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             \author[1]{Nashid Bintey  Hayder}

             \affil[1]{  Institute of Business Administration (IBA), Jahangirnagar University, Dhaka, Bangladesh}

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\date{\small \em Received: 11 December 2016 Accepted: 4 January 2017 Published: 15 January 2017}

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\begin{abstract}
        


The internet in Bangladesh has witnessed phenomenal growth. The Internet has developed into a new distribution channel and very soon the internet payment gateway will open for Bangladeshi people. This has created a need to understand how website factors influences consumer buying behavior.The motivation for this research is the fact that numerous organizations in Bangladesh do not realize the importance of website to their consumer for better service delivery quality and ultimate impact on the organizations portability.The purpose of this research was to examine if there are any particular factors that influence the online consumer. Six factors was identified Ease of use, Product information, Entertainment, Trust, Currency, Customer support. To address the research objective, surveys were administered to 150 university students from public and private universities. Primary data was collected through a survey that was conducted on students at the Jahangirnagar University, American International University -Bangladesh (AIUB) and North South University. Results indicate that all web site factors have influences on consumer buying behavior. These findings suggest that online retailers should emphasize site factors that best suit the involvement/experience profile of their primary users.

\end{abstract}


\keywords{customer, website, consumer behavior, characteristics.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction he internet}]{Introduction he internet}\par
in Bangladesh has witnessed phenomenal growth. Although facing many constraints in expanding internet access and use, development of the internet and information Technology are high government priorities.In April 2010, Akhtaruzzaman Manju, president of Internet Service Providers' Association of Bangladesh, said "we've estimated that nearly 10 million people in the country are using 800,000 internet connections on sharing basis", adding the number of internet users in the country is increasing roughly 15-16 percent a year("Roundup: Internet use," 2010).According to the Pew internet and American Life Project, it takes one to three years for most internet browsers to begin to use online stores \hyperref[b15]{(Green and Hof 2002)}. This means that a large number of consumers can be expected to turn into online buyers in a short period of time.This increased internet penetration will result in a 2.6 per cent contribution to the country's GDP by 2020 (Boston Consulting Group) \hyperref[b13]{(FE Report, 2010)}. Recently, the Bangladesh Bank (BB) has given permission to Online Payment Gateway Service Providers (OPGSPs) to facilitate repatriation of remittance against small value service exports in nonphysical forms, such as data entry/processing, off-shore IT service, and business process outsourcing etc. \hyperref[b35]{(Uddin, 2012)}.\par
Who will capture this profitable market? Certainly businesses, which are able to identify the preferences and concerns of these consumers and then utilize this information to design effective online strategies, should benefit most.\par
The internet is considered a mass medium that provides the consumer with purchase characteristics as no other medium. Certain characteristics are making it more convenient for the consumer, compared to the traditional way of shopping, such as the ability to at any time view and purchase products, visualize their needs with products, and discuss products with other consumers \hyperref[b18]{(Joines et al. 2003)}. \hyperref[b28]{Oppenheim and Ward (2006)} explain that the current primary reason people shop over the internet is the convenience. They also recognize that the previous primary reason for shopping online was price, which has now changed to convenience.\par
To help online Bangladeshi marketers to build effectivewebsites achieve the goal,the objective is to learn about thefactors of websites thatinfluences consumer behavior. 
\section[{a) Problem}]{a) Problem}\par
At any given time there are millions of people online and each of them is a potential customer for a company providing online sales. Due to the rapid development of the technologies surrounding the internet, a company that is interested in selling products from its web site will constantly has to search for an edge in the fierce competition. Since there are so many potential consumers, it is of the out most importance to be able to understand what the consumer wants and needs. The importance of analyzing and identifying factors that influence the consumer when he or she decides to purchase on the internet is vital. As the internet is a new medium for there have been new demands set by the consumer. That is why it is crucial for the online retailers to know what influences the online consumer.\par
Analyzing consumer behavior is not a new phenomenon. There aremany theories that have been used for many years not only to understand the consumer, but also create a marketing strategy that will attract the consumer efficiently. Hence, understanding and identifying the consumer is closely related to the directions a company will take with their marketing strategy. These theories can also be applied to identify the online consumer and to create certain consumer segments. However, some distinctions must still be made when considering traditional consumer behavior and online consumer behavior. Since online retailing is a new retailing medium and online consumer behavior is diverse from traditional consumer behavior, one must identify what influences the online consumer. Analyzing the process that the online consumer goes through when deciding and making a purchase over the Internet, shows some factors that consumers consider. These factors need to be identified and taken into account by online retailers in order to satisfy consumer demands and compete in the online market. 
