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CHAPTER 3

METHODOLOGY

3.0 INTRODUCTION

In Chapter 2 an attempt was made to review the various studies conducted

in the field of quality of work-life and its correlates. The present chapter is

devoted to describe the method, procedure and techniques used to achieve the

objectives of the study. It also gives an account of emergence of Hyderabad as a

BPO hub and narrates the significance of the study by identifying gaps in QWL

literature.

3.1 EMERGENCE OF HYDERABAD AS A BPO HUB

Conventional business wisdom regards most government officials and

bureaucrats as obstacles who get in the way of market forces. But a politician and

his lieutenant get much of the credit for making the Indian city of Hyderabad a

major global centre of Business Process Outsourcing (BPO), the booming

practice whereby companies farm out tasks such as call-centre operations, billing

and claims processing (Knowledge@Wharton, 2003).

It was the vision of Shri. N. Chandrababu Naidu, the then Chief minister

of Andhra Pradesh, to modernize the largely rural region of 76 million people

through information technology and corporate-style government practices. With

this vision in mind, Mr. Naidu established an agency in 2001with the sole

purpose of developing and nurturing the BPO industry in the state. Mr. Randeep

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Sudan, one of his special secretaries, took charge as CEO of this agency.

Relentless efforts of the duo resulted in a harmonious blend of socialism and

capitalism in the state of Andhra Pradesh. The free market policy and the timely

interventions from the government enabled the spectacular growth of BPO

industry, mostly centred in the capital city of Hyderabad. As a result, the revenue

of outsourcing industry in the state increased more than 300% in nine months

ended in December, 2002, to $247 million (Knowledge@Wharton, 2003).Today,

the number of employees working in the BPO industry in Andhra Pradesh is

roughly estimated as 50,000, where as it was only 15,000 in the year 2003.

Entrepreneurs, industrialists, media and the public at large started highlighting

the unique leadership of Mr. Naidu. He was named "South Asian of the Year" by

Time Asia and one of 50 Asian leaders at the forefront of change by Business

Week magazine. NASSCOM praised the performance of both Messrs. Naidu and

Sudan in the month of August, 2002. NASSCOM ranked Hyderabad as the top

destination among nine Indian cities for ITES companies, with the city taking

first place in the sub-category of "policy initiatives." They also suggested the

BPO operations to span all industries and to improve the public transportation

system. Local accent, lack of international airport and environmental challenges

were also brought into notice for improvement. Mr. Naidu made a statement

once, "We have very high ambitions for AP, we are gearing up to capture 50%

of all business that’s likely to come to India and are targeting approximately

500,000 jobs in this sector by 2008-2010".

The visionary statesman’s dream has come true. To quote Kiran Karnik,

president of NASSCOM, "Cities such as Hyderabad and Kochi are emerging as

attractive ITES destinations primarily due to rapid improvements in

infrastructure (power, international bandwidth and urban transportation) and

lower manpower costs due to lower cost of living and lack of alternative

employment opportunities in these cities."

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The leader in Naidu was optimistic in the BPO industry’s prospects.

"Hyderabad was not on the BPO landscape of the world," he said. "In the late

nineties, we identified this as an emerging growth opportunity for India and for

the state of Andhra Pradesh." Now, many multinational corporations have their

back office operations in Hyderabad.

The prominent BPO companies in Hyderabad are HSBC electronic data

processing, Deloitee consulting, HCL, ADP Wilco, CapMark, to mention a few.

Under the leadership of Naidu, the state of Andhra Pradesh saw economic

policies which have been diverse in nature. In order to introduce free market

economy, state curtailed many regulations to encourage entrepreneurs to come up

with innovative ideas and establish business in Hyderabad. It was his government

which amended state law to allow ITES companies to employ women and young

people aged 18 to 21 during the night shift. The government also waived for

ITES firms a rule that an employee must be given overtime pay after eight hours

of work on a given day, as long as the employee doesn’t work more than 48

hours in a week. What’s more, Naidu’s administration has declared the ITES

industry an "essential service", meaning that workers at BPO’s like those in

industries such as milk production and water supply, do not have the right to

strike. ‘AP First’ was established in 2001, which is an agency for promoting and

facilitating investments in remote services and technology.

“AP First’s” mission was "to make Andhra Pradesh the world’s preferred

Business Process Outsourcing / Information Technology Enabled Services

(BPO/ITES) destination". AP First’s strategies included ensuring the easy

availability of trained manpower, supporting the BPO industry through

regulatory changes, assisting in the development of infrastructure such as

telecommunications and office space, and marketing efforts. The government has

been instrumental in the creation of a business park in Hyderabad named ‘HITEC

City’ that is slated eventually to provide 5 million sq. ft. of office space. The

government provided land for the office park and serves as a joint-venture

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partner in the project. Andhra Pradesh officials negotiated directly with telecom

players to secure discounts for local businesses. Reliance Infocom, for example,

offers BPO firms in the state, rates reduced by 30% to 70%. These telecom

companies are now getting into smaller cities and expanding their networks.

Uninterrupted power supply was a major focus to encourage companies to set up

their back office operations in Hyderabad. BPO office parks and facilities also

are exempted from regular power outages that affect the rest of the state. ‘HITEC

City’ is known for its uninterrupted power supply.

Establishment of Indian School of Business in 2001 was a turning point in

the growth of BPO industry in Hyderabad. Formed through a partnership with the

Wharton School, the Kellogg School of Management at North-western

University and London Business School, the institution offers postgraduate and

executive programs. The presence of the Indian School of Business was one of

the key factors in Cap Mark’s decision to choose Hyderabad over Bangalore and

Chennai.

The International Institute of Information Technology (IIIT), formerly

known as Indian Institute of Information Technology, was established in

Hyderabad in 1998. The IT-focused institution stands out for its "corporate

schools" on campus, where companies such as IBM, Motorola and Oracle handle

various courses. This is yet another milestone in the growth of BPO industry in

Hyderabad. The ITES training institute was also launched under the initiative of

the government to impart basic English language course and also specialized skill

for ITES.

Coming up of the State of the Art International Airport also has attracted

many companies to set up their BPO’s in Hyderabad city. The initiative taken by

Naidu was continued by other governments which came afterwards. Thus, as

NASSCOM praised, Hyderabad is India’s most attractive BPO site today.

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3.2 IDENTIFICATION OF GAPS IN QWL LITERATURE –

RATIONALE FOR THIS STUDY

Most of the researches in the BPO sector have addressed only specific

problems related to its environmental analysis like challenges, growth and

opportunities, the problem of attrition, the HRM systems, issues of job stress and

burn out, job satisfaction, individual performance etc. Literature review has also

shown how various researchers have identified a plethora of reasons behind the

escalating problem of attrition in BPO industry and many of them have suggested

recommendations to combat it (Misra 2007, Prakash and Chowdhary 2004, Joshi

2004). Many researchers have also worked on various domains like the HRM

systems and practices (Budhwar et al., 2006), job satisfaction (Sharma 2006, E-

sat survey 2005), and burnout prevention (Kanwar et al., 2008). However, no

systematic and comprehensive work has been found that collaborates all the

facets viz. attrition, retention, employee motivation, involvement etc. to combat

the most smoldering problem of the present times, i.e. quality of the work life of

employees. Another interesting finding that emerged from prior researches and

focused group interviews taken for pilot survey is that reducing attrition may not

always mean increasing retention. Attrition may reduce if the negative

characteristics of the job are taken care of. However, that does not mean

employees increase their willingness to stay in the same organization. Thus,

different set of factors emerged for attrition and retention respectively. Looking

at the big picture of the much realized potential of the BPO industry in India and

the impending curse of attrition in this sector, it can be said that the problem

cannot be overlooked. There is dire need of tackling the problem of attrition in

the BPO industry of India and for this employee motivation has been chosen as

an effective tool. There is need to develop a concurrent strategic method, an

innovative development paradigm that can be utilized to curb the ever-increasing

attrition rate in the BPO industry by enhancing the quality of the work life. Thus

the need for this study can be clearly defined in two points:

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1. Due to the uncongenial work culture prevailing in BPO, the physical and

mental well-being of the employees are at stake. Hence a model should be

developed by which the determining factors of QWL of the employees in BPO

can be identified and enhanced leading to greater employee satisfaction and

improved productivity and thus make them better corporate citizens, spouses and

parents.

2. Dearth of motivation among the BPO employees due to the perceived

non-existence of quality of work-life in BPO is one of the bitter truths that is

responsible for high levels of attrition in this sector. Time has come to identify

the factors which contribute to QWL and adopt measures to enhance them for

better man-management. Attrition is a burning problem-for the promising

industry of BPO, especially because it fails to tap the full utilization of the human

resources and wastes much of its time, money and resources.

