Keywords

Behavioral Change, Perceived Risk, Online Shopping Platform, Covid-19, Pandemic, Consumer Perception.

Introduction

The online purchase of goods and services is not a new phenomenon; it started with the arrival of the first search engine and e-mailing technology, the arrival of the world wide web (www) but at the same time traditional shopping (brick and mortar) is still popular. Professional and techno-savvy customers were purchasing online, but they were also like to have a shopping experience and enjoy leisure (Prasad & Gautam, 2012). The online shopping culture rapidly changed with the onset of Covid-19 pandemic. With multiplicative sporadic propagation of Covid-19, lockdown created chaos, and migration of working classes from working sites to native places. The government took preventive measures and established Covid-19 special hospitals to minimize the impact and urged citizens to follow the health guidelines. All educational and commercial establishments closed, but online orders and distributions remained operational and worked as usual with few precautions.

Due to the closure, consumers limited their consumption in the fear of virus infection. For fulfilling bare minimum needs, consumers chose to shop online instead of going to stores and supermarkets to avoid crowded places. Researchers pointed at multiple determinants of changed consumer behavior and hinted at risks or benefits of online buying. Economic risk contributes to behavioral shift and trust moderates the relationship (Bhatti et al., 2018). Some benefit of online shopping, includes easy to use, awareness of utility, marketing policy, convenience, and perceived service quality (Katawetawaraks & Wang, 2011; Tse et al., 2006; Yan & Dai, 2009; Fang et al., 2016; Baubonienė & Gulevičiūtė, 2015). Most of these benefit variables had a positive correlation with online shopping. Consumer switching behavior due to perceived risk or perceived benefits were addressed in normal situation in extant literature and may not hold true in special situations like Covid-19 Pandemic.

Consumer behavior is crucial for business in any circumstance, especially in a new environment of Covid-19, which is influencing all economic sectors around the world. This study aimed to examine the impact of Covid-19 on shifting customer's buying pattern. Although, human species have encountered fatal epidemics like H1N1, Dengue, SARS, Bird Flu, AIDS etc., the Covid-19 pandemic is different and there is no research models that address the behavioral shift among consumers during Covid 19. Present study used both risk and benefit variables to understand the changing consumer behavior dynamics.

This study is organized in to five sections. Section 1 formulates the problem statement, section 2 presents the literature review, section 3 focuses on research methodology, section 4 presents the data and section 5 highlights the conclusion and implications of the study.

Literature Review

Wang et al. (2020) describes COVID-19 crisis as a worldwide disaster which has profoundly affected the growth and development of the global economy and threatening the survival of firms worldwide. It seems unavoidable that this natural disruption has hit the global economy and produced a huge crisis for firms and industries. This study explores how firms in China is innovating its’ marketing strategies by critically identifying the typology of firms’ marketing innovations using two dimensions, namely, motivation for innovations and the level of collaborative innovations. This research also explores the influence of the external environment, internal advantages (e.g., dynamic capabilities and resource dependence), and characteristics of firms on Chinese firms’ choice and implementation of marketing innovation strategies. This research paper provides valuable insights for firms to respond successfully to similar crisis events in the future.

He & Harris (2020) has offered some initial examination on how Covid-19 pandemic influenced the developments of CSR and marketing. The authors argue that Covid-19 pandemic offers a great opportunity for businesses to shift towards more genuine and authentic CSR and contribute to address urgent global social and environmental challenges. They also discuss some potential directions of how consumer ethical decision making will be shifted due to the pandemic. In their discussion of marketing, they outline how they believe marketing is being affected by this pandemic and how they think this will change, not only the context of marketing, but how organizations approach their strategic marketing efforts. The authors end the research paper with a identifying a number of potentially fruitful research themes and directions.

Skordoulis et al. (2018) proposed the purpose of their research is to analyze e-customer satisfaction, the factors affecting it and the impact of the economic crisis on it. The researchers examined 350 e-commerce websites users' satisfaction. The research results indicate that e-customer satisfaction determinants are the purchasing process, the safety provided by the website, the brand name of the e-commerce firm, the possibility of interaction and communication with the website and other consumers and, the products delivery process. Regarding the utilitarian features of products, e-customers pay more attention to quality than to quantity and price.

Nguyen et al. (2020), attempted to understand the corona virus disease 2019 (COVID-19) pandemic that had a large impact on the publishing industry. The authors aimed to investigate the influences of the COVID19 pandemic situation, utilitarian and hedonic motivations on consumer intention to buy books online. It conceptualizes the effects of the COVID-19 pandemic as situational influences, which involve the closure of physical bookstores, health risks associated with visiting such stores, online shopping trend and additional marketing efforts from online bookstores during the pandemic. Data analysis shows that the COVID-19 pandemic situation has a positive and significant impact on consumer intention toward online book shopping. Furthermore, while utilitarian motivation exerts a strong effect on consumer intention to purchase books online, the relationship between hedonic motivation and online purchase intention is positive but insignificant. These findings would assist key stakeholders such as publishers and online bookstores to improve the quality of their websites as well as develop their marketing campaigns.

