introduction

BVR Mohan Reddy, vice-chairman of NAASCOM explained thatIT companies seem to be dropping bench strengths, which might go losing around 1-2 per cent of last fiscal year. Employees with high experience could be hired on requirement and positioned to projects with least training. The only way of hiring would be from each other and consequently it will increase in attrition.

The latest reports of NAASCOM on Attrition rates exhibited that IT majors like TCS, Infosys and Cognizant were 12.8 per cent, 20.1 per cent and 15.6 per cent respectively for the quarter ended September 30, 2014. The companies had a total workforce of 3.13 lakh, 1.65 lakh and nearly 2 lakh respectively, at the end of the quarter (Shyamala,2015).

Continuous increase in attrition rates yields the idea of lower chances in revenue generation. But NASSCOM ChairmanCP Gumani rubs off the idea by saying fall in recruitment would not blow off the revenue growth which is hooked at 10-11 per cent in this fiscal year. IT industry would witness 20 per cent less recruitments in 2016, as key players like TCS and Infosys arefocused more on automation, leading to hiring less number of people (The Economic Times, 2016).

Considering the economic track record for the past 10 years, the industry has grown over 30 percent (Wadhwa and Koul, 2012) providing an employment of 2.5 million people directly or indirectly (Confederation of Indian Industry, 2011).

Table 1: Attrition Rate

Vendor	4Q12 (in %)	4Q13 (in %)
Cognizant	10.7	14.5
HCL Technologies	13.1*	15.3*
Infosys	15.1	18.1
TCS	11.2	10.9
Tech Mahindra	16.0	17.0
Wipro ITS	12.9	16.3
*Estimated to include both IT services
Table Source: Attrition on the rise in IT firms (Simhan, 2014)

According to Table – 1 it can be stated that out of 6 above mentioned companies 5 companies had an increased in the attrition rate. The problem of attrition has been constantly increasing in the leading IT companies of India (Simhan, 2014)

Scenario in IBM

Lee Conrad in 2015 of Alliance updated in IBM Talking cloud that 250 employees in Boulder Colorado had lost their jobs, 150 employees received pink slips and 202 employees in Dubuque, IOWA were asked to leave their jobs (Arlotta, 2015).

Infosys Suffering but Recuperated

The second largest software company in India,Infosys, had been striving hard with attrition problem, for the past 2-3 years. It was recorded in quarter 4 attrition rates as 18.7% which was a cause for concern.Lately it was observed attrition rates falling by 15% during quarter March which was a result of several employee morale boosting measures taken by its Top Management (Alawadhi, 2015).

TCS putting up a better picture

Compared to Infosys, Wipro and other IT houses, TCS reports a low attrition rate and has shown a growth in workforce by two and half times from 3.2 lacs since December 2008. A US based research firm, named Technology Business Research (TBR) states, barring TCS,other leading software companies clouted 14% rise in the attrition rates, in December 2013which is the end of fourth quarter. Companies like Tech Mahindra and HCL also witnessed a distinctive rise in attrition in Q4 2013; it was only TCS whose rate dipped from 11.2 to 10.9% in the same year (CXOtoday, 2014).

An Opportunity Increase

Though attrition seem to be a major problem hovering in IT companies on a continuous basis, but some companies see a silver lining.It indicates market is growing and offering a greater job opportunity with better profiles and compensation schemes(CXOtoday,2014).

Ajoy Mukherjee, Head of Global Human Resources, TCS said, “The kind of initiatives that we have taken and the overall business environment are definitely helping us in keeping retention levels at this stage”.

SD Shibulal, CEO of Infosys said that “Attrition meant more Opportunities”.

ManojBhat,deputy Chief Financial Officer of Tech Mahindra agreed that due to attrition wages keep increasing but their business model and recruiting model is proficient enough to manage the attrition problems(Simhan,2014).

Background

The information technology industry faced the problem of employee shortage since the employees could have extensive employment opportunities not only at the local level but more importantly at the global level. The expanding employment opportunities with better and better terms of employment made the employees to seek employment with another employer who was willing to hire them with better terms and conditions.

