EFFECTIVE RECRUITMENT AND SELECTION SYSTEM FOR...

EFFECTIVE RECRUITMENT AND SELECTION SYSTEM FOR

THE IT SOFTWARE INDUSTRY IN INDIA Thesis submitted for the award of the Degree of

Ph.D. (BUSINESS ADMINISTRATION)

ABSTRACT

By

Brigadier. P.K. Saxena

Under the guidance of

Dr. Mohammad Israrul Haque Dr Bhupen Srivastava

Professor Professor

Department of Business Administration Department of Organizational Behaviour &

HRM

AMU, Aligarh IMI, New Delhi

DEPARTMENT OF BUSINESS ADMINISTRATION

FACULTY OF MANAGEMENT STUDIES AND RESEARCH

ALIGARH MUSLIM UNIVERSITY

ALIGARH -202002(UP) INDIA

YEAR OF SUBMISSION 2011

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EFFECTIVE RECRUITMENT AND SELECTION SYSTEM FOR

THE IT SOFTWARE INDUSTRY IN INDIA

Introduction

The increasing size and complexity of organizations, the intensive use of

technology, combined with changing socio-cultural norms have contributed with

immense complexity to Human Resources Management aspects of managing people in

organizations. The Information Technology (IT) Software Industry in India has attracted

worldwide attention recording phenomenal rates of growth, achieving the status of an

economy driver for a country, emerging as a superpower of the Twenty First Century. It

is equally vital that this trend continues in the midst of global challenges and competition.

While the system in vogue has been meeting the current challenges, little research has

been done to determine the effectiveness of recruitment and selection system for the IT

Software Industry in India. The purpose of this research is to determine an effective

recruitment and selection system and assist the IT Software Industry in India in meeting

the challenges of the coming decades.

Research Objectives

Keeping in view the global demand of software development and the unique

status acquired by the IT software companies in India in terms of quality, cost

competitiveness, availability of trained manpower and the infrastructure, there is a felt

need to shape the IT software industry in India, for the future. For keeping India’s

competitive advantage from declining, it is imperative that an effective system of

recruitment and selection be identified. It has been accepted that certain attributes are

required for employees employed in the IT software development and these require to be

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tested by a system that can be evaluated for its effectiveness against measurable output

variables that reflect the output / performance of IT software companies. There is also a

requirement to evaluate the domination of certain conditions that software companies

consider such as the academic record, socio economic conditions and work experience,

for their influence on the effectiveness of the recruitment and selection system. Lastly

proper evaluation of other factors influencing effectiveness of output / performance

variables requires to be studied to arrive at a scientific model that will meet the felt need.

This would result in immense savings in time, effort and costs to all stakeholders.

Objectives

The purpose of this research is to determine the key variables in an effective

recruitment and selection system for the IT Software Companies in India. In order to

accomplish the research, the objectives formulated are:

1. To evaluate the various recruitment and selection techniques that IT software

companies in India employ. These include interviews, group tests, psychological tests,

intelligence tests, technical tests and others.

2. To determine key employee skill gauges, like academic record, socio-economic

conditions, overall work experience and experience in a specific field.

3. To determine key company performance metrics, like growth in sales, net profit

margin, revenue/profit growth and average net profit per employee.

4. To determine the relationship between key company performance metrics to

selection techniques, thereby obtaining key variables in an effective recruitment and

selection.

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Review of Literature

A review of literature was undertaken to study the current state of

recruitment and selection in IT Software Industry in India. It has been accepted that the

quality and maturity of the Indian software industry has been realized globally. With

technology and processes undergoing rapid transition, the investment in human resource

and its development can reap rich dividends. It emerges that India is among the choices

for low cost programmers because of several factors and one of them being the

widespread knowledge of English language. Some of the other important factors that

contributed towards this success are the availability of human capital, especially the stock

of engineers available in India. Another important factor that has contributed has been the

geographical location of the country that has helped several nations explore capability to

operate round the clock. It can also be stated that the base of Indians in every part of the

world also helped bridge several gaps related to red tape and human networking. Not

withstanding these, a dispassionate reappraisal of the education facilities to meet the IT

boom amply highlights that the current system is at best only meeting the present

“demand and supply". Literature highlights that like other countries, India may also face

possibility of an upcoming talent shortage problem on account of a few factors.

