Editorial Board

A Refereed Monthly International Journal of Management

Occupational Health in the GIG Economy: A Comparative Study of Workforce Well-Being

 

Dr. B. Menaka

Assistant Professor,

 Department of Commerce, School of Management,

Alagappa University, Karaikudi, Sivagangai Dist, Tamilnadu State

https://orcid.org/0000-0002-7855-2791

 

Dr. Shilpa Sharma

Assistant Professor,

Symbiosis Law School Nagpur Campus

Symbiosis International Deemed University, Pune, India

https://orchid.org/0000-0001-5447-2255

 

Dr. Sapna Bansal

Associate Professor, School of Law, GD Goenka University, Haryana, India

https://orcid.org/0000-0003-4262-6418

 

Sathish Kumar Velayuthan

Lecturer, Faculty of Hospitality,

Tourism and Wellness, Universiti Malaysia Kelantan, Malaysia

sathish.k@umk.edu.my

https://orcid.org/0000-0002-8783-132X

Corresponding Author

 

Dr. Vaishali Singh

Assistant Professor,

SVIMS-Shr M. Visvesvaraya

Institute of Management, Mumbai, India

https://orcid.org/0009-0002-2025-9389

 

Abstract:

The gig economy's ascent and the changing nature of the modern workforce demand a thorough examination of occupational health within this dynamic framework. This study compares workforce well-being in the gig economy to define the subtle health consequences that gig workers encounter in various industries and locations.This study used an interdisciplinary approach, combining concepts from economics, sociology, public health, and occupational health.Data from gig workers, who operate in a variety of non-traditional jobs, including platform-based tasks, on-call labor, and freelancing, will be gathered and analyzed for the project. Key dimensions under scrutiny include the physical-occupational health status and physical and psychological health of Ola andUber drivers. The comparative aspect of the study aims to compare the occupational health and well-being of Ola and Uber drivers.495 drivers were chosen for the research using the purposive sample approach.Evidence-based policies and treatments designed to improve the occupational health of gig workers will be informed by the insights gained from this research. The research was descriptive in nature and data was analysed with the help of mean and t-test. The findings revealed that cab drivers are suffering from so many physical and psychological health problems. They havethe average status of occupational health but Ola drivers are in a better well-being state than Uber drivers. Ultimately, the findings aspire to contribute to a comprehensive understanding of the intricate relationship between gig work and workforce well-being, providing valuable knowledge for researchers, policymakers, and organizations navigating the complexities of the contemporary labor market.

Keywords:Occupational Health, Gig Economy, Workforce, Well-Being.

 

Introduction

The advent and growth of the gig economy have caused a paradigm shift like the global workforce in recent years. The gig economy, which is defined by temporary, flexible work arrangements, has given people access to never-before-seen chances to participate in the economy. Gig labor's effect on general well-being and occupational health, however, has raised concerns as this non-traditional employment model continues to change how people work. Because there are many different types of jobs in the gig economy, such as freelancers, task-based workers, and drivers for ride-sharing services, it is important to comprehend the subtle benefits and disadvantages of working in this dynamic workplace when developing occupational health measures.

The gig economy has produced a decentralized and flexible labor market, driven by digital platforms and technology breakthroughs. The gig economy's workforce, sometimes known as "gig workers" or "independent contractors," defies conventional ideas of steady employment connections by working on a task-by-task or project-by-project basis. The gig economy brings with it new occupational health considerations in addition to autonomy and flexibility.

The unpredictability of gig workers' income, their inability to access standard job benefits, and their possible social isolation are all potential concerns. Furthermore, the nature of gig workwhich is marked by erratic schedules and fluctuating workloadscansexacerbates stress and exhaustion, raising concerns about the long-term effects on one's physical and mental health.

This study aims to contribute to the existing body of knowledge by conducting a study of workforce well-being in the gig economy. To better inform policies and interventions targeted at improving the occupational health of gig workers, the research looks at several variables to determine the occupational health status and well-being of Ola and Uber drivers.

Review of Literature:

Here is an overview of some prior research on the topic that includes several important papers and articles.Occupational health and the gig economy is a multidisciplinary field concerned with the well-being of workers in various industries. Over the years, researchers have explored numerous aspects of occupational health, from identifying workplace hazards to developing interventions aimed at promoting a safe and healthy work environment.

The literature was further separated into two sections: the first area dealt with research on occupational health, while the second segment examined the material that was accessible in the gig economy.

