Keywords

Business cycle; Job satisfaction; Foreign direct investment (FDI); Technology advancements; Government policy; Employee motivation.

Introduction

Today's competitive business scenario is highly dynamic and technology-driven. The automobile industry is a leading industrial sector that contributes a significant portion of GDP share to the country's development. Technology has bought about essential changes in the automobile industry. Industry research, innovation and development have made room for producing self-driving vehicles in the developed world. Autonomous emergency braking (AEB) is a feature found in most modern vehicles. The car industry has entered a time of flux and volatility due to government regulation on pollution control, technological advancements, and rising oil prices. Vehicle manufacturers have responded by developing several internal combustion engines (ICE)-alternative power trains (Sierzchula et al., 2018). Artificial intelligence, IOT, Block chain has bought massive innovation in the auto industry.

Emerging technologies create jobs for skilled workers and it also reduces manual work, risk of error, enhances productivity and generates more employment opportunities for knowledge and skilled workers. Thus up-skilling employees based on technology advancement has a possible positive impact on employee intention to stay.

Employees in the organization should cope with the technology change even though technological advancement is so drastic and disruptive. Larry (2017) stated that we want to create technology that everyone enjoys using and has an impact on livelihoods. We want to build services and applications that are so stunning and intuitive that people use them twice a day.

Some organizational changes necessitate significant restructuring, resulting in considerable life changes for a number of employees. Salary reductions, loss of benefits, job downgrading, job loss, or relocation to another city, state are common changes that have a negative impact on a portion of the workforce. All of these changes can be devastating to employees. Thus, frequent changes in company practices due to government policy framework and work profile affects job satisfaction, influencing voluntary employee turnover.

Consumers, on the other hand, are continually expecting greater product features, service, while expecting lower prices, forecasting future demand is getting increasingly challenging. New channel acquisitions and distribution network adoptions are becoming more widespread. Many businesses strive to cut their manufacturing costs as much as they can under the influence of globalisation. The automobile manufacturers are under pressure in the market, as low demand and consumer spending have reduced the footfall. Higher fuel prices are also impacting the industry from time to time.

An organization must grow and adapt to the changing environment in order to sustain itself globally and retain employees. Existing literature suggests that employee satisfaction with an organization's working conditions and changing environment may result in outcomes such as low job satisfaction or employee’s intention to quit jobs. Job role change purely depends upon the organization's positive and negative assessment of an employee as a change initiative. Businesses can reduce employee turnover by offering proper development programs, rewarding workers for a well-performed job, and cultivating a trusting company culture. This study investigates the business cycle impact on employee turnover, including external, internal factors, FDI, consumer demand and advancement in technology.

Literature Review

Employee turnover has been recognized as a critical problem for every business enterprise in the age of globalization. Organisations want to increase productivity and decrease employee turnover in order to become more profitable. Reducing employee turnover is a requirement for achieving organizational objectives. High turnover will be disastrous for the company in terms of both direct and indirect costs. To combat the pressures of the competitive business environment, management has identified employee turnover as a significant problem and has taken steps to keep them on board (Chowdhury & Md. Nazmul, 2017).

Krishnan et al. (2013) stated that replacing staff is not only expensive but also impacts the reputation of an organization. An organization with a high turnover finds it challenging to attract new talent. No one feels trustworthy dealing with a company which cannot hold onto its employees. It proposes instability, poor management, and a lack of good planning. In addition to the bitterness of the employee during their exit (voluntary or involuntary), the organization is drained of key knowledge and skills learned by the employee on the job.

The retention of employees is tremendously important in a global context. Based on their turnover rate, the majority of companies are evaluated. The turnover rate affects an organization's performance. Low revenues ensure that the organization retains its competent staff by providing them with a superior environment that increases employee performance (Waleed et al., 2013).

Maintaining a strong employer-employee relationship in today's competitive world, where there is a considerable shift in technology, trade agreements, and an increase in the cost of living, is a struggle for any organisation (Gurunathan & Vijayalakshmi, 2012).

Traditional production is exponentially accelerating technology throughout the world with digital transformation (intelligent robotics, autonomous drones, sensors, 3D printings). The pace of change reflects the 'Moore law' on how rapidly changes are driven by information technology. Companies and industries need to change quickly to save themselves from their competitors and to cope with recent development (Deloitte, 2014). Thus, upgrading and innovative government policies will produce a better product, more proficiently, and move into a more skilled zone which will create stickiness among employees.