The Indian Government has been aware of the complexities and has contributed

by launching a Technical Education Quality Improvement program, accepting of

proposals like setting up venture capital funding, as well as tie-up for bilateral

cooperation in IT Sector with 32 countries. India has also set up an efficient organization,

the National Association of Software and Services Companies (NASSCOM), which

provides reliable information, including individual supplier profiles, through the Internet.

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The Indian software industry has shown flexibility in growing acceptance and adoption of

the newly emerging standards of quality management such as Capability Maturity Model

(CMM), a worldwide certification developed by the Software Engineering Institute of

Carnegie Mellon University. The quality maturity of the Indian software industry can be

judged from the fact that India has one of the largest number of companies being quality

accredited and serving the needs of the Fortune 500 companies.

The importance of effectiveness in recruitment and selection for organizational

performance has been amply highlighted as being critical to organizational success. One

of the possible ways is by initiating the screening process to include psychometrics with

other tests for selection and recruitment. It is only logical that such psychometric testing

will prove to be scientific and result oriented but whether the IT Companies are

undertaking such processes and incorporating it in their recruitment and selection system

is the purpose of this research. Studies show that Psychometrics is best used in

conjunction with other assessment and selection tools such as interviews. With quality

and cost assuming greater importance, helping the firm save money by reducing turnover

would help to cut expenses and paperwork in recruitment and contribute to the

organization. Several other advantages come to light while undertaking such tests such as

the speed, efficiency and total-in-time recruitment can really reduce the time to hours and

days rather than weeks. There is however resistance to psychological tests in several

countries including China, because such tests result in a potential loss of face for

applicants, are invasive in privacy and also statutory provisions in some countries

prohibit their use. The laws in India permit use of psychometric tests. These have been

used in organizations like the defense forces, where they have stood the test of time.

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Not withstanding the likely changes in trends of recruitment and selection,

selection for employment in the IT Field is still based on academic performance and

possibly marginally on experience, if considered necessary, by the employer. It is seldom

on aptitude or scientific personality evaluation as done in scientifically selecting

executives or officers as leaders in other industries and organizations like the services. A

query that comes up is if recruitment and selection of a potential IT Industry employee

can be undertaken scientifically, based on aptitude for software programming so as to

make the employment of the professional effective. For this, it is important that IT

companies know what to measure and how to measure when it comes to recruitment.

Countries like China are following closely. Having achieved a great deal to

improve, they are likely to pose a major challenge to India. In comparison with India,

China has an inherent strength as an outsourcing destination. It has a huge domestic

software market that attracts domestic software firms as well as foreign software firms.

Especially, after its accession to WTO, China will promote social and economic

development through the wider use of information technology. China has absorbed

foreign direct investment (FDI) many times than that by India. The information and

telecommunication infrastructure of China is considered superior to that of India. Greater

political and social stability also give the investors more confidence and lastly

competitive advantage of lower cost of manpower in China, is another important factor.

However, there are some aspects, such as quality control processes,

communication/culture etc. where India is far ahead of China. It is vital that this edge

remains with India in the coming decades.

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Setting of the Research Problem

To state briefly, the research purpose is to determine the key variables in

formulating a model for an effective recruitment and selection system for the IT Software

Industry in India. In order to achieve this purpose, each key research objectives, as

identified earlier pertain to determining the key variables for an effective recruitment and

selection system. Based on those techniques, key employee gauges like academic record,

work experience etc. are identified as “Input Variables”. The attributes that companies

consider essential for IT Software professionals were also identified and these also

formed part of the input variables. The companies’ performance depends on its employee

base without question and these performance indicators are identified as “Output

Variables”. Finally, using various statistical methods, an attempt is made to determine the

relationship between key company performance metrics to selection techniques, thereby

obtaining key variables in an effective recruitment and selection. The methodology used

involves testing those hypotheses using a multivariate analysis scheme. A Conceptual

Model for Research is as given below.

