The Impact of the Emerging Coronavirus (COVID-19) on E-Commerce in

the Kingdom of Saudi Arabia

MEHDI ABID1, HOUCINE BENLARIA2, ZOUHEYR GHERAIA2

SAUDI ARABIA

SAUDI ARABIA

Key-Words: - COVID-19, e-commerce, home quarantine, movement restriction, psychological anxiety, Saudi

Several challenges can be encountered when setting

up and implementing an e-commerce platform. When

setting up e-commerce portals, one of the main

difficulties encountered lies in the process of creating

a secure online payment platform. These challenges

are often technological "use of advanced encrypted

technologies", social "consumer education in the use

of online transaction sites" and related to payment

"limited number of online credit card users". In

addition, the United Nations Conference on Trade

economic threat.

The Chinese government soon announced the first

case of Coronavirus, this epidemic swept the world in

a short period not exceeding days, and it soon turned

into a pandemic. Most of the countries in the world

have successively announced the discovery of cases

among their citizens, which led most of the countries

of the world to take precautionary measures at

international airports for those coming from China at

the beginning. However, after a while, the

closing of the geographical borders between them.

After the pandemic has spread sweeping across the

world, in addition to its health effects, it has had an

economic impact on growth, as expectations indicate

that growth in the Middle East and Central Asia

electronic commerce (e-commerce). For this purpose, a questionnaire was distributed to a sample size of 492

individuals in the Kingdom of Saudi Arabia. Further, to analyze the study data, the SEM structural equations method

was used via the Smart PLS. After analyzing and testing the hypotheses of the study, it was found that there is a

direct positive impact of the emerging coronavirus (COVID-19) on customer behavior toward e-commerce in Saudi

Arabia. There is a positive, direct effect of home quarantine on the orientation of individuals toward e-commerce.

Moreover, there is a positive, direct impact of movement restriction on the orientation of individuals toward e-

commerce. Finally, there is a positive, direct effect of psychological anxiety on the attitude of individuals toward e-

commerce.

region will decline from 1.2% in 2019 to 2.8% in

2020 [1]. The Kingdom of Saudi Arabia was also

of March, attendance at workplaces in all ministries,

commercial centers, have been closed, with the

exception of health institutions, pharmacies and

supermarkets. This is part of the Saudi government's

efforts to control the spread of the infectious virus,

including stopping the export of all medical and

laboratory products, supplies, and equipment used to

detect or prevent the virus (Covid 19). In addition,

state revenues have been severely affected due to the

most affected energy sector, as oil prices have fallen

to levels not seen in more than 20 years by about

Afghanistan by 4.2% for the year 2020. Also,

expectations indicate a 2.7% contraction in growth in

the GCC countries for the same year [1].

In light of this current situation, information and

communication technology plays the largest role in

the modern production process, and with the

multiplication of human knowledge, the global

economy has turned into a knowledge-based

economy [2]. Where the key to this knowledge was

the development of technology, which in turn led to

E-commerce has become among the fastest growing

sectors in the global economy, and its role has

emerged in recent years [3]. In light of the Corona

pandemic, this role has emerged clearly. E-commerce

has become an aid to the business sector to mitigate

the effects of this pandemic. The restrictions imposed

in light of this pandemic have also affected consumer

buying behavior, as many consumers have directed to

use different e-commerce channels to meet their

multiple needs, and it should be noted that

government decisions to combat the Corona

pandemic have contributed significantly to this trend

The importance of the study stems from the

cases of mixing, which were among the most

prominent preventive means to limit the spread of

infection among the public. In addition to that, the

main role that electronic commerce plays in general

in economic growth and limiting the effects of the

pandemic through its advantages and its close

relationship with the digital economy, which is one

of the pillars of the Kingdom's 2030 vision. So the

current study seeks to shed light on the extent of the

impact of the pandemic (Covid-19) on the growth of

1 provides the literature review. Section 2 presents

an overview of the impact of the Corona pandemic

on consumer behavior and e-commerce. Section 3

discusses the main models and econometric

methodology. Section 4 discusses the empirical

results of the estimations. Section 5 concludes with a

summary of the findings and policy implications.

3 Literature review and Hypothesis

Development

In this section, we try to present some previous

literature for this research. The impacts of the

emerging corona virus (Covid-19) on e-commerce

have constituted an important subject for researchers.

[5] determined the e-commerce trends in coronavirus

predicament as well as how imminent progress in e-

commerce that might affect consumer behavior in

future. They examined that e-commerce grew due to

coronavirus. E-commerce is become a substitute

retailers provide goods that usually consumers

increased the e-commerce growth of the country and

the state. The research paper here describes the

impact of the corona virus on India's online

commerce. On the analysis, they found that online

businesses are increased due to this pandemic

disease. [7] indicates that as quarantining

considerably restricts recreational opportunities, the

importance of hedonic motives for remaining

activities, such as online shopping, increases. This

appears to reflect changes in the behavior of

quarantine on customer behavior toward e-

commerce.