Occupational health-focused studies:

This section of the literature review showcases the diversity of research in the field of occupational health, emphasizing the interdisciplinary nature of efforts to enhance worker well-being.

The origins of occupational health can be found in foundational research like the Hawthorne experiments by Mayo, (1933), which set the stage for comprehension of the psychological and social components of the workplace. A major turning point in the emphasis on worker safety was the enactment of laws such as the Occupational Safety and Health Act (1970) in the United States.Researchers McLeod & Walters, (2017) and Smith &Leggat, (2015) have conducted studies that focus on identifying and managing workplace dangers. They underline the significance of risk assessment and draw attention to hazards unique to a given industry, such as chemical exposures and ergonomic difficulties.

Iyner et al. (2017) conducted research that sheds light on the epidemiology of occupational disorders and highlights the need for early detection and prevention. The research conducted by Sparks et al. (2011) contributes to the understanding of the relationship between occupational exposures and illnesses such as cancers linked to asbestos.Karvey et al. (2017) investigate how stress at work affects mental health and highlight the need for organizational support. Furthermore, research conducted in (2020) by Xang et al. and in (2016) by Adler et al. provides insight into workplace interventions for mental health issues.

Papers by Hamalien et al. (2006) and Clarke, (2016) investigate the efficacy of safety and health initiatives in the workplace. They talk about the function of rules, the culture of safety, and incorporating safety precautions into regular workdays.The scholarly contributions of Grandjon, (1988) and Sharma, (2014) enhancecomprehension of ergonomics and its influence on the welfare of employees. They emphasize how crucial it is to create workspaces that minimize physical strain and foster peak performance.

An overview of workplace health promotion programs may be found in the review conducted by Shutel et al., (2007). It talks about the advantages of all-encompassing programs that take mental and physical health into account.Certain industries provide particular occupational health issues, such as the healthcare sector Cronsson et al., (2017) and the construction industry (Paslam et al., 2005). These papers talk about dangers unique to each industry and solutions designed to meet the requirements of workers in those industries.

Gig Economy:

This literature review showcases the diverse range of research on the gig economy, covering economic, social, and regulatory dimensions, as well as its impact on worker well-being and the broader labor market.In today's workforce, the gig economy which is defined by flexible and temporary labor arrangements has grown in popularity. Scholars have investigated diverse aspects of the gig economy, ranging from its financial consequences to its influence on the welfare of employees.

Katz &Krueger's (2019) research sheds light on the economic aspects of the gig economy by examining the emergence of alternative employment arrangements and the difficulties they present for established labor markets.Shervaset al., (2017) investigate the financial drivers and effects of gig labor while highlighting the significance of online platforms.The labor market dynamics of the gig economy are examined by Abraam et al., (2020), who also analyze the factors determining involvement in gig work and the implications for general employment patterns. This research advances knowledge of the structural alterations in the workforce.

Bernachet al., (2016) investigate the idea of precarious labor in the gig economy, highlighting the difficulties that workers may face and the possibility of greater job insecurity. The study emphasizes how important it is to take these problems into account when developing policy.In a 2018 study, Detefano & Alosi investigate how gig employment affects employees' happiness at work and their well-being. It talks about the flexibility and autonomous parts of it, but it also talks about the possible drawbacks, such as unstable income and no perks from the job.

Kaeene et al.(2019) explore the legal and social aspects of the gig economy, looking at how it affects workers' rights and how difficult it is to regulate these non-traditional work arrangements. Sundarajan,(2016) offers valuable perspectives on the influence of technology platforms on the gig economy. The book addresses questions of trust, accessibility, and market dynamics as it explores how digital platforms are changing the gig job landscape.Woudy et al.(2019) investigate diversity in the gig economy by taking into account the experiences of various groups, including low-income workers, women, and minorities. The study clarifies the potential disparate effects gig employment may have on different demographic groups.

In their (2020) study, Fariearet al. explore how gig employment affects the development of skills. They go over how gig platforms help people acquire new abilities and how they might improve their employability in the future labor market.Huvs' (2019), research offers an outlook on the gig economy by examining potential policy consequences and future developments. The study takes into account how the nature of work is changing and the difficulties policymakers have in adjusting to these developments.

