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International Journal of Accounting & Business Management

www.ftms.edu.my/journals/index.php/journals/ijabm

Vol. 7(No.2), Nov, 2019

ISSN: 2289-4519 DOI: 10. 24924/ijabm/2019.11/v7.iss2/218.235

This work is licensed under a

Creative Commons Attribution 4.0 International License.

Research Paper

FACTORS AFFECTING ONLINE CONSUMER BUYING DECISION IN

MALAYSIA

Mok Wang Nee

Anglia Ruskin University & FTMS

moksteph@gmail.com

Abstract The aims of this research are to determine the factor affecting online consumer buying

decision in Malaysia. The adopted framework has 4 independent variables namely; web

design, price, security and privacy, and convenience. Dependent variable is Online

Consumer Buying Decision. The theories used in this research, Theory of Reasoned Action

(TRA), Theory of Planned Behaviour (TPB). In order to test the relationship between the

theories and to establish the measurement model validity, the Descriptive and Normality

Analysis was adopted and Multiple Regression was used to test the hypotheses. A sample of

112 is used to collect primary data using online Google survey questionnaire. The target

population of this research study is Malaysian above 18 years old who have online

purchase experience. The data compiled from the questionnaires was recorded and coded

into SPSS. Results revealed that majority are focusing on price and convenience as priority

of their reviews prior to purchase online. Wed design and price as secondary source of

reviews when come to decision an online purchase.

Key Terms: web design, price, privacy and security, convenience and online buying decision.

1. Introduction

The point of this exploration is to find out how different or various components influence web based shopping, demonstrating the connection between perceived usefulness, subjective standards and shopping attitudes (Souiden, Nizar and Marzouki Rani, 2015). This study assists experts with managing their business and to have the capacity to deal with the issues or hindrances (San et al., 2015). An exploration from Al- Debei demonstrates that online shops are blasting at a forward rate since most of their customers are influenced by the fact that they send to the doorstep (Al-Debei et al., 2015). The components influencing online consumers involve the quality of the information, price or offers, security and privacy, customer service, race dependability, perceived attribute and the design or layout of the website. Having to do with security and privacy, when the perceived risk is greater, it demotivates or discourages the customers from buying the goods and therefore it has a negative impact. If the web design is very direct and not complicated to understand, user friendly, the consumers

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will be anticipated to buy goods online with a peace of mind. Generally, Malaysian with multi races still not happy in buying on the web and short of knowledge or education information on web based or online Shopping. One can find various measurable and financial examinations on consumer loyalty and dedication concerning physical store shopping. This study enable us to gather the views and insight of online customers in Malaysia with respect to what they expect and what has made them remain loyal or satisfied to the product or service. The objective of this research work is to find out the problems or challenges faced by customers in online shopping.

The objective of this research is to analyse the factors affecting online consumer buying decision in Malaysia.

To investigate the impact of Web Design as a factor affecting online consumer buying decision

To investigate on the impact of price as a factor affecting online consumer buying decision

To investigate on the impact safety and privacy quality as a factor affecting online consumer buying decision

To investigate on the convenience as a factor affecting online consumer buying decision

2. Literature Review

Definition of Key Concepts

Web Design

According to a study (Afshardost et Al., 2013), the point of view of customers about website design is based on features in a website that meet customers’ needs, requirement and attracted to the total excellence of that website. Website design is the features of the website quality and impression that fulfil and meet the online consumers’ needs (Hasanov and Khalid, 2015). The web design impact and factors affecting online buying behaviour as well as factors discouraging the consumer to buy online. If the web design is not user friendly, not easily access, complicated, it may cause the consumer to look for other user friendly web design online shopping website. Based on a group of researchers (Shaheen et al., 2012), website features have an important role on online purchase decision. An informative website helps and assists customers to differentiate and evaluate product choices, differences and will increase customer satisfaction and contributing to online purchase decision (Hausman and Siekpe, 2009).

