Consumers focused on shopping for essentials

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Research in Transportation Business & Direction.
2022 Jun; 43: 100768.

Consumer responses towards essential purchases during COVID-19 pan-India lockdown

Gopal R. Patil

aSection of Civil Engineering, Indian Institute of Engineering science Bombay, Powai, Mumbai 400076, India

Rutuja Dhore

aDepartment of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India

B.Chiliad. Bhavathrathan

bSection of Civil Engineering, Indian Institute of Technology Palakkad, Kozhippara P. O, Palakkad 678557, Kerala, Bharat

Digvijay Southward. Pawar

cDepartment of Civil Engineering, Indian Constitute of Engineering science Hyderabad, Kandi, Sangareddy District 502285, Telangana, India

Prasanta Sahu

dDepartment of Civil Engineering, Birla Institute of Technology and Scientific discipline Pilani, Hyderabad 500078, Telangana, Republic of india

Asim Mulani

aDepartment of Civil Engineering, Indian Plant of Technology Mumbai, Powai, Mumbai 400076, Bharat

Received 2020 Aug 26; Revised 2021 Nov 13; Accepted 2021 December nine.


Humanity experienced 1 of the worst crises in recent history due to the COVID-19 pandemic. The spread of the disease and the lockdown announced by the government of Republic of india created an emergency, disrupting the supply of essential bolt and creating panic and anxiety among the people. This paper aims at capturing the behavior of consumers purchasing essential commodities before and during the lockdown using an online questionnaire. Responses from 730 households roofing xx states in India were used. The data assay revealed that consumers made a lesser number of trips during lockdown but purchased excess commodities considering the future uncertainties. The local family grocery stores, called
shops served well during the pandemic. During the lockdown, consumers made shorter trips past vehicles and walked extensively. Income was constitute to influence purchase behavior. The disruptions at the organized retail stores for in-store every bit well as online purchases were identified using cistron analysis. Out of the 3 factors identified each for in-store and online purchases, perceived take chances and vendor distrust had major influence respectively. The findings of this study give pointers to many infrastructure and policy initiatives that target tackling such emergencies in the futurity.


Consumer response, Essential commodities, COVID-xix, India, Factor assay

i. Introduction

The consumer products supply-chain, especially related to the fast-moving consumer appurtenances, operates through dynamic global networks. Improved access to data and the expansion of choice sets have made consumers reply to fifty-fifty pocket-size perturbations in this network. The nigh unpredictable context which influences consumer habits is the occurrence of ad-hoc natural disasters such as hurricanes, tsunamis, and global pandemics. As a event, there has been an ever-increasing interest in understanding consumers’ behavioral responses to disruptions. Such understanding is vital in treatment uncertainties, informing decisions, and improving resilience. This paper focuses on studying the response of consumers in Bharat towards purchasing essential bolt considering of the uncertainties created by COVID-19 pandemic and the pan-Republic of india lockdown appear by the Government of Bharat.

The severity of the unprecedented health crisis with the outbreak of COVID-19 was felt when the Globe Health Organization alleged it as a global pandemic on 11 March 2020. By this time COVID-19 had spread in more than than 114 countries with 118,326 active cases and 4292 deaths (WHO, State of affairs Report-51, 2020). The timeline of the virus-spread in India started with the first instance being recorded in Kerala on 30 January 2020 and continued with transmission charge per unit increasing rapidly to date. Apart from the dense population, several factors such as lack of sensation, ignorance of people towards precautionary measures like social distancing, usage of masks, and a considerable proportion of people living below the poverty line intensifies the threat in India (Buckshee, 2020; Kamath, Kamath, & Salins, 2020).
Fig. 1
shows the timeline of COVID-xix cases in Republic of india from xxx January 2020 (detection of the showtime case) to 25 March 2020 (imposition of lockdown). Every bit the full number of cases crossed a 400 marking, a 14-h curfew called ‘Junta curfew’ (people’s curfew) was announced in India past the Prime Government minister on 22 March 2020. Immediately after 3 days, a 21-mean solar day lockdown was imposed from 25 March 2020, which primarily resulted in the closure of all transportation services, public and private offices, businesses, factories, etc., throughout the country.

Fig. 1

Timeline of the total number of cases in India.

The logistics and supply chain sector class an essential role of a country’s economy. This sector was adversely affected during the lockdown considering freight movement about stood yet. Economic slowdown intensified when more than eight one thousand thousand people working in the logistics manufacture all of a sudden became jobless as an firsthand bear upon of the imposed lockdown. Although the transportation of essential goods was notwithstanding functional, about ninety–95% reduction in the motility of freight was observed in the initial 2–3 days of the lockdown (Yabaji, 2020). Co-ordinate to the Indian Foundation of Transport Research and Training (IFTRT), Indian trucking faced a shortage of drivers, and 62.5% fleet owners stopped the piece of work owing to less demand or safety of the drivers (Khan, 2020). The hindered movement of goods in the domestic supply concatenation resulted in increased operational cost, which in turn resulted in inflation (Kumar, 2020). While the need for essential goods kept on increasing, numerous issues effected in supply chain disruptions.

Factors like travel restrictions, closure of shopping malls and supermarkets (Agrawal, Jamwal, & Gupta, 2020) reduced production availability at stores (Mahajan & Tomar, 2021) impacted consumer behavior with respect to shopping of essential commodities. Thus, their priorities chop-chop shifted towards essential bolt in food, healthcare, and personal hygiene categories (Biswas, 2020). On the other mitt, sales of luxury products and services witnessed a slump. Considering of several issues at retail stores such as long queues, uncertainty in opening and closing times, unavailability of items, and restrictions on purchase quantity, (Balachander, 2020; Mishra, 2020) consumers preferred budgeted the local vendors and the family-owned local departmental stores in Indian localities chosen as ‘Kirana’
shops. These stores are not a part of retail chain business and serve every bit a middle for groceries and items of daily employ for local community. Also, consumers became more than price-sensitive due to a fall in their incomes (Hobbs, 2020) or loss of employment, which as well explains their inclining involvement towards local vendors and brands. Another shift observed was in the increased demand for online shopping for groceries and other essential items because of the move restriction, social distancing norms, and the fear of getting infected.