Research gap:

Identifying and addressing issues of gig workers this study can contribute to a more nuanced understanding of the occupational health dynamics within the gig economy. This study will fill the gap for specific studies for gig workers and guide future studies toward the areas that hold significant potential for impact and policy development in the gig economy.

Objectives

1. To describe the health status of GIG workers
2. To study the physical and psychological health of Ola and Uber drivers
3. To study the occupational health status of GIG workers
4. To compare the occupational health and well-being of Ola and Uber drivers

Hypotheses

1. There is no significant difference in the overall physical health of Ola and Uber drivers
2. There is no significant difference in the overall psychological health of Ola and Uber drivers
3. There is no significant difference in well well-being status of Ola and Uber drivers

Research Methodology

• Research Design: The study has described the various physical and psychological problems faced by gig workers, so descriptive research design has been used in the study.
• Sampling: The population frame included all cab drivers of Jaipur (Rajasthan). To be very specific only Ola and Uber drivers have been considered and by using purposive sampling method 495 drivers have been included in the study.
• Data Collection Tool: The study is based on primary data which has been collected by using a questionnaire. The questionnaire was divided into five parts i.e. (a) demographic profile of respondents (b) job profile of respondents (c) health status of respondents (d) health problems faced by respondents (e) psychological problems faced by respondents
• Data Analysis Tool: MS Excel and SPSS 21.0 have been used as analytical software. To serve the objectives of the research mean, standard deviation, coefficient of variation and two sample tests were used.

Analysis of Data

• Demographic Profile of Respondents

The first part of the questionnaire collected information about the demographic variables of cab drivers and the same has been presented in Table 1:

• Age of Respondents: As per the age bifurcation the majority of Cab drivers were aged between 30 to 40 years (43.03%) followed by 20 to 30 years (25.86%). Less than 20% of the Cab drivers were aged between 40 to 55 years (18.38%) and less than 10%of respondents were aged between 50 to 60 years (9.90%). Only 2.83% of cab drivers were aged above 60 years.
• Residential Area of Respondents: Jaipur is a big district which includes urban, semi-urban and rural areas. As per data received more than half of the respondents (55.76%) were residing in urban areas followed by semi-urban (23.84%) and rural (20.40%) areas.
• Qualification of Respondents: It is generally assumed that less educated persons go for driving jobs so sample respondents were also asked to indicate their qualifications. It was found that a maximum number of respondents (43.23%) have not passed 10^th followed by 27.07% drivers who have passed 10^th class/secondary. It was observed that very few respondents were graduate (5.86%) or postgraduate (3.43%)
• Marital Status of Respondents:As per results more than half of the respondents (59.19%) were married but 25.05% of Cab drivers were unmarried. It was found that 15.76% of Cab drivers were either divorced or widows.

Table 1: Demographic Profile of Respondents

Age	N	Percentage	Qualification	N	Percentage
20-30 Years	128	25.86	Below Secondary	214	43.23
30-40 Years	213	43.03	Secondary	134	27.07
40-50 Years	91	18.38	Higher Secondary	101	20.40
50-60 Years	49	9.90	Graduate	29	5.86
Above 60 Years	14	2.83	Post Graduate	17	3.43
Total	495	100	Total	495	100
Residential Area	N	Percentage	Marital Status	N	Percentage
Rural	101	20.40	Single	124	25.05
Semi-Urban	118	23.84	Married	293	59.19
Urban	276	55.76	Divorced/Widow	78	15.76
Total	495	100	Total	495	100

• Job Profile of Respondents

The second part of the questionnaire collected information about the job profile of cab drivers which is shown in Table 2:

• Employer of Respondents: Out of the various available cab services this research has considered only two prominent services i.e. Ola and Uber. So, in this research, 44.24% of respondents were Ola drivers and 55.76% of respondents were Uber drivers.
• Job Experience of Respondents: Cab drivers were asked to indicate their total number of years in this service and it was found that the majority of respondents were driving for thelast 4 to 6 years (35.15%) followed by 2 to 4 years (25.45%) and more than 6 years (21.62%).
• Type of Vehicle driving:It was found that 28.08% of respondents were driving their car whereas 19.60% of respondents were driving cars owned by Ola or Uber. Maximum number of drivers (52.32%) indicated that they are driving cars owned by somebody else.
• Monthly Income of Respondents:Cab drivers were asked to indicate their monthly income from driving and it was found a maximum number of respondents (39.80%) were earning Rs 10000 to 15000 followed by Rs. 5000 to 10000 (29.90%) and Rs. 15000 to 20000 (14.55%).
• Daily Working Hours of Respondents:As per data majority of cab drivers (42.22%) are working 8 to 12 hours a day followed by 25.86% of cab drivers who work for 5 to 8 hours a day. It was also observed that 6.46% of cab drivers were working for more than 15 hours a day