Price

Price is the number one factor in influencing consumers to purchase (Kotler and Keller, 2003). Price is a value of a product or service that a person or consumer willing to pay. Price is a factor that influences a decision on a purchase. Pricing is a useful way for price sensitive consumers to be attracted to purchase a specific product at the lowest prices possible or to obtain the greatest value of money (Brassington and Pettitt, 2006). When there’s a drop in prices for products and services in websites, price sensitive consumers will be aware and respond quickly, based on prices (Pi et al., 2011). Expected price confirm the value accepted for a product or services. The awareness of pricing refers to consumer willingness to buy a service or products at lower prices in order to avoid over pricing services or products (Sinha and Batra, 1999). When consumers disagree with the price, beyond the budget set, dissatisfaction, consumers will source for another website.

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Security and Privacy

Security and Privacy is the privacy and security level of personal data published via internet. It involved in capturing personal data, preferences, and communications. The privacy of personal information’s is a factor that affects or limits the consumer buying online. (Park, Lennon and Stoel, 2005; Pavlou, 2003; Quintal, Lee and Soutar, 2010; Samadi and Yaghoob-Nejadi, 2009) Based on Settle (2000), the problem of online purchasing were privacy and security of information. People whom still reluctant to shop from the Internet mainly due to the privacy concern. According to Elliot and Fowell (2000), consumer worries about security for transaction information resulted in 50 percent of transactions rated as unsatisfactory and unsuitable. Uncovering private data through the conceivable dispersion of individual data without consent of the buyer was likewise seen as dangerous.

Over the years, even though awareness and confidence and security in computer systems are growing rapidly, as a whole, online shopping no doubt is a common practice, perceived risk still impact online shopping behaviour negatively (Zendehdel et al., 2015). Privacy subjects’ effect on consumers’ believes in the online suppliers (Miyazaki and Fernandez, 2001). Based on studies, privacy matters are the main barrier to the advancement of buying online (Littler and Melanthiou, 2006). As privacy problems spiked, there will be increasing consumers that will be wary of sharing or exchanging their personal information (Mwencha et al., 2014).

Convenience

According to Hanson (2000), as cited in Harn et al. (2006), convenience is view from a few angles comprising of time, location to shop and purchasing process or procedures. It adds value and encourages consumers to shop online. Swilley and Goldsmith (2013) stated that the save time and effort as added advantage in online shopping. Harn et al. (2006) online shopping is so much more convenient than other traditional methods of purchasing hassle free from going to one shop to another. It is also a 24-hour availability of online shopping and accessibility makes online shopping more convenient and interesting. Convenience in online shopping includes, time saving, refund policy, satisfaction and etc. Girard, Korgaonkar and Silverblatt (2003) mentioned that, convenience is a stronger motivator to shop online (as cited in Amoroso and Hunsinger, 2008).

Online Consumer buying decision

Consumer behaviour is classified as a process that involved an individual or group to buy a certain products needed to fulfil their needs (Solomon et al., 2013). Consumer is not easy to handle. It’s a psychology or mind-set of consumers that influence or affect the buying decision. It involved various processes from selecting, sourcing, buying, influence and finally purchase of a product or service. It requires various steps before jumping into conclusion on buying decision. Consumer buying decision is linked consumer attitude and intention to buy, and when intention and decision are made, buying online takes place (Jahng et al., 2001). With the advancement of internet and process of information search, each and every search outcomes or details does influence a consumer buying decision. They’re exposed to varieties and choices thus impulse their decision making on purchasing (Nguyen and Gizaw, 2014). Hence, informative products and services, attracts increasing consumers behaviour to change from the traditional

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way of shopping to online shopping platform. In order to capture the consumer transformation, more and more companies change their marketing strategies. Consumer’s decision can be compensated with decision rules, where a product is measured in terms of attributes, usefulness that are weighted and can balance out a negative measured on another attribute (Schiffman and Kanuk, 2007). Besides that, online shopping decision eases the consumer from travelling from one shop to another, without wasting time as per (Childers et al., 2001). Online shopping allows consumers to buy faster, more alternatives and can order products and services with comparative lowest price (Koyuncu and Bhattacharya, 2004).

