“The impact of the COVID-19 pandemic on retailer performance: empirical evidence from India”

The study aims to synthesize the challenges that retailers are facing during the COVID-19 emergency. The research is definitive, informative, and based on a single design of cross-sectional research. Quantitative data based on the research instrument were produced (a questionnaire). Five hundred responses were collected from employees of major retail stores in India. Retailer performance is considered a dependent variable, whereas employee well-being, customer and brand protection, use of technology, government policies, and supply chain are used as independent variables. The current study results indicated that employee well-being and government policies have a significant positive impact on retailer performance, while customer and brand protection, use of technology, and supply chain have a significant positive impact on retailers’ performance. This study will help retailers develop strategies for their employees to protect them and understand that technology is needed in the new normal times. This study highlights the need to be flexible in executing strategic strategies, but retailers need to develop comprehensive action plans, including selecting managers of initiative and defining goals and deadlines. Provided that retailers’ current reality is different from the old normal, no time is lost in taking audacious action.


INTRODUCTION
The retail market of India has over 15 million retailers, both small and large, both conventional and new.Retail hires between 40 and 50 million Indians, with more than six million Indians directly employed through international commerce, contributing almost 12 percent of the country's overall retail consumption.Retail improves roughly.The shutdown to deter the spread of coronavirus in India affected retail businesses significantly -40% of the Indian intake and 10% of the Indian GDP.Most of the stores were shut throughout the country, except stores selling essential food and medicine.Almost all shops have closed down clothes, saris, electronics, cell phones, furnishings, equipment, etc.The revenue decline of non-grocery/food stores is 80 to 100%.However, distributors selling essential goods face risks if they cannot market non-essential goods, meaning that they have better profits.During the pandemic during an economic recession, Indian retailers, especially non-essential products such as clothing and jewelry, were affected (income hit by 75% by the end of March 1).With most national officials halting their operations, many shops are either completely closed or still operating grocery stores.India's 40%-contributed retail sector was affected by COVID-19 in the amount of $30 billion over two weeks ended on April 1, 2020, as given by the CAIT.The company had declined by the end of February 2020 to 20-25%, according to the Retailers Association of India (RAI).It has now decreased to 15 percent over the last month and a half.Indian retailers will need to develop strategies to reduce the disturbance caused by the COVID-19 crisis, 'next' and 'done.'Retailers will urgently focus on managing vital market practices and retaining respect inspired the large workforce.When retailers are adapting to the fluidity of "here," they need to create "next" resilience as the competition starts to recover and "believe".They should simultaneously plan for long-term impacts on a strategic journey towards financial resilience.No one will, with certainty, estimate the financial effect of this situation on the market with the constantly changing COVID-19 pandemic.The actual effect and recovery will depend on the recession time and the level of behavior.Nonetheless, it will check both distributors and those who can respond financially stable, flexible, and responsive employees.
The COVID-19 pandemic (started in early 2020) triggers many short-and mid-term disruptions for companies to adapt.Some retailers responded to the emergency immediately, displaying a variety of different forms of action.The article aims to understand the reaction of the workers to COVID-19 in the retail sector.The retail sector has taken measures to protect the health of workers.The retail sector aims to adapt to technological developments to make it easier for both staff and consumers.The retail industry follows government guidelines.The study found that employee well-being and government policies significantly and positively impact retailer performance, while customer and brand protection, use of technology, and supply chain have a significant positive impact on retailer performance.This review identified a need to be agile in implementing strategic initiatives, but retailers need to develop detailed action plans, including selecting initiative managers and the specification of targets and deadlines.Given that the new reality of retailers is different from the old standard, no time is wasted in taking bold action.