3.3 SIGNIFICANCE OF THE STUDY

The information technology and ITES-BPO industry in India has shown

spectacular growth in the last one decade, registering a Compound Annual

Growth Rate (CAGR) of 26 percent. According to the statistics of the

Department of Information Technology, Government of India (annual report,

2007-08), the number of professionals working in Information Technology and

ITES-BPO industry increased from 284,000 in 1999–2000 to over 1.6 million in

2006–07. The year 2011-2012 marked a significant landmark for the IT-BPO

industry as it crossed the milestone of USD 100 billion in revenues. The industry

performance in the year 2012 demonstrated the sector’s ability to innovate and

deliver differently in order to maintain the growth trajectory. It is estimated that

the addressable market for the Indian IT-BPO industry in this sector would be

approximately USD 70-80 billion in the next 10 years, growing at a CAGR of 14

percent.

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It is estimated that 70% of the people working in IT and ITES-BPO are in

the age group of 26-35. Thus, this industry is a major source of employment for

young adults of the country. Currently Bangalore, Mumbai, the National Capital

Region (NCR), Hyderabad, Pune, Chennai, Kerala and Kolkata account for 90

per cent of the total direct employment in the BPO sector. BPO sector

employment involves, for the most part, such activities as customer care services,

data entry and analysis, payment processing services, audit checks for

companies, medical transcription and so on. Typically, each BPO employee

responds to a minimum of 100 phone calls per shift (Sharma, 2004). Aside from

attractive salaries, the attractive work environment and benefits offered by the

BPO sector have motivated many young adults to seek employment in this sector

(Sharma, op.cit). For example, majority of the BPOs provide provident fund,

gratuity, group medi-claim, insurance schemes (for employees as well as their

spouse, non-earning parents and children), personal accident insurance scheme,

subsidized food and transportation. Many BPO companies provide their

employees with performance based incentives, flexi-time, flexible salary

benefits, paid days off, maternity leave, and employee stock option plan.

Recreational facilities available in BPOs include pool tables, chess tables and

coffee bars and some even have well-equipped gymnasiums and personal

trainers. Many BPO companies organise regular get-together and other cultural

programs for their staff. Some provide company leased (shared) accommodation

for out-station employees, corporate credit card facilities, cellular phones/laptops,

medical check-ups, loans and educational benefits (Sharma, op.cit).

Though BPO sector has opened up vast career opportunities for young

adults, employment in this sector has strong impact on young people’s lives. For

example, several young BPO employees have to relocate to the major metros and

cities where outsourcing hubs are located and live independently. With the

availability of higher disposable income, many young people have reported

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lifestyle changes. Indeed, among financially independent youth, there has been a

visible move towards consumerism (Phukan, 2006).

While employment in the BPO sector has meant that young adults are

reaching their career milestones and financial goals much earlier than before,

surveys and anecdotal evidences show that employees in the BPO sector

experience high levels of stress as a result of working in closely monitored

environments under immense pressure to meet ambitious performance targets.

Strict deadlines and ambitious targets have also resulted in employee “burnout”.

Repetitive tasks, such as responding to telephone calls more than 100 times a

shift have resulted in absenteeism and attrition among many young employees

(Sharma, op.cit). A growing number of employees also experience physical and

emotional problems such as panic attacks, depression, relationship problems,

alcoholism and sleeping and eating disorders (e-sat survey, 2005; Phukan, 2006).

According to a survey of BPO employees, several factors are considered to cause

stress at work including travel time, changing duty shifts, insufficient holidays,

work pressure and long working hours (http:// www.livemint.com/ 2007/11 /

17012831/ Long-working-hours-travel-tim.html). Moreover, as many BPO’s

provide services to countries abroad, employees in this sector are trained to

understand the culture and accent of these countries; in some cases, employees

are required to use a different name, speak with foreign accent and adopt a

different persona at work, which may result in anxiety and related disorders

(Pradhan and Abraham, 2005).

Hence, there is growing concern in various sectors, including the

government, about the health and safety of young BPO employees. The health

ministry is considering to issue guidelines for employers in this sector. However,

lack of reliable information on which to base a response to such concerns poses

serious challenge to safeguard the health and well being of these young

professionals. Hence, the struggle between man and his work environment

continues.

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This relentless struggle and the resulting revolution that is taking place in

the relationship between man and his work has led to a new concept in

management literature known as ‘quality of work life’ which is the hottest

corporate lexicon in the post globalised era. The goal of this movement is

‘humanization of work’ or ‘job designing’ which advocates the management to

treat the employees as the greatest asset of the organization which is not only to

be utilised but also to be developed. The job has to be excellent from the point of

technology and human needs of employees. Moreover, the job should help them

in the utilization of their higher level skills and satisfaction of their higher level

needs.

Origin of the concern for QWL can be found in the humanistic tradition

within the social sciences that tries to highlight the employees’ need for

meaningful and satisfying work experiences and for participation in decision

making that affect their work environment. Therefore, from a historical

perspective, this concern for QWL in organizations can be seen as the latest, and,

in many ways, the culmination of a string of reform movements that have

attempted during the past several decades, to protect the rights and interest of

workers. The concept of QWL is very close to the concept of Human Resource

management (HRM) and Human Resource Development (HRD). The traditional

approach to these concepts under Taylorism and Fordism led to

‘dehumanization’ of work as the emphasis was more on machines than on

people. The human relations movement restored the balance between men and

machine and brought forth the significance of human beings in organizations.

Herzberg’s (1959) distinction between ‘hygiene factors and motivators’

advocated the use of job as a medium for developing and changing organizations

through the program of ‘job enrichment’. Later on, Davis (1966) proposed the

concept of ‘job design’, satisfying the techno-social requirements of the job. This

was followed by ‘work re-organization’ as an extension of the ‘job design’. In

1976, Hackman and Oldham drew attention to what they described as

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psychological growth needs as relevant to the consideration of quality of working

life. Several such needs were identified: skill variety, Task identity, Task

significance, Autonomy and Feedback. They suggested that such needs have to

be addressed if employees are to experience high quality of working life. At the

core of all these programs was the value of treating people in organizations as

human beings and helping them to grow, develop, and take part in the decision

making processes. The goal was to humanize the organizations. Various terms

such as, ‘humanization of work’, ‘industrial democracy’, ‘workplace

democracy’, ‘work redesign’, ‘organizational redesigning’, ‘participative work’

and later on, ‘QWL’ were used interchangeably to describe the same thing.

Thus, the purpose of QWL is to change the climate at the work place, so

that the human-technological interface leads to better QWL and hence better

productivity. The interest in the field of QWL has survived the period of past

three decades. In fact, it is growing in most of the countries of the world,

carrying out lot of research work and producing enormous literature. Time has

come in India to find out what factors in the work environment contribute to

QWL, particularly, in BPO companies, so that attention can be paid by the

companies and managers to enhance these factors. The voluminous literature on

QWL says about stress and burn out in BPOs, attrition and so on, but no study

talks about how QWL can be established and sustained in these organizations.

The findings of this study would be useful for the managers, policy

formulators, government of India, particularly the ministry of health, and those

who are concerned with the health and life of youngsters of India. Hence this

study was taken up.

The suggestions and recommendations which are based on the empirical

findings, if implemented, are expected to bring radical changes in the work

culture of BPO companies, bringing ‘glittering moments of joy and happiness’ in

the life of the employees, society and nation.

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3.4. Statement of the problem

As early as 1950’s researchers have begun to study about the quality of

the work life of employees with the intention of making the work place a better

place for working, learning and living. The ultimate goal was to humanize the

work place under various names such as industrial democracy, work place

democracy and finally enhance the quality of work life. To add to this, numerous

works on work life thereafter proved that what happened in the workplace have

significant impact on individuals and their families. (Greenhaus & Beutell, 1985;

Kossek & Ozeki, 1998; Lewis & Cooper, 1987).

Volatile work environment coupled with highly competitive jobs and

family obligations negatively affected the workforce leading to low morale and

motivation, reduced productivity, and increased burnout and turnover (Galinsky

& Stein, 1990, Benedict & Taylor, 1995). Moreover, the inability of employee to

balance the equally challenging demands of their work and personal life has

contributed to the escalating stress and conflict of today’s workforce (Edwards &

Rothbard, 2000). This in turn leads to significant rise in stress-related health

problems, which translates to financial cost, both to the employer as well as the

government (Johnson, Duxbury & Higgins, 1997, Frone, Russell, & Cooper,

1997).

The problem of the present study is stated as “Socio-psychological

determinants of quality of work-life of employees in BPO Industry in

Hyderabad”.