Critical Reviews of Current Research

Tsai et al. (2011), consumers’ perception of security of online vendors is known as a major concern of decision making by consumers. They found that website design (website features), efficiency service quality, product quality information and transaction and delivery capability as factors that are related to perceived risk of information security and purchase intention. User can’t prove that the information from a shopping website is genuine. Online vendor who is trusted is presumed to be capable to attract larger numbers of online customers. We found that participants in the privacy information condition were more likely to make purchases from websites offering medium or high levels of privacy (even when those sites charged higher prices), while those in the control conditions generally made purchases from the lowest priced vendor. This indicates that individuals are likely to pay a premium for privacy when privacy information is made more accessible. This demonstrates that the observed behaviour cannot simply be attributed to an interest in purchasing from web sites labelled with attractive indicators. Our study was not designed to establish whether the premium consumers were willing to pay for privacy.

Based on Ganguly et al. (2010) , lacking of trust in online transaction and main reason that hinder consumer from online shopping Hence Academic researchers are anxious to find out the importance of website design factors that linked to trust in online shopping . Website design has two features, which are hygiene and motivator elements. These elements contribute to user dissatisfaction and satisfaction. Hygiene elements are those features making the website functional. Its absence will cause user dissatisfaction. Motivator elements add value to the Website. For E- retailing, Webpage design is crucial as it acts as the marketplaces. Website design continuously influence buying decision and lowered the perceived risk. The use of colours, graphics, pictures, various fonts does improve the feel and look of web design and create attraction. The study is solely based on website design factors as antecedents of trust

According to Powers and Jack (2013), online shopper feels good and satisfied and considers no risk taking when buying online with liberal return policies. This flexibility enables online shopper to shop more confidently.

Jiradilok et al. (2014) able to shop from home 365 days, time saving, can access into anything, any brand and any retailers when shopping online; hassle free as don’t need to leave home or office to shop. The results of this research will increase researcher’s comprehension on difference in factors that influence online purchase intentions of experience and inexperienced online purchasers.

Sivanesan et al. (2017) cited that with the vast demand on online shopping it enables shopper to shop faster and more quickly hence save time. Consumer’s shop when and where they want, where they are comfortable with the products and the choice of

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shopping. This study provides mixed results about the factors in influencing of offline and online shoppers in Kanyakumari district.

According to Hadi B et al. (2017), online shopping enable consumer to get a product or services at a lower price since the cost are lower. Hence, purchasers will most likely purchase items at lower costs. At the point when buyers understand that they get items with a similar quality and at lower costs, they might be eager to purchase. When consumers realize that they get products with the same quality and at lower prices, they may be willing to buy.

Conceptual Framework and Hypotheses

In the e-commerce market, web design has been an important role in positively influencing consumers to shop online with satisfaction and more confidently (Lee and Lin, 2005). It should be user friendly, easily accessible, and trusted platform (Dellarocas, 2010). Therefore:

H1: Web Design has a positive impact on online consumer buying behaviour

Price plays a role on affecting online consumer buying behaviour. Consumers will opt for online shopping if the price of the products is competitive or low. With online shopping, it enables seller to reduce cost of the products as they need not bear rental or other business expenses. Thus, price is an advantage influence on online shopping behaviour (Su and Huang, 2011). Therefore:

H2: Price has a positive impact on online consumer buying behaviour

Security and Privacy is about revealing personal information of intend consumers. It is also a positive factor. According to Har Lee et al. (2011), Tsai et al. (2011), Ha and Stoel (2012), security and privacy protections reduce the worries and risk and increases the purchase intention. Therefore:

H3: Security and Privacy has a positive impact on online consumer buying behaviour

Convinience (Chaudary et al., 2014)

Web Design (Ganguly et al. 2010)

Price (Harn et al., 2006)

Security & Privacy (Koyuncu and Bhattacharya 2004)

Online Consumer Buying Decision (Smith and Rupp, 2003)

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Convenience has the highest positive relationship with the acceptance of online shopping cited by Chaudary et al. (2014). To et al. (2007) found that online shopping ease the customers as do not need to leave home and limit by time constraint as online shopping provides service on 24/7 basis and the customer can shop leaving consumers free to shop at their own pace. Therefore:

H4: Convenience has a positive impact on online consumer buying behaviour

3. Research Design and Methodology Research Paradigm

Positivism described the role of research is limited to data collection and interpretation positively and findings are observable and quantifiable. According to Crowther and Lancaster (2008), positivism includes deductive approach, inductive research and are connected with phenomenology philosophy. Positivism depends on quantifiable observations that lead to statistical analyses. Positivism can be verified by since it affirms the value of science, logical proof and also claims everything either false or meaningless. It’s being chosen because it’s a quantitative method and outcomes are measureable. Both Phenomenology and interpretivism is not applicable because it is qualitative.