\section[{b) Research Purpose}]{b) Research Purpose}\par
The purpose of this research is primarily to identify and get insight into what main factors the online consumer takes into consideration when visiting a website, Further;the studyhas investigatedhow the targeted segments related to the identified factors. The findings of this research would be outlined as implications for future Bangladeshi online retailers in order to enhance their consumer knowledge and increase their online marketing strategy effectiveness. c) Research Questions 1. What are the factors that affect the consumer when considering a website for information to purchase a product/service? 2. What is the connection with the identified factors? 3. What is the level of computer and internet experience of the target group for searching product/service information. 
\section[{II.}]{II.} 
\section[{Literature Review}]{Literature Review}\par
The consumers" buying behavior has been always a popular marketing topic, extensively studied and debated over the last decades while no contemporary marketing textbook is complete without a chapter dedicated to this subject. The predominant approach, explaining the fundamentals of consumer behavior, describes the consumer buying process as learning, information-processing and decision-making activity divided in several consequent steps: 1) Problem identification.\par
2) Information search.\par
3) Alternatives evaluation. 4) Purchasing decision. 5) Post-purchase behavior \hyperref[b2]{(Bettman, 1979;}\hyperref[b11]{Dibb et al., 2001;}\hyperref[b17]{Jobber, 2001;}\hyperref[b3]{Boyd et al. , 2002;}\hyperref[b20]{Kotler, 2003;}\hyperref[b4]{Brassington and Pettitt, 2003)}.\par
A distinction is frequently made between high and low involvement purchasing, implying that in practice the actual buying activity can be less or more consistent with this model, depending on the buyer's perceived purchasing risks. High or low degree of involvement is also a question of buyer experience; products purchased for the first time, in general, req uire more involvement than frequently purchased products \hyperref[b3]{(Boyd et al. 2002)}.\par
The web experience as a major parameter of customer influence is crucial for dot.com -type firms but also for multi-channel vendors. For traditional firms expanding their business with internet presence, the quality of online experience they deliver is an issue requiring special attention: poorly designed and dysfunctional Web sites are a potential threat not only to the company's virtual aspirations but also a hazard for their physical activities. The primary means of delivering the Web experience is the corporate Web site. Sites delivering superb Web experience are designed in a way not only addressing the client's product needs and expectations but also assisting the customers through the steps of the buying process. In that respect the back-office e-commerce infrastructure (O'Keefe and \hyperref[b27]{McEachern, 1998}) is also of crucial importance. Web sites must be seen therefore as vital instruments of customer service and persuasion rather than simply as online brochures or catalogues of the company's products.\par
Several academics and practitioners have identified the "online shopping experience" or "virtual experience" as a crucial e-commerce marketing issue. \hyperref[b34]{Tamimi et al. (2003)} define the online shopping experience as a process of four stages describing the successive steps of an online transaction. Considering that an online customer is not simply a shopper but also an information technology user \hyperref[b8]{(Cho and Park, 2001)} one can argue that the online experience is a more complicated issue than the physical shopping experience: the Web experience can be defined as the consumer's total impression about the online company (Watchfire Whitepaper Series, 2000) resulting from his/her exposure to a combination of virtual marketing tools "... under the marketer's direct control, likely to influence the buying behavior of the online consumer"  {\ref (Constantinides,2002, p. 60)}. The Web experience embraces elements like searching, browsing, finding, selecting, comparing and evaluating information as well as interacting and transacting with the online firm. Past research indentifies several candidates (Chen and Wells 1999: Szymanski and Hise 2000; Donthu 2001; Zeithaml, Parasuraman, and Malhotra 2002) considers six website factors that have been widely researched: 1. Ease of use 2. Product information 3. Entertainment 4. Trust 5. Currency 6. Customer support Ease of use implies uncluttered screens, clear organization, logical flow, and ease of navigation, in short, a web design that facilitates one's efficient and effective use of the site. Ease of use should enhance ability to process product and purchase information, reduce cost of search, permit faster search, increase likelihood of a successful search, and increase attitude toward the site. Several studies report a positive relationship between ease of use and attitude toward a web site (Chen and Wells 1999; Stevenson, Bruner, and Kmnar 2000; Kwon, Kim, and Lee 2002; \hyperref[b0]{Bellman and Rossiter 2004)}. Ease of use also appears to increase site credibility  {\ref (Fogg et}  Product information includes the amount, accuracy, and form of information about the product and services offered on a website. Since e-consumer cannot examine a product, they depend on information to identity, compare and select products. Online information includes text, tables, graphs, photos, audio, and video. Better product information should help online shoppers make better decision feel more confident about their decisions, increase satisfaction with the shopping experience, and improve attitude toward a site. Several studies report a positive association between product information and attitude toward website (Chen and Wells 1,999; Donthu 2001; Kwon, Kim and bee 2002). Product information also appears to increase attitude to online shopping (Vijayasamhy and Jones 2000), amount of online shopping  {\ref (Kwak, Fox and Zinkhan 2002)}, online spending (Belhnan, Lohse, and Johnson 1999; Korgaonkar and Wolin1999), and satisfaction with online purchases (Szyrnenski and I-hise 2000).\par
