ABSTRACT Online purchase has become a norm with the improvement of technology where consumers can access to the internet anywhere and anytime. The online retailers are slowly taking over the traditional stores. However, research for purchase intention is still limited in the context of website design. Therefore, this paper will examine the factors affecting consumers online purchase intention in Klang Valley to test the respondents by a modified conceptual framework. Data was collected via face to face interview with tablet for data collection with a sample of 300 Malaysian respondents during October 2016. Data were analysed by descriptive analysis and simple linear regression test. Results indicated that information design, visual design, information design, trust and perceived usefulness has positive link towards purchase intention. The findings of this study are able to provide a valuable insight towards the context of online purchase, which will be able to provide ideas for future improvements on businesses that is related to this area. Keywords: E-Commerce, Information Design, Visual Design, Navigation Design, Trust, Perceived Risk, Purchase Intention Factors affecting consumers online purchase intention in Klang Valley ⦁ INTRODUCTION This chapter presents the Malaysia’s internet and e-commerce background and history, which provides a brief explanation on issues and ideas that are related to the context of online shopping in Malaysia. This chapter will also include other section such as research problem, research objectives and scope of the study as well. ⦁ Background information on Malaysia e-commerce Internet was first started around at the year of 1995 in Malaysia (Paynter and Lim, 2001). MIMOS and Beta Interactive Services had conducted the first survey about internet penetration in Malaysia at the end of 1995 period, which only has one internet user out of a thousand Malaysians (Beta Interactive Services, 1996). Because of the late start of adopting the internet services in Malaysia, which results in a late starter in online shopping as well. At the early stage, not only a slow start but a slow growth as well, with number of internet users grew to only 2.6% in 1998 (Lee, 2000). However, in the past few years, Malaysia’ e-commerce market has increased rapidly. With the telco company in Malaysia offering free basic internet access (Song, 2015), internet has become more popular in Malaysia. In fact, now Malaysia has reached an internet penetration rate of 68.6% (Internet Live Stats, 2016). Internet has become one of the necessities for Malaysian, as it enables people to connect with information and ideas, building up new channel and communities (Paynter and Lim, 2001). At 2011 October, the founder of Alando and Jamba!, Samwer brothers have launched their online ventures businesses in Malaysia including Lazada, Zalora and Foodpanda (Lim, 2012). Because of their success, Lazada has been bought over by China largest online retailer, Alibaba to expand their business in South East Asia, which they see the potential of Malaysia market. Internet is now the world’s largest trading platform. The reason behind its success is because it allows anyone to start up their business with a minimum capital, yet able to target consumers in different region. At 2011, the people shopping online in Malaysia reaches 1.6 million, which is an increase of 44% compare with 2010. More and more traditional retailers have moved to e-commerce with the ease to setup and large population and growing pace. In Malaysia, the growth of e-commerce increase rapidly which now expand to international market as well (Goh, 2013). Malaysia’s e-commerce market is getting more competitive, with the huge increase in number of stores, as well as the varieties of retailer and consumer on the internet. To get a better understanding what is the most important factor that will drive consumer towards a purchase on the internet is become more crucial now. With the online retailing business evolving rapidly, cross-cultural internet marketing has taken more weight in the business and social science research field (Griffith et al., 2006). The penetration rate of internet for online purchase is constantly increasing and getting more popular in Malaysia. Shopping through the internet would be able to provide consumers a much affordable price, faster delivery and more convenient. Therefore, it is getting more complicated to attract consumers’ attention, but this benefits the consumers as they have more opportunity to select and have comparison with all the brands in the market before they make any purchase (Paynter and Lim, 2001). The objectives of this study will be considering the few points. Firstly, to review the literatures in the past to identify and empirically considering the website design factors that affected trust on e-retailer. Secondly, considering the role of culture with the relationship between website design and trust. Lastly, to identify the role of trust between website design and purchase intention. ⦁ Background information on internet and e-commerce In 1961, the internet was first introduced by Leonard Kleinrock, name as the ARPANET, which is the very first version of internet. American Defense Department Network has funded the project to develop the protocols used at that point of time and the internet communication today. ARPANET was first designed for army forces in US to develop educated information (Yulihasri et al., 2011). In the early 1990s, the internet has opened the public and has become the most important and easy way to acquire information for everyone. Internet has fundamentally changed people’s way of communication over the years. In 1991, there are only less than 3 million of internet users worldwide, without any business transaction involved. At 1999, the internet users number has already expanded to a total of 250 million users around the globe, with a total of 63 million users trading actively online which include purchase and selling, generated a value of US$110 billion (Coppel, 2000). The business environment has changed ever since world wide web introduced, with the speed of business to consumer commerce increased rapidly with all different industries considering the online channel (Alden et al., 2006). Internet has changed the way how consumers shop and purchase goods and services, and has brought a huge impact to the traditional brick and mortar store, this has rapidly evolved into a very common phenomenon. With the convenience of reaching customers and cost efficiency, many companies have shifted to the internet platform to stay ahead in the highly competitive market. According to Yu and Abdulai (2000), e-commerce can be described as the transaction took place over the internet where buyer purchase the product from seller over the website and the goods or services will be delivered to the customer. Internet has evolved how business works, provided the flexibility to distribute and consume products differently for businesses and customer compared to previous (Al-Maghrabi et al., 2010). Number of internet users has increased rapidly from 1999 to 2013. In 2014, the internet user population has reached 40%, which is equivalent to 3 billion in the world that currently has an internet connection, compare to 9 years back in 1995, there was only less than 1% of internet users available (International Telecommunication Union, 2014). Since the first evolution of business to consumer electronic commerce, it has been more than a decade. Scholars and practitioners in electronic commerce constantly looking for solution to improve the knowledge of consumer behaviour in the electronic commerce field. ⦁ Problem Statement E-commerce is fundamentally evolving how consumers purchase goods and services. Despite the growing interest in e-commerce industry, there is still lack of knowledge on understanding the factors that affect consumers online purchase intention. Findings from this study will be able to help the marketers and online business to better understand online consumer purchase behaviour. This study will be focusing to investigate