Bhatti et al. (2020) described the Corona virus intensely transformed the global trends. These variations are causative to the high inadvertent and secondary funds that reflected for this virus. The authors has described in this article the e-commerce trends in corona virus predicament as well as how imminent progress in e-commerce that might affect consumer behavior in future. This article examines that e-commerce grew due to corona virus. E-commerce has become a substitute source and considered top in this condition, and e-retailers provides goods that usually consumers bought in superstore traditionally. Corona virus impacted on whole e-commerce industry. Meanwhile, we want to comprehend their efficacy to stability both cost and benefits as well connected actions in coming scenario.

Perceived Risk of Online Purchase (PROP)

Consumers perceive the internet offers bundle of benefits and perceive a higher level of risk when purchasing on the Internet than traditional retail formats (Forsythe et al., 2006). Internet shoppers experience various types of risk that include product performance risk, financial risk, and time/convenience risk. Financial risk is the potential net loss of money. It includes consumers' sense of insecurity regarding online credit card usages, which act as the bottleneck in online purchases (Maignan & Lukas, 1997). Product performance risk, the loss incurred when a brand or product does not perform as expected, is due to the shoppers' inability to evaluate the product's quality online accurately (Bhatnagar et al., 2000). Inconvenience risks result due to navigation difficulty and submitting orders or delays receiving products. Attitudes toward online shopping depend on both functional and non-functional benefits, and their perceptions of the risks associated with purchasing online.

Consumers' awareness impacts the emotional beliefs and purchase intention (Pham et al., 2020; Bhatt et al., 2018). Financial risks' negatively influence the online shopping (Philippou et al., 2009; Yan & Dai, 2009; Bhatti et al., 2018). Literature suggests financial risks impact online shopping in a normal and stable economy and society. Recently, Covid-19 impacted all countries worldwide, which triggered the online purchase. The risk perception in online shopping and hedonic consumption behavior of credit card owners are lower than consumers who do not own credit cards (Şener et al., 2018). The correlation between the perceived consumers’ benefit, perceived risk, hedonic and utilitarian consumption values has been discovered. These facts suggest our study about the idea of measuring the impact of financial risk online shopping during the Covid-19 period and a comparison with the trust of online shopping and losing money before and after Covid-19. Based on the facts, we propose Hypothesis 1 as follow:

H1: There is a change of Indian consumers toward Online Shopping during the Covid-19 pandemic in terms of Financial Risks

Product Risks for online Shopping (PROS)

Yan & Dai (2009) researched many aspects of product risk, including product performance, online product intangibility, and real product compared to customers' expectations (Masoud, 2013). Some product consumers' problems may have in online shopping are its performance not fit the need; what they ordered is different from what they saw online. The goods they ordered could be fake, inferior, or damaged on the way they get or delivery process. We found that most of the literature admitted the negative effects of product risks on online shopping; hence, this research continues to apply this point for the model but in a new circumstance of Covid-19 outbreak. Accordingly, we propose Hypothesis 2 as follows:

H2: There is a change of Indian consumers toward Online shopping during the Covid-19 pandemic in terms of perceived risk

Perceived Time Risks for online shopping (PTR)

As a online customer experience too many steps involved in the shopping process that causes time risks due to waiting, searching, delivery lag, product unavailability (Yan & Dai, 2009; Masoud, 2013). In addition, service time including product maintenance and customer support channel complicates the purchase. Surprisingly, Masoud (2013) hypothesized that time risk negatively influences online shopping, but the result did not support that hypothesis. Therefore, this study aims to test this hypothesis again in the new situation of Covid-19. Based on these arguments, Hypothesis 3 posited as follows:

H3: There is a change of Indian consumers toward Online Shopping during the Covid-19 pandemic in terms of Time Risk

Delivery Risks in Online Shopping (DROS)

Most of the authors from Yan & Dai (2009) to Masoud (2013) have reiterated that delivery service after purchasing in pre-delivery payment might be based on the product or service availability, distance from the store; therefore, delivery risks like late delivery, not in-time in receiving products, and probable damage during handling and moving. The authors found the delivery risks negatively impact the online shopping, accordingly, we propose Hypothesis 4 as follows:

H4: There is a change of Indian consumers toward Online shopping during the Covid-19 pandemic in terms of delivery risk

Social Risks of Online Shopping (SROS)

Iyengar et al. (2009) think that the class of consumers in society reacted to buying Behavior differently because of the different levels of impact from social pressure. The author's inference that there are negative effects on buying Behavior towards high-class customers in society and positive effects on middle-class and no impacts on lower-class buyers. Yan & Dai (2009) also expressed the social contact risk impact the ability to make decisions, which is crucial in the fast and high transmission rate of Covid-19. Therefore, we propose the correlation of personal reputation, social status, and online shopping decision during the Covid-19 period and hypothesized that:

H5: There is a change of Indian consumers toward online shopping during the Covid-19 pandemic in terms of social risk.

