\documentclass[11pt,twoside]{article}\makeatletter

\IfFileExists{xcolor.sty}%
  {\RequirePackage{xcolor}}%
  {\RequirePackage{color}}
\usepackage{colortbl}
\usepackage{wrapfig}
\usepackage{ifxetex}
\ifxetex
  \usepackage{fontspec}
  \usepackage{xunicode}
  \catcode`⃥=\active \def⃥{\textbackslash}
  \catcode`❴=\active \def❴{\{}
  \catcode`❵=\active \def❵{\}}
  \def\textJapanese{\fontspec{Noto Sans CJK JP}}
  \def\textChinese{\fontspec{Noto Sans CJK SC}}
  \def\textKorean{\fontspec{Noto Sans CJK KR}}
  \setmonofont{DejaVu Sans Mono}
  
\else
  \IfFileExists{utf8x.def}%
   {\usepackage[utf8x]{inputenc}
      \PrerenderUnicode{–}
    }%
   {\usepackage[utf8]{inputenc}}
  \usepackage[english]{babel}
  \usepackage[T1]{fontenc}
  \usepackage{float}
  \usepackage[]{ucs}
  \uc@dclc{8421}{default}{\textbackslash }
  \uc@dclc{10100}{default}{\{}
  \uc@dclc{10101}{default}{\}}
  \uc@dclc{8491}{default}{\AA{}}
  \uc@dclc{8239}{default}{\,}
  \uc@dclc{20154}{default}{ }
  \uc@dclc{10148}{default}{>}
  \def\textschwa{\rotatebox{-90}{e}}
  \def\textJapanese{}
  \def\textChinese{}
  \IfFileExists{tipa.sty}{\usepackage{tipa}}{}
\fi
\def\exampleFont{\ttfamily\small}
\DeclareTextSymbol{\textpi}{OML}{25}
\usepackage{relsize}
\RequirePackage{array}
\def\@testpach{\@chclass
 \ifnum \@lastchclass=6 \@ne \@chnum \@ne \else
  \ifnum \@lastchclass=7 5 \else
   \ifnum \@lastchclass=8 \tw@ \else
    \ifnum \@lastchclass=9 \thr@@
   \else \z@
   \ifnum \@lastchclass = 10 \else
   \edef\@nextchar{\expandafter\string\@nextchar}%
   \@chnum
   \if \@nextchar c\z@ \else
    \if \@nextchar l\@ne \else
     \if \@nextchar r\tw@ \else
   \z@ \@chclass
   \if\@nextchar |\@ne \else
    \if \@nextchar !6 \else
     \if \@nextchar @7 \else
      \if \@nextchar (8 \else
       \if \@nextchar )9 \else
  10
  \@chnum
  \if \@nextchar m\thr@@\else
   \if \@nextchar p4 \else
    \if \@nextchar b5 \else
   \z@ \@chclass \z@ \@preamerr \z@ \fi \fi \fi \fi
   \fi \fi  \fi  \fi  \fi  \fi  \fi \fi \fi \fi \fi \fi}
\gdef\arraybackslash{\let\\=\@arraycr}
\def\@textsubscript#1{{\m@th\ensuremath{_{\mbox{\fontsize\sf@size\z@#1}}}}}
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\def\abbr{}
\def\corr{}
\def\expan{}
\def\gap{}
\def\orig{}
\def\reg{}
\def\ref{}
\def\sic{}
\def\persName{}\def\name{}
\def\placeName{}
\def\orgName{}
\def\textcal#1{{\fontspec{Lucida Calligraphy}#1}}
\def\textgothic#1{{\fontspec{Lucida Blackletter}#1}}
\def\textlarge#1{{\large #1}}
\def\textoverbar#1{\ensuremath{\overline{#1}}}
\def\textquoted#1{‘#1’}
\def\textsmall#1{{\small #1}}
\def\textsubscript#1{\@textsubscript{\selectfont#1}}
\def\textxi{\ensuremath{\xi}}
\def\titlem{\itshape}
\newenvironment{biblfree}{}{\ifvmode\par\fi }
\newenvironment{bibl}{}{}
\newenvironment{byline}{\vskip6pt\itshape\fontsize{16pt}{18pt}\selectfont}{\par }
\newenvironment{citbibl}{}{\ifvmode\par\fi }
\newenvironment{docAuthor}{\ifvmode\vskip4pt\fontsize{16pt}{18pt}\selectfont\fi\itshape}{\ifvmode\par\fi }
\newenvironment{docDate}{}{\ifvmode\par\fi }
\newenvironment{docImprint}{\vskip 6pt}{\ifvmode\par\fi }
\newenvironment{docTitle}{\vskip6pt\bfseries\fontsize{22pt}{25pt}\selectfont}{\par }
\newenvironment{msHead}{\vskip 6pt}{\par}
\newenvironment{msItem}{\vskip 6pt}{\par}
\newenvironment{rubric}{}{}
\newenvironment{titlePart}{}{\par }

\newcolumntype{L}[1]{){\raggedright\arraybackslash}p{#1}}
\newcolumntype{C}[1]{){\centering\arraybackslash}p{#1}}
\newcolumntype{R}[1]{){\raggedleft\arraybackslash}p{#1}}
\newcolumntype{P}[1]{){\arraybackslash}p{#1}}
\newcolumntype{B}[1]{){\arraybackslash}b{#1}}
\newcolumntype{M}[1]{){\arraybackslash}m{#1}}
\definecolor{label}{gray}{0.75}
\def\unusedattribute#1{\sout{\textcolor{label}{#1}}}
\DeclareRobustCommand*{\xref}{\hyper@normalise\xref@}
\def\xref@#1#2{\hyper@linkurl{#2}{#1}}
\begingroup
\catcode`\_=\active
\gdef_#1{\ensuremath{\sb{\mathrm{#1}}}}
\endgroup
\mathcode`\_=\string"8000
\catcode`\_=12\relax

\usepackage[a4paper,twoside,lmargin=1in,rmargin=1in,tmargin=1in,bmargin=1in,marginparwidth=0.75in]{geometry}
\usepackage{framed}

\definecolor{shadecolor}{gray}{0.95}
\usepackage{longtable}
\usepackage[normalem]{ulem}
\usepackage{fancyvrb}
\usepackage{fancyhdr}
\usepackage{graphicx}
\usepackage{marginnote}

\renewcommand{\@cite}[1]{#1}


\renewcommand*{\marginfont}{\itshape\footnotesize}

\def\Gin@extensions{.pdf,.png,.jpg,.mps,.tif}

  \pagestyle{fancy}

\usepackage[pdftitle={Customers' Perception towards Online Shopping in Jordan},
 pdfauthor={}]{hyperref}
\hyperbaseurl{}

