Academy of Marketing Studies Journal (Print ISSN: 1095-6298; Online ISSN: 1528-2678)

Research Article: 2021 Vol: 25 Issue: 1S

Impact of Covid-19 Creating a Panic Buying

Sajjan Choudhuri, Associate Professor, Chandigarh University

Abstract

The fear of coronavirus has affected the consumers' mind and that impacted their shopping processes. The problem was people were more panic in this scenario and all of a certain purchase behavior and purchase intention has been changed. Therefore in this paper I studied various factors impacting the customer buying behavior like purchase intentions, Panic buying due to lockdown and increasing cases of COVID, along with that people started panic purchase. I matched COVID-19 prevalence rates and web queries by data analysis period and also performed correlation analyses in India to verify the hypothesis. A link study was carried out to explore the relationship of the incidence rates of COVID-19 with studies for defensive habits, wellbeing literacy and panic acquisitions.

Keywords

Coronavirus, Panic Buying, Consumer Buying Behavior.

Introduction

The COVID-19 pandemic has transformed the environment as we know it radically. People live differently, buy differently and think differently in several respects. Supply chains were checked. Windows are closed for stores. Consumers around the planet are gazing through a different prism at goods and labels. The virus turns the consumer products market in real time and accelerates the fundamental long-term patterns in just weeks. Our analysis suggests that modern patterns that have already been created can last through this crisis, forever transforming what we value, what we store and how we live and function. Consumers react in a number of ways to the crisis. Some sound nervous and anxious over panic shopping staples and grooming items. On the other hand, certain consumers seem oblivious to the pandemic and, pending advice from governmental and health experts, prefer to perform their work as normal. CPG businesses would have to consider how their own customers respond and build customized communication campaigns for each particular customer.

The days of single-size ads have passed. Because of their buying actions, consumers determine if, where, how, where and why they purchase a commodity. The learned customers demonstrate the behavioral improvements in accordance with macro and micro factors. The firms expressed themselves to overcome the problems of customer behavior. No exception is the latest Covid-19 case. Many brands have shown differences in their marketing contact that affect the preference of customers. Due to buyer desire for items or services that support, decreased demand is transient for non-essential goods and services. The Internet has been a main channel for citizens to find knowledge regarding their wellbeing (Kalichman et al., 2003; Reeves, 2001). For example, about six in ten Americans in the United States use search engines such as Google, Bing and Yahoo to navigate the Internet for health details (Pew Research Center, 2013). During the spread of coronavirus, Internet searches have increased. On 11 March 2020 there were almost 20 million coronavirus mentions, relative to four million Trump mentions and fewer than 2 million freshly cancelled NBA games that day (Molla, 2020). Therefore, vast quantities of internet research relevant to coronaviruses enable us to understand how people perceive, sound and act in relation with the COVID-19 pandemic.

And during the Covid-19 crisis, non-essential products / services businesses would bind to customers in a social networking context. The shared contact between consumers and businesses has often impacted customer shopping decisions. The competition security environment is temporary, and after removing the lockout, customers can start shopping with protections. The lockout situation would alter the market scenario. The period of the company will expand. Covid 19 is like a life jacket, and fear of the virus will, to a certain extent, minimize the shopping pressure without a life jacket. The online buying of Covid 19 vaccines will increase and revenues will decrease. Tourism and travel firms have to re-create and reorient their plans since Covid-19 (Du et al., 2020).

The persistence of the COVID-19 crisis primarily contributes to hope worldwide and the improvement of weeks has slight variations. Consumers anticipate long-term habits and finances, and most show a decline in income over the last two weeks (Chen et al., 2018).

Literature Review

Overall Impact of Covid-19

Since the Second World War, the earth experiences the biggest human catastrophe. Virtually any nation was afflicted by Coronavirus Paralysis (COVID-19). China's epidemic has reached across the globe. In recent months, Corona's epicentre has shifted to the United States from China to Europe. More than 1,5 million individuals have been contaminated with COVID19 to date, with about 80,000 deaths worldwide. Indirectly, the worldwide COVID-19 pandemic has infected billions of citizens. This Coronavirus has certainly placed the global economy at great risk. Coronavirus threatens the international exchange commercial structures. This epidemic was defined by pundits as the product of hyper globalisation or the start of de-globalization. However the planet will experience a recession; some experts say that global casualties will equal the combined First and Second World Wars.

McKinsey & Company, 2020

Another research was undertaken by McKinsey & Company to explain how much time the media spent on Coronavirus pandemic adult in India in March 2020 shifted. 582 respondents aged 18+ were contacted in this study. Watching television television grew by 71 percent. Online entertainment viewership grew by 67 percent; watching videos and other programmes grew to 66 percent; texting, chatting, and messaging grew to 58 percent; social networking use grew by 58 percent.

Global Web Index, 2020

Review of literature clearly highlighted that there is progressive growth in Digital in India and hence research have been carried out that have influenced numerous facets of emerging technology. A Global Network Index analysis was undertaken to explain the shopping behaviour of Indians at the time of COVID-19. Any of these surveys are important: 47 percent of respondents agreed that they purchase goods digitally for home delivery, 47 percent agreed that they are investing time testing products online prior to a shop visit, 43 percent said they spend less time in shops.