LITERATURE REVIEW AND HYPOTHESES
In the hope of minimizing deaths in various ways but with significant adverse effects for national economies, industries, and individual workers, India had locked-up wide segments of its markets.
Estimates of economic effects are early in many countries, but some developing markets are considered significant and even catastrophic (Evans, 2020).The Chinese economy is expected to have reached 3.2 percent in 2020 (Fernandes, 2020), a couple of months or so more than other nations.For the US, GDP growth will decrease in the course of its 1.5-month containment strategy by 2.9 percent in 2020 (Fernandes, 2020;Makridis & Hartley, 2020).Fernandes (2020) is projected that in the case of a moderate lockout (1.5 months), GDP growth in most countries will decline by between three and five percent by 2020.An additional two to 2.5% of global GDP growth occurs each month of the shutdown.The retail, hospitality, and touristic sectors will be especially affected (Dolnicar & Zare, 2020) and the hospitality and tourism industries (Fernandes, 2020).More jobs at risk will impact more service-driven markets (Fernandes, 2020).China suggests that the "return to normality" will be followed by a rapid uptick in demand due to market demands, but the turnaround can be confined to a very short-term effect (Deng et al., 2020) restoring the usual rate of psychological anticipation in retail environments.
Nevertheless, the COVID-19 pandemic is global, and since World War II, the democracy of people has not been subject to such drastic restrictions as those imposed by previous democratization regimes, adopted by certain countries (e.g., Italy) full shutdown tactics.In contrast, others restricted the number and activities of companies and utilities (e.g., UK), while others implemented security measures that substantially restrict citizens' right to leave home.Such initiatives seriously affected the daily lives of customers and businesses; reduced tourism led to emergency buying situations (EPS) (Somason & Voyer, 2014) and posed unparalleled retail management challenges.Consumers naturally go shopping differently due to extraor-dinary controls.The "scarcity effect" (Hamilton et al., 2019) has had a dramatic impact on-demand elasticity and storage practices, whereas conventional dissuasions such as "waiting times and perceived crowding are now accepted", as evidenced by long queues entering shops.Many supermarket retailers distribute food availability messages and restrict the number of products to purchase per customer, introduce new forms of online services and home delivery, but often with unsatisfactory results for customer well-being.
The latest research has substantially affected customer preference due to the perceived lack of goods (Hamilton et al., 2019).While the literature accepts that scarcity increases the usefulness of the inaccessible good (Verhallen & Robben, 1994), the shortage of capital leads either to a relative increase in the worth of the scarce goods or to a decrease in the quality of the purchasing sense (Shah et al., 2015).However, the continued loss of brands (because of inventory outages) and shops (because they shut down) could exacerbate customer habits, ultimately impacting the option of commodity shares long after shops return to normal.Panic has also increased the price response of customers.Several determinants of the demand elasticity of goods have been extensively studied in previous literature (Huang et al., 2017;Wakefield & Inman, 2003).The lengthy waiting times in an emergency are not expected to have a negative effect on customer loyalty with the shops.Nevertheless, it is not clear whether the consequences of condition variables remain over time.For example, customer satisfaction with the time spent waiting comes from the contrast with the length of the queue (Kumar, 2005).
The reduced efficiency of the facilities, combined with higher safety issues for customers, has resulted in a growing need for alternate supply sources.Unforeseen laws that enforce social isolation have a huge effect on the preferred shopping platform of consumers.Specifically, although there has been steady but limited growth in online shopping in the last decade (Harris et al., 2017) The study aims to understand the employee's response to COVID-19 in the retail sector.The retail industry has been taking measures to protect the employees' health.The retail industry is trying to adapt to technological changes to make things easier for the employees and the customers.The retail industry is following government guidelines.The following hypotheses were formulated for a more in-depth and thorough study of the set goals: H 0a : There is no significant difference between employee well-being and retailer performance during the COVID-19 pandemic.
H 0b : There is no significant difference between customer and brand protection and retailer's performance during the COVID-19 pandemic.
H 0c : There is no significant difference between the use of technology and retailer performance during the COVID-19 pandemic.
H 0d : There is no significant difference between the government's guidelines and retailer performance during the COVID-19 pandemic.
H 0e : There is no significant difference between supply chain and retailer performance during the COVID-19 pandemic.

METHODS
The data are collected from 500 employees of the organized retail sector in India.The data are collected from the employees of Spencer, Big Bazaar, Future group, and Reliance Retail.A questionnaire was used for data collection.The Likert scale of five points, which ranges from strongly agreed to disagree, measured two major proportions, including the impact of COVID-19 on the retail sector and employees of the retail sector.Each variable had a certain set of questions.The paper aims to know whether there is a significant difference between the impact of COVID-19 and the retail industry of India.SPSS version 23 was used for the data processing, and all experiments were performed in a 5 percent sense standard.Descriptive figures identified the key demographic characteristics of the survey.
There have been 500 absolute and open responses collected from various regions in India.The above table shows that the male respondents were higher than the females.The majority of the customers were in the age group of 20-30 years old.The respondents were senior secondary graduates.The employees had 5-10 years of association with the particular store.