	 \paperwidth210mm
	 \paperheight297mm
              
\def\@pnumwidth{1.55em}
\def\@tocrmarg {2.55em}
\def\@dotsep{4.5}
\setcounter{tocdepth}{3}
\clubpenalty=8000
\emergencystretch 3em
\hbadness=4000
\hyphenpenalty=400
\pretolerance=750
\tolerance=2000
\vbadness=4000
\widowpenalty=10000

\renewcommand\section{\@startsection {section}{1}{\z@}%
     {-1.75ex \@plus -0.5ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large\bfseries}}
\renewcommand\subsection{\@startsection{subsection}{2}{\z@}%
     {-1.75ex\@plus -0.5ex \@minus- .2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large}}
\renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}%
     {-1.5ex\@plus -0.35ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\large}}
\renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}%
     {-1ex \@plus-0.35ex \@minus -0.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\normalsize}}
\renewcommand\subparagraph{\@startsection{subparagraph}{5}{\parindent}%
     {1.5ex \@plus1ex \@minus .2ex}%
     {-1em}%
     {\reset@font\normalsize\bfseries}}


\def\l@section#1#2{\addpenalty{\@secpenalty} \addvspace{1.0em plus 1pt}
 \@tempdima 1.5em \begingroup
 \parindent \z@ \rightskip \@pnumwidth 
 \parfillskip -\@pnumwidth 
 \bfseries \leavevmode #1\hfil \hbox to\@pnumwidth{\hss #2}\par
 \endgroup}
\def\l@subsection{\@dottedtocline{2}{1.5em}{2.3em}}
\def\l@subsubsection{\@dottedtocline{3}{3.8em}{3.2em}}
\def\l@paragraph{\@dottedtocline{4}{7.0em}{4.1em}}
\def\l@subparagraph{\@dottedtocline{5}{10em}{5em}}
\@ifundefined{c@section}{\newcounter{section}}{}
\@ifundefined{c@chapter}{\newcounter{chapter}}{}
\newif\if@mainmatter 
\@mainmattertrue
\def\chaptername{Chapter}
\def\frontmatter{%
  \pagenumbering{roman}
  \def\thechapter{\@roman\c@chapter}
  \def\theHchapter{\roman{chapter}}
  \def\thesection{\@roman\c@section}
  \def\theHsection{\roman{section}}
  \def\@chapapp{}%
}
\def\mainmatter{%
  \cleardoublepage
  \def\thechapter{\@arabic\c@chapter}
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \pagenumbering{arabic}
  \setcounter{secnumdepth}{6}
  \def\@chapapp{\chaptername}%
  \def\theHchapter{\arabic{chapter}}
  \def\thesection{\@arabic\c@section}
  \def\theHsection{\arabic{section}}
}
\def\backmatter{%
  \cleardoublepage
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \setcounter{secnumdepth}{2}
  \def\@chapapp{\appendixname}%
  \def\thechapter{\@Alph\c@chapter}
  \def\theHchapter{\Alph{chapter}}
  \appendix
}
\newenvironment{bibitemlist}[1]{%
   \list{\@biblabel{\@arabic\c@enumiv}}%
       {\settowidth\labelwidth{\@biblabel{#1}}%
        \leftmargin\labelwidth
        \advance\leftmargin\labelsep
        \@openbib@code
        \usecounter{enumiv}%
        \let\p@enumiv\@empty
        \renewcommand\theenumiv{\@arabic\c@enumiv}%
	}%
  \sloppy
  \clubpenalty4000
  \@clubpenalty \clubpenalty
  \widowpenalty4000%
  \sfcode`\.\@m}%
  {\def\@noitemerr
    {\@latex@warning{Empty `bibitemlist' environment}}%
    \endlist}

\def\tableofcontents{\section*{\contentsname}\@starttoc{toc}}
\parskip0pt
\parindent1em
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\newenvironment{reflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemsep}{0pt}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\itshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{sansreflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\itemsep}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\upshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{specHead}[2]%
 {\vspace{20pt}\hrule\vspace{10pt}%
  \phantomsection\label{#1}\markright{#2}%

  \pdfbookmark[2]{#2}{#1}%
  \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}%
  }{}
      \def\TheFullDate{2021-03-15 (revised: 15 March 2021)}
\def\TheID{\makeatother }
\def\TheDate{2021-03-15}
\title{Customers' Perception towards Online Shopping in Jordan}
\author{}\makeatletter 
\makeatletter
\newcommand*{\cleartoleftpage}{%
  \clearpage
    \if@twoside
    \ifodd\c@page
      \hbox{}\newpage
      \if@twocolumn
        \hbox{}\newpage
      \fi
    \fi
  \fi
}
\makeatother
\makeatletter
\thispagestyle{empty}
\markright{\@title}\markboth{\@title}{\@author}
\renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@
\belowdisplayskip \abovedisplayskip
\abovedisplayshortskip \z@ plus2\p@
\belowdisplayshortskip 4\p@ plus2\p@ minus2\p@
\def\@listi{\leftmargin\leftmargini
               \topsep 2\p@ plus1\p@ minus1\p@
               \parsep 2\p@ plus\p@ minus\p@
               \itemsep 1pt}
}
\makeatother
\fvset{frame=single,numberblanklines=false,xleftmargin=5mm,xrightmargin=5mm}
\fancyhf{} 
\setlength{\headheight}{14pt}
\fancyhead[LE]{\bfseries\leftmark} 
\fancyhead[RO]{\bfseries\rightmark} 
\fancyfoot[RO]{}
\fancyfoot[CO]{\thepage}
\fancyfoot[LO]{\TheID}
\fancyfoot[LE]{}
\fancyfoot[CE]{\thepage}
\fancyfoot[RE]{\TheID}
\hypersetup{citebordercolor=0.75 0.75 0.75,linkbordercolor=0.75 0.75 0.75,urlbordercolor=0.75 0.75 0.75,bookmarksnumbered=true}
\fancypagestyle{plain}{\fancyhead{}\renewcommand{\headrulewidth}{0pt}}

\date{}
\usepackage{authblk}

\providecommand{\keywords}[1]
{
\footnotesize
  \textbf{\textit{Index terms---}} #1
}

\usepackage{graphicx,xcolor}
\definecolor{GJBlue}{HTML}{273B81}
\definecolor{GJLightBlue}{HTML}{0A9DD9}
\definecolor{GJMediumGrey}{HTML}{6D6E70}
\definecolor{GJLightGrey}{HTML}{929497} 

\renewenvironment{abstract}{%
   \setlength{\parindent}{0pt}\raggedright
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
   \textcolor{GJBlue}{\large\bfseries\abstractname\space}
}{%   
   \vskip8pt
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
}

\usepackage[absolute,overlay]{textpos}

\makeatother 
      \usepackage{lineno}
      \linenumbers
      
\begin{document}

             \author[1]{Dr. Atalla Fahed  Al-Serhan}

\renewcommand\Authands{ and }

\date{\small \em Received: 12 February 2021 Accepted: 1 March 2021 Published: 15 March 2021}

\maketitle


\begin{abstract}
        


Online shopping is gaining popularity across the globe, thanks to the speedily advancing and easily accessible internet that allowed online marts to transcend the traditional methods of trading. A highly challenging lifestyle is convincing consumers to adopt online shopping as a substitute to traditional retailing to save time and money. Therefore, this study was conducted to examine the perception of customers towards online shopping in Jordan. The research conducted an online survey of 400 customers who bought online products. Trust, convenience, price, customer service, product varieties, and website design were used as the variables on which the customers? perception towards online shopping was examined. 

