Attrition Report

EMPLOYEE ATTRITION ‘An Analysis of Factors Influencing Attrition in the growing Economies’

-NMIMS

INTRODUCTION In the recent decades the Indian industry has changed its outlook. The employment scene has changed its appearance. The factors like skill sets, job satisfaction drive the employment and not just the money. The employer hence faces the heat of continuous employee turnover. Continuous efforts are made by organisations to control the employee turnover rate as it directly affects the

performance of the organisation as many key people leave the organisations for various reasons at crucial points. This turnover is normally known as ATTRITION. Defining attrition: A reduction in the number of employees through retirement, resignation or death. Defining Attrition rate: The rate of shrinkage in size or number. In the best of worlds, employees would love their jobs, like their coworkers, work hard for their employers, get paid well for their work, have ample chances for the advancement, and the flexible schedules so they could attend to personal or family needs as and when necessary. But then there’s the real world. And in the real world, employees do leave, either because they want more money, hate their co-workers, want a change or because their spouse gets a dream job in another state. So, what does the turnover cost? And which employees are likely to have the highest turnover? Who is likely to stay the longest? Impact of attrition Direct impact: A high attrition indicates the failure on the company’s ability to set effective HR priorities. Clients and business get affected and the company’s internal strengths and weaknesses get highlighted. New hires need to be constantly added, further costs in training them, getting them aligned to the company culture, etc.,—all a challenge.

Indirect impact: Problem for the company in attracting potential employees. Typically, high attrition also leads to a chronic or systemic cycle—attrition brings decreased productivity, people leave causing others to work harder and this contributes to more attrition. All this has a significant impact on the company’s strength in managing their business in a competitive environment.

Productivity and profitability are both impacted, either negatively and positively, according to the type of attrition. The cost of hiring is sometimes not less than two to three times the salary of the employee. The impact on work progress is tremendous, particularly if a project is underway and one of the key people leaves. “It leads to dip in entire organizational efficiency, and a lot depends on how it is able to cover the setback,” Organizations should execute top of the line retention policies in the right earnest and consistency. They should be more employee-centered and look for further ways to “bond” employees to their companies. “Company performance is optimally aligned to the skills its employees possess. High attrition implies that certain necessary skills are vulnerable or are not present due to employees being lost. It results in lower than optimal levels of business performance. If the skills are constantly not available, the situation gets compounded into a crisis with key projects, revenues, etc., getting affected. Business is then reduced to just managing crisis.” Reasons why organizations are not able to retain employees 1. Performance goals are unclear. In a fast growing team or business the focus is on getting the thing done today, but rarely are performance goals thought through and employees told as to which resources to approach for help. 2. Reward systems are not transparent. Most employees who get salary increases because they have a rare skill at a particular point of time think they got their raise for excellent performance. Can you share details about how they have been compensated? 3. Perceived equity of reward systems is low. Like it or not, employees discuss salary details and if there is any perceived lack of equity then you have an issue !

4. Goal setting process is not scientific. Most organizations impose a normal curve fitment, but do not train managers to set realistic goals or goals that tie up with organizational or functional goals. This also leads to point number 6 5. External equity is missing too. Don't do an annual compensation survey when the market moves every 3-4 months. If your practitioners feel that externally comparable professionals are being valued more, then they will leave.

6. No communication around total value. If you offer benefits apart from only monetary terms do you communicate that to employees too. Things like being a global or niche industry leader, value of the brand of the organization, should also be made explicit. 7. No career planning. Are people aware of the ways in which they can grow in the organization? Who are the role models within the organization? Do they know what they have to do to gain the competencies to move to various levels? (update: Can you be radical enough and create an internal The decision to choose this project was taken by us for the following reasons: 1. The project was inherently complex and involved imparting objectivity to factors that are inherently subjective in nature. Also the complexity of the project ensures for us a thorough and in-depth understanding of the concepts and fundamentals of marketing research which is the most important aim of this basic course on Research Methodology. 2. The study pertained to the industry and we could easily identify with one of our own breed. Also since we all have experience this process in our daily work lives it was easier for us to short list the major factors that go into the decision making. The project as we see it at present can be divided into the following four major stages: 1. Short listing the most important factors that are considered by employees at various levels while making decisions regarding their own career choices. 2. Preparing a questionnaire that can give us as objective as possible a view about the relative importance of these factors and also of how these factors change with stage life and owing to socio-economic differences.