In this study, we accept analyzed the response of educated respondents towards the purchase of perishable and non-perishable essential bolt. The responses were collected from residents all over India. Our principal business organization is to investigate the shift in frequency, transport way, altitude travelled, type of stores, etc. associated with shopping trips before and during the lockdown. Combined effects of behavioral variables including way of travel, frequency of shopping, and average distance travelled have likewise been studied. Locality wise (city type wise) beliefs of respondents across Bharat with respect to shopping of essential bolt during lockdown has been analyzed. We also attempt to understand if this shift was consequent across the different income groups of Indian society. Factor assay is performed to place the type and frequency of disruptions experienced at the last vendor node that contributed to the shift in the response of the consumers.

2. Literature review

Consumption is habitual every bit well equally contextual. The chief contexts that can influence consumers’ habits are social events (migration, marriage, etc.), the appearance of technology (cyberspace, online shopping), government-imposed rules and regulations (promoting solar cars, etc.), and natural disasters (Sheth, 2020). The consumer appurtenances ecosystem includes consumers on the need-side and firms on the supply side. This ecosystem works well with the iterative process of consumers feeding into the supply side to improve the quality of products and delivery of such improved products feeding into the understanding of consumers (Fortin & Uncles, 2011). The occurrence of the above-mentioned events causes an imbalance in supply and demand, resulting in the disruption of this system.

COVID-19 pandemic has essentially tested the consumer goods industry. As people are trying to adapt to the ‘new normal,’ prominent changes in their attitude, priorities, and habits of purchasing are existence observed. Supply chains take been severely hampered due to lockdown conditions resulting in the supply-side and the demand-side shocks. Demand-side shocks mainly include panic buying, changes in food purchasing patterns, and inverse product priorities; supply-side shocks include a shortage of labor, closure of manufacturing factories, unstable fuel prices, and disruption to transportation and supply networks (Hobbs, 2020).

There are studies in the literature that analyze the changes in consumer beliefs in the context of the occurrence of natural disasters, economical recession, epidemics, wars, etc. Most of them discussed event-induced human being emotions such equally fear, depression, and stress, which influences the purchasing patterns of consumers. Many studies have researched how consumers’ behavior changes the aftermath of natural disasters (Kennett-Hensel, Sneath, & Lacey, 2012; Larson & Shin, 2018; Sneath, Lacey, & Kennett-Hensel, 2009). Several studies talk about environmental (Singh & Chauhan, 2020) and mobility (de Vos, 2020; Park, 2020; Pawar, Yadav, Akolekar, & Velaga, 2020; Pawar, Yadav, Choudhary, & Velaga, 2021; Saha, Barman, & Chouhan, 2020) changes occurring during COVID-nineteen pandemic, but the bear on of a pandemic on consumer beliefs has not been explored much.

Li, Hallsworth, and Coca-Stefaniak (2020), through a survey in China, found a switching behavior with local retailers bouncing back into the market and, on the other hand, farmers losing their customers. According to the study conducted in Canada by Richards and Rickard (2020), a notable shift from foodservice channel to retail channel has been observed owing to the closure of restaurants and other food outlets during the lockdown. Harris, Depenbusch, Pal, Nair, and Ramasamy (2020), through a survey, found out that the disruptions in nutrient systems due to pandemic had severely affected the farmers of India in terms of productions, sales, prices, and income. Laato, Islam, Farooq, and Dhir (2020) proposed a structural model that suggested that increased exposure to the internet during lockdown led to the anxiety-driven unusual purchasing behavior of consumers. Technology has always played a crucial office in transforming consumer beliefs. Basic needs (food, clothing, shelter), too, have shifted to new necessities like mobile phones, internet, and apps (Sheth, 2020). The shift of consumers from in-store to online shopping during lockdown would be of no surprise.

The advent of any natural disaster or wellness crisis arouses a feeling of fearfulness in the community, which greatly influences their shopping behavior. According to Larson and Shin (2018), perceived convenience gets negatively impacted equally panic and fear amongst consumers intensify. Sneath et al. (2009) stated that disaster-induced stress due to losses and perceived lack of command leads to impulsive and compulsive ownership. Kennett-Hensel et al. (2012) found that consumers practice hoarding and stockpiling for alleviating their anxiety. Long and Khoi’s (2020) report demonstrated that high-risk perception led consumers to purchase and hoard goods during the lockdown.

The literature review revealed that although numerous studies on linking consumer personality traits and their perception take been done in the past, very few describe their behavior in events of the crisis. Virtually of these studies highlight the stress and anxiety-driven behavioral changes prevailing during emergencies. The width of such inquiry is limited in the context of developing countries like India. To the all-time of our cognition, there are no studies in the literature that specifically draw consumer beliefs changes during emergencies in the shopping choice, shopping frequency, travel mode, trip length distribution, etc. to perishable and non-perishable essential commodities.

3. Data collection

Information was collected through an online questionnaire survey that targeted responses from consumers all over India. Every bit discussed above, the focus of this study is on consumer behavior towards essential appurtenances. During the pan-Bharat lockdown, transporting or selling non-essentials appurtenances was banned. The perishable essential bolt were restricted to fruits and vegetables only. Other perishable items, such every bit meat and dairy products, are not considered. In India, nosotros have dedicated vendors for non-vegetarian items, and nearly families purchase milk daily. The non-perishable commodities broadly include grains, flour, spices, package food items, laundry items, and other household essentials. Medicines and personal protective products (such as masks) purchased from pharmacies are not considered.

The questionnaire is divided into three broad sections. The kickoff department sought information about socio-demographic characteristics of respondents like the number of earning members, educational qualification, monthly family income, household type, and vehicle ownership. The detailed list of socio-demographic variables included in the survey is shown in
Tabular array ane
. The second and third sections requested details about the behavior of respondents while buying essential commodities during and earlier lockdown, respectively. These sections asked information about the frequency of in-store shopping, frequency of online shopping, fashion of travel, the average altitude travelled, mode of payment, type of stores visited, type of shopping preferred (online or in-store). Too, the futurity catamenia considered while ownership essential commodities was also recorded. Shopping related issues (both in-store and online) during lockdown were analyzed using a Likert scale in the consumer beliefs section.

Table 1

Summary of socio-demographic characteristics of respondents.