3.5 Research questions

Based on the significant gaps identified in QWL literature and as

described in the rationale of the study, the following research questions have

been investigated to achieve the purpose of the study.

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1. Which are the social and psychological factors that contribute to the

quality of the work life of employees working in the BPO industry in

Hyderabad city?

2. What is the impact of the various social and psychological factors

considered in the present study on the perception of employees with

regard to their quality of work life?

3. Does QWL have impact on overall satisfaction of employees?

4. Does QWL have impact on the performance of employees?

5. Are there any significant differences in QWL of employees based on their

age, gender, marital status, education, monthly income, total work

experience and work experience in the current organization?

3.6 OPERATIONAL DEFINITIONS OF IMPORTANT TERMS USED IN

THE PRESENT STUDY

The present study has taken into consideration certain social and

psychological variables which the researcher assumes to contribute to the quality

of work life of employees. They are Transformational leadership, Transactional

leadership, Work culture, Employee attitude to job, Occupational self efficacy,

Employee participation in non-work related activities, Self concept and

Employee perception about the company. “Quality of work-life” is an important

term which is to be defined initially. All the above mentioned important terms

used in the present study and their operational definitions are given in Table 3.1

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Table 3.1

Operational Definitions

S.

No Important terms Definition

1. Quality of work

life

Favourable working environment that supports and promotes

satisfaction by providing employees with rewards, job security

and career growth opportunities (May et al., 1999).

2 Transformational

leadership

Process where leaders and followers engage in a mutual process

of raising one another to higher levels of morality and

motivation (Burns, 1978).

3 Transactional

leadership

Such leadership occurs when one person takes the initiative in

making one contact with others for the purpose of an exchange

of valued things (Burns, 1978).

4 Work culture A pattern of shared basic assumptions that was learned by a

group as it solved its problems of external adaptation and

integration, that has worked well enough to be considered valid

and, therefore, to be taught to new members as the correct way

to perceive, think, and feel in relation to those problem(Schein,

1992).

5 Employee attitude

towards job

An attitude is a mental and neural set of readiness, exerting a

directive dynamic influence upon the individual’s response to

all objects and situations with which it is related (Allport,

1938).

6 Occupational self-

efficacy

The belief in ability and competence to perform in an

occupation (Pethe, Chaudhari and Dhar 1999).

7 Employee

participation in

non-work related

activities

Extent to which employees participate in various non-work

related activities like competitions, cultural programs, sports

and games etc. whether they do it willingly or by coercion from

authorities or when there is no other alternative (Developed by

researcher, 2012).

8 Self concept Totality of the individual thoughts and feelings having

reference to himself as an object (Rosenberg, 1979).

9 Employee

perception about

the company

Process by which individuals organize and interpret their

sensory impressions to give meaning to their environment

(Robbins, 1996).

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3.7 RESEARCH METHODOLOGY

In the following section, the objectives of the present study, variables

considered in the study, hypotheses formulated for testing in the study, research

questions, data collection procedure, sample and sampling technique and

determination of sample size are discussed.

3.7.1 Objectives of the present study

It is clear from the review of earlier researches that urgent solutions are

required to some specific problems of lack of quality of work-life in BPOs,

leading to lack of physical and mental well-being of employees, escalating

attrition, problems related with work life balance, stress and burn out etc. The

broad objective of this thesis is to identify the social and psychological

determinants of quality of work life in BPOs by analyzing the level of employee

motivation, satisfaction and involvement. The study also intends to generate a

model for maximizing the quality of work life of these employees for better

man-management and for sustenance of employees in the organization as highly

enlightened and satisfied employees. The specific objectives of this study are:

1. To examine the influence of demographic variables on each of the select

socio-psychological factors of QWL

2. To examine the influence of demographic variables on QWL

3. To examine the influence of demographic variables on employee

satisfaction

4. To examine the influence of demographic variables on employee

performance

5. To find out whether there is any significant association between

educational qualification and level of QWL; monthly income and level of

QWL and level of QWL and level of employee performance.

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6. To find out whether there exists any significant difference between the

mean ranks towards perception on each of the select socio-psychological

factors of QWL.

7. To find out whether there exists any significant difference between mean

ranks of the employees towards the perception on employee performance.

8. To find out the significant relationship between each of the select socio-

psychological factors and QWL; each of the select socio-psychological

factors of QWL and employee performance and between each of the select

socio-psychological factors of QWL and employee satisfaction.

9. To find out the significant relationship between employee performance

and QWL; employee satisfaction and QWL and between employee

satisfaction and employee performance.

10. To find out the impact of each of the select socio-psychological factors on

QWL.

11. To find out the impact of each of the select socio-psychological factors of

QWL on employee performance

12. To find out the impact of employee satisfaction on employee performance;

QWL on employee performance

13. To test the conceptual model developed by the researcher using Structural

Equation Modelling (SEM).

3.7.2. Variables taken for the study: The dependent variables and independent

variables taken for the study are given in the Table 3.2

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Table 3.2

Variables

S.No Dependent variable Independent variable

1 QWL Leadership style of managers

2 QWL Work culture

3 QWL Attitude of the employees

4 QWL Occupational self-efficacy

5 QWL Self-concept

6 QWL Employee participation in non-

work related activities

7 QWL Employee perception about the

company

8 Employee satisfaction QWL

9 Employees performance Employee satisfaction

Table 3.2 shows that there are three dependent variables and nine

independent variables. The dependent variables taken are QWL, employee

satisfaction and employee performance. The independent variables are leadership

styles, work culture, attitude of the employees to job, occupational self efficacy,

employee participation in non-work related activities, employee perception about

the company, QWL and employee satisfaction.

3.7.3 Statement of hypotheses

On the basis of the theoretical framework described in Chapter 1 and on

the basis of the review of related studies given in Chapter 2, the following twelve

hypotheses were formulated:

The following hypotheses are formulated for the present study:

H1: There is no significant difference between the demographic variables and

each of the select socio-psychological factors of QWL.

H2: There is no significant difference between demographic variables and QWL

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H3: There is no significant difference between demographic variables and

employee satisfaction

H4: There is no significant difference between demographic variables and

employee performance

H5: There is no significant association between educational qualification and

level of QWL; monthly income and level of QWL and between level of QWL

and level of employee performance.

H6: There is no significant difference between the mean ranks of the employees

towards perception on each of the select socio-psychological factors of QWL.

H7: There is no significant difference between mean ranks of the employees

towards the perception on employee performance.

H8: There is no significant relationship between each of the select socio-

psychological factors and QWL; each of the select socio-psychological factors of

QWL and employee performance and between each of the select socio-

psychological factors of QWL and employee satisfaction

H9: There is no significant relationship between employee performance and

QWL; employee satisfaction and QWL and between employee satisfaction and

employee performance.

3.7.4 Data collection

Data were collected through a well-designed questionnaire on the various

factors of quality of work life and the select socio-psychological determinants of

QWL. A sample of 500 employees was selected from various BPO companies

located in Hyderabad.

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3.7.5 Sampling technique

Stratified random sampling was used to select BPO organizations and

employees. Chart.1 shows the sampling plan. The stratification was done on the

basis of size of the company (large, medium and small), areas (urban, semi-

urban), nature of the operation (domestic, MNC), sectors (health care, call

centers, recruitment, IT and banking and others), and departments ( technical,

non technical) .

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Figure 3.1

SAMPLING DESIGN

BPOs

Large Medium Small

A1 A2

A1 A4 A4 A3

Area-wise Area-wise Area-wise

A2

A1

A2 A3

A4

A3

MNC INDIAN MNC INDIAN MNC INDIAN

Sectors Sectors Sectors Sectors

Sectors

Sectors

Dept. Dept. Dept. Dept. Dept. Dept.

S A I A

Emps Emps Emps Emps Emps Emps

M S F A

F H I M

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The sample design in Figure.1 shows that the researcher used five strata

for the present study. Initially BPO’s were divided into large, medium and small.

Secondly, they are divided into area-wise covering different urban and semi-

urban areas in the city of Hyderabad. Thirdly, they are divided into domestic and

MNC. Fourthly, different strata are evolved based on sectors like Health care,

Call centres, Recruitment BPO’s, IT& Banking and others. Finally they are

divided into different departments and then directly to employees. The sampling

plan of BPO’s showing different sectors and the types of BPO based on the

nature of operation is given in Table 3.3

Table 3.3

Sampling Plan of BPOs

S.No SECTORS MNC DOMESTIC TOTAL

1 Health Care 3 2 5

2 Call Centres 1 4 5

3 Recruitment 2 3 5

4 IT & Banking 4 1 5

5 Others 2 3 5

Total 12 13 25

Table 3.3 describes that the researcher has chosen five sectors in BPO’s

for the study. The first sector is of ‘Health Care’ consisting of five companies out

of which three are MNC and two are domestic. The second sector is ‘Call

centres’ consisting of five companies comprising of one MNC and four domestic.