3. Conducting surveys in various organisations and ensuring that we get a proper mix of gender, economic position, competency and other such instrumental characteristics of students in our survey. 4. Analyzing the data we gather through the survey in (3) using various statistical tools available to us and derive conclusions that are in coherence with the objectives of the project.

OBJECTIVES • What are the various factors involved in the process of deciding job objectives for employed people. • To analyze the relative importance of factors discovered in last point. • To find out the similarities and differences in the decision making process for when employee satisfaction with respect to the job profile and the organisation changes. • Find how these factors for employees are influenced by demographic differences. RESEARCH DESIGN This can be divided into two major parts 1. Brainstorming 2. Survey (Questionnaire) Brainstorming We brainstormed for factors drawing largely on the personal experience of the group members. The major influencers that we came up with were: •

Demographics

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Industry segmentation

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Job satisfaction

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Job level

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Miscellaneous influences

Questionnaire (Survey) The design of questionnaire is of paramount importance. It will be used to translate the information needed into a set of questions that the respondent could answer and the answers thus obtained could in turn yield the desired result. Questionnaire attached separately

FOLLOWING FACTORS WERE FOUND OF IMPORTANCE THAT COULD INFLUENCE THE DECISION: •

Salary & Benefits

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Connection between pay and performance

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Security and administration of yours

•

Workload

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Flexibility of work hours

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Physical working environment

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Opportunity for advancement

•

Job security

•

Ability to influence decisions that affect you

•

Opportunity to use new technologies

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Opportunity to work on interesting projects

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Access to company-sponsored training and seminars

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Communication with your supervisor

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Recognition received from your supervisor

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Your supervisors management capabilities

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Your supervisors active involvement in your career development

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Your overall relationship with your supervisor

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Your relationship with your peers

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Your understanding of the business mission

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Your overall satisfaction with your company

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Overall satisfaction with your job

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Work profile

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Position

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Inequality

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Poor work environment and facility

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Long distance Commuting

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Growth opportunity

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Change in industry

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Personal commitments

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Easy Commute RATIONALE OF THE WORK AND EXPECTED OUTCOME The sudden opening up of economy has brought with it exponential increase in the opportunities and options for service class people. People find it difficult to leave a well settled job unless and until there is a strong reason to it. A very common belief for changing job is the remuneration, but to our surprise we found out that the remuneration or salary is the third or fourth in priority if we arrange the parameters in descending order. This study can be used for working on a wider scope project on the same subject. It is tricky to design and execute the project as it involves multiple influences and their interaction. Following are the expected outcome of the research project: To determine Factors Influencing attrition patterns in the industry. The objective is to see what factors are more influential than others, so that proper factors could be used to analyse the factors to reduce attrition rates by HR managers in the industry. Data Collection Source The data collection for this research will be from primary data sources. Though usage of primary data will lead to higher collection cost, and more involved collection process, it will ensure that the data is current, can be easily analyzed and accurately reflects the present day reality. The usage of primary data sources will also help us have more freedom in the choosing the data that we want to use/analyze in course of our research. The sample group for this project is to be the employees at different levels and from different organisations. We are also interested in analysing the demographical features like marital status, sex and educational background. The survey will be having both qualitative and quantitative data collection. As the research field is exploratory, the qualitative data will allow finding out the major factors behind the choices made. The quantitative data will give us information

regarding the relative importance of the various factors from the employee point of view. SAMPLING Sampling is one of the key components of any research design. Details of our sample are as follows: Target Population We are targeting respondents of 2 categories: •

Employees in their first job

•

Employees who have worked for minimum two or more organisations.