Kaiser-Meyer-Olkin (KMO)
The KMO method is used to check the suitability of the sampling for the EFA (Barrett et al., 2011).The KMO value for sampling adequacy is expected to be greater than 0.50 as per Ferry  .000

Total variance explained
As shown in Table 3, the six constructed factors explained 78.483 of the total variances.The six factors explain about 51.454% of the variance.
The second factor explains about 63.464, third factor explains about 68.717, fourth and fifth factors represent 72.653 and 3.980%, respectively, and the last factor explains about 78.483.Table 3 also shows that the total variance explained (TVE), and it can be observed that the eigenvalue for the first factor is good, i.e., 11.145, and the cumulative total variance explained for all the variables was 78.483.

Exploratory Factor Analysis
Factor analysis is the main focus of data reduction and definition analysis.This mathematical method helps to reduce the number of correlated variables independently.The relationship between many different variables can be analyzed using a set of underlying dimensions known as factors or parameters.Exploratory Factor Analysis (EFA) was used to incorporate less of the variables or factors of the original variables to improve the definition.As shown in Table 4, nine items test different structures under analysis.

Reliability analysis
Nunnally (1967) describes the measures as precision, while the explanation for the mistake is some influence differentiating between measurement situations.Reliability means how a system achieves comparable outcomes continuously.Based on the total reliability considered for each element (Hogan, Benjamin, & Brezinski, 2000), Cronbach's alpha is a typical reliability metric.A wide-scale indicates the value of Cronbach's alpha greater than 0.7 (Hair et al., 2010).The Cronbach's alpha value surpassed the amount agreed for every structure, as seen in Table 5.The validity of a system testing what is predicted is stated.An integration and exclusion will determine the utility of the procedure.Inter -item analysis of correlation follows convergent validity.As previous researchers suggested, the correlation values between the sample areas were 0.6 to 0.9 (Kalafatis, 2004).Psychometric scale tests thus ensured the intensity and reliability of the unit.The results show that Cronbach's alpha is the most reliable factor.The value is generally shown to be 0.6-0.8highly reliable.

Confirmatory Factor Analysis
Variables are incorporated into EFA variables, research being confined to factor loads, cross loads, and independence section.Lee et al. (2008) recommended that a Confirmatory Factor Analysis be carried out to resolve this cap set in EFA, which contains all variables in the CFA model after the Exploratory Factor Analysis and reliability evaluation.CFA is a study of factors that measures a direct correlation between latent variables underlying data and the structural hypothesis (Field, 2009).The CFA is a hypothesis-based technique in which the hypothesis establishes a relationship between the unknown and measurable variables.A theory is verified or denied by CFA.Following the covariance observed, the proposed model is used for estimating population covariance.The model is used to approximate covariations and interrelationships between latent houses.CFA is a valuable method for evaluating abstract logic (Reise et al., 1995).

Hypotheses testing results
Regression was used to test the relationship between different constructs of the proposed model.In regression, the maximum likelihood method was used for testing the hypotheses.The study has used regression to test the causal relationship between the COVID-19 pandemic and retailer performance.
The findings in Table 7 show that three significant hypotheses and two non-significant hypotheses are supported.H 0a shows a direct impact of employee well-being on retailer performance (β = 0.456, p = .000).β = 456 indicates that employee well-being affects the dependent variable retailer performance by about 45 percent.Therefore, a 1 percent increase in employee well-being will result in a 45 percent improvement in retailer performance.The p-value (p = 0.000) reveals the positive and direct relationship between employee well-being and retailer performance.Table 7 shows the regression analysis result used to determine the impact of customer and brand protection and retailers' performance during the COVID-19 pandemic.
The result shows the impact is positive (coefficient = 0.041).Further, this impact is statistically significant.Therefore, H 0b is not acceptable.
The regression analysis outcome was used to classify the impact of the use of technology and retailers' performance during the COVID-19 pandemic.
The result shows the impact is positive (coefficient = 0.008).Further, this impact is statistically significant.Therefore, H 0c is not acceptable.The result also shows that there is no significant difference between government policies and retailers' performance.The result shows a positive impact.Hence H 0d is acceptable.The table shows a significant difference between supply chain and retailers' performance during the COVID-19 pandemic.The result shows that the impact was positive.H 0e is not acceptable.