Research Design and Strategy

The research will be based on a cross-sectional strategy. The study will be conducted through a self-administered questionnaire to identify the independent variables on factors affecting online consumer buying decision. Cross-sectional study shows the overall populations they are collecting data about. Cross sectional has been selected because it allows simultaneous comparison of many different variables due to time constraints of this research. Therefore, it can be contrasted with panel surveys for which individual respondents are followed over time. On the other hand, time series / Longitudinal is long and time consuming and is therefore not suitable for this research.

Data Collection Method

Questionnaires are a set of questions developed to obtain primary data. Methods of data collection may be raw or in any form of media (Borgman, 2010). It is divided into two ways of collecting data divided into primary or secondary data. In the form of questionnaires, interviews, focus group interviews, case studies or scientific experiments, primary data can be used. It is possible to classify the secondary sources into two categories; published and unpublished sources such as media, broadcast, foundation of universities, etc. Questionnaire will be chosen because it produces more honest answers and is easier to administer (Furaiji and Łatuszyńska, 2012). For this research simple, clear, straightforward English will be used in questionnaires.

Population & Sample Size

According to Zikmund (1997), population is indicated to the specific or targeted and complete group that related to the research study. This study data will be collected from Respondents aged between 18 to 46 years old and above with online shopping experiences in Malaysia from March 2019 to April 2019.The research objective is examine factors affecting online consumer shopping decision in Malaysia.

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The target sample size for this is 112 respondents using a convenient technique of non-probability sampling that collects and obtains informative data (Zikmund, 1997). Differences are shown through probability and non-probability under sampling techniques. The sample population consists of unrestricted population such as gender, income, and level of education.

Data Analysis Plan Software for the Social Science Statistical Package (SPSS) will be used to cross-examine all collected data. The regression technique is used to examine, for example, the appropriateness of the hypotheses proposed to discover avoid variables that imitate one another (Reinard, 2006). Descriptive and normality analysis and regression testing are chosen for analysed data collected with the intention of examining the relationship between independent variable and dependent variable (Saunders, et. al., (2009). Finally, the results of the analysis of beta coefficient and p value data determine the acceptance or rejection of each hypothesis.

4. Results and Discussion

Demographic Analysis

The Result shows that 51% of the responses were Male and 49% Female. Highest responses on education level are Bachelor's Degree while others are fairly distributed among Diploma, High School, Postgraduate and others. For income analysis the highest is 36.6% of monthly income between RM5001 to RM10000, 22% for monthly income between RM10001 and above 25.9% , for monthly income between RM1500 to RM3000, 18.8% for monthly income between RM3001 to RM5000 18.8%, lowest percentage score for monthly income range of between RM1500-RM3000 and RM3001-RM5000.

Frequency Percent Valid Percent

Cumulative Percent

Gender Female 55 49.1 49.1 49.1

Male 57 50.9 50.9 100.0

Age

>46 & above 40 35.7 35.7 35.7

18-25 15 13.4 13.4 49.1

26-35 18 16.1 16.1 65.2

36-45 39 34.8 34.8 100.0

Income

10001 and above 29 25.9 25.9 25.9

1500-3000 21 18.8 18.8 44.6

3001-5000 21 18.8 18.8 63.4

5001-10000 41 36.6 36.6 100.0

Race

Chinese 83 74.1 74.1 74.1

Indian 13 11.6 11.6 85.7

Malay 10 8.9 8.9 94.6

Others 6 5.4 5.4 100.0

Education Bachelor's Degree 38 33.9 33.9 33.9

Diploma 35 31.3 31.3 65.2

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Table 1: Demographic Analysis Results

Descriptive Analysis

The statistics in Table 2 shows that the frequency distribution where Mean constructs is between 3.7 and 4.3 for Web Design, Price, Security and Privacy, Convenience and Online Shopping Decision. The Mean constructs are near the mid-point of scale which is 2 for this study. As shown in the table below, all independent variables and the mean value of the dependent variable are higher than 3.7. Overall, the answers show that most of the respondents agreed on the construct relationships.