Sampling Technique We will be using the traditional sampling approach of Probability sampling and Non Probability sampling, i.e. the sample will be selected before data collection begins and sampling without replacement will be performed. Sample Size We will include around 253 people in our study. As data is being collected on a large number of variables, a large sample will help in reducing the cumulative effects of sampling error. Execution We distributed our questionnaire among organisations where we work, also sent the questionnaire to friends who are working in different organisations. We have tried to cover basic industry types like engineering, IT, finance, projects, consultants, education etc.

DATA ANALYSIS TOOL The process to be followed to analyze the data collected will be as follows:

The data will be entered into an Excel sheet. The scores to the questions pertaining to a particular factor will be grouped together and then an average score for the factor can be calculated (simple arithmetic mean). A correlation matrix of the influencing factors (mentioned in the questionnaire) can then be generated to highlight the interrelationships. Cluster analysis can be run to identify groupings in the population in the space. The analysis can then be taken to a deeper level. For each of the factors, we can try to find the influence of socioeconomic variables on the strength and direction of each of the factors. This can be done through testing for statistically significant difference in means between classifications based on the relevant socioeconomic factors. Graphical Representations Pie Chart

Column Chart

Gender

Satisfaction/Non Satisfaction

Total

Female 　 　 Female Total Male 　 　 Male Total Grand Total

Neutral Non Satisfy Satisfy

20 13 56

　

89

Neutral Non Satisfy Satisfy 　

59 22 83 164

　

253

Cylinder Chart

FACTORS WHICH EFFECT CAREER DECISIONS The Questionnaire contained questions analysing the satisfaction level of the current job of an individual. The various factors were correlated to the Satisfaction rating provided by the sample respondents Correlation Factors Salary

37%

Benefits

38%

Connection between pay and performance

32%

Workload

24%

Flexibility of work hours

9%

Physical working environment

5%

Opportunity for advancement

46%

Job security

34%

Ability to influence decisions that affect you

46%

Opportunity to work on interesting projects

33%

Your supervisors management capabilities

22%

Your overall relationship with your supervisor

54%

Your relationship with your peers

34%

Salary has a correlation of only 37% where as “Relationship with your superior” had a correlation of 25% Second part of our Questionnaire contained reasons for leaving your Prior Job (if any) and factors that would influence a persons to change Job in future. Depending upon the ranks provided by the individual three important factors were identified

1.Compensation (Our Hypothesis) 2.Work Profile & Equality 3.Growth Opportunity

Reason for leaving prior Job

Weights

Reason leading to Future attrition

Weights

Less Compensation

2.86

Higher Compensation

2.49

Less growth opportunity

3.99

Better work profile

2.67

Stagnant work profile

4.49

Higher position

4.05

Lower position

5.57

Better work environment and facility

5.11

Change in industry

5.85

Faster Growth Opportunity

5.30

Change in work profile

5.93

Able to fulfill Personal commitments

7.01

Inequality

6.33

Easy Commute

7.33

Poor work environment and facility

6.54

Job in a MNC (applicable for people working in Local org)

7.42

Not able to fulfill Personal commitments

6.77

Change in work profile

7.53

Long distance Commuting

6.91

Change in industry

8.04

The detailed analysis is as follows: Independent-Samples T Test

To test the general Hypothesis of Salary being the most influential factor for Attrition, we applied certain Hypothesis test. The Independent-Samples T Test procedure compares means for two groups of cases. Ideally, for this test, the subjects should be randomly assigned to two groups, so that any difference in response is due to the treatment (or lack of treatment) and not to other factors. This is not the case if you compare average income for males and females. A person is not randomly assigned to be a male or female. In such situations, you should ensure that differences in other factors are not masking or enhancing a significant difference in means. Differences in average income may be influenced by factors such as education (and not by sex alone). The Independent-Samples T Test procedure tests the significance of the difference between two sample means. Also displayed are:

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for the difference between the two variables (95% or a value you specify) Descriptive statistics for each test variable

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A test of variance equality

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A confidence interval

The Descriptive table displays the sample size, mean, standard deviation, and standard error for the groups. The procedure produces two tests of the difference between the two groups. One test assumes that the variances of the two groups are equal. The Levene statistic tests this assumption. The t column displays the observed t statistic for each sample, calculated as the ratio of the difference between sample means divided by the standard error of the difference. The df column displays degrees of freedom. The column labelled Sig. (2-tailed) displays a probability from the t distribution with 251 degrees of freedom. The value listed is the probability of obtaining an absolute value greater than or equal to the observed t statistic, if the difference between the sample means is purely random. The 95% Confidence Interval of the Difference provides an estimate of the boundaries between which the true mean difference lies in 95% of all possible random samples of 250 employed people.