the factors affecting consumer online purchase intention in Klang Valley, how would the factors explain the differences in the online purchase behaviour among the online consumers? The main purpose of this study is to better understand the online consumer behaviour, which will provide a better understanding for online business and fine tune their business strategies. ⦁ Research Questions / Objectives The aim of this study is to understand how the factors affect the consumer online purchase intention in Klang Valley. Information design, visual design, navigation design, trust and perceived risk is chosen as the factors to predict the purchase intention of consumer towards online shopping. Therefore, this study aims to: ⦁ To identify whether information design is a significant predictor towards online purchase intention in online shopping. ⦁ To identify whether visual design is a significant predictor towards online purchase intention in online shopping. ⦁ To identify whether navigation design is a significant predictor towards online purchase intention in online shopping. ⦁ To identify whether trust is a significant predictor towards online purchase intention in online shopping. ⦁ To identify whether perceived risk is a significant predictor towards online purchase intention in online shopping. ⦁ To provide useful information for online retailers in decision making. ⦁ Practical Contributions The website design may be an addition of acceptance behaviour towards purchase intention. Therefore, this study will be contributing to the understanding of website design to purchase intention, serve as a foundation for future research to build on with the current proposed model with other aspects that may provide more findings that is related to website design and intention to purchase on the internet. The information gathered from this study will contribute to the field of e-commerce business which the businesses are still currently facing in the online business environment in Malaysia. ⦁ Scope of the study The intention of this study is to investigate the design factors affecting the purchase intention to purchase online among Malaysian consumers and to explore the relationship between factors as well. This study will add on to the previous studies mentioned in the literature review supporting the purchase intention to purchase online. The main objective of this study is to build on the data currently available from the related study of purchase intention on the internet and aimed for further investigation on the design factors affecting purchase intention, including information design, visual design and navigation design. ⦁ Organization of Remaining Chapters This study will be separated into total of seven chapters. In the next chapter, which is chapter 2, literature review will be included related to the design factors that will affect purchase intention on the internet will be discussed. Chapter 3 will be explaining the methodology of this study, including information such as research design, the procedure of sampling and respondent’s procedure, as well as the validity and reliability and the statistical treatment method for the data. Follow by chapter 4 which will include the results that is analysed from the survey. Chapter 5 focusing on explaining the gathered results of this study to prove the hypothesis is valid for the factors applied in this study. Chapter 6 will be summarizing the study and include the limitations of the research. ⦁ Literature Review ⦁ Introduction In this section, literature review in detail will be included to consider the findings from all the previous studies to identify the factors on affecting customers online purchase intention in Malaysia. This section will be exploring the theories that will be used on discuss and explain how website design, trust and culture affect customer’s online purchase intention in Malaysia. ⦁ Website design It is very important to have a website design with quality for online business to attract potential customers. The importance of website design is how information should be display on the website, such as the ease of navigation through the web page, aesthetic of the web page, and the time to navigate through the website as well. In the study conducted by Cho and Park (2001), they have identified that the quality of website design is related to the e-commerce customer’s satisfaction. Website design represents how the content is shown in the website (Ranganathan and Grandon, 2002). Wolfinbarger and Gilly (2003) had argued that customers interact with an e-commerce site, the customers prefer to do so through the technical interface but not directly through any employee. Therefore, customer satisfaction can be highly influenced by the website design, which acts as the interface that plays an important role. Moreover, Lee and Lin (2005) has also found out that the perceived service quality and overall customer satisfaction is positively influenced by the design of the website. Besides that, Ranganathan and Ganapathy (2002) also empirically established the purchase intention is positively affected by the website design as well. However, there are not much findings among researchers on the factors that constitute the design of website. Therefore, in improving the above findings mentioned, in this study, will be investigating on the factors that affect website design. Ganguly et al (2010) mentioned that the website design has too many factors to focus on, and the development of understanding the website design factors would benefit the ecommerce industry. This would help on better understanding how would web design will be affected by trust in business to consumer e commerce. The components of website design can be separated into total four components which include content, structure, interaction and presentation. Content can be defined as the information that is shown on the web page. Structure is the way how the information is arranged. As for interaction, it means how users surf the web page and able to surf the web page with maximum ease (Park and Kim, 2000). Lastly, presentation represents the emotional appeal of the website, such as visual aids shown. Cyr (2008) had separated the design factors into three parts including information design, navigation design and visual design. It is comparable with the architecture perspective of website design. In this current study, we will be implementing the architecture perspective as it is dealing with the details of system implementation. The information design include the structure and content of the information, whereas navigation design consists of the interaction component and the component of the website design is known as visual design. ⦁ Trust in Online Shopping Different authors defined trust differently. In this current study, trust will only be limited in the context of e-commerce. There is not a specific definition for online trust as there is no agreement among previous studies (Yoon, 2002). In e-commerce, it would have a high chance for seller to run away against any fraud or dispute case due to lack of governance as well as geographical reason. Based on the reason mentioned above, trust is considered as one of the most important factor for online business to succeed. In order to keep the business going, and maintain a healthy business relationship with customer, trust is irreplaceable (Lui, 2012). Customers would have a higher trust towards the seller if more information is provided from the seller, and higher chance of transaction will be happening (Chen and Chou, 2011). How usually consumer gain trust is normally through exchanging information such as a face to face interaction. However, in the context of online shopping, interaction process is replaced by computer and technical interfaces. Therefore, the seller’s reputation is important to increase the trust of buyer towards seller (Jarvenpaa and Todd, 2000). Tan and Sutherland (2004) had identified online trust is a multidimensional construct which include three categories of trust including institutional trust, dispositional trust and interpersonal