Entertainment involves all web site elements that promote enjoyment while using a site. These include sensory and hedonic stimuli, like color, music, action, and interactivity. Site designs that promote fantasy or suspense can also provide entertainment, as might games, puzzles, streaming video, and virtual tours. Like conventional shoppers, e-shoppers should prefer experiences that create positive feelings. Past research suggests that entertainment-related factors (vividness, aesthetically pleasing design elements, and engaging material) have positively related to attitude to a web site  {\ref (} Entertainment also appears to increase attitude to online shopping (Jarvenpaa and Todd 1997; Vijayasarathy and Jones 2000), invention to shop online (Lynch, Kent, and Srinivasan 2001), frequency of online purchases \hyperref[b19]{(Korgaonkar and Wolin 1999)}, and e-loyalty \hyperref[b7]{(Childers et al. 2001)}.\par
Customer support complements ease of use. Both factors support the shopping process (search, comparison, choice, ordering, and tracking). But while ease of use involves design elements that directly support the process, customer support has to do with contingent resources that are drawn upon only when normal shopping processes prove inadequate. By analogy, in-store shoppers look for help from salespeople (or other shoppers) when something impedes their shopping process. We suggest that online customer support plays a similar role. It allows disrupted e-shoppers to continue shopping. This use of customer support is similar to the definition of "recovery service" proposed by Zeithaml, Parasuraman, and Malhotra (2002). It is not clear whether good customer support would actually enhance attitude toward a site or only reduce the likelihood of frustration, failure, and unfavorable attitudes. That may depend on whether the support provided merely meets or exceeds one's expectation. In either case, customer support is associated with a stronger attitude to the site. Jarvenpaa and Todd (1997) reports that customer support positively affects intention to shop online. Srinivasan, Anderson, and Ponnavolu (2002) find that customer support positively effects online customer loyalty.\par
Trust is critical for financial transactions. Many e-shoppers fear that personal data will be misused, that undesirable cookies will be planted, and that endless spam will rain down on them from cyberspace. Seventyone percent of US web users distrust online vendors (Pew Foundation 2003). What site factors can help overcome this? According to  {\ref Chen and Dhillon (2003)}, the site factors that drive trust most are likability, credibility, situational normality, and structural assurances. Likability and credibility are well known from the sales and advertising literature. Sites achieve situational normality by adopting a "professional look" (the web's version of a business suit). Structural assurances include return policies, privacy policies, and third party assurances. Such policies and assurances imply that a vendor is trustworthy. If retailers fail to provide them, shoppers are more likely to leave without completing a transaction. Zeithaml, Parasuraman, and Malhotra (2002) and  {\ref Chen and Dhillon (2003)} suggest that trust is an important dimension for retail web sites. \hyperref[b12]{Donthu (2001)} reports that trust is related to attitude toward a web site. Trust also appears to increase attitude to online shopping \hyperref[b16]{(Jarvenpaa and Todd 1997)} Currency implies that everything on a site is upto-date. Currency implies accuracy, an information dimension discussed by \hyperref[b40]{Yang, Peterson, and Huang (2001)}. It also helps to establish normality. That is, if a site looks up-to-date, it is assumed to be in proper order, a precondition for credibility and trust  {\ref (Chen and Dhillon 2003)}. Currency is more than updated data. It includes news, special promotions, and announcements of coming events, anything that refreshes the content or appearance of the site. New page designs, new photos and new headlines can all signal a vendor's commitment to stay current. To be effective, salesmen must look and sound current. To remain effective, traditional retail establishments update their inventory and their shopping environments. By analogy, web content that affirms the up-to-date status of the site should bolster one's con fidence in the site and reduce switching.\par
Anything that calls a site's currency into question should reduce the vendor's perceived credibility and the shopper's attitude toward the site. \hyperref[b14]{Fogg et al. (2001)} reports that currency increases web site credibility, but no previous research explicitly considers the effect of currency on attitude toward a retail web site.\par
Twenty studies investigate the relationship between website quality and consumers online shopping attitudes and behavior from different point of view. Zhang, Von Dran, Small and Barcellousmake an attempt to evaluate website quality from user satisfaction and dissatisfaction perspectives. Their studies showed that website design features can be regarded as hygiene and motivation factors that contribute to user dissatisfaction and satisfaction with a website. Hygiene factors are those whose present make a website functional and serviceable, and whose absence causes user dissatisfaction. Some of hygiene factors were: Privacy and Security, Technical Aspect, Navigation, Impartially and Information Content. Based on the discussion of the relevant website factors, the following hypotheses' is proposed: H1: Consumers' website visit is influenced by web site's ease of use. H2: Consumers' website visit is influenced by product information available on the website H3: Consumers' website visit is influenced by trust generated by the website H4: Consumers' website visit is influenced by up-to-date features of the website.   