Concept of Attrition – Definition:

Attrition concept for any organization is a natural phenomenon of leaving the organization, detaching him with current roles and responsibilities. An employee who has completed his entire service tenure departs from the organization with a satisfaction. But there could be employees who moved away because their job instinct was left dissatisfied. For such employees the companies rely on exit interviews. But as per the studies it would be more relevant to interview the employees during their stay, so as to reduce attrition (Manjunatha V,Nanjegowda H,2016).

Some define attrition as the shrinkage of number. In simple words it could be defined as numbers reducing due to resignations, retirement or death (Ranjitham,2013).The method used by Ranjithamto calculate attrition is:-

Figure 1: Attrition Calculation Method

Attrition rate in percentage (%) = Total number of resigns per month (voluntary/compulsory)

                                                            _______________________________________________

                                                             Total number of employees at the beginning of the month

                                                                                                             +

                                                            Total number of joiners –total number of resignations) x 100

 

Source: Ranjitham, 2013

Real Causes of Attrition

1. Employees with low morale and absenteeism put in high risk of Attrition. Mainly, if the employees start losing interest in their jobs, they will leave.
2. Employees also leave their companies when their skills are in high demand, and other companies entice them for higher pay and better benefits. Hence, being unhappy at work cannot be the sole reason.
3. Other common reasons could be due to lack of progression in career, inadequacy of training and lack of employee’s engagement (MalathiG, Mathi Malar K)

Literature Review

Employee attrition in India is predicted to rise from 26% in 2010 to 26.9% in 2013.This shows employee turnover or attrition rate in India the world’s highest. Indian organizations give serious thought and try to find out strategies on how to increase employee commitment. Firms must focus on employee with a mission to give importance on theircriticalskills, and providing high competencies mostly to employees holding crucial positionsHay Group,(2013).Osteraker (1999) says the success story of any organization is because of employee satisfaction and their aim of retention. He divided the factors of retention into three broad dimensions, i.e., social, mental and physical. The mental dimension comprises work characteristics, employees always prefer flexible work tasks where they can use their knowledge and see the results of their efforts which, in turn, helps in retaining the valuable resources. The social dimension consists of the contacts that the employees have with other people, both internal and external. The physical dimension consists of working conditions and pay. To reduce the level of attrition, companies have changed their approach. It has brought some strategically changes in its concept:

According to a latest study, since 1990s employees working with an employer might shift to another employer the next day and again might move to a third employer the day after tomorrow. Employers were competing against each other to find employees. Consequently, the attrition took a rocketing speed.Angelo S.Denisi and Ricky W.Griffin (2009) said that the fundamental ground why people leave their jobs because they are discontented with their jobs. In the course of research it is discovered that the decision to refrain from jobs can be made in different ways and in response to different stimulus.

Source: Adopted from the book by Cynthia D. Fisher, et. Al. P. 756.

Kahn (1990) describes employee engagement as the ‘attachment of organizational members’ selves to their job roles. In this concept, people involve and articulate themselves physically, cognitively, and emotionally during performing their roles. Thus, employee engagement is that level of dedication and attachment an employeehas towards its organization and its principles. An engaged employee is responsive of businesscircumstance, and works with peers, subordinates to develop performance within the job for the organizational benefit. Thus, this process could be defines as a measuring unit that establishes an association of a person with its organization Vazirani, (May 07)

Research Objectives

This present study attempts to understand the impact of employee satisfactory factors and its impact on attrition in the organization.

Figure 4: Framework of Employee Attrition

SAMPLING

The questionnaire was dispersed among 320 employees from whom 300 were found to be appropriatefor analysis. Only the state of West Bengal was considered for the purpose of sampling. Simple random sampling was used for the process. The simple random sample is meant to be an unbiased representation of a group.