\end{abstract}


\keywords{customer, online shopping, regression, jordan.}

\begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) 
\textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark}
\end{textblock*}

\begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) 
\textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}}
\end{textblock*}


\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
usiness has always existed since the early times of man. Even though it only began with the simplistic barter system, business would not be the same as it is today without the advancements in internet. The role of internet in the business landscape cannot be overstated. All the major industries would fall into a catastrophic collapse if one were to take away internet from business, since majority of business operations and transactions somehow involve the use of internet. Internet has become a crucial and indispensable part of almost every kind of business. Internet in business allows organizations to improve both the performance and overall effectiveness of products, systems and services. The rapid growth in internet usage has brought about a paradigm shift in the way things are done and perceived. It is a great revolution in this decade because it has greatly simplified the way shopping was done. It has dramatically changed the way consumers seek and use information. Earlier, internet was conceptualized as a tool for enchasing information but now it has become an important place of business. Internet has given a dynamic boost to the growth of all sectors and especially retail sector across the globe. Therefore, the words like e marketing or online shopping has come into existence. 
\section[{a) Online shopping and customers' perception}]{a) Online shopping and customers' perception}\par
Online marketing is the process of marketing a product or service using the Internet. Online shopping is also called as web-store, e-store, e-shop, Internet shop, web-shop, online store, and virtual store. The online shopping concept become very popular among the consumers due to the convenience and availability of large varieties of products. Besides, a highly challenging lifestyle is convincing consumers to adopt online shopping as a substitute to traditional retailing to save time and money. It is gaining popularity across the globe because the internet is now more and more accessible to the common man and its benefits are transcended over traditional retailing.\par
The internet penetration in Jordan was 67\%. There were 6.78 million internet users in January, 2020 (Wikipedia). There is great increase in internet users in the decade. The statistics highlighted that there is a bright future for online marketing in Jordan in the coming years. There is surge in the number of firms selling products online. However, selling online is not an easy task as it looks like. There are many challenges in selling online like finding and targeting the right consumer, satisfaction of customers, certain website issues like frauds and hacking. Understanding the perception of customers is usually seen as the most important challenge. Once a firm manages to recognize it, solutions to other problems will come into sight automatically.\par
Understanding of customers' perception in online shopping is more difficult than traditional selling because the customers are not physically available and the sellers could not convince them by communicating properly. Many times, it has been found that customers read the reviews and rating of the product and then takes a decision whether to buy or not. In such a case, the firm lost the customer if the product gets low rating and bad reviews although the product is superior to rival firms. Similarly, they compare the prices of products with rival firms and then take a decision. Sometimes customers' decision is influenced by the attraction of website, its design and features. Therefore, critical examination of customers' attitude is a sine qua non in online shopping. The present research examines the customers' perception towards online shopping. This study comprehensively measures all the important factors like trust, convenience, price, customer service, product varieties, and website design that customers usually taken into consideration while buying products through internet. 
\section[{II. Literature Review and Research Model}]{II. Literature Review and Research Model}\par
a) Literature review Jusoh and Ling (2012) investigated how sociodemographic variables age, income and occupation affect consumers' attitude towards online shopping on a sample of 100 respondents. One way ANOVA was used to assess the differences between independent variable such as age, income, occupation and pattern of online buying (type of goods) and dependant variable such as attitude towards online shopping. The findings revealed that there is no significant difference in attitude towards online shopping among age group but there is a significant difference in attitude towards online shopping were recorded across income groups. Moreover, correlation was also applied to test the relationship between independent variables and dependant variables. The findings revealed that the variables ecommerce experience, product perception, customers' service have a significant relationship with attitude towards online shopping among the respondents. \hyperref[b1]{Bashir (2013)} found that majority of the people bought goods online once in a year. The study found that online shopping was popular in young generation as they feel it more comfortable, time saving and convenient. Besides, the research revealed that time saving; best price and convenience were the important factors affecting online purchase. However, the author revealed that safety of payment was the main barrier in the process of online shopping because people of Pakistan are afraid to share their personal information and financial information on internet. \hyperref[b6]{Masoud (2013)} examined the effect of perceived risks (financial risk, product risk, time risk, delivery risk, and information security risk) on online shopping behavior in Jordan on the sample of 395 online shoppers. The study revealed that financial risk, product risk, delivery risk, and information security risk negatively affect online shopping behavior. The results also showed that the other two dimensions, perceived time risk, and perceived social risk have no effect on online shopping. Yunus (2014) conducted the research on customer perception, towards online shopping in Chennai city. The demographic picture of respondents highlighted that 58\% were married, 52\% were men, 36\% earned income under 40,000, 43\% were graduates, and 39\% were salaried persons. The sample size was 1273 respondents. The results showed that customers' with age 35-45 years, earning monthly income of Rs. 40,001-60,000 are good in online shopping behavior. Convenience risk, financial risk and non delivery risk reduced satisfaction in online shopping by 58\%, 52\%, and 65\% respectively. Satisfaction towards online merchant, good Return policy, Good Infrastructure brings satisfaction towards online shopping by 65\%, 68\%, and 49\% respectively. Bashir, Mehboob, and Bhatti (2015) find out the various factors that affect the consumer behavior towards online shopping. Trust, time, product variety, convenience and privacy, were the variables taken in the study to examine the relationship between consumer-buying behavior towards online shopping. Data was collected through questionnaires. The results revealed that two factors trust followed by convenience have great impact on the decision to buy online or not. Ahluwalia and Sanan (2016) find out the factors influencing online shopping and how these factors affect willingness to purchase online. Data was collected from 200 respondents in Jalandhar city of Punjab with the help of a questionnaire. Factor analysis and Multiple Regression analysis were used as statistical techniques to analyze the data. Results found website security, reliability of the website, return and exchange policy, reasonable prices offered, customer services offered, positive customer reviews regarding website and informative website were the factors