Keywords

E-shopping, COVID 19 Pandemic, Buying Intention.

Introduction

The usage of Internet has exponentially growth since its inception and the spectrum of its use befits individuals to industries for different purposes. The robust use of internet is because of its unique characteristics like flexibility, interactivity, and customization. Ever since the adoption of web 2.0 technology internet becomes an imperative tool in communication, entertainment, education, and trade (Hanjun et al., 2004; Koyuncu & Lien, 2003). Per se, Anuradha & Roopini (2020) elicited the number of transaction in Ecommerce retailing was 1.12 million per day and further added that Indian economy grows consequent at good pace in connection with digital technologies, businesses in India is boosting potentially irrespective of size and scale which in turn increases the economy of India.

Irresistibly the facilitation scuttled by everyone with the advent of internet leverages the use online shopping platform albeit brick and mortar shops remained as an inherent choice of convenience to the consumers. (Ramayah & Ignatius, 2005). To evidence further it was empirically substantiated that 56% study cases hailing from 24 nations desired to do shopping in brick and mortar stores compared to online (Marketing Charts, 2012). The struggle of interpreting the online buyer behavior is difficult due to nil physical communication during the process (Mukherjee & Nath, 2007). The international economic crisis paved way for companies and entrepreneurs to adopt online marketing strategy as it deems to be an inexpensive gateway for promotion and drags numerous consumers in minimal time span (Business Insider, 2015).

Coronavirus COVID-19 outbreak a global pandemic that unquestionably shows a need for social distancing and being quarantined at home causing major disruptions. The economic effects of outbreak are currently being underestimated. The coronavirus outbreak has adversely impacted the global and local economies and its severity depends on the nation (Nuno Fernandes, 2020). As stated by Araz et al. (2020), spreading of COVID-19 is disrupts numerous international Supply Chains. Consequent upon the COVID-19 pandemic resulted in the border closings, isolations, and totally shut downs of numerous vital facilities and marketplaces. The time when World Health Organization (WHO) broadcasted the international pandemic on March 2020 numerous companies ought to bear the brunt of influence (Ivanov, 2020). Consequently, the global supply chain got disrupted which was a detriment to companies of all sizes across the globe. Customers have also altered their purchase patterns, which results in scarcities of various goods throughout the world. According to Hoang Viet Nguyen’s (2020) Multivariate data investigation, the COVID-19 pandemic condition will have a optimistic and substantial influence on buyer intention headed for online book shopping. In view of the momentary shutting of various food-away-from-home sectors, consumer spending on online provisions in the course of the COVID-19 pandemic has improved (Grashuis et al., 2020).

Since the start of the COVID-19 outbreak, major online sellers wanted to attract more customers to their sites (Nguyen et al., 2020). This study targets to examine the effect of COVID- 19 pandemic situation and consumer’s intention to shop online by using the theory of reasoned action (TRA) model.

Based on the literature review and the pandemic situation concerned our research aims to answer the following research questions:

RQ1. Can TRA be used to predict the intention to choose Online Shopping owing to the COVID- 19 pandemic situation?

RQ2. Do attitude and the subjective norm play a significant role to choose Online Shopping in COVID – 19 pandemics?

The present study predicts the constructs that are imperative in studying the online buying behavior by considering the COVID-19 pandemic situation with TRA. Under Literature Review and Theoretical background, various research papers related to online shopping, facilitating condition and COVID-19 as situational factor were analyzed and discussed, followed by the research paper deals about the research model, and hypotheses. Methodology adopted in the study, the results of the data analysis and structural model were presented in the next part of the research paper. The results were discussed in the last section, it consists of theoretical and practical implications, limitations and suggestions for future research.

Literature Review and Theoretical Background

According to Christy Cheung et al. (2003), the works on online shopper behaviour is fairly fragmented. Maximum studies examined intent and acceptance of online shopping while consumer loyalty is certainly under-researched. Upcoming research might use their recommended intention, acceptance and continuance as a foundation to empirically discover the factors that affects the online consumer purchasing process. Therefore, they inspire academicians to discover models from various disciplines and can be used by them to analyse consumers’ acceptance and extension of doing online shopping. The Present study focus on the factors affecting intention of buying online by using TRA and its related theories.