DATA ANALYSES METHODOLOGY

The primary data analysis to find out the impact of the employee satisfaction on the rate of attrition has been done using SPSS 21.0. The primary data analysis mainly focused at 1) Extracting the dimensions of employee satisfaction, and 2) Measuring the impact of those dimensions on the employee’s rate of attrition. Initially,the Reliability Statistics which is Cronbach's Alpha and the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy was done to find out the trustworthiness of the data and to determine the degree of the sample size accuracy respectively. Then, Factor Analysis (PCA) was done to extract the possible factors of employee satisfaction. Finally, Regression Analysis was done to measure the impact of those factors on the employee’s rate of attrition.

EMPIRICS

The demographics of the respondent were presented under the 2 attributes i.e.gender, and age. According to, Table – 2, a maximum of 51.3 percent of the employees in the current study are female employees and the remaining 48.7 percent of the employees are male. It depicts that the dominant gender of the employees is female employees in the present study.

According to Table – 3, the significant age group among the employees is Above 29 as it is constituted of 22.0 percent of total sample size. The other vital age groups are Less Than 23 and 23 – 25, as a whole they are representing 42.7 percentof the total sample size. The other age groups which are 25 – 27 and 27 – 29 represents 17.0 and 18.3 percentage of the total sample size respectively. The analysis reveals that the important age groups of the employees are Above 29, Less Than 23 and 23 – 25 because as a whole they are representing 64.7 percentage of the total sample.

Table:2

Gender
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Male	146	48.7	48.7	48.7
	Female	154	51.3	51.3	100.0
	Total	300	100.0	100.0

Table – 3

Age
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Less Than 23	63	21.0	21.0	21.0
	23 - 25	65	21.7	21.7	42.7
	25 - 27	51	17.0	17.0	59.7
	27 - 29	55	18.3	18.3	78.0
	Above 29	66	22.0	22.0	100.0
	Total	300	100.0	100.0

Reliability Statistics

Table:4

According to Table – 4, the Reliability Statistics which is Cronbach's Alpha was found to be .813 which is fairly high for the 17 variables. Hence, the internal consistency of the dataset is operative and can be consider for further analysis.

Reliability Statistics
Cronbach's Alpha	Cronbach's Alpha Based on Standardized Items	N of Items
.813	.815	17

KMO and Bartlett's Test

According to Table – 5,the KMO = 0.783, this specifies that the sample is suitable. The p-value (Sig.) of .000 < 0.05, hence the Factor Analysis is can be done. The approximate Chi-square is 4576.736with 136 degrees of freedom (Df), which is significant at 95% Level of Significance.

Table:5

KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.783
Bartlett's Test of Sphericity	Approx. Chi-Square	4576.736
	Df	136
	Sig.	.000

Factor Analysis

As there are numerous inter-related variables present for measuring the employee satisfaction, Factor Analysis is used to extract and club the various possible factors responsible for employee satisfaction. Principal Component Analysis (PCA) is used as the technique for extracting the factors along with the Varimax rotation method. The factors which has Eigen-Value of more than 1 has been taken as significant, because Eigen-Value greater than 1 indicates that principal components account for more variance than accounted by one of the original variables in standardized data. This is commonly used cutoff point for which principal components are to be retained (G. F. (n.d.). Principal Component Analysis).

Communalities

According to Table – 6, the communalities of all the variables was higher than 0.64 which depicts that more than 64% of the variations in all the variables were explained by the factors.

Table:6

Communalities
	Initial	Extraction
Dissatisfied salary	1.000	.864
Dissatisfied perquisites / allowance	1.000	.854
Low incentives	1.000	.769
Lack of medical benefits	1.000	.789
Uniformity in rules	1.000	.884
Relationship with superior	1.000	.702
Relationship with subordinates	1.000	.638
Job knowledge	1.000	.762
Skills utilization	1.000	.789
Skills recognition	1.000	.778
Unbiased superiors	1.000	.643
Acknowledgement of work by superior	1.000	.776
Dearth of rewards	1.000	.947
Lack of appreciation	1.000	.893
Unsatisfied work culture	1.000	.828
Unsatisfactory HR policy	1.000	.814
Biased evaluation	1.000	.704

Extraction Method: Principal Component Analysis.

