Influence of Employees' Resilience Emotional Intelligence and Job Satisfaction on Wellbeing with Reference to Textile Apparel Industry

Tesis Doctoral / Disertación , 2018 , 264 Páginas

Política - Región: Asia del Sur

The South Indian Textile apparel industry especially Chennai play a vital role in Indian economy. Being a labor-intensive industry it provides much job opportunities to a large segment of the population. In this rapidly changing global market, employees have to prepare themselves for handling this scenario. Employees who are much influenced by physical, mental and social activities on their work environment have an impact on their individual wellbeing. The psychologist, economists and world health organization’s at the global level are much interested on knowing the work pleasure and pressure, hence the researcher has given thrust on employees wellbeing reflecting through the variables chosen in this study.

On continuous review of various literatures, the researcher was able to identify the research gap giving rise to the following research questions and evolve the objectives of this study. The aim of the research is to find, is there any influence of individual resilience and emotional intelligence on the wellbeing of employees with job satisfaction as mediating variable working in the textile apparel industry. The researcher has adopted descriptive research and has applied convenience and purposive sampling method for data collection. To ensure the reliability and validity of the research instrument, a pilot test was conducted using four sets of self administered standardized research questionnaires. The respective scale, for Resilience framed byG ail Wagnild & Heather Young 1993, Emotional Intelligence scale by Emily Sterrett 2015, Job satisfaction scale by Thompson & Terpening 1983 and Wellbeing scale by Jagsharanbir Singh & Asha Gupta 2001. The results of reliability, Cronbach's Alpha test for items and constructs were found to be in the range of 0.7 and 0.8 above, which holds the internal consistency and the reliability of the questionnaires as recommended by Cornbach 1984, Nunnally 1978.

TABLE OF CONTENTS

ABSTRACT

LIST OF TABLES

LIST OF FIGURES

LIST OF ABBREVIATIONS

1 INTRODUCTION
1.1 INTRODUCTION TO GLOBAL TEXTILE APPAREL INDUSTRY
1.1.1 Introduction to Indian Apparel Industry
1.2 INTRODUCTION TO RESILIENCE
1.2.1 Resilience and Job Satisfaction
1.2.2 Resilience and Wellbeing
1.3 INTRODUCTION TO EMOTIONAL INTELLIGENCE
1.3.1 Emotional Intelligence & Job Satisfaction
1.3.2 Emotional Intelligence & Wellbeing
1.4 INTRODUCTION TO JOB SATISFACTION
1.5 INTRODUCTION TO WELLBEING
1.6 NEED FOR THE STUDY
1.7 STATEMENT OF THE PROBLEM
1.8 RESEARCH QUESTIONS
1.9 RESEARCH OBJECTIVES
1.10 RESEARCH HYPOTHESIS

2 REVIEW OF LITERATURE
2.1 RESILIENCE
2.2 EMOTIONAL INTELLIGENCE
2.3 JOB SATISFACTION
2.4 WELLBEING

3 RESEARCH METHODOLOGY
3.1 INTRODUCTION
3.2 RESEARCH DESIGN
3.3 RESEARCH FLOW CHART
3.4 INDUCTIVE VS DEDUCTIVE
3.5 DESCRIPTIVE RESEARCH
3.6 RESEARCH INSTRUMENT AND SCALES
3.7 MEASURES OF VARIABLES
3.7.1 Measures for Resilience
3.7.2 Measures for Emotional Intelligence
3.7.3 Measures for Job Satisfaction
3.7.4 Measures for Well Being
3.7.5 Demographic Questions
3.7.6 Ethical Considerations
3.8 DESCRIPTION OF SAMPLING TECHNIQUE
3.8.1 Target Population
3.8.2 Population Exclusions
3.8.3 Sample Size
3.8.3.1 Determination of Sample Size
3.8.3.2 Screening of Samples
3.9 DATA COLLECTION METHOD
3.10 PILOT STUDY
3.11 RELIABILITY AND VALIDITY TEST
3.11.1 Reliability Test
3.11.2 Validity Test
3.11.3 Convergent Validity
3.11.4 Discriminant Validity
3.12 NORMALITY TEST
3.13 MULTICOLLINEARITY
3.14 UNIDIMENSIONALITY
3.15 CONFIRMATORY FACTOR ANALYSIS
3.15.1 Fit indices of Resilience
3.15.2 Fit indices of Emotional Intelligence
3.15.3 Fit Indices of Job Satisfaction
3.15.4 Fit Indices of Wellbeing
3.16 PATH DIAGRAM FOR RESEARCH FRAMEWORK
3.17 RESEARCH SOFTWARE PACKAGES
3.18 STATISTICAL TOOLS FOR DATA ANALYSIS
3.18.1 Percentage Analysis
3.18.2 Independent Sample t-Test
3.18.3 Analysis of Variance (ANOVA)
3.18.4 Multi Linear Regression
3.18.5 Measurement Model
3.18.6 Structural Equation Model

4 DATA ANALYSIS
4.1 INTRODUCTION
4.2 PRELIMINARY DATA SCREENING AND DATA CLEANING
4.3 DESCRIPTIVE ANALYSIS OF SAMPLE
4.4 T-TEST
4.4.1 Gender
4.4.2 Marital Status
4.4.3 Type of Family
4.4.4 Nature of Job
4.5 ANOVA
4.5.1 Age
4.5.2 Educational Qualification
4.5.3 Salary
4.5.4 Number of Children
4.5.5 Experience
4.6 MULTIPLE LINEAR REGRESSION
4.6.1 Multiple Linear regression with Wellbeing and Factors of Resilience
4.6.2 Multiple Linear regression with Wellbeing and Factors of Emotional Intelligence
4.6.3 Multiple Linear regression with Job Satisfaction and Factors of Resilience
4.6.4 Multiple Linear regression with Job Satisfaction and Factors of Emotional Intelligence
4.6.5 Multiple Linear regression with Job Satisfaction and Wellbeing
4.6.6 Multiple linear regression with Wellbeing and Resilience, Emotional Intelligence and Job Satisfaction
4.7 MEASUREMENT MODEL WITH JOB SATISFACTION
4.7.1 Introduction
4.7.2 The Measurement Model
4.7.3 Type of Model
4.7.4 Model Identification
4.7.5 Model Estimation Method
4.7.6 Model Evaluation Criteria: Goodness of Fit
4.7.7 Chi Square (CMIN) Goodness of Fit
4.7.8 The Goodness-of-fit Index (GFI & AGFI)
4.7.9 Comparative Fit Index (CFI)
4.7.10 Normed Fit Index (NFI)
4.7.11 Root Mean Square Residual
4.7.12 Root Mean Square Error of Approximation (RMSEA)
4.8 STRUCTURAL EQUATION MODEL ON EMPLOYEES WELLBEING WITH OUT MEDIATING EFFECT
4.8.1 Introduction
4.8.2 The Variables used in the Structural Equation Model
4.8.3 Assessment of model 160
4.8.4 Assessment of Goodness-of-Model Fit Indices Statistic with out Mediating Effect
4.9 STRUCTURAL EQUATION MODEL (SEM) ON EMPLOYEE WELLBEING WITH MEDIATING EFFECT
4.9.1 Variables used in the Structural Equation Model
4.9.2 Testing of SEM Path Hypothesis Testing
4.9.3 assessment of goodness-of-model fit indices statistic with mediating effect
4.9.4 Comparison of Sem Model with and without Mediation Effect

5 FINDINGS AND DISCUSSION
5.1 CONFIRMATORY FACTOR ANALYSIS AND VALIDATION OF THE INSTRUMENT
5.2 SUMMARY OF FINDINGS
5.3 CONCLUSION
5.3.1 Tool on Promoting Resilience, Emotional Intelligence and Wellbeing on Employees
5.4 LIMITATIONS
5.5 FUTUROLOGY
5.6 RECOMMENDATIONS

APPENDIX 1

APPENDIX 2

APPENDIX 3

APPENDIX 4

REFEREFCES

LIST OF PUBLICATIONS

ABSTRACT

The South Indian Textile apparel industry especially Chennai play a vital role in Indian economy. Being a labor-intensive industry it provides much job opportunities to a large segment of the population. In this rapidly changing global market, employees have to prepare themselves for handling this scenario. Employees who are much influenced by physical, mental and social activities on their work environment have an impact on their individual wellbeing. The psychologist, economists and world health organization’s at the global level are much interested on knowing the work pleasure and pressure, hence the researcher has given thrust on employees wellbeing reflecting through the variables chosen in this study.

On continuous review of various literatures, the researcher was able to identify the research gap giving rise to the following research questions and evolve the objectives of this study. The aim of the research is to find, is there any influence of individual resilience and emotional intelligence on the wellbeing of employees with job satisfaction as mediating variable working in the textile apparel industry. The researcher has adopted descriptive research and has applied convenience and purposive sampling method for data collection. To ensure the reliability and validity of the research instrument, a pilot test was conducted using four sets of self administered standardized research questionnaires. The respective scale, for Resilience framed by (Gail Wagnild & Heather Young 1993), Emotional Intelligence scale by (Emily Sterrett 2015), Job satisfaction scale by (Thompson & Terpening 1983) and Wellbeing scale by (Jagsharanbir Singh & Asha Gupta 2001). The results of reliability, Cronbach's Alpha test for items and constructs were found to be in the range of 0.7 and 0.8 above, which holds the internal consistency and the reliability of the questionnaires as recommended by (Cornbach 1984, Nunnally 1978 )

Ensuring the pilot test and estimating the sample size, data collection for the main study was administered to 800 respondents, on screening only 750 could be used for the further research. The hypothesis was framed to meet the objectives of the study and data analysis was done using SPSS ver.21 package and AMOS ver.21, tools involving univariate to multivariate techniques. The Confirmatory Factor Analysis (CFA) loadings on an average were found to be above 0.6 for many items except for few. The results of fit indices were found to be within permissible limits representing a “good model fit” indicates that the model is plausible, (Schermelleh-Engel et al. 2003). Linear multiple regressions analysis to study the relationship between predictor variables namely, resilience, emotional intelligence, job satisfaction and the criterion variable well being of textile apparel employees were performed. The hypothesis was tested and results show resilience, emotional intelligence, and job satisfaction were significantly related to well being. On executing the measurement model the results of convergent validity (AVE > 0.5), and discriminant validity of (AVE > SIC) shows the degree of measurement of result that can be depended and accurate. Finally, the structural model was tested with five postulated hypotheses (H0 1s - H0 5s), and the results of hypotheses supported the relationship between resilience, emotional intelligence, job satisfaction and well being. Also, job satisfaction was found to have a partial mediating effect, strengthening the predicted model between the independent and dependent variables. The implications of the study both theoretical and empirical part have been discussed in successive chapters, followed by its findings, conclusions, specific limitations and further direction for future research .

Key words: Resilience, Emotional Intelligence, Job Satisfaction and Employee Wellbeing.

ACKNOWLEDGEMENTS

During my Ph.D. program, there have been many people helping me on overcoming the challenge, hence I wish to acknowledge them with my heartfelt gratitude. I like to thank Dr.L.Suganthi, Professor & Head, Department of management Studies, Anna University, Chennai. A special thanks to my supervisor, Dr. S.N Geetha, Professor, Department of Management Studies, Anna University, Chennai to accord my Ph.D. degree program & her timely support on tough days for successful completion of my work.

I would like to thank DrL.Prakash Sai Professor & Head, Department of Management Studies IIT Chennai, Dr.Rajendran (Retd. Professor & Head, Department of Management Studies Anna University) and other faculty members. I would like to extend my sincere thank to the doctorial committee members, Dr.P.T.Srinivasan (Retd. Professor and Head, Department of management Studies, Madras University), and Dr. S.Subramanian, Professor, Tamil Nadu Open University, Chennai.

My thanks to personalities behind this success Dr.Joviee, Professor & Ex. HOD Psychology Dept. Madras School of Social Works, Dr.N.R.V.Prabhu, Dr.Charumathi, Dr.Chandra Shaker and Dr.Vijayaragavan Prof. and Principal Jaya Engineering College. I like to thank the managing directors and HR, persons of textile apparel industries, a special thanks to Mr.S.Ashoke Kumar, Mr. Rajesh, Mr.Murugan, Ms.Vijayalakshmi & others during my data collection. I would like to thank my beloved parents Late Mrs.Indrani Elumalei and Mr.Elumalei MA., HDC (Ret.JReg.Co-op), my sister Dr.E.Dhevahi Ph.D., FABMS, my Uncle Er.A.Rangappan, B.E and their budding child R.Malavikka for continuous encouragement, support in all my endeavors and academic goals. Thanks to my friends, research fraternity and student community for their extensive help on successful completion of my research in a useful and informative way.

ANANDHARAJA E

LIST OF TABLES

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LIST OF SYMBOLS AND ABBREVIATIONS

Abbildung in dieser Leseprobe nicht enthalten

CHAPTER 1 INTRODUCTION

1.1 INTRODUCTION TO GLOBAL TEXTILE APPAREL INDUSTRY

Textile is one of the oldest commodities which play a major role in the global economy. The textile industry has widened its wings on the concept ready to wear garments, the so-called apparel industry. Textiles, a renowned art of sewing is around 20,000 years old, which reflects oneself or a group in existence for millennia. “Throughout recorded history, clothing, along with food and shelter has been recognized as one of the primary needs of mankind” (Horn & Gurel 1975). Apparel acts as a physical or utilitarian role in our lifestyle which conveys meaning and values. The classification of society is very much distinguished by human beings on wearing clothing in a unique pattern (Ross 2008). People very often use two basic statements, “This is the person I am”, “This is what I am doing” (Ross, 2008), which reflects the persons attitude and profession. There is also a famous saying in Tamil “Al paathi Adai paathi ” states that equal importance is given to the dress we wear reflecting our personality and social status. Apparel and fashion play a significant hand to glove relationship for continuous growth of the textile industry from the basic need to conspicuous consumption of status and class.

The term apparel relates to cloth, garments, wear, garb, dress, attire, wardrobe etc., which need not be fashionable (Stephens Frings 2002). Fashion is a cyclical phenomenon used by consumers which involves temporary changes (Sproles 1981), while fashion is defined as “style or styles of clothing and accessories worn at a particular time by a particular group of people” (Stone 2012). For the purpose of this study, the term apparel will encompass the word “garments” and the term apparel industry will include the “garment industry”. During the mid-1800s, on inventing the sewing machine it gave way for the first apparel industry, simultaneously the fashion industry began to develop with the help of paper pattern industry and synthetic dyes (Abernathy et al. 1999; Ross 2008).

Once after the invention of the sewing machine, the first ready-made garment factory was established in New York in 1831 producing the fabric mechanically. During the American Civil War the need for ready-made uniforms helped the garment sector grow in the United States (Wikipedia), and by 1951 it has taken up 90 percent of apparel garment industries. The processing of fiber into the fabric in the development of the industrial revolution first took place in Britain and North America (Wilson 1979) at the low cost of production and increased the quantum of supply. Hence for the first time the size of wardrobes increased, accelerated the fashion trends and allowed people to dress better than any other period in the history of inexpensive clothing. Also middle-class people of the social segment aspiring to move up the social ladder presume that best way is to adopt the fashions those of the upper social class. This increased the spending habits of the middle class to imitate the upper class. Relating the Trickle Down theory attributes, Simmel explained the concept of cycle of adaptation and discard of garments bringing the social equalization an expression of status (Simmelʼs 2003). Veblen linked status in terms of consumption, this difference of opinion between Simmel and Veblen acts as a motivator for fashion to be unique from other. The sudden rise of the fashion designer and fashion capitals such as London, Paris, and Milan established a regular seasonal presentation of fashion trends, these transformations increased consumption of apparel to multifold (Breward 1995).

During the second half of the 19th century, the industrialized nations such as Europe and North America faced a noticeable negative environmental and social impact. The working conditions during the early industrial revolution were encountered with more problems in the manufacturing units which employed immigrants. Women and children were subjected to occupational hazards, poor wages and long working hours, but surprisingly those initial working conditions of the apparel industry are still found to be present in developing countries specific to domestic players (Ross 2008). During the 20th century, with increase in the greater mix of culture, gender, class and cosmopolitan lifestyle, there was a remarkable decrease in the formalities of attire in companies which started to introduce the casual wear (Breward 1995).

On implementation of new Liberalization, Privatization, Globalization (LPG) policies accompanied by advanced technology in, industries provided more varieties and attractive garments at lower cost in Europe and North America. As the apparel employees belong to lower segment of people in society with very low literacy level, the companies exploit the sweatshop workers. The great “Triangle Shirtwaist” fire tragedy in New York during 1911, exposed the exploitation of these sweatshop labor of the apparel industry, this tragedy was the seed for formation of unions which strengthened the labor rights regulations of the USA apparel industry. However, it took many decades for establishing the basic standard of good air, water and working environment for the sweatshop labor of textile apparel production. Implementation of these standards, increased the expense of meeting environmental regulation and basic needs of employees which thereby increased the cost of production of textiles apparels and decreased the profit of organizations, this made companies to think about offshore production facilities, manufacturing of goods in foreign countries with inexpensive labor (Abernathy et al. 1999). This phenomenon has transformed apparel and fashion into a trillion dollar global industry at lower costs imports and less polluting the country resources (Sahlins et al. 2012).

1.1.1 Introduction to Indian Apparel Industry

Indian apparel industry is witnessing a highly lucrative market with a large young consuming population of the fastest growing economies in the world. Textile apparel industry play a major role in developing country like India employing a huge population of both unskilled and semiskilled laborers. The inability to automate sewing of garments due to continuous changes of garment styles and pattern of stitching made the specialty of the industry to depend on the labor force which makes apparel industry a typical starter industry providing employment for developing countries due to its low fixed costs (Gereffi 2010, Allwood et al. 2006).

Based on the Ministry of Textiles, Indian brand equity foundation (IBEF) report says next to agriculture, a huge population of 45 million people is employed in the textile industry, of which about 25 million people are directly involved and 20 million people are involved indirectly, they play an indispensable role in building our nation through economic development. Development of the modern ready to wear garment apparel industry was started in India only during 1960’s until a decade ago under newly industrialized economy known as “Asian Tigers”. During 1990’s countries like Bangladesh, Srilanka, Pakistan and Vietnam entered the garment market in a big way. Apparel is one of the oldest commodities and the apparel industry has only continued to grow as the largest global industries. According to the World Trade Organization (WTO 2011 ) from 1980 to 2010 there was an increase in apparel exports from many developing countries and simultaneous drop of export from developed countries on meeting out the standards.

The Multi-Fiber Agreement (MFA) which has been integrated with World Trade Organization (WTO), governed the extent of textile trade between nations since 1962 and this quota was removed during 2005. These changes increased competition in the international market as well as the domestic markets. During 1999 companies likes Nike, Adidas, Puma, H&M, and American Eagle Outfitters formed the Fair Labor Association (FLA) “to find sustainable solutions to systemic labor issues”. FLA serves as a central body to perform independent third-party audits to provide the results of ethically and fair production of products to public forum during the year 2012.

The National Institute of Fashion Technology (NIFT) was set up in 1986 as an autonomous society in collaboration with the Fashion Technology, New York, to train professionals to meet the requirements of the textiles industry. The Institute has pioneered the evolution of fashion business through its network in seven centers at New Delhi, Bangalore, Chennai, Gandhinagar, Hyderabad, Kolkata, and Mumbai. The size of apparel industries is categorized into, big, medium, small and micro level units based upon the number of sewing machines and employees. Ready-made garments is divided into two types, “ Outer clothing such as work wear, uniform, leisure wear and sportswear (e.g. suits, pants, dresses, ladies' suits, blouse, blazers, jackets, cardigans, pullovers, coats, sports jackets, skirts, shirts, ties, jeans, shorts, T-shirts, sports shirts, tracksuits, bathing shorts, bathing suits, bikinis) and Underclothing (underwear): jersey goods, lingerie (e.g. underpants, undershirts, briefs, socks, stockings, and pantyhose) etc.” At present, there are more than 15,160 units as reported by Apparel Export Promotion Council (AEPC) during 2017. Most apparel sector units are family-run businesses having 50-60 sewing machines, often on contract to apparel wholesalers, using old production equipment and methods.

As technology play a central role on driving the textile apparel industries, to handle this global competition, cost-effective operations and maintaining quality conformities, new technology schemes like Technology Upgradation Fund Scheme (TUFS), were implemented in the late sixties and existed till 31.03.2012 for modernization and for setting up new units. The Clothing Manufacturers' Association of India (CMAI) has signed a memorandum of understanding (MOU) with China Chamber of Commerce for Import and Export of Textile (CCCT) to explore potential areas of mutual co-operation for increasing apparel exports from India. Subsidies on machinery, infrastructure, and Revised Restructured Technology Up gradation Fund Scheme (RRTUFS) were also provided to the specified technical textile machinery. As per the report of apparel export promotion council the potential market for apparel are Australia, Canada, France, Spain, Italy, Japan, Germany Netherlands, S. Korea, Mauritius, Turkey UAE, Switzerland, USA, UK and Latin American countries. The textile apparel manufacturing pockets of India are located at Bengaluru, Chennai, Jaipur, Kolkata, Ludhiana, Mumbai, Naraina, Noida, Okhla, and Tirupur. Still India has to position well, ready to take the opportunity of China's falling competitiveness to promote apparel industry at global level.

The demographic and psychographic changes in the Indian consumers supported the domestic apparel market. The largest “ Gen.Y ” population of the global market in the age group of 20’s is also undergoing revolutions in shopping habits through their buying behavior. According to the latest report, Textile Industry & Market Growth in India December 2017, the textile industry contributes 14 percent of overall industrial production and 4% GDP which accounts for nearly 15 percent of total exports. The Apparel clothing is categorized based on age and gender for Men's, Women's, Kids, Infant, Industrial Clothing, and Animal Wear etc. India is one of the few countries that encompass the entire supply chain in close proximity, from diverse fibers to a large market. It is capable of delivering packaged products to customers comprising a variety of fibers, diverse count sizes, cloth of different weight and weave, and a panoply of finishes. This permits the supply chain to mix and match variety in different segments to deliver new products and applications at lower cost.

According to the December 2017, Indian brand equity foundation (IBEF) report India's overall textile exports during FY 2015-16 stood at US$ 40 billion and Ready Made Garment (RMG ) export in the month of April to November of 2016-17 was to the tune of US$ 10962.5 million. The domestic apparel market in India is estimated to grow at a Compound Annual Growth Rate (CAGR) of US$ 1,576 billion by 2026. (Exchange Rate Used: INR 1 = US$ 0.015 as of October 6, 2017). As per June 2017 report from Ministry of Textiles, the central government has planned to implement new textile policy in the next three months, which aims to achieve US$ 300 billion worth of textile exports by 2024-25 and create an additional 35 million jobs. A study by management consultancy firm Wazir Advisors and PCI Xylenes and Polyester also says the country's textile industry, which is currently estimated at US $ 108 billion, can grow to US $ 500 billion by 2025.

Chennai, the capital of Tamil Nadu (TN) is emerging as the global sourcing hub for ready-made garments both for the domestic and international market. The rapid shift in the lifestyle from tailor-made to ready-to-wear apparel is the principal driving force for the growth of apparel industry. Fibre2fashion News Desk-India, states apparel sectors offer tremendous opportunities for job creation and financial support especially for women, which thereby acts as a vehicle for social transformation. In textile apparel industries, women are mostly engaged in activities like tailoring, checking, thread cutting, Kacha (Button stitching) and packing are subject to complex and multiple hierarchies of exploitation in and outside the factory shop floor. Studies by (Mezzadri 2010, Carswell & De Neve et al. 2013) states, unlike in other Asian and Latin American regions, the Indian garment workforce is not greatly feminized. In India only in southern industrial region of the textile apparel industry is much concentrated with women employees specific to places like ,Chennai, Tirupur and Bangalore. There are around 4,000 knitwear and woven garment production units in the Tamil Nadu State providing employment for 5 Lakh persons. State Government of TN has so far promoted 13 Textile Parks in TN at a total project cost of Rs.774.41 crore. Two textile parks under the “Apparel Park for Exports Scheme” and Four projects under the “Textile Centre Infrastructure Development Scheme, seven textile parks under the scheme for Integrated Textile Parks (SITP) to upgrade the Handloom industry with World class infrastructure facilities to meet international standards. Besides SIPCOT and South India Mills Association (SIMA) are setting up a Textiles Processing Park in an area of 317 acres in 3Phase of SIPCOT Industrial Park at Cadalore, TN at a cost of Rs. 450 crores.

Chennai apparel hub was holding more than 600 units and right now only around 450 units exist which includes both small and big units, situated in and around Chennai city at places like Ambathur, Avadi, Amainthakarai, Thiruneermalai, Chrompet, Pallavaram, Madipakkam, Mugappair, Tambaram, Velachary, Sriperumbudur, Thiruvallur, Arakonam and other nearby places. Based on the report from Ministry of Textiles and the Indian brand equity foundation (IBEF), the Indian textile apparel industry looks promising on its future based upon its domestic consumption and export demand with the entry of several international players like Marks & Spencer, Guess and Next into the Indian market. The expected Compound Annual Growth Rate (CAGR) 11.8 percent in future. A Zero Liquid Discharge technology was erected during the last two years serving 3000 SME units, eight apparel units and garment making centers were set up in all North Eastern states and Sikkim to promote garment manufacturing. In spite of the support and interest shown by our Indian Government, as it takes much time to implement these projects into action. As the market is highly volatile the plans become nonconformities; meanwhile. Since the apparel sectors face a set of common challenges like logistics, labor regulations, tax & tariff policy, labor cost and other disadvantages emanating from the international trading environment compared to competitor countries. The space created by China for apparels exports is being taken over by other competitors like Bangladesh and Vietnam.

The Indian apparel firms are relocating to Bangladesh, Vietnam, Myanmar, and Ethiopia. The window of opportunity is narrowing, hence India needs to act fast to regain competitiveness and expand its market share in these sectors. The above situations were much supported from the report of Indian-garment imports 2017, which states, the Indian Government has allowed import of 46 duty-free apparel items into India on intense competition from Bangladesh. The President of Apparel and Handloom Exporters Association Mr. P Shah has reported the raising labor cost and power costs, along with stiff competition from global market is forcing the South India apparel garment units especially at Chennai to struggle for their survival.

Thirty-three leading export-oriented units were recently closed down in and around Chennai leading to job loss. To avoid closure of apparel units in Chennai and surrounding areas, there is an urgent need of actions by state and central Government to save the textile apparel industry. To eliminate the migration of textile apparel units and to ensure a state of progress, Finance Minister Mr. ArunJaitley has addressed the audience on November 26, 2016, on the key point of managing the cash crunch and related credit issues. A large emphasis was placed on exports and reconsideration of the duty structures of exporters in India. The Federation of Indian Export Organizations (FIEO) was quick to point out this inverted duty structure, has undermined growth initiatives, making raw materials more expensive and reducing margins on the finished goods and correcting this duty imbalance. Another issue of concern for exporters has been the delays and uncertainties relating to the passage of the General Sales Tax (GST). A memorandum of understanding (MOU), was signed by the Ministry of Textiles with 20 e-commerce companies, taking the cyber security, and e-tail threats under considerations it has aimed to provide a direct sale of products produced by weavers, handloom and handicraft to the consumer.

According to the “ United Nations (UN) report, the Indian economy is likely to grow by 7.2 percent in 2018 and go up further to 7.4 percent in the following year 2019, the ‘World Economic Situation and Prospects 2018’ report unveiled by United Nations Department of Economic and Social Affairs (UN DESA) states the overall, economic outlook for South Asia is seen largely favorable and steady for the short term, to withstanding significant medium-term challenges, sourced from website fascinating world .

1.2 INTRODUCTION TO RESILIENCE

The term “RESILIENCE” conveys many things to many people, though we believe in our ability to learn, change, and master stressful situations of work and life. Employees who are working in various organizational settings, irrespective of the size, type, level, and location effectively adapt to changes by greater skills to suit environmental pressures, simultaneously the organizations also undergo changes through downsizing, merging and acquisitions in order to survive (De Meuse, Marks & Dai et al. 2011). Wagnild states, at one point of a lifetime, every person is bound to stumble and fall, but each differs on their ability to bounce back and keep going is known as resilience. The Term “Resilience” originated from the Latin word “resilire”, meaning to “leap back” formed from the elements “re” which indicates a backward movement, and the element “salire” to come bouncing back or jump up. In psychology, it represents the ability to positively cope with adverse events in a perspective of well-being and quality of life promotion. The roots of resilience lie in science and mathematics considered to be the “ ability of a strained body ” finally, the term “resilience” originated in the physical sciences to describe properties of non-living things. When adapted to biological sciences it resulted in the term positive psychology which evolved to incorporate the dynamic process of adjustment and transformation, (Kirmayer 2011).

From the words of Einstein, “ The significant problems we face cannot be solved at the same level of thinking we were at when we created them ”(Albert Einstein). The researcher Välikang supported Einstein statement and connects this property of resilience to physical strength “ Resilience is like a muscle” we will need to build it when the tiger is on our tail (LiisaVälikangas et al. 2011), this quality of improving ones required skills will help them to thrive even when strategies fail. Resilience as the ability of an individual to adjust to adversity, maintain equilibrium, retain a sense of control over the environment, which continues to move in a positive manner, therefore an active process which describes a shifting balance between vulnerability and resilience. The environment we live is not same for everyone at every time, contributed by philosopher Lengnick-Hall & Beck (2011), who emphasized “ who is able to adapt continuously to the changes gets the success, shows there capacity to survive ”, one should adapt towards environmental changes.

Though the early resilience research development was based on other areas of clinical studies which have been identified through the study of children who were “thriving with schizophrenic (mental illness) parents” living in poverty and dealing with maltreatment as reported by (Luthar et al. 2000). There are many definitions of the term resilience and the most renowned, Oxford Dictionary of English defined resilience as “ being able to withstand or recover quickly from difficult conditions ”. In the 21st century according to the thoughts of social sciences “resilience” is the term used to build and cultivate a set of skills that help us to overcome stressful circumstances. The American Psychological Association (APA 2014), defined resilience as “ the process of adapting well in the face of adversity, trauma, tragedy, threats or even significant sources of stress ”. APA states stress is inevitable, but developing resilience is the other alternative for overcoming stress. Wagnild defines resilience as, “ a personality characteristic that moderates the negative effects of stress and promotes adaption” the philosopher has also designed (RS25) Resilience Scale (Wagnild & Young 1993), which is adopted in this current study.