Socio-demo. variables Data range Frequency Socio-demo. variables Data range Frequency
Family members: <v years age 0 576 (78.90%) Family members: Age 5–18 years 0 502 (68.77%)
1 125 (17.12%) 1 146 (twenty%)
2 23 (3.xv%) 2 lxx (ix.59%)
3 3 (0.41%) 3 9 (one.23%)
> three iii (0.41%) > 3 3 (0.14%)
Family members: Age 18–35 years 0 172 (23.56%) Family unit members: Age 35–60 years 0 139 (19.04%)
1 208 (28.49%) 1 158 (21.64)
2 253 (34.66%) 2 386 (52.88%)
three 71 (9.73%) 3 27 (3.70%)
> 3 26 (3.56%) > iii 20 (2.74%)
Family members: More than lx years 0 442 (60.55%) Number of earning members 0 6 (0.82%)
1 156 (21.37%) 1 308 (42.nineteen%)
2 110 (15.07%) 2 317 (43.42%)
iii 18 (2.47%) 3 or more 99 (13.56%)
> iii 4 (0.55%)
Highest qualification tenth grade/less 3 (0.46%) Monthly family income (INR ‘000’) <xx 32 (4.38%)
12th/Diploma 6 (0.92%) 20–50 168 (23.01%)
Available caste 141 (21.69%) fifty–100 226 (xxx.96%)
Master’s degree 380 (58.46%) 100–200 168 (23.01%)
PhD 120 (eighteen.46%) >200 136 (xviii.63%)
Two-wheeler buying 0 187 (25.92%) Car ownership 0 285 (39.04%)
1 324 (44.38%) 1 329 (45.07%)
2 178 (24.38%) 2 93 (12.74%)
three or more 41 (5.62%) 3 or more 23 (3.fifteen%)
Household blazon Flat 371 (l.80%)
Row house 193 (26.44%)
Indep. bungalow 157 (21.51%)
Slum nine (1.23%)

The survey was floated across different digital platforms from 29 Apr 2020 to 25 May 2020. At the terminate of the survey, a full of 733 samples were nerveless. Out of the 733 samples, three samples were discarded on account of redundancy. Therefore, 730 sample size has been considered in assay throughout this paper. The state-wise distribution of respondents is depicted in
Fig. 2
. Most 39% of respondents were from the state of Maharashtra, which is the worst affected state with the highest number of COVID-19 cases. Cities in Bharat are in general classified as Tier I, Tier II, and Tier Three. The share of respondents belonging to different classes of cities is represented in
Fig. 3
. About 63% of the respondents belonged to either tier 1 or tier 2 cities. With the participation of respondents from more than 20 states, generalized data depicting the pandemic situation in the country is obtained. Considering the English proficiency and internet penetration, the responses from the people located in remote areas and poor and inadequately educated could not be nerveless.

Fig. 2

Country-wise distribution of respondents.

Fig. 3

Share of respondents belonging to dissimilar city types.

iv. Socio-demographic characteristics

Socio-demographical characteristics play an important part in understanding the behavior of consumers in the time of crisis every bit these characteristics drive changes in need, purchasing patterns, etc. (Cranfield, 2020). Moreover, these characteristics highly touch freight distribution and shopping mobility changes (Nuzzolo & Comi, 2014).
Table 1
represents cross-tabulated and summarized socio-demographic variables with frequencies. All socio-demographic variables in the survey were either chiselled or ordinal. The recorded variables include number of members on the basis of age groups (0, ane, ii, iii, more than than 3), number of earning members in family (0, ane, 2, 3 and more), monthly family income (categories: <20,000; xx,000–50,000; 50,000–100,000; 100,000–200,000; >200,000 (in INR)), highest education qualification in family (categories: 10th form or less, 12th class, Bachelor’due south caste, Master’southward degree, PhD), household type (Categories: flat, independent bungalow, row business firm, slum), ii-wheeler and car ownership (0, ane, 2, 3 or more). Five age groups were identified: <v years (infants), between five and 18 years (kids), betwixt 1835 years (youth), between 35 and lx years (middle-aged), more than 60 years (senior citizens).

five. Comparison of behavior before and during the lockdown Frequency of in-store shopping

During the lockdown, when the stores providing essential commodities just were open, consumers were less probable to visit the stores frequently for buying essential bolt. Access convenience and transaction convenience (Larson & Shin, 2018) factors are likely to crusade this shift in beliefs. It is seen that virtually 56% and 36% of respondents oft (daily and 2–3 times a calendar week) visited markets earlier lockdown for ownership perishable and non-perishable items, respectively, which drastically reduced to 34% and 23% respectively during the lockdown. Usage of the net and subsequent information overload can lead to anxiety, stress, and distress during pandemics (Laato et al., 2020). Considering of this perceived fear, consumers were reluctant to visit stores for shopping. Some of them had stocked upwards essential commodities sensing the uncertainty of the situation in the future. Equally a effect, the percentage of people visiting markets less oft for perishable goods (one time in ii weeks and once in a month) increased from seven.3% to xiv.i% (Fig. 4).

Fig. 4

Frequency of in-shop shopping (comparing).

v.2. Frequency of online shopping

Food supply bondage are believed to be impacted past the developments in digitalization and innovation (Gharehgozli, Iakovou, Chang, & Swaney, 2017). As the due east-commerce industry continues to grow, researchers are keen on studying the behavior of consumers in the context of online shopping (Elms, de Kervenoael, & Hallsworth, 2016; Manus, Riley, Harris, Singh, & Rettie, 2009; Mortimer, Fazal eastward Hasan, Andrews, & Martin, 2016; Picot-Coupey, Huré, Cliquet, & Petr, 2009). Usage of the internet by consumers for shopping is profoundly affected by the prevailing situations (Hand et al., 2009). The lockdown induced feet has compelled them to use the internet for shopping purposes. Our survey findings back up this theory as there has been a rise of viii% and 4% in the number of respondents who performed online shopping for perishable and not-perishable goods, respectively, during lockdown (Fig. 5
). Moreover, nosotros believe, this share will further increment if the online retailers introduce safe and no-contact commitment systems.

Fig. 5

Frequency of online shopping (comparison).

5.iii. Stores visited

Bharat has around 12 meg family-owned grocery stores—called
shops; they are main final node vendors for groceries. Their share is reducing in recent years because of the comfort and convenience of organized concrete and online stores. However, this trend is found to be reversed during the lockdown. Most 88% (increased from 77%) respondents preferred visiting local markets, and vendors for purchasing perishable items and 72% (increased from 44%) preferred visiting the local vendors during lockdown for purchasing non-perishable items (Fig. six
). According to a report past Yadav (2020), there was a ascension of 40% more than than ever at kirana stores since the annunciation of lockdown. This rise of say-so of local family grocery stores during lockdown was supported by several other media reports like Balachander (2020), Mishra (2020) which reported that long queues, frequent stockouts, fluctuations in opening and closing timings of supermarkets have results in this shift. Similar findings are reported past Li et al. (2020) in Mainland china.

