Factors Affecting Quality of Work Life of Private Taxi Drivers

Abstract

This study aims to investigate the factors affecting the quality of work life (QWL) among private taxi drivers in Punjab state of India. This is an empirical study. A well-structured research schedule was used to collect data from 300 private taxi drivers using convenient random sampling technique. The data have been analyzed with the help of Kaiser–Meyer–Oklin (KMO) test, Bartlett’s test of sphericity, correlation matrix and factor analysis to identify the factors. The study identified 12 factors that affect the QWL of taxi drivers, namely, accidental and environmental risk factor, safety, health and well-being, ergonomics risks, unsocial working hours (sleep/fatigue-related risks), job and social security, extent of interpersonal relations (with co-workers), occupational stress, human relations and social aspects of work life, work and life space, adequate and fair compensation, social relevance of working life and effective grievance handling procedure, and in demographics characteristics age, experience and income were used. The present study will be useful for human resource managers, policy makers and government while framing their human resource-related policies and procedures for the labourers working in the unorganized service sector.

Keywords

Quality of work life taxi drivers factor analysis Punjab

Introduction

The evolution of ‘quality of work life (QWL)’ began in the late 1960s emphasizing the human dimensions of work by focusing on the quality of the relationship between the worker and the working environment. The term ‘QWL’ first came into existence in 1972 during an international labour relations conference (Davenport, 1999). Participation programmes emerged from contract bargaining between General Motors Corporation and United Auto Workers Union; the aim was to increase QWL in 1973 which was aimed at increasing workers’ satisfaction with their jobs by giving them more information and a voice in decision-making (Smith, 1983). During the 1980s, emphasis was increasingly given on workers-centred production programmes. Moreover, QWL has emerged due to rising educational levels and employees’ occupational aspirations (Che Rose, Beh, Uli & Idris, 2006).

Human resources are the most important asset of an organization. According to the importance of human capital in attaining the goals of the organization, concentration on the QWL appears to be important. The elements that are relevant to an individual’s QWL include work, the physical work environment, administrative system and relationship between life on and off the job (Cunningham & Eberle, 1990). Quality of work life means the workplace strategies, operations and environment that promote and maintain employees and organizational effectiveness for employees. Quality of work life involves three major factors: occupational health care, suitable working time and appropriate salary. It implies that a safe work environment provides the basis for a person to enjoy working, the work should not pose a health hazard for the person, the employer and the employee should be aware of their risks and rights (Saraji & Dargahi, 2007). Quality of work life not only increases retention but also is useful in reducing labour turnover and improving job satisfaction. Quality of work life does not only contribute to a company’s capability to recruit quality people but also enhances a company’s visibility. Common beliefs support the argument that QWL optimistically cultivates more motivated, loyal and flexible workforce, which is necessary in shaping a company’s competitiveness. In this regard, Walton (1974) commented:

Dissatisfaction with working life is a problem which affects almost all workers at one time or another, regardless of position or status. The frustration, boredom, and anger common to employees disenchanted with their work life can be costly to both individual and organization. Many current organizational experiments seek to improve both productivity for the organization and the quality of working life for its members.

Walton (1974) proposed eight major conceptual categories relating to QWL:

Adequate and fair compensation

Safety and healthy work environment

Opportunity to use and develop human capabilities

Opportunities for continuous growth and security

Constitutionalism in the work organization

Work-life balance and social integration at the workplace

Protection of individual rights

Pride in the work itself and in the organization

Walton proposed the conceptual categories of QWL. He suggested eight aspects in which employees’ perceptions towards their work organizations could determine their QWL. Despite the growing complexity of working life, Walton’s eight-part typology of the dimensions of QWL remains a useful analytical tool (Daud, 2010, p. 76). The empirical literature relating to factors affecting QWL is presented in Table 1.

Table 1.