Some view resilience as a process perspective by (Tusaie & Dyer 2004) who defined resilience as a dynamic, non-static process that results in adaptation and improvement in the face of significant adversity. The earlier research has shown that resilience is a multidimensional component that captured the multidimensionality of the concept defined resilience as,“ the process of navigating, managing and adapting to significant sources of stress or trauma. (Benard et al. 1991) on his studies identified various other terms of resilience such as “ invulnerable ”, “ stress-resistant ”, “ hardiness ”, “ ego-resilient ” and “ invisible ”. During the past three decades, many theories of resilience have been proposed by various researchers, the most predominant theories have described resilience from a variety of perspectives such as, a Trait or personality perspective, a developmental phenomenon and a process perspective.

Though we have marketable capabilities, the life has become more cumbersome, highly volatile, ambiguous and uncertain. Today there is much lesser job security than ever before, the employer-employee relationship was similar to the parent-child relationship during 1960’s where the organization provided employment based on status in the community and job security in exchange with employee hard work and loyalty. After three decades, it has become a partnership between the employer and employee depends on the worker employability rather than job security. Now the system ensures that employees must be career self-reliant, they must continuously update their skills, looking at current requirements and ahead of the future market trends and of the workplace (Collard et al. 1996) this was much emphasized by (Brislin 1993), that organizations reward job security as a compensation for resilient individual.

The rapid changing job descriptions and work environment due to globalization and technology developments deliver an unknown pressure. This pressures is converted as conformities, standards and procedures threaten on meet the global requirements of individuals, organization, society at one point of time. Hence resilience acts as an effective component of health promotion and play a major role in “medicalization” of aging persons (Allen et al. 2010 & Wild et al. 2013). From the findings of (Tugade & Fredrickson 2004) highly resilient people proactively cultivate their positive emotions through optimistic thinking, relaxation and humor, the more protective factors to be present in oneself for more resilience. Seligman & Fowler (2011), Cook (2015) found applications of resilience in military and sports management (e.g., the Comprehensive Soldier Fitness training program and resilience training for athletes). Further, (Luthans 2002, Luthans & Avolio 2015) recognizes resilience in the workplace as found in psychological capital (PsyCap) and positive organizational scholarship. Caza & Milton (2012) says till date the research has examined many factors that foster resilience in the workplace at different levels of analysis and its results were found to influence the work outcomes and employee well being.

Though a number of psychometric scales have been created to seek the measurement of resilience, the resilience (RS25) scale was most predominant, which consist of five personality characteristic of resilience as pronounced by Wagnild & Young, such as “ 1. Perseverance-Having the ability to carry on despite setbacks, 2. Equanimity-A balanced approach to moderate the effects of stress in work and life, 3. Meaningfulness - The realization that life is worth living, 4. Self-reliance-The ability to draw on inner strengths and capabilities from past successes and 5. Existential aloneness- The uniqueness everyone has and the associated experiences of such ”.

As this study relates to the employees of textile apparel industry, creativity and pattern of stitching are much prone to frequent changes trying to meet the customer’s requirements on time and quality conformities. This can disturb the wellbeing of employees under different job nature by which there are many chances of variations on resilience level. Stress in the workplace poses a major problem for both employees and organization as it negatively affects the employees’ general well-being and work performance, which incurs costs to the organization as represented by, National Institute for Occupational Safety and Health (NIOSH 1999). The contribution of Rutter, made changes in the perception of researchers, viewing resilience as a trait who contributed that, “as circumstances change, resilience alters”(Rutter 1984). Majority of theories incorporate the notion that resilience is a dynamic process that changes over time, (Luthans’s 2002) describes resilience as a developable capacity, rather than a stable personality trait, hence organizations should come forward on developing the individual resilience, which in turn enhances employees individual, and organizations wellbeing.

1.2.1 Resilience and Job Satisfaction

At Present workplace has become more tedious with multiple roles, long hours leading to complex and cumbersome life which demands high resilience among employees both in production and services industries to meet the global requirement. One who is able to accept these challenges should be physical and mentally prepared and fit (Glennerster 1995, Burchell Ladipo & Wilkinson 2002). Employee satisfaction is the terminology used to describe how good, happy and contended employees are, fulfilling their desires at the workplace. These challenges are found to be there in workplaces and accepted by many practitioners and researchers through various studies and observations felt by (Gowing Kraft & Quick 1997, Burke & Cooper 2000, Huda & Akhtar 2011, Neog & Barua 2014).

Since 1960’s, our jobs have been entitled both with threats and challenges which encompass components such as pay, working conditions, promotions, recognition, job safety, and benefits, that are typically described as factors promoting wellbeing (Locke 1976). Specific to employees of textile apparel industries, “ The Haddington Road Agreement ” introduced by the Labor Relations Commission Agreement 2013, has changed employee’s work attitude that hypothesized a big factor for job stress and burnout where employees are being made to work unpaid for two hours per week. Calnan & Wainwright (2002) stated resilience determines the perseverance of the employee, further research was supported by (Farber 2014) which also states resilient people recover better and faster from stressful challenges. Though high job demands make people feel depressed, at one point of time the studies of (Wagnild & Young 1993) and (Henderson & Milestein 2003) have projected that resilience helps to gain the ability to manage stress, cope successfully with various difficult issues through the process of adaptation and demands of the task. According to (Tugade & Frederick 2004) resilience is viewed as persistent personality factors, the same was further supported by the contributions of (Luthans et al. 2008), where resilience can be formed and grown in a person, the perception of a problem differs from person to person from time to time, affecting our job satisfaction.

During late fifties literature review on job satisfaction states early satisfaction depends on a set of conditions like pay benefits, promotions, recognition, working conditions etc. If employees are happy with the pay and recognition from the boss they are likely to have job satisfaction (Herzberg 1957). Salvatore Maddi started developing the concepts of resilience in 1975 (hardiness), during this period much substantial resilience research work got well established (Salvatore Maddi 1975). Studies by (Bowling & Hammond 2008) predicts life satisfaction correlates with job satisfaction. (Rain, Lane & Steiner 1991) designed a hypothesis on how job satisfaction and life satisfaction may affect each other, and from the words of (Mckenna 2006) positive influence of job satisfaction is always linked with higher productivity and lower manpower turnout. From the words of (Farkas 2001), intrinsic factor plays a key role in predicting actual job satisfaction than an extrinsic factor. Research studies clearly indicate there is a positive correlation between resilience and job satisfaction, where resilience acts as an individual intrinsic factor which maintains employee performance and job satisfaction which in turn impacts a person's general well being. The intrinsic factor purely includes the working environment of the individual, from the words of Moosavi, the primary aim of resilience is to promote well-being, where employees resilience impacts individual skill on handling work, company problems and solving them which is much related to the levels of job satisfaction (Moosavi 2011).

Communication acts as the backbone of any organization, as the researcher is specific to apparel industry the supervisors act as the link between management and workers of production units, (Teven 2010) observed the verbal and non verbal communication behavior reflects the superior-subordinate relationship, which plays an important role in determining job satisfaction in the workplace. The workers were yelled by supervisors if they don’t reach the production target and their pay was deducted or sometimes they were even deported. According to the workers of apparel units such actions are against the Indian labor laws and the union contract. Hence (Hirschfeld 2000) views job satisfaction as conceptualized and a multifaceted construct which was supported by (Cranny, Smith & Stone 1992, Rothmann & Agathagelou 2000).

Hui & Lee (2000), Testa (2001) observed that an increase in job satisfaction will lead to increased organizational commitment. Studies by Wenburg (2001) shows, organizational success depends on the commitment and accountable workforce consistently performing at the levels far beyond the norms, thus putting the full personal contributions. The research work of (Ekta Sinha 2013) on employee satisfaction measurement with special reference to kribhco, Surat has identified that, innovativeness and creativity of employees working in apparel industries were found to be decreased and almost took the back seats concerned with their level of job satisfaction. The above findings were supported by (Neeraj Kumari 2016) in his study on Employee Satisfaction and Growth Analysis, from a production industry. Further states that employees with job satisfaction are likely to be creative, innovative and come up with the breakthrough that allow a company to grow with positive changes in time with market conditions.

Mueller & Kim (2008) observed two types of job satisfaction based on the level of employees' feelings regarding their jobs. The first, and most analyzed global job satisfaction, which refers to employees' overall feelings about their jobs "Overall, I love my job". Second is job facet satisfaction, which refers to feelings regarding specific job aspects, such as salary, benefits, reporting structure, growth opportunities, work environment and the quality of relationships with one's co-workers "Overall, I love my job, but my schedule is difficult to manage". Findings of (Halbesleben & Buckley 2004) highlights the job demand induces high work pressure, emotional demands and role ambiguity role leading employees to sleeping problems and exhaustion. Other studies show the impact of other job characteristics such as job strain, burnout and work engagement also affect employee wellbeing.

But in the case of Jordanian garment workers report from solidarity center shows a contrary studies expected among workers who reported the highest levels of job satisfaction were reflected from the lower wages paid by the factory compared with other factories. Later the reason was identified that attitude of Jordanian garment workers did not seem to be based on wages when compared to other workplace. To look beyond wages and benefits is the holistic approach on improving job satisfaction and retention of Jordanian workers in the garment factories. Resilience is not a commodity to be purchased, it can only be developed by an individual over a period of time and experience. Our place of work plays a tremendous role in developing our individual resilience which in turn, improves our job satisfaction and wellbeing. (Luthans 2002) defines resilience as a “developable capacity” rather than a stable personality trait as suggested in earlier theories by (Wagnild & Young 1993, Masten 2001 and Windle 2011) studies shows the managers play a vital role in fostering the essential trait within their teams and emphasis the resilient organization relies on a resilient workforce.

1.2.2 Resilience and Wellbeing

The Impact of employee's resilience and enhancement of employees wellbeing are the today’s challenges faced by employees in which it acts as the cutting-edge research to study and address these issues. Over a couple of years, the employees work life has been a prominent area of study, the workplace relationship between, resilience and employee wellbeing in the organization plays an important role in understanding and creating a productive and happy environment. Various authors like (Caza 2012, Kuntz et al. 2014, and Luthans et al. 2015) have emphasized resilient individuals can successfully thrive, rather than survive in the changing environment. Irrespective of the job nature, wellbeing of the employees have to be taken care by their organizations, but unfortunately, only the white collars have been much cared. Specific to the textile apparel industry, though it is a cash cow to our nation these employees were much ignored all along and their wellbeing still remains as a question mark. A statistical survey on workers life of New Zealand on workers life during 2012 reveals that 18.2 percent of employed people often or always felt stressed at workplace over the 12 month period.

Shin et al. (2012) states employees resilience is seen as a protective factor for accepting the changes happening in the workplace, and their wellbeing at work. Research indicates that resilient employees become responsive to necessary organizational changes and possess a greater capacity for recovery from workplace disruptions than non–resilient employees. Though by law it is not possible to make workers more “resilient”, only through proper training employees resilience can be improved.

The innate way of a person to look at the world, in solving problems can influence their resilience and wellbeing, by learning new skills to help them to respond more positively to life’s challenges. Shin et al. (2012) shows that employees with high psychological resilience always have a tendency to support the organizational change because of positive emotions they experience as an individual. Both resilience and wellbeing, depends upon internal and external environmental factors which Play a key role as people develop working models, about social interaction, understanding of opinions by other people.

Bonanno (2004) in his studies on organizational psychology literature has shown, the terms ‘well being’ and ‘resilience’ are consistently associated with each other and is characterized by positive emotions. High resilience individuals have emotional stability, positively adapt to adversity is accepted by various authors like (Bonanno et al. 2001, Masten 2001, Kuntz et al. 2014, Luthans et al. 2015, Luthar et al. 2000, Masten & Wright 2010). Employees resilience in the workplace has been identified to play a key role in developing a productive workforce traced back over a century, this employees resilience sometimes has a direct or indirect effect on the predominant three components of workplace employee wellbeing such, as the physical, mental and social.

The other name of apparel industries employees is sweatshop workers, as they involve high level physical work such as stitching, cutting or ironing etc. Page & Vella-Brodrick (2013) studies show, when employees are healthier they perform better at work, further (Harter et al. 2003) studies emphasis having high levels of employees wellbeing is good for both employees and organizations. The apparel employees also have an equal amount of mental work due to application of different stitching styles, handling work pressure and changes on designing, innovating new patterns on apparels and methods of handling the new machinery. Avey et al. (2011) Emphasizes that psychological wellbeing has a positive relationship that exists between resilience as part of the psychological capital construct.

The apparels employees work is on par with the Information Technology (IT) or electronic industry employees, matching towards the speed of change in the existing technology and new developing concepts. The fashion world report states, that the fashion apparel designs change for every six months on an average at the global level. Bonnano (2004) defines resilience as one’s ability to maintain a stable psychological equilibrium, which is like a counterpart to psychological vulnerability. These numerous definitions of resilience vary from recovery, accounting not one’s ability to bounce back after a negative experience, but for ones ability to maintain a steady psychological state despite changing circumstances as supported by Seery (2011), though (Grych et al. 2015) also shows resilience to include enhancement in psychological well-being following an adverse experience.

The laws of Occupational Health and Safety as observed in New Zealand and other Organization for Economic Co-operation and Development (OECD) countries, is to sustain psychological and physical safe working conditions. Which supports the apparel industry for developing the skills of innovation and creativity much required for their existence and survival of the business. The term ‘wellbeing’ acts as buzz word were the employers should focus on improving the employees wellbeing which leads to comfortable, healthy, happy situation, in the absence of physical and mental illness. Ryan & Deci (2001) describes the wellbeing of the state of “optimal psychological functioning” in an individual and is important in all human endeavor, whether it is a workplace, school, or home activities. Building resilience and wellbeing is important in establishing a holistic approach to health addressing both physical and psychological states. Whereas good health is only absence of sickness, from the studies of (Chiaburu Harrison et al. 2008 and Myers 2000) social interaction and co-worker relations (social connectedness) in the workplace has a strong influence in employees psychological health ,which was further strengthened by (Kalpan et al. 2014) research on social connectedness.

Pipe et al. (2012) and Page (2013), showed a number of contemporary organizational research focusing on employee well being, to be an outcome of resilience leading to enhanced employee productivity, which inturn improves organizational outcomes (Luthans et al. 2010). Studies by (Meyers et al. 2013, Cooper 2015, Sarkar & Curran et al. 2015) showed an initial investment on employees wellbeing initiatives as a modern approach in developing the organizational resilience. Whereas organizations have to understand that promoting employees resilience at workplace has a direct influence on employee wellbeing.

1.3 INTRODUCTION TO EMOTIONAL INTELLIGENCE

Emotional intelligence (EI) is a growing field of behavioral science which attracts the interest of scientific, business world and the general public. In the 21st century, people focus more on self and social aspects, which requires much of (EQ) Emotional quotient than (IQ) Intellectual quotient. Every employee of an organization at one point of time they have to interact with their superiors, subordinates, customers, peers as well as other stakeholders on executing their jobs. It is vital to attract, retain and motivate the most appropriate employees within the organization in order to achieve the organizational goals and objectives (Opatha 2009).

William James argued emotional experience is largely due to the “experience of bodily changes”(James William 1884). Hence the events that cause emotions in organizational settings cannot be ignored even if is relatively minor, the coping process of people strongly influence the individuals to alert emotions according to situational demands.

The world-renowned phrase “First impression is the best impression”, is the outcome of positive emotions, the morning moment as we enter our company the blush and wish right from the security in the entrance of the gate till the managing director of the company at the cabin, knowingly or unknowingly transfer their emotions. Its true from the study of Diener, the employees positive and negative emotions contribute to their well-being (Diener et al. 1991). Some of the greatest moments of human history were fueled by emotional intelligence when Martin Luther King, Jr. presented his dream, that would stir the hearts of his audience “a perfectly balanced outcry of reason and emotion, of anger and hope his tone of pained indignation matched that note for note”.

The term emotion can be defined as “a strong feeling deriving from one’s circumstances, mood, or relationships with others”. The word intelligence can be defined as “the ability to acquire and apply knowledge and skills” (Oxford Dictionaries). Emotional Intelligence (EI) as defined by Goleman, “the ability to, accurately understand and regulate one’s own and others’ emotions” (Daniel Goleman 1995), based upon this further definitions were derived by (George 2000, Druskat et al. 2013, Vidyarthi 2014).

The word "emotion" dates back from 1579, derived from the French word émouvoir, which means "to stir up”. Based on the Latin term “emovere”, which means "without" and movere means "move." The related term "motivation" is also derived from the word more. During the19th century, Charles Darwin in his book described the perspectives of emotions from evolutionary theory “The Expression of Emotions in Man and Animals”, Darwin findings are with clear thoughts that even animals do express their emotions and emotions are not restricted to mankind alone (Charles Darwin 1872). Hochschild (1998) In his study on the sociology of emotion, observed attention on emotion has varied over time, emotions are relatively a brief conscious experience characterized by intense mental activity with a high degree of pleasure or displeasure. Durin 1990’s, sociologists focused on different aspects of specific emotions and how these emotions were socially relevant.

From the words of (Cooley 1992), pride and shame were the most important emotions that drive people to take various social actions. Emotion has its theories stretched back to stories of ancient Greece, Plato and Aristotle times. Emotions play a vital role in all our life, handling emotions both at work and outside environment is an art, how we perceive and manage to use emotions. Keijsers & Laird (2010) through his studies showed experimental pieces of evidence in which, on manipulating the bodily state, a desired emotion is induced. It was Ekman Paul, during the year 1972 developed the classification of basic emotions into anger, disgust, fear, happiness, sadness, and surprise (Ekman et al. 1972 ) same was supported by (Handel & Steven, 2012).

There are other studies which say emotions have different impacts when misused, there are, two types of emotions as described by Hochschild. One is surface acting and other deep acting. The surface acting emotions do not match what the actor is really feeling, the second approach is deep acting here employees try to experience the emotions they are supposed to display (Hochschild 1983 ). At the same time, Judge, Woolf, and Hurst in another meta-analysis found that surface acting was associated with negative mood, emotional exhaustion, and decreased job satisfaction when people identified their role-related emotional displays, then they did not experience emotional exhaustion (Judge et al. 2009).

Abraham (1999) as discussed in his research article, when the emotions expressed do not match the private emotions felt, emotional dissonance occurs (emotions expressed becomes incongruous with the emotions felt) this was supported by (Ashkanasy & Zerbe 2000) on his subsequent research work. To be able to understand perceived and expressed emotions in an appropriate way one can determine whether an individual is successful or not as an employee. which is further strengthened by Spector, who finds the employer and employee relationship is lost if emotional dissonance occurs (Spector et al. 2003) also states emotion is often the driving force behind positive or negative motivation. Substantiating the above conditions (Fehr & Russell et al. 1984) states “emotion is heterogeneous” by nature, whereas several other studies reveal “Emotions are complex”. Our behavior is influenced by physical and psychological changes based on theories of Daniel Goleman (Daniel Goleman 2011).

The term intelligence has added more values and success to life, it was Thorndik from Columbia University first used the term social intelligence which describes the skills of managing other persons and understanding to act wisely in human relations (Thorndik et al. 1920). Goleman article on December 2nd 1986 in Newyork times reported,“ major Personality study finds that Traits are mostly inherited ” which came in the front page of science section, the first few words were, “The genetic makeup of a child has a strong influence on personality than child rearing ” (Goleman 1986).

Meanwhile, Numerous basic questions are yet to be answered, do emotionally intelligent employees produce greater profits for the organization. Does EI enhance well-being of employees at the workplace, are the effects of training in EI likely to result in increased job satisfaction?

A more common notion, if one is intelligent, work can be mobilized easily, but the actual facts are different, Daniel Goleman states “Intellectual Quotient (IQ) contributes only to 20 percent, along with other factors that determine life success, which leaves 80 percent to Emotional Quotient (EQ)”, one should know that despite possessing a high IQ rating success does not automatically follow. Gibbs (1995) reported, “In the corporate world, IQ gets you hired but EQ gets you promoted”. The term emotional intelligence is a multi-dimensional construct that includes other types of intelligence such as social, cultural and emotional intelligence. (Mayer & Peter et al. 1990) published the first explicit article on the term “Emotional Intelligence,” later works by Daniel Goleman’s on emotional intelligence made the concept very popular. Emotions play a vital role in all our life, handling emotions both at work and outside environment is an art, how we perceive and manage to use emotions. After the publication of science writer Daniel Goleman’s in his book ‘Emotional Intelligence, Why It can matter more than IQ’, has gained importance and momentum through New York Times (Daniel Goleman1995) since then EI has become a major topic of interest in scientific circles as well as in public.

Mayer & Salovey et al. (1997) defined emotional intelligence (EI) in terms of four basic abilities, (1) ability to perceive emotions in self and others; (2) ability to assimilate the information in cognitive functioning; (3) ability to understand the role of emotions; (4) ability to use and to manage emotions in decision-making. To discriminate between self and others emotions, one should develop information and skills were emotional intelligence plays an major role (Salovey & Mayer 1990). From the research contributions EI makes a person distinguished among the crowd “what distinguishes top performers in every field of the industrial sector, is not high IQ or technical expertise, it is only EI ”, more empirical research was carried out on his findings.

The employees recruitment process is both an art and science, the employer has to look into both the skills set and how best one can adjust to the organizational changes, synchronizing oneself to the coworkers, “According to (Grandey's 2000) who found individual factors along with organizational factors have an influence on employee well-being. A study on the utilization of EI on workplace evaluation among other factors has also been carried out by (Shekar & Suganthi 2015).

Innovation plays a major role in the field of textile apparel industry for creating new models and designs, through the earlier research by George, it was found employees with high EI contributes more towards innovation, (George 2003) showed EI to be very important determinant of leadership, he also theorized that leaders high on EI are good on creating enthusiasm among their group members.

Hence the secret of success is not what we learn in our school, nor academic excellence, nor a business school degree, not even technical know-how or years of experience, it is only handling the emotions. EI tries to help in distinguishing star performers from the medium performers, it is actually a set of skills that anyone can acquire (Daniel Goleman 1995).

Warr (2002) showed in his studies how people are able to achieve their goals of managing emotions in themselves and handling emotion on others, holding high levels of emotional intelligence and more socially effective. (Salovey Mayer & Caruso 2002) insisted on the ability to regulate emotions as a crucial part of emotional intelligence, and employees with a high level of emotional intelligence are treated as an asset to their organization.

1.3.1 Emotional Intelligence & Job Satisfaction

Emotional intelligence by its name is a combination of ‘emotion’ and ‘cognitive’ part of the term when applied, (Salovey & Mayer 1990, Law, et al. 2004, Mayer et al. 2008). From the statement of (Kafetsios & Zampetakis 2008) investigated and found the existence of strong mediating role of emotional control in the relationship between EI and job satisfaction. The present competitive work environment demands the application of psychological term Emotional Intelligence (EI) which will help to solve the disputes, improve the employer-employee relationship and balance the work life. The workplace is always filled with emotions influencing the interactions between individuals at work (Klem & Schlechter 2008, Gough & Härtel 2008, Wollard & Shuck 2011), the individual emotions impacts work outcomes such as job performance and job satisfaction as researched by (Ashkanasy & Daus 2005, Mayer Salovey & Caruso 2008, Dahl & Cilliers 2012). Further research says that it also affects the general health and wellbeing of employees (Daus & Ashkanasy 2005, Bar-On 2010, Mendes & Stander 2011, Dahl & Cilliers 2012).

Many research studies on EI have high demand for work environments which also involves high social interactions, EI enhances employee’s ability by enhancing job satisfaction through thinking processes and behaviors. Researchers have emphasized the importance of emotional intelligence in high demanding work environments and contexts involving high social interaction. Kahn (1990), May et al. (2004) report states that employee differ themselves cognitively, physically and emotionally as they differ in the degree of impact influenced by their work. Till the last generation, the view on individual emotions was undervalued or not much considered in the workplace. Yalabik et al. (2013) Investigated the job satisfaction, the correlation of positivity with work engagement and negativity with turnover intention. Many earlier researchers like (Dahl & Cilliers 2012) have concluded that emotions play an important role in employees work life at organizations. Some consider emotions as a holistic view of a person in a workplace environment. JorfiYacco & Md Shah (2012), Ford & Tamir (2012) state that every organization should take steps for building a strong relationship between managers and workers, which can be ensured if the concept of emotional intelligence in implied and practiced at all levels which act as an important psychological role for life and organizational success. This was supported by (Gunavathy & Ayswarya 2011, Psilopanagioti & Niakas 2012) stating that emotional intelligence plays an essential role in service industries.

There are evidence showing EI is strongly and positively associated with job satisfaction (Wong & Law 2002, Sy, Tram & O’Hara 2006, Kafetsios & Zampetakis 2008). Since there are very few or no studies on emotional intelligence, job satisfaction and wellbeing of employees on south Indian Textile apparel industry, a strong interest of the researcher has made to convert this into one of the study objectives. But similar studies were carried out in other countries at different organizational level during the 21st century, researchers like (Mendes & Stander 2011, De Castro et al. 2015), has observed that emotional intelligence is more relevant and essential due to the constantly changing nature of work in this global economic context.

There are many works of literature supporting the values and importance of emotional intelligence in the workplace which has its impact on employees job satisfaction are discussed in detail. According to (Damirchi & Daneshfard 2011) the most effective way to identify an employee’s orientation towards work is their job satisfaction, more than 50 years most managers and psychologist believe job satisfaction is directly connected to organizational success. Many philosophers have defined Job satisfaction in many ways and one defined by (Spector 1997) "job satisfaction is simply how people feel about different aspects of their jobs, it is distinct people like or dislike their job".

Spector & Fox (2003) defined job satisfaction “as individuals negative or positive feelings about the work”. Robbins contribution on Job satisfaction linked employees feeling towards their job (Robbins 2005). Studies of (Dong Qingwen & Howard 2006) further described Job satisfaction as the ways used by the organization to determine the level of production in the company, linking the feelings towards the work and workplace. Kumari Pandey (2011) shows Job satisfaction as a combination of pleasurable emotions based on the employee's appraisal, effective reaction and attitude towards the job. Locke (1969), Rogelberg, Allen et al. (2010), Liu et al. (2011), Mafini & Pooe(2013) studies involve various factors like recognition, communication, coworker relations, working conditions, job characteristics, the nature of organization system, policies, procedures, compensation, security and supervisory practices that influence the job satisfaction.

Brief & Weiss (2002) has observed job satisfaction as an attitude influencing the behavior at workplace, both physical and cognitive part. Basically, job satisfaction is associated with quality of life since a person spends most of the time in the work place, it also influences the attitude of the employee’s towards the job and his organization. EI acts as an important predictor in psychology and management for many organizational outcomes including job satisfaction (Barsade & Gibson 2007), much of the EI abilities and traits influence job satisfaction and employees should be selected in the job that demands a high degree of social interaction (Carmeli et al. 2009).

Though emotional intelligence is the ability to perceive and integrate emotion to facilitate, (Perlovsky 2006) has expressed the term emotion as both expressive communications and inner states related to feelings as love, hate, courage, fear, joy, sadness, pleasure, and disgust. From the views of (Badenhorst & Smith 2007), emotions are the biological factor that acts as a neutralizer for a stimulus. Mayer et al. (2008) defined emotion as changes in physiological, motor skills, behavior, cognition and subjective experience as a result of the appraisal of self or situations. Further research carried out by (Fisher & Ashkanasy 2000) represents emotions of employees at work focused on emotional labor, the further emotional expression also influences the mood in the workplace. Whereas (Rivers 2012, Caruso Panter & Salovey 2012) in general focused intelligence research as primary measures of intelligence. Emotional intelligence contributes to different overall job performance (Dulewicz Young & Dulewicz 2005) and in turn sales performance (Wong et al. 2004), EI has been correlated and also associated with stress resilience (Slaski 2001), teamwork skills (Moriarty & Buckley 2003), performance under pressure (Lam & Kirby 2002).

Research on Emotional intelligence (EI) is widely studied in western countries, giving equal importance to intelligence quotient (IQ), currently, emotional intelligence is also given equal weight and vital factor in the workplace. O’Neil (1996) observed 80 percent of employees determine life accomplishment by emotional intelligence and remaining only 20 percent by intelligence quotient, hence on an average every person needs both IQ and EI whereas for a person to become successful in life he needs twice the EI when compared to IQ. Goleman (1995) also claims, people with high levels of EI are more socially effective, to achieve goals by managing the emotion on self and others.

The rise in dissatisfaction among employees was found to be common in majority of the organizations due to various reasons, under the study of (Smith et al. 2016) it was emphasized that the negative effects of job satisfaction are due to long working hours causing fatigue in employees, it was also identified by the researchers who worked on time and job satisfaction found there are lacunae in the information’s on relationship between emotional intelligence and job satisfaction. This lack of information always does not allow the problem to be solved which results in employee dissatisfaction.

1.3.2 Emotional Intelligence and Wellbeing

During 2012 on better appraisal and regulating the study on the feelings of police officers in Australia at the workplace, it was researched that positive morale and job satisfaction was observed in the study of (Brunetto et al. 2012) who demonstrated that EI was positively correlated with positive outcomes of job satisfaction and wellbeing, as everyone wishes to increase the overall wellbeing. (Güleryüz et al. 2008) studies reported nurses who have higher EI are highly satisfied with their job. Sy et al. (2006) studies about the positive association between EI and job satisfaction, employees with high EI are more resilient due to the perseverance to deal with the negative consequences of stress and understand the stress and develop strategies to prevent it. At the same time, employees with low EI are less aware of emotions and possess fewer abilities to cope with emotions during critical situations, thereby stress decreasing the level of job satisfaction.

According to Mafini & Pooe (2013), Robyn & du Preez (2013) employees with higher levels of job satisfaction experience greater well-being, therefore less likely to leave the working organizations. Alarcon & Edwards (2010), Yalabik & Popaitoon (2013), Yalabik & Rayton (2013) defined the concept of job satisfaction as liking and disliking for their job, depending on their needs and have positive implications for enhancing wellbeing. Human resources can be effectively and essentially trained and improved on handling their emotions through EI training.