Perceived Benefits of online shopping

Utility of awareness on online shopping (UAOS)

Lee et al. (2011), considered positively impact the behavioral intention of online shopping and utility awareness; there are many characteristics of utility and awareness measured such as time efficiency, take-away and delivery service, face-to-face negotiation, mailing the text while online shopping. Senecal et al. (2005) discovered that take-away and delivery services serve customers who were afraid of dining out due to the SARS outbreak. It is very useful to analyze utility awareness during Covid-19, which is similar to SARS but highly dangerous. Moreover, the authors also add a mask during Covid-19 into the survey to analyze this variable on online shopping. Accordingly, we propose Hypothesis 6 as follows:

H6: Utility awareness affects online shopping during the Covid-19 pandemic

Convenience for online shopping (COS)

Extant literature suggests the positive impact of Convenience of purchasing on the probability of making a purchase. Most of the author in this domain has clearweed the relationship between convenience and purchase decisions. Yan & Dai (2009), Katawetawaraks & Wang (2011), Bhatti et al. (2018) and Lee et al. (2011) had a similar result in which perceived ease of use is considered to impact the behavioral intention of online shopping positively. Fang et al. (2016) showed that perceived e-service quality related to the capacity to provide comprehensive delivery service would bring higher value to customers to explain the awareness of easy to use; using the latest technology smoothly by the advanced shoppers is one of the most important factors of easy to use which influence the decision of customers. In some of the authors, we also have a contrasting perspective of online shopping's negative side. Yan & Dai's (2009) findings explain that the possibility of service risk from online shopping might be inconvenient to exchange a product, not consulted by a face-to-face personal contact compared to traditional shopping way. Accordingly, we propose Hypothesis 7 as follows:

H7: Convenience affects online shopping during the Covid-19 pandemic

Marketing strategy awareness to consumer (MSAC)

Authors like Katawetawaraks & Wang (2011) indicated that marketing strategies could influence the customers on an easy-to-access website, advertisement campaigns that create curiosity to purchase backed together with its capacity to access consumers easily. After the Covid-19 lockdown in India, some companies offered many promotions in the FMCG sector, durable products, vehicles, etc. The new point of this study applied is the analysis of the company's marketing strategy and return policy based on the facts of marketing strategy during Covid-19 in the new context. Accordingly, we propose Hypothesis 8 as follows:

H8: Perception of marketing strategy affects online strategy during Covid-19pandemic

Consumer awareness of Price and Cost (CAPC)

Online stores give customers opportunities to compare prices between various products. Price is an essential determinant for online shopping. Yan & Dai (2009) concluded in his research paper that the probability of making purchase decisions might be based on the low-cost while Baubonienė & Gulevičiūtė (2015) stated additional incurred costs could be factors influence customers experience in online shopping. Therefore, we propose other online shopping costs in the survey, such as application installation and Internet during the buying process. Accordingly, we propose Hypothesis 9 as follows:

H9: Perception of consumer awareness of price and cost affects online shopping during the Covid-19 pandemic

Control Variables

To analyze and understand customers' decision-making process when they buy products online, research of Fang et al. (2016) brought out a framework in which gender and age took the role of moderator variables affecting shoppers' perceived value. We used the demographic characteristics like education, occupation, financial status (monthly income), and family size for analysis of online shoppers' decision-making process (Yan & Dai, 2009). The paper of Baubonienė & Gulevičiūtė (2015) also investigated these elements motivating customers to shop online, while the work of Baker et al. (2020) explored the response of household's consumption in the context of Covid-19 Pandemic.

The Internet has provided various shopping experiences to bring more benefits to customers when shopping online (Yan & Dai, 2009). However, the limited experience of young people led them to seek and use more information to decide their purchase on online stores' electronic service (Ganesan‐Lim et al., 2008). Hence, the purchasing rate somehow has an impact on the experience in online shopping of customers. Education levels, web experience, purchase quantity, and purchase frequency acted as control variables (Fang et al., 2016) for investigating shopping motives and predicting customers' perceived value.

Hence, this research constructs socio-demographic characteristics as the control variables involving gender, age profile, purchasing rate, educational level, occupation, monthly income, family size, living geographical area, shopping experience, together with adding two more specific information about shopping categories, and reasons of purchase in the proposed model.

Moderator Variables

As we mentioned above, the Covid-19 pandemic could influence demand, psychology, emotion, customer heterogeneity, trust between the parties, a perception which was indicated as a factor affecting customer's switching Behavior (Ahmed et al., 2015; Keaveney, 1995; Nimako & Winneba, 2012; Lopez et al., 2006). Hence, this article concentrated on the impact of the Covid-19 pandemic and concentrated on two factors, including Affection of Covid- 19 information and Awareness of Covid-19 as moderator variables.