EFFECTIVE RECRUITMENT &

SELECTION SYSTEM

CONTROLLED FACTORS (X1, X2, ..,….Xn)

UNCONTROLLED FACTORS (Z1, Z2 ….Zn)

OUTPUT RESPONSE (Y1, Y2, ….….Yn) INCOMING

WORKFORCE

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Identifying Key Variables

Broadly the recruitment and selection system could be analyzed in terms of their

criteria for selection, methodology of testing to include number of tests conducted, the

preference given to academic qualification / experience and other back ground

information of potential candidates and lastly the effect of other factors including size

and turnover of the Company as it affected their system of recruitment and selection. The

Pilot Questionnaire therefore elicited response to some of such queries, such as “job

specification”, the criterion taken as a screening factor for inclusion of the IT Software

Companies for the sample taken for research. The next response was “attributes”

considered by IT Software Companies, as essential for software professionals. Based on

the response from IT Companies, a list of 16 attributes (X1-X16) was compiled.

Attributes Input Variables Name

QUES 5 INTELLIGENCE X1

PROBLEM SOLVER X2

CREATIVE X3

FLEXIBLE X4

QUICK TO LEARN X5

PERSEVERING X6

PASSION FOR TECHNOLOGY X7

TEAM SPIRIT X8

PRIORITISES X9

RESULT ORIENTED X10

DESIGN MAKER X11

MOTIVATED X12

DYNAMIC X13

LONG TERM PLANNER X14

ANALYTICAL SKILLS X15

MAINTAIN FOCUS X16

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Though several other attributes were also listed, these attributes were listed by most of

the companies and hence taken as main attributes for this study.

The next response analysed and taken as input variable has been the number tests

IT Software Companies conducted comprising Interviewing, Group Testing,

Psychological Testing, Intelligence Testing, Technical Testing etc.

QUES 3 INTERVIEW X17

GROUP TEST X18

PSYCHOLOGICAL TEST X19

INTELLIGENCE TEST X20

TECHNICAL TEST X21

OTHERS X22

The preference given to academic qualification / experience and other back

ground information of potential candidates were analyzed as the next set of input

variables.

QUES 4 ACADEMIC RECORD X23

OVERALL WORK EXPERIENCE X24

EXPERIENCE IN THE FIELD X25

SOCIO-ECONOMIC BACKGROUND X26

OTHERS X27

The effect of other factors including size and turnover of the Company as it

affected their system of recruitment and selection were taken as per variables planned

below.

Uncontrolled Factor

Size of company Z1 1-4 1 is <= 1000 employees, 2, 1001-10000, 3

10001-25000 and 4 is > 25000 employees

Turnover </>Rs1500

crores

Z2 1-2 1 less than and 2 is more than Rs1500

crores

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The key outputs variables (Y1-Y4) for the study, to assess the effectiveness of

the model were percentage growth of sales; net profit margin, revenue/profit growth and

finally and the average net profit per employee, as given below.

Growth in Sales (%age) Y1

Net Profit Margin Y2

Revenue/Profit Growth Y3

Average Net Profit/Employee Y4

Assumptions for the Study

From the response during the interactions and interviews as also to the Pilot

questionnaire served to the IT Companies, it emerged that there is a varying nature of

recruitment and selection system that is unique to each Company. The assumptions

therefore taken at the time of the research are that the assessment and answers given by

the companies will hold for the next five years, the subjects fully understand the

questions and responded honesty; the response given by IT Companies is based on their

company position at the time the questionnaire has been served. It has been accepted that

changing environment can affect economic and output state.

Research Procedure

The research being undertaken is to identify a system of employment selection

that could lead to greater effectiveness of the IT Software Industry and provide a greater

cutting edge to meet future challenges. The research design and the procedure undertaken

for conducting the study comprised instrument development and an initial pilot survey

followed by a compilation of the feedback and statistically testing its validity and

reliability. The data was compiled and a main questionnaire was evolved. The

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questionnaire for pilot testing comprised a questionnaire design as non- disguised and

structured with approximately 12-18 questions. Due to the variety of respondents in

terms of their turnover, number of employees and diversity in services provided, the

collection of data was unstructured. The professionals who are interviewed or to whom

questionnaires were administered comprised Project Managers, HR Managers, Industry

experts and software professionals who gave grass root inputs. From the size of the

population available, the objective is to have a sample, which represented at least a

population sample with about 95 percent confidence level. The response provided by the

IT Software Companies has been positive but their perception of corporate secrecy and

willingness to part with confidential financial data has been far from positive. Quota

sampling is used for the study of the population.