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The third sector is, ‘Recruitment BPOs’ and out of 5 companies visited, two are

MNC and three are domestic. The fourth sector is of ‘IT & Banking’ consisting

of another five companies and four are from MNC and one is domestic. The fifth

sector is under ‘Others’ and this also comprises five companies; two are from

MNC and three are from domestic. Thus, the total MNCs are 12 and domestic

BPO’s are 13 and altogether the researcher had taken 25 BPO companies for the

study.

3.7.6 Determination of sample size

A number of formulae have been devised for determining the sample size

depending upon the availability of information. A few formulae are given below:

n =

d

Z

Where n = sample size

z = Value at a specified level of confidence or desired degree of precision

s = Standard deviation of the population

d = Difference between population mean and sample mean

Similarly, the sample size can be determined from the formula for determining

the standard error of the mean, i.e.

n

x

=

n

x2 = σ

2 or n =

2

2

Also from the formula for calculating standard error of proportion, the

sample size can be determined:

σp = n

pq or σ2p =

n

pq or n =

2

p

pq

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For the present study the final sample was 500. Andhra Pradesh houses

around 1300 IT-BPO organizations, [Source: IT department and STPI( Software

Technology Parks of India), 2012] of which, over 800 are operational,

approximately 700 of which, work from Hyderabad alone and provide direct

employment to 2.75 lakh people and indirect employment to 11 lakh

professionals. From 1300 IT-BPO companies, 25 BPO companies were selected

by the researcher. Since the total number of BPO companies in Hyderabad and

the total number of employees working in these companies are not exactly

known, the researcher chose a sample size of 25 BPO companies and 500

employees on a judgemental basis. (A minimum of 200 samples is essential to

test the conceptual model using SEM). The sample in each sector and their

proportion is given in Table 3.4

Table 3.4

Sample Proportion

Sl.No Sectors Sample size

Proportion

(%)

1 Health Care 107 21.4

2 Call Centres 225 45.0

3 Recruitment 72 14.4

4 IT & Banking 39 7.8

5 Others 57 11.4

Total 500 100

Table 3.4 shows that the final usable number of questionnaires is 500. The

samples taken from Health Care are 107 which comprises 21.4%, Call centres are

225 which comprises of 45.0%, Recruitment are 72 comprising 14.4%, IT &

Banking are 39 comprising 7.8% and others are 57 comprising 11.4%.

205

3.8. Development of the tool to measure the quality of work life

A measurement tool was developed and standardized to measure the

perception of employees working in BPO industry about their quality of work

life (QWL).

Initially hundred items (statements) were pooled in consultation with

academicians and industry experts. An extensive literature survey was also done

to identify the variables. These items were classified into thirty major categories.

They are as follows:

Burnout, physical work environment, turnover rate, remuneration, social

support, self-esteem, self-actualization, job satisfaction, job security, rewards,

flexi time, skills, team work, role conflict, performance measurement, suggestion

schemes, role ambiguity, job content, organizational commitment, autonomy, job

enrichment, lack of recognition, shift-work, work place integration, open

communication, creativity, support from superiors, participative problem solving,

participative decision making, constructive feedback and certain BPO specific

issues.

Some items were modified, some were deleted, based on expert opinion

and a draft scale consisting of 80 statements was prepared. This is a Likert type

scale. The items have to be rated on a five point scale which are:

1. Strongly agree

2. Agree

3. Neutral

4. Disagree

5. Strongly disagree

206

3.8.1 Try out

The scale was administered to two hundred employees selected from

various BPO organizations. Appropriate representations were given to large,

medium and small organizations, domestic and MNCs, health care, insurance,

customer care and financial sectors, urban and semi-urban.

3.8.2 Pilot study

Before carrying out the main study, a pilot study were conducted to assess

the reliability of the instrument using Cronbach’s Alpha and also to ascertain the

viability of the data collection. 200 respondents were selected from a population

similar to those who were surveyed in the main study. These respondents were

working in the various BPO companies located in different parts of Hyderabad.

The data collected from the pilot study was subjected to reliability test

using Cronbach’s Alpha to check the internal consistency. Cronbach’s Alpha is

the most prominent reliability coefficient. It measures the reliability of a set of

indicators. “Values” ranges between zero and one, if all indicators have positive

correlation. Any value above .7 is acceptable to establish the reliability of the

scale.

3.8.3. Exploratory Factor Analysis (EFA)

Exploratory Factor Analysis (EFA) was done to identify the factors which

are the index of quality of work life. Principal component method and Varimax

rotation were the main tools of analysis. Cronbach alpha was also established.

The total variance explained after rotation is given in Table 3.5

207

Table. 3.5

Total Variance Explained

Comp

onent

Initial Eigen values Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total % of

Variance

Cumulative

% Total

% of

Variance

Cumul

ative

%

Total % of

Variance

Cumulative

%

1 10.195 12.744 12.744 10.195 12.744 12.744 6.751 8.439 8.439

2 7.835 9.793 22.537 7.835 9.793 22.537 4.886 6.107 14.547

3 5.740 7.175 29.712 5.740 7.175 29.712 4.125 5.157 19.703

4 3.216 4.020 33.732 3.216 4.020 33.732 2.884 3.606 23.309

5 2.547 3.184 36.916 2.547 3.184 36.916 2.750 3.438 26.747

6 2.262 2.828 39.744 2.262 2.828 39.744 2.375 2.969 29.715

7 2.052 2.565 42.309 2.052 2.565 42.309 2.264 2.830 32.546

8 2.011 2.514 44.822 2.011 2.514 44.822 2.231 2.788 35.334

9 1.811 2.264 47.086 1.811 2.264 47.086 2.226 2.783 38.117

10 1.770 2.212 49.298 1.770 2.212 49.298 2.201 2.752 40.868

11 1.677 2.096 51.394 1.677 2.096 51.394 2.162 2.703 43.571

12 1.538 1.923 53.316 1.538 1.923 53.316 2.072 2.590 46.161

13 1.499 1.874 55.190 1.499 1.874 55.190 1.952 2.440 48.601

14 1.413 1.766 56.956 1.413 1.766 56.956 1.887 2.358 50.960

15 1.320 1.650 58.607 1.320 1.650 58.607 1.809 2.261 53.221

16 1.291 1.614 60.220 1.291 1.614 60.220 1.796 2.245 55.466

17 1.263 1.579 61.799 1.263 1.579 61.799 1.784 2.230 57.696

18 1.216 1.521 63.320 1.216 1.521 63.320 1.768 2.210 59.906

19 1.191 1.489 64.809 1.191 1.489 64.809 1.765 2.206 62.111

20 1.129 1.411 66.220 1.129 1.411 66.220 1.698 2.123 64.234

21 1.113 1.391 67.612 1.113 1.391 67.612 1.665 2.081 66.315

22 1.070 1.338 68.950 1.070 1.338 68.950 1.586 1.982 68.297

23 1.032 1.289 70.239 1.032 1.289 70.239 1.554 1.942 70.239

208

Table 3.5 reveals that from the cumulative percentage column, twenty

three components which are extracted together account for 70.23% of the total

variance (information contained in the original eighty variables/statements). This

is a pretty good bargain, because the researcher could economize on the number

of variables (80 variables/statements have been reduced to 33 statements or 23

underlying factors), while only about 30% of the information content is lost (70%

is retained by the 23 underlying factors extracted out of the 29 components which

comprised 80 statements). A graph was plotted using scree plot showing the

eigen values against the factors which is shown in Graph 1.