Research Methodology

With extant literature review, research gap was identified that need addressal for influencing the online shopping during Covid-19. Present study encapsulates factors emergent from literature review and some new factor are added measure consumer behavior's change before and during the Covid-19 outbreak. This study will link all these variables into two different groups, including a group of Perceived risks and a group of Perceived benefits. Perceived risks, which negatively affect online shopping, include product risks, time risks, delivery risks, information, social risks, and financial risks. Simultaneously, benefit variable groups from purchase decisions have some characteristics of Convenience, marketing strategies, price, and cost. However, the above papers in the review section analyzed consumer behavior in a normal period without special support from society. Therefore, this study will examine Indian online shopping behavior during this special time.

Population, sampling, and Data collection Methods

The present study is conducted in Uttar Pradesh (the most populated state of Indian province). A population of 2600000 accounts for 16.4 percent of the country's population and more than 45 % youngster. It is also the fourth largest state in a geographical area covering 6.88 percent of the country's geographical area, encompassing 2,43,290 square kilometers and 75 districts. The population density in the state is 829 people per square kilometers as against 382 in the country. The total population of the state was 19.9 cores in 2011. The state income structure shows that the primary sector's contribution has declined to 41 percent of the state income, though the sector still sustains 73 percent of the total working force. Further, we have selected the state capital Lucknow for our study, which has a total population of 28.2 lac as per the census of 2011. We have followed the sample size determination method out of available methods, and we proposed the study hypothesis as above. The sample could present the population with a given confidence level of 90% and 95% based on the Yates method (Box et al., 1987). We selected enough samples to investigate the overall population (Smith et al., 2016). Thus, we performed the size calculation according to the Yamane (1976) formula out of available methods like Cochran (1977) and GlennD Israel (1992).

The sample size is 'n'; the population is 'N'; and 'e' is a significant level at 0.05 (5%) and 0.1 (10%). Thus, according to the calculation results, the minimum sample that the research needs to perform is at least 400 samples as genuine respondents.

Data Collection Methods

The collection of genuine data from the sample is one of the most tedious tasks in countries like ours (India), where respondents avoid many questions before giving the response. Systematic random sampling directed the selection of respondents for the study. The survey questionnaire includes various questions, including informational questions, questions for measuring independent variables, moderator variables, and the dependent variable using the Likert's scale 5 (1 equal to disagree completely and five equivalents to agree, respectively completely). Initially, we conducted the pilot test with 50 respondents and adopted the Cronbach's Alpha analysis to ensure the reliability of question items before conducting a formal survey. Eventually, we found 371 valid samples out of 400.

Data Analysis and Interpretation

Initially, we used Cronbach's Alpha coefficient for each variable to test our design's reliability and developed a scale (questionnaire). We find Cronbach's Alpha is greater than 0.968, which presents the best reliability as presented in Table 1.

Table 1 The Reliability and Validity Test (Cronbach's Alpha and KMO and BARTLETT'S Test)
Reliability Statistics
Cronbach's Alpha		N of Items
0.968		10
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.			0.944
Bartlett's Test of Sphericity	Approx. Chi-Square		4900.437
	df		45
	Sig.		0.000

Secondly, we used the explanatory factor analysis (EFA) to check each variable's items' trust and confidence and thereby established the most accurate and true representative variables. As per the EFA test protocol, P-value in Bartlett's test should be less than 5% and the KMO coefficient should be higher than 0.5. For reliability, each item in the same construct should have a factor loading greater than 0.5. The factors with eigenvalue more than one forms the final factor structure and the total Variance extracted for all extracted (Norusis, 1994; Hair et al., 2010).

Thirdly, the authors use hierarchical regression to inspect the research hypothesis, including moderators. Our paper examined the hypothesis of moderators by the widely used mean centering method (Aiken et al., 1991; Cohen et al., 2003). We have to find approx. 78% cumulative coverage of the research matters and objects as presented in Table 2 This is a collaborative method, determining which variables are used in the suggested model from previous models based on the three following equations of regression:

Table 2 Total Variance Explained
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	7.787	77.873	77.873	7.787	77.873	77.873
2	0.814	8.135	86.008
3	0.429	4.289	90.297
4	0.222	2.224	92.521
5	0.192	1.924	94.446
6	0.182	1.816	96.262
7	0.115	1.150	97.413
8	0.098	0.983	98.396
9	0.088	0.881	99.276
10	0.072	0.724	100.000
Extraction Method: Principal Component Analysis.

Z = α₀ + α₁*SB (1);

Z = α₀ + α₁*SB + α₂*K (2);

Z = α₀ + α₁*SB*MV + α₂*MV+ α₃*SB*MV (3)

Where: α₀, α_i: are regression weights.

1. The regression equation (1) shows the impact on the dependent variable by independent variables.

2. The regression equation (2) shows the independent variable's impact on the dependent variable; the moderator variable Y is considered an independent variable in the model.

3. The regression equation (3) shows the independent variable's impact and the interaction variable (Y.K) on the dependent variable. If the interaction variable has a significant level of less than 0.05 or 0.10 indicates that the variable K acts as a moderator variable.