Sean McGann (2004) stated that environmental influences combined with certain issues as the dynamic changes in the modern product development lifecycles and speedy implementation procedures, which reduce the parting of design and use in time and space. The gross outcome is the requirement for users to turn into “designers”. This further shapes improvisation, where rapid implementation of methodologies related to shopping based on the environmental factors. Previous research indicates that this method of ad hoc modification by users in the design and use space is often affects the means of familiarizing a system with its chore environment. This research meets this need by exploring how users meet these changing requirements.

Mohammad Al-Nasser et al. (2014) says that attitude is mentioned to as a positive or negative assessment of people’s, actions, thoughts, objects, occasion, or just about everything in the environment. The facets can generally be divided into three namely, behavioural intention, actual behaviour and attitude toward behaviour.

According to Ivanov & Dolgui (2020), the worldwide pandemic and disruption of various Supply Chains and the marketplaces, portray the significance of the supply chain viability research. Although the firms have greatly experienced the resilience of their universal supply chains initiated by various severe natural and man-made calamities and adapted a set of beneficial approaches such as risk mitigation warehouses, commissioning capacities, holdup supply and transportation infrastructures, and data-driven, real-time monitoring and visibility systems, it is uncertain to find out whether those methods can be useful to supply chain survivability investigation. They suggested the example of coronavirus (COVID-19) outbreak to describe some future research angles towards online shopping.

Hypotheses

H1. Subjective norm influences the buyer’s intention to choose E shopping (i.e. Ajzen & Fishbein, 1980; Pettinger et al., 2004)

H2. Attitude will be positively related to Situational Factor

(i.e. Jagdish sheth, 2020)

H3. Attitude (Perceived Risk) will have negative impact towards facilitating condition

(i.e. Yong-Hui Li & Jing-Wen Huang, 2009)

H4. Attitude is positively associated to purchase intention to shop online.

(i.e. Ajzen, 1985; Chang, 1998; Dindyal, 2003).

H5. Subjective norm will be positively associated to the purchase intention to shop online.

(i.e. Shatenstein & Ghadirian, 1997; Tarkiainen & Sundqvist, 2005).

H6. Situational factor (Covid -19) positively influences Purchase intension

(i.e. Samuli Laato et al., 2020)

H7. Facilitating condition positively influences intention to choose e-shopping.

(i.e. Vinay Kumar & Sumit Mishra, 2013)

Theory of Reasoned Action (TRA)

Dulany’s (1968) theory of propositional control lead to the development of theory of reasoned action (TRA; Ajzen & Fishbein, 1980). TRA explains what predicts the behavioral intention of people, which further predicts their actual behavior. TRA suggested that a person’s optimistic attitude combined with the individuals’ thought created the behavioral intention of that person. The theory assumes that there are two conceptually independent determinants of behavioral intention attitude and subjective norm. The first determinant is attitude, which is evaluated by the individual’s belief towards a particular object and the assumed outcome of performing the behavior. The second determinant of Theory of Reasoned action is of subjective norm. It reflects an individual’s belief towards approving or conflicting others behavior, or about pressure from society of performing or not performing the behavior. In Fishbein’s summation theory of attitude, which is advanced as expectancy value model (Fishbein & Ajzen, 1975), individual’s complete attitude to a psychological item is determined by the subjective values or assessments of the attributes related with the item and through the strength of that associations. According Dulany’s model, this normative belief is weighted (multiplied) by the individual’s inspiration to cope up with the referent’s perceived expectation. Though, in the TRA, it is expected that individuals can grasp normative beliefs from more than one referent individual or cluster. The normative beliefs related to such social referents are gathered to formulate an overall perceived social pressure or subjective norm. In the area of online consumer behavior, only a few studies have examined consumers’ intention towards online purchase due to COVID-19 using TRA as the theoretical base.

TRA and Intention to Choose Online Shopping

Theory of Reasoned Action (TRA) projected by Ajzen and Fishbein (1980) is a generally used theory in marketing studies. This theory was projected to neglect the drawbacks of the outmoded attitude behavior studies that provide feeble associations among attitude towards the object measures and behavior performance (Hale et al., 2002). TRA is a wide-ranging theory and it does not indicate a particular belief that may be used in a specific field of study. It is employed for the forecast of various behaviors comprising finance, marketing, shopping, health etc. Thus, Theory of Reasoned Action is apt to be used in the extent of online shopping studies. Christy M. K. Cheung et al. (2003), demonstrates that the Theory of Reasoned Action (TRA) and its family theories along with the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) are the leading theories in online consumer behavior. The results show that maximum authors depend mostly on theories from the TRA family (TPB, TAM, and TRA), whereas other useful theories like the flow theory are ignored. Previous studies in online consumer behavior mainly sought to sightsee how consumers adopt and practice online purchase. Precisely, the importance was on the antecedents of consumer online purchasing intention and adoption.