Empirical Literature on Factors Affecting Quality of Work Life

Sr. No.	Author (year)	Objective of the Study	Sample Size and Technique	Test	Findings (factors affecting QWL)
1.	Lewis et al. (2001)	To test whether extrinsic or intrinsic or prior traits test predict satisfaction	Random sample, questionnaire received 1,819 out of 5,486 (33%)	Regression method and factor analysis	Pay, supervisor style, commitment discretion
2.	Daud (2010)	To investigate the relation between quality of work life (QWL) and organizational commitment amongst employees in Malaysian firms	Random sample, questionnaire, 500 employees	Factor analysis	Growth and development, participation, physical environment, supervision, pay and benefits, social relevance and workplace integration
3.	Tabassum et al. (2012)	To study factors affecting QWL among male and female employees of private commercial banks in Bangladesh	Convenient sampling, questionnaire, 192 bank employees	Factor analysis and consistency, Wilcoxon analysis, Mann–Whitney U test and Bartlett’s test	Job design, employee relation, working environment, socialization efforts, adequate and fair compensation, opportunities to develop, human growth and development, flexible work schedule, job assignment, work and total lifespan
4.	Chitraa et al. (2012)	To study the employees’ perception on QWL and job satisfaction in a manufacturing organization	Convenience sampling, , questionnaire, received 251 out of 460 respondents	Factor analysis, Bartlett’s test and Kaiser–Meyer–Olkin	Meaningfulness, pessimism about organizational change and self-determination, health care benefits, working environment, flexible work, relationship with peers and superiors
5.	Sinha (2012)	To study factors affecting QWL: Empirical evidence from Indian organizations	Convenience sampling, questionnaire, 100 respondents	Factor analysis, Bartlett’s test and Kaiser–Meyer–Olkin	Career growth and development, organizational culture, emotional supervisory support, flexible work arrangement, employee motivation, organizational commitment, job satisfaction, rewards and benefits and compensation
6.	Taneja and Kumari (2012)	To study the factors affecting QWL among bank employees	Convenient sampling, questionnaire, 250 respondents	Factor analysis, descriptive statistics, T-test and (one-way) ANOVA	Equitable and growth environment, self-esteem, job security, time pressure, organization’s effectiveness, self-determination, decision-making, fringe benefits and welfare measures, social and physical environment, challenges in job, union–management relations, opportunity for continued growth and discipline enforcement
7.	Indumathy et al. (2012)	To investigate the factors affecting the overall perception of QWL	Cluster sampling, questionnaire, 100 respondents	Spearmen’s rank correlation technique	Fair competition, growth security, work and life system, development of human capacities, social integration, social relevance
8.	Noushin et al. (2013)	To study the relationship between QWL and organizational commitment	Random stratified sampling, questionnaire	Pearson correlation coefficient and Kolmogorov–Smirnov test	Fair and enough payment (salary and allowances), health security and work conditions, balance in work and other life, social integration, cohesion and general space of life
9.	Arif et al. (2013)	To find out the dimensions of QWL that affect the life and attitude of teachers at private universities and perception of QWL	Simple random sampling, 370 returned out of 500 (72%) respondents	Factor analysis and binary logistic regression	Value of work, work climate, work-life balance and satisfaction, attitude, perception dimensions of work life such as work-life climate, work-life balance, satisfaction with relationship in life
10.	Sadri and Goveas (2013)	To study sustainable QWL and job satisfaction among employees engaged in a freight forwarding and clearing house in Mumbai	Simple random sampling, observation	Chi-square	Safe and healthy working conditions, adequate and fair compensation, opportunity to utilize individual skills and talent, develop human capabilities, provide career and growth opportunities

Source: Compiled from various research studies.

Literature Review

According to B.T. Basvanthappa, ‘The review of literature is defined as a broad, comprehensive in depth, systematic and critical review of scholarly publications, unpublished scholarly print materials, audio visual materials and personal communications.’

To become familiar with the theoretical background of QWL, basic theoretical and empirical aspects and QWL’s predictors, a review of literature has been carried out. General literature observing organizational behaviour, quality of life and well-being has been consulted, as well as particularly QWL and specific components of QWL. The important findings of the different studies are presented for the present analysis.

Research Design

Rationale of the Study

The research article in question is a part of a large study carried out for the PhD work on the title ‘Quality of Work Life: A Case Study of Private Taxi Drivers in Punjab State of India’. It is a general perception that the life of drivers in general and working conditions in private sector are very hard and miserable. Taking this aspect in mind, the present study was planned and accordingly review of literature was carried out on the basis of previous studies. The review of literature supported our apprehension in regard to the poor working and living conditions of taxi drivers working in private sector. Moreover, the review of literature reveals that much research has not been carried out in this area in developing countries in general and India in particular. Taking these facts into consideration, the present study was planned.

The research on QWL had begun in India with differences in the cultural, social, political and economic areas about four decades ago. Most of the research work in India dealt with QWL in descriptive, theoretical and conceptual contexts, or generally in an action research framework to introduce some desirable change in the plan of work system. However, specific studies are lacking with the help of which one can understand factors influencing and affecting QWL.

Furthermore, it is not easy to best conceptualize the QWL concepts and elements (Seashore, 1975). This study examines what private taxi drivers perceive about their QWL experiences employed by private taxi owners in Punjab state of India.

Objectives of the Study

The major objective of this study is to identify the factors affecting QWL of private taxi drivers in Punjab.

Database and Research Methodology

To achieve the above-said objective, standardized methodology has been followed. To identify the factors affecting QWL of private taxi drivers in Punjab, a well-drafted, pretested research schedule was designed. The research schedule has been divided into two sections. The first section of the questionnaire covered demographic profile and the second section comprised of statements related to QWL-based five-point Likert scale, assigning 5 if they strongly agree to the statement and 1 if they strongly disagree. Factor Analysis is used in order to analyze the collected data.

The present article focuses on the identification of the factors that affect QWL of workers working as private taxi drivers in Punjab. The researchers developed a research schedule by consulting various QWL scales. A set of statements were retained as per the requirements of the study. The questionnaire was refined with the help of three steps. First, content validity was examined through a group of experts in industrial relations. These experts were managers of tourist and travel agencies, operators and owners of taxi stands and academicians from tourism industry. This procedure resulted in the removal of 6 items, leaving a pool of 59 items (statements) for further item analysis. Further, it is desirable to identify those items which have a low relation with scale.

Universe of the Study

The universe of the study is the state of Punjab which is situated on the India–Pakistan border. Three districts were selected, namely, Amritsar, Jalandhar and Ludhiana. These three cities represent the three regions of Punjab, namely Majha, Doaba and Malwa. Further, a sample of 300 respondents was taken by selecting 100 respondents from each city which happen to be district headquarters also. Convenient-cum-random sampling has been used for the selection of 300 respondents; for this purpose, important taxi stands of these cities were identified to contact the respondents for the collection of data.