Graph 3.2

The scree plot (Graph.1) shows the Eigen value against the factor number. It is

seen that from twenty third factor onwards the line is almost flat. This means that

each successive factor after twenty third factor accounts for smaller and smaller

amounts of the total variance. In the second phase, the researcher, using her

discretion, raised the cut off value from 0 .5 to 0 .7, as a result, 18 highly

meritorious statements were obtained with 16 components. The rotated

component matrix containing the factor loadings for each factor is shown in

Table 3.6 below:

209

Table.3.6

Exploratory Factor Analysis Results

Rotated Component Matrix

Factors Component

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Family Weekends .13 .77 .11 .12

Login and logout .23 .27 .14 .11 .21 -.12 -.18 .14 .36 .26 -.30 .22 .10 -.13

Career opportunities .64 -.22 -.13 .11 .17 .13 -.13 .12 .25 -.16 .13

Commitment to team goals .13 .15 .22 .20 .13 -.22 .13 .14 -.17 .27 -.12 .12 -.35 .12 .37

Adjust to sudden shifts .26 .27 .14 .40 .25 .17 .13 .19 -.16 -.16 .12 .13

Self-confident .10 .11 -.11 .11 .78 .12

Brainstorm ideas .35 .25 .21 .17 -.16 .51 -.10 .12 .11 .17 .20

Flexible work timings .11 .78 .13

Participative decision making .72 -.12 .10 .20 -.18 .15

Skipping company outings -.10 .11 .13 .76 .14

Co-workers provide support -.10 -.13 .57 .14 .10 -.11 .12 .22 .11 .13 .19 .27

Able to adjust night shifts .16 .18 .17 .63 .29 .21 .19 .11

Ask help from superior -.14 .15 .10 .12 .11 .12 .23 .21 .13 .61

Contribution to society -.11 .18 .14 .61 .18 -.18 .15 -.14 .19 .16

Identify with Organization goals .95

Employee turn over .14 .11 .11 -.14 .14 .16 .72

Divided my time properly -.12 -.18 .49 -.10 .29 .10 .50 .14 -.11 .10 .14

Long queues in cafeteria .13 .19 .79

Award for winning suggestions .30 .20 .10 .21 .17 .42 -.13 .11 .19 -.20 -.22 .10 -.15 .22

Work more than 55 hrs. per week .58 .11 -.19 .16 .14 -.20 .12 -.43

Fair pay compared to others -.10 .81 .12 .11

Know other people .19 .28 .43 .12 .39 -.11 .13 .14 .15 -.16 .20 .22

Good physical surroundings -.43 .13 .28 .38 .17 .12 .23 .30 .13 .24 -.12

Report to duty during high call

volume -.10 .39 .32 .29 .16 .11 .31 .33 .20

Freedom to take decisions .41 -.26 -.14 .34 .18 .30 -.20 -.16 -.17 -.14 .26

Shifts affects biological rhythm -.33 .58 .22 .14 .15 .24 -.20 .13 .13 .10

Monotonous job .51 -.28 .34 .15 .13 -.12 .11 -.17 -.28

Assignment without adequate

resources .65 -.16 .13 .14 -.13 .17 -.15 -.11 .12 -.11 -.18

Poor fit between skills and job needs .49 .17 -.14 -.26 .16 .14 -.15 .14 -.10 .14 .16 .21 -.11 .14 .21

210

Pressure and anxiety -.26 .57 .27 .17 .13 -.14 .14 .38 .14

Work for org. for fl. consequences .36 .24 .18 .25 -.11 -.10 .28 -.10 -.11 .41

Accept myself and others as they are .17 .14 .17 .12 .15 .71

Reward accepted suggestion

liberally .10 .13 .28 .12 .20 .22 .24 .24 .20 .13 .31 .11 .31 .10

Parched throat due to non-stop

talking .37 .22 .24 .22 -.59

Work more than 10 hrs/day .35 .19 -.20 .25 -.12 .20 -.13 .12 -.45 .13 .21 .23 -.18 -.13

Generally satisfied with the work .13 .17 -.15 .13 .18 .47 .29 .22 -.11 .14 .10 .33 -.22

Dual career families -.16 .21 .32 .24 .16 .20 .54 .23 .15 -.11 .14 .11

Pursue other interests .30 -.11 -.22 .11 .66

Constructive feedback .20 .74 .10 .10

Exposure to variety of jobs .39 -.10 .22 .17 .16 .14 -.10 -.26 .37 .20 .34 .10

Grievance with merit -.15 .19 .20 .10 .12 -.24 .20 .21 .48 .12 -.13 .13 .15

Self esteem -.14 .60 .29 .13 .13 .22 .14 .18 .12

Monetary rewards system .16 .21 .21 .13 .58 .14 .15 .23 -.26 .12

No meal with other family members .67 .19 -.13 -.11 -.23 -.11 .16 .20

Intellectually stimulating job .40 .17 -.11 .24 -.14 .14 .27 .10 .49

Shown career path .59 -.15 -.34 .19 -.18 .28 .11 .17 -.12 .19

Participate in decision making .52 -.11 -.11 .16 -.16 .37 .10 .15 .23 -.28 -.15 .10 .13

Flexi-time increases morale .18 .14 .17 .19 .17 .30 .22 .12 .28 -.15 -.10 .38 .13 -.17

Less than 7 hrs. of sleep at night .52 .32 .21 .33 -.10 -.10 -.10

Breaks during duty hrs .45 .37 .18 -.24 .18 .13 .27 .29 -.12

Lot of aches and pains -.21 .67 .12 -.11 -.18 .18 -.14 .21 -.18

Steps for job enrichment -.15 .16 .31 .18 .27 .19 .32 -.14 .27 .10 -.22

Shift system -.13 .62 .12 .26 .16 -.11 -.15 -.12 .10 -.11 -.25 .11 -.15

Long queues in cafeteria .19 .67 -.10 .10 .28 .22 .15

Present career opportunities .15 .17 .15 -.13 .64 .10 .21 .26 .19

Get irritated easily .13 .14 .75

Family life satisfaction .54 -.27 .13 .10 .13 -.14 .25 -.14 -.18 .17 .17

Self critical .12 .93

Recognized for service .27 .13 .11 .22 .11 .17 .11 .54 .13 -.15 .16

My superior gives full support -.11 .10 .14 .21 .54 -.11 .22 .16 .11 .21 -.10 -.11 .22 -.28

Safe work environment -.16 .37 .22 .20 -.30 .18 .15 .43 .12 -.12 .11 .17 .11

Not seen other family members for

weeks .70 .19 -.23 .12 .15 .17 -.16

Concrete goals .29 .17 .15 -.11 .22 .12 .20 .19 -.30 .42 -.16 .24

Not always paid overtime for extra

work .58 .23 -.23 -.10 -.12 -.11 -.12 .35 .10

211

Interesting job .25 -.13 .52 .32 .21 .13 .17 -.11 .24 -.14

Instructions well communicated .11 -.10 .13 .21 -.10 .10 .73

Beyond shift hours .16 .81

Good use of skills and abilities .59 -.39 .21 .10 .10 .21 -.15 -.12 -.10

Too much of responsibility .12 .15 .65 .10 .16 -.13 .12 -.11

Current recognition program .31 .12 .16 .41 .28 .13 -.17 .21 -.20 -.42 .11

Satisfied with salary -.15 .10 -.10 .19 .71 .15 .15 -.17 .12

Lose job in 6 months .14 .11 .59 .27 .11 .24 -.10 -.16 .16 .13 -.14

Challenging work .14 .37 .38 -.19 .25 .13 .22 -.12 -.12 -.31 .11 .15

Trust supervisor ‘s feedback -.10 .54 .35 .13 -.17 .20 .29 .18 .14

Long shifts .12 .69 -.10 -.15 .11 .15 -.10 .16 .17 .10 -.14

Know my responsibilities .16 .60 .29 .30 .12 .14 .15

Shift system interferes with domestic

life

-.18 .62 .12 .10 .11 -.10 -.13 .20 -.20 .19 .10 .23

Participative decision making .10 .18 .15 .73 .13 .14

Pursuing wanted career .50 -.22 .13 .10 .19 -.11 .11 .23 .11 -.37 .16 .21

Balance between job and personal

life .44 -.27 -.24 -.14 .16 .34 .19 .29 .13 .16 .15 -.13

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 77 iterations.

Table.3.6 contains the factor loadings, which are the correlations between

the variable and the factor. Because these are correlations, possible values range

from -1 to +1.

Factors: The columns under factors are the rotated factors that have been

extracted. Here the researcher used 0.7 as the cut-off value in the factor loadings

which is considered as a high loading. Through this method, 16 components

from the 23 components earlier extracted (Table.3.5) were identified. They are

explained below:

Factor 1: Looking at the Rotated Component Matrix, it can be noticed

that variable no’s 9 and 62 have high loadings of 0.721 and 0.707 on factor 1.

This suggests that factor 1 is a combination of these two variables. So it’s a

212

combination of ‘Participative decision making’ and ‘I have not seen other family

members for weeks on end’. Therefore factor 1 can be named as “Nature of Job”

Factor 2: Looking down the factor column 2, it is seen that variable

number 67 alone has the highest loading of 0.811. The variable corresponding to

this value it is “I am asked to stay back beyond the shift hours’. Therefore factor

2 is named as “Beyond shift hours”/Extra work

Factor 3: The factor column 3 has highest loading for variable number 78

and the loaded value is 0.731. The variable used was ‘Participative Decision

making gives me job satisfaction’. Hence it can be labelled as “Participative

Decision-making.”