trust. Trust about relating to the medium of online shopping is known as institutional trust. Dispositional trust is more complex where it combines a few aspects including the individual’s openness, conscientiousness, agreeableness, extroversion and neuroticism. Lastly, the interpersonal trust is defined as the trust between both parties on a business. In the situation of e-commerce, it would categorize as the interpersonal trust as it deals with two parties participating in the business which is the online retailer and the customer. Interpersonal trust can be split into several categories which consists of credibility, integrity and predictability and benevolence. Online retailer’s reputation on providing a good service is described as predictability, whereas integrity is to believing in the online retailer will be honest and follow the standards on doing business. Both predictability and credibility is perceiving as the credibility of the online retailer which includes consistency and honesty. In the eyes of consumer, credibility and benevolence is perceived as the conceptualise trust on the e-commerce environment (Ganguly et al., 2010). Credibility can be defined as the belief of buyer trusting the seller would do their job effectively, and benevolence is the buyer’s belief in the positive intention of the seller (Ganesan, 1994). Moreover, similar definition has been applied in the e-commerce context by some other authors such as Stephens (2004) and Dash and Saji (2007). ⦁ Perceived Risk in Online Shopping Perceived risk is defined as the uncertainty of customer where they are not able to foresee the consequences on the intention of purchase. Whereas in the online situation, the uncertainty is even higher as the internet is connected globally and virtual as well. Buying goods from the online store would not be able to physically feel the item, which creates a higher uncertainty, and translates into a higher perceived risk in online shopping context (Chellappa, 2005). If there is any problem happened during the transaction, the seller is not bound to bear the expenses, there will be no assurance that customers will get what he or she sees on the internet as well. A higher perceived risk will also reduce the intention to purchase online (Jarvenpaa and Tractinsky, 1999). Yoon (2002) has identified that there are three different aspects when it comes to comparing online and offline trust. The first difference is that the distance of both buyer and seller can be huge. Secondly, there is no direct physical contact between the buyer and seller as well. Tan and Guo (2005) mentioned that the internet is a world of chaos, therefore, it is very likely that customers who are having high uncertainty on avoidance have a higher perceived risk from using purchasing through the internet as well. Nath and Murthy (2004) also found out that customers who has a higher uncertainty avoidance are more reluctant to purchase from the internet. Lastly, there is no sales person available like the traditional retail store. Therefore, trust plays an important role in reducing the perceived risk in the online purchase context. Several studies conducted by few author including Jarvenpaa and Tractinsky (1999), Pavlou, (2003) and Harridge-March (2006) shows that the higher the trust on the online retailer, the lower the perceived risk. ⦁ Purchase Intention in Online Shopping The two most mentioned aspects that affect trust in online shopping were perceived risk and purchase intention. Purchase intention can be defined as the likelihood to purchase items on the internet. It is crucial to increase the acceptance of online purchase if wanted to increase the purchase intention of consumers. Purchase intention is the most important part for consumers when it comes to completing the transaction in purchase a product or service. Study conducted by Jarvenpaa and Tractinsky (1999) have mentioned that if the online seller are able to evoke customer’s trust, the more likely customer will purchase from the store. The results from few studies have shown a positive effect on increasing purchase intention if customer’s trust increases (Gefen et al., 2003; Suh and Hun, 2003; Kim and Kim, 2005). ⦁ Proposed Conceptual Framework This study proposes a conceptual framework to further investigate the factors affecting consumers online purchase intention in Klang Valley. There are total of five variable in the below framework, including information design, visual design, navigation design, trust and perceived risk. Figure 1: Website design, trust and perceived risk (independent variable) as the antecedents and purchase intention as the dependent variable. ⦁ Hypothesis Main issues are evaluated from the previous section, which help to formulate the following hypotheses below: H1: Information design is positively related to purchase intention. H2: Visual design is positively related to purchase intention. H3: Navigation design is positively related to purchase intention. H4: Trust is positively related to purchase intention. H5: Perceived risk is positively related to purchase intention. ⦁ Research Design and Methodology ⦁ Introduction This chapter will be describing the purpose of the study, research design, research instrument, validity and reliability, ethical issues and statistical treatment for data. ⦁ Purpose of the study The conceptual model of this study is adapted from Cyr (2008). The trust component will be adopting the questionnaire from Suh and Han (2003) and Chellappa (2005). Purchase intention part will be adopted from Suh and Han (2003) and perceived risk from Chan and Lu (2004) to describe the factors that influences the purchase intention. The behaviour of everyone may be different; therefore, each specific individual may have different perception. The purchase intention of the online consumers will be described to have a better understanding on how shoppers make purchase through online shopping. ⦁ Research design Quantitative research using questionnaire will be conducted for this study. The questionnaire is designed to measure on website design and trust. The target population of this study will consist of randomly chosen respondent in Klang Valley, Malaysia. Respondents for this study will be online shoppers that have made a purchase through the internet for the past 6 months. The reason to include a criterion is to enable to minimize the chances of gathering opinion from non-online purchaser which is inapplicable. By adding in the criteria would be able to screen out non-qualifier, and avoid inability and unwillingness of participation. Malhotra (2004) mentioned that filter question should be included in the questionnaire to measure the familiarity before the main question. This study will be using online as the questionnaire distribution platform, which is more appropriate and convenient to look for online shoppers. Online surveys are also better compared to traditional surveys in a few ways. This enable to achieve a faster response rate and respondents can answer the survey whenever they are free. Hogg (2003) highlighted that it is much more comfortable for respondent doing an online survey whenever he or she feels to, instead of asking questions during inconvenient time. Moreover, Evans and Mathur (2005) highlighted the benefit of using online survey is that respondents must answer questions in order, which enables to avoid bias by not allowing respondents to view questions ahead. ⦁ Research Instrument The questionnaire of this survey was separated into two sections. First section includes the variables to test in this survey including information design, visual design, information design, trust, perceived risk and purchase intention in Klang Valley. All the attitudinal questions on first section will be using five point likert scale and respondents can answer base on their level of agreement on each specific statement (Morris and Adley, 2000). The second part of the questionnaire includes demographic variables such as gender, race, age, monthly income and level of income of the respondent. ⦁ Validity and Reliability The questionnaire was