Descriptive Statistics

N Minimum Maximum Mean Std.

Deviation

Statistic Statistic Statistic Statistic Statistic

Web Design 112 1.4 5.0 4.346 .6797

Price 112 1.0 5.0 4.027 .7624

Security and Privacy 112 1.4 5.0 4.136 .6599

Convenience 112 1.0 5.0 4.339 .7223

Online Shopping Decision 112 1.67 5.00 3.7440 .82744

Table 2: Descriptive Statistic for the aggregated constructs

Normality Analysis For each of the constructs, the statistics for the normality analysis are shown as excellent from the table 3 below. Because of this, these constructs ' Skewness and Kurtosis are normally distributed.

High School or Equivalent 25 22.3 22.3 87.5

Others 5 4.5 4.5 92.0

Postgraduate 9 8.0 8.0 100.0

Job Executive Level 33 29.5 29.5 29.5

position Executive Management Level 18 16.1 16.1 45.5

level Managerial Level 30 26.8 26.8 72.3

Non-Executive Level 31 27.7 27.7 100.0

Categories

Books 5 4.5 4.5 4.5

Computer Products 13 11.6 11.6 16.1

Cosmetic 6 5.4 5.4 21.4

Fashion/Clothes 52 46.4 46.4 67.9

Groceries 5 4.5 4.5 72.3

Handphone/Mobile 10 8.9 8.9 81.3

Others (Foods , Beverages, etc) 21 18.8 18.8 100.0

Frequencies

1-2 times a week 79 70.5 70.5 70.5

3-4 times a week 10 8.9 8.9 79.5

4-5 times a week 6 5.4 5.4 84.8

Never 17 15.2 15.2 100.0

Online Website

11Street 2 1.8 1.8 2.7

Lazada 45 40.2 40.2 42.9

Others such as Tmall ,Mudah, Superbuy, Ebay and etc

27 24.1 24.1 67.0

Shopee 27 24.1 24.1 91.1

Taobao 10 8.9 8.9 100.0

Total 112

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N Minimum Maximum Mean Std.

Deviation Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic Std.

Error Statistic Std.

Error

Web Design 112 1.4 5.0 4.346 .6797 -1.377 .228 2.473 .453

Price 112 1.0 5.0 4.027 .7624 -.912 .228 1.307 .453

Security and Privacy 112 1.4 5.0 4.136 .6599 -.864 .228 1.625 .453

Convenience 112 1.0 5.0 4.339 .7223 -1.435 .228 3.105 .453

Online Shopping Decision

112 1.67 5.00 3.7440 .82744 -.235 .228 -.566 .453

Valid N (listwise) 112

Table 3: Normality Analysis using Skewness and Kurtosis for aggregated constructs

Reliability Analysis According to Table 4, the overall Cronbach's Alpha show an excellent reliability of 0.934

for Overall, follow by a good strength of WD(0.853), PR(0.853), CONV(0.858),

questionable SP(0.670) and acceptable OSD(0.770).This instruments shows excellent

reliability in term of internal consistency.

Variables No of items Cronbach's Alpha Strength of Association

OVERALL 23 0.934 Excellent

WD 5 0.853 Good

PR 5 0.823 Good

CONV 5 0.858 Good

SP 5 0.670 Questionable

OSD 3 0.770 Acceptable

Table 4: Sample Adequacy for construct variables

Assumption for Multiple Regressions

Autocorrelation From Table 5, model summary table showed that R Square at 0.555.It indicates that

55.5% variance of online shopping decision is explained by the variance of Convenience,

Price, Security and Privacy and Web design.

Model Summaryb

Model R R Square Adjusted R Square Std. Error of the

Estimate Durbin-Watson

1 .745a .555 .538 .56221 1.784

a. Predictors: (Constant), Convenience, Price, Security and Privacy, Web Design

b. Dependent Variable: Online Shopping Decision

Table 5: Analysis of Autocorrelation

Multicollinearity The table below shows that tolerance falls well below the recommended value between 0.6 and 0.7. While VIF is below 10, it is considered good.