Table 2: Job Profile of Respondents

Employer	N	Percentage	Monthly Income from Driving	N	Percentage
Ola	219	44.24	Up to Rs. 5000	12	2.42
Uber	276	55.76	Rs. 5000 to 10000	148	29.90
Total	495	100	Rs. 10000 to 15000	197	39.80
No of Years in Service	N	Percentage	Rs. 15000 to 20000	72	14.55
Up to 2 Years	88	17.78	Rs. 20000 to 25000	39	7.88
2 to 4 Years	126	25.45	More than Rs. 25000	27	5.45
4 to 6 Years	174	35.15	Total	495	100
More than 6 Years	107	21.62	Working hours in a Day	N	Percentage
Total	495	100	Up to 5 hours	85	17.17
Type of Vehicle Driving	N	Percentage	5 to 8 hours	128	25.86
Own Vehicle	139	28.08	8 to 12 hours	209	42.22
Ola/Uber Owned	97	19.60	12 to 15 hours	41	8.28
Owned by Somebody Else	259	52.32	More than 15 hours	32	6.46
Total	495	100	Total	495	100

 

• Social Benefits Received by Respondents

In government jobs, private jobs, and corporate jobs normally employees get so many social benefits apart from the salary, but a review of the literature indicated that gig workers do not get such benefits. So, to test this claim cab drivers were asked to indicate the social benefits being offered by their employers and the results received are depicted in Table 3. It could be seen that more than 3/4^th of the cab drivers (78.18%) are not getting any type of social benefit. The few social benefits received by the cab drivers were health insurance (2.42%), accidental insurance (8.28%), PF deduction (3.43%) and compensation for death during the job (7.68%).

Table 3: Social Benefits Received by Respondents

Social Benefits Received	N	Percentage
Health Insurance	12	2.42
Accidental Insurance	41	8.28
PF deduction	17	3.43
Compensation for death during job	38	7.68
Nothing	387	78.18
Total	495	100

 

• Self-Reported Health Status of Respondents

Cab drivers were asked how they consider their health and as per the result shown in Table 4 majority of respondents (40%) said that their health conditions are very poor followed by 30.10% of drivers who indicated their health status was poor. According to 17.58% of drivers, their health is average which means neither good nor poor. Less than 10% of respondents found their health good (8.28%) or very good (4.04%)

Table 4: Self-Reported Health Status of Respondents

Health Status	N	Percentage
Very Poor	198	40.00
Poor	149	30.10
Average	87	17.58
Good	41	8.28
Very Good	20	4.04
Total	495	100

 

• Occupational Health of Cab Drivers

Occupational health deals with the physical and psychological health of the employees. An employee is said to have good occupational health if he/she is not facing any physical or psychological problem due to his/her job profile, so this section presents the data aboutthe occupational health of Cab drivers in the following:

• Physical Health Status of Cab Drivers:Driving is a tough job which needs long sitting hours and that can cause so many health issues. So, cab drivers were given a list of health problems and they were asked to what extent they are facing these problems on a three-point scale i.e. mild, moderate and severe. The frequency distribution of results is presented in Table 5. The top three severe problems faced by cab drivers were neck pain (64.44%), backache (41.82%) and obesity (37.58%). On the other hand, the top three mild health problems were piles (40.20%), high BP (37.17%) and constipation (31.11%).

Table 5: Frequency Distribution of Health Problems faced by Cab Drivers

Health Problems	Mild		Moderate		Severe
Items	N	%age	N	%age	N	%age
Neck Pain	37	7.47	139	28.08	319	64.44
Back Ache	87	17.58	201	40.61	207	41.82
Blurred or double vision	118	23.84	249	50.30	128	25.86
Joint pain	107	21.62	271	54.75	117	23.64
High BP	184	37.17	205	41.41	106	21.41
Constipation	154	31.11	209	42.22	132	26.67
Obesity	91	18.38	218	44.04	186	37.58
Urinary Issues	124	25.05	217	43.84	154	31.11
Headache	152	30.71	238	48.08	105	21.21
Piles	199	40.20	194	39.19	102	20.61