adapted from the existing study about e-commerce. The questions used to measure information, navigation and visual design were adapted from Cyr (2008). The trust component will be adopting the questionnaire from Suh and Han (2003) and Chellappa (2005). Purchase intention part will be adopted from Suh and Han (2003) and perceived risk from Chan and Lu (2004). The questions are measured on a 5-point Likert scale across all variables from “strongly disagree (1) to strongly agree (5). A pilot test is conducted before the actual survey to measure the reliability of the questionnaire. Pilot test could measure without bias and error providing a consistent measurement across the various items in the questionnaire (Cavana et al., 2001). The sample size for a pilot testing should fall between 15 to 30 samples. Therefore, 30 samples were chosen for this study to conduct the pilot test before the actual survey. A total of 30 respondents were chosen to participate in the survey before distributing the actual survey to respondents. Samples were chosen around Subang area and the requirement of the survey is for those who have intention to make purchase on the internet. The Cronbach’s Alpha result of the 30 samples gathered showed that all variables have more than 0.60 which indicates that the questionnaire is reliable for actual survey (Cavana et al., 2001). ⦁ Ethical Issues In this survey, any questions related to respondents’ personal information or background such as name, phone number, email-address and address will not be recorded. All respondents participated voluntarily and they can terminate the survey anytime. A brief introduction about the study is provided to the respondents before they start answering the questionnaire. Instruction to allow respondents to quit whenever he or she wants without any obligation is also clearly stated. ⦁ Statistical Treatment for Data ⦁ Descriptive Analysis The frequencies and descriptive of each demographic variable were used to identify and examine the number of respondents participated in the survey. Detail results are analysed and interpreted in the next chapter. ⦁ Simple Linear Regression A simple linear regression analysis is done to test the hypothesis listed on previous section. The analysis is to describe the relationship between independent variable and dependent variable in a straight line. ⦁ Findings and Analysis ⦁ Introduction This chapter will be presenting the results of data analysis as well as the findings of this study. Several sections will be divided in this chapter. Demographic of the respondents will be included in this section where as other analysis including descriptive analysis, box plot and reliability test will also be included in the sections below. Simple linear regression method will be used for the hypothesis testing. The results will be presented in the order suggested by the research question mentioned in previous chapter. ⦁ Demographic Profiles Data gathered from the survey will be analysed by using the descriptive analysis to find out the demographic profiles of the respondents. Respondents’ data are presented with frequency as well as percentage. The basic demographic variables will be included such as gender, race, age, income, level of education and marital status. Table 4. 1 Frequency Distribution of Demographic Characteristics Demographic Characteristics Sample Frequency Percentage Gender Male 162 54.0 Female 138 46.0 Age Less than 18 30 10.0 Between 18-25 118 39.3 Between 26-35 76 25.3 Between 36-45 51 15.0 Above 46 25 8.3 Race Malay 161 53.7 Chinese 100 33.3 Indian 39 13.0 Profession Professional/ Manager/ Executive 61 20.3 Other White Collar 95 31.7 Skilled/ Semi-skilled Workers 44 14.7 Labourer/ Other Blue Collar 24 8.0 Student 76 25.3 Level of Education Secondary or equivalent 27 9.0 SPM or equivalent Undergraduate (Diploma/Degree) Postgraduate (Master / PhD) 85 28 154 51.3 34 11.3 Monthly Income Under RM 2,000 65 21.7 RM 2,000 - RM 3,999 57 19.0 RM 4,000 - RM 5,999 115 38.3 RM 6,000 - RM 9,999 45 15.0 RM 10, 000 above 18 6.0 Table 4.1 shows that there was a total of 300 respondents participate in the survey. According to the results collected, there were total of 162 males or 54.0% and 138 females or 46.0%. The age group between 18 to 25 has the most respondents with 118 or 39.3%, follow by the elder group between 26 to 35 with 76 or 25.3%, the respondents between age 36 to 45 has a total sample of 51 or 15.0%, and the remaining falls under below 18 with sample of 30 respondents or 10.0% and above 46 with 25 or 8.3%. Malay respondent is the majority for this survey with 161 or 53.7%, follow by Chinese respondents with 100 or 33.3% and Indian with 39 or 13.0%. Majority of the respondent’s occupation from this survey falls under other white collar category with 95 respondents or 31.7, student was next for 76 respondents or 25.3%, professional / manager / executive was 61 respondents or 20.3%, skilled / semi-skilled workers was 44 respondents or 14.7% and labourer / other blue collar was respondents 24 or 8.0%. Majority of the respondents in this survey falls under undergraduate (diploma / degree) level with 154 or 51.3, follow by SPM or equivalent qualification with 85 respondents or 28%, Postgraduate (Master /PhD) with 34 respondents or 11.3% and lastly with secondary or equivalent qualification with 27 respondents or 9.0%. Majority of the respondents has an income level between RM4,000 to RM5,999 with 115 respondents or 38.3%, the second would be the group of RM2,000 to RM3,999 with 57 respondents or 19.0%, follow by those who earn less than RM2,000 with 65 respondents or 21.7%, RM6,000 to RM9,999 with 45 respondents or 15.0% and RM10,000 above with 18 respondents or 6.0%. ⦁ Box Plot Box plot is used to present the data graphically to show the data for summaries on five number: the smallest observation (minimum of sample), lower quartile (Q1), median (Q2), upper quartile (Q3), the largest observation (maximum of sample). A simple box plot analysis was used to test the variables in the study to identify any outliers is captured in the sample (University of Reading, 2011). The results of box plots are shown in appendices including all the variables, information design, visual design, navigation design, trust, perceived risk and purchase intention. No outliers were found in the box plot analysis and the median falls between 3 to 4 (appendix 1,2,3,4,5,6). ⦁ Descriptive Analysis Table 4. 2 Descriptive Analysis Results for Each Variable Mean SD Skewness Kurtosis Information Design 3.8717 0.75361 -0.831 0.092 Visual Design 3.9183 0.7005 -0.566 0.067 Navigation Design 3.8456 0.6882 -0.300 -0.136 Trust 3.7086 0.6507 -0.566 -0.311 Perceived Risk 3.7650 0.6636 -0.076 -0.347 Purchase Intention 3.6489 0.4949 0.037 -0.320 All independent variables including information design, visual design, navigation design, trust and perceived risk and dependent variable including purchase intention is included in the descriptive analysis. All the variables have similar mean and standard deviation. Information with the mean of 3.8717 and standard deviation of 0.75361, visual design with mean of 3.9183 and standard deviation of 0.7005, navigation design with mean of 3.8456 and standard deviation of 0.6882, trust with mean of 3.7086 and standard deviation of 0.6507, perceived risk with mean 3.7650 and standard deviation 0.6636 and purchase intention with mean 3.6489 and standard deviation 0.4949. The results indicated that the responses on average were close to the mean which is much closer to 0. Additional analysis of skewness and kurtosis is measured in this survey. Skewness is to measure the extent on distribution of values that deviates from symmetry around the mean. If the value is closer to zero, it shows a symmetric distribution whereas a positive skewness indicates a higher number of smaller values and negative value shows higher number of larger values. If the value of skewness falls within +1/-1 is considered good for most psychometric users, whereas a +2/-2 number is still acceptable. Kurtosis is to measure the flatness or peakedness of the distribution. A kurtosis with value near to zero indicates the shape is close to normal or flat. Negative value shows distribution which has a higher peak than normal; whereas a positive kurtosis indicates a flatter distribution than normal. The acceptability of kurtosis is same as skewness which falls within +1/-2 to +2/-2 (Illinois State University, n.d.). ⦁ Reliability Table 4. 