\section[{d) Data analysis tools}]{d) Data analysis tools}\par
Simple Liner Regression. 
\section[{e) Data analysis techniques}]{e) Data analysis techniques}\par
Frequency distribution, Bi-variate Regression. 
\section[{f) Variables and Scale}]{f) Variables and Scale}\par
There were seven variables of this study. Variables were: a. Ease of use, Product Information, Entertainment, Trust, Currency, and Customer Support are independent variable; and b. Consumer buying behavior of university students as dependent variable. 
\section[{g) Sample}]{g) Sample}\par
In order to investigate the influence of website characteristics on consumer behavior, students ofJahangirnagar University, American International University and North South University were targeted. Both male and female students were selected on the basis of the convenience of the surveyor. A questionnaire was developed contained thirty two questions regarding factors of website characteristics and students' buying behavior. The response rate was 84.44\% because total distributed questionnaires were 180 out of which 151 were retrieved.\par
V.  
\section[{Analysis \& Findings a) Respondents Profile}]{Analysis \& Findings a) Respondents Profile} 
\section[{b) Reliability Tests}]{b) Reliability Tests}\par
A composite score for each section of the questionnaire was obtained by summing the scores of individual statements. Reliability tests were run to determine how strongly the attributes were related to each other and to the composite score. The internal consistency reliability test is deemed to be acceptable for basic research when the reliability coefficient exceeded Nunnally's reliability criterion of 0.70 levels  {\ref (Nunnally, 1978)}. All dimensions in both sections (expectations and perceptions) of the questionnaire were tested and the Cronbach alpha ranged from 0.733 to 0.84. 
\section[{c) Descriptive Statistics i. Most Visited Bangladeshi websites among the respondents Table 02}]{c) Descriptive Statistics i. Most Visited Bangladeshi websites among the respondents Table 02}\par
The table shows that the respondents visit e-commerce and some retailer's website. The table results give an idea that after the payment gateway opens then retailers website may get more traffic.  
\section[{Most Visited Bangladeshi websites}]{Most Visited Bangladeshi websites} 
\section[{Most Visited Bangladeshi websites}]{Most Visited Bangladeshi websites}\par
ii. 
\section[{Purpose of Internet use among the Respondents}]{Purpose of Internet use among the Respondents}\par
Table  {\ref 03} The table shows that the studentsuses internetmainly for games,music and program and movies downloading secondly for social networking.   
\section[{d) Results of Regression Analysis H1: Consumers' website visit is influenced by web site's ease of use:}]{d) Results of Regression Analysis H1: Consumers' website visit is influenced by web site's ease of use:} 
\section[{F test:}]{F test:}\par
From the table 06 above we see that, the critical value of F-statistic at Degree of Freedom of numerator 1 and Degree of Freedom of denominator 148 is 3.9051, and the calculated value of F-statistic is 214.745. The calculated value > the critical or tabulated value.From this we can conclude that, there is a statistically significant relationship between consumer behavior and Ease of use.  
\section[{t-Test:}]{t-Test:}\par
The critical value of t-statistic at degree of freedom df=148 and level of significance 0.05 is 1.9761. Calculated value of t-statistic of independent variable is less than the critical value. Therefore, we can conclude that Ease of use(.000) have a highly significant relationship with consumer behavior. Correlation is significant at the 0.01 level (2-tailed).\par
Ease of use have a positive and significant impact on the behavior of the consumers towards website visitbecause the P value is less than .05 which means that Consumer's website visit is influenced by web sites ease of use, this is support H1.  
\section[{Purpose of Internet use}]{Purpose of Internet use}\par
Others 
\section[{H2: Consumers' website visit is influenced by product information available on the website}]{H2: Consumers' website visit is influenced by product information available on the website}\par
The result of the regression analysis demonstrates that the independent variables significantly influence the consumer behavior (table \hyperref[tab_7]{10}) (Appendices).The high adjusted r 2 value (.678) reflects the significance of these variables in the model. The adjusted R-square of the model indicates 68\% variation in consumer behavior can be explained by the regression model. The unexplained part of the model is the error term.  
\section[{F test:}]{F test:}\par
From the table 10 above, we can see that, the critical value of F-statistic at Degree of Freedom of numerator 1 and Degree of Freedom of denominator 148 is 3.9051, and the calculated value of F-statistic is 314.091. The calculated value > the critical or tabulated value.From this we can conclude that, there is a statistically significant relationship between consumer behavior and Product Information. 
\section[{t-Test:}]{t-Test:}\par
The critical value of t-statistic at degree of freedom df=148 and level of significance 0.05 is 1.9761. Calculated value of t-statistic of independent variable is less than the critical value. Therefore, we can conclude that Product Information (.000) have a highly significant relationship with consumer behavior. Correlation is significant at the 0.01 level (2-tailed).\par
Product Information have a positive and significant impact on the behavior of the consumers towards website visitbecause the P value is less than .05 which means that Consumer's website visit is influenced by web sites Product Info, this is support H2. 