Sample Characteristics

As far as the demographic profile of the respondents is concerned, age, education, driving experience, marital status and monthly income were considered. The demographic background of the sampled respondents is presented in Table 2. The table shows that the majority of the respondents (28 per cent) belonged to the age group of above 50 years of age, followed by 31–40 years (26 per cent), up to 30 years (24 per cent) and 41–50 years (22 per cent). With regard to the marital status of the respondents, most of the respondents, that is, 179 (59.67 per cent) respondents in the sample were married. Table 2 also shows the educational level of the sampled population. The data reveal that 132 (44 per cent) of the respondents were matrics followed by under matric (32 per cent) and 10+2 (16 per cent), while 6 per cent of the respondents had diploma/certificate courses and 2 per cent of the respondents were graduates. The income categorization shows that 50 per cent respondents belonged to the income category of ₹3,000–5,000 followed by 26 per cent who belonged to the income category of below ₹3,000. Only 8 per cent fall in the income category of above ₹5,000. Majority of respondents (34 per cent) had driving experience of 6–10 years, followed by above 20 years (30 per cent) and 16–20 years (16 per cent).

Table 2.

Demographic Profile of Respondents

	Demographic Variables	No. of Respondents (percentage)
Age (Yrs)	Up to 30 years	72 (24)
	31–40 years	78 (26)
	41–50 years	66 (22)
	Above 50 years	84 (28)
	Total	300 (100)
Marital Status	Married	179 (59.67)
	Single	121 (40.33)
	Total	300 (100)
Education Level	Below Matric	96 (32)
	Matric	132 (44)
	10+2	48 (16)
	Graduation	6 (2)
	Technical	12 (4)
	Diploma/certificate courses	6 (2)
	Total	300 (100)
Personal Monthly Income (Rs.)	Below 3,000	126 (42)
	3,000–5,000	150 (50)
	5,000–8,000	12 (4)
	Above 8,000	12 (4)
	Total	300 (100)
Job Experience	6–10 years	102 (34)
	11–15 years	60 (20)
	16–20 years	48 (16)
	Above 20 years	90 (30)
	Total	300 (100)

Source: Compiled from primary data.

Key Statistics Associated with Factor Analysis

Before proceeding to further analysis in factor analysis, the following terms have been explained to understand the correlation matrix generated on the basis of primary data with the help of Software Package for Social Sciences (SPSS) which is a powerful tool of data analysis used in the social sciences.

Correlation Matrix

A correlation matrix is a lower triangle matrix showing the simple correlations (r) between all possible pairs of variables included in the analysis. The diagonal elements, which are all 1, are omitted. The correlation coefficient value ranging from 0.10 to 0.29 is considered weak, from 0.30 to 0.49 is considered medium and from 0.50 to 1.00 is considered strong (Wei, Govindan, Alain, Keng-Boon & Seetharam, 2009). On the other side, Field (2005) indicated that multicollinearity may arise if correlation coefficient is found to be more than 0.80. But in the present data, the highest correlation is 0. 625 which implies that data are free from the problem of multicollinearity.

Reliability Analysis for QWL Scale

Reliability analysis was performed to test if the research performed at a similar setting and environment can generate the same result. Hair, Anderson, Tatham and Black (1998) suggest that ‘the higher value indicates the higher reliability’. It may be stated that its value moves from 0 to 1, but the acceptable value needs to be more than 0.6 for the scale to be reliable (Malhotra, 2002). According to these standard measures, the Cronbach’s alpha value for the present study falls between 0.60 and 0.912, which implies the existence of internal consistency among the items of the constructs. At the initial stage, the computed value of Cronbach’s alpha of reliability was 0.776. After that, the items with very low Cronbach’s alpha were deleted from the scale. After the removal of these items, the value of Cronbach’s alpha improved from 0.776 to 0.807. It implies that 80.70 per cent of data were found to be reliable in the present study (Table 3). Hence, 58 items are retained for the factor analysis.

Table 3.

Reliability Analysis

No. of Items	Cronbach’s Alpha
58	0.807

Source: Calculated through SPSS.

Validity of Research Instrument

Validity of the research instrument is vital and essential to understand and appreciate how well the concept is defined by the measures. The validity as defined by Hair, Black, Babin, Anderson and Tatham (2007) refers to ‘the degree to which a measure accurately represents what it is supposed to’. The validity may be defined ‘as the extent to which differences in observed scale scores reflect true differences among objects on the characteristics being measured, rather than systematic or random error’ (Malhotra, 2002). There are three types of validity, namely, content validity, construct validity and predictive validity, which are normally tested. According to Malhotra (2010), content validity, known as the face validity, ‘assesses the relation between the individual items and the concept it measures’. Construct validity has often been defined as ‘the practical demonstration of a test to measure the construct it claims to be measuring’ Malhotra (2010). In the present study, content validity is assessed with the help of literature review, while construct validity is recognized through the factorial analysis.