Better psychological and physical well-being is delivered by emotional intelligence as stated by (Salovey & Mayer 1990). Moreover, it was further supported by (Carmeli et al. 2009 and Landa et al. 2010), emphasized that satisfaction and psychological well-being can be characterized as indicators of good mental functioning, which is more required for creativity and innovation for employees working in the textile apparel industry. Another study on North Americans found EI significantly correlated with wellbeing in the study of 3,571 North Americans by (Bar-On 2005; Ciarrochi Chan & Caputi (2000) viewed EI as individual’s ability to regulate the emotions suitably using the information to guide the thinking and their actions. Ashkanasy & Daus 2002, Jordan Ashkanasy & Härtel (2000), MacCann (2010), Crowne (2013) and MacCann et al. (2014) suggest EI can be developed only over the lifespan and with proper training.

1.4 INTRODUCTION TO JOB SATISFACTION

The success of any organization depends on employee's Job satisfaction, it attempts to explain employee’s behavior in the organization when employees are satisfied. The level of employee job satisfaction will reflect the condition or the extent to which employees are treated by the organization. It may also reflect the employee’s emotional state and sense of wellbeing. There is a general saying, that positive attitudes indicates job satisfaction and negative attitudes indicate dissatisfaction, but in reality not one factor decides the level of job satisfaction. The Literature search on the history of job satisfaction dates back from the period 1918, which was further enlightened by Hawthrone studies during 1920, followed by a systematic approach to studying job satisfaction. It was Hoppock who first conducted a study explicitly on job satisfaction and reported that it is affected by both the nature of the job and the relationships with the worker, co-workers, and supervisors (Hoppock 1936). Various views and concept have been developed on job satisfaction by researchers and practitioners over a period of time.

Locke (1976), has given the most widely used definition"a pleasurable or positive emotional state resulting from the appraisal of one's job or job experiences", relating both emotions raised in self and job experiences, the roots of job satisfaction. There are two distinct factors that affect job satisfaction, it is either intrinsic or extrinsic, the level of employee job satisfaction will reflect the condition to the extent being treated by the organization. The supervisors play an important role, specific to textile apparel industry a supervisor who expresses his friendliness, and who has open communication with nonverbal immediacy receives a positive feedback and high job satisfaction towards subordinate. Conversely, we can see the difference when the supervisor who is autocratic, unwilling to communicate will naturally receive negative feedback and create a low job satisfaction in subordinates at their workplace.

Hassan et al. (2012) defined workers job satisfaction as, “ the workers overall effective state of mind on all aspects of his work ”, employees may feel poor working conditions will only provoke negative performance since the apparel jobs demand both physical and mental attention for job satisfaction. The working environment in textile apparel industry has its impact on job satisfaction, ie the space required for workers to move between machines, illumination at workplace, proper ventilation, and hygienic factors like good drinking water, toilet facilities, safety arrangements including the medical facilities first aid boxes etc., play a prime role. The tragedies of poor working condition has taken the life of hundreds of skilled innocent workers have been the source of new policies example, the building collapse of Rana Plaza accidents and outbreaks of fire accident at the Tazreen Garment in December 2012, are an eyewitness of poor and unsafe conditions. Being textile apparel industry, though people work with low wages and sub-standard living condition there are other reasons for employees dissatisfaction in the readymade garment industry such as fine cotton dust, poor lighting, ventilation, hygiene, high noise, more working hours, sexual harassment and lack of transportation are all factors of job satisfaction supported by (Tiotangco & Nunag 2012). According to Taylor (2008) job satisfaction of RMG workers play a prime role, which depends on the nature of work and overall comfort provided to them are important. Excess work, informal recruitment, irregular payment, sudden termination, wage discrimination and abusing child labor, sexual harassment, all the above mentioned factors are considered to be the prime reasons of job satisfaction in the apparel garment sector are to be addressed. These living standards of the apparel garment workers which is put to a question mark may also affect their wellbeing. If the workers find the work much interesting and comfortable work environment along with cooperative supervisor and helpful co-workers, these situation will improve workers job satisfaction and wellbeing at the workplace. The work pattern and inadequate practices in this sector makes the absence of trade unions.

1.5 INTRODUCTION TO WELLBEING

Individuals Wellbeing plays a central role at workplace for creating a happy society and a flourishing nation, were the term wellbeing has moved beyond the “business case”. The evidence have become more stronger that people are more creative, loyal, productive, and provide better customer satisfaction with high standards of well-being at work. The success of any organization is determined by the wellbeing of the workers, hence we have to take wellbeing of workers seriously.

Shah and Marks considered wellbeing as more than just happiness, a state of wellbeing do not end on developing a persons fulfillment but to contribute towards community (Shah & Marks 2004). Much of our time is spent at our workplace to meet our needs which impacts our wellbeing. In ancient Greece, Galen states employment as “nature’s physician, essential to human happiness”. Dolan also highlighted the term well-being is ‘intangible’ and considered to be non-measurable. Other research by Thomas also reflects the evidence, from academic and practitioner that wellbeing is intangible, difficult to define and even harder to measure. The comprehensive model of mental well-being in psychology, consist of the hedonic and eudemonic part of well-being. Hedonic concerns with the achievement of happiness by pleasure and eudemonic part of wellbeing concerns with happiness through achievement and accomplishments by oneself. Though Wellbeing is a growing area of research, yet the question of how it should be defined remains unanswered. This ‘multi-disciplinary’ term explores past attempts to define wellbeing and provides an overview of the work from days of Aristotle till now. Lee et al. describes wellbeing as "a complex, multi-faceted construct that has continued to elude researchers' attempts to define and measure it" (Pollard & Lee 2003).

The knowledge of historical background on the study of wellbeing is essential in defining wellbeing. During the year 1948, World Health Organization (WHO), facilitated health promotion by defining health as a “state of complete physical, mental and social well-being and not merely the absence of the disease or infirmity”. Bradburn’s (1969) defined “wellbeing as classic research on psychological wellbeing”. The changing roles of men and women at workplace, the long hour's culture, the glass ceiling for women and other diversity issues, the technological revolution has transformed the work-life balance. Pfeffer argues, if organizations take care of the people, the people will take care of the organization, thus signifying the important relationship between the health and well-being of employees and the profitability and success of the organization (Pfeffer 1998). Burkeman spontaneously says experiencing pleasure at work is certainly a desirable by-product of one’s work, hence it should be contrived to “inject fun and quirkiness” into the workplace (Burkeman 2013). A happy life is one in which an individual has daily experiences that generate a good balance between fun on the one hand and fulfillment on the others. The researcher has used the term resilience, for checking its influence on wellbeing, the earlier research by Brunetto has strengthened this field of study.

Basically, the term “ sweatshop workers ” used for textile apparel employees is a negatively connoted term for the employees who work long hours with low wages for high economic growth. Very frequently changing and challenging employees nature of work both in terms of skill and tasks, the global drift of urban populations to cities, the increasing proportion of women in the workforce and the emergence of a 24/7 culture has disrupted traditional patterns of work-life balance, which affects the psychological contract between the organization and employee (Dolan et al. 2005). As the researcher study deals with employees of export-oriented units, absenteeism and time delay on delivery cannot be entertained.

Rath et al. (2010) described that employees strongly agree with the view point that as employees well-being increases employers costs for absenteeism go down. It is not only due to absenteeism the operating cost increases, in developed countries presenteeism also accounts up to 40 percent of time lost, which is on an average 1.5 times the cost of absenteeism. Many factors could influence the employee's wellbeing, of which wages and salary have been identified as a most important factor affecting employees wellbeing. A number of organizations like, AstraZeneca, BT, Shell, Unilever, Nestlé, with supporting measures developed to define wellbeing. The UK Office for National Statistics has produced the most comprehensive model of personal or (subjective) wellbeing. In an extensive piece of research by Donal et al. (2005), 16,000 employees across 15 different organizations in UK were studied, covering the workplaces in public and private sectors, including manufacturing plants, a local education authority, a country council, police forces, universities, a prison service, and other service providers, spanning a range of occupations, from professional to administrative and manual roles, they found ‘higher employee productivity was associated with better psychological wellbeing.

The UK organizations Business community has created the “Work well Model” to demonstrate the business benefits for employers who take a proactive approach to the prevention of illness, the promotion of wellbeing and a focus on the quality of work. There are some organizations who have already begun to seriously consider the well-being of their employees. One such organization is the US-based online shoe retailer, Zappos. Zappos was formed by entrepreneur, Tony Hsieh, who later sold the company to Amazon for over a billion dollars. Following his success with Zappos, Tony wrote the best-selling book, Delivering Happiness, about his experiences as an entrepreneur and the happiness-centered approaches he has adopted for wellbeing.

Kimberly Almeida senior program manager at Levi Strauss Foundation, addressed the Harvard University students about (SHINE) Sustainability and health initiative for Net Positive Enterprise summit, on good practices related to sustainability and health supporting workers wellbeing. It was Levi Strauss & Co (LS & Co.) the first global company to adopt a code of conduct for their supplier factories, which established a set of labor and environmental standards. “The idea is to raise the bar so that codes of conduct are the floor, not the ceiling and embed worker well-being initiatives into our supplier relationships and sourcing strategy,” Almeida continued the speech stating LS & Co. has taken the bold step partnering with Harvard University’s SHINE in an attempt to measure the well-being of apparel workers in its supply chain. By 2020, most of the Companies will start producing 80 percent of its volume of product in locations with worker well-being programs which will reach 140,000 and above workers. In developed nations like UK, the government has taken prominent steps on measuring National Wellbeing Program, periodically. Hence wellbeing is considered to be the most required parameter of the worker's community, were increased awareness is required on mental health and our overall wellbeing.

1.6 NEED FOR THE STUDY

Chennai city consists of textile apparel industries of different sizes big, medium, small and numerous micro-enterprises, categorized based on the number of machines and employees size, scattered in and around Chennai at a radius across 20 to 40 kilometers in search of low wages and skilled labor. Based on several personal discussions with apparel employees the researcher came to know they undergo increasing production pressure (standard complaint) which were also similar to the report of GATWU. The information’s collected by the researcher through word of mouth about the employees work environment does not reflect a healthy conditions, with some apparel units neglecting even the basic needs like sufficient toilets, good drinking water and proper ventilation. The researcher came to know that common issues present in the apparel industries are, high physical and mental work as required by the nature of job by itself. This is due to continuous changes in the apparel designs, pattern of stitching, moving with different cultural people, customization, buyer-driven market, low wages, supervisors pressure and managers harassment to work for extra hours to meet the customers ends. There are other factory tragedies like Tazreen factory fire accidents, collapses of building infra structures in Bangladesh leads to unsafe conditions which affects the employees wellbeing, which becomes a difficult situation for employees specifically working at shop floor level.

The employees also undergo individual problem affecting their physical and mental state specific to apparel industries. For instance, the tailoring job so happens with a continuous sitting postures, strong focused vision, high level of concentration and speed on completion of work. At the same time they are bound to take care of self life safety, safe handling of machines and materials, emotion handling with others to avoid sexual harassment. Employees working in textile apparel industry face regular health issues like back pain, neck pain, eye problems, Knee pain, and asthma complaints due to hazardous fine cotton dust particles etc. This environment not only increases health concern but also reduces their innovativeness and creativity which is utmost essential for employees of apparel industry. Only export textile apparel units are well designed with infrastructure, good ventilation, proper lighting, and sufficient moving place etc., meet the conformities of apparel units. On the whole wellbeing of the employees are put to a question mark.

Employees are the most valuable asset in any organization. The UN Guiding Principles on Business and Human Rights also emphasize to "Protect, Respect and Remedy", employees. Looking at the real working environment of the textile apparel industry, which is far off from the one’s stated in the Hawthorne experiments, the researcher’s concern about the overall wellbeing of the employees, interested him to take up this study. Therefore this study will investigate the factors that contribute to wellbeing of the employees with respect to resilience, emotional intelligence and job satisfaction on a select textile apparel manufacturing company in and around Chennai city.

1.7 STATEMENT OF THE PROBLEM

The researcher seeks to find out, whether Resilience, Emotional Intelligence, Job satisfaction and Wellbeing of employees working in Textile Apparel Industry be high. The role of Job satisfaction is also investigated for its mediating effects on the relationship between resilience, emotional intelligence, and wellbeing.

1.8 RESEARCH QUESTIONS

To examine the extent to which resilience, emotional intelligence, job satisfaction are oriented toward wellbeing of employees specific to textile apparel industry, the researcher has developed the following research questions:

1. What are the factors which are closely linked with resilience, emotional intelligence, job satisfaction and well-being of the employees working in Textile Apparel Industry.
2. Is there any significant influence of the demographic characteristics on resilience, emotional intelligence, job satisfaction and well-being of employees working in Textile Apparel Industry.
3. Is there any direct influence of various facets of resilience, emotional intelligence and Job satisfaction on employees’ well-being.
4. How to develop a model depicting employee job satisfaction as a mediating variable with resilience, emotional intelligence, and wellbeing of employees.

1.9 RESEARCH OBJECTIVES

1. To ascertain the factors which are closely associated with the resilience, emotional intelligence, job satisfaction and well-being of the employees working in Textile Apparel Industry.
2. To find the variation in the level of resilience, emotional intelligence, Job satisfaction and well-being based on the demographic factors (Age, gender, education, income, etc.) of employees working in Textile Apparel Industry.
3. To analyze the direct influence of various facets of resilience, emotional intelligence and Job satisfaction on employees’ well-being.
4. To develop a model depicting the relationship between employees resilience, emotional intelligence, and wellbeing under mediating effect of job satisfaction.

1.10 RESEARCH HYPOTHESIS

Based on the literature reviews, the research gap, research objectives and the research hypothesis were developed:

H01a : There is no significant difference between male and female employees of textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and wellbeing .
H01b : There is no significant difference between Single and Married employees of textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and wellbeing .
H01c : There is no significant difference between employees belonging to Nuclear and Joint family working in textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and wellbeing .
H01d : There is no significant difference between employees of Permanent and Temporary job status working in textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and wellbeing .
H01e : There is no significant difference among employees of different age Group working in textile apparel industry with respect to overall Resilience, Emotional Intelligence, Job satisfaction and wellbeing.
H01f : There is no significant difference among employees of different Education Qualification working in textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and wellbeing.
H01g : There is no significant difference among employees of different Monthly Salary working in textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and wellbeing .
H01h : There is no significant difference among employees with discrete number of children working in textile apparel industry with respect to Resilience, Emotional Intelligence, well being and Job satisfaction of the employees.
H01i : There is no significant difference among employees with different level of work experiences working in textile apparel industry with respect to Resilience, Emotional Intelligence, well Being and Job satisfaction of the employees.
H2a : There is no significant influence of the factors of resilience on wellbeing of employees working at Textile Apparel Industry.
H2b : There is no significant influence of the factors of emotional intelligence on wellbeing of employees working at Textile Apparel Industry.
H3a : There is no significant influence of the factors of resilience on Job satisfaction of employees working at Textile Apparel Industry.
H3b : There is no significant influence of the factors of emotional intelligence on Job satisfaction of employees working at the Textile Apparel Industry.
H4a : There is no significant influence of job satisfaction on well-being of employees working at Textile Apparel Industry.
H4b : There is no significant influence of resilience, emotional intelligence and job satisfaction on well-being of employees working at Textile Apparel Industry.
H05: There is no mediating effect of job satisfaction on resilience, emotional intelligence and wellbeing.

CHAPTER 2 REVIEW OF LITERATURE

2.1 RESILIENCE

Montero et al. (2015) conducted his cross-sectional study on “Mindfulness, Resilience, and Burnout Subtypes in Primary Care Physicians: The Possible Mediating Role of Positive and Negative Affect”. Administered his research on 622 Spanish primary care physicians by online survey using a set of questionnaire on, The Mindful Attention Awareness Scale (MAAS), Connor-Davidson Resilience Scale (CD-RISC), Positive and Negative Affect Schedule (PANAS), and Burnout Clinical Subtype Questionnaire (BCSQ-12). The data were analyzed using weighted least squares (ULS) method for developing structural equation modeling. His study results showed that mindfulness and resilience were high associations (φ = 0.46). The model had a very good fit to the data (GFI = 0.96; AGFI = 0.96; RMSR = 0.06; NFI = 0.95; RFI = 0.95; PRATIO = 0.96). Their results showed, mindfulness and resilience influence subtypes of burnout and potentially mediate others by the positive and negative effect.

Anna Faircloth (2017) this cross-sectional study was conducted to find the relationship between negative life events, well-being, and resilience under the title “ Resilience as a Mediator of the Relationship Between Negative Life Events and Psychological Well-Being”. The researcher has used a sample size of 325 (166 female; 158 male) college students as respondents for this study, the required data were collected through a survey by using the required set of questionnaires, Connor-Davidson Resilience Scale (CD-RISC), and Ryff Scales of Psychological Well-Being (RSPWB). On analyzing the data results of the study portrayed the directions that resilience partially mediated the relationships between negative life events and well-being (i.e., Self-Acceptance, Positive Relations with others, Autonomy, Environmental Mastery, Purpose in Life, and Personal Growth), of the respondents.

Tonkin et al. (2016) at the University of Canterbury executed the research dissertation under the title “ Building Employee Resilience through Wellbeing in Organizations”, i.e., how individual employee trait-level resilience interact and influence organizational resilience. The researcher has used online survey with a sample size of 145 respondents taking part in a workplace wellbeing intervention, followed by a second survey. The results of the study supported the hypothesis, stating a positive relationship between employee trait resilience and organizational level resilience, stating that a resilient organization consists of resilient employees. A similar study by (Lengnick-Hall et al. 2011) using Multiple factors influencing organizational resilience, was also referred by the author.

Campbell & Eley et al. (2016) the researcher has applied a cross-sectional study under the title “The relationship between resilience and personality traits in doctors: implications for enhancing well being” in Australia. A sample size of 479 family practitioners were surveyed with seven basic dimensions of personality and resilience scale provided an overall measure of resilience used in this study. The relationship between resilience and personality were examined by Pearson product-moment correlation coefficients and multiple regression analyses under 95% confidence level. The results of the analysis were found to have a strong to medium positive correlations between “ Resilience and Self-directedness (r D: 614, p <: 01), Persistence (r D :498, p <: 01), and Cooperativeness (r D :363, p < :01) and negative with Harm Avoidance (r D :555, p < :01)” The results found to support the inclusion of resilience as a component of optimal functioning and well being of doctors.

Bruggenwirth (2016) in his study on Psychology from Behavioral Management & Social Sciences on promoting resilience and well-being has administered this research on a sample size of 120 respondents and the data were collected through online. The entire trial of proceedings and schedule were made through questionnaires regarding resilience, well-being, and positive emotions, to gain more insight in the effects. The data was subject to further analysis using tools like ANOVA, correlation and multiple regression analysis. The results were non-significant towards resilience or well-being. But this research provided a starting point for further research to promote resilience and well-being.

Murphy (2014) the researcher has applied a cross-sectional study with an objective on exploration of the relationship between resilience, optimism, job satisfaction, and emotional intelligence on predicting burnout at the workplace. The study was tested on general staff in a private hospital, with a sample size of (n=130), further five self-administered questionnaires were used for data collection and subjected to quantitative analysis. On considering the study factors like gender, age and department differences in job satisfaction there was no significant differences found in the results. But factors like staff length of services have significant relation to work stress which is frequent and varied on considering with experienced and new employees .

Oladipo & Idemudia (2015) in their study to determine the validity and reliability of the 25-item Wagnild and Young’s resilience scale, the researcher has administered the RS25 on Nigeria population for a sample size of n = 284 of which (154 males and 130 females). The data were analyzed and results of varimax rotation produced 3 factors, against the 5 factors scale. The results of KMO test produced 0.91 and Alpha reliability coefficient of the total scale was 0.867, it was concluded that 22 out of the 25 items on the scale were relevant hence the scale was found to be reliable and valid for Nigerian population. Though there was no strong evidence for dimensional structure, the three factors ‘Meaningfulness’, ‘Perseverance’ and ‘Self-reliance’ was maintained.

Rüya-Daniela Kocalevent et al. (2006) researched on the topic “ Resilience in the General Population: Standardization of the Resilience Scale (RS-11)among the general Population ”. The survey was conducted on face-to-face verbal consent, the study questionnaires were filled with household members from Germany population during 2006, for an sample size of n = 5,036. The data of the respondents were subject to confirmatory factor analysis, and the scale was found to be uni-dimensional with all items loading substantially on a latent factor of resilience. The findings of the fit analysis values of TLI and CFI were found to be (TLI = 0.911, CFI = 0.929). On assuming the possible limitation and uni-dimensionality the model was considered to be fit. The overall results of the study were found to have a strong association with the factors like self-esteem, mindfulness and empowerment and associated with better psychological functioning, and resilience processes.

Shueh-Yi Lian & CaiLian Tam (2014) in a research work concluded on work stress, coping strategies and resilience among working females. Though a country like Malaysia, design their social policies supporting the working females especially working mothers, in reality it is not being adopted fully by most of the corporations. Very few research literature were found to be on working females specific to stressful work situations in Malaysian, hence the researcher has attempted to take this area of study on building the factors of resilience to enhance the coping strategies and assist the working females to thrive and sustain in the work environment. The final results proved that the work environment has caused more stress to working females when compared to men.

Rutter (2012) in his research topic “Resilience as a dynamic concept ”, identified there was a huge heterogeneity in people’s responses to all manner of environmental adversities. The inference based on resilience was an evidence that some people have a better outcome than others who have experienced a comparable level of adversity. The researcher says in some circumstances, exposure to stress may be followed by an increased resistance called the “steeling effect” and some time the negative experience may be sensitized leading to adversity. Rutter has conducted various studies like Brain plasticity, Depriving care, Hypothesis-driven strategies and group studies on resilience, Research on animal models and both cross-sectional and longitudinal studies on the general population. From the report of Rutter on using the power of statistics to detect interactions, the results were found to be inevitably less than the power in determining the associations with a heterogeneity of positive outcomes. He concluded that the heterogeneity in people’s responses is bound to happen to all manner due to environmental changes.

Windle (2010) in his study on individual resilience under the topic “A Practical Measure of Workplace Resilience Developing the Resilience Work Scale”. Developed and measured the resilience at the workplace on an individual to know the work-related performance and emotional distress contexts. The study was administered to 397 participants with exploratory factor analysis. The Results gave way to a 20-item scale measuring seven aspects of workplace resilience was developed in the expected directions. The item scale explains 67% of the variance, but this raw scale is not a panacea but acts as a “tool, not a rule”, which can act as an additional vehicle for protecting the health and welfare of the employees where organization can protect the worthy human assets.

Khalid Mahmood & Abdul Ghaffar (2014) researched on the topic “The Relationship between Resilience, Psychological Distress and Subjective Well- Being among Dengue Fever Survivors” with a sample size of n=100 among the survivors of dengue fever. Using the non-probability, purposive sampling technique an equal distribution of fifty male and fifty female dengue survivor respondents were selected. The data were collected using three scales Trait Resilience Checklist (Hiew et al. 2000), Psychological Distress Scale (Kessler et al. 1992) and Subjective Happiness Scale (Lyubomirsky 1999) to measure resilience, psychological distress, and subjective well-being respectively. The data were statistically analyzed using SPSS version 20, which showed a significant positive correlation between resilience and subjective well-being among survivors of dengue fever, hence the hypothesis was rejected. There are a number of other studies with similar results carried out by various author like, (Suarez et al. 2004, Jhanjee et al. 2013, Carroll et al. 2006, Stoeckle et al. 1964 and McGarry et al. 2013) holds that the level of subjective well-being is same on both genders.

Gray et al. (2016) in his study on “Developing resilience and well being for healthcare staff during an organizational transition: The salutogenic approach” evaluated the impact of resilience and wellbeing on staff working in United Kingdom NHS, during the ‘burnout’ while trying to deliver a high profile change in service delivery. The author designed the study to ascertain whether training program supported employees to remain engaged with work during workplace transition difficulties. The program adopted pedagogical methods related to transformational learning, and to facilitate a micro/meso/ macro salutogenic coaching approach. The study concluded showing there is an impact at micro, meso and macro levels work as a self-help tool for participants to manage a very stressful working environment and remain engaged with a high profile change in service delivery. Further, this study introduced the importance of wellbeing through team culture, interdependence, and interconnectedness.

Rahmawati (2013) though the concept of resilience is much popular in the clinical, social and educational field the researcher has imparted the concept of resilience to the industrial and organizational environment. In this study, the researcher has aimed to examine the correlation between the resilience of tax consultant’s on their perceived level of employee’s job satisfaction. Hence the researcher has used two sets of questionnaires, one the Minnesota Satisfaction questionnaire for job satisfaction and other by Wagnild and Young employees resilience scale to measure the variable resilience. Hence the researcher has administered the above set of questionnaire on 52 employees of tax consulting firm, to know the impact of employee resilience on the level of job satisfaction. The results on data analysis were found to be positive that resilience has the positive relationship with job satisfaction, and a similar earlier study by Johnson’s (2008) also supported the researchers work.

David Fletcher et al. (2013) in his study on “Psychological resilience: A review and critique of definitions, concepts, and theory” it was much concerned to the field of Sport, Health and Social Sciences. Much of the reviews and critiques on psychological resilience were categorized into three main sections. In the first section, the psychological research literature and definitions of resilience were considered. Section two examines the concept of resilience as a trait / process to explore how it was distinct from a number of related other terms and in the third section, resilience theories were reviewed, critically examined and cited in resilience literature. Finally, the reviews were concluded stating that resilience can be built by implications for policy, practice, and research to manage individual’s immediate environment or to develop and promote protective factors.

Wagnild & Young (1993) in their research on “Development and psychometric evaluation of the resilience scale”, though much research was made only on children the researcher has taken much new attempt towards general community adults, the questionnaire was administered on a sample size of n = 810, adults respondents. The data were statistically tested using factor analysis followed by confirming factor analysis, a positive correlation towards adaptation and a negative correlation towards depression. The overall results supported the reliability and concurrent validity to measure resilience.

Sigurd Hystad & Jarle Eid (2011) the researchers have conducted the study on the title “ Effects of Psychological Hardiness, Job Demands, and Job Control on Sickness Absence ” on the Norwegian Armed Forces. This study was administered to 7,239 civilian and military employees. The sickness absence data were collected for statistical analyses and further subjected to regression test. The results showed the existence of the relationship, that participants who were high in hardiness tended to have lower health care costs and claims. To the best of the researcher's knowledge, it was first time to take hardiness to demonstrate the relationship between hardiness and medically certified sickness absence.

Shawn Utsey et al. (2008) from the Department of Psychology, Virginia Commonwealth University, the researchers were interested to study on the topics ego resilience, Cultural orientation, and optimism as predictors of subjective well-being among African Americans. In this study, a portion of a model proposed by Constantine and Sue was tested and further examined to know if any level of certain attitudes, beliefs, and behaviors were consistent with the cultural orientation. A similar study was designed to ensure if it differs from the worldview of African Americans that would predict ego resilience, optimism, and subjective well-being in a sample of African American. For this purpose, college students were used as respondents for an sample size of (n=¼ 215), and the data was collected on a survey method to check the hypothesis that cultural orientation would predict ego resilience, optimism, and subjective well-being. The analysis was carried out using Structural equation modeling. The results of part model of (Constantine & Sue 2006) states there were only limited proofs on conceptualizations of people and color with optimal psychological functioning and well-being. Specific to this study the respondents exhibited a high level of racism and more resilience which in turn increased well-being. The finding of this results was well supported by prior research that has linked racial pride with well-being by Fischer & Shaw (1999), Sellers & Shelton (2003), Utsey et al.(2008) Wilson & Constantine (1999).

Jiseon Shin et al. (2012) the researchers from the University of Maryland has worked on the topic “The relationships of organizational inducements and psychological resilience to employees’ attitudes and behaviors toward organizational change”. The researchers have used hierarchically structured samples ie., samples were collected at three different levels such as (level 1) 234 samples from the employee were nested within 45 work units (level 2), which in turn were nested within 16 division-level groups (level 3). These data were collected using the survey method and analyzed by hierarchical linear modeling similar to (Bryk & Raudenbush 1992) who have used it as the primary statistical procedure for data analyses. On examining the two hypothesized resources - organizational inducements and employee psychological resilience, the results exhibited a positive relationship.

Mohamad Khaledian et al. (2016) a report from World Scientific News, a study conducted by researchers under the topic “The relationship of psychological hardiness with irrational beliefs, emotional intelligence, and work-alcoholism”. To execute this work the researchers has used a sample size of 100 respondents which included both male and female high school teachers who were working in schools at Ghorveh city (In IRAN). The aim of the researchers was to “investigate the relationship of psychological hardiness with irrational beliefs, emotional intelligence and work-alcoholism among high school teachers”. The researcher has administered, Kobassa Psychological Hardiness Questionnaire, Jones Irrational Belief Questionnaire with 100 questions, Bar-on questionnaire with 90 questions about emotional intelligence and Aghabeigi work-alcoholism questionnaire was used. The analysis was done using both descriptive statistics (frequency, percentage, mean and standard deviation) and inferential statistics (t-test and regression analysis). The results showed there was a negative significance relationship between psychological hardiness with irrational beliefs and work-alcoholism. But a high significant difference was observed between the psychological hardiness and emotional intelligence, and no significant difference between the emotional intelligence of men and women sampled.

Emma Hansen et al. (2016) in his thesis titled “The Effects of Mindfulness on Work-Related Stress, Wellbeing, Recovery Quality, and Employee Resilience”, this study was designed to find the relationship between work-related stress, employees resilience, wellbeing and recovery from work stress among managers with moderating effect of mindfulness. This study was administered on 181 respondents through an online survey and used a cross-sectional design to measure five variables of interest (work stress, mindfulness, employee resilience, recovery quality, and wellbeing). The results on analysis showed that the work-related stress was negatively related to psychological wellbeing, recovery quality, and employee resilience, suggesting that the levels of mindfulness acts as buffer effects when work stress acts on employee resilience, hence it was concluded that there is an increase in employees’ resilience among the respondents.