[8] investigated the effects of Coronavirus spread on

stock markets. Coronavirus spread has been

measured by cumulative cases, new cases,

cumulative deaths and new deaths. This has been

applied on the worst 6 countries (according to

number of cumulative cases), on daily basis over the

period from March 1, 2020 till April 10, 2020.

Coronavirus spread has been measured by numbers

negative effect of Coronavirus spread on stock

market return for China, France, Germany and Spain.

Italy and United States. [9] studied the impact of the

coronavirus on the online business of Malaysia. On

analyzing it has found that online businesses are

seriously hampered due to this pandemic disease.

The results showed that since the maximum number

of products comes from China and the maximum

industries are closed, which means that there is no

import and export of the product. We therefore posit

the following hypothesis:

movement restriction and customer behavior toward

people are afraid as they struggle to adjust to a new

normal. Fear rises as people reflect on what this crisis

means for them, but more importantly, what it means

for their family, friends and society in general.

Consumers are responding to the crisis in various

ways. Some people feel anxious and worried, which

encourages compulsive purchases of basic and

hygiene products. At the other extreme, some

consumers remain indifferent to the pandemic and

marketing are over. Consumers fear the economic

impact of COVID-19 more than their health.

Therefore, we hypothesise that:

HP3: There is a positive relationship between

psychological anxiety and customer behavior toward

e-commerce.

[12] investigated the effects of the spread of COVID-

19 on global ecommerce companies, where the five

largest e-commerce companies in the world were

chosen in terms of revenues and market value, and

virus cases" and the "new corona virus deaths" daily,

the dependent variable reflects the response of the

global e-commerce market to the impact of the

spread of the corona virus and is measured by the

daily returns of the shares of ecommerce companies

to the global financial markets. This was applied on a

daily basis from 15 March 2020 to 25 May 2020. The

results indicates that the percentage of the effect of

coronavirus spread varied from one company to

another, depending on the country to which it

belonged, where the American company Amazon and

the United kingdom company ASOS were the

and this is consistent with that they are the most

influential variable “cumulative deaths”.

[13] investigate the factors that affect consumer’s

online shopping behavior. The study results suggest

affected by several factors like demographic factors,

social factors, consumer online shopping experience,

knowledge of using internet and computer, website

design, social media, situational factors, facilitating

conditions, product characteristics, sales promotional

scheme, payment option, delivery of goods and after

The use of demographics by researchers in the online

shopping literature is common, however, they are

typically constructed as either moderators or control

factors. Little attention has been given to explicitly

modelling the predictive utility of demographics. [14]

studies the impact of nine demographics, six social

connectedness measures and five prior online

purchases. A large and representative data set was

used. The results show that a model on the basis

predictive power of demographic variables is product

specific. Overall, the results strongly support the use

by practitioners of demographics as powerful

predictors for direct targeting of online shoppers.

Prior research examining the effect of gender on

willingness to shop online revealed that men are

more likely to conduct online transactions than

women ([15]; [16]; [17]). There are some studies

where opposing, or mixed, conclusions were

reported; however, these appear to be exceptions to

the general pattern, such as the purchase of clothing

by women ([18]; [19]). Several explanations have

differences including risk perception a general

[25]). Therefore, we propose the following

hypothesis:

HP5: There is a positive relationship between

sample's views regarding e-commerce and

demographic variables.

was prepared for use in the collection of data and

information. The questionnaire was submitted to the

arbitrators to test its suitability for all data. It was

modified at the discretion of the arbitrators.

The questionnaire was divided into three parts:

• The first section consisted of the personal data of

the sample population and had six items.

• The second section dealt with variables that

express COVID-19 and was divided into three axes:

A 5-point Likert scale was used in the questionnaire

492 met the requirements for adequately representing

the total population.

To analyze the study data and test the validity of the

hypotheses, we used one of the most effective

equations [26].

variables were filtered to measure those variables

well and honestly.

measurement model and making the necessary

adjustments, the second step was carried out. This

step assessed the validity of the differentiation of the

was analyzed, as shown in the following table.

Table 3 shows that all the items of the study had a

high level of saturation on the underlying variables.

variables exceeded the minimum, which is the value

of 0.7, as some items were excluded due to the low

Fig. 1: Research Framework

It is observed in the figure above that the cross-

loading coefficients for each latent variable with the

same variable were higher than the cross-loading

coefficients for the rest of the other latent variables,

which, in total, exceeded the value of 0.7. This is an

indication of the quality of the structure of the

measurement model.

5.3 Assessment of Structural Model

To evaluate the quality of the structural model, we

used the R-Squared measure, which shows the

predictive strength of the model in the study sample

and also explains the interpretation of the external

latent constructs for the amount of variance in the

dependent variable.

[27] suggested that the value of R2 that is above 0.67

is considered high, while values ranging between

dependent variable. According to Cohen (1988), if

the F2 is above 0.35, the size of the effect is

considered to be large.

The Q2 scale, which measures the predictive power

of the study model outside of the research sample,

was also used. According to [29], this value must be

greater than zero. When the model predicts

dependent latent variables outside of the study