Exploratory Factor Analysis

According to Hair et al. (1998), ‘It is necessary before conducting Exploratory Factor Analysis (EFA) to make sure that sufficient variance exists within the variables.’ The Bartlett’s test of sphericity and Kaiser–Meyer–Oklin (KMO; sampling adequacy test) test are generally applied to examine the correlation among variables and adequacy of sample. Kaiser (1974) recommended that ‘The value of KMO greater than 0.5 should be acceptable.’ The results of Bartlett’s test of sphericity (homogeneity of variance) and KMO are presented in Table 4. Table reveals that the KMO is 0.744 which implies that the data is suitable for Factor Analysis. Similarly, Bartlett’s test sphericity is significant with value (p < 0.001), which implies that significant correlation exists between the variables to carry on with the analysis. The Bartlett’s test statistic is approximately distributed and it may be accepted when it is significant with value less than 0.05, indicating that the correlation matrix is consider significantly different from an identity matrix, if correlations between variables are all zero. So the results of Bartlett’s Test of Sphericity and KMO show that data is fit to run factor analysis.

Table 4.

KMO and Bartlett’s Test

Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.744
Bartlett’s Test of Sphericity	1.015E4	10130.266
	1653	630
	0.000	0.000

Source: Calculate through SPSS.

Principal Component Analysis

Further, principal component analysis (PCA) of factor analysis was applied to reduce the 58 items into a fix and small number of dimensions and varimax rotation method has also been applied to rotate the factors in the present study. The initial communalities represent the relation between the variable and all other variables (i.e., the squared multiple correlation between the item and all other items) before rotation. If many or most communalities were found to be low (<0.30), a small sample size is more likely to distort results. Appendix 1 shows communalities of different items (statements) of the scale. The extraction communalities are functional as these were calculated using the various extracted factors. Extraction communalities for a variable provide the total amount of variance in that variable, accounted by all the factors. The higher the value of communality for a particular variable after extraction, higher is its amount of variance explained by the extracted factors. In Appendix 1, the rows specify the various components taken into consideration to examine and scan the factor analysis. There are 58 variables (statements) which are converted into 15 factors. Third column (initial) indicates that ‘what will be the total weight of each of the components if there is only one component’. The forth column (extraction) indicates that ‘in existence of all the components what will be the weight of all the components individually’. Further, Appendix 1 describes the mean score and standard deviation (SD) of 58 variables independently.

Variance Analysis

Table 5 summarizes the total variance explained by the EFA solution and describes the number of useful factors. This table has three parts and each part has three columns. The first column of first part, titled initial eigenvalues, gives the variance explained by all the possible factors. There are a total of 58 statements, which is the same as the number of variables that go through the EFA. The first column under initial eigenvalues provides the eigenvalues for all the possible factors in a descending order. This is followed by the variance as a percentage of all the variance and cumulative variance. Table 5 shows that cumulative value of the 12 attributes becomes approximately 65.18 per cent. It means these 12 factors are strong enough to overpower the remaining factors. The factors with eigenvalues greater than 1 are considered important. However, rotated component matrix shows that three factors carried only two statements on each factor, so these factors were excluded. The review of literature helps us to decide that only factors with eigenvalues greater than 1 are retained and considered significant, while values less than 1 are considered insignificant and should be deleted for further analysis. Malhotra (2002) recommended that ‘the minimum 50 per cent of the variance should be accounted for explaining the variation of factors.’ In the present study, 12 factors explained 57.91 per cent (Table 5) of the total variance, which is considered acceptable.

Table 5.

Total Variance Explained

Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	Percentage of Variance	Cumulative Percentage	Total	Percentage of Variance	Cumulative Percentage	Total	Percentage of Variance	Cumulative Percentage
1	7.053	12.161	12.161	7.053	12.161	12.161	5.021	8.657	8.657
2	6.548	11.289	23.450	6.548	11.289	23.450	4.511	7.777	16.434
3	4.544	7.834	31.284	4.544	7.834	31.284	3.812	6.573	23.006
4	3.019	5.205	36.489	3.019	5.205	36.489	2.895	4.991	27.998
5	2.387	4.116	40.605	2.387	4.116	40.605	2.498	4.306	32.304
6	2.085	3.596	44.200	2.085	3.596	44.200	2.360	4.068	36.372
7	1.952	3.366	47.566	1.952	3.366	47.566	2.286	3.942	40.314
8	1.770	3.052	50.618	1.770	3.052	50.618	2.270	3.913	44.227
9	1.536	2.648	53.267	1.536	2.648	53.267	2.153	3.712	47.940
10	1.400	2.414	55.681	1.400	2.414	55.681	2.066	3.562	51.502
11	1.292	2.228	57.909	1.292	2.228	57.909	1.911	3.295	54.797
12	1.257	2.167	60.075	1.257	2.167	60.075	1.824	3.144	57.941
13	1.140	1.965	62.040	1.140	1.965	62.040	1.760	3.035	60.976
14	1.103	1.901	63.941	1.103	1.901	63.941	1.458	2.514	63.490
15	1.031	1.777	65.718	1.031	1.777	65.718	1.293	2.229	65.718

Source: Generated from SPSS output.

Note: Extraction method: Principal component analysis.

Factor Loadings

After performing factor analysis, the selected 58 items were reduced to 12 factors. These 12 factors obtained with the help of varimax rotated method are shown in Table 6. Table 6 shows the factor loadings that have been used to measure the correlation between variables and the factors. It is argued that a loading close to 1 indicates strong correlation between a variable and the factor, while a loading close to zero indicates weak correlation. The factors were rotated with the use of varimax with Kaiser normalization rotation method and PCA method for factor extraction. Only those factors having loadings greater than 0.40 were retained for the interpretation purpose and factors with loading less than 0.40 were dropped from further analysis.