influencing online buying behavior. Website security was the most important predictor of willingness to buy online. \hyperref[b10]{Rahman et al. (2018)} found that Bangladeshi online shoppers are young (mostly below 40 years) similar to other parts of the world. They do online shopping because it saves time, offer home delivery, provides ease in shopping and offers more variety of products for apparels, accessories, and ticketing than that of brick and mortar stores. They mostly rely on price and their experience as the basis of the quality judgment of items in online shopping and for payment system they prefer cash on delivery option. However, the study revealed that privacy and inability to touch and feel are the most disliking factors for online shoppers. Perera and Sachitra (2019) examined the factors influencing customer satisfaction towards online shopping. The moderating effect of income level on the relationship between factors influencing customer satisfaction on online shopping was also examined. The sample size was 380. The survey method was used to collect data through a standardizes questionnaire which takes five variables namely customer satisfaction, convenience, security, website functionality and customer service. The survey findings revealed that convenience, web site functionality, customer service have significant influence on customer satisfaction on online shopping. The results also indicated that the income level has a significant moderating effect on the relationship between convenience, web site functionality, security and customer service and customer satisfaction on online shopping. 
\section[{b) Research Gap}]{b) Research Gap}\par
The review of literature highlights that numerous studies have been carried out on online shopping, online marketing, consumer behaviour and the like topics. But, an empirical gap has been revealed with respect to the studies in Jordan. Therefore, this study is identical from previous studies as it examines the customers' perception towards online shopping in Jordan. 
\section[{c) Research Model}]{c) Research Model}\par
Figure \hyperref[fig_1]{1} highlights the research model of the study. The present study used six variables namely trust, convenience, price, customer service, product varieties, and website design to examine the customers' perception towards online shopping. All these variables were used in previous empirical studies. All these are discussed below.  The first variable used in the research was trust. Because no transaction either online or offline could be done in absence of trust between seller and buyer. Therefore, trust was used to measure the attitude of customer towards online buying. Secondly, the surge in online buying is because of convenience. An individual might buy goods at any time suitable to him/her as the services are available 24X7. This is an era of busy schedule. All the professionals get free at the time where the physical market closed. Therefore, convenience was used as the second variable to measure the attitude of customers' towards online buying. Moreover, reasonable price in online selling is another important factor which persuade customers' towards online buying. Besides, a customer has the chance to make a comparison with different sellers simultaneously which was not possible while purchasing goods in physical market. During certain occasions like Eid or on New Year, they are providing huge discounts which ignite a fire in customers' to buy online. Hence, price was used as third component in the study to measure the attitude of customers towards online buying. 
\section[{Trust}]{Trust}\par
Customer service was used as fourth component in the study because a number of individuals reported that they are getting good quality of customer service if they bought goods online. In many previous studies, it was also used. Furthermore, a customer wants to get a number of varieties for the product he/she would like to buy. It is the nature of customer that he needs many categories so that he/she could select the best ones. It is not only confined to expensive products. But even for very cheap products like buying a shampoo or biscuit, a customer needs to check different other similar categories available in the market. So far online shopping is concerned, customers prefer it because of having number of varieties which becomes easy for them to select the best product according to their budget, taste, likes, and preferences. Therefore, it was used as the fifth variable in the study. Furthermore, website design, website reliability and website security are the attractive features which influence the perception of the consumer towards online buying. So, website design was used as the sixth variable in the study.   Table \hyperref[tab_0]{1} highlights the attitude of customers towards online shopping on different components. The questionnaire asked about the major reason for buying online, barriers faced by them, frequency of buying, and the type of goods bought in the year. It was found that highest percentage of customers (32\%) bought products online due to low prices. 24\% respondents told that they bought due to convenience and to save their time. Besides, 14\% customers bought because they can compare prices with rival firms easily which is not possible in offline purchase. A small number of consumers (thirty six each) told that they bought goods online due to trust and brand image. Moreover, it was found that 12\% customers bought goods after studying the reviews of the products. It means that they prefer such products which get more rating and good reviews.\par
Figure  {\ref 2} highlights the major barriers that the customers faced while buying goods online. 35\% customers reported that difficulty in getting warranties in electronic items was the important barrier regarding online purchase. Some reported that they have less trust in online purchase. High shipping cost was also found an obstacle in online purchase as 12\% reported that they paid high shipping cost. A very small number of consumers raised their concerns regarding safety of payment.\par
Table \hyperref[tab_0]{1} further highlights that the maximum customers were those who bought goods once in three months. A small percentage of customers were those who bought once in a year. The customers who bought frequently were 19\%. Besides, the table further shows that 67\% customers told that they do not visit to a retail store before buying online. It means that they have trust on online shopping. Furthermore, the questionnaire was also asked about the type of products bought online. It was found that highest percentage of customers (37\%) bought clothes. It means that clothes followed by electronic goods were the highest selling products in online mode. Home essentials like soaps, detergents, and groceries were the least bought goods online. 
\section[{III.}]{III.} 
\section[{Research Design}]{Research Design}\par
In this section, I'll present how I conducted the research to collect the primary data and reach to the conclusion of the research and will also explain which different types of methodology that were used. 
\section[{a) Objectives of the study}]{a) Objectives of the study}\par
The objective of the study is to examine the customers' perception towards online shopping in selected cities of Jordan. 
\section[{b) Hypotheses of the study}]{b) Hypotheses of the study}\par
Following null hypotheses have been developed for the study: Ho 1 : A significant and positive relationship does not exist between trust and customers' perception towards online shopping.\par
Ho 2 : A significant and positive relationship does not exist between convenience and customers' perception towards online shopping. Ho 3 : A significant and positive relationship does not exist between price and customers' perception towards online shopping.   
\section[{c) Population and Sampling Method}]{c) Population and Sampling Method}\par
Identification of target population is the first and foremost step in developing a sampling design. The population of this study includes all consumers of Jordan who buy goods online.\par