As per Lutz (1991), TRA offers two significant attributes. The primary is to identify the consumer purchase behavior, in which it is essential to measure a person’s attitude in accomplishing that behavior, instead of just the overall attitude towards the object around which the purchasing behavior is. For example, though an individual’s attitude toward online shopping is positive, yet the individual may never shop online. Secondly, the behavior towards attitude: subjective norm. Social norm is intended to measure the subjective norm on individual’s behavior. According to Anthony d. Miyazaki and Ana Fernandez (2005), Risk perceptions related to Internet privacy and security have been identified as issues for both new and experienced users of Internet technology.

Based on the discussion above, the current study used Theory of Reasoned Action as a base theory. The current study’s literature review revealed that no previous study has tried to connect the prevailing COVID-19 pandemic situation with the online shopping behavior of people and examine the progress of online consumer purchase as a whole. Thus, this study adopts TRA model as presented in Figure 1 to analyze the intention of the consumer towards online shopping owing to the COVID-19 pandemic situation.

Research Model

Research Methodology

Design and Sample

Explanatory research design was adopted as it poised to identify and understand the precursor of choosing e-shopping during the covid-19 pandemic. As India accounts to 1.6 percent of retail sales through online (“Online retail sales in India,” 2019) humongous opportunities could be garnered due to fear of traditional shopping victimizing global corono virus, similarly creates a paradigm shift in consumer behaviour (Aneesh Reddy, 2020). Non-probability sampling technique was adopted to choose the study subjects, and a well-structured questionnaire was used to collect responses. Though the respondents were informed about the purpose of survey we could receive only 100 usable responses through online, hence with the help of franchised distributors of ecommerce companies located in different parts of the southern part of India 300 questionnaires were self-administered during July 2020 and the sample size had for our study was 400 which is in line with Hair et al. (2006). Table 1 shows the background variables of the samples used in the study.

Table 1 Demographic Details of Samples (N=400)
Background variables		Frequency	Percentage
Gender	Male	221	55.25
	Female	179	44.75
Age	< 20 years	17	4.25
	20-29 years	101	25.25
	30-39 years	148	37
	40-49 years	95	23.75
	50-59 years	29	7.25
	60 years and above	10	2.5
Educational qualification	Diploma	97	24.25
	Graduate	102	25.5
	Postgraduate	127	31.75
	Others	74	18.5
Experience in e-shopping	< 1year	116	29
	1-3 years	101	25.25
	4-6 years	97	24.25
	> 6 years	86	21.5

Measures Development

The initial part of the questionnaire demands to know the demographic characteristics of the respondents. The second part of the questionnaire consists of five constructs with 14 items, except for the construct “Influence of Covid - 19” others were validated through prior studies, however as a new construct has been added exploratory factor analysis (EFA) has been employed. The Table 2 shows the exploratory factor analysis for all the constructs used. By employing Likert’s scale that ranges from 1 showing “strongly disagree” to 7 “strongly agree the items of the constructs were measured. In order to suit the prior items to the context of this study we rephrased some of the words.

Table 2 Results of EFA for E-Shopping Intention
Item	Factor Loading					Communality
	1	2	3	4	5
I have the knowledge necessary to do e-shopping	.828					.721
e-shopping is compatible with other shopping that I do	.799					.681
I have the resources necessary to do e-shopping	.787					.659
I can get help from others when I have difficulties in e-shopping	.676					.476
My Family members prefer e- Shopping		.839				.748
Most people who are important to me choosing the e- Shopping		.8				.686
My Friends would think that I should choose e- Shopping		.781				.690
I am likely to choose e- Shopping in future			.831			.734
I plan to choose e- Shopping			.824			.736
I will choose e- Shopping			.790			.696
I restrained traditional shopping because of covid 19				.924		.908
I do prefer e- Shopping due to covid 19				.838		.788
Choosing e- Shopping is a good idea					.826	.840
I like to choose e- Shopping					.8	.736
Eigen Values	4.737	2.442	1.757	1.365	1.091
% of variance	33.8	17.4	12.5	9.7	7.7