\section[{H3: Consumers' website visit is influenced by trust generated by the website}]{H3: Consumers' website visit is influenced by trust generated by the website}\par
The result of the regression analysis demonstrates that the independent variables significantly influence the consumer behavior (table \hyperref[tab_12]{15}) (Appendices).The high adjusted r 2 value (.555) reflects the significance of these variables in the model. The adjusted R-square of the model indicates 56\% variation in consumer behavior can be explained by the regression model. The unexplained part of the model is the error term.   
\section[{F test:}]{F test:}\par
From the table 16 above, we can see that, the critical value of F-statistic at Degree of Freedom of numerator 1 and Degree of Freedom of denominator 148 is 3.9051, and the calculated value of F-statistic is 186.958. The calculated value > the critical or tabulated value.From this we can conclude that, there is a statistically significant relationship between consumer behavior and Trust. The critical value of t-statistic at degree of freedom df=148 and level of significance 0.05 is 1.9761. Calculated value of t-statistic of independent variable is less than the critical value. Therefore, we can conclude that Trust(.000) have a highly significant relationship with consumer behavior. Correlation is significant at the 0.01 level  {\ref (2-tailed)}.\par
Trust have a positive and significant impact on the behavior of the consumers towards website visitbecause the P value is less than .05 which means that Consumer's website visit is influenced by web sites Trust, this is support 
\section[{H4: Consumers' website visit is influenced by up-to-date features of the website}]{H4: Consumers' website visit is influenced by up-to-date features of the website}\par
The result of the regression analysis demonstrates that the independent variables significantly influence the consumer behavior (table  {\ref 20}) (Appendices).The high adjusted r 2 value (.693) reflects the significance of these variables in the model. The adjusted R-square of the model indicates 69\% variation in consumer behavior can be explained by the regression model. The unexplained part of the model is the error term.  
\section[{F test:}]{F test:}\par
From the table 21 above we can see that, the critical value of F-statistic at Degree of Freedom of numerator 1 and Degree of Freedom of denominator 148 is 3.9051, and the calculated value of F-statistic is 337.107. The calculated value > the critical or tabulated value.From this we can conclude that, there is a statistically significant relationship between consumer behavior and Currency. The critical value of t-statistic at degree of freedom df=148 and level of significance 0.05 is 1.9761. Calculated value of t-statistic of independent variable is less than the critical value. Therefore, we can conclude that 337.107 Currency (.000) have a highly significant relationship with consumer behavior. Correlation is significant at the 0.01 level  {\ref (2-tailed)}.\par
Currency have a positive and significant impact on the behavior of the consumers towards website visitbecause the P value is less than .05 which means that Consumer's website visit is influenced by web sites Currency, this is support H4. 
\section[{H5: Consumers' website visit is influenced by entertainment facilities/options available in the website}]{H5: Consumers' website visit is influenced by entertainment facilities/options available in the website}\par
The result of the regression analysis demonstrates that the independent variables significantly influence the consumer behavior (table  {\ref 25}) (Appendices).The high adjusted r 2 value (.666) reflects the significance of these variables in the model. The adjusted R-square of the model indicates 67\% variation in consumer behavior can be explained by the regression model. The unexplained part of the model is the error term. The critical value of t-statistic at degree of freedom df=148 and level of significance 0.05 is 1.9761. Calculated value of t-statistic of independent variable is less than the critical value. Therefore, we can conclude that Entertainment (.000) have a highly significant relationship with consumer behavior. Correlation is significant at the 0.01 level (2-tailed).\par
Entertainment have a positive and significant impact on the behavior of the consumers towards website visitbecause the P value is less than .05 which means that Consumer's website visit is influenced by web sites Entertainment, this is support H5. 
\section[{H6: Consumers' website visit is influenced by customer support of the website}]{H6: Consumers' website visit is influenced by customer support of the website}\par
The result of the regression analysis demonstrates that the independent variables significantly influence the consumer behavior (table  {\ref 30}) (Appendices).The high adjusted r 2 value (.648) reflects the significance of these variables in the model. The adjusted R-square of the model indicates 65\% variation in consumer behavior can be explained by the regression model. The unexplained part of the model is the error term. 
\section[{F test:}]{F test:}\par
From the table 31 above we can see that, the critical value of F-statistic at Degree of Freedom of numerator 1 and Degree of Freedom of denominator 148 is 3.9051 and the calculated value of F-statistic is 275.133. The calculated value > the critical or tabulated value.From this we can conclude that, there is a statistically significant relationship between consumer behavior and Customer Support.\par
The critical value of t-statistic at degree of freedom df=148 and level of significance 0.05 is 1.9761. Calculated value of t-statistic of independent variable is less than the critical value. Therefore, we can conclude thatCustomer Support t(.000) have a highly significant relationship with consumer behavior. Correlation is significant at the 0.01 level  {\ref (2-tailed)}.\par
Customer Support have a positive and significant impact on the behavior of the consumers towards website visitbecause the P value is less than .05 which means that Consumer's website visit is influenced by web sites Customer Support, this is support H6. 