Fig. 6

Type of stores visited (comparison).

v.iv. Mode of payment

Before lockdown, about 55% of respondents were performing cashless transactions for purchasing groceries, out of which near 24% were performing mostly cashless payment, indicating they were either buying online or from retail chain stores. During the lockdown, the percentage of ‘generally cashless’ payments reduced to about fifteen%. This is the result of consumers shifting from retail stores to the local store (Fig. 7
). The shift is primarily due to the disruptions at the retail chain stores and they beingness further from residences. Most of the local stores do not take card payments, although a few of them have payment through e-wallets such as Google pay and Paytm.

Fig. 7

Mode of payment (comparison).

5.v. Style of travel

The imposition of lockdown resulted in motility restriction of citizens. Well-nigh of the respondents visited stores/markets for buying essentials past walking since they might be visiting places that are located nearby their areas of residence. The use of two-wheelers and cars significantly reduced during the lockdown equally compared to their apply before lockdown (Fig. 8
). The bicycle as a mode of travel while visiting stores gained significance during the lockdown.

Fig. 8

Mode of travel (comparison).

5.6. Trip length distribution

The trip length distribution combined with the mode choice behavior is an important input for the policymakers to arrange to cater to the transport needs during emergencies such as the present pandemic. The trip length distributions for not-perishable and perishable bolt earlier and during lockdown are presented in
Fig. 9
. Respondents did not prefer stores that are located across 2 km of the residence during the lockdown. Consumers visiting stores within 1 km distance increased during the lockdown. Overall, respondents did not prefer travelling big distances for buying essentials. Some of them also started performing online shopping to reduce the risk of getting infected.

Fig. 9

Average altitude travelled for essential commodities (comparing).

Nosotros tested the fitting of normal, lognormal, gamma, and exponential distributions to the boilerplate distance travelled by the respondents earlier and during the lockdown. Kolmogorov-Smirnov (Grand—S) examination was used to measure out the goodness of fit for these distributions. Grand—Due south exam value is the maximum altitude between the empirical distribution role of the sample and the cumulative distribution office of the reference distribution. The nil hypothesis for the Yard—S test is that the sample follows a specific distribution. We reject the null hypothesis when the test statistic is greater than the critical value. The statistical parameters for fitting these distributions are presented in
Table 2
. It was seen that average distance travelled by the respondents for buying essential bolt (both perishable and non-perishable) before lockdown followed exponential distribution and during the lockdown followed gamma distribution (Fig. nine).

Tabular array two

Statistical parameters for average distance travelled by respondents.

Type of commodity During lockdown (Chiliad—South test values)

Before lockdown (K—S test values)

Critical value⁎⁎
Nl. Lognl. Gam. Exp. Nl. Lognl. Gam. Exp.
Non-perishable 0.500 0.539 0.361 0.405 0.506 0.539 0.447 0.405 0.483
perishable 0.517 0.539 0.319 0.405 0.502 0.539 0.469 0.405 0.483

v.7. Panic and excessive buying

The sudden announcement of the 21-day lockdown aroused a feeling of fear, which eventually led to panic buying by the consumers. From the survey findings, nearly 48.63% respondents purchased essentials before the declaration of national lockdown, out of which 24.04% bought goods before the announcement of Junta curfew (22nd March), and 24.59% shopped essentials in the period between Junta curfew (22nd March) and the proclamation of National lockdown (25th March). Despite the assurance given past the government regime that essentials would be fabricated bachelor during the lockdown, consumers preferred to hoard essential goods for a longer period as a precautionary measure. About 37.54%, eight.87%, 2.73% (total 49.14%) respondents purchased items considering the future period as 1 calendar month, 2 months, and three months respectively.

vi. Combined effect of frequency of shopping with style of payment and boilerplate distance travelled

half dozen.1. Respondents visiting stores more frequently

The combined effect of manner of payment preferred and average distance travelled for shopping during lockdown for respondents who more frequently visited stores for buying essential commodities is represented in
Fig. 10
. Respondents visiting stores daily, ii–3 times a week and once in a week are considered as more than ofttimes visiting consumers. It is evident from the chart that 42% of more than frequently visiting respondents preferred travelling <0.5 km distance and 53% of them preferred greenbacks payments while ownership from nearby stores. Respondents oft visiting stores far abroad from their homes preferred mixed cash and cashless payments.

Fig. 10

Mode of payment and average distance travelled by frequent store visitors during lockdown.

Fig. 11
represents combined effect of manner of travel and boilerplate distance travelled for shopping during lockdown for respondents who more than ofttimes visited markets for buying essential commodities. Near 42% of more ofttimes visiting respondents preferred travelling <0.5 km altitude and majority of them (72%) preferred walking for visiting stores. Whereas 90% of ofttimes shop visiting consumers travelling across five–8 kms (2%) travelled by cars.

Fig. 11

Mode of payment and manner of travel by frequent store visitors during lockdown.

six.2. Respondents visiting stores less frequently

Fig. 12
represents combined effect of mode of payment preferred and average distance travelled for shopping during lockdown for respondents who less frequently visited markets for buying essential commodities. Respondents visiting stores- once in 2 weeks and in one case in a calendar month are considered as less often visiting consumers. It is evident from the chart that 27% of less ofttimes visiting respondents preferred travelling up to three km distance and 51% of them preferred greenbacks payments while buying. Respondents frequently visiting stores far away from their homes preferred mixed cash and cashless payments.

Fig. 12

Mode of payment and distance travelled past not-frequent visitors during lockdown.

Fig. 13
represents combined effect of mode of travel and boilerplate distance travelled for shopping during lockdown for respondents who more often visited markets for buying essential commodities. Nigh 27% of less oftentimes visiting respondents preferred travelling up to 3 km distance and majority of them (threescore%) preferred 2-wheeler as a style for visiting stores. Whereas 57% of less ofttimes store visiting consumers travelling beyond v–eight kms (15%) travelled past cars during lockdown.

Fig. 13

Manner of travel and distance travelled by not-frequent visitors during lockdown.

7. Shopping behaviour comparison for different metropolis types

This section discusses behavioral changes of respondents with respect to their surface area of residence. About 63% of the respondents belonged to either tier one or tier 2 cities (refer
Fig. 3).
Tabular array 3
represents locality-wise comparison of behavior of respondents before and during lockdown. It was seen that several factors such as shift towards visiting local stores, increased online shopping, reduced frequency of visiting stores, increased expenditure for buying essential commodities were more or less the same beyond respondents residing in all the locations in India.