2.2 EMOTIONAL INTELLIGENCE

Zahyah et al. (2016) the researcher on knowing the values and importance of emotional intelligence designed a study on the title “Relationship between Emotional Intelligence and Academic Achievement in Emerging Adults: A Systematic Review”. The study aimed at finding the relationship between emotional intelligence and academic achievement on emerging adults between the age group of 18- 25 years. The data was collected on using five widely used emotional intelligence (EI) measurements (EQ-I, ECI, TEIQ, MSCIT & WLEIS). This was administered on 885 respondents, further on analysis the results projected only 8 articles which showed the measures of emotional intelligence (MSCIT, & WLEIS) and 18 articles showed self-reported EI measures (EQ-I, ECI & TEIQ), 13 studies have reported a significant positive relationship between emotional intelligence and academic achievements, out of which only 2 studies are very strong but indirect. 2 studies to be insignificant but negatively associated and 2 studies demonstrate no relationship between EI and academic achievements. But still, the study finally provided a base for the research intended on determining the relationship between emotional intelligence and academic achievement in emerging adulthood.

Annamaria Di Fabiola et al. (2016) conducted his study on “Promotion of well being: The Contribution of Emotional Intelligence” aimed to examine competencies with the potential to enhance well-being. This study has used Bar-On Trait EI Questionnaire and wellbeing Questionnaire which was administered on 157 Italian high school students as respondents to analyze the effects of Emotional intelligence and personality traits. The final results of this study highlights the trait Emotional Intelligence in explaining both hedonic and eudemonic wellbeing. The findings of the data analysis on the Italian high school students confirms the relationships between self-report measures of Emotional Intelligence and hedonic wellbeing.

Rajkrishna, Ravikumar et al. (2017) on this growing field of emotional intelligence in other industry, the researcher was interested to study the success rate of emotional intelligence practiced in the field of medicine. Hence he took the topic on “A Study of Emotional Intelligence Among Postgraduate Medical Students in Delhi” which was administered on 200 postgraduate clinical specialties medical students who were randomly selected from two medical colleges in Delhi. A self-administered questionnaire was used for data collection, on analysis the mean scores of participants were 124.4 with a standard deviation of 12.8. The age of the participants was positively associated with emotional intelligence where (r = 0.187, p = 0.008) and also found to decrease with the increase in total workload, such as night duty and emergency. Further concluded that for more accurate assessment on impact of emotional intelligence workload of resident doctors can be taken for further research.

Sakunthala Rathnakara et al. (2014) the researcher has worked on her topic titled “ The Impact of Emotional Intelligence on Psychological Well- being of Public and Private Sector Executives: Perspective of Postgraduate Students”. The researcher states that an employee in any organization has to deal with a range of tasks, duties, and responsibilities to accomplish individual and organizational objectives successfully, handling on self and others emotions is very important. The term emotional intelligence has become more influential concepts to monitor ones thinking and action to guide ones’ self. The researchers have used a structured questionnaire for a sample consisting of 200 participants, on screening only182 participants data were used for analysis. Both univariate and bivariate analysis were used and the results of the study was found to have moderately positive relationship between emotional intelligence and psychological well-being. The emotionally intelligent employees will possess a higher level of psychological well-being and which will positively impact on the success of their work and life.

Joyce Kportufe (2014) in his study on “ Assessing the impact of emotional intelligence on employee customer service delivery: A case study of the banking sector in Ghana”. The aim of the researcher is to find the efforts of bank managers to improve customer service that has lead to enhancement of different policies. Convenience Sampling techniques were used for collecting a samples size of 100 from employees and 100 from customers on circulating the questionnaires in the area within the Accra and Tema Metropolis on five banks. The study results showed banks were benefited from higher employee emotional intelligence which increased the base customers. The employees were benefited through training accompanied with technical skills and expertise knowledge, access to management, counseling and reputation for improving emotional intelligence. Moreover, banks recognized the potential advantages of seeking out and hiring employees with higher emotional intelligence or bank managers can train the employees to acquire high levels of emotional intelligence to act as the mentor for subordinates to solve customer service-related problems. This study also insists that government policymakers should play a vital role as both facilitators and educators to encourage educational institutions and business organizations to train students and employees to promote emotional intelligence.

Nuno Da Camara et al. (2015) has conducted a cross-sectional research under the topic “The relationship between perceptions of organizational emotional intelligence and turnover intentions amongst employees: the mediating role of organizational commitment and job satisfaction”. The researcher has taken this work one shade away from individual emotional intelligence (EI) level to organizational level as the individual was deemed to be a critical indicator of organizational performance. This study engaged 173 respondent i.e., who belong to the employees of the UK-based charity organization. On analysis of the data, it was found both job satisfaction and affective commitment mediate the impact of organizational emotional intelligence, a moderate amount of variance was found in a focal construct. However majority of the mediation has occurred through job satisfaction with a less mediation effect for affective commitment. The study explains the wider understanding of organizational emotional intelligence impacts on employee attitudes toward the organization and the job; and, in turn, how the attitudes impact on turnover intentions. The earlier research work by Da Camara (2013) has supported with a wider understanding of how organizational emotional intelligence impacts on important individual-level work outcomes, specific to turnover intentions, by the mediating effect of employee attitudes focusing at the job and organizational levels and generalized these results within the non-profit, public, and private sectors.

Ramya et al. (2012) studied on “Relationship between Emotional Intelligence and Psychological Well Being among Young Adults” The researcher has used 60 participants with equal gender both male and female within the age group of 20-40 this research. Further he has administered two questionnaires one (MEII) Mangal's Emotional Intelligence Inventory for Emotional Intelligence and other (Ryff Psychological Well-Being Scale) to demonstrate psychological Well-Being among the study groups. Pearson’s product moment correlation was used to study the relationship between Emotional Intelligence and Psychological Well- being. The results showed a positive correlation between Emotional Intelligence and Psychological Well-Being among study groups, hence rejected the null hypothesis. It was concluded that gender difference was not significant among the variables Emotional Intelligence and Psychological Well being.

Harinder Kaur Gujral (2012) conducted his study on “Emotional intelligence – an important determinant of well-being and employee behavior: A study on young professionals”. Looking into the difficulties of young professionals who were forced to cope up with turbulent changes, the researcher emphasized the role of emotional intelligence in well being and employee behavior, how critical it affects their job performance. Hence the researcher has administered a standardized emotional intelligence scale and general well-being questionnaire on 87 young professionals, where the employee behavior information were collected directly from managers. On analysis by appropriate statistical tools, the results interpreted that emotional intelligence was an important determinant of well- being which also contributed to employee behavior influencing job performance.

Praveen Kulkarni (2009) conducted his study under the topic “The impact of Emotional Intelligence and Employee Performance in an automobile industry, Belgaum, Karnataka, India”. The researcher has administered a questionnaire for evaluating the influence of Emotional Intelligence on the level of job performance among the managers and supervisors of Automobile Industries. From the analysis, it was found that emotional intelligence have an impact on performance level of managers, hence the hypothesis was rejected. But still the managers showed a lower level of emotional intelligence on the key areas like achievement drive, teambuilding, flexibility, and adaptability, as these factors are very much vital for the job and that of supervisors showed a low level of job performance. Hence the company decided to conduct a training program on developing emotional intelligence for managerial and supervisors which is deemed as basics skills to apply it in the workplace.

Harminder Kaur Gujral et al. (2012) in this study on Emotional Intelligence, the researcher has administered a standardized emotional intelligence and general well-being questionnaire on 250 young professionals. The data was collected for analysis using SPSS statistical tools, and the results reflected 16% of employees belong to very high category of EI, 20% of employees fall into the high category, and 2% belong to low category of EI which reflects that 62% of the young professionals fall in the above average category of EI. The mean score between the young professionals showed that young professional with high EI were reported to be more competent with positive attitude, better team players, more trustworthy with better stress management capacity than the lower category of EI and well being leading to better job satisfaction.

Natalio et al. (2010) conducted their study on “Emotional intelligence and its relation with hedonic and eudemonic well-being: A prospective study” find the relationship between emotional intelligence and levels of hedonic and eudemonic wellbeing. The researcher has administered a standardized questionnaire for emotional intelligence among undergraduate students and collected the data for a sample size of 349 respondents. On data analysis, the results were found to show emotional intelligence scores to be moderate and significantly related to hedonic and eudemonic well-being measures. The findings provided some preliminary evidence on the prospective value of ability emotional intelligence in the maintenance of positive mood and a better outlook on life (hedonic) and specifically in the development of aspects of human functioning (eudemonia).

Emma et al. (2004) the researcher worked under the title “The relative importance of psychological acceptance and emotional intelligence to workplace well-being” showed two hypothesis that affects well-being (i.e. general mental health, physical well-being, job satisfaction) and performance at work. The researcher has collected samples of data from United Kingdom workers. On analysis, the results showed that EI did not significantly predict any of the well-being outcomes, after accounting for acceptance and job control. But one’s thoughts and feelings may have a greater impact on mental well-being than attempting consciously to regulate emotional intelligence.

Brackett & Mayer (2001) to ensure the scalability to measure emotional intelligence skills, the researcher has used, Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT) scale. This test was administered on a set of respondents in general public, on predicting its important outcomes of validity and reliability. This study engaged 21 experts to exhibit superiors levels of opinion relative to the general sample, it was found that the experts opinion and general consensus criteria agreed to the same answers as correct (r -0.91). The results were supported by Mayer et al. (2002b) on a similar study conducted on 2,112 members of the standardization samples on testing the reliability, and the possible factor structures of emotional intelligence.

Jijo George (2012) in the study of “ Emotional Intelligence and Job Satisfaction”, aimed to find the Correlation between emotional intelligence and job satisfaction. The researcher has also tried to analyze how designation, experience and marital status of employee affect their emotional intelligence and job satisfaction. This study was administered to 208 employees of the international electronic firm operating in India as respondents. On using the standard statistical tools the findings reflected a good high positive relationship between Emotional Intelligence and Job Satisfaction.

Nina Ogińska-bulik (2005) the researcher has taken up the study on “emotional intelligence in the workplace: exploring its effects on occupational stress and health outcomes in human service workers” with the aim to explore how emotional intelligence is related with perceived stress in the workplace. The researcher has surveyed 330 respondents using a questionnaire developed in Poland, and the general health questionnaire. On analysis of data the results were found to reflect that emotional intelligence plays a strong role in occupational stress, hence stress managing training programs were recommended.

2.3 JOB SATISFACTION

Kef & Meininger (2018) the authors have taken strong steps to find out the factors associated with job satisfaction. This study was administered to 117 employees of the intellectual activity which is most required for creativity and innovation, the data were collected with the help of a well-standardized research questionnaire. The results of the study were found to be much supporting that age was positively associated with job satisfaction. But in the case of Moderation analyses results, workers with a high intellectual disability, conscientiousness have less job satisfaction.

Hossain et al. (2016) this study was related with Job Satisfaction of Ready-Made Garments (RMG) Workers in Bangladesh. The researcher has used the employees of RMG workers as respondents of this study and surveyed the data using standard questionnaire, later applied Smart PLS, and SPSS tools for data analysis, to identify if there is any relationship between some of the factors of the work environment and overall satisfaction of the RMG workers. The results of the study showed the RMG workers in Bangladesh are significantly related to the job satisfaction. Further it was suggested that the policymakers have to focus more on factors like salary benefits, supervisor’s behavior, work, and family life which are important for job satisfaction.

Vshal Chandr Jaunky et al. (2017) conducted a cross-sectional research on job Satisfaction among migrant employees of Textile Apparel Industry. The researcher has applied face to face survey to analyze the predictors of job satisfaction among 291migrant employees working in four different ready-made garment factories in Mauritius. The study was much related to factors like age, gender, marital status, nationality, working location and accommodation quality, cultural dimensions, power distance, individuals and long-term reorientation. The results concluded that personal factors directly influence the job satisfaction of migrant employees based on the place of origin like Chinese, India, and Bangladesh and other Asian countries. Further suggestions were recommended, to textile apparel industries to act culturally sensitive on both extrinsic and intrinsic rewards to improve employee morale and motivate them to proceed their job in a positive manner to improve job satisfaction.

Nilgün Anafarta (2015) professor at Akdeniz University Department of Business Administration, Turkey in his study “Job Satisfaction as a Mediator between Emotional Labor and the Intention to Quit” on health workers has used 348 healthcare workers of Turkey as respondents of his study. The data was analyzed using structural equation model by SPSS. The results of the findings indicated that surface acting has a direct negative effect on job satisfaction, while deep acting has a direct positive effect on job satisfaction and a positive relation between surface acting and the intention to quit. When employees attempt to prefer to be with naturally felt emotions to their expressed emotions, their job satisfaction increases. That is, deep acting will lead to positive outcomes for health workers. However, contrary to expectation, workers’ expression of their naturally felt emotions does not directly affect their job satisfaction.

Earlier research by (Chu Baker & Murrmann 2012) has achieved higher emotional adjustment and job satisfaction, therefore, increase in an emotional conflict of workers acting superficially may lead to a decrease in their job satisfaction. Yet another study by Lee & Lee (2011) reflected higher job satisfaction are more likely to have a naturally felt emotions and the emotions they wish to feel can be bridged by deep acting, same as the work by (Walsh & Bartikowski 2013).

Oraman Yesemin et al. (2011) studied on “Work motivation and job satisfaction dynamics of textile employees” at the Department of Agricultural Economics, Faculty of Agricultural, Namik Kemal University. The effectiveness of psycho-social, economic organizational and managerial tools over individual’s motivationamong the employees of textile enterprise was analyzed using factor analysis and regression models. The results showed that the economic tools of employee was positive and significant (p=0.001) and also the regression model plays an important role in determining the level of job satisfaction. Further, the author also found that the economic and psychological tools has a positive effect on increasing employee motivation, on implementations which can increase employee satisfaction at work.

Chun-hsiVivian Chen et al. (2011) the researcher has carried out his work under the topic “Emotional Intelligence in the Workplace: Exploring its Effects on Journalists’ Perceived Work Stress, Job Satisfaction, and Organizational Commitment”. This work was administered on 179 journalists who were working as employees in various fields and organizations. On analysis of the collected data through questionnaires, the results showed that employees with higher emotional intelligence score high in their job satisfaction and were also more committed to their organization. It was also found that employees with higher emotional intelligence perceived less stress on their work. Moreover, employees’ perception of stress was negatively related to their job satisfaction and organizational commitment. Through findings, it was suggested that regular and relevant training programs should be organized to their employees, and care be given during selection of employees for higher emotional intelligence are conducive on reducing the level of perceived stress, this will enhance greater job satisfaction and affective commitment on employees.

Nuno Da Camara et al. (2015) has conducted a cross-sectional research on the topic “the relationship between perceptions of organizational emotional intelligence and turnover intentions amongst employees: the mediating role of organizational commitment and job satisfaction”. The researcher has taken this work one shade away from individual Emotional Intelligence (EI) level to organizational level as the individual was deemed to be a critical indicator of organizational performance. This study engaged 173 respondent i.e., who belong to the employees of the UK-based charity organization. On analysis of the data it was found that both job satisfaction and affective commitment mediate the impact on organizational emotional intelligence. A moderate amount of variance was found in a focal construct and a similar work was supported by Da Camara (2013).

Ian Chauvet (2016) worked on industrial and organizational psychology at the University of South Africa to understand the relationships between emotional intelligence, job satisfaction, work engagement and the turnover intention of employees. The researcher has used different types of organizations for this study which included two private higher education institutions, a management consulting/outsourcing company, an information technology company and a packaging company for collecting the samples.

Using a cross-sectional study the researcher has collected the samples under convenient samples method from the employees of above-mentioned industries in the Durban area of South Africa. Though the researcher has executed the survey on 274 respondents, only a sample size of n=200 was fit for using for this research purpose. The survey was administered at different scales for each variable, one for the Emotional Intelligence, Job Satisfaction, Work Engagement and Turnover Intention Scale. On applying the statistical tool structured equation modeling the results were found, the overall effect of emotional intelligence upon the job satisfaction and the impact of work engagement on employees explaining their turnover intention could not be established. But a known fact is that, emotional intelligence factors of positive emotions, managing emotions, recognizing emotions and utilizing emotions had a significantly positive impact on the cognitive, emotional and physical engagement of employees, the results of this study were also found to have a significant effect on the work engagement of employees but not on job satisfaction. The variable Job satisfaction was found to have a significant effect on the turnover intention of employees.

Andrew Smith & Hugo Smith (2017) both the researchers have made a new attempt to measure positive and negative aspects of wellbeing, using a research instrument Smith Wellbeing Questionnaire (SWELL) having a good psychometric properties, measures positive and negative aspects of wellbeing and is based on a simple wellbeing model. For the first time the researcher used international survey of staff in the business process outsourcing industry. The survey data was subjected to multivariate analysis which allowed identification of key predictor variables. The results on analysis showed, that the measured variables reflect both the positive and negative aspects of wellbeing, high levels of control/support were associated with greater job satisfaction. The employees of outsourcing industry could feel the pinch of stress in terms of job demands and lack of proper control and support for the employees, losing the job satisfaction which acts as a key predictor of happiness at work.

Zito et al. (2018) the researchers of this study have aimed on detecting the role of emotional dissonance on job satisfaction and wellbeing of the workers. This study has used 318 respondents from Italian Telecommunication Company call center employees. The data were analyzed using a set of descriptive statistics through SPSS 22, and furtherpath analysis was tested to find both direct and indirect effects. The results found that emotional dissonance has revealed a negative relation with job satisfaction and a positive relation with a turnover. Moreover, job satisfaction is negatively related to turnover and mediates by the way supporting the relationship between job satisfaction and turnover. To increase job satisfaction and reduce turnover intentions among the call center employees, through this study it was decided to identify theoretical considerations and practical implications to promote well-being, it was also decided to offer specific training programs to make employees and supervisors aware about the consequences of emotional dissonance and improve their wellbeing.

Muhammad Ashraf et al. (2014) knowing the importance of today’s competitive business environment the researcher has taken Emotional Intelligence and Job Satisfaction as variables of the study which are closely related concepts influencing the personal and organizational life of the employee. To know this relationship the research has administered the BarOn scale, on a sample size of 100 respondents, further the data were subject to statistical analysis using the statistical tools, ANOVA, correlation and multiple regressions. The results of the study concluded that there was evidence of a significant relationship between job satisfaction and emotional intelligence on the demographic factors like working experience and marital status.

Sedatkula (2005) in his study under the topic “occupational stress and work-related wellbeing of Turkish national police members”. The researcher has administered the survey method through a set of questionnaires on 538 respondents, one set of 131 ranked police officers and 407 regular police officers who belong to the police department. The collected data was subjected to statistical analysis using structural equation modeling under the theoretical framework of Kahn & Osiere’s (1990) causal theory. The results of the study were found to have an indirect causal effect of both organizational and operational stresses on job satisfaction with supervisor support as mediator. Supervisor support mediated the relationship between operational stress and job satisfaction fully, and partially mediated the relationship between organizational stress and job satisfaction. The findings of this study illustrated the need for reform in internal policy and managerial change how the executives of Turkish national police members have to organize their agencies and policies for improving wellbeing.

Emmanuel Olatunde & Odusanya (2015) this study was carried out under the topic “Job Satisfaction and Psychological Well-being among Mental Health Nurses ”. On understanding the importance of job satisfaction and psychological wellbeing of nurses and to improve their working conditions, the researcher has surveyed 110 mental health nurses using systematic random sampling technique in Nigeria, using General Health Questionnaire (GHQ- 12). The researcher aimed to assess the prevalence and correlates of job satisfaction and psychological wellbeing. On analysis of data, the results showed that 5.5% reported for low job satisfaction, 60%, and 34.5% reported for average and high level of job satisfaction respectively. Majority of these nurses reported positive psychological wellbeing that is (84.5%), while 15.5% had psychological distress. The results reflected a positive significant relationship between job satisfaction and psychological wellbeing.

Anton Vorina et al. (2017) as the researchers were much interested on knowing the relationship between employee engagement and job satisfaction, a study was conducted on the title “An Analysis of the Relationship Between Job Satisfaction and Employee Engagement”. The researchers have used survey method for a sample size of 594 respondents who are employed in the public and non-public sector in Slovenia. The statistical analysis was done using SPSS 20 at 5 % significance level. The results confirmed the existence of relationship between employee engagement and job satisfaction and positive significance, the results of linear regression forF (1, 583) =296.14, p-value = 0.000, R-square = 0.337. The results also showed there was statistical significance between employee engagement and gender, moreover, there was no statistically significant difference between job satisfaction and gender.

Sanaz Aazami et al. (2017) the researchers were much interested to know the influence of employees work environment who designed this cross-sectional study on the topic “The Relationship Between Job Satisfaction and Psychological/Physical Health among Malaysian Working Women”. As much of the employees spend more time on workplace the stressor can influence employees at psychological and physical health statuses. The aim of this study was to assess the multi-dimensions of job satisfaction and the relationship between the nine facets of job satisfaction (i.e., sleep disorders, headache, gastrointestinal and respiratory problems) specific to women employees. The data were collected using a self-administered questionnaire conducted among 567 Malaysian women respondents working in the public sector. The results showed there was a strong link between job satisfaction and overall health status of employees, psychological distress, and four somatic complaints.The job satisfaction with the nature of work was the strongest predictor of psychological distress, sleep disorders, headaches and gastrointestinal problems. In addition, it was also concluded that job satisfaction levels vary across different dimensions and can even differ from an individual’s feelings of global job satisfaction. Hence policies and practices should focus on improving working conditions to enhance job satisfaction of the employees.

2.4 WELLBEING

Santos et al. (2017) this study was carried out to find the relationship between the psychological effects of job characteristics and indicators of wellbeing among the nurses of Portuguese and Brazilian hospitals. The researcher has used a sample size of 620 respondents who are hospital nurses i.e., (335 Portuguese and 285 Brazilian) samples were collected by means of an online survey. The data was tested using the statistical package meant for multi-group analysis and structural equation modelling. The results of data analysis supported the full mediation model, explained the relationships between hospital nurses' perceived social worth and their well-being.

Kun et al. (2017) the researcher has conducted this study on wellbeing under the title “Development of the Work-Related Well-Being Questionnaire Based on Seligman’s PERMA Model” The author has conducted a pilot study on employee wellbeing using M.Seligman’s multidimensional PERMA model questionnaire. Based on the factor analysis and reliability analysis, the author was able to reduce 56 PREMA items to 35 items. The researcher has administered this 35 item questionnaire to 397 respondents, who were employees of postgraduate courses at the Budapest University of Technology and Economics (BME). On completion of the analysis the results were found to be theoretically relevant to Work-Related Well-Being which support the multidimensional approach on defining and measuring multidimensional employee wellbeing.

De Simone et al. (2014) has conducted a research on organizational behaviour at national research council, under the topic “Conceptualizing wellbeing in the workplace”, described that measures of wellbeing at a workplace may vary, therefore the approach is to think in terms of relatively stable differences between people. On analysis the results showed, important factors were associated with well-being at the workplace and discussed two organizational frameworks, namely leadership and motivation drawn from the interdisciplinary perspective. During analysis since wellbeing can be measured and expected to remain constant over a considerable period of time further studies on research can be done to analyze the maximum desirable state for employees wellbeing that predicts an important outcome for organizations.

Ajay Jain et al. (2013) the researcher who belongs to the department of psychology and behavioural sciences, Aarhus University, has aimed to study and investigate the mediating effect of organizational commitment on the relationship between organizational stressors, employee health and well-being. A sample size of 401 respondents who were employees at operator level working in business process outsourcing organizations (BPO’s)in Delhi were chosen for this study. The data were analyzed using structural equation modelling on AMOS software, the results of the analysis were found to have mediation effect of stressors on physical well-being and psychological well-being of BPO employees. Moreover, the fit indices were meeting the standard as model fit, hence it was decided to improve the wellbeing of call centre industry employees.

Luthans et al. (2013) these researchers have taken steps to increase the wellbeing of the employee through their psychological capital consisting of resources like hope, efficacy, resiliency, optimism which is the desired outcomes of today’s organizations’. An empirical cross-sectional study was conducted on a sample size of (n=523), both “Relationship PsyCap” and “Health PsyCap” were related along with the already well-established work satisfiers to obtain the desired objective outcomes of overall well-being. This well-being was in turn found to help their leaders meet the challenges.

Alireza Choobineh (2016) in their study “Prevalence of work-related musculoskeletal symptoms among Iranian workforce and job groups”, aimed to develop Musculoskeletal disorders a problem among Iranian workforce. To strengthen this work the researcher has used 8004 employees from 20 Iranian industries throughout the country as respondents. The study was carried out by using simple random sampling method and Nordic MSDs questionnaires as a research instrument. The results on data analysis were found to have the highest prevalence rate of Musculoskeletal disorders of (90.3%) among health-care provider and workers with dynamic activities. Most of the symptoms were reported in the area of the lower back (48.9%), shoulders (45.9%), neck (44.2%), upper back (43.8%), and knee (43.8%). Based on the above results it was recommended to implement ergonomics interventional program in Iranian industrial settings.

Hartfiel et al. (2011) the study was aimed to find the effectiveness of yoga on improving the resilience and well-being of the employees through the topic “The effectiveness of yoga for the improvement of well-being and resilience to stress in the workplace”. The researcher has adopted a randomized controlled trial at a British university using 48 employees as respondents. The yoga training class was offered by the certified Dru Yoga instructor at lunchtime for six weeks comprising of 60-minute class per week whereas the control group did not receive such classes. The data assessment were done using a Profile of Mood States–Bipolar (POMS-Bi) and the Inventory of Positive Psychological Attitudes (IPPA). On analysis the results concluded that even for a short yoga programmes there was an effective enhancement of emotional well-being and resilience.

Premchopra et al. (2009) has published his topic on “Mental health and the workplace: issues for developing countries” in International Journal of Mental Health Systems, who insisted that the capacity to work productively is a key component of health and emotional well-being. The author also says that the emotional well being has been associated with adverse impact on workplace stress and is linked with of risk of common mental disorders. Further reviews on the evidence for mental health promotion and intervention studies, the relationship between workplace environment and psychiatric morbidity etc., may help in improving mental health wellbeing research of employees.

Freak et al. (2014) in his study on “Change in well-being amongst participants in a four-month pedometer-based workplace health program”, the researcher aims to prevent chronic disease of workplace physical activity for good psychosocial health. The researcher has used 762 adult respondents, who has voluntarily enrolled in a physical activity program were taken from ten Australian worksites. The data was collected using a self-administered five-item scale at various times intervals as a baseline. On completion of four to eight months duration the data was further analyzed and the results reflected 49.5% have moved into the positive well-being category immediately after program completion.

Peter Hancock et al. (2015) this study was conducted on the title “Female workers in textile and garment sectors in Sri Lankan Export Processing Zones (EPZ): gender dimensions and working conditions”. The study explores the conditions of poor social respect, derogatory comments and exposure to harsh or poor working conditions of female workers in textile and garment sectors in Sri Lankan. The researcher has chosen two sets of sample study one were (n=1878) employees of garment factories working in export processing zones and the second set of study samples (n=426) compared to those employed in other factory types. This was further subject to statistical analysis like Mann-Whitney U tests, Pearson’s chi-square test. The results expressed that, although the women workers of other industries were not subject to abuse and harassment or if at all very low, the textile and garment workers were more exposed to frequent and hard verbal abuse.

CHAPTER 3 RESEARCH METHODOLOGY

3.1 INTRODUCTION

This chapter deals with the research methodology adopted in this study to generate the data required for this research, governed by the research objectives and executed within available resources and constraints. It narrates the details about the type of research, research design-flow chart and inductive or deductive nature of research. Various sections of this chapter discuss the research instrument, type of scales and measures of each variable in detail. Description of sampling techniques involves the population, sample size, sampling frame and sample adequacy. The researcher has made the pilot study as it involves large samples size and population for ensuring the appropriateness and requisite iteration needed for the final questionnaire. Reliability & validity was checked for the items and factors before final data collection. In addition, this chapter also discusses the statistical tools used in this research which concludes with an overview of the research analysis.

3.2 RESEARCH DESIGN

The purpose of research design is to bring out the relationship between independent and dependent variables, it is also a process of finding exact answers to research objectives. (Burns & Grove 2003) has defined the research design as “a blueprint for conducting a study with maximum control over factors that may interfere with the validity of the findings”. Polit et al. (2001) has also defined the research design as “the researcher’s overall for answering the research question or testing the research hypothesis”. Research design explains the type and subtype of research question, sampling, data collection, hypothesis-testing, statistical analysis and finding the results, it attempts to coherent different components in a logical way. “Research design was developed as a framework to find answers to research questions” (Muaz & Jalil Mohammad 2013).

3.3 RESEARCH PROCESS FLOW CHART

Abbildung in dieser Leseprobe nicht enthalten

Figure 3.1 Research process flow chart

3.4 INDUCTIVE VS DEDUCTIVE

In practice, we often refer to the two broad methods of reasoning in research, that is deductive and inductive approaches. An inductive approach is concerned with the generation of new theory emerging from the data. While the deductive approach is aimed at testing the theory. This deductive reasoning is informally called as "top-down" approach, works from the more general to the more specific objective. In this study, the researcher has adopted the deductive approach, which helps to tests the theory through a series of test related to reliability, validity, assumptions of specific hypotheses, data collection, analyse and ultimately relating towards theories.

3.5 DESCRIPTIVE RESEARCH

Descriptive research helps to describing the various aspects of phenomenon and characteristics of the sample population. Though the researcher does not have any control over the variables, still the statement of the problem are well explained. Descriptive research involves gathering data that describe events and then organizes, tabulates, depicts, and describes the data collected (Glass & Hopkins 1984). Descriptive research is “aimed at casting light on current issues or problems through the process of data collection that enables them to describe the situation more effectively than was possible without employing this method” (Fox & Bayat 2007). The descriptive research allows inclusion of more number of variables that can be subjected to three main operations e.g., describing, explaining and validating research findings, (Borg & Gall 1989). Descriptive studies are closely associated with observational and survey methods which are popularly used for data collection which can also integrate the qualitative and quantitative methods, as supported by (Bor g & Gall 1989). This type of research describes what exists and what may help to uncover new facts and meaning observe, describe and document the situation as it naturally occurs (Polit & Hungler 1999). The National Assessment of Educational Progress (NAEP) education statistics of research, and the International Association for the Evaluation of Education Achievement (IEA), adopts the descriptive research as stated by (Borg & Gall 1989). The prime advantage and effectiveness of a descriptive research is that it can analyse non-quantified topics and issues, a phenomenon in a completely natural un tampered environment. Hence the current research uses descriptive research as a tool to organize data into patterns that emerge during analysis, on comprehending the qualitative study and its implications.