\section[{VI.}]{VI.} 
\section[{Conclusion \& Recommendation a) Conclusion}]{Conclusion \& Recommendation a) Conclusion}\par
In summary, the study shows that respondent have positive perceptions about company's website. The findings indicate that in order to influence these consumer companies should carefully design secure and easy to use web sites with a rich with up to date product information and easy to reach customer service. Bangladeshi Online marketers should also be very receptive to personal consumer characteristics. This study, past research, and advancements in technology suggest that many of these concerns may be addressed now, for instance, with a more careful design of online stores, more effective marketing and online selling processes that take into consideration the needs and expectations of today's online browsers. Future research should help online vendors develop and test most effective ways that address these concerns. 
\section[{b) Recommendation}]{b) Recommendation}\par
In Bangladesh there is very limited websites are present for giving information for their product. But as internet user increases local website traffic will increase. As all the hypotheses were accepted, it is suggested that? a. The identified factors should be concerned while designing the website for a company to promote online depending company's businesses.\par
All the factors have influence on consumer buying behavior. So for any kind of online store these factors should be present.  \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Fig. 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-6.png}
\caption{\label{fig_4}H5:}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-7.png}
\caption{\label{fig_7}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{01} \par 
\begin{longtable}{P{0.19870129870129868\textwidth}P{0.34588744588744585\textwidth}P{0.11038961038961037\textwidth}P{0.195021645021645\textwidth}}
Demographics\tabcellsep Items\tabcellsep N\tabcellsep Percentage (\%)\\
Gender\tabcellsep Male Female\tabcellsep 103 47\tabcellsep 68.67 31.33\\
Total\tabcellsep \tabcellsep 150\tabcellsep 100\\
\tabcellsep 18 -20 yrs old\tabcellsep 46\tabcellsep 30.67\\
Age\tabcellsep 21 -23 yrs old 24 -26 yrs old\tabcellsep 84 20\tabcellsep 56 13.33\\
\tabcellsep 27 yrs +\tabcellsep 0\tabcellsep \\
Total\tabcellsep \tabcellsep 150\tabcellsep 100\\
Level of Education\tabcellsep Undergraduate Post Graduate\tabcellsep 135 15\tabcellsep 90 10\\
Total\tabcellsep \tabcellsep 150\tabcellsep 100\end{longtable} \par
 
\caption{\label{tab_0}Table 01 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{04} \par 
\begin{longtable}{P{0.4428571428571429\textwidth}P{0.09999999999999999\textwidth}P{0.12857142857142856\textwidth}P{0.03214285714285714\textwidth}P{0.08214285714285714\textwidth}P{0.02857142857142857\textwidth}P{0.03571428571428572\textwidth}}
\tabcellsep Model\tabcellsep Sum of Squares\tabcellsep df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\\
\tabcellsep Regression\tabcellsep 628.562\tabcellsep 1\tabcellsep 628.562\tabcellsep 214.745\tabcellsep .000 a\\
1\tabcellsep Residual\tabcellsep 433.198\tabcellsep 148\tabcellsep 2.927\tabcellsep \\
\tabcellsep Total\tabcellsep 1061.760\tabcellsep 149\tabcellsep \tabcellsep \\
\multicolumn{3}{l}{a. Predictors: (Constant), Ease\textunderscore of\textunderscore use}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{b. Dependent Variable: Consumer\textunderscore Behavior}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 04 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{05} \par 
\begin{longtable}{P{0.20786026200873362\textwidth}P{0.21157205240174673\textwidth}P{0.1410480349344978\textwidth}P{0.0705240174672489\textwidth}P{0.12248908296943231\textwidth}P{0.0482532751091703\textwidth}P{0.0482532751091703\textwidth}}
\tabcellsep Model\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients}\tabcellsep Standardized Coefficients\tabcellsep t\tabcellsep Sig.\\
\tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \tabcellsep \\
1\tabcellsep (Constant) Ease\textunderscore of\textunderscore use\tabcellsep 3.891 .924\tabcellsep .507 .063\tabcellsep .769\tabcellsep 7.669 14.654\tabcellsep .000 .000\\
\multicolumn{3}{l}{a. Dependent Variable: Consumer\textunderscore Behavior}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table 05 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{06} \par 
\begin{longtable}{P{0.36791044776119397\textwidth}P{0.11840796019900497\textwidth}P{0.15223880597014924\textwidth}P{0.03805970149253731\textwidth}P{0.09726368159203981\textwidth}P{0.03383084577114428\textwidth}P{0.04228855721393035\textwidth}}