Cattell’s Scree Test

According to Cattell (1966), ‘It is a diagrammatically presentation of numbers of factors. It is a plot of the eigen values against the number of factors in order of extraction.’ Figure 1 shows a sharp break in sizes of eigenvalues which results in a change in the slope of the plot from steep to shallow. It can be observed that the slope of the scree plot changed from steep to shallow after the first 12 factors. This suggests that the 12 factors are appropriate solution.

Interpretation of Factor Analysis

The various factors and the subsequent variables, along with their loadings, eigenvalues, variance, reliability alpha (Cronbach’s alpha) and factorial mean values, are presented in Table 6. Table 6 shows that the factor analysis contains 51 statements covered with 12 dimensions which accounted for 57.91 per cent of total variance. To check the internal consistency of the scale, Cronbach’s alpha reliability analysis was performed of the newly created factors. It may be noted in Table 6 that all factors’ reliability alpha (internal consistency) is greater than 0.6 which is considered significant. The newly constructed factors are renamed according to variables loaded on these factors. The names of the factors, the statement labels and factor loadings are summarized in Table 6.

Table 6.

Summary of Factors Affecting Quality of Work Life of Private Taxi Drivers

Sr. No.	Factor Name	Label	Variables	Loadings	Eigenvalue	Variance Percentage	Cumulative Percentage of Variance	Cronbach’s Alpha	Factorial Mean
I.	Accidental risk factor	X50	Risk of road accidents due to lengthy driving periods, especially at night, under bad weather, uneven terrain (e.g., cobblestones) and poor road conditions and through traffic jams.	0.844	7.053	12.161	12.161	0.605	3.4608
		X27	Rain, snow or ice and pavement surfaces with reduced frictional properties (e.g., tiles, cobblestones) that increase the potential and further increase the risk of slipping (poor climate).	0.831
		X53	Noise, illumination and vibrations in working place are irritating.	0.741
		X38	There is a need for use of tobacco/alcohol/stay-awake drugs to feel fresh.	0.739
		X42	Risk of explosion of overinflated tyres and car battery, exposure to dust (especially on desert roads, etc.) and exposure to various automobile fluids (e.g., battery fluid, brake fluid), chemicals burns, acute intoxication in drivers by dangerous chemicals, etc..	0.687
		X46	Cab design, car ventilation and car vibration affect health badly	0.630
		X51	Obstacles along the kerb (e.g., parked cars) that could restrict visibility and create confined spaces in which to perform the task.	0.539
		X26	Risk of skin and other physical diseases/conditions (dermatitis, skin sensitization, eczema, etc.) caused by chemicals, such as cleaning compounds, antifreeze and brake fluids, gasoline, diesel oil and other oils, and acute poisoning by exhaust gases, including carbon monoxide, etc.	0.415
II.	Safety, health and well-being	X12	Welfare measures such as cafeteria and shed with electrical fans, etc. available are sufficient.	0.728	6.548	11.289	23.450	0.612	3.41
		X7	Welfare and social security measures are inadequate to help in increasing mental peace, so state of mind not remains relatively peaceful.	0.716
		X33	Workers are insured against life hazards such as health accidents.	0.697
		X31	Working conditions are dehumanized and have risk of illness and injury.	0.695
		X8	Feel physically unsafe at my working environment.	0.668
		X32	Safety measures are sufficient.	0.664
		X20	I go for regular medical and periodical physical check-up.	0.641
		X25	Organization gives high priority for the safety of its workers.	0.572
III.	Ergonomics risks	X23	Psychic disorders caused by mental and emotional stress factors while performing a job.	0.847	4.544.	7.834	31.284	0.912	2.377
		X52	Pains in the low back and in the joints (of legs and hands/arms) caused by prolonged driving, sometimes over bumpy roads and development of lumbago due to poor vehicle suspension, uncomfortable seat and/or inadequate seat, etc.	0.839
		X18	Overexertion while moving or otherwise handling steering wheel and psychological discomfort as a result of possibility of permanent control by cellular phone or radio communication equipment.	0.838
		X55	Visual discomfort, noise and eye problems caused by inadequate illumination and eye strain (especially when driving in the dark on interurban roads).	0.817
IV.	Human relations and social aspects of work life	X11	The employer is not a source of socio-emotional support to all the employees	0.882	3.019	5.205	36.489	0.780	3.333
		X40	Receive equal treatment in all matters, such as employee compensation, job and security, and there is ‘one best way for all ages’.	0.877
		X5	Employer asks opinion in making decisions which affects my work, health and well-being.	0.639
		X37	There is trusting and open work environment, and messages concerning health of the driver and vehicle faults are received positively.	0.555
V.	Job and social security	X19	I have a secure job if I perform well.	0.706	2.387	4.116	40.605	0.758	2.645
		X17	There is job security and no fear of loss of job.	0.705
		X14	Social security benefits are sufficient.	0.659
VI.	Extent of interpersonal relations (with co-workers)	X45	Spend my leisure time in recreational activities with my friends.	0.702	2.085	3.596	44.200	0.840	2.692
		X57	The relationship with colleagues is good.	0.666
		X49	I am happy with my friends circle.	0.648
VII.	Occupational stress	X9	No need to worry about poor health and to adjust lifestyle due to shift work and excessive stress at work.	0.731	1.952	3.366	47.566	0.670	2.5013
		X35	Bad treatment by clients and smoking inside cabin contributing to health deterioration.	0.691
		X21	Employer provides good security equipment and health care measures.	0.563
		X16	No need to change lifestyle and eating habits due to irregular sleeping and eating, long and unsocial working hours and stress (digestive tract disorders).	0.563
		X24	Exposure to peer violence (e.g., in roadside cafeterias, etc.) and to petty and gang (including organized) crimes attracted by cash and valuable cargo so there is risk of carrying large sums of money.	0.471
VIII.	Unsocial working hours (Sleep/fatigue-related risks)	X54	Work overload makes me feel fatigued and sleepy.	0.723	1.770	3.052	50.618	0.628	2.765
		X41	I take appropriate rest breaks for the safety and to reduce fatigue.	0.633
		X6	I do not take enough night sleep and regular/adequate sleep during trips.	0.546
		X47	Night driving and dusk driving affect the life of drivers.	0.477
IX.	Work and life space	X29	Neglect health due to social and individual requirements.	0.875	1.536	2.648	53.267	0.757	2.663
		X58	Enough time for activities, such as shopping, playing with children, visiting relatives home, spending time with parents, watching TV, attending family functions, other household activities like helping spouse in household activities, and for pursuing hobbies which I enjoy because working hours and work pattern suit my personal circumstances while working as a taxi driver.	0.816
		X44	I am happy with social and working life in this profession as one can attend to family and social obligations without much problem.	0.587
X.	Adequate and fair compensation	X1	Gross emoluments offered by employer are commensurate of your worth, ability, skill and experience.	0.767	1.400	2.414	55.681	0.640	2.683
		X2	The income from job alone is enough to meet your family needs.	0.719
		X30	Workers association is a part of management.	0.402
XI.	Social relevance of working life	X28	Organization functions as a socially responsible unit as employer has improved my social security schemes and takes serious efforts in a campaign to quit smoking/alcohol and drugs.	0.700	1.257	2.167	60.075	0.610	3.226
		X22	Organization is aware of the methods for prevention of industrial pollution.	0.560
		X15	Employer takes serious efforts in a campaign to quit smoking/alcohol and drugs.	0.557
		X36	Job increases social prestige.	0.437
XII.	Effective grievance handling procedure	X10	In the organization, rules are equally applicable to all.	0.738	1.140	1.965	62.040	0.620	2.95
		X43	In this organization, people caught while committing serious offenses and irregularities are not left with minor punishment.	0.632
		X48	Grievance handling procedure available in this organization enables to sort out grievances effectively.	0.458