Another analysis was performed by the Global Site Index to understand what digital features Internet users in India consider to be more relevant. Here, 60% of participants claimed the most valuable aspect is free shipping, 52% preferred digital shopping because of reliable delivery, 47% chose digital shopping as the product of free return policies, 45% committed to digital shopping because the platform was reliable and 36% said that they shopped online because

The COVID-19 has changed life just as we know it – and we will hold each other healthy and adjust our routines drastically as we do all. The pandemic prevention initiatives needed have influenced the global economy and have modified customer attitudes, behavior and purchase behavior. Fresh challenges have emerged in manufacturing chains, storage, retail stores and staff (Google, 2020). Google witnessed corporations all around the world – including our own – adjust to these modern realities. Such are exceptional times but we have seen businesses start thinking in three stages – responding, fixing and reframing – on the way to economic growth, each with distinct priorities. Companies, vertical companies and markets are influenced differently at any point, with some shifts in pace than others but the overwhelming majority are still centred on reacting (Google, 2020).

Objectives of the Study

1. To study the impact of covid-19 on consumer buying behavior

2. To analyze various buying behavior and patterns of consumer

Purpose of this Research

Consumers react in a number of ways to the crisis. Some sound nervous and anxious over panic shopping staples and grooming items. On the other hand, certain consumers seem oblivious to the pandemic and, pending advice from governmental and health experts, prefer to perform their work as normal. I am trying to analyze various pointers which will focus on consumer behavior (Shive, 2010; Hood et al., 2013).

Research Methodology

The present research used freely accessible data and the authors had no contact with participants; the ethical analysis of the human topic was therefore not relevant. COVID-19 details have been collected from the Johns Hopkins Coronavirus Resource Center website (Johns Hopkins Coronavirus Resource Center, 2020). For India, we have used COVID-19 data since the pandemic started Table 1. I analyzed data from the Google search in India, particularly related to panic transactions and changes in search patterns, which may indicate the effects of COVID-19 purchasing behavior (Chen et al., 2017). To analyse whether data from Google Patterns represented people's COVID-19 desires and intentions, we collected search volume data using the 'Coronavirus' keyword. I have studied the following variable through the use of Google trends and establish relation using correlation through data collected from google trends Hypothesis.

Table 1 Cronbach’s Alpha for Sample Data
Variables Search Terms Cronbach’s alpha
Purchase Intention ‘N-95 mask’ ‘Sanitizer’ 0.94
Health related knowledge ‘Vaccine’ ‘Corona symptoms’ 0.97
Panic buying ‘Online groceries’ ‘Corona test’ 0.81
Online shopping ‘Buy medicine online’ ‘Oximeter’ 0.85
Protective Behavior ‘Antigen test’ ‘Corona vaccine’ ‘Medicine online’ 0.85

H1: Panic buying has significant impact on Purchase intentions.

H0: Panic buying does not have significant impact on Purchase intentions.

Data Analysis

I also matched COVID-19 prevalence rates and web queries by data analysis period and performed correlation analyses in India to verify the hypothesis. A link study was carried out to explore the relationship of the incidence rates of COVID-19 with studies for defensive habits, wellbeing literacy and panic acquisitions. In addition, we carried out pattern analyses in Google to evaluate the mediation impact of fear searches in the correlations of COVID-19 incidence rates, in searches for defensive habits, health awareness and panic purchasing. Correlation was carried out using the SPSS in Figure 1 to Figure 6.

Figure 1 We Collected Search Volume Data Using the 'Coronavirus' Keyword

Figure 2 Google Trend Pattern for Selected Keywords on Purchase Intention

Figure 3 Google Trend Pattern for Selected Keywords on Health Related Knowledge

Figure 4 Google Trend Pattern for Selected Keywords on Panic Buying

Figure 5 Google Trend Pattern for Selected Keywords on Online Shopping

Figure 6 Google Trend Pattern for Selected Keywords on Protective Behavior

The above graph indicates that the market purchasing behaviour has shifted considerably since the advent of Covid-19 in the pandemic. Health analysis has been indexed by the search volume of data with two keywords: vaccination and immunity, which represent people's propensity to locate medical knowledge such as the availability of a vaccine and the methods of immunity. All kinds of awareness of health demonstrated high similarities. Our observation that searches for anxiety act as mediators does not rule out the probability of action searches as mediators. For instance, COVID19 might improve such forms of conduct (e.g. panic buying), thus intensifying emotions of terror. These assumptions may be focused on other ideas that are outside the reach of the present research and should be more investigated in future in Table 2.