The pilot survey was sent to about 204 IT Companies identified as part of quota

sampling. The research variables identified were specific qualities and weight-ages, if

any considered for these in effectiveness of the recruitment and selection system. The

number of tests companies employ at the time of recruitment and selection, comprising

combination of interviews, psychological tests, group tests, technical tests and

intelligence tests etc. The numbers of tests conducted by companies were collated and

response was thereafter categorized into three groups. The first group A comprised

companies that undertook 4/5 prescribed tests out of Interviews, Group Tests,

Psychological Tests, Intelligence Tests, Technical Tests and others. The second group B

comprised companies that took 3/5 tests and the last group C comprised companies that

took less than two of such tests. IT Companies were also to respond to the minimum

educational qualities acceptable in terms of 10+2 level, engineering graduate and non

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engineering graduate, preference of the IT Software Companies to the nature of entry

preferred in terms of IT engineering graduate, other engineering graduate, graduate from

non engineering stream, diploma holders and others. The other variables were the

forecast to meet challenges of the IT Industry in the next 5 years, the evolution of specific

criteria to meet the effectiveness in recruitment and selection of IT Software Companies

and the viability of undertaking psychological testing of the software professionals. A

response was also elicited to identify the possible stage at which the centralized system of

testing could be introduced.

Hypotheses Formulation

Based on the research objectives, the instrument for research (main questionnaire)

helped formulate 12 hypotheses to test relationship between the variables indentified

earlier. The level of significance for rejecting null hypothesis was kept at .05.

Descriptive statistics were computed for all variables before testing the null hypothesis

for means, standard variables, frequencies, percentages and to correlated response values

between variables. The key hypotheses relating to the questions are given below.

While it is widely perceived that greater number of tests would assist in sieving

better candidates in the selection and recruitment, the research tests for hypotheses H1- H4

posit that there is no significant difference in the growth in sales, net margin of profit,

revenue / profit growth or average net profit per employees of the IT Companies.

H1. Increased combination of interviews, psychological tests, intelligence tests,

technical tests and others taken at the time of recruitment and selection for the IT

Software Companies in India does not improve the relationship of growth in sales of IT

Software Companies.

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Software Companies in India does not improve the relationship of net profit margin of IT

Software Companies.



Software Companies in India does not improve the relationship of revenue/profit growth

of IT Software Companies.



Software Companies in India does not improve the relationship of average net profit per

employee, of IT Software Companies.

Considerable thought is given by IT Companies to the academic record, socio

economic conditions, overall work experience and experience in the IT field while

undertaking recruitment and selection in respective companies but only few IT Software

Companies, across the board, gave any weight-age to the socio economic back ground of

candidates, whereas in assessing the Overall Work Experience, there was much more

agreement. Hypotheses H5-H8 addresses the relative importance of these variables.

H5. The academic record has no domination while overall work experience,

experience in field and socio-economic conditions, have more effect on the recruitment

and selection for the IT Software Companies in India that affects growth in sales of IT

Software Companies.

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and selection for the IT Software Companies in India that affects the net profit margin of

IT Software Companies.



and selection for the IT Software Companies in India that affects the revenue/profit

growth of IT Software Companies.



and selection for the IT Software Companies in India that affects the average net profit

per employee, of IT Software Companies.

Based on the feedback from companies, a generally common list attributes was

evolved that would constitute the variables. Hypotheses H9-H12 tests the relative

contributions of those parameters.

H9. The combination of various factors considered in the study at the time of

recruitment and selection for the IT Software Companies in India affects growth in sales

of IT Software Companies of all the factors considered for evaluation of the output

variables of most factors did not affect the growth in sales of IT Software Companies.


recruitment and selection for the IT Software Companies in India affects net profit

margin of IT Software Companies of all the factors considered for evaluation of output

variables, most factors did not affect the net profit margin of IT Software Companies.

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recruitment and selection for the IT Software Companies in India affects revenue/profit

growth of IT Software Companies of all the factors considered for evaluation of the

output variables, most factors did not affect the revenue / profit growth.


recruitment and selection for the IT Software Companies in India affects average net

profit per employee of IT Software Companies of all the factors considered for evaluation

of output variables, most factors did not affect the average net profit per employee of IT

Software Companies.