3 Overall Mean Scores Cronbach’s Alpha Independent Variable 0.762 0.727 0.769 Information Design Visual Design Navigation Design Trust 0.900 Perceived Risk 0.845 Dependent Variable 0.627 Purchase Intention Overall 0.954 Reliability test is done for this study to analyse the data collected through questionnaire to identify the Cronbach alpha value for each variable. Cronbach alpha is to identify the reliability of the questionnaire. The test results show that the Cronbach alpha value for information design, visual design, navigation design, trust, perceived risk and purchase intention were 0.762, 0.727, 0.769, 0.900, 0.845, and 0.627 respectively. The overall of all variables combined is 0.954. ⦁ Simple Linear Regression Simple linear regression is done for each dependent variable including visual design, information design, navigation design, trust and perceived usefulness towards purchase intention. Table 4. 4 Correlation for Information Design and Purchase Intention (Model 1) Information Design Purchase Intention Pearson Correlation Information Design Purchase Intention 1.000 .589 .589 1.000 P-value (1 – tailed) Information Design .000 Purchase Intention .000 Model 1 Summary ANOVA Coefficient Collinearity Statistics R R Square Mean Square Sig. B Tolerance VIF 0.589 0.347 29.380 0.000 0.387 1.000 1.000 The quality prediction on the independent variable information design is represented by R. The R value of 0.589 shows a good level of predicting the variable. R2 value will be representing the proportion of variance in the dependent variable that could be described by the independent variable in this study. The model 1 summary showed R2 value is 0.347, which can be described as a total of 34.7% of the variable is explainable by information design. The F-ratio shown in ANOVA table can predict the significance of the dependent variable, F (1, 298) = 158.034, p <0.05. Results indicated that there is a significant relationship between information design and purchase intention as the p-value is shown less than 0.05 (p <0.000). Therefore, the hypothesis is supported by the results. The correlation of 0.589 which also indicates that the strength of correlation is strong as well (Dancey and Reidy, 2004). Therefore, information design is a significant predictor of purchase intention in this study. Table 4. 5 Correlation for Visual Design and Purchase Intention (Model 2) Visual Design Purchase Intention Pearson Correlation Visual Design Purchase Intention 1.000 .557 .557 1.000 P-value (1 – tailed) Visual Design .000 Purchase Intention .000 Model 2 Summary ANOVA Coefficient Collinearity Statistics R R Square Mean Square Sig. B Tolerance VIF 0.557 0.310 22.711 0.000 0.393 1.000 1.000 The quality prediction on the independent variable visual design towards purchase intention is represented by the R value listed in table 4.5. The R value of 0.557 shows a moderate level on predicting the variable. R2 will be presenting the proportion variance in the dependent variable that could be described by the independent variable in this study. Model 2 summaries indicated that the R2 value of 0.310 which indicate that 31.0% of the variable can be explained by purchase intention. F-ratio shown in the ANOVA table showed that it is significant on predicting the dependent variable, F (1, 298) = 133.948, p < 0.05. The result proves that the relationship between visual design and purchase intention is significant with the p-value less than 0.05 (p < 0.000). Therefore, the hypothesis is supported. Correlation between visual design and purchase intention is 0.557 which shows a moderate correlation (Dancey and Reidy, 2004). Based on the result we can conclude that visual design is a significant predictor of purchase intention in this study. Table 4. 6 Correlation for Navigation Design and Purchase Intention (Model 3) Navigation Design Purchase Intention Pearson Correlation Navigation Design Purchase Intention 1.000 .621 .621 1.000 P-value (1 – tailed) Navigation Design .000 Purchase Intention .000 Model 3 Summary ANOVA Coefficient Collinearity Statistics R R Square Mean Square Sig. B Tolerance VIF 0.621 0.386 28.282 0.000 0.447 1.000 1.000 The quality prediction on the independent variable navigation design towards purchase intention is represented by the R value listed in table 4.6. The R value of 0.621 shows a strong level on predicting the variable. R2 will be presenting the proportion variance in the dependent variable that could be described by the independent variable in this study. Model 3 summaries indicated that the R2 value of 0.386 which indicate that 38.6% of the variable can be explained by purchase intention. F-ratio shown in the ANOVA table showed that it is significant on predicting the dependent variable, F (1, 298) = 187.467, p < 0.05. The result proves that the relationship between navigation design and purchase intention is significant with the p-value less than 0.05 (p < 0.000). Therefore, the hypothesis is supported. Correlation between navigation design and purchase intention is 0.621 which shows a strong correlation (Dancey and Reidy, 2004). Based on the result we can conclude that navigation design is a significant predictor of purchase intention in this study. Table 4. 7 Correlation for Trust and Purchase Intention (Model 4) Trust Purchase Intention Pearson Correlation Trust Purchase Intention 1.000 .740 .740 1.000 P-value (1 – tailed) Trust .000 Purchase Intention .000 Model 4 Summary ANOVA Coefficient Collinearity Statistics R R Square Mean Square Sig. B Tolerance VIF 0.740 0.548 40.104 0.000 0.563 1.000 1.000 The quality prediction on the independent variable trust towards purchase intention is represented by the R value listed in table 4.7. The R value of 0.740 shows a strong level on predicting the variable. R2 will be presenting the proportion variance in the dependent variable that could be described by the independent variable in this study. Model 4 summaries indicated that the R2 value of 0.548 which indicate that 54.8% of the variable can be explained by purchase intention. F-ratio shown in the ANOVA table showed that it is significant on predicting the dependent variable, F (1, 298) = 360.688, p < 0.05. The result proves that the relationship between trust and purchase intention is significant with the p-value less than 0.05 (p < 0.000). Therefore, the hypothesis is supported. Correlation between trust and purchase intention is 0.740 which shows a strong correlation (Dancey and Reidy, 2004). Based on the result we can conclude that trust is a significant predictor of purchase intention in this study. Table 4. 