Source: Compiled from the results of SPSS.

Naming of Factors

Table 6 revealed that factor I is a linear combination of variable numbers X50, X27, X53, X 38, X42, X46, X51 and X26 (α = 0.605). Factor II is a linear combination of variable numbers X12, X7, X33, X31, X8, X32, X20 and X25 (α = 0.612). Factor III is a linear combination of variable numbers X23, X52, X18 and X55 (α = 0.912). Factor IV is a linear combination of variable numbers X54, X41, X6 and X47 (α = 0.780). Factor V is a linear combination of variable numbers X19, X17 and X14 (α = 0.758). Factor VI is a linear combination of variable numbers X45, X57 and X49 (α = 0.840). Factor VII is a linear combination of variable numbers X9, X35, X21, X16 and X24 (α = 0.620). Factor VIII is a linear combination of variable numbers X11, X40, X5 and X37 (α = 0.628). Factor IX is a linear combination of variable numbers X29, X58 and X44 (α = 0.707). Factor X is a linear combination of variable numbers X1, X2 and X30 (α = 0.640). Factor XI is a linear combination of variable numbers X28, X22, X15 and X36 (α = 0.610). Factor XII consists variable numbers X10, X43 and X48 (α = 0.620). All the factors have been given appropriate names according to the variables that have been loaded on each factor. Mean score ranking was used to compare the relative importance of all factors.

Figure 1.

Source: Authors’ own.

Factor 1: Environment and Accidental Hazards

Table 6 shows that the first factor, environment and accidental hazards, is comprised of eight items relating to environment and accidental hazards. It explained 12.161 per cent variation in the data, with an eigenvalue of 7.053 and α = 0.605. The factor loading on this factor ranged from 0.415 to 0.844. It contained eight items related to environment and accidental risk, such as noise, illumination, vibration, temperature and radiation. This has also been supported by other studies as Dhar (2008) found that QWL was related to working conditions, the quality of the buses driven, external conditions such as noise, pollution, smell of diesel and heavy work demands like the unruly and noisy and uncooperative commuters, of the bus drivers of Pune Municipal Corporation in a metropolitan city. Ranjan and Prasad (2013) also found that

The extremely irregular working hours constitute an added workload for the railway drivers. The physical work environment can also give rise to workload; this includes, for e.g. noise (or distressful noise levels), vibrations or an uncomfortable cab conditions (too hot, too cold, draughty). The railway drivers are also exposed to a demanding psychosocial work environment, which includes solitary work, limited opportunities for social contact and a heavy responsibility for operating the train (in terms of both safety and adhering to the timetable). The heavy noise, dust pollution, excess heat, high voltage electricity in the electric locomotive and diesel smell in the diesel locomotive are contributing to early fatigue to the crew.