Data Analysis

To analyse the data we used Microsoft Excel, SPSS 23.0 and AMOS. In line with the proposition of Anderson and Gerbing (1988) we employed the two-step structural equation modelling (SEM) in order to understand the relation between endogenous and exogeneous factors as specified through hypothesis. To test the hypothetical models as MacCallum & Austin (2000) recommended to use SEM, we first used confirmatory factor analysis (CFA) which is its initial phase to test the validity and reliability aspects of factors after conducting EFA. As Hair et al. (2017) recommended to conduct SEM, provided the loadings are high in CFA which also shows the sample adequacy we conducted SEM. Maximum likelihood estimation technique was employed to explore the structural model estimates.

Factor Analysis

Exploratory factor analysis was performed to extract five factors considered as the precursors to choose e-shopping in the proposed hypothetical model. Initially we calculated the Kaiser- Meyer-Olkin measure of sample adequacy as .773 which is greater than .70 and Bartlett’s test shows significant at p =.000 show a good level of measure (Osborne, Costello, & Kellow, 2008). Subsequently all the 14 items were converged into five factors based on the eigen value greater than one as recommended by Kaiser (1960) and the eigen values arrived are 4.737, 2.442. 1.757, 1.365 and 1.091 for attitude, subjective norm, covid – 19 influence, facilitating condition and intention to choose e-shopping respectively and accounts to 72.138 of variance by the first five factors. The EFA results are presented in Table 2 and the results of scree plot obtained from EFA is presented in Figure 2.

Results

Description on Demographic Profile of Respondents

Socio-demographic details and the experience in using e-shopping is presented in Table 1. Our study was made with the help of 55.25% male and 44.75% female respondents with an extrapolation of 60% male internet users in 2020 (Statista, 2010). Relating to age 37% of the respondents age fall between 30-39 years which is almost similar with the age groups most active with e-commerce portals (Quartz India, 2016) and most of the respondents in our study used eshopping below one year which befits to the purpose to which this study was intended.

Results of Measurement Model

First in employing CFA we examined the reliability, convergent and discriminant validity which is imperative when hypothetical model is used. Descriptive statistics of the scale items and factor loadings are depicted in Table 3. Measures in our study embraces exceptional content and convergent validity as the item loadings score above 0.68, and the average variance extracted (AVE) scores above 0.60. Moreover, the threshold level of discriminant validity has been met as the values of AVE were higher than maximum shared variance (MSV) and average shared variance (ASV) (Hew and Kadir, 2018). The values of composite reliability (CR) for all factors as greater than .86 adjudged as adequate. The critical level of cronbach’s alpha value as pronounced by Nunnally and Bernstein (1994) is 0.70, however the values derived in our study falls between 0.86 and 0.91 is determined as good which appears in Table 4. Leong, Hew, Ooi, & Lin, (2012) as suggested to use the goodness of fit indices to measure unidimensionality and lessen measuring bias we calculated fit indices from measurement model. The respective fit indices are CMIN/DF = 2.57; GFI = 0.941; CFI = 0.967 and RMSEA = 0.063, which existed within the recommended level (Leong, Hew, Ooi, & Lin, 2012). Subsequently, internal consistency (reliability) and discriminant validity as examined are shown in Table 4. To test the discriminant validity first the diagonal values in the correlation matrix table should be the square root of AVEs and the intercorrelation scores are lesser than the corresponding diagonal value.

Table 3 Results of Measurement Model
Construct			Items	Mean & SD	Loadings	AVE, MSV & ASV		Composite Reliability
Attitude			A1 A2	2.72, 1.33 2.78, 1.49	0.811*** 0.968***	0.797, & 0.146	0.213	0.886
Subjective norm			S1	3.04, 1.43	0.825***	0.702.	0.213	0.876
			S2	2.97, 1.44	0.853***	& 0.115
			S3	3.13, 1.43	0.836***
Covid	-	19	C1	3.53, 1.67	0.871***	0.844,	0.148	0.915
Influence			C2	3.85, 1.69	0.964***	& 0.098
Facilitating			FC1	3.47, 1.52	0.807***	0.626,	0.132	0.869
Condition			FC2	3.38, 1.66	0.849***	& 0.074
			FC3	3.45, 1.53	0.817***
			FC4	3.87, 1.67	0.681***
e-shopping			I1	2.67, 1.36	0.833***	0.706,	0.115	0.878
intention			I2	3.30, 1.54	0.861***	& 0.093
			I3	3.23, 1.69	0.826***