Tabular array 3

Behavioral changes during and before lockdown in different city types (comparison).

Behavioral variables Data range Tier I cities

Tier Ii cities

Tier Iii cities and other

During lockdn Earlier lockdn During lockdn Before lockdn During lockdn Before lockdn
Frequency of shopping (physical) Daily iii% vii% ii% x% iv% 10%
2–3 times a week 18% 27% 20% 31% 23% 28%
Once in a calendar week xxx% 23% 24% 22% 32% 26%
Once in 2 weeks 18% 24% 30% 17% 16% 21%
Once in a month 18% 15% 12% 16% 12% eleven%
Not applicable 13% 5% 12% five% 12% 4%
Frequency of shopping (online) Daily 1% one% 2% ane% 0% 2%
2–3 times a week 4% 3% 4% ii% 1% 2%
Once in a week 10% 5% v% two% 3% 5%
One time in 2 weeks 16% 6% 9% six% 7% four%
One time in a month 17% 17% 8% xvi% 5% 11%
Not applicative 51% 78% 74% 70% 81% 68%
Types of stores visited Mostly local vendors/stores 93% 42% 89% 45% 88% 47%
Mostly retail store chains 2% 34% one% 30% 3% 27%
Both local and retail stores 5% 24% 10% 25% 9% 26%
Amount of money spent on buying essential bolt (monthly) Average (in INR) 7254 6791 6789 6559 6544 63.06

8. Influence of income on the purchase behavior

We included five ranges of household monthly income in the questionnaire. To analyze how the income influenced different aspects of purchasing; we merged them into three groups as beneath:

  • Group I: <50,000 INR (Lower income grouping)

  • Group II: between 50,000–200,000 INR (Middle income group)

  • Group Iii: more than than 200,000 INR (Higher income group)

The shares of respondents in group 1, grouping 2, and group 3 are 27.39%, 53.97%, and 18.63%. The behavior of these groups with reference to frequency of in-store shopping, frequency of online shopping, travel mode, the average distance travelled, and type of stores preferred is presented in
Table 4,
Table v
Table four
is for non-perishable commodities, and
Table 5
is for perishable commodities. A Chi-foursquare examination is performed to test the association betwixt the three income groups and their corresponding behavior during the lockdown. In other words, we are interested in finding whether the group-wise behavior is different. The null hypothesis causeless that the behavior of consumers during lockdown is contained of the grouping or income of the family unit in full general. Results from
Table four,
Table 5
propose that the frequency of in-store shopping (non-perishable items), frequency of online shopping (both perishable and non-perishable items), style choice used while visiting during lockdown varied within the income groups of respondents (at significant level
p = 0.05).

Table 4

Descriptive statistics of behavioral variables and chi-square test results (non-perishable commodities).

Behavioral variables Information range Income group I Income group II Income grouping Iii P value
Frequency of in-store shopping (non-perishable) Daily nine (6.42%) iv (1.47%) two (2.44%) 0.015
2–3 times a week thirty (21.42%) 52 (nineteen.xi%) 21 (25.6%)
In one case in a week 35
92 (33.82%) 30 (31.78%)
Once in 2 weeks 31 (22.xiv%) 68 (25.0%) 13 (22.6%)
Once in a month 18 (12.85%) 43 (15.8%) 10 (xiv.37%)
Not applicable 17 (12.14%) 13 (4.77%) 6 (7.28%)
Frequency of online shopping (non-perishable) Daily 1 (0.71%) 3 (1.11%) 1 (1.21%) 0.042
2–3 times a calendar week 7 (5.0%) 5 (1.83%) 3 (3.65%)
Once in a week 3 (2.14%) 10 (3.67%) 6 (7.31%)
Once in 2 weeks 5 (3.57%) 29 (10.66%) seven (8.5%)
Once in a calendar month 10 (7.fourteen%) 27 (nine.9%) 13 (15.85%)
Not applicable 119 (81.42%) 198
Travel mode (non-perishable) Walk 51 (36.4%) 105 (38.6%) 37
Bicycle 8 ( 4 (1.47%) ane (1.2%)
2-wheeler 67 (47.85%) 107
19 (23.17%)
car 10 (7.i%) 52 (nineteen.11%) 25 (30.48%)
Machine-rickshaw/Taxi 1 (0.71%) 1 (0.36%) 0 (0.00%)
Not applicable 3 ( 3 (1.1%) 0 (0.00%)
Average distance travelled (non-perishable) <0.v km 47 (33.57%) 91
0.5–i km 42 (30%) 86 (31.61%) 27
i–3 km 32 (24.28%) 76 (27.94%) 20 (24.39%)
iii–five km 11 (7.85%) 11 (iv.04%) 6 (7.31%)
5–8 km 3 (two.14%) 4 (1.47%) 2 (two.43%)
8–12 km 0 (0.00%) 2 (0.735%) 0 (0.00%)
Not applicable 3 (2.xiv%) 2 (0.735%) 0 (0.00%)
Stores preferred (not-perishable) By and large local stores 111 (79.28%) 138
Mostly retail stores 11 (vii.85%) 38 (13.97%) xi (thirteen.41%)
Both local and retail stores xviii (12.85%) 51 (xviii.75%) xiv (17.07%)

Table 5

Descriptive statistics of behavioral variables and chi-square test results (perishable commodities).