Table 3 presents the model summary of r, r² and adjusted r² along with the variance significance (Field, 2005). Here, r has a value 0.504; this value represents the simple correlation between group size CAPC (Customer awareness for price and cost), PROP (Perceived risk online platform), COS (Convenience of online shopping), PROS (perceived risk in inline shopping), SROS (Social risk in online shopping), MSAC (Marketing strategy and awareness of consumer), PROS (Perceived risk in online shopping), UAOS (Utility awareness in online shopping), SROS (Social risk in online shopping, PTR (Perceived time risk in online shopping).

Table 3 Model Summary Through Regression and Analysis of Variance
Model Summary
Model		R		R Square		Adjusted R Square			Std. The error of the Estimate
1		0.504a		0.254		0.233			0.99672
a. Predictors: (Constant), CAPC, PROP, COS, PBOS, DROS, MSAC, PROS, UAOS, SROS, PTR
ANOVA
Model			Sum of Squares		df		Mean Square	F		Sig.
1	Regression		121.356		10		12.136	12.216		0.000b
	Residual		356.650		359		0.993
	Total		478.007		369
a. Dependent Variable: S.B.
b. Predictors: (Constant), CAPC, PROP, COS, PBOS, DROS, MSAC, PROS, UAOS, SROS, PTR

"r²" is a measure of how much variability in the outcome is accounted for by the predictors (Field, 2005). The value of r² is 0.254, which tells us that these ten variables can account for 25.1% of the variation in online shopping's overall role.

The adjusted R-Square suggests the generalizability of the model (Field, 2005). Result showed the difference between r² and adjusted r² is 0.1% that indicates the model fits to the larger population set also. Hence we become sure that all our identified variables, chosen tools and techniques, our thought process for the present research study has been scientific, and we have followed the right research methodology, which is scientific and reliable.

Data Analysis

We have used both the descriptive and inferential statistics to analyze the collected and tabulated data in a coded form, which as under.

As the data collected from the survey and tabulated properly with coding as presented in Table 4 shows, the male respondents are 188 (50.7%). The female constitutes a share of 183 (49.3), almost near to male online buyers.

Table 4 Frequency of Gender in Online Shopping
Gender
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Male	188	50.7	50.7	50.7
	Female	183	49.3	49.3	100.0
	Total	371	100.0	100.0

Table 5 presents a view of the respondents' age profile in which we find that majority online shoppers are in the age slot of 15 years to 35 years (90%). This is because of the technology adaptability among youngsters, and they have adopted technology as usual in all spheres of life.

Table 5 AGE Group of the Respondents
Age Group (In years)
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	15-25 Yrs	189	50.9	50.9	50.9
	26-35 Yrs	164	44.2	44.2	95.1
	36-45 Yrs	15	4.0	4.0	99.2
	Above 45 Yrs	3	0.8	0.8	100.0
	Total	371	100.0	100.0

Table 6 shows the combination of online shoppers, which denote that businesspeople have a high frequency in the online platform. This may be because, in the Covid pandemic, they had the only option of carrying out business activities, and hence they upgraded them to technology savvy. Second, the students are already online platform either their learning or buying goods or services for the family requirements. These two segments constitute 279 (75.2%) of the sampled respondents.

Table 6 The Profession/ Occupation of the Sampled Respondents
Profession
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Student	122	32.9	32.9	32.9
	Businessman	157	42.3	42.3	75.2
	Service	28	7.5	7.5	82.7
	Professional	63	17.0	17.0	99.7
	Others	1	.3	.3	100.0
	Total	371	100.0	100.0

Table 7 shows that graduates and postgraduates form the largest share of online purchase activities and constitute 361 (97%) of the total sampled population.

Table 7 Share of the Respondents' Academic Qualification
Qualification
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Higher Secondary	11	3.0	3.0	3.0
	Graduate	104	28.0	28.0	31.0
	Postgraduate	256	69.0	69.0	100.0
	Total	371	100.0	100.0

Table 8 depicts the decreasing frequency of respondents for online shopping during the Covid-19 pandemic Rs' earners. Twenty thousand per month have shown higher online shopping approach 170 (45.8%) whereas Rs. 21000 to 30000 monthly earners are 108 in numbers our of 371 sample (29.1%). Rs 41000 to 50000 monthly earners have a 10.8% share in online shopping platforms during the period and last Rs. 31000 to 40000 have shown the lowest share of 14.3 %, i.e., 53 respondents out of 371 responses. We interpret that the lower-income group has been more technology savvy and online users during the pandemic.

Table 8 Monthly Incomes of the Sampled Respondents
Monthly  Income ( Rs. in Lacs)
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Rs. 20000	170	45.8	45.8	45.8
	21000-30000	108	29.1	29.1	74.9
	31000-40000	53	14.3	14.3	89.2
	41000-50000	40	10.8	10.8	100.0
	Total	371	100.0	100.0

Correlation Analysis

We have used Simple linear correlation as a statistical technique to determine the existence of relationships between two different variables (i.e., Shifting Behavior and other independent variables). It shows how much shifting shopping behavior will change when there is a change in the independent variable. This basic question of "whether or not two variables are related or the strength of relationship between the variables" stimulates virtually all quantitative research in consumers' shifting behavior.