3.6 RESEARCH INSTRUMENT AND SCALES

Research instrument is a generic term for measuring devices administered by the researcher, but these are actually facts finding tools. The rational appropriateness of research instrument depends on the validity and reliability of the instrument used for study, “ Validity is the extent to which an instrument measures what it is supposed to measure and performs as it is designed to perform ” and “ reliability check, whether the instrument consistently measure what it is intended to measure”. The instrument used for data collection are of different types, such as Questionnaire, Interview, Observation etc. In this study the researcher has administered four set of structured questionnaires, for the Resilience scale, the one developed by (Gail Wagnild & Heather Young 1993) which consists of 5 factors, for Emotional Intelligence scale developed by (Emily Sterrett 2014) which consists of 6 factors, for Job satisfaction scale developed by (Thompson & Terpening 1983) consists of 8 items and wellbeing scale by (Jagsharanbir Singh & Asha Gupta 2001) which consists of 3 factors. The measurement was carried out with the help of effective scaling designed during the construction of the instrument, which associates qualitative constructs with quantitative metric units for measuring abstract concepts. Among the different forms of measuring scales which are commonly used in research questionnaires, the researcher has used a Likert scale.

3.7 MEASURES OF VARIABLES

Each variable requires a different measuring scale, the level of measurement refers to the relationship among the values that are assigned to the attributes for a variable. The specific purpose is to accurately represent the research variables numerically. The measure is commonly referred to levels of four different scales such as “nominal”, “ordinal”, “interval”, and “ratio”. The characteristics of each variable could be observed and recorded with the help of level of measurement. These measurements of variables influence the type of statistical analysis to be administered, are also quantifiable on the basis of the presence or absence of the concerning attribute (Kothari 2004).Variables can be measured, controlled and manipulated, due to the aforementioned reasons. The researcher has chosen nominal and ordinal scale to measure the variables.

3.7.1 Measures for Resilience

The Resilience scale was developed by Gail Wagnild & Heather Young (1993) which consists of 5 factors such as Equanimity, Perseverance, Self Reliance, Meaningfulness, Existential Aloneness, with each comprising of 5 items and total 25 items used in this research. The scale adopted in this instrument is Likert scale, which ranges in its degree of response from strongly disagree to strongly agree, on a seven-point scale, with a statement of responses ranging from strongly disagree (1) to strongly agree (7) in the case of resilience.

3.7.2 Measures for Emotional Intelligence

Emotional Intelligence scale was developed by (Emily Sterrett 2014), which represents the Knowledge, Attitude and Behaviour (KAB) model each representing two factor, a total sum of 6 factors such as Self Awareness, Self Confidence, Self Control, Empathy, Motivation, Social Competency, with each comprising of 5 items and total 30 items is used in this research variable. The scale adopted in this instrument is typically a five-point Likert scale, contains an odd number of options1 to 5, with a statement of responses ranging from (1) Virtually never on the left to (5) Virtually always on the right, and middle of the scale is (3) for neutral, in the case of Emotional Intelligence .

3.7.3 Measures for Job Satisfaction

The Job satisfaction measure used in this study is a unidimensional scale developed by (Thompson & Terpening 1983) consisting of 8 items using a typical five-point Likert scale that contains an odd number of options,1 to 5, with a statement of responses ranging from strongly disagree for (1) to strongly agree for (5) and middle of the scale is (3) for neutral, in the case of Job satisfaction .

3.7.4 Measures for Well Being

Wellbeing scale developed by (Jagsharanbir Singh & Asha Gupta 2001) is used in this research which consists of 3 factors Physical, Mental and Social Well Being with each consisting of 10 items, with total 30 items. The scale adopted in this instrument is typically a five-point Likert scale, contains an odd number of options 1 to 5, with a statement of responses ranging from (5) very much on the left to (1) not so much on the right, and middle of the scale is (3) on average, in the case of Emotional Intelligence. Each sub-scale is summated and aggregated. The final score obtained by averaging the total subscale factor.

3.7.5 Demographic Questions

The demographic questions administered includes Gender, Age, Marital status, Monthly income, Number of children, Family status, Temporary or permanent employee, Education qualification, and Experience etc. The above information’s were gathered to gain insight about the respondents and their characteristics of the population.

3.7.6 Ethical Considerations

All participants were given clear instructions about the purpose of this study and their contributions towards this research. The confidentiality of the respondents was assured on not revealing their personal and organization identity (such as names of participants or names of employers). Except for examiners, if necessary, during the presentation of the thesis, no other information will be revealed to others, nor it will be available to the public or stated in any papers. Also, all participating industries were thanked for their support extended for this research work and will be provided with a copy of the results if needed on request only.

3.8 DESCRIPTION OF SAMPLING TECHNIQUE

There are two major sampling techniques such as Probability and Non-probability. Probability sampling is often not possible and suitable for business research as recommended by (Saunders et al. 2012), it may not be appropriate to answer a research question, hence non-probability sampling was chosen. The Non-probability sampling includes, self-selection sampling, quota sampling, convenience sampling, snowball sampling and purposive sampling. The researcher has used convenience and purposive sampling methods for the entire sample selection which was implemented at two levels, first the convenience sampling was applied at unit level ei., only the textile apparel units in and around Chennai radius which gave permission for carrying out the research work were chosen for this study. Second at the respondent level, only the permanent employees of the textile apparel units having three years and above experience were purposively chosen for the study. The researcher felt they would be the right persons to provide opinion for the study variables based on their experience, termed as potential sample. The process of selecting the sample representing the entire population is known as sampling (LoBiondo-Wood & Haber 1998, Polit & Hungler 1999).

3.8.1 Target Population

The term population is represented by ‘N’, representing the potential group or subject for study, it is always important and good to be precise. (Polit & Hungler 1999) “ refer to population as an aggregate or totality of all the objects, subjects or members that conform to a set of specifications”. In this study the source of population was identified from textile apparel employees in Chennai, capital of Tamil Nadu, the Southern part of India. Due to very large population sizes, every individual in the population cannot be taken for test, which is expensive and time consuming, and difficult to conduct research using entire population (Desmarais 2005). The textile apparel units are spread out in and around 40 kilometres of radius to Chennai city covering a total number of 669 registered apparel units with a finite population of 1,90,800 permanent employees, classified into 118 Export units, 436 job working units and 115 domestic players. The textile apparel units which is the area of study interest consists of employees population with different age groups, gender, races, education, socio-economic status, family setup, number of children, experience etc. To make a uniform distribution on selected samples, the entire territory of the population was split into four segments and samples were drawn for the research, which extends till Tambaram and Guduvanchari to the south, Sriperumbudur to the west, and till Redhills to the north and central Chennai.

3.8.2 Population Exclusions

The term population exclusions, relates to removal of subjects from the population based on specific grounds or reasons. The exclusion criteria of this research work was that, among the subjects of textile apparel units, temporary and contract workers were not considered for the study. Moreover, among the permanent employees, those with less than three years experience were also excluded from this research.

3.8.3 Sample Size

Statistical results and analysis are prone to uncertainties like sampling errors, which is strongly related to the sample size. There is high possibility of misleading conclusions even for a well-designed research if the study sample is not representative of the population. Quality and strength of statistical inference also depends largely on the size of the sample selected, (Gardner & Altman 1989). Important and appropriate inclusion and exclusion criteria are to be incorporated before collecting the required samples. “A general rule of the thumb is to always use a large samples possible”. The larger the sample the more representative of the population, smaller samples produce less accurate results because they are likely to be less representative of the population (LoBiondo-Wood & Haber 1998). Hence sample adequacy test was carried out to determine the adequacy of sample size and found to be 750 samples required.

3.8.3.1 Determination of Sample Size

A sample size is a part of the population chosen for a survey or experiment. There are different methods for calculating the sample size, the researcher has adopted this confidence level assumption method using the equation for calculating “ n” , the sample size.

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Hence from the determination of the sample size using the equation the researcher could arrive, the sample size required for executing this research project is seven hundred and fifty samples .

3.8.3.2 Screening of Samples

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Figure 3.2 Samples screening flow chart

3.9 DATA COLLECTION METHOD

Data collection is the process of collecting and measuring information on all the variables of interest and study in a systematic fashion that enables one to find the answer to stated research questions, test hypotheses, and evaluate outcomes. “A formal data collection process is necessary as it ensures that the data gathered are both defined and accurate and that subsequent decisions based on arguments embodied in the findings are valid”(Roger Sapsford 1995). Data were collected from employees using the personal approach after explaining the purpose of the study in detail and how to respond to the questions.

According to (Mouton 1996),“ adequate training of interviewers, research assistants and field workers is a precondition of any research”. The researcher has approached the human resource department people for assisting the data collection process on filling the questionnaire from respondents. The researcher has spent two hours on training the research assistants prior to the field work. The training includes their specific role as, how to approach the subject, discussing how to answer all the questions in the questionnaire, and how to clarify the common doubts arising in this process. To eliminate the barrier of communication, the questionnaire was also typed in regional language (Tamil), which was directly administered by the researcher and research assistants to the employees. Being an export apparel units and units catering large volume of domestic suppliers, employees could be accessed only during the break hours and end of the day.

3.10 PILOT STUDY

The term 'pilot studies' or 'feasibility studies' is carried out prior to undertaking final data collection exercise or distribution of the final questionnaire to the respondents. The importance and thrust on pilot study by De Vos et al. (1998) states, the effectiveness of the investigation lies in the pilot study and must be carried out as the main study. The success of any research project depends on administering prerequisite of a pilot study, “ no amount of intellectual exercise can substitute for testing an instrument designed to communicate with ordinary people” (Backstrom & Hursch 1963). The pilot study helps the researcher on minimizing the Type I or Type II error and for executing the appropriate and necessary modifications of the research instruments to be used for main study. Though the researchers have used a well established standardized and validated measuring instruments, they are basically constructed for the western population. To eliminate bias and ensure reliability and validity of the constructs and scale items pilot test was executed. “The literature suggests that the characteristics of the pilot sample should resemble those of the target population (Zaltman & Burger 1975). A sample of 100 employees working in textile apparel industries at Chennai city was used for the above pilot test, as “a costume rehearsal for the actual empirical investigation” (Welman et al. 2009).

The Cronbach's Alpha is a measure of internal consistency of items in the research questionnaire, the theoretical value of alpha varies from zero to one, alpha closer to one represents greater the internal consistency of the items in the questionnaire. A rules of thumb was given by George & Mallery (2003) which provides the following standards, “ ≥ 0.9 is Excellent, ≥ 0.8 is Good, ≥0.7 is Acceptable, ≥ 0.6 is Questionable, ≥ 0.5 is Poor, and <0.5 – Unacceptable” for item reliability. A total number of 93 items were consolidated from all the four set of instruments apart from demographic variables. Further after conforming with the validity test the same instruments was used for final data collection.

3.11 RELIABILITY AND VALIDITY TEST

3.11.1 Reliability Test

To ensure the reliability of the scale consistency, Cronbach’s alpha (Cronbach 1951) test was run and values were estimated, to assess the internal consistency of the question, scales used for data collection. If the Cronbach’s alpha value is greater than 0.7, it is acceptable, and alpha values less than 0.6 indicate an unreliable scale (Nunnally 1978).

Table 3.1 Cronbach'sAlpha Reliability Test

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From Table 3.1the reliability coefficient values of Cronbach’s alpha test for all the research factors were found to be above 0.8, except for Meaningfulness 0.759 and empathy 0.789, as recommended and supported by (George & Mallery 2003). Based upon the recommended standards and obtained results a good reliability is found to be present in the items and factors used.

Table 3.2 Validation Measurement Test

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3.11.2 Validity Test (Validation of the Measurement Model)

The term validity means genuine or authentic is the strength of any research. The scale validity is study-specific and must be considered before an instrument is chosen for the study, to ensure the items consist validity, the American Educational Research Association, National Council on Measurement in Education & Psychological Association, also support that the findings truly represent the phenomenon we are claiming to measure. It is the degree to which evidence and theory support the interpretations of test scores.

3.11.3 Convergent Validity

Convergent validity is a subset of construct validity, refers to the degree to which two measures of constructs are theoretically related, assesses the convergence of each item loading on the latent construct it is measuring. The Average Variance Extracted (AVE) is computed as the sum of the squared standardized factor loadings divided by the number of items. “A good rule of thumb is the Average Variance Extracted (AVE) should be greater than (≥ 0.5) indicates adequate convergent validity” as recommended (Fornell & Larcker 1981). If AVE is less than 0.5 it indicates an average value, there is more error remaining in the items than variance explained by the latent factor structure that is imposed on the measure. The results generated by the statistical measurement model is tabulated in table 3.2. From the results we could infer the AVE values are 0.759 for resilience, 0.607 for emotional intelligence, 0.785 for job satisfaction and 0.849 for well being. On an average, all the values are higher than recommended value of 0.5 which ensures high Convergent validity is present in the corresponding items, as proposed by Hair et al. (2010), and (Fornell & Larcker 1981).

3.11.4 Discriminant Validity

Discriminate validity is also a subset of construct validity. It is the extent to which a construct is truly distinct from other constructs. Discriminate validity measures the level up to which each item loading of a construct is distinct and does not measure other constructs. Statistically, The AVE of each of the latent constructs should be higher than the highest squared correlation with any other latent variable. If that is the case, discriminate validity is established on the construct level. “A good rule of thumb is that all construct (AVE) estimates should be larger than the corresponding squared inter construct correlation estimates (SIC) ”. In this study from Table 3.2, we could infer that all the highest value of SIC, for resilience is 0.651, for emotional intelligence 0.566, for wellbeing 0.741 and for job satisfaction 0.671, is lesser than AVE. That all average variance extracted estimates in the above table are larger than the corresponding squared inter construct correlation estimates (AVE > SIC). Hence, we conclude that our construct possesses discriminate validity, measured through the measurement model. Hence this ensures high Discriminant validity is present in the corresponding construct, as proposed by (Hair et al. 2010) and (Straub Boudreau & Gefen 2004).

3.12 NORMALITY TEST--BASED ON CENTRAL LIMIT THEOREM (CLT)

The researcher has applied the central limit theorem for checking the normality of the sample. A number of statistical tests, such as the Student's t-test and ANOVA require a normally distributed sample population. A normality test is used to determine whether sample data has been drawn from a normally distributed population (within some tolerance). Under the condition, if the population has a normal distribution, then the sample means will also have similar normal distribution. The normal distribution, also known as the Gaussian or standard normal distribution, is the probability distribution that plots all of its values in a symmetrical fashion, and most of the results are situated around the probability's mean. Values are equally likely to plot either above or below the mean. Grouping takes place at values close to the mean and then tails off symmetrically away from the mean.

The Central Limit Theorem (CLT) is a statistical theory which states that, given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population. On applying the (CLT), If the sample size is sufficiently large, and even if the population is not normally distributed, still the sample mean will be normally distributed.

The Central Limit Theorem basically says that for a non-normal data, the distribution of the sample means has an approximate normal distribution, no matter what the distribution of the original data looks like, as long as the sample size is large enough (usually at least 30) and all samples have the same mean. “The significance of the central limit theorem lies in the fact that it permits us to use sample statistics to make inferences about population parameters without knowing anything about the shape of the frequency distribution of that what we can get from the sample” ( Levin 1999).

3.13 MULTICOLLINEARITY

Multicollinearity is a state of very high inter correlations among the independent variables. The Variance Inflation Factor (VIF), is the most commonly used diagnostic representation for multicollinearity. VIF is estimated using the ‘R [2] ’ value of regression .

Table 3.3 Variance inflation factor & Tolerance in multicollinearity

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The statistical phenomenon of two or more predictor (Independent variables) being highly correlated in the multiple regression model is known as multicollinearity i.e., one can be linearly predicted from the others with a substantial degree of accuracy. The Variance Inflation Factor (VIF) identifies the multicollinearity, that is how much variance of a regression coefficient is inflated due to multicollinearity in the model. Variance inflation factors range from 1 to10 and upwards. The VIF numerical value tells us what percentage the variance is inflated for each coefficient. A rule of thumb for interpreting the variance inflation factor, 1 represents not correlated, between 1 and 5 is moderately correlated, and greater than 5 represents highly correlated. To check the influence of multicollinearity the Variance Inflation Factors (VIF) where calculated and found to be with a maximum value of 3.3 as shown in table which is at a permissible limit as recommended by Hair et al. (1998). To establish convergence and unidimensionality of the factors, we used CFA which is discussed in the next section.

3.14 UNIDIMENSIONALITY

This ensures only one indicator represents towards the related factor. There are two conditions required to be fulfilled for representing unidimensionality. The measures must satisfy two conditions. First there should be an strong association of empirical item towards the construct, which can be attained by suppressing the factor loadings below 0.5; and next, the item must be associated with one and only one construct which can be confirmed by discriminant validity (Hair et al. 1998, Phillips & Bagozzi 1986).

3.15 CONFIRMATORY FACTOR ANALYSIS

Confirmatory factor analysis (CFA) is a statistical tool, a special form of factor analysis frequently used in social research either to confirm or reject the measurement theory (Kline 2010). The number of constructs is measured using the multivariate statistical procedure measuring the variables. The CFA which is an special case of the structural equation model (SEM).

The concept of unidimensionality between construct error variance and within construct error variance were maintained on keeping a minimum of five items for each factors used in this study. CFA are employed to understand shared variance of measured variables that is believed to be attributable to a factor or latent construct, and moreover evaluates the hypotheses which is largely driven by theory. It is absolutely necessary to establish convergent and discriminant validity, as well as reliability, when doing a CFA. The statistical tests helps to determine model fits to the data. “Note that a good fit between the model and the data does not mean that the model is “correct”, or even that it explains a large proportion of the covariance. A “good model fit” only indicates that the model is plausible”, (Schermelleh & Engel 2003 )

Confirmatory Factor Analysis (CFA), is particularly useful in the validation of scales for the measurement of specific constructs. The main constructs used in this research work were Resilience, Emotional intelligence, Job satisfaction and Wellbeing. Specific to wellbeing each individual factors such as physical, mental and social wellbeing were subject to confirmatory factor analysis test.

3.15.1 Fit Indices of Resilience

The goodness of fit, model represented in Figure 3.3 was evaluated using absolute and relative normed and comparative indices. The fit indices calculated were (1) χ[2] / df (2) Goodness of Fit Index ( GFI ), (3) Adjusted Goodness-of Fit Index (AGFI), (4) Comparative Fit Index (CFI), (5) Normed Fit Index (NFI), (6) the Root Mean Square Error of Approximation (RMSEA); (7) The fit indices calculated are given below in Table 3.4.

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Figure 3.3 Path diagram for resilience

Table 3.4 Fit indices for resilience

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The path diagram of resilience represented in Figure 3.3, is a one-factor model which is the standardized solution. The latent variable values are manifested by five observed variables, the oval represents a latent variable and rectangle represent a manifest variable. The numbers of arrows from the latent variable to observed variables are standardized factor loadings which are represented as regression weights. Among the five factors, Meaningfulness has larger factor loadings of 0.91, which also appear to be the best indicators of values. All the five factors, Equanimity, Perseverance, Self Reliance, Meaningfulness and Existential aloneness have larger factor loadings above 0.78 and above, of which Meaningfulness appear to be the best indicators of values. R[2] is a standardized factor loading squared which means the extent that a factor can explain the variance in a manifest variable. In the above CFA, the latent variable values explain about 83 percent (0.912 ) of the variance in Meaningfulness.

CMIN / DF: χ[2] is a classic goodness-of-fit measure to determine overall model fit, the difference between the two matrices is expressed in χ[2], with degrees of freedom (df) equating the number of covariances. χ[2] really a “badness-of-fit” index so, dividing χ[2] by df called normed chi-square (NC). χ2 should be small enough - in relation to df, χ2/df (2.0 or 3.0 or even 5.0) considered acceptable. In the above CFA for employee Resilience, we find CMIN/DF to be 1.24 less than 2 well within the recommended value hence which is acceptable.

GFI: Goodness- of fit index ( GFI) represents the overall degree of fit of the model. It is a non statistical measure ranging in value from 0 (poor fit) to 1.0 (perfect fit). Higher values indicate better fit. In the above CFA for Resilience, we find GFI is 0.99 which is above the recommended value of (> 0.95) hence deemed to be a good fit and acceptable as recommended by (Baumgartner & Hombur 1996 ).

AGFI: The adjusted goodness-of-fit is an extension of the GFI, adjusted by the ratio of degrees of freedom from the proposed model to the degree of freedom for the null model. A recommended acceptance level for AGFI is a value greater than or equal to 0.80. In the above CFA for employee Resilience, the AGFI was found to be 0.985 which is above the recommended value of (>0.8) hence deemed to be a good fit and acceptable as recommended by, Baumgartner & Hombur (1996).

CFI: Another measure which represents comparisons between the estimated model and a null or independence model is the comparative fit index (CFI). The values lie between 0 and 1.0, and larger values indicate higher levels of goodness-of-fit. In the above CFA for employee Resilience, the CFI was found to be 0.97 which is above the recommended value of (> 0.95) hence deemed to be a good fit which is acceptable as recommended by (Hu & Bentler 1999).

NFI: One of the more popular measures is the normed fit index, a measure ranging from 0 (no fit at all) to 1.0 (perfect fit). Again, the NFI is a relative comparison of the proposed model to the null model. In the above CFA for employee Resilience, the NFI value was found to be 0.99 which is above the recommended value of (> 0.90) hence deemed to be a good fit and acceptable as recommended by (Hu & Bentler 1999).

RMR: The root mean square residual, is simply the mean absolute value of the covariance residuals of the discrepancy between the sample covariance matrix and the model covariance matrix, (Hooper et al 2008). The standardized root means square residual removes this difficulty in interpretation, and ranges from 0 to 1, with a value of 0.08 or less being indicative of an acceptable model (Hu &Bentler 1999). A "badness of fit" index (in that higher numbers mean worse fit) values of standardized RMR < 0.10 generally considered adequate. In the above CFA for employee Resilience the RMR value was found to be 0.040, which is less than the recommended value of (<0.08) hence deemed to be a good fit and acceptable as recommended by (Hu & Bentler 1999).

RMSEA: Another measure that attempts to correct for the tendency of the chi-square statistic to reject any specified model with a sufficiently large sample is the root mean square error of approximation (RMSEA).The value is representative of the goodness-of-fit that could be expected if the model were estimated in the population, not just the sample drawn for the estimation (Hair et al. 2010). Values ranging from 0.05 to 0.08 are deemed acceptable. An empirical examination of several measures found that the RMSEA was best suited to use in a confirmatory model strategy with larger samples (Ringdon 1996). In the above CFA for employee Resilience, the RMSEA value was found to be 0.018 which is less than the recommended value of (< 0.05) hence deemed to be a good fit and acceptable as recommended by (Hair et al. 2010). Thus it is found that employee Resilience comprising of five components such as Equanimity, Perseverance, Self-reliance, Meaningfulness and Existential aloneness is recommended as a good fit.

3.15.2 Fit Indices of Emotional Intelligence

The goodness of fit, model represented in figure 3.4 was evaluated using absolute and relative indices. The fit indices calculated were (1) χ2 / df (2)Goodness -of- Fit Index (GFI), (3) Adjusted Goodness-of-Fit Index (AGFI), (4) Comparative Fit Index (CFI),(5) Normed Fit Index (NFI), (6)Root Mean Square Error of Approximation (RMSEA); (7)Root Mean Square Residuals (RMR), The fit indices calculated are given below in table 3.5.

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Figure 3.4 Path diagram for emotional intelligence

Table3.5 Fit indices for emotional intelligence

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The path diagram of emotional intelligence represented in Figure 3.4, is also a one-factor model which represents the standardized solutions. The latent variable values are manifested by six observed variables, the oval represents a latent variable and rectangle represent a manifest variable. The numbers of arrows from the latent variable to observed variables are standardized factor loadings which are represented as regression weights. All the six factors, Self Awareness, Self Confidence, Self Control, Empathy, Motivation and Social Competency have larger factor loadings, of which Self Confidence appear to be the best indicators of values. R[2] is a standardized factor loading squared which states the extent that a factor can explain the variance in a manifest variable. In the above CFA, the latent variable values explain about 75 percent (0.86) of the variance in Self Confidence.

CMIN/DF: In the above CFA for Emotional Intelligence, we find CMIN/DF to be 1.38 less than 2.0 well within the recommended value hence which is acceptable.

GFI: In the above CFA for Emotional Intelligence, we find GFI is 0.997 which is above the recommended value of ( > 0.95 ) hence deemed to be a good fit and acceptable as recommended by ( Baumgartner & Hombur 1996 ).

AGFI: In the above CFA for employee Emotional Intelligence, the AGFI was found to be 0.987 which is above the recommended value of (>0.8) hence deemed to be a good fit and acceptable as recommended by (Baumgartner & Hombur 1996).

CFI: In the above CFA for employee Emotional Intelligence, the CFI was found to be 0.969 which is above the recommended value of (> 0.95) hence deemed to be a good fit and acceptable as recommended by (Hu & Bentler 1999).

NFI: In the above CFA for employee Emotional Intelligence, the NFI value was found to be 0.998 which is above the recommended value of (>0.90) hence deemed to be a good fit and acceptable as recommended by (Hu & Bentler 1999).

RMR: In the above CFA for employee Emotional Intelligence, the RMR value was found to be 0.040, which is less than the recommended value of (<0.08) hence deemed to be a good fit, and acceptable as recommended by Hu & Bentler (1999).

RMSEA : In the above CFA for employee Emotional Intelligence, the RMSEA value was found to be 0.023 which is less than the recommended value of (< 0.05) hence deemed to be a good fit and acceptable as recommended by (Hair et al. 2010).

Thus it is found that employee Emotional Intelligence comprising of six components such as Self Awareness, Self Confidence, Self Control, Empathy, Motivation, and Social Competency, which fall within the recommended values is a good fit.

3.15.3 Fit Indices of Job Satisfaction

The goodness of fit, model represented in Figure 3.5 was evaluated using absolute and relative indices. The fit indices calculated were (1) χ2 / df (2) Goodness -of- Fit Index (GFI ), (3) Adjusted Goodness-of-Fit Index (AGFI), (4) Comparative Fit Index (CFI),(5) Normed Fit Index (NFI), (6) Root Mean Square Residuals (RMR), (7) the Root Mean Square Error of Approximation (RMSEA); The fit indices calculated are given below in Table 3.6.

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Figure 3.5 Path diagram for job satisfaction

Table 3.6 Fit indices for job satisfaction

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The path diagram of job satisfaction represented in Figure 3.5, is a latent variable, values manifested by eight items as observed variables, the oval represents a latent variable and rectangle represent a manifest variable. The numbers of arrows from the latent variable to observed variables are standardized factor loadings which are represented as regression weights. Among the eight items, JS5 have larger factor loadings of above 0.78, of which item JS.5, appear to be the best indicators of values. R[2] is a standardized factor loading squared states the extent that a factor can explain the variance in a manifest variable. In the above CFA, the latent variable values explain about 61 percent (0.78 ) of the variance in JS.5.

CMIN/DF : The difference between the two matrices is expressed in χ2, with degrees of freedom (df) equating the number of covariance’s. χ2 really a “badness-of-fit” index so, dividing χ2 by df called normed chi-square (NC). χ2 should be small enough - in relation to df, χ2/df (2.0 or 3.0 or even 5.0) considered acceptable. In the present CFA for job satisfaction, we find χ2/df to be 1.205 less than 2.0 which indicate an acceptable and a good fit.

GFI : In the above CFA for job satisfaction, we find GFI to be above 0.994 is closer to one which is on the higher side of recommended value (>0.95) which indicate a good fit as supported by (Baumgartner & Hombur 1996).

AGFI :In the present study, the AGFI was found to be 0.977 for job satisfaction which meets the recommended value of (>0.8) hence deemed to be a good fit and acceptable supported by (Baumgartner & Hombur 1996).

CFI :In the CFA conducted for employee job satisfaction, the CFI was found to be 0.989 which suggests a good fit (Hu & Bentler 1999 ).

NFI :In the CFA done on employee job satisfaction, the NFI was found to be 0.966 which meets the recommended standards (≥ 0.90) hence deemed to be a good fit (Hu & Bentler 1999)

RMR :The root mean square residual, a "badness of fit" index (in that higher numbers mean worse fit) values of standardized RMR(<0.10) generally considered adequate. In the CFA done on employee overall well being, the RMR value was found to be 0.045, which is less than 0.1, is commonly recommended as acceptable

RMSEA : An empirical examination of several measures found that the RMSEA was best suited to use in a confirmatory model strategy with larger samples (Ringdon 1996), RMSEA in the present CFA is 0.015, thus it is found that employee job satisfaction suggests a good fit.

3.15.4 Fit Indices of Wellbeing

The goodness of fit, model represented in Figure 3.5 was evaluated using absolute and relative indices. The fit indices calculated were (1) χ2 / df (2) Goodness -of- Fit Index ( GFI ), (3) Adjusted Goodness-of-Fit Index (AGFI), (4) Comparative Fit Index (CFI 5) Normed Fit Index (NFI), (6) Root Mean Square Residuals (RMR), (7) the Root Mean Square Error of Approximation (RMSEA); The fit indices calculated are given below in Table 3.7.

Abbildung in dieser Leseprobe nicht enthalten

Figure 3.6 Path diagram for physical wellbeing

The path diagram of physical well being represented in Figure 3.6, is a one-factor model which is the standardized solutions. The latent variable values are manifested by eight items as observed variables, the oval represents a latent variable and rectangle represent a manifest variable. The numbers of arrows from the latent variable to observed variables are standardized factor loadings which are represented as(regression weights). All the eight items, WB.8 to have larger factor loadings of above 0.76, of which item WB.8, appear to be the best indicators of values. R[2] is a standardized factor loading squared that means the extent that a factor can explain the variance in a manifest variable. In the above CFA, the latent variable values explain about 58 percent,(0.762) of the variance in WB.8 and items WB.3, WB.9 and WB.10 are showing less than 0.60-factor loading, which is lesser than other items hence considered to have poor factor loadings, suggesting that they appear to indicate other factors.