Behavioral variables Data range Income group I Income group II Income group III P value
Frequency of in-store shopping (perishable) Daily ten (vii.14%) 8 (2.94%) 3 (3.65%) 0.254
two–3 times a week 46 (32.85%) 87 (31.98%) 26 (31.5%)
One time in a calendar week 56 (40.0%) 119 (43.75%) 37 (45.12%)
Once in 2 weeks 13 (9.28%) 43 (15.8%) 11 (xiii.41%)
Once in a calendar month 4 (2.85%) 4 (one.47%) 0 (0.00%)
Non applicative 11 (7.85%) 11 (4.04%) v (6.09%)
Frequency of online shopping (perishable) Daily ane (0.71%) 1 (0.36%) 1 (1.21%) 0.0027
two–3 times a week two (1.42%) 12 (iv.41%) 2 (2.43%)
Once in a week 4 (2.85%) 7 (2.57%) viii (9.75%)
Once in 2 weeks ii (1.42%) 14 (5.14%) v (6.09%)
One time in a month ii (one.42%) thirteen (4.77%) viii (9.75%)
Not applicable 129
Travel mode (perishable) Walk 59 (42.14%) 127 (46.69%) 46 (56.09%) 0.0113
Wheel 7 (v.00%) 4 (ane.47%) two (two.4%)
2-wheeler 59 (42.fourteen%) 96 (35.29%) twenty (24.39%)
car 10 (vii.14%) 42 (fifteen.44%) 14 (17.07%)
Auto-rickshaw/Taxi 1 (0.714%) 0 (0.00%) 0 (0.00%)
Not applicable 4 (two.8%) 3 (i.1%) 0 (0.00%)
Boilerplate distance travelled (perishable) <0.5 km 55
0.5–1 km 41 (29.28%) 78 (28.67%) 29 (35.36%)
ane–three km 27 (nineteen.28%) 68 (25.0%) 16 (19.51%)
3–5 km 10 (7.14%) 7 (2.57%) 3 (3.half-dozen%)
5–eight km 3 (two.14%) three (one.1%) 1 (1.21%)
8–12 km 0 (0.00%) 1 (0.36%) 0 (0.00%)
>12 km 4 (2.85%) 0 (0.00%) 0 (0.00%)
Stores preferred (perishable) Generally local stores 127
Mostly retail stores 1 (0.714%) 9 (3.3%) ii (2.44%)
Both local and retail stores 12 (eight.57%) 28 (10.29%) seven (8.53%)

Table 4,
Table 5, the daily visits to stores are college in income groups I and 2, probably considering these consumers practice purchase commodities that are currently needed. The shares of online purchases decrease with decreased income ranges. The online buy of perishable commodities is lower for each income group. While considering the travel modes, the walk is widely used across all income groups for both perishable and non-perishable commodities; the share of the walk is higher for perishable goods. This is also reflected in the trip length distribution equally the significant share of trips was <1 km. Two-wheeler is the near preferred mode past group I and group Ii respondents, where the walk is the most preferred by group III. It may exist noted that the share of <1 km trips is approximately the same for all groups. One of the possible reasons for this observation is the walkability in the neighborhood of the respondents.

9. Disruptions experienced at final vendor node

Likert scale responses were considered for accessing the consumer experience during the lockdown for both in-store shopping and online shopping. Exploratory factor analysis (EFA) is used to determine the number and nature of the factors that explain maximum covariance in the data. EFA is a statistical method that helps to identify the fewest possible constructs which tin reproduce the original data (Gorsuch, 1997). Out of 733 respondents, 603 of them responded to the Likert scale questions respective to in-store shopping, and 366 respondents answered questions related to online shopping. The significant reduction in the afterward was considering it was non asked those respondents who did only in-store purchasing.

The suitability of the respondent data for factor analysis can be assessed using two tests- Bartlett’due south test of sphericity and Kaiser-Meyer-Olkin (KMO) test. Bartlett’s sphericity exam checks whether the correlation matrix is an identity matrix, and KMO test tests the sample capability of data. If the P-value for Bartlett’s test of sphericity is <0.05 and the KMO examination value is more than than 0.5, then data is said to be suitable for exploratory factor analysis (Williams, Onsman, Brown, Andrys Onsman, & Ted Chocolate-brown, 2010). The purpose of cistron extraction is to reduce a big number of items into a few factors. According to Thompson (2004), parallel analysis method is the all-time and ordinarily used method for deciding the number of factors to extract. This method compares the actual eigenvalues with the randomly ordered eigenvalues (from simulated and resampled data) and retains factors when the divergence betwixt the two is minimum (Williams et al., 2010). Rotation farther helps in minimizing the complexity of gene loadings and thus producing the best fit solution. The rotation method used in this study is ‘varimax’ technique, which is unremarkably used in factor analysis (Thompson, 2004). This rotation method produces cistron structures that are uncorrelated.

The results and interpretations of this assay are presented in the subsequent sub-sections for both in-shop and online shopping data samples. Nine research components were included for in-store shopping and online shopping, each equally shown in
Table v,
Tabular array viii. The Likert scale ranged from one = ‘strongly disagree’ to 5 = ‘strongly agree.’ For each factor, the corresponding variance explained, and cistron loadings were described. Pilus, Anderson, Tatham, and Black (2000) categorized loadings ±0.3 as minimal, ±0.4 as necessary, and ±0.five as practically significant. Here, loadings >0.four were considered for identifying the factors. Factor analysis was performed using Rstudio software.

Table viii

Residual examination results.

Indicators Values
Root Mean Square of the Residuals (RMSR) 0.02
Tucker Lewis Index (TLI) 0.953
Root Mean Square Error of Approximation (RMSEA) 0.043

9.ane. In-store shopping

Tabular array 6
shows the list of enquiry components used to sympathize the respondents’ experience for in-store shopping. Bartlett’s test results gave a P-value <0.05, and the KMO value obtained is
(>0.5). Thus, the success of both sphericity and KMO tests makes the information suitable for factor analysis. The parallel analysis test helps to determine the number of factors to exist extracted by comparing the eigenvalues obtained from observed information and simulated (or random) data. The observed eigenvalues higher than their corresponding random eigenvalues are more likely to course meaning factors, and accordingly,
Fig. xiv
suggests the retention of 3 factors.

Table half dozen

Components used in the questionnaire (offline shopping).

Component notation Measurement
P1 Limitations on buying
P2 Some regular items not available
P3 Increased prices of some items
P4 Restrictions on payment way
P5 No proper precautions taken at the stores
P6 Absence of crowd management forces at the stores
P7 Fluctuation in opening and closing timings of stores
P8 Bug non sorted within 2 weeks
P9 Risk of getting infected if visited the stores

Fig. 14

Parallel analysis scree plot (in-shop shopping).

Later conducting factor assay using 3 factors and rotating them by the ‘varimax’ method,
Tabular array 7
shows the loadings of items on different factors. The three factors resulted are labeled as
perceived threat
supply-chain side disruption
(SSD), and
excessive pricing
(DP). The cumulative variance explained by these three factors is
Tabular array eight
shows the residual test results. The root mean foursquare of residuals is
0.02, which is in the range of credence of closer to zero. The root hateful foursquare mistake of approximation (RMSEA) is
showing a good model fit every bit the value is well below 0.05. The Tucker–Lewis index (TLI) here is
0.953, while the cut off for TLI is 0.nine.