Knox and Irving (1997) stated that

strength and weaknesses of the work environment plays an important role in determining QWL. Therefore, safety activities in transport sector should include modification of the environment using alternative procedures such as containing hazard; limiting exposure to hazards by reducing time of exposure; using protective devices e.g. heating suit, glasses, ear protector, gloves, boots cooling suit; health education to generate workers awareness of health related issues; continuous monitoring; pre-placement examination and periodic examination of workers.

Factor II: Safety, Health and Well-being

The second dimension, ‘safety, health and well-being’, has explained 11.29 per cent variation in the data, with eigenvalue of 6.548 and α = 0.612. The factor loading ranged from 0.728 to 0.572. It included eight items. This study is supported by Adhikari and Gautam (2010) who claim that ‘the main outcomes of an effective QWL program are improved working conditions for employees and greater organizational effectiveness for employers’. In the case of service sector workers, QWL of a worker reflects impulsively in mental and physical well-being (Holman, 2002). Quality of work life is the desirable conditions and environments of the workplace that concentrate on the welfare and well-being of workers (Huang, Lawler & Lei, 2007). Cooper and Mumford (1979) found that ‘The growing importance of the quality of working life has engendered efforts to identify major variables which have impact on the well-being of individuals at work.’

Factor III: Ergonomics Risks

The third factor, ‘ergonomics risks’, is responsible for 7.83 per cent of variation, having an eigenvalue of 4.544 and α = 0.912. The factor loading ranged from 0.817 to 0.847. It consists of four items. All the statements loaded on this factor are related to ergonomics and physical risks. These problems result from the poor quality of the vehicle and lack of safety measure available to the taxi drivers.

Factor IV: Unsocial Working Hours (Sleep/Fatigue-related Risks)

The fourth dimension ‘unsocial working hours (sleep/fatigue-related risks)’ is a combination of six items. This dimension explains 5.21 of the total variance and has an eigenvalue of 3.019 (α = 0.780). The factor loading ranged from 0.555 to 0.822. This finding is also supported by Bragard et al.’s (2012) study who ‘suggested that prevention should focus on reduction of work hours’.

Factor V: Job and Social Security

The fifth dimension, ‘job and social security’, is responsible for 4.116 per cent variation, with an eigenvalue of 2.387 and α = 0.758. The factor loading ranged from 0.659 to 0.706. This finding is supported by the research conducted by the Organisation of Economic Cooperation and Development (OECD, 1996) which highlighted that ‘job security is the most controversial issue in contemporary work environment’. Job security is the central and core aspect of QWL. Hence, providing a sense of stability and security is important to enhance the sense of confidence among the respondents which may further improve the QWL.

Factor VI: Extent of Interpersonal Relations (With Co-workers)

The sixth dimension, ‘extent of interpersonal relations (with co-workers)’, is a combination of four items. It explained 3.596 of the total variance with an eigenvalue of 2.085 and α = 0.840. Feldman (1993) has also argued that QWL is influenced by the quality of relationship between the staff and the environment. Therefore, improved interpersonal relations of the drivers may enhance QWL.

Factor VII: Occupational Stress

The seventh factor, ‘occupational stress’, comprised of three items and elucidated 3.366 per cent of the variance in the data, having an eigenvalue of 1.952 with α = 0.670. The factor loading ranged from 0.471 to 0.731. This finding is similar to Baba and Jamal (1991) who described work role overload and job stress as indicators of QWL.

Factor VIII: Human Relations and Social Aspects of Work Life

The eighth dimension, ‘human relations and social aspects of work life’, is based on four items with 3.052 of the total variance and an eigenvalue of 1.770 with α = 0.628, emphasizing the socio-emotional support and equal treatment. The factor loading ranged from 0.477 to 0.723. Rossmiller (1992) found that ‘QWL positively influenced the respect and participation in decisions affecting their work environment.’ The level of support offered by the organization was also measured and considered as an indication of the work-life quality (Dixon & Sagas, 2007; Rhoades & Eisenberger, 2002). So the findings of our study also reveal that the feeling of emotional support and sense of belongingness influence the QWL.

Factor IX: Work and Life Space

The ninth factor consisted of three items with the loading in the range of 0.875–0.5.87 and α = 0.757. It accounted for 2.648 per cent of the total variance with an eigenvalue 1.536. Eurofound (2013) also found that the ‘quality of work life has to do with better jobs and more balanced ways of combining working life with personal life’.

Factor X: Adequate and Fair Compensation

Six items are loaded on this factor sharing 2.414 per cent of the total variance with an eigenvalue of 1.400. The lowest loading was 0.402 and highest was 0.767 and α = 0.640. This factor had been named as ‘adequate and fair compensation’. Hackman and Oldhams (1980) supported that ‘personal needs are satisfied when rewards from the organization such as compensation meet workers expectations and in so doing results in an excellent quality of work life’. Therefore, the present study highlights the importance of this factor.

Factor XI: Social Relevance of Working Life

The eleventh factor emphasized the social relevance of working life. It shared 2.17 per cent of the total variance with an eigenvalue of 1.257. It includes three items and the factor loading ranged from 0.437 to 0.700 and α = 0.610. Relevance of working life is a strong indicator of good QWL; this is also supported by Walton (1975).