Factor 4: In the factor column 4, we see that the variable no. 39 has the

highest loading of 0.745. And the variable (item) is ‘My manager provides me

constructive feedback’. Therefore factor 4 can be given a name as “Constructive

feedback”.

Factor 5: In the above factor column 5 of rotated component matrix,

variable no.8 has loaded highest value of 0.789. And the corresponding statement

was ‘Flexible work timings motivate me to be more productive’. So this factor is

named as “Flexi-time”/Flexible work timings”.

Factor 6: The factor column 6 shows that variable no.5 has loaded highest

value of 0.750 and the corresponding statement was “I am expected to adjust to

sudden shifts”. So this factor can be called “Adjustment to sudden shifts”.

Factor 7: In the factor column 7, it is noticed that there are two variables

with high loadings above 0.7 such as 0.958 and 0.930. So this factor 7 is a

combination of ‘I identify with the goals of the organization’ and ‘I am self-

critical about what I do’. Therefore it is named as “Goal Congruence”

Factor 8: From the factor column 8, we come to know that the variable

no.70 has recorded a highest loading of .713. And the variable used was ‘I am

213

satisfied with my salary in this organization’. Hence it can be called “Salary

contentment”.

Factor 9: From the factor column 9, the observed highest loading was

0.814 for variable no.21 which is ‘I am getting fair pay compared to others doing

similar work’. So it is labeled this “Fair Pay”

Factor 10: In the factor column 10, the highest loaded variable is no.6

with factor loading of .786 which was explained as ‘I am confident of doing any

difficult task.’ Hence it can be called “Self-efficacy/Self-confidence”.

Factor 11: The factor column 11 shows that the variable no.66 alone has

the highest loading of 0.730 and its statement (item) was ‘Instructions are well

communicated in my company’ and hence this factor can be named “Well-

Communicated Instructions”.

Factor 12: The factor column 12 reveals that variable no.1 is having the

highest loading of 0.771. And the statement was ‘Weekly Breaks was spent

entirely with my family’, so it is called “Weekly Breaks/Leisure time/family

weekends.

Factor 13: Looking down the factor column 13, we realize that the

variable no.31 has the highest loading of 0.716. The statement is ‘I continue to

work for the organization because I fear the financial consequences of leaving’.

Thus it can be called “Uncertain future”.

Factor 14: The factor column 14 tells that the variable 10 ha the highest

loading of 0.765 with the statement ‘Skipping Company outings implies lack of

commitment and team bonding’ and so it is labeled as “Team

Bonding/Organizational Commitment”.

Factor 15: The factor column 15 shows that the variable no.16 has

recorded highest loading of .723 and the variable described as ‘Recognizing

214

employee performance reduces employee turnover’. So it was be named as

“Employee Turnover”

Factor 16: The last factor column 16 tells that variable no.54 has the

highest loading of 0.792 with statement ‘Long queues in cafeteria forces me to

choose fast-food’. So this last factor can be named as “Fast food”

A careful selection of the items/statements was made on the basis of the

merit of the statements after item analysis. The final scale constituted statements

selected on the basis of factor loading. Statements having factor loading .7 and

above were considered for the final scale. Both positive and negative statements

of approximately equal number were included. Care was taken to include

maximum possible aspects of QWL.

The number of items/statements in the final scale is eighteen. They are

arranged randomly. The scale is preceded with an introductory note carrying

instructions to the respondents. The final scale is given in Appendix. A

3.8.4. Reliability and validity of the tool/scale.

The data collected from the pilot study were subjected to reliability test

using Cronbach’s Alpha to check the internal consistency. The overall reliability

coefficient alpha value is given in Table 3.7

Table.3.7

Reliability Statistics

Cronbach's Alpha No. of Items

.848 80

215

Table 3.7 reveals that overall Cronbach alpha value for the QWL items is

0.848. The items extracted from the pilot study for QWL items along with their

Cronbach alpha values are given in Table 3.8

Table.3.8

Item-Total Statistics

Items Scale Mean if

Item Deleted

Scale

Variance if

Item

Deleted

Corrected Item-

Total

Correlation

Cronbach's

Alpha if Item

Deleted

Weekly Breaks is entirely spent with my

family 192.8844 617.678 .187 .847

I am expected to adjust to sudden changes in

shifts 192.3970 604.806 .374 .844

I am confident of doing any difficult task 192.7789 618.032 .208 .846

Flexible work timings motivates me to be

more productive 192.8342 614.897 .250 .846

Participative decision making is time

consuming 191.8693 607.165 .314 .845

Skipping company outings implies lack of

commitment to the team and team bonding 192.2111 618.410 .132 .848

I identify with the goals of the Organization 192.7186 600.516 .194 .849

Recognizing employee performance reduces

employee turn over 192.1960 611.512 .256 .846

Long queues in the cafeteria forces me to skip

the meal in order to log back on time 191.6583 607.347 .320 .845

I am getting fair pay compared to others doing

similar work 191.9698 616.191 .214 .846

I continue to work for the org. Because I fear

the financial consequences of leaving 191.5879 609.314 .291 .845

My manager provides me with constructive

feedback 192.7085 616.642 .243 .846

Long queues in cafeteria forces me to choose

fast food 192.0352 605.771 .356 .844

I am self critical about what I do 191.5779 587.892 .125 .863

I have not seen other family members for

weeks on end 191.1709 603.476 .340 .844

I am asked to stay back beyond shift hours 191.7990 611.838 .247 .846

I am satisfied with my salary in this

organization 191.8543 621.176 .102 .848

Participative decision making gives me job

satisfaction 192.5578 606.743 .435 .844

216

Table 3.8 shows that all the Cronbach alpha values range from 0.847 to

0.844. So the pilot study results showed that the constructs’ alpha coefficients

had an acceptable level (> 0.70) which is considered sufficient.

The final tool is presented in Appendix A. This tool can be used by other

research workers and academicians for similar purpose.

3.9 MEASUREMENT TOOL TO MEASURE THE SELECT SOCIO-

PSYCHOLOGICAL FACTORS

To measure the select socio-psychological variables namely, leadership

styles, work culture, employee attitude to job, occupational self efficacy,

employee participation in non-work related activities, self concept and employee

perception about the company, adapted version of the tools developed by

Abraham (2011), Pareek and Rao (2003), Chirayath (1992), Chirayath (1992),

Nair (1980) and Chirayath (1992) were used. The reliability coefficients,

Cronbach alpha for all these measurement tools were established. They are given

in Table.3.9

3.10 Administration of the questionnaire

The questionnaire can be administered individually or in group. In order to

make the employees feel free, the manager’s presence was avoided. Moreover,

the respondents should remain incognito. That may give them greater sense of

security. The time generally taken for completing the questionnaire was thirty

minutes.

The purpose of the questionnaire was to secure a description of the

different ways in which employees and managers behave in the organizations and

the situations and conditions in which they work for the goal attainment of the

organization. The items in the questionnaire describe typical behaviours or

conditions/situations that occur within a BPO company in Hyderabad. The items

are not to be evaluated in terms of “good” or “bad” behaviour, but they are to be

217

responded in terms of how well the statement best describes the typical

behaviour/situation of employees in group and the manager.

The scale against which the respondents indicated the extent of agreement /

disagreement with reference to the characteristics of his/her organization is

defined by the following five categories.

1. Strongly agree

2. Agree

3. Neutral

4. Disagree

5. Strongly disagree

3.11 SCORING OF THE QUESTIONNAIRE

The five categories of the responses were scored by simply assigning

numbers to the respective categories. Any five successive integers such as 1,

2,3,4,5 can be converted from one scale to another by merely adding or

subtracting a constant and this can be done without affecting the variance. In the

present study, the following scoring pattern was followed:

Strongly agree - 5

Agree - 4

Neutral - 3

Disagree - 2

Strongly disagree - 1

Negative statements were scored assigning the scores of 1,2,3,4 and 5. To

find out the raw scores for each employee, the scores of all items in the

questionnaire answered by him/her were added. This gave the score of that

218

particular employee regarding his/her perception of quality of work life and the

select socio-psychological factors in his/her organization.

3.12 PSYCHOMETRIC CHECKS

As mentioned earlier, a structured questionnaire developed by the

researcher was used as the instrument for data collection for the study. Items

selected for the constructs were mainly adopted from prior studies to ensure

content validity. However, the instrument was validated for the main study again

for the sample size of 500.

3.12.1 Reliability

Reliability, also called consistency and reproducibility, is defined in

general as the extent to which a measure, procedure, or instrument yields the

same result on repeated trials (Carmines & Zeller, 1979). It can be used to assess

the degree of consistence among multiple measurements of variables (Hair,

Anderson, Tathman, & Black, 1998).