\tabcellsep Model\tabcellsep Sum of Squares\tabcellsep df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\\
1\tabcellsep Regression\tabcellsep 721.696\tabcellsep 1\tabcellsep 721.696\tabcellsep 314.091\tabcellsep .000 a\\
\tabcellsep Residual\tabcellsep 340.064\tabcellsep 148\tabcellsep 2.298\tabcellsep \tabcellsep \\
\tabcellsep Total\tabcellsep 1061.760\tabcellsep 149\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{a. Predictors: (Constant), Product information}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{b. Dependent Variable: Consumer Behavior}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 06 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{08} \par 
\begin{longtable}{P{0.3702970297029703\textwidth}P{0.11782178217821783\textwidth}P{0.15148514851485148\textwidth}P{0.03787128712871287\textwidth}P{0.09678217821782178\textwidth}P{0.033663366336633666\textwidth}P{0.04207920792079208\textwidth}}
\tabcellsep Model\tabcellsep Sum of Squares\tabcellsep df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\\
1\tabcellsep Regression\tabcellsep 592.625\tabcellsep 1\tabcellsep 592.625\tabcellsep 186.958\tabcellsep .000 a\\
\tabcellsep Residual\tabcellsep 469.135\tabcellsep 148\tabcellsep 3.170\tabcellsep \tabcellsep \\
\tabcellsep Total\tabcellsep 1061.760\tabcellsep 149\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{a. Predictors: (Constant), Trust}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{b. Dependent Variable: Consumer\textunderscore Behavior}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 08 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{07} \par 
\begin{longtable}{P{0.22884615384615384\textwidth}P{0.17163461538461539\textwidth}P{0.14711538461538462\textwidth}P{0.07355769230769231\textwidth}P{0.13076923076923078\textwidth}P{0.04903846153846154\textwidth}P{0.04903846153846154\textwidth}}
\tabcellsep \tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep Standardized\tabcellsep \\
\tabcellsep Model\tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep Coefficients\tabcellsep t\tabcellsep Sig.\\
\tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \\
1\tabcellsep (Constant)\tabcellsep 3.290\tabcellsep .454\tabcellsep \tabcellsep 7.238\tabcellsep .000\\
\tabcellsep Product\textunderscore info\tabcellsep .760\tabcellsep .043\tabcellsep .824\tabcellsep 17.723\tabcellsep .000\\
\multicolumn{3}{l}{a. Dependent Variable: Consumer\textunderscore Behavior}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_5}Table 07 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{09} \par 
\begin{longtable}{P{0.27045454545454545\textwidth}P{0.09015151515151515\textwidth}P{0.15883838383838383\textwidth}P{0.08156565656565656\textwidth}P{0.13737373737373737\textwidth}P{0.055808080808080814\textwidth}P{0.055808080808080814\textwidth}}
\tabcellsep \tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep Standardized\tabcellsep \tabcellsep \\
\tabcellsep Model\tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep Coefficients\tabcellsep t\tabcellsep Sig.\\
\tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \tabcellsep \\
1\tabcellsep (Constant) Trust\tabcellsep 3.607 .933\tabcellsep .563 .068\tabcellsep .747\tabcellsep 6.411 13.673\tabcellsep .000 .000\\
\multicolumn{4}{l}{a. Dependent Variable: Consumer\textunderscore Behavior}\tabcellsep \tabcellsep \tabcellsep \\
t-Test:\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table 09 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{10} \par 
\begin{longtable}{P{0.3773170731707317\textwidth}P{0.11609756097560976\textwidth}P{0.14926829268292682\textwidth}P{0.037317073170731706\textwidth}P{0.09536585365853659\textwidth}P{0.033170731707317075\textwidth}P{0.041463414634146344\textwidth}}
\tabcellsep Model\tabcellsep Sum of Squares\tabcellsep df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\\
\tabcellsep Regression\tabcellsep 737.830\tabcellsep 1\tabcellsep 737.830\tabcellsep 337.107\tabcellsep .000 a\\
1\tabcellsep Residual\tabcellsep 323.930\tabcellsep 148\tabcellsep 2.189\tabcellsep \\
\tabcellsep Total\tabcellsep 1061.760\tabcellsep 149\tabcellsep \tabcellsep \\
\multicolumn{3}{l}{a. Predictors: (Constant), Currency}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{b. Dependent Variable: Consumer\textunderscore Behavior}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_7}Table 10 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{11} \par 
\begin{longtable}{P{0.27321428571428574\textwidth}P{0.09974489795918368\textwidth}P{0.15612244897959185\textwidth}P{0.07806122448979592\textwidth}P{0.13877551020408163\textwidth}P{0.05204081632653061\textwidth}P{0.05204081632653061\textwidth}}