8 Correlation for Perceived Risk and Purchase Intention (Model 5) Perceived Risk Purchase Intention Pearson Correlation Perceived Risk Purchase Intention 1.000 .681 .681 1.000 P-value (1 – tailed) Perceived Risk .000 Purchase Intention .000 Model 5 Summary ANOVA Coefficient Collinearity Statistics R R Square Mean Square Sig. B Tolerance VIF 0.681 0.464 34.001 0.000 0.508 1.000 1.000 The quality prediction on the independent variable perceived risk towards purchase intention is represented by the R value listed in table 4.8. The R value of 0.681 shows a strong level on predicting the variable. R2 will be presenting the proportion variance in the dependent variable that could be described by the independent variable in this study. Model 5 summaries indicated that the R2 value of 0.464 which indicate that 46.4% of the variable can be explained by purchase intention. F-ratio shown in the ANOVA table showed that it is significant on predicting the dependent variable, F (1, 298) = 258.236, p < 0.05. The result proves that the relationship between perceived risk and purchase intention is significant with the p-value less than 0.05 (p < 0.000). Therefore, the hypothesis is supported. Correlation between perceived and purchase intention is 0.681 which shows a strong correlation (Dancey and Reidy, 2004). Based on the result we can conclude that trust is a significant predictor of purchase intention in this study. ⦁ INTEPRETATION AND ANALYSIS OF FINDINGS ⦁ Introduction References will be drawn from the previous literature review and conceptual framework section which has been discussed in chapter 2 to support the analysis done by this study. To identify the factors affecting consumers online purchase intention in Klang Valley and to explore the relationship between different factors. ⦁ The Effects of Website Design on Purchase Intention of Online Shopping Based on the statistical analysis done in chapter 4, it was found that information design is supported by the simple linear regression test. All three-independent variable including information design, visual design and navigation design is significantly related to purchase intention. Wolfinbarger and Gilly (2003) had argued that customers interact with an e-commerce site, the customers prefer to do so through the technical interface but not directly through any employee. Therefore, customer satisfaction can be highly influenced by the website design, which acts as the interface that plays an important role. Fang and Salvendy (2003) has also conducted a study and found out that attributes of an ideal website includes clean homepage layout, aesthetically pleasing graphics, easy navigation and search, logical categorization of products as few of the important cue on website design. Jeong, Fiore, Niehm and Lorenz (2009) has also found out that user’s pleasure and arousal will be increased by rich sensory design elements enhanced entertainment and aesthetic experiences, which in turn increasing the purchase intention of users. The finding from this study is consistent with the findings of previous study, consumers who perceive that website design is important will be more likely to make a purchase through the website. All three-independent variable including information design, visual design and navigation design is significantly related to purchase intention. Online retailer should focus on improving the website design where consumers will be more likely to make a purchase with a better website design. ⦁ The Effects of Trust on Purchase Intention of Online Shopping H4 was supported by the simple linear regression analysis conducted. Trust was found to be significant on increasing the purchase intention of online shopping. The findings are consistent with other previous reseasrch as trust has been long recognized as a critical success factor for e-commerce (Torkzadeh and Dhillon, 2002). How usually consumer gain trust is normally through exchanging information such as a face to face interaction. However, in the context of online shopping, interaction process is replaced by computer and technical interfaces. Therefore, the seller’s reputation is important to increase the trust of buyer towards seller (Jarvenpaa and Todd, 2000). Trust in the context of e-commerce has been extensively addressed over the years as one of the popular research topic. Reason is because it is an important variable for online consumer behaviour as it influences the usage intention of an e-commerce website. Several studies have been tested out on trust for online purchase such as online purchase Chen and Chou (2011) and Jarvenpaa and Todd (2000). If consumer wants to purchase online, they will first need to build trust with the website to make a purchase. If consumers have a higher trust towards the website, the more likely they will make a purchase from it. However, there are also studies have found that trust does not affect the purchase intention of a websites. Khalifa and Limayem (2003) argued that the violation of privacy that reduces trust do not cause impact to influence purchase intention. Pechtl (2003) also mentioned that financial risk and trust do not significantly affect consumer’s online purchase intention. Foucault and Scheufele (2005) have also reported that security and privacy which is related to trust do not affect the buying behaviour of online shoppers. ⦁ The Effects of Perceived Risk towards Purchase Intention on Online Shopping H5 was supported which found that perceived risk affects purchase intention significantly with simple linear regression analysis. This is similar with the findings of Jarvenpaa and Tractinsky (1999) as they found that the increased level of perceived risk will also reduce the intention to purchase online. Consumers are reluctant to provide information to someone on the internet because the fear of unauthorised person misused personal information. Tan and Guo (2005) mentioned that the internet is a world of chaos, therefore, it is very likely that customers who are having high uncertainty on avoidance have a higher perceived risk from using purchasing through the internet as well. Nath and Murthy (2004) also found out that customers who has a higher uncertainty abvoidance are more reluctant to purchase from the internet. Choi and Lee (2003) have also empirically shown the increase of perceived risk will reduce the intention to make a purchase. Therefore, e-retailers should do something to lower the perceived risk of consumers such as, providing a detail company information and hotline for contact, which will able to lower the perceived risk of consumers to increase the purchase intention. ⦁ Theoretical Implication Based on the findings of this study, it shows that the variables used in this study is applicable to the findings of this research. Results gathered from survey proves consistency with the findings of extant literature on purchase intention for all variables, including information design, visual design, navigation design, trust and perceived risk. In this study, the purchase intention of online shopper is tested, factors used in this study is to study the Klang Valley’s online shopper purchase intention. Website design including information design, visual design and navigation design and trust and perceived risk is found to be positively significant with purchase intention. ⦁ Practical Implication The findings of the present study could provide some valuable insights that able to motivate consumers to make online purchase on the internet. Having a better understanding for the online retailers and online businesses. Findings gathered from this study can provide a better understanding on both current and future potential customers for businesses to implement and plan future marketing strategies for the online sales channel. To better attract consumers to purchase from website, online retailer should first design their website to become more user friendly and more interactive which able to gain the attention of consumers. Online business can also consider increasing the usability of website to improve website design, to explore easily, faster and to be more interactive. For instance, having a clear site navigation would be to explore easier. Providing steps or tutorial to make order and payment for new customers. Moreover, to increase trust and lower the perceived risk. Online website should set up a physical presence such as an office, shop lot or kiosk if budget is limited even when the focus is for online business. This would be able to provide a distinct advantage by improving the trust of consumers with the physical presence because consumers can go and look for enquiry when a problem turns up (Garvey, 2013). If business with limited capital is having difficulty in having physical presence, a hotline to contact seller should be the minimum requirement. For example, providing a hotline through skype or an online customer service page such as Facebook to aid customers whenever required. To lower the perceived risk of consumers, online business should consider having a better transaction security such as PayPal and having a multi-tier security system to ensure customers information is safe guarded. Working with PayPal or local bank to provide online transaction is more reliable. Traditional