Total Variance Explained

From Table – 7, it can be described that the 1^st Factor which was consider to summarize 5 variables was able to explain 24.55% of variance, the 2^nd Factor which was consider to summarize 4 variables was able to explain 19.42%, the3^rd Factor which was consider to summarize 4 variables was able to explain 17.56% of variance and the remaining 4 variables was able to explain 17.51% of variance forming the 4^th Factor. All together these4 Factors were able to explain 79.03% of the variance in total.

Table:7

Total Variance Explained
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	4.593	27.017	27.017	4.593	27.017	27.017	4.173	24.548	24.548
2	3.355	19.733	46.750	3.355	19.733	46.750	3.301	19.418	43.966
3	2.789	16.406	63.156	2.789	16.406	63.156	2.985	17.556	61.522
4	2.698	15.873	79.029	2.698	15.873	79.029	2.976	17.507	79.029
5	.810	4.765	83.794
6	.516	3.034	86.828
7	.416	2.450	89.278
8	.332	1.951	91.228
9	.288	1.697	92.925
10	.279	1.641	94.566
11	.233	1.371	95.937
12	.180	1.058	96.995
13	.157	.921	97.915
14	.124	.728	98.643
15	.117	.691	99.334
16	.066	.386	99.720
17	.048	.280	100.000
Extraction Method: Principal Component Analysis.

Rotated Component Matrix

According to Table – 8, the 1^st Factor was formed with the 5 variables namely Dearth of rewards (.970), Lack of appreciation (.943), Unsatisfied work culture (.907), Unsatisfactory HR policy (.889) and Biased evaluation (.830) all together it accounted for24.55% of variance.

The 2^nd Factor was formed with 4 variables namely Dissatisfied salary (.925), Dissatisfied perquisites / allowance (.924), Low incentives (.876) and Lack of medical benefits (.884) all together it accounted for19.42% of variance.

The 3^rd Factor was formed with 4 variables namely Skills utilization (.886), Skills recognition (.879), Unbiased superiors (.793), and Acknowledgement of work by superior (.873) all together it accounted for17.56% of variance.

The 4^th Factor was formed with 4 variables namely Uniformity in rules (.936), Relationship with superior (.827), Relationship with subordinates (.795), and Job knowledge (.871) all together it accounted for 17.51%. Altogether the 4 Factors collectively were able to explain 79.03% of the variance.

Table – 8

Rotated Component Matrixa
	Component
	1	2	3	4
Dissatisfied salary	.027	.925	.084	.017
Dissatisfied perquisites / allowance	.017	.924	.024	.017
Low incentives	.006	.876	.035	-.007
Lack of medical benefits	.063	.884	.060	.006
Uniformity in rules	.065	.041	.048	.936
Relationship with superior	.125	-.024	.047	.827
Relationship with subordinates	.066	-.038	-.021	.795
Job knowledge	.019	.059	.022	.871
Skills utilization	.034	-.024	.886	.043
Skills recognition	.061	.011	.879	.033
Unbiased superiors	.079	.089	.793	.003
Acknowledgement of work by superior	.026	.119	.873	.013
Dearth of rewards	.970	.034	.031	.059
Lack of appreciation	.943	.009	.027	.062
Unsatisfied work culture	.907	.044	.022	.054
Unsatisfactory HR policy	.889	.090	.118	.040
Biased evaluation	.830	-.041	.045	.109
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a
a. Rotation converged in 5 iterations.

Nomenclature of the Factors

These 4 factors which were finally extracted have factor loading of more than 0.793 and these factors have been referred as the dimensions of employee job satisfaction. The following Table – 9,shows the factors nomenclature as well as the variables loading point for each factors.