The path diagram of mental well being represented in Figure 3.7, is a one-factor model which is the standardized solutions. The latent variable values are manifested by eight items as observed variables, the oval represents a latent variable and rectangle represent a manifest variable. The numbers of arrows from the latent variable to observed variables are standardized factor loadings which are represented as(regression weights). All the eight items, WB.16 to have larger factor loadings of above 0.78, of which item WB.16, appear to be the best indicators of values. R[2] is a standardized factor loading squared that means the extent that a factor can explain the variance in a manifest variable. In the above CFA, the latent variable values explains about 60 percent, (0.782) of variance in WB.16 and items WB.11, WB.12, WB.13, WB.14, and WB.20 are showing less than 0.60 factor loading, which is lesser than other items hence considered to have poor factor loadings, states that they appear to indicate other factors.

The path diagram of social well being represented in Figure 3.8, is a one-factor model which is the standardized solutions. The latent variable values are manifested by eight items as observed variables, the oval represents a latent variable and rectangle represent a manifest variable. The numbers of arrows from the latent variable to observed variables are standardized factor loadings which are represented as(regression weights). All the eight items, WB.27 to have larger factor loadings of above 0.74, of which item WB.27, appear to be the best indicators of values. R[2] is a standardized factor loading squared that means the extent that a factor can explain the variance in a manifest variable. In the above CFA, the latent variable values explain about 55 percent, (0.742) of the variance in WB.27 and items WB.21, WB.22 and WB.23are showing less than 0.60 factor loading, which is lesser than other items hence considered to have poor factor loadings, suggesting that they appear to indicate other factors.

Abbildung in dieser Leseprobe nicht enthalten

Figure 3.7 Path diagram for Mental wellbeing

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Figure 3.8 Path diagram for Social wellbeing

Table3.7 Fit indices for wellbeing

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CMIN/DF : In the present CFA for all the three components of well being, we find Cmin / df which is to be less than five such as 1.877 for physical well being, 1.746 for mental well being, 1.464 for social well being indicates an acceptable fit.

GFI :. In the present CFA for wellbeing, we find GFI for all the components of wellbeing to be above 0.9 which indicate a good fit as acceptable and recommended by (Baumgartner & Hombur 1996).

AGFI : In the present study the AGFI is 0.9 and above for all the three components of employee wellbeing, which meets the recommended standards of (>0.8) hence deemed to be fit and acceptable as recommended by (Baumgartner & Hombur 1996).

CFI : In the above CFI for all the three components of employee wellbeing is 0.99 which is above the recommended value of (> 0.95) hence deemed to be a good fit and acceptable as recommended by ( Hu & Bentler 1999).

NFI : In the above NFI is found to be 0.98 for physical well being, 0.98 for mental well being, 0.99 for social well being which is above the recommended value of (> 0.90) hence deemed to be a good fit and acceptable as recommended by ( Hu & Bentler 1999).

RMR : The root mean square residual, a "badness of fit" index in the above RMR which is found to be 0.009 for physical well being, 0.010 for mental well being, 0.009 for social well being which is above the recommended value of (>0.08) hence deemed to be a good fit and acceptable as recommended by (Hu & Bentler 1999).

RMSEA : In the above RMSEA it was found to be 0.034 for physical well being, 0.031 for mental well being, 0.024 for social well being which is above the recommended value of (<0.05) hence deemed to be a good fit and acceptable as recommended by ( Hair et al. 2010).

Thus it is found that employee wellbeing comprises of three components such as physical well being, mental, social well being which suggests a good fit.

3.16 PATH DIAGRAM FOR RESEARCH FRAMEWORK

Abbildung in dieser Leseprobe nicht enthalten

Figure 3.9 Path diagram for research framework model

3.17 RESEARCH SOFTWARE PACKAGES

In Research statistics application software are used to simplify and get accurate results in short time period. The WINKS and SPSS statistical data analysis software are specialized computer programs, of which the researcher has used SPSS program which is a widely used for, health research, survey companies, marketing, data miners and others, as referred by (Wellman 1998). In a statistical analysis, the main features of the base software lie in data management and data documentation. Research statistical tools are used for data analysis and to arrive substantial inferences. The researchers have used Statistical Package for the Social Sciences (SPSS) ver. 21 for one set of tools. Though there are different software packages like LISREL, EQS, AMOS, and laavan package in R, using covariance method for running measurement Model, and Structural Equation Modeling the researcher has adopted the statistical packages (AMOS) Analysis Of Moment Structures, Ver.21, for the executing the model.

3.18 STATISTICAL TOOLS FOR DATA ANALYSIS

- Percentage Analysis
- Independent Sample t-Test
- Analysis of Variance (ANOVA)
- Multi Linear Regression
- Measurement Model
- Structural Equation Modeling (SEM)

3.18.1 Percentage Analysis

Percentage analysis is a commonly used method to find how the data is scattered. It is widely used to segregate the demographic attributes of the respondents. In the present study, percentage analysis is used to find how the respondent’s demographic attributes are distributed.

3.18.2 Independent Sample T-Test

The Independent-sample t-Test is an inferential statistics which is used to compare the significant difference between the means of two groups. In the present study independent sample t-Test is used to compare gender and marital status of respondents with respect to study variables. The output of the SPSS will display statistics like mean, t-value, significance value and standard deviation. Only t-Test and significance values are taken into consideration to find the significant difference. If the significance value is less than 0.05 it is considered that there is a significant difference between the study variables and the hypothesis is accepted.

3.18.3 Analysis of Variance (ANOVA)

The ANOVA can be used in quantifiable outcome with a categorical explanatory variable that has two or more levels of treatment. In this research study, ANOVA is used to compare demographic attributes of the respondents like age, income, educational qualification, experience, designation, family status and a number of children. If the significance value is less than 0.05 it is assumed that there is a significant difference between groups. The one-way ANOVA analysis is an extent of the independent sample t-Test. The researcher has used (ANOVA) to determine the significant differences and means of two or more independent groups and more than three group simultaneously To identify groups post-hoc analysis have been performed using Duncan method.

3.18.4 Multi Linear Regression (MLR)

Multi-Linear Regression a most common form of linear regression analysis, was used to explain the relationship between one dependent variable and two or more independent variables. In this research well being is the dependent variable, resilience and emotional intelligence are the independent variables, and job satisfaction acts as a mediating variable. In multilinear regression, residuals must be normally distributed for the absence of multicollinearity. To execute MLR, enough data is needed to provide reliable estimates of the correlations by which we can understand the calculation and interpretation of R [2] in a multiple regression setting. MLR uses several explanatory variables to predict the outcome of a response variable since the present study involves more than one independent variable MLR was preferred.

3.18.5 Measurement Model

The measurement model is the part which relates measured variables to latent variables. The structural model is the part that relates latent variables to one another. Statistically, the model is evaluated by comparing two variance/covariance matrices. From the data, a sample variance/covariance matrix is calculated. The measurement model in Figure 4.7.1 portrays the relationship between latent variables and their measures. The structural model is the relationship between the latent variables.

3.18.6 Structural Equation Modeling (SEM)

In order to determine the relationship between the variables of the proposed model, structural equation modeling technique is employed. SEM comprises of two mechanisms measurement model or Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) or path analysis. Measurement model (i.e.) Confirmatory Factor Analysis (CFA) was carried out to identify the items of each construct and to evaluate the measurement model. Structural Equation Modeling (SEM) is employed to develop a comprehensive model which identify the hypothesized relationship between study variables. Both CFA and SEM were performed using AMOS software which is widely used for structural equation modeling estimations.

CHAPTER 4 DATA ANALYSIS

4.1 INTRODUCTION

This chapter consists of analysis and interpretation of data which helps in strengthening the concept and theory behind it and also acts as a central step of any research process. This kind of data analysis performed on a set of data will be influenced by the goals identified at the outset that triggered the research. Interpretation is the amplification and searches for the broader meaning of findings, which cannot be fulfilled without analysis, hence each stage is interrelated.

The analysis of data helps to derive the stated objectives on testing the hypotheses, arrive at the findings and conclusions of the study. Both descriptive and inferential statistical techniques were used considering the merits and shortcomings of each technique.

Descriptive statistical analysis helps to gain insight of the data by presenting numerical and graphical summaries, distribution of values, patterns of skewness etc., thus describing and summarizing of data in a meaningful way. There are two types of descriptive statistics; the first type of statistics measures the central tendency of data covering the central limit theorem. This form of statistics is described by finding the mean, median, and mode of the data that we have collected. The second type of descriptive statistics is typically used to measure the spread out of data. The limitation of this descriptive statistics is that we cannot make any conclusions beyond the set of data that is being analyzed.

Inferential statistics comes into play when we don’t have access to the entire population. Inferential statistics is carried out by taking several sufficient samples that accurately represent the population as a whole, which is tested for generalizations. There are two methods used in inferential statistics, the first involves estimating the parameter and the second involve testing the statistical hypothesis.

4.2 PRELIMINARY DATA SCREENING AND DATA CLEANING

Data Screening and cleaning is the process of ensuring the collected data is clean without errors and ready for conducting the statistical analyses. Screening process also ensures validity, reliability, and usability of data for the research process. While screening the data care must be taken to ensure that not more than 10% of the responses are eliminated, which may cause several problems while running the data for analyses and computing the estimates.

In this study the researcher has identified some of the respondents have not completed the questionnaire. To the best, the researcher has identified the respondents and filled the same, but some of the respondents who have left the organization or who could not be traced, those unfilled questionnaires have not been included for this research. Fang & McNabb (2013) enumerates, when sample size increases the random and sampling error decreases. Summing up the samples collected from ten different industries, after undergoing screening and cleaning of data it was identified 750 sample data were fit for analysis and the response rate was found to be 94 percent.

4.3 DESCRIPTIVE ANALYSIS OF SAMPLE

The descriptive statistical analysis describes the quantitative part of the collection of information (Mann & Prem 1995), it summarizes the data into measures of simple graphics. In descriptive statistics, the percentage analysis is one of the statistical measures used to describe the characteristics of the population for easy and sensible interpretation.

4.4 t-TEST

HYPOTHESIS I

4.4.1 G ender

H01a: Null Hypothesis: There is no significant difference between male and female employees of textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and Wellbeing.

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Figure 4.1 Frequency Distribution of employees based on gender

Table 4.1 t-test for significant difference between male and female employees with respect to Resilience, Emotional Intelligence, Job satisfaction and Wellbeing of the employees .

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Note : 1. ** denotes significant at 1% level

2. * denotes significant at 5% level

From Table 4.1, since P-value is less than 0.01, the null hypothesis H01a is rejected at 1% level with regard to emotional intelligence. Hence there is a significant difference between male and female employees of textile apparel industry with regard to emotional intelligence. Based on the mean score, female employees have high emotional intelligence, than the male employees. These gender variations are assumed to be present in all social spheres including family and work life (Desmarais & Alksnis 2005).

Since P-value is less than 0.05, the null hypothesis H01a is rejected at 5% level with regard to resilience, job satisfaction and well being. Hence there is a significant difference between male and female employees with regard to resilience, job satisfaction and well being. Based on the mean score, female employees have better in resilience, job satisfaction and well being than male employees.

HYPOTHESIS - II

4.4.2 Marital Status

H01b : Null Hypothesis: There is no significant difference between Single and Married employees of textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and Well Being .

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Figure 4.2 Frequency Distribution of employees based on Marital Status

Table 4.2 t-test for significant difference between Single and Married employees with respect to Resilience, Emotional Intelligence, Job satisfaction and Wellbeing

Abbildung in dieser Leseprobe nicht enthalten

Note : 1. ** denotes significant at 1% level

2. * denotes significant at 5% level

From Table 4.2, since P-value is less than 0.05, the null hypothesis H01b is rejected at 5% level with regard to resilience, emotional intelligence and well being. Hence there is a significant difference between employees of single and married status with regard to resilience, emotional intelligence, and wellbeing. Based on the mean score, single status employees have higher values of resilience, emotional intelligence, and wellbeing than married employees of the textile apparel industry.

Since P-value is greater than 0.05, there is no significant difference between employees of single and married status with regard to job satisfaction.

HYPOTHESIS - III

4.4.3 Type of Family

H01c: Null Hypothesis: There is no significant difference between employees belonging to Nuclear and Joint family working in textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and Wellbeing.

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Figure 4.3 Frequency Distribution of employees based on type of family

Table 4.3 t test for significant difference between employees belonging to Nuclear and Joint family with respect to Resilience, Emotional Intelligence, Job satisfaction and Wellbeing

Abbildung in dieser Leseprobe nicht enthalten

Note : 1. ** denotes significant at 1% level

2. * denotes significant at 5% level

From Table 4.3, since P-value is less than 0.01, the null hypothesis H01c is rejected at 1% level with regard to resilience and emotional intelligence. Hence there is a significant difference between employees in nuclear and joint family with respect to resilience and emotional intelligence. Based on the mean score, the employees of nuclear family have higher values of resilience and emotional intelligence than employees of the Joint family working in the textile apparel industry.

Since P-value is less than 0.05, the null hypotheses H01c is rejected at 5% level with regard to Job Satisfaction, and Wellbeing. Hence there is a significant difference between employees in nuclear and joint family with respect to wellbeing and job satisfaction. Based on the Mean score, statistically significant differences were found on resilience and emotional intelligence, which reflects that in today lifestyle employees who emerge from nuclear family setup have better resilience, emotional intelligence, Job satisfaction and Wellbeing than employees reporting from the joint family setup of the textile apparel industry.

HYPOTHESIS – IV

4.4.4 Nature of Job

H01d : Null Hypothesis: There is no significant difference between employees of Permanent and Temporary job status working in textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and Wellbeing.

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Figure 4.4 Frequency Distribution of employees based on nature of Job

Table 4.4 t-test for significant difference between employees of Permanent and Temporary job status with respect to Resilience, Emotional Intelligence, Job satisfaction and well being

Abbildung in dieser Leseprobe nicht enthalten

Note : 1. ** denotes significant at 1% level

2. * denotes significant at 5% level

From Table 4.4, since P-value is less than 0.01, the null hypothesis H01d is rejected at 1% level with regard to job satisfaction. Hence there is a significant difference between employees of Permanent and Temporary nature of Job with regard to job satisfaction. As the P-value less than 0.05, the null hypothesis H01d is rejected at 5% level with regard to well being, of the employees in the textile apparel industry.

As the P-value is greater than 0.05, there is no significant difference between employees of Permanent and Temporary nature of Job with respect to resilience, emotional intelligence, hence the null hypothesis H01d is found to be accepted . Based on the mean score, the employees in Permanent job status have higher values of resilience, emotional intelligence, job satisfaction and wellbeing than Temporary job status of employees working in the textile apparel industry.

4.5 ANOVA

HYPOTHESIS V

4.5.1 Age

H01e: Null Hypothesis: There is no significant difference among employees of different age group working in textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and Wellbeing.

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Figure 4.5 Frequency Distribution of employees based on age

Table 4.5 ANOVA for significant difference among employees of different age group with respect to Resilience, Emotional Intelligence, Job satisfaction and Wellbeing

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Note : 1. The value within bracket refers to SD

2. ** denotes significant at 1% level.

3. * denotes significant at 5% level.

4. Different alphabet among age group denotes significant at 5% level using Duncan Multiple Range Test (DMRT)

From Table 4.5, since P-value is less than 0.01, the null hypothesis H01e is rejected at 1% level with regard to emotional intelligence. Hence there is significant difference between employees of different age group with regard to emotional intelligence. Based on the Duncan Multiple Range Test (DMRT), the emotional intelligence of the employees influences the age group above 30 years significantly differ from the age group of the employees between 18-20 years, 26-30 years at 1% level and also employees in the age group of 21-25 years differ with emotional intelligence.

Since P-value is less than 0.05, the null hypothesis H01e is rejected at 5% level with regard to, Job satisfaction and Wellbeing. Hence there is a significant difference between employees in different age group of with regard to job satisfaction and wellbeing. Based on Duncan Multiple Range Test (DMRT), with regard to the wellbeing, employees in the age group above 30years is significantly differed with the age group of employees between 18-20 years, 26-30 years at 1% level and also employees in age group of between 21-25 years. There is no significant difference among the employees of age group with regard to resilience. Since P-value is greater than 0.05, the null hypothesis H01e is accepted with regard to resilience.

HYPOTHESIS VI

4.5.2 Educational Qualification

H01f : Null Hypothesis: There is no significant difference among employees of different Education Qualification working in textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and Wellbeing.

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Figure 4.6 Frequency Distribution of employees based on Educational Qualification

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Table 4.6 ANOVA for significant difference among employees of different education qualification with respect to Resilience, Emotional Intelligence, Job satisfaction and Wellbeing

Abbildung in dieser Leseprobe nicht enthalten

Note : 1. The value within bracket refers to SD

2. ** denotes significant at 1% level.

3. * denotes significant at 5% level.

4. Different alphabet among educational qualification denotes significant at 5% level using Duncan Multiple Range Test (DMRT)

From Table 4.6, since P-value is less than 0.01, null hypothesis H01f is rejected at 1% level with regard to Emotional Intelligence and Job satisfaction. Hence there is significance difference between employees with different education qualification with regard to emotional intelligence and job satisfaction in textile apparel industry. Based on Duncan Multiple Range Test (DMRT), the level of Emotional intelligence of employees with the education qualification up to HSc. and above is significantly different with employees who are Illiterate and Primary school at 1% level and also employees in HSc and above, differ with Middle school and High School in emotional intelligence.

Since P-value is less than 0.05, the null hypothesis H01f is rejected at 5% level with regard to resilience, and well-being. Hence there is a significant difference between employees with different education qualification with regard to resilience, and well-being.

Based on Duncan Multiple Range Test (DMRT), level of wellbeing of employees in the education qualification, who are Illiterate have a significant difference with employees who are in Middle school, High school, HSc. & above at 5% level and employees who are Illiterate also differ with Primary school employees in wellbeing.

HYPOTHESIS VII

4.5.3 Salary

H01g : Null Hypothesis: There is no significant difference among employees of different Monthly Salary working in textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and wellbeing.

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Figure 4.7 Frequency Distribution of employees based on salary

Table 4.7 ANOVA for significant difference among employees of different Monthly Salary with respect to Resilience, Emotional Intelligence, Job satisfaction and wellbeing.

Abbildung in dieser Leseprobe nicht enthalten

Note : 1. The value within bracket refers to SD

2. ** denotes significant at 1% level.

3. * denotes significant at 5% level.

4. Different alphabet among monthly salary denotes significant at 5% level using Duncan Multiple Range Test (DMRT)

From Table 4.7, Since P-value is less than 0.01, null hypothesis H01g is rejected at 1% level with regard to, resilience, Job satisfaction, and wellbeing. Hence there is a significant difference between the employees with different levels of monthly salary with regard to resilience, Job satisfaction, and wellbeing. Based on Duncan Multiple Range Test (DMRT), employees in the monthly salary level of employees above 6000 rupees is significantly different from the employees of monthly salary up to 4000 rupees, employees in monthly salary up to 4001 to 5000 rupees at 1% level, and differ with employees of monthly salary up to 5001-6000 rupees in Resilience.

Since P-value is less than 0.05, the null hypothesis H01g is rejected at 5% level with regard to Emotional Intelligence. Hence there is a significant difference between employees with different levels of monthly salaries with regard to Emotional Intelligence. Based on Duncan Multiple Range Test (DMRT), the employees with monthly salary level above 6000 rupees significantly differ from employees of monthly salary, up to 4000 rupees and 4001 to 5000 rupees at 5% level, and differ with employees of monthly salary up to 5001-6000 rupees with regard to Emotional Intelligence.

HYPOTHESIS VIII

4.5.4 Number of Children

H01h : Null Hypothesis: There is no significant difference among employees with discrete number of children working in textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and wellbeing of the employees.

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Figure 4.8 Frequency Distribution of employees based on number of children

Table 4.8 ANOVA for significant difference among employees with number of children of textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and well being of the employees

Abbildung in dieser Leseprobe nicht enthalten

Note : 1. The value within bracket refers to SD

2. ** denotes significant at 1% level.

3. * denotes significant at 5% level.

4. Different alphabet among number of children denotes significant at 5% level using Duncan Multiple Range Test (DMRT)

From the above Table 4.8, since P-value is less than 0.01, null hypothesis H01h is rejected at 1% level with regard to Emotional Intelligence, and job satisfaction. Hence there is a significant difference between the employees with discrete number of children with regard to Emotional Intelligence and job satisfaction. Based on Duncan Multiple Range Test (DMRT), there is no significant difference among employees with discrete number of children at 1% level with regard to emotional intelligence.

Since P-value is less than 0.05, the null hypothesis H01h is rejected at 5% level with regard resilience, and wellbeing. Hence there is a significant difference between employees with discrete number of children with regard to resilience and wellbeing. Based on Duncan Multiple Range Test (DMRT), there is no significant difference among employees with discrete number of children at 5% level with regard to wellbeing.

HYPOTHESIS IX

4.5.5 Experience

H01i : Null Hypothesis: There is no significant difference among employees with different level of work Experiences working in textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and Wellbeing of the employees.

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Figure 4.9 Frequency Distribution of employees based on work experience

Table 4.9 ANOVA for significant difference among employees with different level of work Experiences of textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and wellbeing of the employees.

Abbildung in dieser Leseprobe nicht enthalten

Note : 1. The value within bracket refers to SD

2. ** denotes significant at 1% level.

3. * denotes significant at 5% level.

4. Different alphabet among experience in years denotes significant at 5% level using Duncan Multiple Range Test (DMRT)

From Table 4.9 Since P value is less than 0.05, the null hypothesis H01i is rejected at 5% level with regard to resilience, Emotional Intelligence, and job satisfaction. Hence there is significance difference between employees with different level of work experience with regard to resilience, Emotional Intelligence, and job satisfaction. Based on Duncan Multiple Range Test (DMRT), employees above 5 years of experience are significantly different from employees above 3 years of experience and differ with employees with in 4- 5 years of experience in resilience.

4.6 MULTIPLE LINEAR REGRESSION

4.6.1 Multiple Linear regression with Wellbeing And Factors of Resilience

H2a : There is no significant influence of the factors of resilience on wellbeing of employees working at Textile Apparel Industry.

Here the dependent variable is wellbeing, and Independent variables are factors of resilience.

Dependent variable : Well Being (Y)

Independent Variable

(Resilience) 1. Equanimity (X1)

2. Perseverance (X2)

3. Self Reliance (X3)

4. Meaningfulness (X4)

5. Existential Aloneness (X5)

Table 4.10 Model Summary on factors of resilience and well being

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Table 4.11 Variables of resilience and wellbeing in Multiple Regression Analysis

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Note: ** Denotes significant at 1% level

The multiple correlation coefficients ‘R’ is 0.820, explains the amount of relationship between the real values and the predicted values of wellbeing. The predicted values are linear combination of resilience factors, such as Equanimity (X1), Perseverance (X2), Self-Reliance (X3), Meaningfulness (X4), and Existential Aloneness (X5), the coefficient value of 0.820 indicates that the relationship between wellbeing and the five independent variables are quite strong and positive.

The value of R[2] is 0.672, which represents about 67.2 percent of variation is found in the dependent variable wellbeing which is explained by the estimated linear combination of sub factor of independent variable Resilience, such as Equanimity (X1), Perseverance (X2), Self-Reliance (X3), Meaningfulness (X4), and Existential Aloneness (X5), at 1% significant level.

The multiple regression equation is

Abbildung in dieser Leseprobe nicht enthalten (4.1)

Here the coefficient of (X1) 2.290 represents the partial effect of equanimity on wellbeing holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score increase by 2.290 for every unit increase in the equanimity and this coefficient value is significant at 1% level. The coefficient of (X2) 0.866 represents the partial effect of perseverance on wellbeing, holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score would increase by 0.866 for every unit increase in perseverance and this coefficient value is significant at 5% level.

The coefficient of (X3) 1.129 represents the partial effect of Self Reliance on wellbeing, holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score would increase by 1.129 for every unit increase in Self Reliance and this coefficient value is significant at 5% level. The coefficient of (X4) 0.991 represents the partial effect of Meaningfulness on wellbeing, holding the other variables as constant. This estimated positive sign implies that such effect can be positive and the adjustment score may increase by 0.991for every unit increase in Meaningfulness and were the coefficient value is at 5 % significant level.

The coefficient of (X5) which represents the partial effect of Existential Aloneness on wellbeing is 2.438, holding the other variables as constant. The estimated value has positive impact were the adjustment score would increase by 2.438 for every unit increase in Existential Aloneness and this coefficient value is significant at 1% level.

Based on standardized coefficient adjustment score, Existential Aloneness (2.438) is the most important factor followed by Equanimity (2.290), Self-reliance (1.129), Meaningfulness (0.991) and Perseverance (0.866).

4.6.2 Multiple Linear Regression with Wellbeing and Factors of Emotional Intelligence

H2b : There is no significant influence of the factors of emotional intelligence on wellbeing of employees working at Textile Apparel Industry.

Here the dependent variable is Wellbeing score, and Independent variables are factors of Emotional Intelligence.

Dependent variable : Wellbeing (Y)

Independent Variable

(Emotional Intelligence) : 1.Self Awareness (X1)

2. Self Confidence (X2)

3. Self Control(X3)

4. Empathy(X4)

5. Motivation (X5)

6. Social Competency (X6)

Table 4.12 Model Summary on factors of emotional intelligence and well-being

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Table 4.13 Variables of emotional intelligence and wellbeing in Multiple Regression Analysis

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Note: ** Denotes significant at 1% level

The multiple correlation coefficient is 0.802 measures the degree of relationship between the actual values and the predicted values of wellbeing. Because the predicted values are obtained as a linear combination of Emotional Intelligence factors, such as Self Awareness (X1), followed by Self Confidence (X2), Self Control (X3), Empathy (X4), Motivation (X5), and Social Competency (X6), the coefficient value of 0.802 indicates that the association between Overall wellbeing and the four positive independent variables are strong .

The value of R[2] is 0.643 which means about 64.3 percent of variation is found in the dependent variable wellbeing which is explained by the estimated linear combination of sub factor of independent variable Emotional Intelligence such as, Self Awareness (X1), Self Confidence (X2), Motivation (X5), and Social competency at 1% significant level.

The multiple regression equation is

Abbildung in dieser Leseprobe nicht enthalten (4.2)

Here the coefficient of (X1) 3.376 represents the partial effect of Self awareness on wellbeing holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score would increase by 3.376 for every unit increase in Self Awareness and this coefficient value is significant at 1% level. The coefficient of (X2) 2.680 represents the partial effect of Self Confidence on wellbeing, holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score would increase by 2.680 for every unit increase in Self Confidence and this coefficient value is significant at 1% level.

The coefficient of (X3) is -0.019 represents the partial effect of Self Control on wellbeing, holding the other variables as constant. The estimated negative sign implies that such effect is negative and the adjustment score would decrease by -0.019 for every unit increase of Self Control and this coefficient value is not significant. The coefficient of (X4) is -0.386 represents the partial effect of Empathy on wellbeing, holding the other variables as constant. The estimated negative sign implies that such effect is negative and the adjustment score would decrease by -0.386 for every unit increase in Empathy and this coefficient value is not significant.

The coefficient of (X5) 0.185 represents the partial effect of Motivation on wellbeing, holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score would increase by 0.185 for every unit increase in Motivation and this coefficient value is significant at 1% level. The coefficient of (X6) 3.549 represents the partial effect of Social Competency on overall wellbeing, holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score would increase by 3.549 for every unit increase in Social Competency and this coefficient value is significant at 1% level. Based on the regression equation we can find that only four factors have a positive impact on wellbeing, and factor like, Social Control and Empathy contribute negatively.

Based on standardized coefficient of adjustment score, social competency (3.549) is the most important factors, followed by self-awareness (3.376), self-confidence (2.680), motivation (0.185).

4.6.3 Multiple Linear regression with Job Satisfaction and Factors of Resilience

H3a : There is no significant influence of the factors of resilience on Job satisfaction of employees working at Textile Apparel Industry.

In this study, the dependent variable is Job Satisfaction score, and Independent variables are factors of resilience.

Dependent variable : Job Satisfaction (Y)

1. Equanimity (X1)

2. Perseverance (X2)

3. Self Reliance (X3)

4. Meaningfulness (X4)

5. Existential Aloneness (X5)

Table 4.14 Model Summary on factors of resilience and wellbeing

Abbildung in dieser Leseprobe nicht enthalten

Table 4.15 Variables of resilience and job satisfaction in Multiple Regression Analysis

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Note: ** Denotes significant at 1% level

The multiple correlation coefficients are 0.723 measures the degree of relationship between the actual values and the predicted values of wellbeing. Because the predicted values are obtained as a linear combination of Resilience factors, such as Equanimity (X1), Perseverance (X2), Self Reliance (X3), Meaningfulness (X4), and Existential Aloneness (X5), the coefficient value of 0.723 indicates that the relationship between wellbeing and the five independent variables are quite strong and positive. However (X2), and (X3), being non significant hence not discussed further.

The value of R[2] is 0.523 which means about 52.3 percent of the variation is found in the dependent variable job satisfaction is explained by the estimated linear combination of sub factor of independent variable Resilience factors, such as Equanimity(X1), Meaningfulness (X4), and Existential Aloneness (X5), at 1% significant level.

The multiple regression equation is

Abbildung in dieser Leseprobe nicht enthalten (4.3)

Here the coefficient of (X1) 0.811 represents the partial effect of equanimity on job satisfaction holding the other variables as constant. The estimated positive sign implies that such effect is positive that adjustment score would increase by 0.811 for every unit increase in equanimity and this coefficient value is significant at 1% level. The coefficient of (X4) 0.369 represents the partial effect of Meaningfulness on job satisfaction, holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score would increase by 0.369 for every unit increase in Meaningfulness and this coefficient value is significant at 1% level.

The coefficient of (X5) 0.565 represents the partial effect of Existential Aloneness on job satisfaction, holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score would increase by 0.565 for every unit increase in Existential Aloneness and this coefficient value is significant at 1% level.