Table 9 depicts the strength of relationships that range from -1 to +1. We find most variables have strong correlation whereas few variables weak strengths of relationship between shifting Behavior with other independent variables like PROS. PROP, PTR, DROS, UAOS, COS, CAPC.

Table 9 Correlation Between the Dependent Variable (S.B.) and Independent Ten Variables
Correlations (Strength of relationships between variables)
Control Variables		PROP	PROS	PTR	DROS	COS	MSAC	CAPC	UAOS	PBOS	SROS
Shifting Behavior of the customers during Covid-19 pandemic (S.B.)	PROP	1.000	0.877	0.855	0.770	0.639	0.723	0.301	0.736	0.730	0.796
	PROS		1.000	0.858	0.817	0.661	0.755	0.323	0.769	0.779	0.834
	PTR			1.000	0.863	0.654	0.789	0.309	0.773	0.757	0.813
	DROS				1.000	0.684	0.752	0.350	0.765	0.764	0.832
	COS					1.000	0.747	0.405	0.799	0.725	0.713
	MSAC						1.000	0.373	0.855	0.804	0.806
	CAPC							1.000	0.342	0.320	0.298
	UAOS								1.000	0.839	0.804
	PBOS									1.000	0.864
	SROS										1.000

Hypothesis Test with Respects shifting Shopping to Behavior

To test the hypothesis as hunched in the research study process, we have used the test of homogeneity and Levine statistics about Variance as presented in Table 10.

Table 10 Test of Hypothesis with the help of Homogeneity test (Levine Statistics) and Variance
Test of Homogeneity of Variances
	Levine Statistic		df1			df2		Sig.
PROP	6.020		16			353		0.000
PROS	3.375		16			353		0.000
DROS	4.104		16			353		0.000
SROS	5.289		16			353		0.000
PBOS	3.705		16			353		0.000
UAOS	3.509		16			353		0.000
COS	2.397		16			353		0.002
MSAC	2.977		16			353		0.000
CAPC	.590		16			353		0.891
ANOVA
		Sum of Squares		df	Mean Square		F		Sig.
PROP	Between Groups	94.351		16	5.897		5.379		0.000
	Within Groups	387.024		353	1.096
	Total	481.374		369
PROS	Between Groups	126.414		16	7.901		7.528		0.000
	Within Groups	370.478		353	1.050
	Total	496.892		369
DROS	Between Groups	127.995		16	8.000		7.775		0.000
	Within Groups	363.204		353	1.029
	Total	491.199		369
SROS	Between Groups	112.950		16	7.059		7.061		0.000
	Within Groups	352.911		353	1.000
	Total	465.861		369
PBOS	Between Groups	129.407		16	8.088		7.620		0.000
	Within Groups	374.665		353	1.061
	Total	504.072		369
UAOS	Between Groups	131.868		16	8.242		8.411		0.000
	Within Groups	345.907		353	0.980
	Total	477.775		369
COS	Between Groups	74.155		16	4.635		6.567		0.000
	Within Groups	249.126		353	0.706
	Total	323.281		369
MSAC	Between Groups	101.344		16	6.334		6.831		0.000
	Within Groups	327.295		353	0.927
	Total	428.639		369
CAPC	Between Groups	27.839		16	1.740		2.916		0.000
	Within Groups	210.660		353	0.597
	Total	238.499		369

We find that all ten (10) hypotheses are accepted, and the facts that we hypothesized are true scientifically. The entire hypothesis summarized as-there is a change of Indian consumers toward Online Shopping during the Covid-19 pandemic in terms of Financial Risks, terms of perceived risk, terms Time Risk, terms of social risk, terms of delivery risk, in terms of social risk, Utility awareness affects online shopping, Convenience affects online shopping, marketing strategy affects online strategy, price and cost affects online shopping. All the tentative assumptions from H:1 to H:10 is true and found correct in the research investigation.

Conclusion and Managerial Implication

Our research approach has tried to find out the shifting behavioral changes of Indian customers in the Covid-19 pandemic period in terms of risks and benefits and how the moderators have interfered in the process of online purchase decision making; the study develops various hypotheses, which based on the contribution from previous literature to support businesses a better understanding of Covid-19's influence as well as a better precautionary plan in the future.

In most research studies or research articles, the dependent variables such as shifting consumer buying behavior heterogeneity, income, occupation, education, and gender frequency affect online shopping behavior. However, with the appearance of Covid-19, the effects of these control variables on online shopping were significant. The out puts from the SPSS software how that all determinants used in the study equation are reliable, and the validity and reliability of each construct are good. The study identified no effect of the dependent variable group on online shopping behavior. However, there are significant changes in the risk perceptionofIndiancustomers'buyingbehaviorduringtheCovid-19epidemic.

According to the result, the time risks variable has changed from positive to negative, while financial risks, information risks, delivery risks, and social risks still positively affect. Thus, the related issues such as delivery time, product search time, or online shopping are still major barriers to Indian consumers when doing online shopping. There are two variable reject hypotheses regarding benefits perception, including awareness of utility and awareness of marketing policy. That means the awareness of utility and marketing policy has no effect on shopping behavior during the Covid-19 period. This result states a clear aspect of marketing policy on online purchasing compared to other authors' results. While the easy to use of awareness of price and cost, three factors that significantly correlate the buying decision.