The automobile industry is always one of the first to adopt new technologies. Every year, new methods are developed to make automobiles safer, easier to repair, and even more efficient. These are some of the most powerful technologies that have influenced the modern automobile industry.

Meanwhile, new manufacturers are entering the market, displacing critical components of the established OEMs. As more start-ups enter the e-mobility market, competition will heat up even more in the future. As a result of this reversal of trend, capabilities from previous hybrid solution developments will have a more significant impact said (Schönfeld, 2018). These changes require the organization to make modifications that can promote productivity, and otherwise, enhance organizational growth (Daniel, 2019).

Park et al. (2018) Countries all over the world are working hard to develop and discover alternative fuel sources and new technology to minimise their reliance on fossil fuels due to increased energy consumption, variable fuel prices, and strict air pollution restrictions in the road transport sector. Furthermore, in order to reduce hazardous emissions to the air in city centres and the usage of fossil fuels, an emphasis has recently been placed on the development of cleaner alternative fuels generated from renewable sources, as well as the enhancement of hybrid vehicles. This is linked to many other significant and crucial concerns in business.

Wasif (2016) stated that employees are the most valuable assets and a critical component of any organization that manages all modes of production. It is human nature for employees to be satisfied or dissatisfied with the changes in the work they are assigned. If employees were not satisfied with the work they are assigned there will be low productivity, low organization commitment, leading to increase in the turnover rate. This is the organization's primary goal in developing and implementing policies that will allow an employee to be satisfied with their assigned job.

FDI plays an important role in emerging and developed countries during the globalisation period. Among the world's top economies, India has the most favourable and rapidly rising economy for attracting FDI. FDI has been highlighted as a vehicle for economic progress in our country, as it strengthens domestic capital, productivity, and employment. India has a big population and land mass, as well as a diversified culture and huge wealth differences between its regions. There is a large pool of managerial and high-tech talent in the country (Dhande & Magar, 2018).

Rajalakshmi and Ramachandran (2011) stated in India has become a popular destination for foreign direct investment (FDI) from the US and other countries in recent years. Its fast-growing economy, low salaries, and well-educated workforce have attracted FDI in the services and manufacturing industries to serve both Indian and third world markets.

Mishra and Palit (2020) stated that inflow of FDI increases job opportunities, foreign companies are allowed to set up their business in the host countries, they usually develop their own production methods. Therefore, employees in the host country are hired who are sufficiently skilled and efficient as per their production needs, and others are forced to lose their jobs. It is partly acceptable by referring to several articles that FDI inflows, indirectly increases unemployment in the host country.

Organizations are also having difficulty adapting to changing circumstances. An organization's culture has a substantial impact on its effectiveness by influencing how decisions are made, human resources are used, and people respond to environmental challenges. The factor organizational culture is taken into account on employee retention because organizational culture is increasingly being pushed as an essential selling feature as companies try to attract and retain valued employees (Anitha & Begum, 2016). When employees are adaptable to change in organization culture it will create stickiness in the organization.

The volatility of consumer demand within the various automobile segments has outpaced the overall market volatility. Hence, automobile manufacturers are under pressure in the market, as low demand and consumer spending have reduced footfall, while higher fuel prices discourage them from making new purchases which in turn lead to a reduction in production - consequently impacting employment opportunities. Standard emission norms are good for the environment, where a frequent change in emission norms will increase production cost affecting the business cycle.

Research Framework

Research framework was developed on the basis of literature study which is depicted below Figure 1.

Objective of the Study

The primary objective of the research is to investigate the changes in the regulatory framework and its impact on business cycles leading to voluntary turnover.

Hypothesis

H1: Regulatory framework and government norms have an impact on business cycles resulting in ET

H2: Changes in work nature have an impact on job satisfaction, which in turn may influence ET

Research Methodology

The study is descriptive in nature and is a combination of theoretical and empirical research. The structured questionnaire was designed, and data was gathered using linked-in. A total of 15 questions were posed, 14 of which were on the Likert scale and one of which was descriptive in nature. During the period, the sample was collected from employees of the automobile industry in Chennai (1^st March 2021 to 31^st May 2021). SPSS, a statistical tool, will be used to analyze the data collected. Correlation, regression, and Chi-square tests were used to determine whether or not the framed hypothesis was statistically significant. Dimension taken for the study are given in Table 1.