Coefficientsa

Model Unstandardized Coefficients

Standardized

Coefficients t Sig. Collinearity Statistics

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B Std. Error Beta Tolerance VIF

1 (Constant) .035 .380 .093 .926

Web Design -.290 .126 -.238 -2.299 .023 .388 2.575

Price .316 .099 .291 3.203 .002 .505 1.982

Security and

Privacy .194 .124 .154 1.560 .122 .424 2.356

Convenience .667 .120 .583 5.551 .000 .378 2.648

a. Dependent Variable: Online Shopping Decision

Table 6: Analysis of Multicollinearity

Normality of the Dependent Variable According to Doane and Seward (2017), the histogram reflected as a bell-shaped curve is commonly referred to as the "normal distribution." As shown in the diagram below, bell-shaped is clearly seen where researchers can conclude that data is normal (Ghasemi and Zahedials, 2012).

Figure 1: Histogram of Normality of the Dependent Variable

Normality of Residuals As shown in the p-p plot chart below, there is an almost straight line where the points come from normal distribution showing a strong correlation between their actual outcome and their model predictions.

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Figure 2: Normality of the Residuals using Probability Plot

Analysis of Homoscedasticity

According to Byrne (2010), the general guidelines of Homoscedasticity [similar variable] are important to linear models of regression. General guideline for homoscedasticity is “having the same scatter” where the set of data must be generally in the same line. As shown in the all the partial regression plot diagram below researcher can assume that there is no homoscedasticity from this study as the data shown to be dispersed (Osborne, 2010).

Figure 3: Homoscedasticity

Regression Analysis

Model Fitness Hair et al. (2013) suggested that the R Square (R2) values of 0.75 would be substantial

as a rule of thumb, 0.50 would be moderate or 0.25 would be weak for latent

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endogenous variables. For this study, R Square value of 0.555 indicates moderate

fitness.

Model Summaryb

Model R R Square Adjusted R Square Std. Error of the Estimate

Durbin-Watson

1 .745a .555 .538 .56221 1.784

a. Predictors: (Constant), Convenience, Price, Security and Privacy, Web Design

b. Dependent Variable: Online Shopping Decision

Table 7: Model Summary

Model Significance - ANOVA Table

The sig value in the ANOVA table below is 0.00 which proves that the model for this study is extremely important. This is correct because this research is based on Malaysian population. This therefore indicated a fit model of regression as shown in the table below. Result from Table 8 can interpreted as significant and accepted with the F value of 33.359 is significant at level of less than 0.05 (p=0.000 < 0.05) therefore can summarise that the mediator (Online Shopping Decision) in the regression model can be used to forecast Online Shopping Decision in Malaysia.

ANOVAa

Model Sum of

Squares df Mean Square F Sig.

Regression 42.176 4 10.544 33.359 .000b

Residual 33.820 107 .316

Total 75.996 111

a. Dependent Variable: Online Shopping Decision

b. Predictors: (Constant), Convenience, Price, Security and Privacy, Web Design

Table 8 : Model Significance - Anova

Hypotheses Testing As shown in the table below, the good value of Tolerance of 0.505 and 0.378 with the p-value is less than 0.05 is Price and Convenience. The others dependent variable was rejected as indicated by the p-value greater than the 0.05.

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.

Collinearity Statistics

B Std. Error Beta Tolerance VIF

1 (Constant) .035 .380 .093 .926

Web Design -.290 .126 -.238 -2.299 .023 .388 2.575

Price .316 .099 .291 3.203 .002 .505 1.982

Security and Privacy .194 .124 .154 1.560 .122 .424 2.356

Convenience .667 .120 .583 5.551 .000 .378 2.648

Table 9: Coefficients

Hypothesis Summary Table Hypothesis Sig Value

(p <0.05)

Standardized Beta

Coefficients

Result Interpretations

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H1: There is a

significant impact of

Web Design on Online

Consumer Decision

0.023 -0.238 Rejected The beta coefficients of

-0.238 interprets that

Web Design carry 2%

negative impact on

Online Consumer

Decision although p-

value is less than 0.05

hence hypothesis is

rejected.

H2: There is a

significant impact of

Price on Online

Consumer Decision

0.002 0.291 Accepted The beta coefficients of

0.291 indicates Price

reflected 29.1%

positive impact on

Consumer Purchasing

Decision p-value is less

than 0.05, so the

hypothesis is accepted.