Table 4 Reliability and Validity Of Measures
	Cronbach’ s alpha	Facilitating Condition	Attitude	Subjective norm	Covid – 19 Influence	e-Shopping
Facilitating Condition	0.866	0.791
Attitude	0.877	0.363	0.893
Subjective norm	0.876	0.135	0.461	0.838
Covid – 19 Influence	0.912	0.180	0.385	0.373	0.919
e-Shopping	0.873	0.339	0.303	0.303	0.270	0.840

Analysis of Common Method Bias

Harman’s single factor analysis was performed to tackle the common method bias (Hew, & Kadir, 2016) as a common instrument was used to gather independent and dependant factors (Hew, & Kadir, 2016). We arrived a value of 31.9% through factor analysis for the first factor which is lesser than the threshold value of 50% and determines that our study is away from CMB issues (Wong, Tan, Hew, & Ooi, 2016).

Structural Model

In the second stage of the SEM analysis research hypotheses posited were tested and the respective fit indices are; CMIN/DF = 3.01; GFI = 0.93; AGFI = 0.90; NFI = 0.93; CFI = 0.95; and RMSEA = 0.07. The fit indices that were arrived are based on the recommendations of Hair et al. (2010). Table 5 shows the results of hypotheses testing based on path co-efficient analysis. The relationship between subjective norm and attitude was found to be statistically significant (β = 0.378; p < 0.001) therefor H1 is supported. In addition, H2 too is supported as (β = 0.268; p < 0.001) and a relationship could be seen between covid -19 influence and attitude, however there does not exist any relationship between attitude and e-shopping intention as (β = 0.074; p > 0.05). Subsequently relationship exist between attitude and facilitating condition as (β = 0.354; p < 0.001) and H3 is supported. Similarly, subjective norm has relationship with e-shopping intention and H5 is supported based on (β = 0.187; p < 0.01). The relationship between covid – 19 influence and e-shopping intention was significant at p < 0.05with β = 0.135 and facilitating condition shows relationship with e-shopping intention at p < 0.001 and β = 0.268. It is further stated that there was no multicollinearity issue as the variance inflation factors were tested and lesser than the 10 (Brace, et al. 2003)

Table 5 Results of Hypotheses Testing
Hypotheses	Endogenous Constructs	Exogenous constructs	Beta value	P Value	VIF
Hypothesis 1	Attitude	Subjective norm	0.378	***	1.000
Hypothesis 2	Attitude	Covid – 19 Influence	0.268	***	1.000
Hypothesis 3	Facilitating Condition	Attitude	0.354	***	1.000
Hypothesis 4	e-shopping Intention	Attitude	0.074	0.241	1.000
Hypothesis 5	e-shopping Intention	Subjective norm	0.187	0.002*	1.000
Hypothesis 6	e-shopping Intention	Covid – 19 Influence	0.135	0.013**	1.000
Hypothesis 7	e-shopping Intention	Facilitating Condition	0.268	***	1.000

Discussion

The results of our study hold up the predictive soundness of majority of the factors. The goodness of fit indices within the threshold level differed between the measurement and structural equation model confirms the uniqueness of the data. All the study factors were sound as fulfills the reliability and validity concerns. Influencing the attitude of consumers is a major concern of all the marketers and design advertisements and publicity are made accordingly. Though attitude was one of the most influential factors based on prior studies it predicted the facilitating condition than the e-shopping intention. It is very obvious to understand the impact of subjective norm on change in attitude and e-shopping intention which signifies that subjective norm is one of the dominating factors in bringing changes. As this study was made with the objective of understanding the underlying reason of consumers towards the intention of e-shopping, situational factor (Covid – 19 influence) was considered which significantly impacts shopping and facilitated e-shopping among the respondents. It was evidenced from pragmatic and axioms ousted by ecommerce companies that severe changes had been taking place in the consumer behaviour because of covid – 19 pandemic (Aneesh Reddy, 2020). The results of our study go hand in hand to the statements made. Al-Swidi, Huque, Hafeez, & Shariff, (2014) though found positive relationship between attitude and purchase intention our study is of the view that attitude does not show positive attitude towards e-shopping intention. However, our study confirms with Voon, Ngui, & Agrawal, (2011) that subjective norm significantly affects e-shopping intention and facilitating condition also facilitates e-shopping which is in line with Verkijika, (2018). In line with the research by Nicholls et al. (1996) our study confirms that situational factor (Covid-19) affects e-shopping. Though several voices had been viewed in continuation with the change in purchase behaviour because of covid – 19 pandemic this study might be the first empirical evidence to suggest subjective norm; facilitating condition and covid -19 pandemic towards eshopping intention.