The internal reliability of the measurement models was tested using

Cronbach’s alpha and Fornell’s composite reliability (Fornell and Larcker 1981).

The Cronbach’s reliability coefficients of all variables should be higher than the

minimum cut-off score of 0.70 (Nunnally 1978; Nunnally and Bernstein, 1994).

The construct reliability coefficient alpha arrived at from the pilot study

data has been presented in Table 3.9 for all the constructs.

219

Table 3.9

Instrument’s Cronbach’s Alpha Reliability

S.No Constructs No. of items Cronbach’s

Alpha

1. Quality of Work Life 18 0.8480

2. Leadership Styles 6 0.8231

3. Work Culture 6 0.7253

4. Employee Attitude to Job 6 0.6496

5. Occupational Self Efficacy 6 0.7557

6. Employee Participation in Non-

work Related activities 6 0.8107

7. Self Concept 6 0.6555

8. Employee Perception about the

company 6 0.7213

Table 3.9 shows the reliability analysis results. The values range from

0.84 to 0.72. Three constructs out of eight, have alpha values greater than 0.80,

(QWL, Leadership styles and Employee participation in non-work related

activities), three have values greater than 0.70 (work culture, occupational self

efficacy and employee perception about the company) and two have alpha scores

above 0.6 (employee attitude to job and self concept)

3.12.2 Validity

A scale is said to be valid if it measures correctly what it is expected to

measure. In other words, a scale is valid only when it is real and correct. The

validity of a questionnaire relies first and foremost on reliability. If the

questionnaire cannot be shown to be reliable, there is no discussion of its

validity.

Researchers use different methods of establishing the validity of the

instrument which they have developed. They are: content validity, convergent

220

validity, discriminant validity and nomological validity. In the present study the

content validity was established. It is given in the following section.

3.12.2.1 Content Validity

For the content validity, a thorough review of the literature was

conducted. As mentioned earlier, all items of the constructs have been drawn

from well established studies to ensure content validity. The questionnaire was

also validated by having a panel of experts (human resource managers working

in the BPO industry and academicians) review it, after which necessary changes

were incorporated to improve both the content and clarity of the questionnaire.

The instrument was tested through two stages. In the first stage, two English

faculty members reviewed the instrument to ensure the clarity of items and the

accuracy of the language. In the second stage, a panel of experts was selected to

establish face and content validity of the instrument. The panel of experts

consisted of six individuals- four human resource managers of the BPO industry,

who had earlier participated in the instrument development and two PhD students

who were fluent in English and who have experience in fields related to the

instrument design and technology use. The reviewed questionnaire was then

followed to ensure the validity, clarity, and consistency with the main purpose of

this research.

3.13 Statistical analysis

Sophisticated statistical techniques were used in the analysis of the

collected data. SPSS software (version 16) and AMOS software (version 16)

were used for this purpose. The statistical tools used for the analysis along with

its purpose are presented in Table 3.10

221

Table 3.10

The statistical analysis performed

S.

No

Statistical tool Purpose

1 Exploratory factor

analysis

To reduce the number of initial QWL components into

few meaningful factors

2 Descriptive Statistics

(Percentage analysis)

To describe the sample in terms of their demographic

characteristics

3. T test

To find out whether there is any significant relation

between QWL and gender (male-female), marital

status (married-unmarried), and nature of the job

(technical &non-technical)

4. One way ANOVA

To find out the relationship between the demographic

variables and socio-psychological factors of quality of

work life

5. Chi-square test To find out the association between demographic

variables and level of quality of work life

6. Friedman test To find out the significant difference between mean

ranks towards perception on QWL and the socio-

psychological factors of QWL

7. Correlation (Pearson r)

To find out the relation between QWL and the select

socio-psychological variables

8 Regression analysis

To measure the impact of the select socio-psychological

variables on QWL

9 SEM To test the conceptual model which the researcher has

developed

These different statistical techniques namely, Exploratory Factor

analysis(EFA), Descriptive statistics, t test, one way ANOVA, Chi-square test,

Friedman test, Correlation (Pearson r), Regression analysis and Structural

Equation Modelling (Table.3.10) enabled the researcher to test the various

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hypotheses formulated in the present study to achieve the objectives of the

study. A detailed explanation of these tests is given in the following section.

3.13.1 Exploratory Factor analysis

In multivariate statistics, exploratory factor analysis (EFA) is a statistical

method used to uncover the underlying structure of a relatively large set

of variables. EFA is a technique within factor analysis whose overarching goal is

to identify the underlying relationships between measured variables. It is

commonly used by researchers when developing a scale (a scale is a collection of

questions used to measure a particular research topic) and serves to identify a set

of latent constructs underlying a battery of measured variables. It should be used

when the researcher has no a priori hypothesis about factors or patterns of

measured variables. In fact, “measured variables” means “any one of several

attributes of people that may be observed and measured”. An example of a

measured variable would be one item on a scale. EFA procedures are more

accurate when each factor is represented by multiple-measured variables in the

analysis. There should be at least 3 to 5 measured variables per factor.

EFA is based on the common factor model. Within the common factor

model, measured variables are expressed as a function of common factors,

unique factors, and errors of measurement. Common factors influence two or

more measured variables, while each unique factor influences only one measured

variable and does not explain correlations among measured variables.

An assumption of EFA is that any indicator/measured variable may be

associated with any factor. When developing a scale, researchers should use EFA

first before moving on to confirmatory factor analysis (CFA). EFA requires the

researcher to make a number of important decisions about how to conduct the

analysis because there is no one set method.

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3.13.2. Percentage analysis

“Percentage method” refers to a specified kind of statistical analysis which

is used in making comparison between two or more series of data. Percentages

are based on descriptive relationship. It compares the relative items. Since

the percentage reduces multiple observations to a common base, this method

allows meaningful comparison among various set of data.

Percentage = 100xsrespondentofnoTotal

responsesofnumberTotal

3.13.3 Independent samples t test

The independent samples t test allows researcher to evaluate the mean

difference between two populations using the data from two samples. This test is

used in situations where a researcher has no prior knowledge about either of the

two populations being compared. The general purpose of the independent

samples t test is to determine whether the sample mean difference obtained is a

real difference between the two populations or simply the result of sampling

error.

In this study t test is used to find out the significance difference between

means of two independent samples. The two independent samples considered in

this study are “male” and “female”, “married” and “unmarried” and “technical”

and “non-technical” staff.

3.13.4 One Way ANOVA

ANOVA is a statistical technique for examining the differences among

means for two or more populations. The null hypothesis, typically, is that all

means are equal. In one way ANOVA, the dependent variable is denoted by Y

and the independent variable by X. X is a categorical variable having c

categories. There are n observations on Y for each category of X.

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In examining the differences among means, one way analysis of variance

involves the decomposition of the total variation observed in the dependent

variable. This variation is measured by the sums of squares corrected for the

mean (SS). Analysis of variance is so named because it examines the variability

or variation in the sample (dependent variable) and, based on the variability,

determines whether there is reason to believe that the population means differ.

In analysis of variance, two measures of variation are estimated: within

groups and between groups. Within groups variation is a measure of how much

the observations, Y values, within the group vary. This is used to estimate the

variance within a group in a population. It is assumed that all groups have the

same mean; the variance of all observations cannot be calculated together. The

variance for each of the groups must be calculated individually, and these are

combined into an “average” or “overall” variance.

In the present study one way analysis is used to find out the difference, if

any, with respect to socio-psychological factors of QWL based on the

demographic characteristics of employees, namely, age, education levels, income

levels, and work experience both total work experience and work experience in

the current organization.

3.13.5 Cross-tabulation

Cross-tabulation is one of the most useful analytical tools and is a main-

stay of the market research industry. One estimate is that single variable

frequency analysis and cross-tabulation analysis account for more than 90% of

all research analyses.

Cross-tabulation analysis, also known as contingency table analysis, is

most often used to analyze categorical (nominal measurement scale) data. A

cross-tabulation is a two (or more) dimensional table that records the number

(frequency) of respondents that have the specific characteristics described in the

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cells of the table. Cross-tabulation tables provide a wealth of information about

the relationship between the variables.

Cross-tabulation analysis has its own unique language, using terms such

as “banners”, “stubs”, “Chi-Square Statistic” and “Expected Values.”

3.13.5.1 Chi-square test

A Chi-square is a statistical measure used in the context of sampling

analysis for comparing a variance to a theoretical variance. As a non-parametric

test, it can be used to determine if categorical data shows dependency or the two

classifications are independent. It can also be used to make comparisons between

theoretical populations and actual data when categories are used. Thus, the chi-

square test is applicable in large number of problems. The test is, in fact, a

technique through the use of which it is possible for all researchers to (1) test the

goodness of fit (2) test the significance of association between two attributes, and

(3) test the homogeneity or the significance of population variance.