Based on standardized coefficient adjustment score, equanimity (0.811) is the most important factor, followed by existential aloneness (0.565), meaningfulness (0.369), perseverance (0.111) and self-resilience (0.033).

4.6.4 Multiple Linear Regression with Job Satisfaction and Factors of Emotional Intelligence

H3b : There is no significant influence of the factors of emotional intelligence on Job satisfaction of employees working at the Textile Apparel Industry.

Here the dependent variable is job satisfaction score, and independent variables are factors of Emotional Intelligence.

Dependent variable: Job satisfaction (Y)

Independent Variable

(Emotional Intelligence) :1.Self Awareness ( X1)

2. Self Confidence(X2)

3. Self Control(X3)

4. Empathy (X4)

5. Motivation (X5)

6. Social Competency (X6)

Table 4.16 Model Summary on emotional intelligence and job satisfaction

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Table 4.17 Variables in the Multiple Regression Analysis factors of emotional intelligence on job satisfaction

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Note: ** Denotes significant at 1% level

The multiple correlation coefficients are 0.744 measures the degree of relationship between the actual values and the predicted values of job satisfaction. Because the predicted values are obtained as a linear combination of emotional intelligence factor, such a self-awareness(X1), followed by self-confidence(X2), self-control(X3), Empathy(X4), motivation(X5), and social competency(X6). The coefficient value of 0.744 indicates that the relationship between job satisfaction and the six independent variables is quite strong and positive.

The value of R[2] is 0.554 which means about 55.4 percent of variation is found in the dependent variable job satisfaction which is explained by the estimated linear combination of sub factor of independent variable Emotional Intelligence such as, Self Awareness (X1), Self Confidence (X2), Social competency (X6), at 1% significant level.

The multiple regression equation is

Abbildung in dieser Leseprobe nicht enthalten (4.4)

Here the coefficient of (X1) 0.776 represents the partial effect of Self Awareness on job satisfaction holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score would increase by 0.776 for every unit increase in Self Awareness and this coefficient value is significant at 1% level. The coefficient of (X2) 0.813 represents the partial effect of Self Confidence on job satisfaction, holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score would increase by 0.813 for every unit increase in Self Confidence and this coefficient value is significant at 1% level.

The coefficient of (X3) - 0.071 represents the partial effect of Self Control on Job satisfaction, holding the other variables as constant. The estimated negative sign implies that such effect is negative that adjustment score would decrease by - 0.071 for every unit increase in Self Control and this coefficient value is also not significant. The coefficient of (X4) 0.172 represents the partial effect of Empathy on Job satisfaction, holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score would increase by 0.172 for every unit increase in Empathy and this coefficient value is not significant.

The coefficient of (X5) 0.115 represents the partial effect of Motivation on Job satisfaction, holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score would increase by 0.115 for every unit increase in Motivation and this coefficient value is not significant. The coefficient of (X6) 0.795 represents the partial effect of Social Competency on Job satisfaction, holding the other variables as constant. The estimated positive sign implies that such effect is positive and the adjustment score would increase by 0.795 for every unit increase in Social Competency and this coefficient value is significant at 1% level.

Based on the un-standardized coefficient of adjustment score, Self Confidence (0.813) is the most important factor, followed by Social Competency (0.795), Self-Awareness (0.776), Empathy (0.172) and Motivation (0.185)

4.6.5 Multiple Linear regression with Job Satisfaction and Wellbeing

H4a: There is no significant influence of job satisfaction on well-being of employees working at Textile Apparel Industry.

Here the dependent variable is Well Being score, and Independent is job satisfaction.

Dependent variable : Well Being (Y)

Independent variable : Job satisfaction (X)

Table 4.18 Model Summary on job satisfaction and wellbeing

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Table4.19 Variables in the Multiple Regression Analysis job satisfaction and wellbeing

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Note: ** Denotes significant at 1% level

The multiple correlation coefficients of 0.877 measures the degree of relationship between the actual values and the predicted values of wellbeing. Because the predicted values are obtained as a linear regression the coefficient value of 0.877 indicates that the relationship between wellbeing and independent variables job satisfaction are quite strong and positive.

The value of R[2] is 0.769 which means about 76.9 percent of the variation is found in the dependent variable wellbeing is explained by the estimated linear regression were the independent variables job satisfaction at 1 % significant level.

The multiple regression equation is

Abbildung in dieser Leseprobe nicht enthalten (4.5)

Here the coefficient of X1 is 3.172 represents the partial effect of job satisfaction on wellbeing holding the other variables as constant. The estimated positive sign implies that such effect is positive that adjustment score would increase by 0.811 for every unit increase in Job satisfaction is significant at 1% level.

4.6.6 Multiple Linear Regression Wellbeing with Resilience, Emotional Intelligence and Job Satisfaction

H4b : There is no significant influence of resilience, emotional intelligence and job satisfaction on well-being of employees working at Textile Apparel Industry.

Here the dependent variable is wellbeing, and Independent variables are resilience, Emotional intelligence and Job satisfaction.

Dependent variable : Well Being (Y)

Independent variables : Resilience (X1)

Emotional intelligence (X2)

Job Satisfaction (X3)

Table 4.20 Model Summary on wellbeing , resilience, Emotional intelligence and Job satisfaction

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Table 4.21 Variables in the Multiple Regression Analysis with resilience, emotional intelligence, job satisfaction and wellbeing

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Note: ** Denotes significant at 1% level

The multiple correlation coefficients is 0.923 measures the degree of relationship between the actual values and the predicted values of wellbeing. Because the predicted values are obtained as a linear combination of Resilience (X1), Emotional intelligence (X2), and Job satisfaction (X3). The coefficient value of 0.923 indicates that the association between wellbeing and the three positive independent variables are strong.

Thus, the value of R[2] is 0.852 simply states about 85.2 percent of the variation is found in the dependent variable wellbeing, the dependent variable is explained by the estimated linear combination of subfactor of independent variable Resilience(X1), Emotional intelligence(X2), and Job satisfaction(X3), at 1 % significant level.

The multiple regression equation is

Abbildung in dieser Leseprobe nicht enthalten (4.6)

Here the coefficient of (X1) 0.652 represents the partial effect of Resilience on wellbeing holding the other variables as constant. The estimated positive sign implies that such effect is positive that adjustment score would increase by 0.652 for every unit increase in Resilience and this coefficient value is significant at 1% level.

The coefficient of (X2) 0.341 represents the partial effect of Emotional intelligence on wellbeing, holding the other variables as constant. The estimated positive sign implies that such effect is positive that adjustment score would increase by 0.341for every unit increase in Emotional intelligence and this coefficient value is significant at 1% level. The coefficient of (X3) 1.931 represents the partial effect of Job satisfaction on wellbeing, holding the other variables as constant. The estimated negative sign implies that such effect is negative that adjustment score would decrease by 1.931 for every unit increase of Job satisfaction and this coefficient value is not significant. Based on the regression equation we can find that all the factors have a positive impact on overall wellbeing.

Based on standardized coefficient adjustment score, job satisfaction (1.931) is the most important factor, followed by resilience (0.652) and emotional intelligence (0.494).

4.7 MEASUREMENT MODEL WITH JOB SATISFACTION

4.7.1 Introduction

The measurement model is the first part of Structural Equation Model (SEM) which relates the measured variables to latent variables, and the SEM relates latent variables to one another. Statistically, the measurement model estimates the covariance matrix between the constructs which serves as input to estimate the structural coefficients between constructs or latent variables, on the whole, the model is evaluated by comparing two covariance matrices. The measurement model is run to test the fit, which gives way for executing the structural model. The researcher has used AMOS, version 21 to evaluate the model fit of the measurement model to confirm the hypothesized structure.

4.7.2 The Measurement Model

The measurement model shown in Figure 4.10 comprises of four factors. The resilience factor is measured with five observed variables, emotional intelligence with six observed variables, the wellbeing with three observed variables and finally job satisfaction with eight items. The reliability of which is influenced by random measurement error, as indicated by each associated error term. Each of these observed variables is regressed into its respective factor and finally, all the four factors are shown to be inter-correlated.

4.7.3 Type of Model

This is a uni-directional model termed as recursive, by nature these recursive models are the most straightforward and have two basic features such as their disturbances are uncorrelated, and all causal effects are unidirectional.

4.7.4 Model Identification

To identify the type of Structural model to which it belongs among the three, representations such as just-identified, over-identified, or under-identified. A just identified model is one in which there is a one to one correspondence between the data and the structural parameters. That is, the number of data variances and covariance’s equals the number of parameters to be estimated. An under-identified model is one which the number of parameters to be estimated exceeds the number of variances and covariance’s. An over-identified model is one which the number of estimable parameters is less than the number of data points (i.e. variances and covariance’s of the observed variables). This results in positive degrees of freedom that allow for rejection of the model thereby rendering it for scientific use. The aim in SEM, therefore, is to specify a model which is over-identified.

The Structural Equation Model is identified based on two basic requirements, (1) There must be at least as many observations as free model parameters (df ≥ 0), and (2) Every unobserved (latent) variable must be assigned a scale (metric). The proposed model in this study is an over-identified model with positive degrees of freedom, which is positive (greater than zero), hence the model is an over identified one.

Abbildung in dieser Leseprobe nicht enthalten

Figure 4.10 Measurement model with job satisfaction

4.7.5 Model Estimation Method

The most widely used estimation method is the Maximum Likelihood (ML). It describes the statistical principle that underlies the derivation of parameter estimates. The estimates make the data to the likelihood of a sample drawn from this population which is actually observed (Winer Brown & Michels 1991). The ML estimation is a normal theory in which the population distribution for the endogenous variables is multivariate normal. In fact, the use of an estimation method other than ML requires explicit justification (Hoyle 1995). In this study the minimum iteration was achieved, thereby providing an assurance that the estimation process yielded an admissible solution, eliminating any concern about multicollinearity effects.

Table 4.22 Fit indices of measurement model

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4.7.6 Model Evaluation Criteria: Goodness of Fit

The Structural Equation Model primary interest is how best the hypothesized data “fit”, or adequately describes the sample data. The model fit is derived from a variety of perspectives which is based on several criteria. The Goodness Of Fit (GOF) shows how best the specified model represents the observed covariance matrix among the indicator items. The GOF is part of a model fitting process which determines the fit between the hypothesized model and the sample data.

Since GOF measure was developed by researchers, striving to define new measures reflect various facets of the model’s ability to represent the data. A number of alternate measures were identified which is unique in their operations, basically, these measures were classified into three general groups: absolute measures, incremental measures, and parsimony fit measures. After estimating the model specified, the model fit compares the theory to reality by assessing the similarity of the estimated covariance matrix (theory) to reality (the observed covariance matrix). If the theory is found to be perfect, the observed and estimated covariance matrices would be the same. The values of any GOF measure result from a mathematical comparison of these two matrices. A better model fit exist if the values of these two matrices are closer to each other, the description of the goodness-of-fit indicators used to evaluate model fitness.

The closer the values of these two matrices, better the model is said to fit. Given below is the description of goodness-of-fit indicators used to evaluate model fitness in measurement Model.

4.7.7 Chi-Square (CMIN) Goodness of Fit

The term chi-square / Degrees of freedom is 4.9 which indicates a good fit, the criterion for acceptance varies across researchers, ranging from less than 2 (Ullman 2001) to less than 5 (Schumacker & Lomax 2004).

4.7.8 The Goodness-of-fit Index (GFI & AGFI)

The value of GFI is in the range 0–1.0, however, GFI = 1.0 indicates perfect model fit, GFI > 0.90 indicate a good fit, and values close to zero indicate very poor fit. In Table 4.23 the value of GFI=0.957, which is closer greater than the recommended value of 0.95 hence considered as acceptable fit. The fit indices value of AGFI=0.871, though the recommended value is 0.8 and the obtained value is greater than 0.85 it is considered as an acceptable fit (Hu & Bentler 1999).

4.7.9 Comparative Fit Index (CFI)

The value of CFI from Table 4.21is 0.952, which is above 0.9 the recommended value CFI values above 0.90 are usually associated with a model that fits well. But a revised cut off value close to 0.95 was suggested by Hu & Bentler (1999).

4.7.10 Normed Fit Index (NFI)

The value for NFI ranges from 0 to 1 with higher values indicating better fit. Values greater than 0.90 are interpreted as indicating acceptable fit (Kaplan 2000). From Table 4.21, the value of NFI=0.941, which is greater than recommended value of 0.9 hence an acceptable fit.

4.7.11 Root mean square residual

The RMS should be less than 0.08 (Browne & Cudeck 1993) and alternatively, the upper confidence interval of the RMS should not exceed 0.08 (Hu & Bentler 1998). From Table 4.21 the value of RMR is 0.06 which is within the limit as recommended, hence model fit is exhibited.

4.7.12 Root Mean Square Error of Approximation (RMSEA)

Root Mean Square Error Approximation (RMSEA), the lower value indicate better fit as <0.05 recommended by suggested by (Browne & Cudeck 1993), (Hu & Bentler 1999) have suggested a value of <0.06 a good fit. From Table 4.21 the measured value of RMSEA is 0.035, which is less than the recommended value, hence the model represents a good fit.

4.8 STRUCTURAL EQUATION MODEL ON EMPLOYEES WELLBEING WITH RESILIENCE AND EMOTIONAL INTELLIGENCE

4.8.1 Introduction

In statistical analysis, Structural Equation Model (SEM) provides a simple and convenient framework. Researchers of various disciplines and specific to social sciences prefer SEM as a next level extension of several multivariate techniques-which includes confirmatory factor analysis, path analysis, partial least squares path modeling, and latent growth modeling, (Clen Kline 2011). Structural equation model provides the ability to possess multiple interrelated dependence relationships in a single model that is the dependent variable in one equation can be an independent variable in other equation.

In Exploratory and confirmatory factor analysis models, only the measurement part is represented, but SEM contains the structural part. SEM is often visualized as a graphical path diagram, also implies a structure for the covariance’s between the observed variables and termed as covariance structure modeling. As per Wight’s notation, the observed (measured) variable is represented by a squared or rectangle box, and the latent (unmeasured) variable are represented by a circle or an ellipse, further, the boxes and circles were connected with arrows based upon path diagram, (Wright’s 1921). Statistically, the single-headed arrow or ‘paths’ represents the regression coefficients and the double-headed arrow indicates covariance or correlations. The fit indices of structural equation model help to establish the overall model acceptance if the model is acceptable, the researchers can ensure the specific path significantly.

As a first step, the reliability and validity of the survey instrument were analyzed with the help of Measurement model using AMOS version 21, the Measurement model fit ensures the further step for executing the Structural Equation Model as recommended by (Anderson & Gerbing 1988). SEM often cites a measurement model which defines latent variables using one or more observed variables to assess the unobservable 'latent' constructs. The independent regression equation is used to link the constructs, for analyzing the suitability of the model based on the collected samples.

As an initial phase, various facts on theory was collected based on previous research experience. The pictorial path diagrams represent how the independent variables predict each dependent variable to reflect the interrelationships. The arrow marks drawn in the path diagram indicates the casual relationship from one construct to another, which forms the basis for path analysis. The framed hypotheses are established using the path analysis as an outcome of SEM and the model best fit the data reflecting the underlying theory as recommended by, Hopper, Coughlan & Mullen (2008).

4.8.2 The variables used in the structural equation model,

Observed endogenous variables

1. Equanimity
2. Perseverance
3. Self Reliance
4. Meaningfulness
5. Existential aloneness
6. Self Awareness
7. Self Confidence
8. Self Control
9. Empathy
10. Motivation
11. Social Competency
12. Physical Wellbeing
13. Mental Wellbeing
14. Social Wellbeing

Unobserved, endogenous variables

Well being

Unobserved, exogenous variables-fifteen error terms with,

Abbildung in dieser Leseprobe nicht enthalten

Abbildung in dieser Leseprobe nicht enthalten

Figure 4.11 Standardized co-efficient of Structural Equation Model on Employee Well being with Resilience, Emotional Intelligence.

Table 4.23 Variables in the Structural Equation Model Analysis

Abbildung in dieser Leseprobe nicht enthalten

Note: ***denotes significant at 1% level

4.8.3 Assessment of Model

Having estimated the model, interpretation is carried out after analysis, the estimated paths are tabulated and presented in table 4.22 as a path model. The impact of each variable is discussed below.

Here the coefficient of Equanimity on resilience is 2.954 represents the partial effect of resilience on equanimity, holding the other variables as constant. The estimated positive sign implies that such effect is positive that equanimity would increase by 2.954 for every unit increase in resilience and this coefficient value is significant at 1% level.

The coefficient of Perseverance on resilience is 3.119 represents the partial effect of resilience on perseverance, holding the other variables as constant. The estimated positive sign implies that such effect is positive that perseverance would increase by 3.119 for every unit increase in resilience and this coefficient value is significant at 1% level.

The coefficient of Self Reliance on resilience is 3.268 represents the partial effect of resilience on Self Reliance, holding the other variables as constant. The estimated positive sign implies that such effect is positive that Self Reliance would increase by 3.268 for every unit increase in resilience and this coefficient value is significant at 1% level.

The coefficient of Meaningfulness on resilience is 3.137 represents the partial effect of resilience on Meaningfulness, holding the other variables as constant. The estimated positive sign implies that such effect is positive that Meaningfulness would increase by 3.137 for every unit increase in resilience and this coefficient value is significant at 1% level.

The coefficient of Existential aloneness on resilience is 3.817 represents the partial effect of resilience on existential aloneness, holding the other variables as constant. The estimated positive sign implies that such effect is positive that existential aloneness would increase by 3.817 for every unit increase in resilience and this coefficient value is significant at 1% level.

The coefficient of Self Awareness on emotional intelligence is 2.597 represents the partial effect of emotional intelligence on Self Awareness, holding the other variables as constant. The estimated positive sign implies that such effect is positive that Self Awareness would increase by 2.597 for every unit increase in emotional intelligence and this coefficient value is significant at 1% level.

The coefficient of Self Confidence on emotional intelligence is 1.768 represents the partial effect of emotional intelligence on Self Confidence, holding the other variables as constant. The estimated positive sign implies that such effect is positive that Self Confidence would increase by 1.768 for every unit increase in emotional intelligence and this coefficient value is significant at 1% level.

The coefficient of Self control on emotional intelligence is 1.510 represents the partial effect of emotional intelligence on self control, holding the other variables as constant. The estimated positive sign implies that such effect is positive that self control would increase by 1.510 for every unit increase in emotional intelligence and this coefficient value is significant at 1% level.

The coefficient of Empathy on emotional intelligence is 0.970 represents the partial effect of emotional intelligence on empathy, holding the other variables as constant. The estimated positive sign implies that such effect is positive that empathy would increase by 0.970 for every unit increase in emotional intelligence and this coefficient value is significant at 1% level.

The coefficient of motivation on emotional intelligence is 1.317 represents the partial effect of emotional intelligence on motivation, holding the other variables as constant. The estimated positive sign implies that such effect is positive that motivation would increase by 1.317 for every unit increase in emotional intelligence and this coefficient value is significant at 1% level.

The coefficient of Social Competency on emotional intelligence is 3.051 represents the partial effect of emotional intelligence on Social Competency, holding the other variables as constant. The estimated positive sign implies that such effect is positive that Social Competency would increase by 3.051 for every unit increase in emotional intelligence and this coefficient value is significant at 1% level.

The coefficient of physical well being on wellbeing, is 1.000 represents the partial effect of well being on physical wellbeing, holding the other variables as constant. The estimated positive sign implies that such effect is positive that physical wellbeing would increase by 1.000 for every unit increase in wellbeing and this coefficient value is significant at 1% level.

The coefficient of mental wellbeing on wellbeing, is 1.118 represents the partial effect of well being on mental wellbeing, holding the other variables as constant. The estimated positive sign implies that such effect is positive that mental wellbeing would increase by 1.118 for every unit increase in wellbeing and this coefficient value is significant at 1% level.

The coefficient of social wellbeing on well being is 1.125, represents the partial effect of well being on social wellbeing holding the other variables as constant. The estimated positive sign implies that such effect is positive that social wellbeing would increase by 1.125 for every unit increase in social wellbeing and this coefficient value is significant at 1% level.

The coefficient of resilience on well being, is 5.231 represents the partial effect of well being on resilience holding the other variables as constant. The estimated positive sign implies that such effect is positive that resilience would increase by 5.231 for every unit increase in wellbeing and this coefficient value is significant at 1% level.

The coefficient of emotional intelligence on well being, is 3.674 represents the partial effect of well being on emotional intelligence holding the other variables as constant. The estimated positive sign implies that such effect is positive that emotional intelligence would increase by 3.674 for every unit increase in wellbeing and this coefficient value is significant at 1% level.

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Figure 4.12 Path diagram model with estimates

Table 4.24 Goodness-of-fit statistic without mediation

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4.8.4 Assessment of Goodness-of-Model Fit Indices Statistic with Out Mediating Effect

Having discussed the model and its related hypothesis, discussing the interpretation of model fit indices has its importance. The "fit" of an estimated model helps to determine how well it models the data which acts as a basis for accepting or rejecting the models. Assessment of fit essentially calculates how similar the predicted data are to matrices containing the relationships in the actual data.

The researcher has chosen absolute fit indices to determine how well a proposed conceptual model fits the sample as recommended by McDonald & Ho (2002). A structural model was constructed to examine the postulated relationship among the constructs by using the following indices: model Chi-square /dfvalue, Goodness-of-Fit Index (GFI), the Adjusted Goodness-of-Fit Statistic (AGFI), Normed-Fit Index (NFI), and Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA). The following model fit indices were used for the study.

Chi-square value / df

The term chi-square, goodness of fit is also called the discrepancy function, in AMOS the chi-square value is called CMIN. The relative chi-square is also called the normed chi-square, this value equals the chi-square index divided by the degrees of freedom. Chi-square value / df is a measure for the evaluation of overall model fit and evaluates the magnitude of discrepancy between the sample population and covariance’s matrices. “The 𝑥[2] has an asymptotically large sample distribution under an assumed distribution and the hypothesized model for the population covariance matrix, Hu & Bentler (1999)” This index is less sensitive to sample size, however many researchers disregard this index if the sample size exceeds 200 or so by which the acceptance is decided by other indices. The criterion for acceptance varies across researchers, ranging from less than (< 2) Ullman (2001), to less than (< 5) Schumacker & Lomax (2004), and Hair et al. (1998). From Table 4.22 it is found that the calculated Chi-square value / df, is 4.538 which is lesser than 5.00 indicates with in the acceptable limit as recommended.

Goodness of-Fit Index (GFI)

The Goodness of-Fit Index, test measure the relative amount of the variances and covariance’s in covariance matrix, (Joreskog & Sorbom 1989). GFI was formed by Jöreskog & Sorbom (1993) to calculate the proportion of variance. The GFI was developed to adjust for the bias occurring due to model complexity,(Tabachnick & Fidell 2007).The closeness of the model is reflected by variances and covariance’s accounted in the model, which replicate the observed covariance matrix, Diamantopoulos & Siguaw (2000). The statistics for fit ranges from 0 to1, higher value reflecting a good fit. The usual “rule of thumb is 0.95 for a good fit, greater than 0.90 are indicating an acceptable fit, (Schermelleh-Engel & Moosbrugger 2003)”. The GFI ensures the probability test is greater than or equal to 0.85 for a good fit (Hu & Bentler 1999). In this research study, from Table 4.22 the GFI value is 0.958 which is above the recommended value of 0.9 hence deemed fit.

Adjusted goodness-of-fit Index (AGFI)

The adjusted goodness-of-fit Index (AGFI) is also used to measure the relative amount of the variances and covariance’s in covariance matrix, (Joreskog & Sorbom 1989). The AGFI plays a major role to adjust the GFI based on the complexity of the model, this index also ranges between 0 and 1 with larger values indicating a better fit (Tabachnick & Fidell 2007). A rule of thumb for this index is that (≥0.80) is indicative of good fit relative to the baseline model, the obtained value of AGFI from Table 4.22 is, 0.917 which is greater than recommended value hence regarded as an acceptable fit as recommended by, Hair et al. (2006).

Comparative fit index (CFI)

Comparative fit index (CFI) is a revised form of the NFI, which is also known as the Bentler Comparative Fit Index. The CFI can be measured and performed even with small sample size are least affected as supported by, Hopper et al. (2008), the CFI can represent the extent to which the model of interest is better than independence model and index ranging from 0 to 1 with higher values indicating better fit. “A rule of thumb for CFI is 0.90 a sign of good fit, (Schermelleh-Engel & Moosbrugger, 2003)”,and not effective if the correlations between variables approach towards zero. In Table 4.23 the CFI value was found to be 0.984,which is above > 0.90 as recommended by Hu & Bentler (1999).

Normed-fit index (NFI)

Normed-fit index (NFI) was developed by Bentler & Bonnett (1980) to assess the model by comparing the 𝑥[2] value. The NFI equals the difference between the chi-square of the null model and the chi square of target model, divided by the chi square of the null model. Both Non Normed and Normed adjust for complexity of the model. When the samples are small, the fit is often underestimated (Ullman 2001) in contrast to the TLI., but not so in case of NNFI. Values for fit index ranges from 0 to1 with higher values indicating better fit. The NFI value from table 4.8.2 is 0.980which is greater than (≥0.90) which indicates the model is fit as recommended by, Hu & Bentler (1999) and supported by Kaplan(2000).

Root mean square error of approximation (RMSEA)

Root mean square error of approximation (RMSEA) fit index helps to evaluate the covariance structure models, (Steiger& Lind 1980). A high values of fit index represents a poor fit rather a value of zero indicates the best fit in RMSEA, Kline (2011). This fit index is a measure of approximation in the population and it is concerned with the discrepancy due to approximation. The special feature of RMSEA is the presence of coherent estimation strategy exists for both a point estimate and a confidence interval, (Nevitt & Hancock 2000). A vast research work recommends an RMSEA of 0.1 or more indicates poor fit (Hu &Bentler 1999). The obtained value of RMSEA from Table 4.23 is 0.069 which is lesser than the recommended value of (≤0.08 ), a smaller size of RMSEA is a good approximation as per norms supported by, Hair et al. (2006), Steiger (2007).

4.9 STRUCTURAL EQUATION MODEL (SEM) ON EMPLOYEES WELLBEING WITH MEDIATING EFFECT

H05 : There is no mediating effect of job satisfaction on resilience, emotional intelligence and wellbeing.

4.9.1 Variables used in the structural equation model

Observed variables

1. Equanimity
2. Perseverance
3. Self-Reliance
4. Meaningfulness
5. Existential aloneness
6. Self-Awareness
7. Self Confidence
8. Self-Control
9. Empathy
10. Motivation
11. Social Competency
12. Job satisfaction
13. Job satisfaction
14. Job satisfaction
15. Job satisfaction
16. Job satisfaction
17. Job satisfaction
18. Job satisfaction
19. Job satisfaction
20. Physical well being
21. Mental well being
22. Social well being

Unobserved, variables- Twenty four error term with,

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Hence number of variable in the SEM are,

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Figure 4.13 Standardized co-efficient of Structural Equation Model on Employee Well being with job satisfaction as mediating variable.

Table 4.25 Variables in the Structural Equation Model Analysis

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Note: ***denotes significant at 1% level

4.9.2 Testing of SEM Hypothesis

Table 4.26 Results of SEM path hypothesis testing

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Structural equation model has been used to test the hypothesized relationships as shown in Figure 4.13. The following statistical reports were obtained for the same structural model as shown in Table 4.26 The standardized loading (β), t-values and significance are discussed below. In this research work, five hypotheses were postulated as (H1s, H2s, H3s, H4s, H5s), and the four hypotheses were found to support the designed model and also found to be significant at a 1% level.

Testing hypothesis between Resilience and Wellbeing (H1s)

With reference to the first hypothesis (H1s) from the Table 4.26, the impact of resilience has a positive influence on well-being. The path relations between two constructs are (β=0.565; t=14.718 ; P<0.001), which is also highly significant, hence it supports the hypothesis (H1s). In support of this hypothesis, the results of (Luthans et al. 2010), and (Shin et al. 2012) also indicate resilience has an influence on wellbeing.

Testing hypothesis between Emotional intelligence and Wellbeing (H2s)

With reference to the second hypothesis (H2s) from the Table 4.26, the impact of Emotional intelligence has a positive influence on Wellbeing. The path relations between two constructs are (β=0.299; t=7.429 ; P<0.001), which is also highly significant, hence it supports the hypothesis (H2s). This is further in line with the results of (Slaski & Cartwright 2002) and (Dulewics et al. 2003) on the relationship between Emotional intelligence and Wellbeing.

Testing hypothesis between Job satisfaction and wellbeing (H3s)

With reference to the third hypothesis (H3s) from the Table 4.26, the impact of Job Satisfaction has a positive influence on Wellbeing. The path relations between two constructs are (β=0.168; t=7.216 ; P<0.001), which is also highly significant, hence it supports the hypothesis (H3s). This influence is in support of the research work of (Judge & Watanabe 1993), and work carried out (Weiss & Cropanzano 1996) on cognitive assessment of employee working conditions.

Testing hypothesis between Resilience and Job satisfaction (H4s)

With reference to the fourth hypothesis (H4s) from the Table 4.26, the impact of resilience has a negative influence on job Satisfaction. The path relations between two constructs (β = -0.002 ; t = -0.238 ; P = NS), which is insignificant, hence it does not support the hypothesis (H4s). hence it does not support the study on employees resilience by (Garmezy 1991), but the results of this research is in line with (Udechukwu 2008) or (Luthans et al. 2008), where the relationship between resilience and job satisfaction is not established.

Testing hypothesis between Emotional intelligence and Job satisfaction (H5s)

With reference to the fifth hypothesis (H5s) from the Table 4.26, The impact of Emotional intelligence has a positive influence on Job satisfaction. The path relations between two constructs are (β= 0.469; t= 6.136 ; P<0.001), which is also highly significant, hence it provides support to the hypothesis (H5s). This research work goes with the study of (Sy et al. 2006) which concludes the existence of positive association between emotional intelligence and job satisfaction, Further is also supports the studies made by (Lopes, Grewal & Kadis 2006).