Although the Covid-19 outbreak has just happened since the beginning of 2020, there is increasing research of this topic in healthcare (Raude et al., 2019) and rarely in the business aspect. Most of the authors analyzed the effects of Covid-19 in China, the United States, Germany and Italy, but our paper contributes another view in terms of Covid-19 in India. From other paper's limitation, which recommended other hidden factors affecting perceived valued (Fang et al., 2016) and government policy, emotional, involuntary, subjective norms, psychological could be caused and proposed by Covid-19 pandemic (Nimako & Winneba, 2012), this article continues to analyze Covid-19 as a moderator while other research used to trust (Zhang et al., 2014) and age, gender (Fang et al., 2016) as a moderator. This study of the moderator of Covid-19 in India is distinct different among other papers in which there is no moderator, or moderator variable is not Covid-19. The results show that Covid-19 significantly impacts Indian customer behavior toward online shopping. However, without Covid-19 as a moderator, their purchasing behavior is still the same as before Covid-19. The present study confirms the Covid-19 impacts on switching behavior and helps the business to understand its role in buying Behavior to respond in time in the new circumstance.

Managerial Implications

This paper offers multiple managerial implications. First, in terms of the group of perceived risk factors, although there is a change in buying goods and services, perceived risks are no longer issues with the appearance of Covid-19. After the pandemic period is over, these risks are still barriers that cause customers to not ready for online shopping. The model indicates time risks, delivery risks, and social risks reduce more during Covid- 19 outbreak. However, other risks still may eliminate gradually in favor of the customer. Some solutions are suggested such as diversifying forms of payment including online payment, cash on delivery, pay via e-wallet, etc.; clearer delivery information and time to avoid late delivery for uncontrollable reasons; easy, clear and transparent return policy; return/exchange policy with a flexible way and time. Besides that, governments should create favorable terms and conditions for businesses doing online and encourage people to sell online. In the meantime, tightly control information to make sure accuracy and safety when customers buy online.

Second, the group perceived benefits' impact has some inherent results with the previous studies but could be explored better in the future. Organizations need to pay more attention to marketing policy awareness during epidemics to meet customer needs than the period without the Covid-19 outbreak. Companies can provide more choices, promotions, and discounts for their online purchasing consumers. Particularly, in Vietnam, the affection of society is an important factor impacting consumers' shopping decisions. They often consult relatives before making a buying decision. Evaluation information of relatives or previous buyers also significantly affects their buying behavior. Thus, online companies can give the consumers, who evaluated after buying, reviewed on the website and application, rated, accumulated points, e-coupons, discount code, etc. In other words, businesses and regulators need to do more professional, change faster, capture technology trends to better satisfy consumers' demand during and after the epidemic.

Third, information on disease risks is scary concerning the moderator group, but not enough to change consumer behavior. Therefore, businesses should not consider epidemics as an opportunity to change customers, but rather to take advantage of this period to reduce risk and increase benefits factors for customers.

Limitations and Future Research

Like other research papers and researchers in management and social science, the present research study has no exception, and it contains some potential problems. First, online shopping trust is proposed by the previous researchers as a moderator variable. Present study tested the model in Covid-19 epidemic context, for generalizability future studies can test it in another context also. Second, some previous studies have shown a strong impact of age and gender factors on online shopping, but this paper's results do not include the multi group analysis. Future research can study the impact of this factor and expand the survey in more countries. Also, cultural factors are one of the issues that this paper has not addressed to study online shopping behavior changes that future research can complement.