Table 7

Gene pattern matrix for rotated loadings.

Sr. no Items of measurement Notation MR1 (xvi%) MR three (thirteen%) MR 2 (12%)
1 Absence of oversupply management forces at the stores P6 0.85 0.32 0.05
2 Proper precautions not being taken at stores P5 0.65 0.25 0.06
iii Limitations imposed on buying P1 −0.02 0.55 0.06
4 Fluctuation in opening and closing timings of shops P7 0.19 0.47 0.03
5 Some regular items not available P2 0.05 0.43 0.26
6 Increased cost on some items P3 0.21 0.20 0.96
7 Restrictions on payment mode P4 0.29 0.33 0.21
8 Issues not sorted within ii weeks P8 0.23 −0.04 0.eleven
9 Adventure of getting infected P9 0.25 0.39 0.01
SS loadings 1.42 1.17 1.05
Proportion Variance 0.16 0.13 0.12
Cumulative Variance 0.16 0.29 0.41
Proportion explained 0.39 0.32 0.29
Cumulative Proportion 0.39 0.71 1.00

The factor MR1, named every bit
perceived fear/risk, explained 16% of the total variance. The items loaded to MR1 were P6 (Absence of crowd management forces at the stores) and P5 (Proper precautions not being taken at stores). Factor MR3 is named as
supply-side disruption. It explained 13% of the total variance. The items loaded to MR3 were P1 (Limitations imposed on ownership), P7 (Fluctuation in opening and closing timings of shops), and P2 (Some regular items not available). The factor MR2, named as
excessive pricing, explained 12% of the full variance. The particular loaded to MR2 is P3 (Increased cost on some items).

Internal reliability analysis gave the value of Cronbach alpha value for all the ix items every bit
(should be >0.six as per Butts & Michels, 2006), which is satisfactory. The reliability of subscales was likewise found out. Cronbach alpha for the factor supply-side disruption is 0.72; 0.62 for dynamic pricing and 0.57 for a perceived threat.

Descriptive analysis on the data collected propose that consumers did not prefer visiting stores for ownership essential commodities during the lockdown. More inclination towards online shopping, authorities-imposed restrictions and pandemic driven anxiety could be the major reasons for this shift. The results of factor assay demonstrate a considerable explanatory data that can be used in future studies. This will enable retail marketers to prioritize their resources effectively and efficiently. For instance, vendor side disruption was majorly because of the consumer’s perceived risk while physically visiting stores for shopping. Therefore, vendors and governmental bodies should make sure of a safe environment thus, ensuring a stress-gratis shopping experience for consumers.

9.two. Online shopping

Table 9
shows the list of items that were used to assess the online shopping experience of respondents. Bartlett’s examination results gave a P-value <0.05, and the KMO value obtained is
(>0.5). Thus, the success of both sphericity and KMO tests makes the data suitable for factor analysis. The parallel analysis test suggests that the observed eigenvalues college than their respective random eigenvalues are more likely to form significant factors. Accordingly,
Fig. xv
suggests the retention of 3 factors.

Tabular array 9

Items used in the questionnaire (online shopping).

Items (note) Measurement
Q1 Limitations on amount of buying online
Q2 Some regular items not available online
Q3 Increased prices of some items
Q4 No delivery slot available
Q5 Fear of getting poor quality detail
Q6 High delivery charges being applied
Q7 Some orders were canceled
Q8 Not able to place lodge because of high need
Q9 Issues non sorted inside 2 weeks

Fig. 15

Parallel assay scree plot (online shopping).

After conducting cistron analysis using 3 factors and rotating them by the ‘varimax’ method.
Table 10
shows the loadings of items on different factors. The three factors used in factor assay were named as vendor distrust (VD), supply-chain disruption (SD) and order-placing difficulty (OD). These three factors explained
variance in the data.
Table 11
shows the rest test results. The root mean foursquare of residuals is
0.03, which is in the range of acceptance of closer to zero. The root mean square error of approximation (RMSEA) is
showing a good model fit as the value is more than 0.05. The Tucker–Lewis index (TLI) here is
0.948, while the cutting off for TLI is 0.9, then is acceptable.

Tabular array 10

Factor pattern matrix for rotated loadings.

Sr. no Items of measurement Note MR1 (25%) MR2 (17%) MR3 (10%)
1 High delivery charges being applied Q6 0.82 0.08 0.00
2 Fear of getting poor quality detail Q5 0.69 0.22 0.02
three Some of the orders getting canceled Q7 0.51 0.26 0.41
iv Increased prices of some items Q3 0.50 0.28 0.02
5 Some regular items not available online Q2 0.18 0.97 0.xv
vi Limitations imposed on corporeality of ownership items online Q1 0.32 0.33 0.10
7 Not able to place online order due to higher need Q8 0.48 0.28 0.66
8 Commitment slot not available Q4 0.40 0.41 0.44
9 Issues not sorted out inside two weeks Q9 −0.11 0.00 0.29
SS loadings ii.21 1.50 0.92
Proportion Variance 0.25 0.17 0.10
Cumulative Variance 0.25 0.42 0.52
Proportion explained 0.48 0.32 0.20
Cumulative Proportion 0.48 0.80 1.00

Tabular array 11

Residual test results.

Indicators Values
Root Mean Square of the Residuals (RMSR) 0.03
Tucker Lewis Index (TLI) 0.948
Root Mean Square Error of Approximation (RMSEA) 0.06

The factor MR1, named equally
vendor distrust, explained
of the full variance. The items which were loaded highly to MR1 were Q6 (high delivery charges), Q5 (fear of getting poor quality particular), Q7 (some orders getting canceled), and Q3 (increased prices of some items). Factor MR2 is named as
supply-chain disruption. It explained
of the total variance. The items loaded to MR2 were Q2 (some regular items non available online) and Q4 (delivery slot not available). The gene MR3, named as
order-placing difficulty, explained
of the total variance. The highly loaded items to MR3 were Q8 (non able to place the order online due to high demand) and Q7 (some of the orders getting canceled).

Internal reliability analysis is conducted, which produced Cronbach’s blastoff value (α) for all 9 items as 0.8 (significant if α > 0.6 as per Butts & Michels, 2006), which is satisfactory. Subsequently, the reliability of subscales is also found out. Cronbach blastoff for factor vendor distrust (MR1) is 0.75, for supply concatenation disruption (MR2) is 0.64, and for society-placing difficulty (MR3) is 0.59. All of them were establish to be satisfactory, i.e., were greater than or equal to 0.6.