Table 1 Dimension of the Study
Q1	External factors impact on business
Q2	Change in government policy framework
Q3	Fuel efficiency standards
Q4	Change in emission norms
Q5	Struggles of export firm to week globalization
Q6	Frequent change in tax policy and regulatory issue
Q7	Impact of rise in fuel price
Q8	Change in company strategies
Q9	Shift in employee job role
Q10	Consumer demand and market dynamics
Q11	Change In production process and business verticles
Q12	Technological linkage on learning
Q13	Advancement in technology
Q14	Management investment on skill development
Q15	Reason for employee exit

Results

Reliability Statistics

Cronbach's alpha is a useful tool for determining the test variable's reliability and internal consistency. A total of 75 valid samples were collected for examination. According to Table 2, the value obtained is 0.707, which is a good and acceptable number.

Table 2 Reliability Statistics
Cronbach’s Alpha	Cronbach’s Alpha Bases on Standardized items	No of items
0.707	0.703	15

The descriptive statistics is depicted in Table 3 as shown above.

Table 3 Descriptive Statistics
		Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Q14	Q15
N	Valid	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
	Missing	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Mean		1.75	2.09	1.92	1.92	1.87	1.57	1.63	2.29	2.27	2.31	2.39	1.75	1.87	1.79	1.67
Std. error of Mean		0.095	0.108	0.107	0.128	0.118	0.066	0.08	0.138	0.141	0.131	0.146	0.097	0.11	0.108	0.124
Median		2	2	2	2	2	2	2	2	2	2	2	2	2	2	1
Mode		2	2	2	2	2	2	1	2	2	2	2	1	2	1	1
Std Deviation		0.824	0.932	0.926	1.112	1.018	0.574	0.693	1.194	1.223	1.139	1.262	0.84	0.949	0.934	1.07
Variance		0.678	0.87	0.858	1.237	1.036	0.329	0.48	1.426	1.495	1.297	1.592	0.705	0.901	0.873	1.144
Skewness		1.699	1.146	1.21	11.736	1.538	0.808	0.655	0.825	0.881	0.72	0.761	1.357	1.345	1.67	2.002
Std. Error of Skewness		0.277	0.277	0.277	0.277	0.277	0.277	0.277	0.277	0.277	0.277	0.277	0.277	0.277	0.277	0.277
Kurtosis		4.797	2.004	1.404	2.619	2.283	2.246	-0.694	-0.27	-0.107	0.033	-0.445	2.51	2.021	3.606	3.534
Std. Error of Kurtosis		0.548	0.548	0.548	0.548	0.548	0.548	0.548	0.548	0.548	0.548	0.548	0.548	0.548	0.548	0.548
Range		4	4	4	4	4	3	2	4	4	4	4	4	4	4	4
Minimum		1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Maximum		5	5	5	5	5	5	5	5	5	5	5	5	5	5	5

Pearson correlation matrix of Table 4; correlations were calculated to find the relationship between the variable. Most of the correlations are positively correlated. All the correlations are ranging from +1 to -1. The significant (2 tailed indicates the p - value associated with the variables.