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Figure 4.14 Path diagram model estimates with job satisfaction mediation

Table 4.27 Goodness-of-fit statistic with mediation

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4.9.3 Assessment of Goodness-of-model Fit Indices Statistic with Mediating Effect

Having discussed the model and its related hypothesis, discussing the interpretation of model fit indices has its importance. The "fit" of an estimated model helps to determine how well it models the data which acts as a basis for accepting or rejecting the models. Assessment of fit essentially calculates how similar the predicted data are to matrices containing the relationships in the actual data.

The researcher has chosen absolute fit indices to determine how well a proposed conceptual model fits the sample as recommended by (McDonald & Ho 2002). The structural model was constructed to examine the postulated relationship among the constructs by and the following indices were found as represented in table 4.27, chi-square (𝑥[2]/df) 4.96, Goodness-of-fit statistic (GFI) is 0.907, the Adjusted goodness-of-fit statistic (AGFI) is 0.913, Comparative fit index (CFI) is 0.953, Normed-fit index (NFI) is 0.942 and the Root mean square error of approximation (RMSEA) is 0.073, on comparing the above obtained fit indices of the mediation model with the standard recommended values, the overall model fit is established.

4.9.4 Comparison of SEM Model with and Without Mediation Effect

Structural equation modeling (SEM) is a very popular and powerful multivariate technique which is more appropriate to inference framework for mediation analyses. It uses a conceptual model, path diagram and system of linked regression-style equations to capture complex and dynamic relationships within a web of observed and unobserved variables. The mediation process was first tested using a series of regression equations, which fundamentally requires three variables under any study (i.e. intervention, mediator and response). But in some cases, certain conditions must be met in order to claim the occurrence of mediation (Kenny et al. 1998; MacKinnon et al. 2002). In concern with this study the researcher has used job satisfaction as the mediating variable , with resilience and emotional intelligence as independent variables and wellbeing as dependent variable which helps to gain insight and acquire deep understanding about the mechanism of job satisfaction.

On executing the Structural equation mediation model, the null hypothesis H05 was rejected, which represents that partial mediation intervenes in this model. Though the variations are found to be very marginal, there is an increases in the level of R[2] value ie., (R[2] = 0.79) for the Structural equation modeling with mediation is higher than the value of SEM without mediation in which the ( R[2] = 0.78), via the direct, indirect and total effects.

Various factors of resilience, emotional intelligence and job satisfaction also show significant influence on well-being of employees working at textile apparel industry. In the outcome of SEM model it was found there is a partial mediating effect of job satisfaction on wellbeing of the employees.

The direct and indirect effect of dependent and independent variable through the mediating variable were estimated as direct and indirect effect for estimating the total effect.

Table 4.28 Direct, indirect and total effect of independent and dependent variable

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The Path coefficient of emotional intelligence is positive, which has both direct and mediating effect. The path coefficient of resilience to wellbeing is positive for direct effect but the path coefficient of resilience to job satisfaction shows a negative path coefficient.

This reflects that employees are not job satisfied though they are resilient, but attain their wellbeing through direct effect. (Just because they are resilient they are able to overcome their job dissatisfaction). Further the magnitude of the mediation effect is very less or only some residual direct effect is found on the outcome, it clearly represents a partial mediation effect in this research study model.

CHAPTER 5 FINDINGS AND DISCUSSION

This chapter deals with the significant outcomes of the research work. It also drafts the implications of the results of the study to human resource management practices. Further, this chapter highlights the key contributions of the study. In addition, it contains a brief note on the limitations as well as scope for future research.

This research mainly focuses on the influence of Resilience and Emotional intelligence on Wellbeing of employees working in the Textile apparel Industry. Moreover, the mediating effect of Job satisfaction on the above hypothesized relationship is investigated.

5.1 CONFIRMATORY FACTOR ANALYSIS AND VALIDATION OF THE INSTRUMENT

This study consists of three, second order latent constructs viz., Resilience, Emotional intelligence and Wellbeing. Equanimity, Perseverance, Self-Reliance, Meaningfulness And Existential Aloneness are considered as the dimensions of Resilience. The factors identified for measuring Emotional Intelligence are Self-Awareness, Self-Confidence, Self-Control, Empathy, Motivation And Social Competency. Although Well being scale includes Physical, Mental, Social, Psychological and Spiritual Well being developed by (Jagsharanbir Singh & Asha Gupta 2001), this study has considered only Physical wellbeing, Mental Wellbeing And Social Wellbeing as the dimensions of wellbeing. One first order latent construct Job satisfaction consisting eight items were chosen for this study. Confirmatory Factor Analysis (CFA) was carried out for all the constructs in order to confirm whether the unobserved constructs were measured by reliable items and dimensions. The results of CFA shows a good GFI (Goodness of Fit Index), meets the recommended value (>0.95) and hence establish the validity of the variables. The Cronbach’s alpha, for the dimensions of all constructs, is above 0.7 which shows evidence of reliability.

5.2 SUMMARY OF FINDINGS

- A significant difference was found between male and female employees of textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction and Wellbeing. Based on the mean scores, the female employees have high Resilience (97.39), Emotional Intelligence (125.86), Job satisfaction (31.25) and Wellbeing (116.78) than the male employees.
- A significant difference was found between Single and Married employees of textile apparel industry with respect to Resilience, Emotional Intelligence, and Well Being. Based on the mean scores, the single status employees have high Resilience (98.63), Emotional Intelligence (126.39), and Wellbeing (118.34) than married employees of the textile apparel industry
- A significant difference was found between Nuclear and Joint family of employees in the textile apparel industry with respect to Resilience, Emotional Intelligence, Job satisfaction, and Wellbeing. Based on the mean scores, the employees of nuclear family have high Resilience (98.79), Emotional Intelligence (126.50), Job Satisfaction (31.72) and Wellbeing (117.99) than employees of the Joint family working in the textile apparel industry.
- A significant difference was found between employees with Permanent and Temporary job status with respect to job satisfaction and wellbeing. No significant difference was found with respect to Resilience, the Emotional intelligence of the employees in the textile apparel industry. Based on the mean scores, the employees in Permanent job status have high Job satisfaction (31.42) and Wellbeing (116.53) than Temporary job status of employees working in the textile apparel industry.
- A significant difference was found between the employees of different age group with respect to Emotional intelligence, Job satisfaction, Wellbeing and no significant difference was found with respect to the resilience of the employees in the textile apparel industry. Based on the mean scores, the employees within the age group 18-20 having high Emotional intelligence (128.95), Job satisfaction (32.94) and Wellbeing (121.42) than the remaining age group of textile apparel industry employees.
- A significant difference was found between employees of different educational qualification with regard to Resilience, Emotional intelligence, Job satisfaction and Wellbeing working in the textile apparel industry. Based on the mean scores, the employees with education qualification up to HSC and above have high Resilience (99.29) and Emotional intelligence (127.91) than the other groups and the employees with education qualification up to High school have high level of Job satisfaction (32.30) and Wellbeing (119.75) than the other qualified groups of employees of the textile apparel industry.
- A significant difference was found between employees with different monthly salary with regard to Resilience, Emotional intelligence, Job satisfaction, and Wellbeing working in the textile apparel industry. Based on the mean scores, the employees with a salary above 6000 have high Resilience (99.49), Emotional intelligence (127.23), Job satisfaction (32.37) and Wellbeing (120.5) than the employees belonging to other salary groups of the textile apparel industry.
- A significant difference was found between employees with an unalike number of children with regard to Resilience, Emotional intelligence, Job satisfaction, and wellbeing working in the textile apparel industry. Based on the mean score, the employees with two children have high Resilience (98.78), Emotional intelligence (126.87), Job satisfaction (32.29) and Wellbeing (119.00) than the other employees with no children, one child and three children of the textile apparel industry.
- A significant difference was found between employees with a different level of work experience with regard to Resilience, Emotional Intelligence, and Job satisfaction, except with regard to the wellbeing of employees working in the textile apparel industry. Based on the mean score, the employees with experience above 5 years having high Resilience(99.18), Emotional intelligence (12.25) and Job satisfaction (32.35) than other experienced employees of the textile apparel industry
- The factors of resilience have a significant influence on the wellbeing of the employees working at textile apparel industry. The ‘R’ value of 0.820 represents the degree of relationship between the actual values and the predicted values of wellbeing which is positive and establishes a strong relationship between the independent variables and the dependent variable. The R[2] value 0.672 denotes that 67.2% of the variation found in the dependent variable wellbeing is explained by the linear combination of the five factors of resilience viz., equanimity, perseverance, self-reliance, meaningfulness and existential aloneness.
- The factors of Emotional intelligence have a significant influence on the wellbeing of the employees working at textile apparel industry. This ‘R’ value of 0.802 represents the degree of relationship between the actual values and the predicted values of wellbeing which is positive and ascertain a strong relationship between the independent variables and the dependent variable. The R[2] value 0.643 denotes that 64.3% of the variation found in the dependent variable wellbeing is explained by the linear combination of the four factors of Emotional viz., self-awareness, self-confidence, motivation and social competency. But the factors like Self Control, Empathy are negatively influenced and these factors are also not significant. This insignificance may be due to moderate co-linearity existing between the independent factors. The reciprocity shows a negative coefficient which can be ignored as it is insignificant
- The factors of resilience have a significant influence on the Job satisfaction of the employees working at textile apparel industry. The positive ‘R’ value of 0.723 represents the degree of relationship between the actual values and the predicted values of resilience and is found to have a strong relationship between the independent variables and the dependent variable. The R[2] value 0.523 denotes that 52.3% of the variation found in the dependent variable wellbeing is explained by the linear combination of the three factors of resilience viz., Equanimity, meaningfulness, existential aloneness. But the factors like Perseverance and Self-Reliance are not significant.
- The factors of Emotional intelligence have significant influence on the Job satisfaction of the employees working at textile apparel industry. The ‘R’ value of 0.744 represents the degree of relationship between the actual values and the predicted values of Emotional Intelligence and is found to be strong between the independent variables and the dependent variable. The R[2] value 0.554 denotes that 55.43% of the variation found in the dependent variable Job satisfaction is explained by the linear combination of the three factors of Job satisfaction viz., awareness, self-confidence and social competency. But the factors like Empathy and Motivation are not significant This again may be due to moderate co-linearity existing between the independent factors. Self Control is negatively influenced and not significant and hence can be ignored.
- Job satisfaction of the employees has significant influence on their Wellbeing working at textile apparel industry. The ‘R’ value of 0.877 represents a strong degree of relationship between the independent variables and the dependent variable. The R-value is above 0.5 which is positive and has a strong relationship between the independent variables on the dependent variable. The R[2] value 0.769 denotes that 76.9% of the variation found in the dependent variable Wellbeing is explained by Job satisfaction.
- The factors of Resilience, Emotional intelligence and job satisfaction have significant influence on the Wellbeing of the employees working at textile apparel industry. The ‘R’ value of 0.923 concludes a strong degree of relationship between the actual values and the predicted values of wellbeing between the independent variables and the dependent variable..The R[2] value 0.852 denotes that 85.2% of the variation found in the dependent variable wellbeing is explained by the linear combination of independent variables Resilience, Emotional intelligence and Job satisfaction.
- The output of (SEM) model without job satisfaction as mediating variable, showed 78 percent of explained variance, and for the model with job satisfaction as mediating variable represented 79 percent of explained variance. Though the value of variance is very meagre between the models, still job satisfaction has a partial mediating effect on wellbeing of the employees. Moreover on discussing the results on five postulates of (SEM) hypotheses (H1s, H2s, H3s, H4s, H5s), it was found that the path relationship of Resilience-->Wellbeing, Emotional Intelligence -->Wellbeing, Job Satisfaction--> Wellbeing, Emotional Intelligence --> Job satisfaction were found to be significant at 1 % level, and the estimates were positive for all the four hypothesis, but not significant for path relationship between Resilience--> Job satisfaction, which also shows negative value on estimates.
- The values of Chi-square value/df, goodness-of-fit statistic (GFI),the adjusted goodness-of-fit statistic (AGFI), normed-fit index (NFI), and comparative fit index (CFI), root mean square error of approximation (RMSEA) for the model without job satisfaction as mediating variable and for the model with job satisfaction as mediating variable fall within recommended values by (Hair et al. 1998) and hence establishes the fitness of both the models.

5.3 CONCLUSION

Textile apparel industry occupies a unique place in the India economy which ranks to be the second largest employment provider in India. This research has been conducted as an attempt to study the employee's wellbeing through the variables like resilience, emotional intelligence and job satisfaction of textile apparel industry which is first of its kind in Chennai. This chapter relates the values of literature theory, the statistical analyses, and the findings of this study to support and enhance knowledge.

Wellbeing, a construct of long-standing importance in the field of psychology, needs added attention by employees for achieving individual, organizational, and societal goals. The importance of employee wellbeing at the workplace has taken a new phase of the industrial revolution in developed countries. On looking into the facts of organizations productivity, employees absenteeism, sickness, and economic losses, it is right time for employers to share the best practices on improving employee wellbeing even in developing countries. The term well-being is beyond wellness, the successes of life are decided on the concepts of wellbeing. We try to influence or alter the circumstances that impinge upon wellbeing for pleasure, passion, and purpose. This wellbeing acts as an ultimate universal goal of human existence, practiced before Aristotle’s time until now. Galen a Greek physician, has said, employment is “nature’s physician, essential to human happiness”. Majority of the lifetime is spent at our workplace where job and income are key ingredients to human life for economic empowerment.

The global apparel sector is more challenging, complex and highly volatile. However, the apparel sector plays a major role in providing more job opportunity in developing countries specific to women workers who aim for financial independence. In many developing countries like Vietnam, Bangladesh, and India, employee wellbeing has been very much under-acknowledged. A healthy organization hallmarks itself by the type of employees it possess, EI of employees, organizational climate, employee resilience, creativity, the product they make, the customers, employee wellbeing and not merely on monetary benefits. Employee involvement can be a stand-alone practice that improves employee wellbeing .

Employers are rolling out workplace wellbeing strategies at a rate never seen before. Most wellbeing strategies are carefully-formulated and being implemented in workplaces, to improve employee wellbeing. This implies that employers have started to view wellbeing as a vital component for the development of individual, organizational, and societal outcomes. Hence organizations are more willing to allocate all types of resources on improving employee well-being.

The UK Government has executed its “Annual Population Survey” with the help of National Statistics which has taken strong steps to “protect and improve the health and well-being of working age people”.

The world renowned company Levi Strauss Foundation, Manager Kimberly Almeida has implemented “Worker well-being (WWB) concept not only at LS & Co. but also in all their supply chain partnering companies through the “aha” moment, stating by 2020, 80% of its products will be produced through worker well-being initiative. To find out the reality of facts from employees, a survey was conducted through the creation of happiness at Work survey. The results of the survey have reported that individuals working in smaller organizations tend to experience higher levels of well-being at work than those working for larger organizations.

Jenn Lim, the CEO and Chief Happiness Officer found, that US-based shoe retailer of Zappos’ Tony Hsieh to help companies unlock the business potential of happiness, who clearly states the business successes is sustained only on keeping the employees happy. The United Kingdom Wellbeing policy development places employees wellbeing at its center and has implemented many schemes for establishing Health and Wellbeing Boards and consortia meant for wellbeing.

Work can make you sick or happy, the earlier fact of research is that stressful working conditions were found to be negatively related to employee well-being. In addition to work-related stress, anxiety and depression cannot be eliminated in an apparel export unit who work on a time frame which causes physical, psychological and social problems.

“Wellbeing” and “Happiness,” are more commonly used interchangeable terms, with a positive meaningful outcome. Employee wellbeing differs among employees due their job, duties, responsibility, expectations, stress coping level, and work environment. A high level of health hazards in the textile apparel industry is byssinosis ie., causing lung disease and cancer due to prolonged inhalation of cotton fiber dust. But wellbeing is more than the absence of diseases. In the present 21st-century employees work plays a significant role in their physical, mental and social wellbeing as reported by World Health Organizations.

The Government of United Kingdom designed a tool to promote mental wellbeing at New Zealand. The New Economics Mental Health Foundation designed a free online game to promote mental wellbeing.

The study conducted by Harter and Arora through Gallup World Poll data on seven Regions at the global level reported that, if employees skills and desires match their job, a good relationship exists with well-being.

Employees wellbeing is a positive meaningful outcome account to many aspects of employees work environment, which differs by work cultures and contexts. There is no one single factor which can determine individual well-being. Hence the researcher has proposed a model on employee well-being to identify the contribution of variable factors like resilience, emotions intelligence and job satisfaction on “well-being”.

In this study, the research samples belong to the textile apparel industry, where the nature of work requires meeting out both the quantitative and qualitative ends. Irrespective of both international and domestic customers, this type of work environment can very much cause physical and mental stress to employees. Though employees of textile apparel play a vital role on national income, they also have a social stigma of “sweat shop” workers, hence, by all means, they have to prepare themselves on meeting out the day to day challenges of life.

Here plays the role of individual employee resilience which explains how employees meet out their demands on reducing the physical and psychological problems to maintain their wellbeing.

Resilience is a buzzword, the term resilience is a multidimensional variable which has the ability to maintain a stable equilibrium, where ‘resilience’ and ‘wellbeing’ are consistently associated with each other in the organizational psychology literature which is characterized by positive emotions and also referred to as a reciprocal effect.

A recent survey by Buck Consultant at Xerox in America, reported 22 percent of companies have already implemented resilience development programs which have become the fastest growing wellness program in corporate of America. The research carried out at the University of Western Sydney found employee resilience improves employee health and performance in workplace.

The past results of research on resilience have shown that resilience increases positive emotions which in turn influences wellbeing. The research on positive psychology has made to know the characteristics of resilient employees. Every employee in a working environment differs person to person based on individual abilities to overcome the adversities. Only some employees grew up on handling the life problems to attain the success. The apparel employees show a greater resilience by consistent performance even during conditions of crisis and economic volatility. Resilient people are more flexible to adopt new situations, they cope with stress, crisis and quickly re bounce back to normalcy. The challenges and hardship faced by an individual act as a catalyst that promotes the opportunity for further growth and increases resilience.

Emotional intelligence is another very important independent variable used to examine the influence on employee well-being in this research. It helps the employees to understand the perceived and expressed emotions in a work environment which is hypothetically linked to employee’s job satisfaction and wellbeing.

The World Economic Forum’s has listed emotional intelligence in the sixth rank out of the top 10 skills needed for employees to possess for thriving in the workplace.

Emotional intelligence acts as a day to day decision player on making promotions, hiring, firing and motivating employees. Managers high in emotional intelligence are good team players and belong to a group of top performers. EI is not meant just for senior executives, it is necessary for all levels of the organization. Psychologist Martyn Newman states “Businesses depend on the people who work for them to be highly engaged, to be able to adapt quickly to internal and external changes, and to show fresh thinking and come up with new ideas,”. A Research on training young adults in Belgium demonstrated an increase in the level of EI within a span of six months period by Nelis et al. (2009), which clearly states EI can be easily adaptable. Same was observed in the case of EI intervention on positive personality characteristics development among Belgian university students.

Job satisfaction contributes more on life satisfaction, the level of satisfaction differs with each employee based on their personal requirements like pay, skills, job safety, basic needs, hours of work and life safety, etc., In this times of global economic drain, getting a job is tough even for a qualified persons, but majority of employees in textile apparel industry are illiterates or literate at schooling level. These employees belong to the lower socio-economic group where they have to depend on the similar type of industry or adjust to the environment, if not satisfied with their job.

Job satisfaction leads to pleasurable positive emotion for those who are good at handling the situations or improved level of resilience. It is the role of employers to examine how employees feel at work. The “pet milk theory” and other research states that keeping the employees happy will lead to high performance and wellbeing. Concerned with this study the researcher has used employee job satisfaction as a mediating variable in the proposed model to identify the degree of influence on wellbeing. This researcher has also investigated the mediating role of job satisfaction, between resilience and emotional intelligence on well-being.

Our findings show that all the five factors of resilience viz., Equanimity, Perseverance, self-reliance, Meaningfulness, Existential aloneness show significant influence on their wellbeing. The psychological construct Resilience is a global culture which has the capacity to overcome stressful life situations and overcome significant adversity in the work place.

On looking at the factors of emotional intelligence, out of six factors only four factors like Self Awareness, Self Confidence, Motivation, and Social Competency were found to show significant influence towards well being of the employees. Hence the organization should try to enhance the levels of these factors to improve the well being of its employees.

The findings of the Structural Equation Model with job satisfaction as the mediating variable showed a partial mediation effect and hence reiterates the importance of job satisfaction in improving the well being of employees.

The most recommended factors for employers to improve employees wellbeing are to develop Resilience, Emotional Intelligence and Job satisfaction apart from providing them with fair pay, job security, work environment etc.,. The researcher also stresses the importance of enlightening the employee community on educating through proper training and personal awareness programs parenting the importance of wellbeing .On improving individual wellbeing, the organizational wellbeing can be improved and at large the society can be benefited, as employees are part of society.

5.3.1 Tools On Promoting Resilience, Emotional Intelligence And Wellbeing On Employees

The wellbeing of employees is long been overlooked in many wellness programs, a good fact is that these variables can be taught and enhanced among the employees in any type of industry. Hence the employers before starting the training programs should target individuals with low levels of resilience, emotional intelligence, and wellbeing to build wellbeing at the workplace for improving individual, organizational and societal wellbeing. As the variables are intangible, these qualities cannot be easily transferred. It is only out of practice and handling the problems and stressful situations at work, over a period of time, the improvements can be visualized by their actions. Hence, HR Managers and employers should encourage and promote programs for developing employee’s wellbeing. Some of the suggested training activities will include,

- The science-backed techniques and evidence-based training programs through trained “coaches”.
- Employees training Program on positive thinking.
- Employees training Program on how to build and maintain strong social supports.
- Employees training Program on developing strategies for responding to stressors.
- Employees training Program on Smarter work design on time management.
- Employees training Program on building better work cultures.
- Employees training Program on Employee Assistance Programs (EAPs)
- Employees training Program on Support recovery for return-to-work programs.
- Employees training Program on Increase awareness of physical and mental health.
- Train and prepare employees to face failures and mistakes through resilient programs ( Meet challenges).
- Train employees to be commitment at work where personal relationships with families, friends, and social contacts improve.
- Train employees on personal control to feel empowered and confident towards executing their jobs.
- There are tools designed and developed by Mental Health Foundation of New Zealand as employees’ free online game to promote mental wellbeing.

5.4 LIMITATIONS

The samples collected for this research consists of both domestic and export-oriented units. Hence the results cannot be exactly identified pertaining to employees of particular units, and it represents a more generalised result of the employees of textile apparel industry.

Most of the readymade garment industries were reluctant to disturb their employees during busy working hours, disclose their internal policy and confidentiality. Hence getting the permission for data collection and counselling the respondents on filling the questionnaires was a Herculean task unlike online process.

Though the study area lay emphasis on the data of Chennai textile apparel industries, the radius of collecting the samples were extended in and around to twenty kilometre radius of Chennai city which includes some samples from the semi urban sector. Hence the results of the study cannot be specifically stated to be pertaining to Urban population only.

This research framework and hypothesis developed can be expanded on adding more variables. Moreover, the employees were surveyed only at one point of time and hence conclusion about cause and effect could not be quantified.

5.5 FUTUROLOGY

In this fast-moving turbulent world subject to changes, organizations rarely find time to attend to the worker's wellbeing. Hence there is a wide scope of future research in the developing country like India. The study in terms of resilience, emotional intelligence and well being in the workplace is more common in western countries, but still a new field of study in south India, specific to the textile apparel industry.

As we know people spend most of their valuable time in the work environment, individual is subject to physical, psychological and social changes specific to any industry. The textile apparel industry whether domestic or export oriented, encounters time pressure and continuous work orders, long work hours, low wages, poor work environment etc which highly disturbs the wellbeing of employees. Hence further research can be carried out with different mediators and moderators affecting the wellbeing of the employees, and new models can be designed to promote wellbeing of the employees. Moreover, as we have done the research only to the employees of the textile apparel industry in and around Chennai, the same can be counter checked in other territorial areas and for other type of industries. All focus and investigations on further research are required to understand the better relationship between Resilience, Emotional intelligence, job satisfaction and wellbeing. Hence Government and organisations should come forward for more research work to be carried out for the human kind benefiting the individual, organization and the society at large on sustaining and promoting employee wellbeing.

5.6 RECOMMENDATIONS

The shift in the status of export apparel industry can have a threat in near future. This high instability in global apparel business becomes a disadvantage for investors to improve the infrastructure and add more welfare measures. Based on the outcome of the research findings, it was found that most of the employees are resilient and emotionally stable but their level of Job Satisfaction is low. Employers should come forward to improve this employee status.

The following recommendations are made for improving, resilience, job satisfaction and wellbeing of the employees

- Companies should implement counseling activities to handle and reduce work related stress.
- Companies should improve job skills of the employees through training programs which enhances their job satisfaction and in turn their degree of Well Being.
- Empowering employees workplace environment to improved Job Satisfaction. To practice some of the activities like Connect, Be Active, Take Notice, Keep Learning, and Give, among employees helps to improve their wellbeing.
- Considering the physical wellbeing, organizations should bring onsite health clinics specific to domestic apparel units.
- Government and Private organizations should adopt wellness measures to improve the Well Being of textile apparel employees as part of their Corporate Social Responsibility. Companies should appoint ‘Wellbeing Managers’ to check the state of being happy, physically and psychologically fit.

APPENDIX 1

QUESTIONNAIRE FOR RESEARCH

Dear Respondent,

Warm Greetings. I am E.Anandharaja , pursuing my Ph.D at Anna University under the title “A STUDY ON INDIVIDUAL EMPLOYEE RESILIENCE, EMOTIONAL INTELLIGENCE AND WELLBEING IN TEXTILE APPAREL INDUSTRY IN CHENNAI ”. I would be very grateful to you if you spare your valuable time in responding this questionnaire. I assure you all the information collected is strictly for academic purposes only and will be kept confidential. Thank you for your kind support.

DEMOGRAPHIC INFORMATION

Please read the following data and kindly indicate your answer by putting a tick { ü } mark in the appropriate option.

1. Initial : S.B Ex-( S. BABU )

2. Gender : □ Male □ Female

3. Age : ----------Years.

4. Educational Level : ------------------.

5. Monthly Income : Rs---------------.

6. Marital Status : □ Single □ Married □ Divorced □ Widowed .

7. Family Status : □ Joint Family □ Nuclear

8. No. of Children : □ One □ Two □ More than two □ Zero (Nil)

9. Designation : ------------------------------.

10. Experience : ---------------------------------.

11. Nature of job : □Permanent □ Temporary □ Contract.

*6-Matital status-(single-A, Married-B, Divorced-D, Widowed-W)

*7-Family status-(Joint-J, Nuclear-S)

*8-Children-( Nil-0)

*11-Nature of job-(Permanent-A, Temporary-B, Contract-C)

SECTION-I

Please read the following statements and put an tick mark{ ü } to your option. To the right of each you will find seven numbers, ranging from “1”( Strongly Disagree ) on the left to “7”(Strongly Agree) on the right. For example, if you strongly disagree (SD) tick “1”, if you disagree(D) tick “2”, if you slightly disagree(SLD) tick “3”, if you are neutral(N) tick “4”, and if you slightly Agree(SLA)tick “5’’ , if you slightly agree(A) tick “6”, if you strongly agree(SA) tick “7’’, I request you to answer all questions given below.

Abbildung in dieser Leseprobe nicht enthalten

SECTION-II

Please read the following statements. To the right of each you will find five numbers, ranging from “1(Virtually never) on the left to “5”(Virtually always) on the right. Tick the below number which best indicates your feelings about that statement. For example, if you say virtually never with a statement, tick the number “1”. If you are neutral, tick “3”, and if you virtually always, tick “5’, etc. I Request you to answer all question with out fail.

Abbildung in dieser Leseprobe nicht enthalten

SECTION-III

Please read the following statements. To the right of each you will find five numbers, ranging from “5’’on the left to “1” on the right. Tick the below number which best indicates your feelings about that statement. For example, if you say very much with a statement, tick the number “5”. If you are average, tick “3”, and if you say not so much, tick “1”, etc. I Request you to answer all question with out fail.

Abbildung in dieser Leseprobe nicht enthalten

SECTION-IV

Please read the following statements. To the right of each you will find five numbers, ranging from “1”(Strongly Disagree ) on the left to “5”(StronglyAgree) on the right. Tick the below number which best indicates your feelings about that statement. For example, if you strongly disagree (SD) with a statement, tick “1”,if you disagree(D) with a statement, tick “2”, if you are neutral(N), tick “3”, and if you agree(A) tick “4’’, if you Strongly agree(SA) tick “5’’, etc I request you to answer all questions given below.

Abbildung in dieser Leseprobe nicht enthalten

Thanks for your kind support.

* * * * * * * * * * * * * * * *

APPENDIX 2

POST HOC TESTS

HOMOGENEOUS SUBSETS

Abbildung in dieser Leseprobe nicht enthalten

APPENDIX 3

CRONBACH'S ALPHA RELIABILITY TEST FOR PILOT STUDY

Abbildung in dieser Leseprobe nicht enthalten

APPENDIX 4

STRUCTURAL EQUATION MODELING

Regression Weights for SEM-With out mediation

Abbildung in dieser Leseprobe nicht enthalten

Standardized Regression Weights: (Group number 1 - Default model)

Regression Weights for SEM-With mediation

Abbildung in dieser Leseprobe nicht enthalten

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LIST OF PUBLICATIONS

International Journals

1. Anandharaja, E &; Geetha, SN 2016, ‘Employee Resilience as a Strategy for Surviving in the Work Place- A Study of Textile Apparel Industries at Chennai’, Asian Journal of Research in Social Sciences and Humanities, ISSN 2249-7315, vol. 6, no. 10, pp. 1348-1358 (Annexure).

[...]

• Autor: E. Anandharaja (Autor)
• Año de publicación: 2018
• Páginas: 264
• No. de catálogo: V1373048
• ISBN (PDF): 9783346908391
• ISBN (Libro): 9783346908407
• Idioma: Inglés
• Etiqueta: influence, employees, resilience, emotional, intelligence, satisfaction, wellbeing, reference, textile, apparel, industry