References

1. Ahmed, Z., Gull, M., & Rafiq, U. (2015). Factors affecting consumer switching behavior: Mobile phone market in Manchester-United Kingdom. International Journal of Scientific and Research Publications, 5(7), 1-7.
2. Aiken, L.S., West, S.G., & Reno, R.R. (1991). Multiple regression: Testing and interpreting interactions. Sage.
3. Baubonienė, Ž., & Gulevičiūtė, G. (2015). E-commerce factors influencing consumers' online shopping decisions.
4. Bhatnagar, A., Misra, S., & Rao, H.R. (2000). On risk, convenience, and Internet shopping behavior. Communications of the ACM, 43(11), 98-105.
5. Bhatti, A., & Rehman, S.U. (2020). Perceived benefits and perceived risks effect on online shopping behavior with the mediating role of consumer purchase intention in Pakistan. International Journal of Management Studies, 26(1), 33-54.
6. Bhatti, A., Akram, H., Basit, H.M., Khan, A.U., Raza, S.M., & Naqvi, M.B. (2020). E-commerce trends during COVID-19 Pandemic. International Journal of Future Generation Communication and Networking, 13(2), 1449-1452.
7. Bhatti, A., Saad, S., & Gbadebo, S.M. (2018). Convenience risk, product risk, and perceived risk influence on online shopping: Moderating effect of attitude. International Journal of Business Management, 3(2), 1-11.
8. Cohen, B.J. (2003). Theory and practice of psychiatry. Oxford University Press.
9. Fang, J., Wen, C., George, B., & Prybutok, V.R. (2016). Consumer heterogeneity, perceived value, and repurchase decision-making in online shopping: The role of gender, age, and shopping motives. Journal of Electronic Commerce Research, 17(2), 116.
10. Forsythe, S., Liu, C., Shannon, D., & Gardner, L.C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. Journal of Interactive Marketing, 20(2), 55-75
11. Ganesan‐Lim, C., Russell‐Bennett, R., & Dagger, T. (2008). The impact of service contact type and demographic characteristics on service quality perceptions. Journal of Services Marketing.
12. Gliem, J.A., &Gliem, R.R. (2003). Calculating, interpreting, and reporting Cronbach's alpha reliability coefficient for Likert-type scales. Midwest Research-to-Practice Conference in Adult, Continuing, and Community Education.
13. Hair, Jr, J.F., Babin, B.J., & Anderson, R.E. (2010). A GLOBAL PERSPECTIVE.
14. He, H., & Harris, L. (2020). The impact of Covid-19 pandemic on corporate social responsibility and marketing philosophy. Journal of Business Research, 116, 176-182.
15. Israel, G.D. (1992). Determining the sample size.
16. Iyengar, R., Han, S., & Gupta, S. (2009). Do friends influence purchases in a social network?. Harvard Business School Marketing Unit Working Paper, (09-123).
17. Keaveney, S.M. (1995). Customer switching behavior in service industries: An exploratory study. Journal of Marketing, 59(2), 71-82
18. Lee, M.K., Shi, N., Cheung, C.M., Lim, K.H., & Sia, C.L. (2011). Consumer's decision to shop online: The moderating role of positive informational social influence. Information & management, 48(6), 185-191.
19. Lopez-Nicolas, C., & Molina-Castillo, F.J. (2006). Customer Knowledge Management and E-commerce: The role of customer perceived risk. International Journal of Information Management, 28(2), 102-113.
20. Maignan, I., & Lukas, B.A. (1997). The nature and social uses of the Internet: A qualitative investigation. Journal of Consumer Affairs, 31(2), 346-371.
21. Masoud, E.Y. (2013). The effect of perceived risk on online shopping in Jordan. European Journal of Business and Management, 5(6), 76-87.
22. Nguyen, H.V., Tran, H.X., Van Huy, L., Nguyen, X.N., Do, M.T., & Nguyen, N. (2020). Online Book Shopping in Vietnam: The Impact of the COVID-19 Pandemic Situation. Publishing Research Quarterly, 36, 437-445.
23. Nimako, S.G., & Winneba, K.G. (2012). Consumer switching behavior: a theoretical review and research agenda. Research Journal of Social Science and Management, 2(3), 74-85.
24. Nimako, S.G., & Winneba, K.G. (2012). Consumer switching behavior: a theoretical review and research agenda. Research Journal of Social Science and Management, 2(3), 74-85.
25. Norusis, M.J. (1994). SPSS advanced statistics, 6.1. SPSS.
26. Pham, V.K., Nguyen, T.L., Do, T.T.H., Tang, M.H., & Thu Hoai, H.L. (2020). A Study on Switching Behavior toward Online Shopping of Vietnamese Consumer during the Covid-19 Time.
27. Philippou, S., Keating, A., &Ortloff, D.H. (2009). Citizenship education curricula: comparing the multiple meanings of a supra‐national citizenship in Europe and beyond. Journal of Curriculum Studies, 41(2), 291-299.
28. Senecal, S., Kalczynski, P.J., & Nantel, J. (2005). Consumers' decision-making process and their online shopping behavior: a clickstream analysis. Journal of Business Research, 58(11), 1599-1608.
29. Şener, A., Ateşoğlu, L., & Coşkun, A. (2018). The effect of utilitarian and hedonic shopping values on consumers' perceived benefits and risks in online shopping. Akademikaraştırmalarveçalışmalardergisi (akad), 10(18), 12-28.
30. Skordoulis, M., Kaskouta, I., Chalikias, M., & Drosos, D. (2018). E-commerce and e-customer satisfaction during the economic crisis. Journal for International Business and Entrepreneurship Development, 11(1), 15-29.
31. Wang, Y., Hong, A., Li, X., & Gao, J. (2020). Marketing innovations during a global crisis: A study of China firms’ response to COVID-19. Journal of Business Research, 116, 214-220.
32. Yan, X., & Dai, S. (2009). Consumer's online shopping influence factors and decision-making model. In SIGeBIZ track of the Americas Conference on Information Systems (pp. 89-102). Springer, Berlin, Heidelberg.
33. Zhang, B., Zhai, F.Y., Du, S.F., & Popkin, B.M. (2014). The China Health and Nutrition Survey, 1989-2011. Obesity Reviews, 15, 2-7.