Besides, random sampling plan was implemented in the study because all consumers have equal chance of being including in the sample. The main advantage is that the large number of respondents can be obtained quickly and conveniently at lower cost. 
\section[{d) Sample Size}]{d) Sample Size}\par
The required number of sample to conduct the research was identified through the formula particularly where large and unknown population.\par
Where, Z = Confidence limit = 1.96 P = 0.5 (Proportion for unknown Population) Q = 1-P = 1 -0.5 = 0.5 B = 5\% significance level 5\% = 0.05 According to the formula, 385 is the required sample size. Therefore, researcher distributed 600 questionnaires because many questionnaires might be wrong, unsuitable, and many might not return. 
\section[{e) Data collection Method}]{e) Data collection Method}\par
A self-administered questionnaire was used for collecting primary data. It is considered as a superior mode for minimizing bias and improving response rates. With regards to questions, care was taken to eliminate words with ambiguous meaning. The questionnaire was designed to be short and simple. 
\section[{f) Pilot survey}]{f) Pilot survey}\par
When the final questionnaire was ready, then pilot online survey was conducted before actual survey. Random sampling method was used to select a small group of consumers for pilot survey. A total of 80 questionnaires were distributed to confirm the clarity of measurement items. Consumers were requested to fill the questionnaire with overall comments. A total of 60 usable responses were collected and analyzed. 
\section[{g) Distribution of Questionnaires}]{g) Distribution of Questionnaires}\par
A total of 600 questionnaires after successful pilot survey were distributed to the consumers via email living in major cities of Jordan who met the sampling requirements. Amman, Zarqa, Madab, Irbid, Mafraq, Aqaba, and Ma'an were the cities selected in the study. A total of 425 questionnaires were returned wherein 400 questionnaires were considered valid for data analysis. Table \hyperref[tab_2]{2} highlights the questionnaires distributed, rejected and accepted.  
\section[{Source: Primary Data h) Duration of field survey and Statistical tools used}]{Source: Primary Data h) Duration of field survey and Statistical tools used}\par
The data collection period was four months from September, 2019 to December, 2019. Cronbach alpha was used to test reliability of data. Correlation and simple linear regression was used to test hypotheses of the study. 
\section[{IV.}]{IV.} 
\section[{Data Analysis a) Reliability Analysis}]{Data Analysis a) Reliability Analysis}\par
Before hypotheses testing, reliability of all components as well as all statements of questionnaire was examined with the application of cronbach alpha. Table \hyperref[tab_3]{3} highlights the reliability of all statements under study. The values of all components were ranging from 0.7 to 0.9 and hence it can be said that the data was reliable for testing hypotheses.  Ho 1 : A significant and positive relationship does not exist between trust and customers' perception towards online shopping.\par
Ha 1 : A significant and positive relationship exists between trust and customers' perception towards online shopping. Simple linear regression was used as the statistical tool to examine the relationship between trust and customers' perception. Trust was taken as independent variable whereas customers' perception was the dependent variable. Table \hyperref[tab_4]{4} highlights the regression model-1 in abridged form. The adjusted R square value was 0.84 which indicates that 84\% variations in the customers' perception can be predicted from trust. Moreover, ANOVA shows the model significance. The overall model is significant because the F value is significant at 95\% confidence level. Furthermore, the unstandardized beta value shows the impact of the predictor variable (trust) on the dependent variable (customers' perception). It suggests that for one unit increase in trust, there will be 0.691 unit increase in customers' perception. Finally, the null hypothesis is rejected because P<0.05 and it can be said that there is a significant and positive relationship exists between trust and customers' perception towards online shopping.\par
Ho 2 : A significant and positive relationship does not exist between convenience and customers' perception towards online shopping. Ha 2 : A significant and positive relationship exists between convenience and customers' perception towards online shopping. Simple linear regression was used as the statistical tool to examine the relationship between convenience and customers' perception. Convenience was taken as independent variable whereas customers' perception was the dependent variable. Table \hyperref[tab_5]{5} highlights the regression model-2 in abridged form. The adjusted R square value was 0.704 which indicates that 70\% variations in the customers' perception can be predicted from convenience. Moreover, ANOVA shows the model significance. The overall model is significant because the F value is significant at 95\% confidence level. Furthermore, the unstandardized beta value shows the impact of the predictor variable (convenience) on the dependent variable (customers' perception). It suggests that for one unit increase in convenience, there will be 0.637 unit increase in customers' perception. Finally, the null hypothesis is rejected because P<0.05 and it can be said that there is a significant and positive relationship between convenience and customers' perception towards online shopping. Ho 3 : A significant and positive relationship does not exist between price and customers' perception towards online shopping.\par
Ha 3 : A significant and positive relationship exists between price and customers' perception towards online shopping. Simple linear regression was used as the statistical tool to examine the relationship between price and customers' perception. Price was taken as independent variable whereas customers' perception was the dependent variable. Table \hyperref[tab_6]{6} highlights the regression model-3 in abridged form. The adjusted R square value was 0.49 which indicates that 49\% variations in the customers' perception can be predicted from price. Moreover, ANOVA shows the model significance. The overall model is significant because the F value is significant at 95\% confidence level. Furthermore, the unstandardized beta value shows the impact of the predictor variable (price) on the dependent variable (customers' perception). It suggests that for one unit increase in price, there will be 0.506 unit increase in customers' perception. Finally, the null hypothesis is rejected because P<0.05 and it can be said that there is a significant and positive relationship between price and customers' perception towards online shopping. Ho 4 : A significant and positive relationship does not exist between customer service and customer's perception towards online shopping.\par
Ha 4 : A significant and positive relationship exists between customer service and customers' perception towards online shopping. Simple linear regression was used as the statistical tool to examine the relationship between customer service and customers' perception. Customer service was taken as independent variable whereas customers' perception was the dependent variable. Table \hyperref[tab_7]{7} highlights the regression model-4 in abridged form. The adjusted R square value was 0.459 which indicates that around 46\% variations in the customers' perception can be predicted from customer service. Moreover, ANOVA shows the model significance. The overall model is significant because the F value is significant at 95\% confidence level. Furthermore, the unstandardized beta value shows the impact of the predictor variable (customer service) on the dependent variable (customers' perception). It suggests that for one unit increase in customer service, there will be 0.266 unit increase in customers' perception. Finally, the null hypothesis is rejected because P<0.05 and it can be said that there is a significant and positive relationship between customer service and customers' perception towards online shopping.  