H3: There is a

significant impact of

Security and Privacy

on Online Consumer

Decision

0.122 0.154 Rejected The beta coefficients of

0.154 indicates that

Security and Privacy

carry 15.4% positive

impact on Consumer

Purchasing Decision

and p-value is more

than 0.05 hence

hypothesis is rejected.

H4: There is a

significant impact of

Convenience on Online

Consumer Decision

0.000 0.583 Accepted The beta coefficients of

0.583 indicates Price

reflected 58.3%

positive impact on

Consumer Purchasing

Decision p-value is less

than 0.05, so the

hypothesis is accepted.

Table 10: Hypothesis Summary Table

5. Conclusion

This research is aimed to explore on the factors affecting online consumer buying

decision in Malaysia. The proposed framework in chapter 2 contains four independents

variables that affect consumers’ buying decision in Malaysia. The four variables are

product web design, security and privacy, price and convenience. In this section, we will

be focus on the two variables except for web design and price as it did not pass the

reliability test, to discuss further with the emphasis on the variables on factor affecting

online consumer buying decision in Malaysia.

From the findings, it is concluded that web design has insignificant impact on factors

affecting online consumer buying decision. The outcome is aligned with research by

Alam et al. (2008) shows that web design has an insignificant influence. Results from

this research do not support the stated hypothesis as it is rejected with p value of 0.023

and beta coefficient of -0.238. In addition, Ranganthan and Ganapathy (2002) found in

their research that web design should have an aesthetic appealing outlook. However

results showed otherwise. This could be due to the analysis, more than 35%

respondents were 46 years old and above therefore with the higher income and

purchasing power, they have in mind what to buy thus web design does not hinder their

buying decision.

The exploration of price is supported by the study done by Hadi et al. (2017) that online

shopping offer lower price due to the reduction of cost from retailer; saving money

impact consumer decision positively. The overall reliability for Price is good with a

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0.002 p value with beta correlation of 0.291 which is strong correlation. Thus this

finding is supported by Harn et al. (2006), since price attract attention, new online

retailer use price as a main weapon to attract online customers with competitive prices

and deals, which benefits consumers (Hanson, 1999.). Since the samples size shows

35% are above 46 years old and with stable income, they will weight price as more

important due to the intention to buy is of higher possibility.

Based on the results obtained, Security and Privacy p value is at 0.122 and beta

correlation of 0.154, therefore hypothesis was rejected. For security and privacy, the

results were consistent with the findings from Shergill and Chen (2005), security

basically is perceived as very important factor to determine customers' trust in online

shopping. Additionally, it also induces consumers' external trust value and level that

contribute to on online shopping satisfaction. A reason for privacy risk being the most

perceived risk while shopping online without affecting online shopping behaviour may

be that this risk is the most present, but least estimable risk in the online environment.

When personal data is lost or stolen on the Internet, consumers cannot really estimate

what is really happening with their data (Lim, 2003). However this finding contradicted

with the study of Ha and Stoel (2012) which indicates that security and privacy was not

significant to online shopping. In this research security and privacy results as not so

important, however, according to Subramaniam and Andrew (2016), security as a

whole represent a very solid evidence in determining customers' extend of trust as it

also induces consumers' external trust levels towards payment method, data

transmission and data storage therefore consumers' should not underestimate the

importance of security and privacy.

According to Wang et al. (2005), convenience is one of the most significant reasons for

consumers' to shop online. It allows you to shop 24 hours a day, 7 days in a week, even

after office hours, anytime, anywhere at your own pace without unnecessary stress.

Wide range of products and online retailers are easily access just by your finger tip. The

results are consistent with the findings whereby convenience p value is at 0.000 being

the most significant hypothesis of 4 independent variables and beta correlation of

0.583. Convenience allow consumers to avoid unnecessary like traffic jam which in turn

added travelling cost, face to face interaction with salesperson, parking space , and etc.

All these factors strongly support the findings on convenience. Research by The Tech

Faq (2008) also highlighted that consumers' choose online shopping over traditional

shopping in order to avoid crowds and wailing lines especially in holiday shopping and

this really save time and hassle of consumers'.

References

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