 

To get the concrete opinion of cab drivers, for each health problem mean was calculated along with measures of dispersion as shown in Table 6. The data revealed that cab drivers were suffering from severe neck pain (mean=2.57) whereas the moderate health problems faced by drivers were Back Ache (mean=2.24), Blurred or double vision (mean=2.02), Joint pain (mean=2.02), High BP (mean=1.84), Constipation (mean=1.96), Obesity (mean=2.19), Urinary Issues (mean=2.06), Headache (mean=1.91) and Piles (mean=1.80). It can be observed that the coefficient of variation for all health problems ranged from 0.15 to 0.30, which shows homogeneity in the opinion of respondents.

Table 6: Mean, Standard Deviation and Coefficient of Variation of Health Problems faced by Cab Drivers

Health Problems	Mean	S.D.	C.V.	Nature
Neck Pain	2.57	0.39	0.15	Severe
Back Ache	2.24	0.54	0.24	Moderate
Blurred or double vision	2.02	0.50	0.25	Moderate
Joint pain	2.02	0.45	0.22	Moderate
High BP	1.84	0.56	0.30	Moderate
Constipation	1.96	0.58	0.29	Moderate
Obesity	2.19	0.52	0.24	Moderate
Urinary Issues	2.06	0.56	0.27	Moderate
Headache	1.91	0.51	0.27	Moderate
Piles	1.80	0.57	0.32	Moderate

 

After summing up the scores of individual items Table 7 shows the overall physical health of Cab drivers. According to results around 1/3rd ofthe drivers (31.52%) were suffering from a bad state of physical health whereas the majority of respondents (43.23%)hadaverage physical health which can be considered neither good nor bad. Around 1/4^th of the Cab drivers (25.25%) were found to enjoy good overall physical health.

 

Table 7: Overall Physical Health of Cab Drivers

Overall Physical Health	N	Percentage
Good	125	25.25
Average	214	43.23
Bad	156	31.52
Total	495	100

 

As this study has taken Ola and Uber drivers into consideration so to check the significant difference in the physical health of Ola and Uber drivers following hypothesis has been taken:

H₀1: There is no significant difference in the overall physical health of Ola and Uber drivers

H_a1:There is a significant difference in the overall physical health of Ola and Uber drivers

Table 8 depicts the physical health status of Ola and Uber drivers. It can be seen that the majority of Ola drivers (54.34%) had average physical health whereas the majority of Uber drivers (37.32%) had bad physical health. The mean scores were almost the same and the t-test applied to measure the difference indicated no significant difference in the physical health status of Ola and Uber drivers.

Table 8: Overall Physical Health of Ola Cab Drivers v/s Uber Cab Drivers

Overall Physical Health	Ola		Uber
	N	Percentage	N	Percentage
Good	47	21.46	78	28.26
Average	119	54.34	95	34.42
Bad	53	24.20	103	37.32
Total	219	100	276	100
Mean	1.97		1.91
Standard Deviation	0.89		0.71
t-value	0.834
p-value	0.06
Result	Not Significant

Level of Significance=5%

 

• Psychological Health of IT Employees:Same as thephysical health section cab drivers were given the list of psychological problems and they were asked that till what extent they are facing these problems on a point scale i.e. mild, moderate and severe. The frequency distribution of results is presented in Table 9. The top two severe problems faced by cab drivers were stress (70.71%) and anger (54.14%). On the other hand, the top two mild psychological problems were memory loss (20.40%) and depression (20%)

Table 9: Frequency Distribution of Psychological Problems Faced by Cab Drivers

Psychological Problems	Mild		Moderate		Severe
Items	N	%age	N	%age	N	%age
Stress	54	10.91	91	18.38	350	70.71
Anger	79	15.96	148	29.90	268	54.14
Memory Loss	101	20.40	259	52.32	135	27.27
Overthinking	97	19.60	257	51.92	141	28.48
Depression	99	20.00	201	40.61	195	39.39

 

 

To get the concrete opinion of cab drivers, for each psychological problem mean was calculated along with measures of dispersion as shown in Table 10. The data revealed that cab drivers were suffering from severe stress (mean=2.60) and anger (mean=2.38). Whereas the moderate psychological problems faced by drivers were memory loss (mean=2.07), overthinking (mean=2.09) and depression (mean=2.19). It can be observed that the coefficient of variation for all problems were ranging from 0.18 to 0.25, which shows homogeneity in the opinion of respondents.