Table 4 Correlation
		Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Q14	Q15
Q1	Pearson correlation	1	0.436**	0.203	0.14	-0.089	0.083	0.021	0.214	0.202	0.228*	0.174	0.121	0.112	0.104	0.01
	Sig. (2-tailed)		0.009	0.08	0.231	0.447	0.48	0.855	0.065	0.082	0.049	0.136	0.302	0.34	0.372	0.931
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q2	Pearson correlation	.436**	1	0.15	0.099	-0.015	-0.076	-0.134	.230*	0.227	0.189	0.095	-0.073	0.06	-0.101	0.208
	Sig. (2-tailed)	0.009		0.2	0.4	0.897	0.516	0.253	0.047	0.05	0.104	0.416	0.534	0.609	0.389	0.074
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q3	Pearson correlation	0.203	0.15	1	.440**	-0.083	0.113	0.226	.266*	.293*	0.139	0.096	0.043	0.111	-0.004	0.055
	Sig. (2-tailed)	0.08	0.2		0.007	0.478	0.335	0.051	0.021	0.011	0.235	0.412	0.714	0.345	0.97	0.642
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q4	Pearson correlation	0.14	0.099	.440**	1	0.038	0.136	-0.092	.303**	.473**	.254*	.234*	0.079	0.105	0.113	-0.068
	Sig. (2-tailed)	0.231	0.4	0.007		0.745	0.243	0.433	0.008	0.001	0.028	0.043	0.499	0.37	0.333	0.561
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q5	Pearson correlation	-0.089	-0.015	-0.083	0.038	1	0.017	0.12	0.099	-0.112	-0.221	0.062	-0.087	-0.075	-0.073	-0.066
	Sig. (2-tailed)	0.447	0.897	0.478	0.745		0.885	0.305	0.397	0.338	0.057	0.599	0.455	0.525	0.534	0.573
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q6	Pearson correlation	0.083	-0.076	0.113	0.136	0.017	1	.274*	0.126	-0.028	0.038	-0.124	.306**	.291*	0.206	-0.015
	Sig. (2-tailed)	0.48	0.516	0.335	0.243	0.885		0.017	0.281	0.81	0.749	0.29	0.008	0.011	0.076	0.9
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q7	Pearson correlation	0.021	-0.134	0.226	-0.092	0.12	.274*	1	0.15	-.232*	0.079	0.044	.276*	0.19	0.126	0.012
	Sig. (2-tailed)	0.855	0.253	0.051	0.433	0.305	0.017		0.198	0.045	0.503	0.71	0.016	0.102	0.282	0.918
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q8	Pearson correlation	0.214	.230*	.266*	.303**	0.099	0.126	0.15	1	.279*	.340**	.282*	0.169	0.142	-0.028	-0.007
	Sig. (2-tailed)	0.065	0.047	0.021	0.008	0.397	0.281	0.198		0.015	0.003	0.014	0.146	0.223	0.812	0.952
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q9	Pearson Correlation	0.202	0.227	.293*	.473**	-0.112	-0.028	-.232*	.279*	1	.358**	0.213	-0.052	0.054	0.003	0.048
	Sig. (2-tailed)	0.082	0.05	0.011	0.001	0.338	0.81	0.045	0.015		0.002	0.067	0.659	0.643	0.979	0.681
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q10	Pearson Correlation	.228*	0.189	0.139	.254*	-0.221	0.038	0.079	.340**	.358**	1	.330**	0.096	0.126	0.088	0.074
	Sig. (2-tailed)	0.049	0.104	0.235	0.028	0.057	0.749	0.503	0.003	0.002		0.004	0.41	0.282	0.454	0.528
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q11	Pearson Correlation	0.174	0.095	0.096	.234*	0.062	-0.124	0.044	.282*	0.213	.330**	1	0.004	0.077	-0.044	-0.083
	Sig. (2-tailed)	0.136	0.416	0.412	0.043	0.599	0.29	0.71	0.014	0.067	0.004		0.97	0.509	0.71	0.477
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q12	Pearson Correlation	0.121	-0.073	0.043	0.079	-0.087	.306**	.276*	0.169	-0.052	0.096	0.004	1	.720**	.481**	-0.095
	Sig. (2-tailed)	0.302	0.534	0.714	0.499	0.455	0.008	0.016	0.146	0.659	0.41	0.97		0.001	0.001	0.416
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q13	Pearson Correlation	0.112	0.06	0.111	0.105	-0.075	.291*	0.19	0.142	0.054	0.126	0.077	.720**	1	.623**	-0.058
	Sig. (2-tailed)	0.34	0.609	0.345	0.37	0.525	0.011	0.102	0.223	0.643	0.282	0.509	0.001		0.003	0.623
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q14	Pearson Correlation	0.104	-0.101	-0.004	0.113	-0.073	0.206	0.126	-0.028	0.003	0.088	-0.044	.481**	.623**	1	-0.14
	Sig. (2-tailed)	0.372	0.389	0.97	0.333	0.534	0.076	0.282	0.812	0.979	0.454	0.71	0.001	0.002		0.232
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75
Q15	Pearson Correlation	0.01	0.208	0.055	-0.068	-0.066	-0.015	0.012	-0.007	0.048	0.074	-0.083	-0.095	-0.058	-0.14	1
	Sig. (2-tailed)	0.931	0.074	0.642	0.561	0.573	0.9	0.918	0.952	0.681	0.528	0.477	0.416	0.623	0.232
	N	75	75	75	75	75	75	75	75	75	75	75	75	75	75	75

Test of Hypothesises

To answer the primary research question, the following hypotheses were developed.