3.13.5.2 Cross-tabulation with Chi-square analysis

The Chi-square statistic is the primary statistic used for testing the

statistical significance of the cross-tabulation table. Chi-square tests whether or

not the two variables are independent. If the variables are independent (have no

relationship), then the results of the statistical test will be “non-significant” and

“are not able to reject the null hypothesis”, meaning that it is believed there is no

relationship between the variables.

If the variables are related, then the results of the statistical test will be

“statistically significant” and “are able to reject the null hypothesis”, meaning

that it can be stated that there is some relationship between the variables.

The chi-square statistic, along with the associated probability of chance

observation, may be computed for any table. If the variables are related (i.e. the

observed table relationships would occur with very low probability, say only 5%)

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then it can be said that the results are “statistically significant” at the “.05 or 5%

level”. This means that the variables have low chance of being independent.

In the present study crosstabs were done using Chi-square analysis to find

out the association between the demographic variables i.e. educational

qualification, monthly income of the employees and QWL. Crosstabs were also

done to find out the association between level of QWL and level of performance.

3.13.6 Friedman test

The Friedman test is a non parametric test. Similar to the parametric

measures like ANOVA, it is used to detect differences in treatments across

multiple test attempts. The procedure involves ranking each row (or block)

together, then considering the values of ranks by columns.

In the present study, Friedman test is used to test the significance

difference between mean ranks towards perception of each of the select socio-

psychological factors and also towards QWL.

3.13.7 Correlation analysis

The degree of relationship between the variables under consideration is

measured through the correlation analysis. The measure of correlation or

correlation index summarizes in one figure the direction and degree of

correlation. The correlation analysis refers to the techniques used in measuring

the closeness of the relationship between the variables. Thus, correlation is a

statistical device which helps in analysing the co-variation of two or more

variables. The detection and analysis of correlation (i.e., co-variation) between

two statistical variables requires relationship of some sort which associated the

observation in pairs, one of each pair being a value of each of the two variables.

In the present study correlation (Pearson r) is used to find out the

correlation between each of the select socio-psychological variables and quality

of work life, employee performance and employee satisfaction. It was also used

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to find out the relationship between quality of work life and employee

performance, quality of work life and employee satisfaction.

3.13.8 Multiple Regression Technique

Multiple regression analysis is a statistical technique that allows the

researchers to use more than one independent variable to predict a single

dependent variable. It can also show how a set of independent variables explain a

proportion of the variance in a dependent variable at a significant level. Brace,

Kemp, and Snelgar (2006) specify four conditions for using multiple regression

technique in statistical analysis:

There are linear relationships between the predictor and dependent

variables (i.e., the relationship follows a straight line).

The criterion variable is measured on a continuous scale such as interval

or ratio scale

The predictor variables are measured on a ratio, interval, or ordinal scale.

When there are a large number of observations, the number of participants

must substantially exceed the number of predictor variables used in the

regression. The absolute minimum is five times the number of many

participants as predictor variables.

In the present study multiple regressions is applied to find out the impact of

the select socio-psychological variables i.e. leadership styles, work culture,

employee attitude to job, occupational self efficacy, employee participation in

non work related activities, self concept and employee perception about the

company on QWL. It was also used to find the impact of socio-psychological

factors, employee satisfaction and QWL on employee performance.

3.13.9 Structural Equation Modelling

Structural Equation Modelling (SEM) is a family of statistical models that

seek to explain the relationships among multiple variables. In the process, the

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structure of interrelationships expressed in a series of equations is examined,

similar to a series of multiple regression equations. These equations depict all the

relationships among constructs (both the dependent and the independent).

Constructs are unobservable or latent factors represented by multiple variables. A

latent construct is a hypothesized and unobserved concept that can be represented

by observable variables. It is measured indirectly by examining consistency

among multiple measured variables, also referred to as manifest variables or

indicators. SEM’s foundation lies in two familiar multivariate techniques: factor

analysis and multiple regression analysis.

Structural Equation Modelling (SEM) is widely used in behavioural research.

SEM is used in the present study because of the following three distinct

characteristics:

1. Estimation of multiple and inter-related dependence relationships.

2. Ability to represent unobserved concepts/latent variables in these

relationships and check for measurement error in the estimation process

3. Defining a model to explain the entire set of relationship

The most obvious difference between SEM and other multivariate techniques

is the use of separate relationships for each set of dependent variables. In simple

terms, SEM estimates a series of separate, but interdependent, multiple

regression equations simultaneously by specifying the structural model used by

the statistical program. Some dependent variables become independent variables

in subsequent relationships, giving rise to the interdependent nature of the

structural model. The structural model expresses these relationships among

independent and dependent variables, even when a dependent variable becomes

an independent variable in other relationships.

The proposed relationships are then translated into a series of structural

equations (similar to regression equations) for each dependent variable. This

feature sets SEM apart from multivariate analysis of variance and canonical

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correlation- in that they allow only single relationship between dependent and

independent variables.

AMOS (Analysis of Moment Structures) is an easy to use Structural Equation

Modelling (SEM) program that tests relations among observed and latent

variables and then uses models to test hypotheses and confirm relationships.

Some of the advantages of AMOS are: graphical language, no need to write

equations or type commands, easy to learn user-friendly features such as drawing

tools, configurable toolbars, and drag and drop capabilities, fast. Models that

once took days to create can now be completed in minutes using AMOS.

In the present study SEM is used to test whether the model developed by the

researcher is fit or not and also the interrelationships are examined. The

conceptual model was developed initially by the researcher. In this model, there

are seven socio-psychological variables which are known as Independent

variables. They are: Leadership styles, work culture, employee attitude to job,

occupational self efficacy, employee participation in non-work related activities,

self concept and employee perception about the company. These seven socio-

psychological variables directly contribute to quality of work life (QWL). This

QWL leads to employee satisfaction which ultimately leads to employee

performance. Thus some dependent variables become independent variables in

subsequent relationships, giving rise to the interdependent nature of the structural

model. The conceptual model was tested using SEM and it was found that the

model developed was fit.

3.14 Limitations of the study

The present study, though carefully planned and executed, is not free from

certain limitations. They are given below:

The variables under study may be affected by external factors which the

study does not take into consideration.

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Since this research investigation covers only one area, the city of

Hyderabad, the problem of generalization may occur. Due to time and financial

constraints, the study was conducted in the Hyderabad city only. There are 234

BPO’s in Hyderabad, out of that only 25 companies were selected for the study.

The sample comprises only 500 employees. Due to the peculiar nature of BPO

industry, it was very difficult to meet the employees. Many companies did not

allow meeting the employees.

The data relating to QWL were analyzed to identify the perception of

employees working in BPO companies. Only sixteen components of QWL were

identified and data pertaining to only those components were analyzed. With

regard to the socio-psychological determinants of QWL, only seven variables

were studied.

The conclusions apply only to this population and any broader

generalization beyond this population will not be justified. Obviously, no

perfection is claimed and it is admitted without any hesitation that the present

study is only a fragmentary attempt in this field.

3.15. CONCLUSION:

The research methodology adopted in the study is explained in detail in

this chapter. Identification of significant gaps in QWL literature is the rationale

of the present study. As such the present study intends to fill up those gaps by

developing a new paradigm for establishing a congenial work environment in the

BPO companies in Hyderabad and sustain high levels of quality of work life for

the young professionals working there. It should be remembered that BPO

industry is a major employer of young adults in the country. Hence their physical

and mental well-being is of interest to the IT department, health ministry and also

to the public at large. As mentioned in the significance of the study, surveys and

anecdotal evidences show that employees in the BPO sector experience high

levels of stress and burn out. Solutions should be found out for the various

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physical and emotional problems experienced by the employees so that attrition

which is a major issue in BPO industry can be checked. Since 70% of the work

force in BPO belongs to Gen Y, managers should possess effective mind-set and

key competencies to manage the millennial and help demystify manager-

millennial interactions that sometimes lead to frustration and conflict (Espinoza,

2011). It should always be remembered that employees leave the managers and

not the organizations. Hence equipping the managerial leaders with required

competencies is the best way to address the challenge of integrating the

millennial into the work force.

The chapter also discussed the objectives of the study and the hypotheses,

data collection method, sample and sampling technique, development of the tool

to measure QWL, psychometric checks such as reliability and validity of the

scale developed. The limitations of the study are also stated in this chapter.

The analysis and interpretation of the data are given in Chapter 4