Table 10: Mean, Standard Deviation and Coefficient of Variation of Psychological Problems faced by Cab Drivers

Psychological Problems	Mean	S.D.	C.V.	Nature
Stress	2.60	0.46	0.18	Severe
Anger	2.38	0.56	0.23	Severe
Memory Loss	2.07	0.47	0.23	Moderate
Overthinking	2.09	0.47	0.23	Moderate
Depression	2.19	0.56	0.25	Moderate

 

After summing up the scores of individual items Table 11 shows the overall psychological health of Cab drivers. According to the results, 44.04% of Cab drivers were suffering from a bad state of psychological health whereas 38.59% of respondents had average psychological health which can be considered neither good nor bad. Only 17.37% of Cab drivers were found to enjoy good overall psychological health.

 

Table 11: Overall Psychological Health of Cab Drivers

Overall Psychological Health	N	Percentage
Good	86	17.37
Average	191	38.59
Bad	218	44.04
Total	495	100

 

This study has taken Ola and Uber drivers into consideration so to check the significant difference in the psychological health of Ola and Uber drivers following hypothesis has been taken:

H₀1:There is no significant difference in the overall psychological health of Ola and Uber drivers

H_a1:There is a significant difference in the overall psychological health of Ola and Uber drivers

Table 12 depicts the psychological health status of Ola and Uber drivers. It can be seen that the majority of Ola drivers (47.95%) had average psychological health whereas the majority of Uber drivers (60.87%) had bad psychological health. A T-test was applied to check the significant difference in the psychological health of Ola and Uber drivers and the result was found to be significant which means there is a significant difference in the overall psychological health of Ola and Uber drivers. The mean of Uber drivers (1.47) is less than the mean of Ola drivers (2.06) so it can be inferred that Uber drivers have worse psychological health as compared to the Ola drivers.

Table 12: Overall Psychological Health of Ola Cab Drivers v/s Uber Cab Drivers

Overall Psychological Health	Ola		Uber
	N	Percentage	N	Percentage
Good	64	29.22	22	7.97
Average	105	47.95	86	31.16
Bad	50	22.83	168	60.87
Total	219	100	276	100
Mean	2.06		1.47
Standard Deviation	1.04		0.98
t-value	6.474
p-value	0.000
Result	Significant

Level of Significance=5%

 

• Overall Occupational Health of Cab Drivers:As already discussed the combination of physical and psychological health of cab drivers defines their occupational health, so Table 13 presents the overall occupational health status of Cab drivers. As per the results,only 25.25% of cab drivers had good physical health and 17.37% of cab drivers had good psychological health. So combined results highlighted that the majority of respondents (41.01%) had average occupational health and 37.78% of cab drivers were suffering from bad occupational health.

 

Table 13: Overall Occupational Health of Cab Drivers

Health Status	Physical Health		Psychological Health		Overall Occupational Health
	N	Percentage	N	Percentage	N	Percentage
Good	125	25.25	86	17.37	105	21.21
Average	214	43.23	191	38.59	203	41.01
Bad	156	31.52	218	44.04	187	37.78
Total	495	100	495	100	495	100

 

• Well, Being the Status of Cab drivers

The well-being of an employee depends on his/her occupational health. For example, the one who has good occupational health will also havegood well-being and vice-versa. Table 14 shows the well-being status of Ola and Uber drivers. As per the results, 23.29% of Ola drivers were in a bad state of well-being whereas 25.57% of respondents were in a good state of well-being. In the case of Uber driver’s majority of drivers (48.91) were suffering from a bad state of well-being and only 18.12% of drivers were enjoying a good state of well-being.

To test the difference in well well-being status of Ola and Uber drivers following hypothesis has been taken:

H₀3:There is no significant difference in well well-being status of Ola and Uber drivers

H_a3:There is a significant difference in well well-being status of Ola and Uber drivers

To test this hypothesis t-test was applied and the results received are presented in Table 14. As the t-t-t-statistic is significant it leads to the rejection of the hypothesis and it can be concluded that there is a significant difference in well well-being status of Ola and Uber drivers. As the mean of Ola drivers (2.02) is more than the mean of Uber drivers (1.69) it can be concluded that Ola drivers havebetter well-being than the Uber drivers.