Table – 9

Factors of employee job satisfaction extracted from factor analysis
Factors	Variables	Factor Loading	Name of the Factors (Latent Variable)
1	Dearth of rewards	.970	Uncongenial Organizational Culture
	Lack of appreciation	.943
	Unsatisfied work culture	.907
	Unsatisfactory HR policy	.889
	Biased evaluation	.830
2	Dissatisfied salary	.925	Insufficient Compensation
	Dissatisfied perquisites / allowance	.924
	Low incentives	.876
	Lack of medical benefits	.884
3	Skills utilization	.886	Job Satisfaction
	Skills recognition	.879
	Unbiased superiors	.793
	Acknowledgement of work by superior	.873
4	Uniformity in rules	.936	Sociable Organizational Practice
	Relationship with superior	.827
	Relationship with subordinates	.795
	Job knowledge	.871

Correlation

According to Table – 10, all the independent variables are having significant correlations with the dependent variable which is “Possibility of staying in the existing organization”. Out of 4 independent variables, 1^st, 2^nd and 4^th independent variables are significant at the 0.01 level, and the 2^ndindependent variable is significant at the 0.05 level. The first 2 independent variables which are “Uncongenial Organizational Culture” and “Insufficient Compensation” are having negative correlation while the remaining last 2 independent variables which are “Job Satisfaction” and “Sociable Organizational Practice” are having positive correlation.

Table – 10

Correlations
		Possibility of staying in the existing organization	Uncongenial Organizational Culture	Insufficient Compensation	Job Satisfaction	Sociable Organizational Practice
Possibility of staying in the existing organization	Pearson Correlation	1	-.708**	-.250**	.143*	.516**
	Sig. (2-tailed)		.000	.000	.013	.000
	N	300	300	300	300	300
Uncongenial Organizational Culture	Pearson Correlation	-.708**	1	.000	.000	.000
	Sig. (2-tailed)	.000		1.000	1.000	1.000
	N	300	300	300	300	300
Insufficient Compensation	Pearson Correlation	-.250**	.000	1	.000	.000
	Sig. (2-tailed)	.000	1.000		1.000	1.000
	N	300	300	300	300	300
Job Satisfaction	Pearson Correlation	.143*	.000	.000	1	.000
	Sig. (2-tailed)	.013	1.000	1.000		1.000
	N	300	300	300	300	300
Sociable Organizational Practice	Pearson Correlation	.516**	.000	.000	.000	1
	Sig. (2-tailed)	.000	1.000	1.000	1.000
	N	300	300	300	300	300
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

The 2 dimensions of employee satisfaction are “Uncongenial Organizational Culture” and “Insufficient Compensation” is having negative correlation of -0.708 and -0.250 respectively with the employees’ “Possibility of staying in the existing organization”. The more the employees identify the incompatible nature of organizational culture and inadequate disbursement of compensation, the more is the chance of quitting the organization.

On the other hand, remaining 2 dimensions of employee satisfaction are “Job Satisfaction” and “Sociable Organizational Practice” is having positive correlation of 0.143 and 0.516with the employees’ “Possibility of staying in the existing organization”. The higher the employee notices job gratification level and friendly organizational practices, the more is the chance of retaining in the organization.

Regression Model

The regression analysis was directed with the dependent variable as possibility of staying in the existing organization and the independent variables as the dimensions which got extracted from the factor analysis i.e. Uncongenial Organizational Culture, Insufficient Compensation, Job Satisfaction, and Sociable Organizational Practice.

According to Table – 11, it shows the regression model fit summary, theRvalue which is .922, signifies that 92.2% of correlation is present between the dependent and independent variables. Next, the R²value which is .849, it depicts that the linear regression explains 84.9% of the variance in the dataset when all the independent variables in the model affects the dependent variable, and the Adjusted R²value which is .847 shows that 84.7% of variation is explained by only those independent variables that in actuality affect the dependent variable. Then, the Durbin-Watson d = 2.103, which is between the critical value of 1.5 < d < 2.5 and hence we can assume that there is no first order linear auto-correlation in the dataset.