Table 2 Correlations
  Purchase Intention Health related knowledge Panic buying Online shopping Protective Behavior
Spearman's rho Purchase Intention Correlation Coefficient 1.000 0.242 0.259 0.027 0.226
Sig. (2-tailed) 0.000 0.084 0.064 0.848 0.107
N 52 52 52 52 52
Health related knowledge Correlation Coefficient 0.242 1.000 0.808** 0.484** 0.740**
Sig. (2-tailed) 0.084 0.000 0.000 0.000 0.000
N 52 52 52 52 52
Panic buying Correlation Coefficient 0.259 0.808** 1.000 0.254 0.735**
Sig. (2-tailed) 0.064 0.000 0.000 0.069 0.000
N 52 52 52 52 52
Online shopping Correlation Coefficient 0.027 0.484** 0.254 1.000 0.623**
Sig. (2-tailed) 0.848 0.000 0.069 0.000 0.000
N 52 52 52 52 52
Protective Behavior Correlation Coefficient 0.226 0.740** 0.735** 0.623** 1.000
Sig. (2-tailed) 0.107 0.000 0.000 0.000 .
N 52 52 52 52 52
**. Correlation is significant at the 0.01 level (2-tailed).

Panic Buying and Online shopping variable were found to be significant value (0.254) p>0.01 which can lead us to rejection of null hypothesis and we can accept that Panic buying has changed consumer behavior of shopping online.

Conclusion and Managerial Implications

In an early effort to provide objective data on how e-commerce impacts the behaviour of food sales, the study reveals the drawback that e-commerce will offer to society when e-commerce sites run food in severe circumstances. Much of today's literature discuss how e-commerce has strengthened customer well-being, including lower cost structure, increased versatility, wider selection of services and size, greater transparency and quicker transactions under normal conditions (Kabango & Asa, 2015; Zheng et al., 2020). Restricted as a literature review explores how e-commerce can play in acute scenarios where demand exceeds the availability on e-commerce platforms. This makes it impossible to establish constructive guidelines in extreme circumstances while launching a successful application of panic inventories. A second implication of management is that demand and supply align. There were persistent shortages of customers in a recession in any distributor spanning from pharmacies and hypermarkets to pharmacy shops owing to hoards and "run on the bank." The supply chain, the logistics and the warehouse processes are essential roles that must be combined with unpredictable demand variations. In other terms, unlike the existing method of keeping goods on the shelves with a stock back up in the back of the shop, on-line supplies and reverse the phase from the merchandise waiting in the shelf for consumers to buy first and the retail warehouse distribution and shipping to customers would be more and more important. As already stated, consumers who come to the shop are not the same as customers.

The lockdown and social isolation to battle the Covid-19 virus have contributed to major customer behaviour disturbances. All use is time-limited and place-limited. With time versatility yet rigidity of venue, customers have been imaginative and inventive in improvisation. The borders of working life are blurred today, when people work at home, research at home and rest at home. Since the customer cannot go to the market, the shop must come to the consumer.

References

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Chen, M.H., Jang, S.S., & Kim, W.G. (2007). The impact of the SARS outbreak on Taiwanese hotel stock performance: an event-study approach. International Journal of Hospitality Management, 26(1), 200-212.

Du, H., Yang, J., King, R.B., Yang, L. and Chi, P. (2020), COVID‐19 Increases Online Searches for Emotional and Health‐Related Terms. Appl. Psychol. Health Well‐Being. https://doi.org/10.1111/aphw.12237

Global web index. (2020). Coronavirus Research: Multi-Market Research Wave 3. Retrieved from: https://www.globalwebindex.com/coronavirus

Hood, M., Kamesaka, A., Nofsinger, J., & Tamura, T. (2013). Investor response to a natural disaster: Evidence from Japan's 2011 earthquake. Pacific-Basin Finance Journal, 25, 240-252.

Kabango, C.M., & Asa, A.R. (2015). Factors influencing E-commerce development: implications for the developing countries, International Journal of Innovation and Economic Development, 1(1), 59-66.

Kalichman, S.C., Benotsch, E.G., Weinhardt, L., Austin, J., Luke, W., & Cherry, C. (2003). Health-related internet use, coping, social support, and health indicators in people living with HIV/AIDS: Preliminary results from a community survey. Health Psychology, 22(1), 111-116.

McKinsey & Company. (2020). COVID-19 India Consumer Pulse Survey'. Retrieved from: https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/survey-indian-consumersentiment-during-the-coronavirus-crisis

Molla, R. (2020). How coronavirus took over social media. Retrieved from: https://www. vox.com/recode/2020/3/12/21175570/coronavirus-covid-19-social-media-twitter-faceb ook-google

Pew Research Center. (2013). Pew internet and American life project. Retrieved from: https://www.pewresearch.org/fact-tank/2013/02/01/majority-of-adults-look-online-forhealth-information/

Reeves, P.M. (2001). How individuals coping with HIV/AIDS use the internet. Health Education Research, 16(6), 709-719.

Shive, S. (2010). An epidemic model of investor behavior. Journal of Financial and Quantitative Analysis, 169-198.

Zheng, Q., Chen, J., Zhang, R., & Wang, H.H. (2020). What factors affect Chinese consumers’ online grocery shopping? Product attributes, e-vendor characteristics and consumer perceptions, China Agricultural Economic Review, 12(2), 193-213. doi: 10.1108/CAER-09-2018-0201.

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