Research Results and Discussion

For quantitative data sets, the inputs were normalized on a scale of 1-5 to make

them quantitative, with 1 being the least importance and 5 being the most importance. For

qualitative data sets, the inputs were marked as Y/N (Yes/No) and treated as nominal

variables.

For working out the effectiveness of the recruitment and selection system, the

output data had to be evaluated against the input variables. In order to analyze the output

variables, data pertaining to the growth in sales, net margin of profit were gathered from

the data provided by companies and in most cases obtained from the sites showing the

financial results that were public. Thereafter, the revenue/profit growth was worked out

to finally arrive at the average net profit per employee.

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Results

The results of hypothesis testing for H-1 to H-4 are as given below:

The next vital consideration was the preference to the socio economic conditions,

academic record, overall work experience and experience in the IT field while

undertaking recruitment and selection in respective Companies. The IT Companies across

the board did not give any weightage to socio-economic background of candidates so that

was dropped from the testing. To test the null hypotheses H5 to H8, the response of the

Companies to the question of preference to the academic record, overall work experience

and experience in the IT field was classified into two categories, Group D -Companies

which value academic record but do not value overall work experience and experience in

the field and Group E Companies which value overall work experience and experience

in the field but do not value academic record as much.

Null Hypothesis Test Sig. Decision

1 The distribution of Y1 is the same

across categories of Group Q3

Independent Samples

Kruskal-Wallis Test

0.054 Accepted

2

The distribution of Y2 is the same


Independent Samples

Kruskal-Wallis Test

0.619

Accepted

3



Independent Samples

Kruskal-Wallis Test

0.164

Accepted

4



Independent Samples

Kruskal-Wallis Test

0.447

Accepted

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Based on this classification, the results of the test are below:-

Null Hypothesis Test Sig. Decision

1

The distribution of Y1 is the

same across categories of Group

Q4

Independent

Samples Mann-

Whitney U Test

0.002

Failed to accept

2



Q4

Independent

Samples Mann-

Whitney U Test

0.741

Accepted

3



Q4

Independent

Samples Mann-

Whitney U Test

0.892

Accepted

4



Q4

Independent

Samples Mann-

Whitney U Test

0.397

Accepted

To test the hypotheses H9 to H12, the approach used here is different. A linear

effect model for all inputs was constructed and was fitted using least squares method for

the output responses. These were then fitted using least square method for the out put

variables growth in sales, net margin of profit, revenue / profit growth or average net

profit per employees of the IT Companies. The results of any such model being

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dependent on the revenue, due consideration had to be given to the uncontrolled variables

and so out of turnover of IT Companies Z-1 and Z-2 , the latter Z-2 was blocked.

For the overall model, the highest correlation is shown for growth in sales with a

significance of 0.024 which indicates >97% confidence in the model. All other factors

show less correlation. Within the model, the attributes (X1-X16) where F is shown ~ > 3

are considered significant. Thus, for the output variables Y1-Y4, the results for

significant effects (with F-factors below) are:

Hypothesis 9 Y1: Quick to Learn (X-5), Team Spirit (X-8), Long Term Planner (X-14)

and Maintains Focus (X-16).

Hypothesis 10 Y2: Maintains Focus (X-16).

Hypothesis 11 Y3: Analytical Skills (X-15) and Passion for Technology(X-7).

Hypothesis 12 Y4: Dynamic (X-13).

Thus, we can say that only for H9 and H11 the alternate hypothesis holds

true. This is also corroborated by the result where for example the growth in sales is

correlated to hiring with real world experience rather than academic tests. For H10 and

H12, the null hypothesis holds true.

Hypothesized Relationship Results

H1. Increased combination of interviews, psychological tests

and intelligence tests taken at the time of recruitment and selection for the

IT Software Companies in India does not improve the relationship

of growth in sales of IT Software Companies.

Accepted

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H2. Increased combination of interviews, psychological tests, intelligence

tests, technical tests and others taken at the time of recruitment and selection

for the IT Software Companies in India does not improve the relationship of

net profit margin of IT Software Companies.

Accepted




revenue/profit growth of IT Software Companies.

Accepted




average net profit per employee, of IT Software Companies.

Accepted

H5. The academic record has no domination while overall

work experience, experience in field and socio-economic conditions,

have less effect on the recruitment and selection for the IT

Software Companies in India that affects growth in sales of IT

Software Companies.