Simple linear regression was used as the statistical tool to examine the relationship between product variety and customers' perception. Product variety was taken as independent variable whereas customers' perception was the dependent variable. Table \hyperref[tab_8]{8} highlights the regression model-5 in abridged form. The adjusted R square value was 0.263 which indicates that 26\% variations in the customers' perception can be predicted from product variety. Moreover, ANOVA shows the model significance. The overall model is significant because the F value is significant at 95\% confidence level. Furthermore, the unstandardized beta value shows the impact of the predictor variable (product variety) on the dependent variable (customers' perception). It suggests that for one unit increase in product variety, there will be 0.423 unit increase in customers' perception. Finally, the null hypothesis is rejected because P<0.05 and it can be said that a significant and positive relationship exists between product variety and customers' perception towards online shopping. Ho 6 : A significant and positive relationship does not exist between website design and customers' perception towards online shopping. Ha 6 : A significant and positive relationship exists between website design and customers' perception towards online shopping. Simple linear regression was used as the statistical tool to examine the relationship between website design and customers' perception. Website design was taken as independent variable whereas customers' perception was the dependent variable. Table \hyperref[tab_9]{9} highlights the regression model-6 in abridged form. The adjusted R square value was 0.385 which indicates that 38\% variations in the customers' perception can be predicted from website design. Moreover, ANOVA shows the model significance. The overall model is significant because the F value is significant at 95\% confidence level. Furthermore, the unstandardized beta value shows the impact of the predictor variable (website design) on the dependent variable (customers' perception). It suggests that for one unit increase in website design, there will be 0.497 unit increase in customers' perception. Finally, the null hypothesis is rejected because P<0.05 and it can be said that a significant and positive relationship exists between website design and customers' perception towards online shopping.\par
V. 
\section[{Concluding Remarks}]{Concluding Remarks}\par
Internet is a great revolution in this decade. It has greatly simplified the way shopping was done. Internet has given a dynamic boost to the growth of all sectors and especially retail sector across the globe. Online shopping is one of the offshoots of internet. Online shopping/marketing is the process of marketing/shopping a product or service using the Internet. Online shopping is also called as webstore, e-store, e-shop, Internet shop, web-shop, online store, and virtual store. The online shopping concept become very popular among the consumers due to highly challenging lifestyle. It is gaining popularity across the globe. Taking this into cognizance, the present research examined the customers' perception towards online shopping in Jordan.\par
A total of 600 questionnaires after successful pilot survey were distributed randomly to the consumers living in major cities of Jordan who met the sampling requirements. Amman, Zarqa, Madab, Irbid, Mafraq, Aqaba, and Ma'an were the cities selected in the study.\par
The data collection period was four months from September, 2019 to December, 2019. A total of 400 questionnaires were considered valid for data analysis. Cronbach alpha was used to test reliability of data. It was found that the data was reliable for testing hypotheses. Moreover, simple linear regression was used as the statistical tool to examine the relationship between online shopping and customers' perception. The present study used six variables namely trust, convenience, price, customer service, product varieties, and website design to examine the customers' perception towards online shopping. All these variables were statistically significant at 95\% confidence level. It means that a significant and positive relationship exists between all independent variables and customer's perception towards online shopping. The highest beta value was recorded on trust and the least value was recorded on customer service. The findings are in line with the previous empirical studies \hyperref[b1]{Bashir (2013)} This research found that 32\% respondents bought products online due to low prices whereas 24\% respondents told that they bought due to convenience and to save their time. However, the major barrier that the customers faced while buying goods online was difficulty in getting warranties in electronic items. 35\% customers reported it. A very small number of consumers raised their concerns regarding safety of payment. Therefore, all the firms have to take this issue seriously and try to improve it and ensure that customer will get same warranties as they get in offline purchase otherwise they stop buying online.\par
Selling online is not an easy task. There are many challenges which are found in selling online like understanding consumer behavior, certain website issues like frauds and hacking and the like. All these issues must be tackled by the firms as soon as possible to get higher degree of customer satisfaction which leads to customer loyalty which is the basis of their survival in the long run.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_1}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{24}\includegraphics[]{image-3.png}
\caption{\label{fig_2}Figure 2 : 4 Global}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-4.png}
\caption{\label{fig_3}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-5.png}
\caption{\label{fig_4}Ho 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-6.png}
\caption{\label{fig_5}Ho 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7}\includegraphics[]{image-7.png}
\caption{\label{fig_7}7 Global}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{58}\includegraphics[]{image-8.png}
\caption{\label{fig_8}Ho 5 : 8 Global}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.6948033707865169\textwidth}P{0.1074438202247191\textwidth}P{0.047752808988764044\textwidth}}
Main Reason for online Shopping\tabcellsep Frequency\tabcellsep Percent\\
Convenience \& time saving\tabcellsep 96\tabcellsep \\
Less Price\tabcellsep 128\tabcellsep \\
Trust\tabcellsep 36\tabcellsep 9\\
Brand Image\tabcellsep 36\tabcellsep 9\\
Price comparison available\tabcellsep 56\tabcellsep \\
Product reviews available\tabcellsep 48\tabcellsep \\
Total\tabcellsep 400\tabcellsep 100\\
Major Barriers\tabcellsep \tabcellsep \\
Safety of payment\tabcellsep 20\tabcellsep 5\\
Low trust level of online store / Brand\tabcellsep 96\tabcellsep \\
Warranty and claims\tabcellsep 140\tabcellsep \\
Refund Policy\tabcellsep 96\tabcellsep \\
High Shipping Cost\tabcellsep 48\tabcellsep \\
Total\tabcellsep 400\tabcellsep 100\\
No of times you buy online\tabcellsep \tabcellsep \\
Frequently or at least once a month\tabcellsep 76\tabcellsep \\
Once in three months\tabcellsep 232\tabcellsep \\
Once in six months\tabcellsep 60\tabcellsep \\
Once in a year\tabcellsep 32\tabcellsep 8\\
Total\tabcellsep 400\tabcellsep 100\\
Do you go to a retail store first before making your final purchase online?\tabcellsep Frequency\tabcellsep Percent\\
Yes\tabcellsep 92\tabcellsep \\
No\tabcellsep 307\tabcellsep \\
Total\tabcellsep 400\tabcellsep 100\\
Type of Products bought online\tabcellsep Frequency\tabcellsep Percent\\
Electronic products\tabcellsep 148\tabcellsep \\
Books\tabcellsep 44\tabcellsep \\
Clothes\tabcellsep 168\tabcellsep \\
Home essentials and Groceries\tabcellsep 40\tabcellsep \\
Total\tabcellsep 400\tabcellsep 100\\
Source: Primary Data\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_1}Table 1}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.18449612403100774\textwidth}P{0.16472868217054265\textwidth}P{0.15813953488372093\textwidth}P{0.3426356589147287\textwidth}}
Questionnaires\tabcellsep Questionnaires\tabcellsep Questionnaires\tabcellsep Questionnaires\\
Distributed\tabcellsep Returned\tabcellsep Rejected\tabcellsep Accepted for analysis\\
600\tabcellsep 480\tabcellsep 80\tabcellsep 400 [Sample Size]\end{longtable} \par
 