business should start considering online channel to attract more potential customers to expand sales. Setting up a website may be costly; therefore, social media is a good platform to start off as Malaysia is one of the highest usage of Facebook with 46.9% are currently active in Facebook (Consortium, 2012). Setting up a Facebook page to promote business by creating campaign or events to attract consumers to share the promotion within their social network, such as providing referral program for users who successfully referred their friends. Other social media platform can also be used at the same time to target different apps users such as Instagram, Twitter, Snapchat and e-commerce site such as Lelong and Mudah to expand the sales channel. ⦁ CONCLUSION AND RECOMMENDATION ⦁ Conclusion The aim of this study is to examine the variables that will affect the purchase intention in the Klang Valley area. The overall objectives of this study are to find out: (1) whether information design is a significant predictor towards online purchase intention in online shopping, (2) whether visual design is a significant predictor towards online purchase intention in online shopping, (3) whether navigation design is a significant predictor towards online purchase intention in online shopping, (4) whether trust is a significant predictor towards online purchase intention in online shopping, and lastly (5) whether perceived risk is a significant predictor towards online purchase intention in online shopping. After analysing the results from the study, it showed that consumers will have a higher purchase intention if the website design is better, trust is higher and perceived risk is lower. A website with a more detailed information, better visualisation and better navigation will increase the purchase intention. The purchase intention will be higher if trust between seller and buyer is higher and perceived risk is lower as well. ⦁ Limitation of Future Research There are several numbers of limitation in this study which needs to be taken into consideration. Questionnaire is the only survey instrument in this study, where observation and interview can also be included to have a more robust finding to increase the data reliability. The scope of this study is only limited to Klang Valley as well, whereas for future study should focus on whole Malaysia instead of only focusing in Klang Valley. Because the perception of respondents may differ from Klang Valley; therefore, it may not be representative for whole Malaysia. In future research, it is recommended to conduct study on consumers across Malaysia to have a more robust finding. ⦁ CRITICAL REFLECTIONS The number of consumers purchase online grow in terms numbers of purchase and the money spent through purchasing online. Shopping online is getting more popular among the consumers instead of relying on the traditional retail store. Reason due to the lower prices that attracted the consumers as well as having a lower cost without the need to pay for rental for business. Moreover, communicating and selling over the internet is much more cost effective for a business compare to hiring a sales person. Operating an online business can allow the company to directly contact their customers by eliminating the intermediaries such as distributor and wholesaler. However, shopping online also raised some issues as consumers are concerned about their personal information being leaked to seller and how the information is being used. The perception of consumers towards risk has been discussed since the early 60s that will directly affect consumer behaviour. Traditional store is more safe compare to online shopping because online shopping does not provide a physical evidence which is riskier. Customers may be worried about the after sales service as well as the product and service quality which cannot be verified through internet. With the intense competition in online sales channel, businesses need to innovate continuously to provide a more user friendly website to attract and retain customers. Spending effort into creating a website which is more detailed information, better visualisation and better navigation will increase the purchase intention. Despite the needs of improvement for website, trust still plays an important role and one of the key influencer for online shopping. Although enhancement needs to be made, but addressing the issue on improving trust of consumers is equally important as well. References Al-Maghrabi, T., Dennis, C. and Halliday, S.V., 2010. Antecedents of continuance intentions towards e-shopping: the case of Saudi Arabia, Journal of Enterprise Information Management, Vol. 24 No. 1, pp. 85-111. Beta Interactive Services., 1996. Malaysian Internet shopping survey. [online]14 May, available at: http://jaring.my/biz/cybersquare/survey.htm [Accessed 6 September 2016]. Chellappa, R. K., 2005. Consumers’ trust in electronic commerce transactions: the role of perceived privacy and perceived security; pp.1–46 in Fulk et al. (1987). Chen, Y.T. and Chou, T.Y., 2011. Exploring the continuance intentions of consumers for B2C online shopping: Perspectives of fairness and trust. Online Information Review, Vol. 36, No. 1, pp. 104-125. Cho, N. and Park, S., 2001. Development of electronic commerce user – consumer satisfaction index (ECUSI) for internet shopping, Industrial Management and Data Systems, Vol. 101, No. 8, pp.400–405. Choi, J. And Lee, K.H., 2001. Risk perception and e-shopping: across cultural study, Journal of Fashion Marketing, Vol. 7, No. 1, pp. 49-64. Consortium., 2012, Malaysia: Passion, Pins and Politics Driving Social, available at: http://dconsortium.wordpress.com/2012/05/29/malaysia-passion-pins-and-politics-driving-social/ [Accessed 20 September 2016]. Copple, J., 2000. E-commerce: impacts and policy changes, OECD Economics Department Working Papers, No. 252, OECD Publishing, Paris. Cyr, D., 2008. Modeling website design across cultures: relationships to trust, satisfaction, and e-loyalty, Journal of Management Information Systems, Vol. 24, No. 4, pp.47–72. Dancey, C., and Reidy, J. (2004), Statistics without Maths for Psychology: using SPSS for Windows, London: Prentice Hall. Dash, S.B. and Saji, K.B., 2007. Role of self-efficacy and website social-presence in customers’ adoption of b2c online shopping: an empirical study in the Indian context, Journal of International Consumer Marketing, Vol. 20, No. 2, pp.33–48. Evans, J.R. and Mathur, A., 2005. The value of online surveys, Internet Research, Vol. 15 No. 2, pp. 195-219. Fang, X. And Salvendy, G., 2003. Customer-centered rules for design of e-commerce web sites. Communications of the AMC, Vol. 46 No. 12, pp. 332-336. Foucault, B.E., Scheufele Laroche, M., Yang, Z., McDougall, G.H.G. and Bergeron, J., 2005, Internet versus bricks and mortar retailers: an investigation into intangibility and its consequences, Journal of Retailing, Vol. 81 No. 4, pp. 251-67. Ganesan, S., 1994. Determinants of long-term orientation in buyer-seller relationship, Journal of Marketing, Vol. 58, No. 2, pp.1–19. Ganguly, B., Dash, S.B., Cyr. D. and Head, M., 2010. The effects of website design on purchase intention in online shopping: the mediating role of trust and the moderating role of culture, International Journal of Electronic Business, Vol. 8 No. 4/5, pp. 302-330. Garvey, E., 2013, How to Boost Consumer Trust When Launching an Online Business, available at: https://smallbusiness.yahoo.com/advisor/boost-consumer-trust-launching-online-business-134040194.html [Accessed 20 September 2016]. Gefen, D., Karahanna, E. and Straub, D.W., 2003. Trust and TAM in online shopping: an integrated model, MIS Quarterly, Vol. 27, No. 1, pp.51–90. Goh, G., 2013. Huge opportunities for SMEs in cross-border e-commerce: Paypal. [online]9 May, available at: https://www.digitalnewsasia.com/digital-economy/huge-opportunities-for-smes-in-cross-border-e-commerce-paypal [Accessed 6 September 2016]. Griffith, D.A., Myers, M.B. and Harvey, M.G., 2006. An investigation of national culture’s influence on relationship and knowledge resources in interorganizational