H1. Regulatory framework and government norms have an impact on business cycles resulting in ET

The above hypothesis was tested to determine whether all of the above factors impact business, resulting in employee turnover. Frequent changes in the organization's framework and policies will have a negative impact on its development. It is extremely difficult for any organization to implement changes quickly. Employees, on the other hand, will struggle to adjust to the changing environment. Thus, changes in government policy framework are affecting industrial growth, consequently, leading to employee turnover. Change in tax policies; regulatory issues and procedures will have an impact on the performance of small and medium manufacturing enterprises which in turn impacts the industry.

Model Summary

Total Direct effects on path model: R= 0.3726; P=0.001; R-SQUARE= 0.1389; CO-EFF= 0.4121; standardized coefficient = 0.3726; SE=0.1201; T=3.431

We see that in the first (simple) regression, Regulatory framework and government norms is a significant (positive) predictor of employee turnover (b= 0.4121, s.e. = 0.0.1201, p = 0.001<0.05) (Figure 2). The coefficient reflects the direct effect of regulatory framework on Employee Turnover within the path model. (Notice that the standardized path coefficient is also provided, which is 0.3726.)

Regulatory framework and government norms is a significant predictor of employee turnover

Analysis on Regulatory Framework on Business Cycle

R = 0.1040; P = 0.3746; R-SQUARE = 0.0108; CO-EFF = 0.1074; standardized coefficient = 0.1040; SE = 0.1202; T = 0.8934

We notice that in this regression, regulatory framework on mediator business cycle is a not - significant (positive) predictor of Business cycle (b = 0.1074, s.e. = 0.0.1202, p = 0.08934>0.05) (Figure 3). This coefficient reflects the direct effect of Regulatory framework on Business Cycle within the path model. Notice that the standardized path coefficient is also provided, which is 0.1040.

Regulatory framework and government norms is not significant predictor of business cycle

Total, Direct and Indirect effect of X on Y

Total effect is significant 0.4121, where p value is 0.001 which is less than 0.05. Here direct effect is also significant the effect value is 0.3899 because the p value is 0.0016 which is less than 0.05 (Table 5). Total effect of regulatory norms and government framework is significant predictor of employee turnover. Direct effect of regulatory norms and government framework is significant predictor of employee turnover.

Table 5 Total & Direct Effect of X on Y
	Effect	se	T	P	LLCI	ULCI	C’_PS	C’_CS
Total effect of X on Y	0.4121	0.1201	3.431	0.001	0.1727	0.6515	0.5005	0.3726
Direct effect of X on Y	0.3899	0.119	3.2772	0.0016	0.1527	0.627	0.4735	0.3525

Table 6 shows the indirect effect s of X on Y.

Table 6 Indirect Effects of X on Y
	EFFECT	BOOTSE	BOOTLLCI	BOOTULCI
BS_CY_av	0.0222	0.327	-0.328	0.1009

The overall impact of X on Y, computed as DE + IE = 0.3899 + 0.0222 = 0.4121. Because zero (the null) does not fall between the lower and upper bound of the 95% confidence interval, we infer that total effect of Regulatory framework on Employee Turnover is significantly different from zero. It is found that all direct effects, partial direct effects, and total effects are statistically significant as null does not appear between the lower and upper limits. All partial indirect effects are insignificant as null is included between lower and upper control limits (Figure 4).

Indirect effect of regulatory norms and government framework is not significant predictor of employee turnover.

H2 Changes in work nature have an impact on job satisfaction, which in turn may influence ET

If work profile changes frequently in the organization, the productivity of the employees will have an impact. Proper training will be required at all the stages; investing in employees is very important for the organization's development and employee job satisfaction. Technology is redefining work, the work place and having a linkage on learning, growth and employee retention. Therefore, advancement in technology increases organizational productivity and employee satisfaction resulting in employee’s intention to stay. Therefore, management investment on skill, knowledge and behavioural development enhances value to employees and creates stickiness and stability.

Table 7 shows total and direct effect of X on Y.