Factor X: Grievance Handling Procedure

The last factor is ‘grievance handling procedure’. Three statements are loaded on this factor and together account for 1.965 per cent of the total variance with α = 0.620. The factor loading ranged from 0.458 to 0.738. This factor is also supported by two studies conducted by Stephen (2012) in India who highlighted the importance of grievance handling system for the workers.

Appendix 1.

Communalities and Descriptive Statistics

Statements	Initial	Extraction	Mean	SD
X1	1.000	0.650	2.6033	0.74015
X2	1.000	0.662	2.6567	0.71214
X3	1.000	0.597	2.6100	0.70205
X4	1.000	0.694	2.4867	0.75613
X5	1.000	0.628	2.4100	0.74638
X6	1.000	0.484	3.3500	0.61815
X7	1.000	0.634	3.6433	0.75145
X8	1.000	0.563	3.4867	0.66178
X9	1.000	0.615	2.5033	0.72462
X10	1.000	0.590	2.2533	0.79872
X11	1.000	0.617	3.8000	0.77157
X12	1.000	0.651	2.4433	0.74966
X13	1.000	0.641	2.7400	0.55986
X14	1.000	0.633	2.7767	0.58417
X15	1.000	0.631	2.2200	0.77477
X16	1.000	0.655	2.4733	0.74238
X17	1.000	0.626	2.6300	0.69861
X18	1.000	0.846	2.3833	0.74267
X19	1.000	0.640	2.5267	0.74687
X20	1.000	0.552	3.5700	0.69284
X21	1.000	0.467	2.4533	0.71381
X22	1.000	0.677	3.5400	0.79404
X23	1.000	0.866	2.3767	0.74159
X24	1.000	0.622	2.4833	0.76047
X25	1.000	0.566	3.3767	0.69020
X26	1.000	0.491	3.3933	0.70257
X27	1.000	0.793	3.5267	0.74687
X28	1.000	0.699	3.4767	0.79891
X29	1.000	0.856	2.2733	0.72598
X30	1.000	0.546	2.7900	0.61140
X31	1.000	0.549	2.5467	0.72773
X32	1.000	0.652	3.6800	0.79983
X33	1.000	0.665	3.5333	0.77301
X34	1.000	0.484	2.8000	0.59541
X35	1.000	0.628	2.5933	0.72782
X36	1.000	0.567	3.6767	0.83333
X37	1.000	0.545	2.4233	0.79974
X38	1.000	0.831	3.4633	0.80674
X39	1.000	0.654	2.5733	0.79586
X40	1.000	0.544	2.4233	0.74339
X41	1.000	0.836	3.3833	0.66170
X42	1.000	0.641	2.7233	1.24309
X43	1.000	0.659	3.3267	0.73604
X44	1.000	0.686	3.3500	0.78925
X45	1.000	0.753	2.9500	0.28479
X46	1.000	0.576	3.5400	0.85489
X47	1.000	0.664	3.2800	0.78973
X48	1.000	0.612	3.2700	0.73410
X49	1.000	0.612	2.6767	0.77078
X50	1.000	0.824	3.4800	0.81146
X51	1.000	0.469	3.5533	0.82224
X52	1.000	0.831	2.4467	0.76754
X53	1.000	0.830	3.4667	0.80273
X54	1.000	0.823	3.3200	0.75686
X55	1.000	0.779	2.4400	0.81770
X56	1.000	0.702	3.4433	0.82607
X57	1.000	0.675	2.4500	0.85827
X58	1.000	0.834	2.3667	0.83739

Source: Calculated through SPSS.

Note: Extraction method: Principal component method.

Conclusion

The main focus of this study was to identify the factors affecting QWL of private taxi drivers in Punjab state of India. Factor analysis identified 12 factors that affect the working life of drivers, namely, environmental and accidental risk, safety, health and well-being, ergonomics risks, unsocial working hours (sleep/fatigue-related risks), job and social security, extent of interpersonal relations (with co-workers), occupational stress, human relations and social aspects of work life, work and life space, adequate and fair compensation, social relevance of working life and effective grievance handling procedure. All these factors play a significant role in determining QWL which are supported by other researchers also (Stephen, 2012; Walton, 1974). The analysis of various dimensions reveals that the factors which have lower mean score are the factors on which the drivers have low level of satisfaction. Occupational stress dimension has lowest factor mean which indicates that taxi drivers least agree with this factor and face high level of occupational stress which in turn gives rise to other problems that affect both personal and work life. Therefore, taxi owners should frame appropriate policy with regard to reduce the pressure due to overtime and occupational hazards. This could be achieved by working on different factors which have been identified with the help of factor analysis.

The QWL of taxi drivers could be enhanced by focusing on the variables which share maximum percentage of variance, namely, lower road accident risk, maintenance of vehicles, educating drivers about the harmful impact of drugs, introduction of welfare and social security measures, health insurance, etc. It could be a win–win situation. Many studies have pointed out that improvements in QWL lead to higher productivity and might have a positive impact on the performance of the taxi drivers in the long run. The study also recommends that respective governments should introduce strong regulatory measures to provide protection to the taxi drivers for the upliftment of their level of living.

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