DISCUSSION
More than 15 million retailers in the Indian retail industry are small and large, traditional and new.Retail directly employs 40-50 million Indians, of whom modern trade employees more than 6 million Indians, equivalent to approximately 12% of the country's overall retail intake.Retail leads to approximately 40% of India's production and 10% of India's GDP.
Retail business has been greatly affected by the lockdown to limit the risk of COVID-19 in the region.Most supermarkets have been shut down throughout the country, except for stores selling vital food and groceries, clothing (including saris), electronics, cell phones, furniture, appliances, etc. Virtually all shops are closed.Nongrocery/food retailers are announcing revenue declines of 80 to 100 percent.Also, retailers with essential goods face losses because they cannot sell non-essential products, which would give them greater profits.
Around 85% of retail costs are fixed costs, which brings retailers under a range of financial stresses.The business faces significant funding problems that can lead to unemployment on a large scale.The industry's revenue stream has come to a halt, whereas the fixed operating expenses stay unchanged.The Retailers Association of India, concerned with the state of affairs, surveyed retailers (including groups and nongroups) to determine their views on the effect of COVID-19 on their company and workforce.

CONCLUSION
The key point of the crises mentioned above is that the recession had the most retail-oriented effects customer spending habits are expected to be irreversible.A long recession has a heavy effect on the buying behavior of customers.It takes much time for customers to stop purchasing, sales, and rehabilitation.That means retailers need to recognize that all of these habits are irreversible and must plan for a significant shift in their business model.Furthermore, the shift in behavior becomes less lasting as problems are handled easily, and customers can only re-start when they have to go shopping.Both food and drink have seen steady recovery at the end of the depression.However, rebound for discretionary categories such as clothes took time after the recession.In India, the authors expect that similar trends of recovery will emerge depending on the time of the crisis.Since COVID-19 has a medium-term effect on customer investment and will increase online spending, relevant, inexpensive categories and greater online penetration are expected to rebound more rapidly.The current study found that employee well-being and government policies have a significant positive impact on retailer performance.In contrast, customer and brand protection, use of technology, and supply have a significant positive on retailer performance.
In the present scenario, unorganized supermarket and district Kirana appear to be the winners.This paves the way for shops to be found in the future and further.Digital shopping continues to expand.In several markets, the recession leads to restructuring, and the retail sector will have an equal share of the effect (this time will be difficult to endure for smaller consolidated or financially constrained retailers) with few large retailers with a distinct premium plan.Retailers are compelled to worry about what they're all like when they enter places where they never went to float before during the ongoing recession.Some of these peripheries can continue to become their core.
Retailers are compelled to worry about what they are all like when they enter places where they never went to float before during the ongoing recession.Some of these peripheries can continue to become their core.Following demonetization, COVID-19 is a further catalyst for India's digital payments, with the public refusing cash transactions out of fear or comfort.In general, this should remind organizations that such an unforeseen event will occur anywhere and may involve strategic preparation if firms want to continue to help their clients through a period of interruption.

Figure 1
Figure 1.Regression model (Miltgen et al., 2016;Carpenter et al., 2016)17)ample, anxiety, stress, and depression(Holmes et al., 2020), but at least partly online shopping could reduce the negative influence of social isolation(Dennis et al., 2015;Papagiannidis et al., 2017).Besides, counter-measures between retailers and policymakers that contribute to greater adoption of biometric authentication (e.g., body scan, facial recognition, GPS tracking, etc.), which may also impact expectations of privacy over time.Biometrics are used and divulged to induce users to fret about their safety, though with cultural variations(Miltgen et al., 2016;Carpenter et al., 2016).This requires retailers to understand how they interact with their partners (i.e., consumers, suppliers, staff, etc.).This would reduce reaction times and streamline processes.More coordination with stakeholders is required to make them feel part of the process rather than just vendors or end consumers (e.g., demanding input during operation).
(Sjodin et al., 2020;Gordon et al., 2020., 2017)row.For, e.g., retailers like Ocado (UK) and Carrefour (France) have been compelled to allow online queues or use the platforms digitally to meet unsustainable demand (Mintel, 2020).Social isolation is a social exclusion process that adversely affects consumers' health(Dennis et al., 2015;Papagiannidis et al., 2017).Some stores like Ted Baker (UK) have received support alerts, either to remind buyers that they are sending them home notifications and passwords or to temporarily close all shops (limiting internet purchases only) while allowing shoppers to be healthier and healthier, such as Louis Vuitton, Paul Smith, and Mulberry.The alteration in buying patterns triggered by an incident is one of the most affected by luxury retailers(Tsai, 2006).Others have introduced new behaviors that wish to be especially socially involved in this development and have specifically indicated they matter about society(Janssen et al., 2017).(Schriber&Lowstedt,2019).An agile approach to shifts in demand and vendor responses(Sjodin et al., 2020;Gordon et al., 2020) may minimize the lead-time.

Table 1 .
Demographics of respondents

Table 4 .
Exploratory Factor Analysis

Table 3 .
Total variance explained