Table:11

Model Summaryb
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Durbin-Watson
1	.922a	.849	.847	.477	2.103
a. Predictors: (Constant), Sociable Organizational Practice , Job Satisfaction, Insufficient Compensation , Uncongenial Organizational Culture
b. Dependent Variable: Possibility of staying in the existing organization

According to Table – 12, the F-test with a high value of 415.883 and degree of freedom (df) with value of 299, states that there is no linear relationship between the any two variables in the model. The p-value (Sig.) of .000 < 0.05, which is less than 0.05, indicates that, in general the regression model is statistically significant and predicts the outcome variable.

Table – 12

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	378.649	4	94.662	415.883	.000b
	Residual	67.147	295	.228
	Total	445.797	299
a. Dependent Variable: Possibility of staying in the existing organization
b. Predictors: (Constant), Sociable Organizational Practice , Job Satisfaction, Insufficient Compensation , Uncongenial Organizational Culture

According to Table – 13, the 1^st independent variable “Uncongenial Organizational Culture” is having a beta value of -0.864 and a Pearson Correlation coefficient of -0.708 which is significant at 0.01 level, this particular factor contributes significantly and quite largely to the employee rate of attrition. It can be stated that Dearth of rewards, Lack of appreciation, Unsatisfied work culture, Unsatisfactory HR policy, and Biased evaluation are the variables which are putting negative impact on the rate of Possibility of staying in the existing organization, as because the incompatible organizational principles demotivates the employees. A 1 unit increase in the X1 (Uncongenial Organizational Culture) will decrease 0.864 unit in the Y (Possibility of staying in the existing organization).

The 2^nd independent variable “Insufficient Compensation” is having a beta value of -0.305 and a Pearson Correlation coefficient of -0.250 which is significant at 0.01 level, this independent variable also appeared as an domineering contributing factor of attrition. This factor is a grouping of different kinds of unhappiness arising from Dissatisfied salary, Dissatisfied perquisites / allowance, Low incentives, and Lack of medical benefits. A 1 unit increase in the X2 (Insufficient Compensation) will decrease 0.305 unit in the Y (Possibility of staying in the existing organization).

The 3^rd independent variable “Job Satisfaction” is having a beta value of 0.174 and a Pearson Correlation coefficient of 0.143 which is significant at 0.05 level. Appropriate skills utilization and skills acknowledgment are some vital measure which increases the rate of employee retention, because monotonous nature of jobs has no diversity. Unbiased superiors and acknowledgement of work by superior plays increase the self-confidence of the employees. A 1 unit increase in the X3 (Job Satisfaction) will increase 0.174 unit in the Y (Possibility of staying in the existing organization).

The 4^th independent variable “Sociable Organizational Practice” is having a beta value of 0.630 and a Pearson Correlation coefficient of 0.516 which is significant at 0.01 level. Uniformity in rules and proper job knowledge for every single employee is significant for eluding the biasedness in the working circumstance. Relationship with superior and subordinates should be good and healthy, because it leads to positive motivation of employees and provided self-satisfaction too. Relationship building is a significant part of confirming operative and transparent superior-subordinate communications. A 1 unit increase in the X4 (Sociable Organizational Practice) will increase 0.516 unit in the Y (Possibility of staying in the existing organization).

Table – 13

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Pearson Correlation
		B	Std. Error	Beta
1	(Constant)	2.897	.028		105.161	.000
	Uncongenial Organizational Culture	-.864	.028	-.708	-31.313	.000	-.708**
	Insufficient Compensation	-.305	.028	-.250	-11.058	.000	-.250**
	Job Satisfaction	.174	.028	.143	6.309	.000	.143*
	Sociable Organizational Practice	.630	.028	.516	22.825	.000	.516**
a. Dependent Variable: Possibility of staying in the existing organization
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

The equation which emerged after the process was as follows: -

Y= 2.897 - 0.864X1 - 0.305X2 + 0.174X3 + 0.630X4

Where,

Y = Possibility of staying in the existing organization

X1 = Uncongenial Organizational Culture

X2 = Insufficient Compensation

X3 = Job Satisfaction

X4 = Sociable Organizational Practice

Proposed Model of Employee Attrition

All the above mentioned associations have been represented in a diagrammatic illustration as below in Figure – 5