Table 7 Total and Direct Effects of X on Y
	EFFECT	SE	T	P	LLCI	ULCI	C’_PS	C’_CS
Total effect of X on Y	-0.0352	0.1503	-0.2342	0.8155	-0.3348	0.2644	-0.0428	-0.0274
Direct effect of X on Y	-0.0521	0.1516	-0.3437	0.7321	-0.3543	0.2501	-0.0633	-0.0405

The total effect of X on Y, computed as DE + IE = -0.0521 + 0.0169 = 0.1503. Because zero (the null) falls between the lower and upper bound of the 95% confidence interval, we infer that total effect of Changes in work nature on Employee turnover is not-significantly different from zero.

Total effect of change in work

Total effect of Change in work nature is not a significant predictor of employee turnover.

Direct effect of Change in work nature is not a significant predictor of employee turnover

Table 8 shows the indirect effect s of X ON Y

Table 8 Indirect Effect of X on Y
	EFFECT	BOOTSE	BOOTLLCI	BOOTULCI
Job satisfaction	0.0169	0.0306	-0.0160	0.1036

The un-standardized indirect impact (0.0169) of job satisfaction is calculated as the product of paths ‘a’ (0.1470) and b (0.1149) from the previous regression models (Figure 5). This indirect effect is tested using bootstrap standard errors and confidence intervals. The total effect, direct effect, indirect effect none of them were statistically significant.

Findings

• Regulatory norms and government framework has significant impact on employee turnover.

• Business cycle is not affecting the relationship between government regulatory norms and employee turnover.

• Total effect of regulatory norms and government framework is significant predictor of employee turnover. (Refer Table 5)

• Direct effect of regulatory norms and government framework is significant predictor of employee turnover. (Refer Table 5)

• Changes in work practices have no impact on employee turnover. (Refer Table 7)

• Changes in work practices have no impact on job satisfaction. (Refer Table 7)

• Employee satisfaction has no impact on employee turnover. (Refer Table 7)

• Total effect of Change in work nature is not a significant predictor of employee turnover.

• Direct effect of Change in work nature is not a significant predictor of employee turnover.

Conclusion

The current study concluded that this piece of research has examined the internal and external changes in government norm, practices, framework, and technological advancement that impact the business cycle, resulting in employee turnover. Managing employee retention is a critical strategic action to keep employees motivated ensuring continuous training and development and engagement which are essential factors in retaining employees.

Singh and Jain (2013), states when it comes to managing their staff, one of the most challenging areas for today's managers is job satisfaction. In order to motivate their employees, policymakers and managers have shifted their attention to providing various incentives to them. Employee job satisfaction can be increased by providing a positive work atmosphere and good working circumstances. Under such circumstances, employees will strive to do their best, which will enhance value to the organisation.

Employee morale, motivation, and other factors are affected by changes in the business cycle. Given regulatory changes, norms, and reforms, it is preferable to retain trained and experienced resources. Furthermore, replacing employees is expensive and difficult because talent is scarce. If they can't prevent change, employees will look for avenues to adjust to the new circumstance or exit. Employees if they lose interest in their work and do not "adapt" or "fail to adjust" to organisational change, it will impact productivity and morale at the workplace. As a result, any organisational changes must be communicated to staff and they have to be taken into confidence before rolling out change measures.

Organizational change practitioners agree that clear and concrete change communication is essential for generating favourable employee response to change. Transparent change communication appears to be the only way to ensure favourable employee outcomes. This is merely a measure of an employee's willingness and competence to step in when management fails (Paula, 2017).

Regulatory framework and government norms and business cycle independently impact on employee turnover. However changes on work practices and job satisfaction have found not to be having an impact on employee turnover. This study improves our grasp of this conceptual structure, encompassing economic considerations and the business cycle, both academically and conceptually.

To conclude reforms are required, government policy changes are to be consistent with global trends for unification and harmonisation of laws. But the key to success of any enterprises is its ability to communicate the changes, help employees to adapt, up-skill and rotate jobs to ensure employees remain motivated, embrace change and focus on development at all times. This will avoid stress owing to uncertainty and prevent employee turnover.

Scope for Further Studies

The sample size could be larger and the coverage could be extended to the whole geography of India. Also, various other impact factors could be considered within the same scope for enhancing the value of the study. Impact of globalisation in the pandemic scenario could also be probed for finding new insights.

Limitations

• The study was carried out during the pandemic situation, personal meetings were not possible and all interactions were over email or phone

• The research was restricted to Chennai and its surrounding industrial belts

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