Failed

to accept

H6. The academic record, socio-economic conditions, overall

work experience and experience in field have no domination during

the recruitment and selection for the IT Software Companies in India

that affects the net profit margin of IT Software Companies.

Accepted

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H7. The academic record, socio-economic conditions, overall

work experience and experience in field have no domination during

the recruitment and selection for the IT Software Companies in India

that affects the revenue/profit growth of IT Software Companies.

H8. The academic record, socio-economic conditions, overall work

experience and experience in field have no domination during the

recruitment and selection for the IT Software Companies in India that affects

the average net profit per employee, of IT Software Companies.

Accepted

Accepted

H9. The combination of various factors considered in the study at the time

of recruitment and selection for the IT Software Companies in

India affects growth in sales of IT Software Companies of all the factors

considered for evaluation of the output variables, most factors did

not affect the growth in sales of IT Software Companies.

Failed

to accept


of recruitment and selection for the IT Software Companies in India affects

net profit margin of IT Software Companies of all the factors considered for

evaluation of output variables, most factors did not affect the net profit

margin of IT Software Companies.

Accepted


of recruitment and selection for the IT Software Companies in India affects

revenue/profit growth of IT Software Companies of all the factors

considered for evaluation of the output variables, most factors did not affect

the revenue/profit growth of IT Software Companies.

Failed

to accept

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H12. The combination of various factors considered in the study at the

time of recruitment and selection for the IT Software Companies in India

affects average net profit per employee of IT Software Companies of all the

factors considered for evaluation of output variables, most factors did not

affect the average net profit per employee of IT Software Companies.

Accepted

Conclusions

The results of the present research lead to the conclusion that there are attributes

that most IT software companies consider as essential for the potential employees

involved in software development. The research brings out that significant increase in

number of tests; during the selection process does not of yield a major advantage for a

better recruitment and selection system for the IT Software Companies. Also, while

socio-economic conditions do not have any major impact on the selection system, some

impact is experienced in the growth rate of companies while considering academic

record. Considering the attributes for aptitude identified by the IT software companies,

the research has identified these attributes along with certain other input factors, which

could be utilized for evolving a model that can give better growth in sales, margin of

profit, revenue / profit growth and finally average net profit per employee. Thus by

utilizing the model evolved by employing input, controlled and output variables used in

this research, the IT software companies have a feasible model for an effective

recruitment and selection system for the IT Software Industry in India.

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Implications

A few of the implications of the study are that a systematic study of recruitment

models has been lacking and this study will hopefully prove to be an example of

quantitative analysis for recruitment. As is clear from testing hypotheses H1-H4,

increasing the length of the selection process does not necessary always result in

obtaining a pool of candidates that affect the company’s bottom line. For key parameters

like growth in sales, relying on antiquated academic screening criterions also does not

help. It is thus imperative that each selection team be very clear on their organizational

objective and thereby can tailor the selection criterion while recruitment for maximum

efficiency. Hypotheses H5-H8 reflects that most aptly. One can envision a multiple

parameter correlation study being conducted similar to Hypotheses H9-H12 testing where

once a model has been built that correlates the input variable in form of attributes to the

output variables. With this study, the company can do targeted selections based on their

needs. For example, growth in revenue is most linked to candidates “Passion for

Technology”. Once the company is in a growth phase, it can hire candidates from schools

which have a strong research and development wing that attracts people with a passion

for technology. The conclusion arrived to from the analysis presented above can change

significantly if either the sample size for the study or the variable set used is different. A

very good example to showcase this effect is the blocking variable. In our study, the

turnover for a company is used to segregate the data/structure differences between the

two data sets and is used as a blocking variable. If not used, the data from small turnover

companies will corrupt the analysis for the large turnover companies. A selection strategy

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would combine elements of various hypotheses depending upon the output variable that

would need to be maximized

Suggestions for Future Studies

Based on the results and the limitations of this study, further research are

proposed for extending input variable correlation between management’s vision of how

IT Software professionals are managed and how IT Software professionals perceive

management’s vision. This study also did not investigate the differences between the

measured variables with respect to the respondents’ demographic characteristics and sex

ratios (male vs female) of employees. IT Software Companies who have different

demographic characteristics and sex ratios of employees may have different results.

Lastly, longitudinal studies are needed in order to validate the predictive dimension of the

model as has been worked out.

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