Theoretical Contribution

Amidst the ample studies that poised to understand the antecedents of purchase intention, this study adventures with a poignant situational factor (covid – 19) as one of the antecedents to predict e-shopping. The study is based on Theory of Planned Behaviour (TPB), however future researchers are encouraged to use Technology Acceptance Model (TAM) and a blend of TPB and TAM could be done with stimuli-organism-response (s-o-r) framework. The results of this study certainly add new knowledge to the existing literature of e-shopping and situational factor. Though several studies were made on situational factors relating to mall shopping and impulsive shopping, this study considered the global pandemic factor and related with e-shopping intention which is irrefutably novel. Facilitating condition to do e-shopping contributes significantly towards eshopping similarly subjective norm. We understood that mere situation cannot change the attitude of the people, however it would take its own sequential time period to bring changes in attitude and manifestation of attitude towards e-shopping. Though e-shopping has become a trend, its prominence in several places is very remote because of attitude towards e-shopping however situational factor prompts or drives to e-shopping without changes in attitude.

Practical Implications

Our research offers a gamut of contributions to the society that seems to be radical nevertheless imperative. It is evidenced that the digital buyers in India accounts to 329.1 billion in 2020 against 974.86 million internet users (Statista. 2020). Hence substantial untapped market potential could be garnered through e-commerce. Purchase of products is a routine indelible event that everyone does. Based on the results, e-commerce companies need to bring significant changes in the attitude of the consumers nevertheless it cannot be changed immediately. Hence consistent nudging should be made to spur and kindle the emotions of consumers towards e-shopping. As stated in prior studies Indian consumers emotionally attach towards the brands which they buy. Covid-19 pandemic is one of the situational factors that prompted the traditional shoppers to shop online, however they would revert once the situation favors to shop traditional after the shops and malls open. Hence e-commerce companies need to do advertisement and publicity by means of promoting products and services. The results of our study also show light on the influence of facilitating condition and subjective norm towards e-shopping intention. As discussed earlier the major facilitating condition to do e-shopping is the use of internet, however a smaller number of internet users do e-shopping which further needs suitable strategies in driving them to shop online. Another major factor that we found was subjective norm, strategies and techniques are further needed to fuel subjective norm which further amplifies e-shopping.

Limitations and Future Research Directions

Albeit our study intended to contribute significantly to the literature of e-shopping by considering the situational factor certain limitations that are fettered could give opportunities for future research. The respondents used for the study should be studied after post-covid to test whether they would continue e-shopping and accordingly e-commerce companies are advised to design strategies to retain them. Besides the precursors used to study e-shopping intention other antecedents also can be used to enable the predictive power of e-shopping in future researches. The present study as adopted non-probability sampling to identify study subjects suggests the future researchers to use probability sampling. Moreover, this study is not made upon the respondents who bought similar products through online, hence future researchers are encouraged to study based on product category. Theory of Trying can be used to test whether the consumers would continue trying e-shopping. Mediation and moderation analysis could be done by considering demographic and predictor variables like facilitating condition to design strategies by the e-commerce companies accordingly.

Conclusion

Based on the robust information about the e-shopping and change in consumer behaviour this study has considered covid – 19 pandemic as the major situational factor along with other antecedents of e-shopping intention the study was executed. Based on the theory of planned behaviour we carried out this study. The study used 400 non-probability samples were collected through online and self-administered form. Structural equation modelling was used to identify the relationships between the endogenous and exogeneous factors. Facilitating condition and subjective norm were the strongest predictor of e-shopping in addition with situational factor. Covid – 19 influence was the new predictor used in the study after employing EFA. Attitude was not explaining e-shopping intention, which specifies that situational factors per se cannot influence attitude of consumers for continued action. Hence marketers are suggested to design strategies through advertisements and publicity to influence the attitude of consumers. Finally, future researchers are suggested to include other antecedents along with situational factor to predict eshopping intention.

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