Since the significance value of the test is less than 0.05, you can safely conclude that the average employees’ leaving the job is not due to chance alone. The priority of changing the job is as follows 1. Opportunity for advancement 2. Connection between pay and performance 3. Salary Paired-Samples T Test The Paired-Samples T Test procedure compares the means of two variables for a single group. The procedure computes the differences between values of the two variables for each case and tests whether the average differs from 0. One of the most common experimental designs is the "pre-post" design. A study of this type often consists of two measurements taken on the same subject, one before and one after the introduction of a treatment or a stimulus. The basic idea is simple. If the treatment had no effect, the average difference between the measurements is equal to 0 and the null hypothesis holds. On the other hand, if the treatment did have an effect (intended or unintended!), the average difference is not 0 and the null hypothesis is rejected. The Paired-Samples T Test procedure is used to test the hypothesis of no difference between two variables. The data may consist of two measurements taken on the same subject or one measurement taken on a matched pair of subjects. Additionally, the procedure produces: •

Descriptive statistics for each test variable

•

The Pearson correlation between each pair and its significance

•

A confidence interval for the average difference (95% or a value you specify)

The Descriptive table displays the mean, sample size, standard deviation, and standard error for both groups. At 0.065, the correlation between the baseline and less compensation and high compensation is not statistically significant. Levels were lower overall, but the change was inconsistent across subjects. On the other hand, the Pearson correlation between the baseline and Overall job satisfaction is 0.792, a strong correlation. The Sig. (2-tailed) column displays the probability of obtaining a t statistic whose absolute value is equal to or greater than the obtained t statistic The lower or higher compensation has no effect on the satisfaction but the job satisfaction must have some effects with other work related factors which gives one a job satisfaction.

Correlations

In this example, the Partial Correlations table shows both the zero-order correlations (correlations without any control variables) of all three variables and the partial correlation controlling of the first two variables controlling for the effects of the third variable. The zero-order correlation between Communication and overall satisfaction is, indeed, both fairly high (0.669) and statistically significant (p < 0.001). Similarly recognition received and the overall satisfaction has a correlation factor of 0.510 which is also fairly high. ANALYSE RESULTS 1. From the above observation we can see that the salary is not the only parameter of changing job. 2. The two factors for which we see people tend to change the job are the job satisfaction and the relation with the immediate supervisor. 3. The other demographic features like commutation, marital status do make an influence on job changing decision but not as a significant factor.

4. The factors like educational qualification, change in industry also tends to influence but not significantly. 5. The fact of salary and recognition being important criteria they are compromised on if the other factors are favourable.

LIMITATION OF THE WORK Despite our best efforts, we feel that our study has a few limitations which are listed below: •

The samples collected may carry a bias of field of work and position as there is a large amount of data from lower or middle management people.

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We could not test all possible combinations as there are large no of interactions prevalent in the data which needs expert advice.

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The demographic influence needs more objective data to analyse it against the job satisfaction and attrition criteria.

CONCLUSIONS From the findings of our study we could conclude that: •

Relationship with immediate supervisor is the most important criteria for changing jobs.

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Recognition monitory or non monitory from the supervisor makes a difference even in cases of lower salary.

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Banking and construction sector are experiencing the large amount of attrition apart from IT industry.

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Middle management has higher attrition rate as it faces heat from both the ends.

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The compensation for the job and compensation for the performance are valued equally as an employee.

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The satisfaction is a comparative term amongst colleagues and can not be treated as independent entity. This calls for the transparency in the evaluation system of any organisation.