Although, online shopping gained popularity during lockdown, disruptions were seen at final vendor node there also. The results of factor analysis suggest that the factor ‘vendor distrust’ has major touch on disruptions experienced in online shopping. Therefore, online shopping service providers should build systems that are more reliable, user friendly and affordable to common people.

ten. Give-and-take and policy suggestions

The data collected embrace 20 states in India, indicating wide geographical applicability of the study findings. The initial spread in India was slow, and the people in India were exposed to information overload from China, Europe, the USA, and other affected countries with COVID-nineteen, where cities and regions were placed under lockdown to control the spread of the disease. As the number of cases rose past early March, so did the social anxiety. Anecdotes on toilet-paper shortage and empty supermarket racks from the worst-hit countries urged some consumers in Republic of india to prepare, including hoarding essential bolt, for the possible replications of these events in other countries. The Junta curfew and the subsequent sudden proclamation of a iii-calendar week pan-India lockdown with very short notice created uncertainty and feet in the minds of consumers, store owners, transporters, and suppliers. This study focuses on consumers’ responses to these events regarding essential commodities.

The behavioral changes reported in this study are the combined outcome of the pandemic and the lockdown. It is concluded that the frequency of visiting stores for both perishable and not-perishable bolt reduced during the pandemic (Fig. three). A primary reason for this beliefs is the fright of getting infected. It is natural that when consumers want to reduce the frequency, they will purchase considering longer future needs. The actress purchase is also a preparation for a possible quarantine. Close to 50% of consumers purchased groceries, because a future period of more than than 1 month. Another reason for hoarding commodities is the fear of their shortage in the virtually future. The critical factors for in-shop purchasing that resulted from these situations are perceived fear/risk, supply-side disruptions, excessive pricing. The noesis that such exigencies are likely can motivate policymakers to ensure sufficient supply of essential commodities at the terminal vendor nodes to instill conviction in consumers, thereby controlling excessive purchase. Enforcement measures that control opportunist pricing would also benefit, as excessive pricing is a disquisitional gene. The factors that disrupted online purchasing are vendor distrust, supply-side disruption, and order-placing difficulty. Vendor distrust being the major factor, retailers too as online shopping service providers should build systems that are more reliable, user friendly and affordable to mutual people.

The share of online purchasing of groceries is <20% in Republic of india; thus, some limited vendors tin can be trusted for the quality and pricing of bolt. Similar the in-shop purchase, online stores were also affected past supply disruptions. The express number of well-recognized vendors were not prepared to handle the drastic rising in demand. Considering the rapid growth in online order and their importance during emergencies, businesses and policymakers must brand conscious efforts to maintain and improve the multi-modal nature of purchase options.

The fear of infection, restriction on the travel, and the disruptions at the organized retail stores shifted many to the local
stores. Although they are facing stiff contest from organized retail stores,
stores still are the key final node vendors of essential household commodities for the people from all income groups. They have proven to exist very effective in an emergency, simply well-nigh of them lack the convenience of electronic payment and online ordering. It is suggested that necessary support should be provided past the regime to better their efficiency and improve their reach. This volition besides encourage walking, reduce longer trips, and is efficient in many dimensions, including that of the environment and employment. The persistent ignorance of pedestrian-friendly infrastructure has been degrading the walkability in Indian cities. Information technology is vital, especially in anticipation of emergencies, but not express to them, to create walkable neighborhoods where day-to-day subsistence demand non depend on motorized mobility.

11. Conclusions

In this study, data related to consumers’ responses apropos essential commodities during and earlier the pan-India lockdown was collected using an online questionnaire. The data were collected from 20 states in Bharat, but responses are restricted from those who can read and write English and use the internet. The questionnaire included three broad sections, 1 each for socio-economic characteristics, before lockdown purchase activeness, and during lockdown purchase activeness. Overall, data from 733 households were collected, though a few optional questions were left unanswered past some respondents. Thus, the number of information-points varies beyond different components of our analysis. Descriptive analyses are presented with reference to frequency of purchasing, type of stores visited, mode of payment, trip length distribution, way of travel, and panic and excessive buying. The effect of income group on these attributes is also assessed. Factor analysis was performed to identify the factors that limited the experience of consumers during lockdown for in-shop and online purchasing of essential commodities.

Fright of infection and lockdown restrictions caused a reduction in the frequency of essential purchase but resulted in panic and excessive buying. The increasing share of organized retail stores reversed during the lockdown because of their inability to cater to the excessive demand and the proximity of
stores. Short trips of <1 km increased during the lockdown, and walk is found to be a pop style of travel across all income groups. Two-wheelers are the main option for the group I (income less than INR fifty,000) and group 2 (income between INR l,000 to 200,000) respondents, whereas walk is the almost preferred by group III (income greater than INR 200,000). The distribution fitting to the trip length distribution revealed that the exponential distribution is the best fit for the before lockdown travel for in-shop purchase, whereas gamma distribution is the best fit during the lockdown. The behavior of the consumers is found to be influenced past income grouping. The gene analysis identified perceived fear/adventure, supply-side disruption, and excessive pricing as disruptive factors for in-store purchasing. The factors affecting online purchasing are vendor distrust, supply-side disruptions, and difficulty in placing orders.

The findings from the study signal to suggestions that will help manage emergencies in pandemic situations similar COVID-19. A few important suggestions are 1) ensuring sufficient (more than what is usually bachelor) supply of essential commodities to instill confidence, 2) enforcement to avoid opportunist pricing, 3) making retail store operators and consumers follow the rules such equally social distancing and wearing masks, 4) improving walkability in cities, 5) facilitating local
stores for electronic payment and online ordering, 6) convincing organized retail stores to heighten their in-store purchase and online ordering capabilities. The findings can likewise be useful in developing urban freight demand models for emergencies. Developing a comprehensive emergency freight demand model analyzing dissimilar disruption scenarios is the demand of the hour and can help in tackling such emergencies effectively in the future.

CRediT authorship contribution argument

Gopal R. Patil: Conceptualization, Methodology, Writing – review & editing, Supervision.
Rutuja Dhore: Information curation, Formal analysis, Writing – original draft.
B.K. Bhavathrathan: Methodology, Writing – review & editing.
Digvijay Southward. Pawar: Methodology, Writing – review & editing.
Prasanta Sahu: Methodology, Writing – review & editing.
Asim Mulani: Data curation.


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