\tabcellsep \tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep Standardized\tabcellsep \tabcellsep \\
\tabcellsep Model\tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep Coefficients\tabcellsep t\tabcellsep Sig.\\
\tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \tabcellsep \\
\tabcellsep (Constant)\tabcellsep 3.714\tabcellsep .417\tabcellsep \tabcellsep 8.908\tabcellsep .000\\
1\tabcellsep Currency\tabcellsep .964\tabcellsep .053\tabcellsep .834\tabcellsep 18.360\tabcellsep .000\\
\multicolumn{4}{l}{a. Dependent Variable: Consumer\textunderscore Behavior}\tabcellsep \tabcellsep \tabcellsep \\
t-Test:\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_8}Table 11 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{12} \par 
\begin{longtable}{P{0.40345622119815666\textwidth}P{0.1096774193548387\textwidth}P{0.14101382488479264\textwidth}P{0.03525345622119816\textwidth}P{0.0900921658986175\textwidth}P{0.03133640552995392\textwidth}P{0.0391705069124424\textwidth}}
\tabcellsep Model\tabcellsep Sum of Squares\tabcellsep df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\\
1\tabcellsep Regression\tabcellsep 709.117\tabcellsep 1\tabcellsep 709.117\tabcellsep 297.608\tabcellsep .000 a\\
\tabcellsep Residual\tabcellsep 352.643\tabcellsep 148\tabcellsep 2.383\tabcellsep \tabcellsep \\
\tabcellsep Total\tabcellsep 1061.760\tabcellsep 149\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{a. Predictors: (Constant), Entertainment}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{b. Dependent Variable: Consumer\textunderscore Behavior}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
F test:\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_9}Table 12 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{13} \par 
\begin{longtable}{P{0.2650990099009901\textwidth}P{0.11782178217821783\textwidth}P{0.1556930693069307\textwidth}P{0.07574257425742574\textwidth}P{0.13465346534653466\textwidth}P{0.05049504950495049\textwidth}P{0.05049504950495049\textwidth}}
\tabcellsep \tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep Standardized\tabcellsep \\
\tabcellsep Model\tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep Coefficients\tabcellsep t\tabcellsep Sig.\\
\tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \\
1\tabcellsep (Constant)\tabcellsep 3.236\tabcellsep .470\tabcellsep \tabcellsep 6.890\tabcellsep .000\\
\tabcellsep Entertainment\tabcellsep 1.007\tabcellsep .058\tabcellsep .817\tabcellsep 17.251\tabcellsep .000\\
\multicolumn{3}{l}{a. Dependent Variable: Consumer\textunderscore Behavior}\tabcellsep \tabcellsep \tabcellsep \\
t-Test:\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_10}Table 13 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{14} \par 
\begin{longtable}{P{0.425\textwidth}P{0.1043859649122807\textwidth}P{0.13421052631578947\textwidth}P{0.03355263157894737\textwidth}P{0.08574561403508771\textwidth}P{0.02982456140350877\textwidth}P{0.03728070175438596\textwidth}}
\tabcellsep Model\tabcellsep Sum of Squares\tabcellsep df\tabcellsep Mean Square\tabcellsep F\tabcellsep Sig.\\
\tabcellsep Regression\tabcellsep 690.386\tabcellsep 1\tabcellsep 690.386\tabcellsep 275.133\tabcellsep .000 a\\
1\tabcellsep Residual\tabcellsep 371.374\tabcellsep 148\tabcellsep 2.509\tabcellsep \\
\tabcellsep Total\tabcellsep 1061.760\tabcellsep 149\tabcellsep \tabcellsep \\
\multicolumn{4}{l}{a. Predictors: (Constant), Customer\textunderscore support}\tabcellsep \tabcellsep \\
\multicolumn{3}{l}{b. Dependent Variable: Consumer\textunderscore Behavior}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_11}Table 14 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{15} \par 
\begin{longtable}{P{0.22452830188679243\textwidth}P{0.18443396226415096\textwidth}P{0.14433962264150943\textwidth}P{0.07216981132075471\textwidth}P{0.12830188679245283\textwidth}P{0.048113207547169815\textwidth}P{0.048113207547169815\textwidth}}
\tabcellsep \tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep Standardized\tabcellsep \\
\tabcellsep Model\tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep Coefficients\tabcellsep t\tabcellsep Sig.\\
\tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \\
1\tabcellsep (Constant)\tabcellsep 3.247\tabcellsep .487\tabcellsep \tabcellsep 6.663\tabcellsep .000\\
\tabcellsep Customer\textunderscore support\tabcellsep .988\tabcellsep .060\tabcellsep .806\tabcellsep 16.587\tabcellsep .000\\
\multicolumn{3}{l}{a. Dependent Variable: Consumer\textunderscore Behavior}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_12}Table 15 :}\end{figure}
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