Conclusion

Most important contributing factors for high attrition rate of employees in IT sector in West Bengal are identified in this particular objective through primary data and the factors were defined after using factor analysis. The factors defined were “Uncongenial Organizational Culture”, “Insufficient Compensation”, “Job Satisfaction” and “Sociable Organizational Practice” All the factors were found significant at 0.05 level. Taking all these factors as independent variable and the “Possibility of staying in the existing organization” as dependent variable, the Regression analysis was done in order to find out the contribution of these factors to the dependent variable (Possibility of staying in the existing organization).

The factors “Uncongenial Organizational Culture” and “Insufficient Compensation” were found to negatively correlate with the dependent variable, which means as there will be increase in these two factors, the possibility of stay in the current organization will decrease, that will lead to an increased attrition rate.

While on other hand the factors “Job Satisfaction” and “Sociable Organizational Practice” were found to positively correlate with the dependent variable, which means as there will be increase in these two factors, the possibility of stay in the current organization will also increase, which will lead to a decreased attrition rate.

Reference

[1]	Alawadhi, N. (2015, April 24). Infosys arrests attrition at 13.4%; continues to promote design thinking. Retrieved July 09, 2016, from http://articles.economictimes.indiatimes.com/2015-04-24/news/61493903_1_attrition-rate-design-thinking-vishal-sikka
[2]	Arlotta, C. J. (Ed.). (2015). CJ Arlotta. Retrieved July 09, 2016, from http://talkincloud.com/author/cj-arlotta
[3]	Denisi, A. S., & Griffin, R. W. (2000, April). Human resource Management.
	Hay Group. (n.d.). Retrieved July 14, 2016, from http://www.haygroup.com/en/
[4]	Indian IT Sees High Attrition As Market Rebounds. (2014, February 27). Retrieved July 09, 2016, from http://www.cxotoday.com/story/indian-it-sees-high-attrition-as-market-rebounds/
	IT hiring may drop by 20% this year: Nasscom - The Economic Times. (2016, April 21). Retrieved July 14, 2016, from http://economictimes.indiatimes.com/jobs/it-hiring-may-drop-by-20-this-year-nasscom/articleshow/51930291.cms
[5]	Kahn, W. A. (1990). Psychological conditions of personal engagement and disengagement at work. Academy of Management Journal,33, 692-724.
[6]	Manjunatha, V., &Nanjegowda, H. (2016, January). EMPLOYEE ATTRITION Management in software companies-a conceptual study. Journal of Applied Research,6(1). ISSN-249-555X
[7]	Mathi, K. M., &Malathi, G. (n.d.). Analyzing the causes of attrition in it industries - opinion of employee and employer. Asia Pacific Journal of Research,1(4). ISSN 2320-5504
	Mobley, W. H. (1982). Employee turnover: causes, consequences, and control. Reading, MA: Addison-Wesley.
[8]	Osteraker, M. C. (1999). Measuring motivation in a learning organization. Journal of Work Place Learning.
[9]	Ranjitham, D. (2013). Attrition and retention at BPO companies in Chennai: An analytical study. Journal of Exclusive Management Science,2(7). ISSN 2277 – 5684
	Shyamala, S. (2015, January 5). Attrition picks up with growth in IT | mydigitalfc.com. Retrieved July 14, 2016, from http://www.mydigitalfc.com/it/attrition-picks-growth-it-611
[10]	SIMHAN, T. R. (2014, February 26). Attrition on the rise in IT firms. Retrieved July 09, 2016, from http://www.thehindubusinessline.com/info-tech/attrition-on-the-rise-in-it-firms/article5730053.ece
[11]	Vazirani, N. (May 07). Employee Engagement. SIES College of Management Studies Working Paper Series, wps05.
[12]	Wadhwa, P., &Koul, S. (2012). Critical factors affecting retention. Ninth AIMS International Conference on Management.