\caption{\label{tab_2}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.22513513513513514\textwidth}P{0.3491891891891892\textwidth}P{0.07351351351351351\textwidth}P{0.20216216216216218\textwidth}}
No.\tabcellsep Dimensions\tabcellsep Statements\tabcellsep Cronbach Alpha\\
1\tabcellsep Trust\tabcellsep 5\tabcellsep 0.894\\
2\tabcellsep Convenience\tabcellsep 4\tabcellsep 0.746\\
3\tabcellsep Price\tabcellsep 4\tabcellsep 0.914\\
4\tabcellsep Customer Service\tabcellsep 5\tabcellsep 0.834\\
5\tabcellsep Product Variety\tabcellsep 3\tabcellsep 0.799\\
6\tabcellsep Website Design\tabcellsep 4\tabcellsep 0.804\\
\multicolumn{2}{l}{Source: Output of SPSS\textunderscore 18}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.5332699619771863\textwidth}P{0.019391634980988594\textwidth}P{0.054942965779467674\textwidth}P{0.019391634980988594\textwidth}P{0.06787072243346008\textwidth}P{0.04201520912547528\textwidth}P{0.07433460076045627\textwidth}P{0.03878326996197719\textwidth}}
Model\tabcellsep R\tabcellsep Adjusted R 2\tabcellsep B\tabcellsep Standard Error\tabcellsep t value\tabcellsep ANOVA F Value\tabcellsep P Value\\
1\tabcellsep 0.917\tabcellsep 0.840\tabcellsep 0.691\tabcellsep 0.44429\tabcellsep 45.779\tabcellsep 2050.720**\tabcellsep 0.000\\
\multicolumn{3}{l}{Predictors: (Constant), Trust}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Dependent Variable: Customers' Perception}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{B: Unstandardized Coefficient}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{**Significant at 5\%}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Source: Output of SPSS\textunderscore 18}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.5423507462686568\textwidth}P{0.019029850746268655\textwidth}P{0.053917910447761196\textwidth}P{0.019029850746268655\textwidth}P{0.0666044776119403\textwidth}P{0.04123134328358209\textwidth}P{0.06977611940298507\textwidth}P{0.03805970149253731\textwidth}}
Model\tabcellsep R\tabcellsep Adjusted R 2\tabcellsep B\tabcellsep Standard Error\tabcellsep t value\tabcellsep ANOVA F Value\tabcellsep P Value\\
2\tabcellsep 0.839\tabcellsep 0.704\tabcellsep 0.637\tabcellsep 0.60987\tabcellsep 30.741\tabcellsep 944.989**\tabcellsep 0.000\\
\multicolumn{3}{l}{Predictors: (Constant), Convenience}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Dependent Variable: Customers' Perception}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{B: Unstandardized Coefficient}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{**Significant at 5\%}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Source: Output of SPSS\textunderscore 18}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_5}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.5353053435114503\textwidth}P{0.019465648854961833\textwidth}P{0.055152671755725184\textwidth}P{0.019465648854961833\textwidth}P{0.0681297709923664\textwidth}P{0.042175572519083965\textwidth}P{0.07137404580152672\textwidth}P{0.038931297709923665\textwidth}}
Model\tabcellsep R\tabcellsep Adjusted R 2\tabcellsep B\tabcellsep Standard Error\tabcellsep t value\tabcellsep ANOVA F Value\tabcellsep P Value\\
3\tabcellsep 0.701\tabcellsep 0.490\tabcellsep 0.506\tabcellsep 0.76243\tabcellsep 19.546\tabcellsep 382.060**\tabcellsep 0.000\\
\multicolumn{3}{l}{Predictors: (Constant), Price}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Dependent Variable: Customers' Perception}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{B: Unstandardized Coefficient}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{**Significant at 5\%}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Source: Output of SPSS\textunderscore 18}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.5520295202952029\textwidth}P{0.018819188191881917\textwidth}P{0.053321033210332096\textwidth}P{0.018819188191881917\textwidth}P{0.06586715867158671\textwidth}P{0.037638376383763834\textwidth}P{0.06586715867158671\textwidth}P{0.037638376383763834\textwidth}}
Model\tabcellsep R\tabcellsep Adjusted R 2\tabcellsep B\tabcellsep Standard Error\tabcellsep t value\tabcellsep ANOVA F Value\tabcellsep P Value\\
4\tabcellsep 0.677\tabcellsep 0.459\tabcellsep 0.266\tabcellsep 1.06663\tabcellsep 7.232\tabcellsep 52.297**\tabcellsep 0.000\\
\multicolumn{3}{l}{Predictors: (Constant), Customer service}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Dependent Variable: Customers' Perception}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{B: Unstandardized Coefficient}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{**Significant at 5\%}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Source: Output of SPSS\textunderscore 18}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_7}Table 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8} \par 
\begin{longtable}{P{0.546875\textwidth}P{0.01875\textwidth}P{0.053125\textwidth}P{0.01875\textwidth}P{0.065625\textwidth}P{0.040624999999999994\textwidth}P{0.06875\textwidth}P{0.0375\textwidth}}
Model\tabcellsep R\tabcellsep Adjusted R 2\tabcellsep B\tabcellsep Standard Error\tabcellsep t value\tabcellsep ANOVA F Value\tabcellsep P Value\\
5\tabcellsep 0.513\tabcellsep 0.263\tabcellsep 0.423\tabcellsep 0.96172\tabcellsep 11.919\tabcellsep 142.068**\tabcellsep 0.000\\
\multicolumn{3}{l}{Predictors: (Constant), Product variety}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Dependent Variable: Customers' Perception}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{B: Unstandardized Coefficient}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{**Significant at 5\%}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Source: Output of SPSS\textunderscore 18}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_8}Table 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9} \par 
\begin{longtable}{P{0.5457564575645756\textwidth}P{0.018819188191881917\textwidth}P{0.053321033210332096\textwidth}P{0.018819188191881917\textwidth}P{0.06586715867158671\textwidth}P{0.040774907749077494\textwidth}P{0.06900369003690036\textwidth}P{0.037638376383763834\textwidth}}
Model\tabcellsep R\tabcellsep Adjusted R 2\tabcellsep B\tabcellsep Standard Error\tabcellsep t value\tabcellsep ANOVA F Value\tabcellsep P Value\\
6\tabcellsep 0.621\tabcellsep 0.385\tabcellsep 0.497\tabcellsep 0.87832\tabcellsep 15.796\tabcellsep 249.502**\tabcellsep 0.000\\