relationships between Japan and the United States, Journal of International Marketing, Vol. 14 No.3, pp. 1-32. Harridge-March, S., 2006. Can the building of trust overcome consumer perceived risk online?, Marketing Intelligence & Planning, Vol. 24, No. 7, pp.746–761. Internet Live Stats, 2016. Malaysia Internet Users. [online] Available at: http://www.internetlivestats.com/internet-users/malaysia/ [Accessed 6 September 2016]. International Telecommunication Union, 2014. ITU releases 2014 ICT figures: Mobile-broadband penetration approaching 32 per cent, Three billion Internet users by end of this year. [press release] 5 May 2014. Available at: http://www.itu.int/net/pressoffice/press_releases/2014/23.aspx#U58HtsanreU [Accessed 6 September 2016]. Jarvenpaa, S.L. and Todd, P.A., 1997. Consumer reactions to electronic shopping on the world wide web, International Journal of Electronic Commerce, Vol. 1 No. 2, pp. 59-88. Jarvenpaa, S.L. and Tractinsky, N., 1999. Consumer trust in an internet store a cross-cultural validation, Journal of Computer-Mediated Communication, Vol. 5, No. 2, pp.1–35. Jeong, S.W., Fiore, A.M., Niehm, L.S. and Lorenz, F.O., 2009. The role of experiential value in online shopping: The impacts of products presentation on consumer responses towards an apparel website. Internet Research, Vol. 19, No. 1, pp. 105-124. Khalifa, M. and Limayem., M. 2003, Drivers of Internet shopping, Communication of ACM, Vol. 46, No. 12, pp. 233-239. Kim, Y.H. and Kim, D.J., 2005. A study of online transaction self efficacy, consumer trust and uncertainty reduction in electronic commerce transactions, Proceedings from 38th Hawaii International Conference on System Sciences, Hawaii. Lee, G.G. and Lin, H.F., 2005. Customer perceptions of e-service quality in online shopping, Journal of Retail and Distribution Management, Vol. 33, No. 2, pp.161–176. Lee, W., 2000. Internet took the crown as top IT growth area in Malaysia. [online]21 April, available at: http://malaysia.cnet.com/news/2000/04/21/200000421j.html [Accessed 6 September 2016]. Lim, Y.H., 2012. Asia’s Rising E-Commerce Nation: A Q&A With Rakuten Malaysia CEO Masaya Ueno. [online]28 December, available at: http://www.forbes.com/sites/limyunghui/2012/12/28/asias-rising-e-commerce-nation-a-qa-with-rakuten-malaysia-ceo-masaya-ueno/ [Accessed 6 September 2016]. Lui, C.M., 2012. Factors Affecting Consumers Purchasing Decisions in Online Shopping in Hong Kong. Institute of Textiles & Clothing. Malhotra, N.K., 2004. The technology acceptance model: a meta-analysis of empirical findings, Journal of Organizational and End User Computing, Vol. 16 No. 1, pp. 59-72. Morris, A.H. and Adley, C.C., 2000, Genetically modified food issues: Attitudes of Irish University scientists, British Food Journal, Vol. 102 No. 9, pp. 669-691. Nath, R. And Murthy, N.R.V., 2004. A study of the relationship between Internet diffusion and culture, Journal of Information Technology and Information Management, Vol. 13, No. 2, pp. 123-132. Park, J. and Kim, J., 2000. Contextual navigation aids for two world wide web systems, International Journal of Human Computer Interaction, Vol. 12, pp.193–217. Pavlou, P.A., 2003. Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model, International Journal of Electronic Commerce, Vol. 7, No. 3, pp.101–134. Paynter, J. and Lim, J., 2001. Drivers and Impediments to E-Commerce in Malaysia, Malaysian Journal of Library & Information Science, Vol. 6 No. 2, pp. 1-19. Pechtl, H., 2003, Adoption of online shopping by German grocery shoppers, Int Rev. of Retail, Distribution and Consumer Research, Vol. 13-2, pp. 145-159. Ranganathan, C. and Ganapathy, S., 2002. Key dimensions of business-to-consumer websites, Information and Management, Vol. 39, pp.457–465. Ranganathan, C. and Grandon, E., 2002. An exploratory examination of factors affecting online sales, Journal of Computer Information Systems, Vol. 42, No. 3, pp.87–93. Song, H., 2015. Prepaid Plans with Free Basic Internet: Digi Smart Prepaid vs. Xpax Magic SIM vs. Maxis #Hotlink. [online] Available at: https://www.lowyat.net/2015/66753/prepaid-plans-with-free-basic-internet-digi-smart-prepaid-vs-xpax-magic-sim-vs-maxis-hotlink/ [Accessed 6 September 2016]. Stephens, R.T., 2004. A framework for the identification of electronic commerce design elements that enable trust within the small hotel industry, ACMSE ’04, 2–3 April, Huntsville, Alabama, USA. Suh, B. and Han, I., 2003. The impact of customer trust and perception of security control on acceptance of electronic commerce, International Journal of Electronic Commerce, Vol. 7, No. 3 pp.135–164. Swaminathan, V., Lepkowska-White, E. and Rao, B.P., 1999. Browsers or Buyers in Cyberspace? An Investigation of Factors Influencing Electronic Exchange, Journal of Computer-Mediated Communication, Vol.5 No. 2. Tan, H. And Guo, J., 2005. Some methods to depress the risks of the online transactions, ICEC’05, 15 – 17 August, Xi’an, China Tan, F.B. and Sutherland, P., 2004. Online consumer trust: a multi-dimensional model, Journal of Electronic Commerce in Organizations, July–September, Vol. 2, No. 3, pp.40–58. University of Reading. (2011), PASW(SPSS) Tip sheet 5: Boxplots, available at: http://www.reading.ac.uk/web/files/maths/spss_tip_sheet_5.pdf [Accessed 15 December 2016]. Wolfinbarger, M. and Gilly, M.C., 2003. eTailQ: dimensionalizing, measuring and predicting etail quality, Journal of Retailing, Vol. 79, pp.183–198. Yoon, S-J., 2002. The antecedents and consequences of trust in online purchase decisions, Journal of Interactive Marketing, Vol. 16, No. 2, pp.47–63. Yu, C.M. and Abdulai, D.N., 2000. E-commerce and the new economy: The Proceedings of International Conference On Electronic Commerce, Emerging Trends in E-Commerce, Kuala Lumpur, Malaysia, November, Multi Media University. Yulihasri. I.A. and Daud, K.A.K., 2011. Factors that Influence Customers’ Buying Intention on Shopping Online, International Journal of Marketing Studies, Vol. 3 No. 1. Zimmerman, K.A., 2001. Internet History Timeline: ARPANET to the World Wide Web. [online] Available at: http://www.livescience.com/20727-internet-history.html [Accessed 6 September 2016]. APPENDICES Appendix 1: Questionnaire Identifying factors affecting consumers online purchase intention in Klang Valley Dear Sir/Madam, I am postgraduate student, who currently pursuing Masters in Business Administration in Segi College Subang Jaya. As part of my study requirements, I am conducting a research on identifying factors affecting consumers online purchase intention in Klang Valley. I would be grateful if you spend five minutes answering all the following questions. With all respect, your response to this questionnaire will be treated as private and confidential. Section A (Please √ only one for each question) ⦁ Do you have the intention to purchase online in future? ⦁ Yes ⦁ No Section B (Please √ only one for each question) Please tick the appropriate answer (1= strongly disagree; 2= disagree; 3=neutral; 4=agree; 5= strongly agree) Section C (Please √ only one for each question) ⦁ What is your gender ⦁ Male ⦁ Female ⦁ What is your age? ⦁ Under 20 ⦁ 20 – 34 ⦁ 35 – 44 ⦁ 45 Above ⦁ What is your race? ⦁ Chinese ⦁ Malay ⦁ Indian ⦁ Other:_____________(Specify) ⦁ What is the highest level of education that you have completed? ⦁ SPM ⦁ Undergraduate (Diploma/Degree) ⦁ Postgraduate (Master/PhD) ⦁ Other:_____________(Specify) ⦁ What is your approximately monthly income ⦁ Under RM2000 ⦁ RM2000 – RM3999 ⦁ RM4000 – RM5999 ⦁ RM6000 – RM9999 ⦁ Above RM10000 Appendix 2: Box Plot Information Design Appendix 3: Box Plot Visual Design Appendix 4: Box Plot Navigation Design Appendix 5: Box Plot Trust Appendix 6: Box Plot Perceived Risk Appendix 7: Box Plot Purchase Intention Appendix 8: Simple Linear Regression (Information Design and Purchase Intention) Appendix 9: Simple Linear Regression (Visual Design and Purchase Intention) Appendix 10: Simple Linear Regression (Navigation Design and Purchase Intention) Appendix 11: Simple Linear Regression (Trust and Purchase Intention) Appendix 12: Simple Linear Regression (Perceived Risk and Purchase Intention)