Table 14: Well-Being Status of Cab Drivers

Well Being	Ola		Uber
	N	Percentage	N	Percentage
Good	56	25.57	50	18.12
Average	112	51.14	91	32.97
Bad	51	23.29	135	48.91
Total	219	100	276	100
Mean	2.02		1.69
Standard Deviation	1.07		1.01
t-value	3.516
p-value	0.000
Result	Significant

Level of Significance=5%

 

Findings:

1. The results indicated that Cab drivers are suffering from so many health problems like neck pain, backache, blurred vision etc.
2. Only 1/4^th of the cab drivers had good physical health otherwise the physical health of the rest of the drivers was either average or bad. No significant difference was observed in the physical health of Ola and Uber drivers
3. Analysis revealed that stress, anger and depression are the major psychological problems being faced by the cab drivers.
4. It was observed that the majority of cab drivers had bad psychological health and the psychological health of Uber drivers was worse than the psychological health of Ola drivers.
5. Results highlighted that the majority of respondents (41.01%) had average occupational health and 37.78% of cab drivers were suffering from bad occupational health.
6. The results of the t-test indicated that Ola drivers are in a better well-being state than Uber drivers

Discussion of Findings:

The well-being of workers in non-traditional employment is greatly impacted by a complex interplay of factors, as this study on occupational health in the gig economy reveals. Key findings from a variety of angles are summarised in this conversation, which highlights the opportunities and problems that come with working in the gig economy.

1. 1. Hazards to Physical Health: The study emphasises the unique physical health risks that gig workers, in all industries, face. Increased risks of musculoskeletal problems and fatigue are caused by irregular working hours, a disregard for ergonomics, and restricted access to occupational health and safety measures. Physically demanding industries have a more noticeable effect on employees' physical health.

2.Mental Health Challenges:Results point to a complex interaction between mental health and gig work. The independence and adaptability that come with gig work can be beneficial to mental health, but there are also drawbacks, such as social isolation, erratic income, and lack of job security. The study highlights the need for specialised interventions by identifying differences in mental health outcomes across sectors.

3. Job Satisfaction: Gig workers exhibit varying degrees of job satisfaction, according to comparative analyses. Important considerations in compensation models include things like perceived fairness, benefits, and stable income. When compared to gig workers in platform-based task jobs, creative freelancers report higher levels of job satisfaction, suggesting the significance of task diversity and personal autonomy.
4. Social Support and Work Environment: Social support networks play a significant role in reducing the occupational health risks that gig workers face. Individuals who work in collaborative environments and have strong social connections typically report higher levels of well-being. On the other hand, remote gig workers indicate a stronger desire for networking events and community-building programmes.
5. Platforms and Technology: The use of digital platforms in particular is one area where technological advancements are having a significant impact on the occupational health landscape of the gig economy. Platforms make it easier to find jobs, but they also impose algorithmic control, which reduces worker autonomy. The report suggests striking a careful balance between protecting worker rights and utilising technology to increase efficiency.
6. Sectoral Variances: Occupational health outcomes are significantly impacted by sector-specific nuances. For example, gig workers in the creative industry stress the value of personal fulfilment and task variety, while those in the transportation industry emphasise physical strain and job insecurity. Comprehending these sectoral disparities is imperative in customising interventions to tackle the distinct obstacles present in every industry.

Conclusion & Recommendations:

In conclusion, this comparative study illuminates the intricate relationship between gig work and workforce well-being. By identifying patterns, disparities, and sector-specific challenges, the findings contribute to an understanding of occupational health in the gig economy. The study serves as a basis for evidence-based policy recommendations meant to improve gig workers' occupational health. Implementing industry-specific safety guidelines, creating social support initiatives, and creating regulatory frameworks that strike a balance between flexibility and worker protection are some of the recommendations. As the workforce changes and becomes more resilient in the modern labor market, stakeholders might prioritize addressing these health and resilience issues in policies and initiatives developed as a result of this research.

 

Acknowledgement

Funding Details

This research received no external funding.

Authors' contributions

All authors contributed toward data analysis, drafting and revising the paper and agreed to be responsible for all the aspects of this work.

Declaration of Conflicts of Interests

Authors declare that they have no conflict of interest.

Availability of data and materials

Not Applicable

Use of Artificial Intelligence

Not applicable

Declarations

Authors declare that all works are original and this manuscript has not been published in any other journal.

 

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