\multicolumn{3}{l}{Predictors: (Constant), Website design}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Dependent Variable: Customers' Perception}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{B: Unstandardized Coefficient}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{**Significant at 5\%}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Source: Output of SPSS\textunderscore 18}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_9}Table 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_10}}\end{figure}
 			\footnote{( ) E Customers' Perception to wards Online Shopping in Jordan © 2021 Global Journals} 			\footnote{© 2021 Global Journals} 		 		\backmatter  			 
\subsection[{Appendix}]{Appendix}			 			  				\begin{bibitemlist}{1}
\bibitem[Jordan]{b7}\label{b7} 	 		\textit{},  		 			Effect\textunderscore Of\textunderscore Perceived\textunderscore Risk\textunderscore On\textunderscore Online\textunderscore Shopping\textunderscore In\textunderscore  Jordan 		.  		 	 
\bibitem[]{b9}\label{b9} 	 		\textit{},  		 \xref{http://dx.doi.org/10.9734/ajarr/2019/v6i230149}{10.9734/ajarr/2019/v6i230149}.  		 \url{DOIhttps://doi.org/10.9734/ajarr/2019/v6i230149}  		 	 
\bibitem[Handa and Gupta ()]{b3}\label{b3} 	 		‘A study of the relationship between shopping orientation and online shopping behavior among Indian youth’.  		 			M Handa 		,  		 			N Gupta 		.  		 \url{https://www.researchgate.net/publication/271927781\textunderscore A\textunderscore Study\textunderscore of\textunderscore the\textunderscore Relationship\textunderscore between\textunderscore Shopping\textunderscore Orientation\textunderscore and\textunderscore Online\textunderscore Shopping\textunderscore Behavior\textunderscore among\textunderscore Indian\textunderscore Youth}  	 	 		\textit{Journal of Internet Commerce}  		2014. 13  (1)  p. .  	 
\bibitem[Khare et al. ()]{b5}\label{b5} 	 		‘Attracting shoppers to shop online-Challenges and opportunities for the Indian retail sector’.  		 			A Khare 		,  		 			A Khare 		,  		 			S Singh 		.  		 \xref{http://dx.doi.org/10.1080/15332861.2012.689570}{10.1080/15332861.2012.689570}.  		 \url{https://doi.org/10.1080/15332861.2012.689570}  	 	 		\textit{Journal of Internet Commerce}  		2012. 11  (2)  p. .  	 
\bibitem[Bashir ()]{b1}\label{b1} 	 		\textit{Consumer behavior towards online shopping of electronics in Pakistan},  		 			A Bashir 		.  		 \url{https://www.theseus.fi/bitstrea}  		2013. Pakistan.  	 	 (Master Thesis) 	 (Seinäjoki university of applied sciences) 
\bibitem[Customer perception, awareness and satisfaction towards online shopping-a study with reference to Chennai city ()]{b15}\label{b15} 	 		\textit{Customer perception, awareness and satisfaction towards online shopping-a study with reference to Chennai city},  		 \url{http://hdl.handle.net/10603/200056}  		2014. Chennai, India.  		 			University of Madras 		 	 	 (Doctoral Thesis) 
\bibitem[Perera ()]{b8}\label{b8} 	 		‘Customer Satisfaction towards Online Shopping in Sri Lanka: Moderating Effect of Income Level’.  		 			Sachitra Perera 		.  	 	 		\textit{Asian Journal of Advanced Research and Reports}  		2019. 6 p. .  	 
\bibitem[Rahman ()]{b10}\label{b10} 	 		‘Customers' perception towards online shopping: An empirical study on Dhaka city’.  		 			M A Rahman 		.  		 \xref{http://dx.doi.org/10.1080/23311975.2018.1514940}{10.1080/23311975.2018.1514940}.  		 \url{http://dx.doi.org/10.1080/23311975.2018.1514940}  	 	 		\textit{Bangladesh. Cogent Business \& Management}  		2018. 5.  	 
\bibitem[Thamizhvanan and Xavier ()]{b13}\label{b13} 	 		‘Determinants of customers' online purchase intention: an empirical study in India’.  		 			A Thamizhvanan 		,  		 			M J Xavier 		.  		 \url{https://www.deepdyve.com/lp/emerald-publishing/d-of-customers'-online-purchase-intention-an-empirical-study-R205RavXfw}  	 	 		\textit{Journal of Indian Business Research}  		2013. 5  (1)  p. .  	 
\bibitem[Bashir et al. ()]{b2}\label{b2} 	 		‘Effects of online shopping trends on consumerbuying behavior: an empirical study of Pakistan’.  		 			R Bashir 		,  		 			I Mehboob 		,  		 			W K Bhatti 		.  		 \url{http://admin.umt.edu.pk/Media/Site/SBE1/SubSites/JMR/FileManager/JMR/Paper\textunderscore 1.pdf}  	 	 		\textit{Journal of Management and Research}  		2015. 2  (2) .  	 
\bibitem[Jusoh and Ling ()]{b4}\label{b4} 	 		‘Factors influencing consumers' attitude towards ecommerce purchases through online shopping’.  		 			Z M Jusoh 		,  		 			G H Ling 		.  	 	 		\textit{International Journal of Humanities and Social Science}  		2012. 2  (4)  p. .  	 
\bibitem[Swapana and Padmavathy ()]{b12}\label{b12} 	 		‘Factors influencing online shopping experience-a conceptual model and implications’.  		 			M Swapana 		,  		 			C Padmavathy 		.  		 \url{https://www.sonamgmt.org/journal/previous-issues/june-2017/factors-influencing-online-shopping.pdf}  	 	 		\textit{Sona Global Management Review}  		2017. 11.  	 
\bibitem[Ahluwalia and Sanan ()]{b0}\label{b0} 	 		‘Online Buying Behavior: A Case Study of Jalandhar City in Punjab’.  		 			A K Ahluwalia 		,  		 			P Sanan 		.  		 \url{http://www.pbr.co.in/2016/2016\textunderscore month/January/3.pdf}  	 	 		\textit{Pacific Business Review International}  		2016. 8 p. .  	 
\bibitem[Rose et al. ()]{b11}\label{b11} 	 		‘Online customer experience: A review of the business-to consumer online purchase context’.  		 			S Rose 		,  		 			N Hair 		,  		 			M Clark 		.  		 \xref{http://dx.doi.org/10.1111/j.1468-2370.2010.00280.x}{10.1111/j.1468-2370.2010.00280.x}.  		 \url{https://onlinelibrary.wiley.com/doi/full/10.1111/j.1468-2370.2010.00280.x}  	 	 		\textit{International Journal of Management Reviews}  		2011. 13  (1)  p. .  	 
\bibitem[Masoud ()]{b6}\label{b6} 	 		‘The Effect of Perceived Risk on Online Shopping in Jordan’.  		 			E Y Masoud 		.  		 \url{https://www.researchgate.net/publication/340438323\textunderscore The\textunderscore }  	 	 		\textit{European Journal of Business and Management}  		2013. 5  (6) .  	 
\bibitem[Tseng ()]{b14}\label{b14} 	 		‘The impact of electronic-service quality on online shopping behavior’.  		 			Wen-Chin Tsao \& Ya-Ling Tseng 		.  		 \xref{http://dx.doi.org/10.1080/14783363.2011.593869}{10.1080/14783363.2011.593869}.  	 	 		\textit{Total Quality Management \& Business Excellence}  		2011. 22  (9)  p. .  	 
\end{bibitemlist}
 			 		 	 
\end{document}